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无损音乐格式APE/FLAC/WAV/DFF/DSF有什么不同？_音频

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无损音乐格式APE/FLAC/WAV/DFF/DSF有什么不同？

2020-07-31 13:39

来源:

QCJPSZS

原标题：无损音乐格式APE/FLAC/WAV/DFF/DSF有什么不同？

1、 前三种都是属于俗称的“无损”音频文件格式。

APE和FLAC的文件相比WAV体积要小一些，在音频的提取和转化过程中，也经过一定的压缩，因此和原始录音对比，APE和FLAC文件本质也是“有损”，只不过损失很小，接近可以忽略不计；其中APE算法有较好的压缩率，相比之下FLAC压缩率有所不如，因此同品质的FLAC文件往往略大。

WAV是由微软公司开发的一种电脑音频格式，属于数字化的无损音频格式，诞生较早，也比较常见，一般而言WAV音频的文件体积都较大。

2、 DFF和DSF，都是采用DSD编码的音频格式。

DSD是Direct Stream Digital的缩写，表示直接比特流数字编码，是SACD(Super Audio CD)的编码模式，由Sony与Philips在1996年共同开发诞生，与采用PCM编码的DVD-AUDIO阵营竞争。

DSD编码用1bit比特流的方式取样，采样率2.8224MHz （CD 44.1kHz取样的64倍）的高取样方式，直接把模拟音乐信号波形以脉冲方式转变为数字信号，并以约四倍于传统CD音频文件的空间储存音乐，因此可以提供更为优秀的声音效果。由于取样次数高，所以取样过的波形很圆顺，十分接近模拟波形。

展开全文

DFF和DSF就是采用DSD编码所制作的音频文件，由于DSD由索尼和飞利浦共同开发，因此两家也分别采用了DFF和DSF作为文件格式名，DFF文件最初被索尼用于PS游戏机上的音频，后来进一步的衍生为专用于音乐产品的SACD，并深受发烧友推崇。

DFF和DSF主要用于没经过压缩的双声道录音，体积比WAV文件更大。由于DSD编码的音频文件体积庞大，对于数据量更大的多声道音频，一般会另外采用经过压缩的DST格式。

在网络年代，数字音乐早已成为主流，甚至在汽车音响领域，数字音乐取代传统的CD播放也已成为趋势，而在这个热潮之中，上述无损格式音频，凭借着更佳的音质，也受到了发烧级听众的追捧。然而，要聆听无损音频，无论哪种格式，首先都需要播放设备支持对相关格式的解码，而目前的汽车音响业界，能够全面支持APE,FLAC,WAV,DFF,DSF等格式的高品质音源还是较为稀缺。

近期，英国Gypsy Sound（吉普赛之声）即将推出的首款数字播放器，就会全面支持上述各种无损音频格式，并且在音频的播放品质上，也达到让人惊艳的高水准，对于每一位的Car HI-FI玩家而言，这款数字播放器的面世，相信都将会是2020年里令人难忘的一份巨大惊喜。返回搜狐，查看更多

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Object reference not set to an instance of an object.

Object reference not set to an instance of an object.

Server Error in '/' Application.

Object reference not set to an instance of an object.

Description: An unhandled exception occurred during the execution of the current web request. Please review the stack trace for more information about the error and where it originated in the code.

Exception Details: System.NullReferenceException: Object reference not set to an instance of an object.

Source Error:

Line 28: if (Request["lang"] == null && Session["lang"] == null)

Line 29: {

Line 30: Session["lang"] = Request.UserLanguages[0];

Line 31: }

Line 32: else if (Request["lang"] != null)

Source File: e:\InetPub\dsfhome.1\App_Code\basePage.cs    Line: 30

Stack Trace:

[NullReferenceException: Object reference not set to an instance of an object.]

Localization.basePage.InitializeCulture() in e:\InetPub\dsfhome.1\App_Code\basePage.cs:30

ASP.default_aspx.__BuildControlTree(default_aspx __ctrl) in e:\InetPub\dsfhome.1\Default.aspx:1

ASP.default_aspx.FrameworkInitialize() in e:\InetPub\dsfhome.1\Default.aspx.cs:912308

System.Web.UI.Page.ProcessRequest(Boolean includeStagesBeforeAsyncPoint, Boolean includeStagesAfterAsyncPoint) +56

System.Web.UI.Page.ProcessRequest() +80

System.Web.UI.Page.ProcessRequestWithNoAssert(HttpContext context) +21

System.Web.UI.Page.ProcessRequest(HttpContext context) +49

ASP.default_aspx.ProcessRequest(HttpContext context) in c:\Windows\Microsoft.NET\Framework\v2.0.50727\Temporary ASP.NET Files\root\fb509fc4\c07da73a\App_Web_duy7nmsk.1.cs:0

System.Web.CallHandlerExecutionStep.System.Web.HttpApplication.IExecutionStep.Execute() +181

System.Web.HttpApplication.ExecuteStep(IExecutionStep step, Boolean& completedSynchronously) +75

Version Information: Microsoft .NET Framework Version:2.0.50727.8745; ASP.NET Version:2.0.50727.8745

如何播放.dsf文件？ | Sony China

如何播放.dsf文件？ | Sony China

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文章ID : S800009143 / 最近修改 : 2018-02-22打印如何播放.dsf文件？

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Differential Scanning Fluorimetry (DSF) | Center for Macromolecular Interactions

Differential Scanning Fluorimetry (DSF) | Center for Macromolecular Interactions

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Differential Scanning Fluorimetry (DSF)

For information on access fees, policies and getting started at the CMI, see the CMI Access Page.

DSF at the CMI

Differential Scanning Fluorimetry (DSF) measures protein unfolding by monitory changes in fluorescence as a function of temperature. Conventional DSF uses a hydrophobic fluorescent dye that binds to proteins as they unfold. NanoDSF measures changes in intrinsic protein fluorescence as proteins unfold.

The CMI has a modified Life Technologies Quant Studio 6/7, for conventional DSF. The CMI has the Life Technologies Protein Thermal Shift Analysis software for DSF data fitting.

The CMI has a Prometheus NT.Plex instrument from NanoTemper Technologies with aggregation optics. The CMI has these Data collection and analysis software packages: PR.ThermControl for thermal stability data collection, PR.ChemControl for chemical stability data collection, PR.TimeControl for time interval data collection, and PR.Stability Analysis for advanced data analysis.

DSF (Conventional DSF, Protein Thermal Shift Analysis)

Conventional Differential Scanning Fluorimetry (DSF) uses a real-time PCR instrument to monitor thermally induced protein denaturation by measuring changes in fluorescence of a dye that binds preferentially to unfolded protein (such as Sypro Orange, which binds to hydrophobic regions of proteins exposed by unfolding).  This experiment is also known as a Protein Thermal Shift Assay, because shifts in the apparent melting temperature can be measured upon the addition of stabilizing or destabilizing binding partners or buffer components.

NanoDSF

NanoDSF is a modified differential scanning fluorimetry method which monitors intrinsic tryptophan and tyrosine fluorescence as a function of temperature, time, or denaturant concentration. Tryptophan and tyrosine fluorescence intensity and wavelength maximum will vary as the local chemical environment changes, with significant changes occurring as buried or packed aromatic side chains become solvent exposed upon unfolding.  NanoDSF measures fluorescence intensity at 350 nm and 330 nm and compares the ratio as a function of temperature or denaturant concentration. NanoDSF can be used for a broader range of protein samples than traditional DSF and has significantly higher throughput and lower sample consumption than DSC or CD. Free energies of folding and temperatures of unfolding measured using NanoDSF are comparable to values determined by DSC for a range of sample types. The only significant limitation is that the protein of interest must contain aromatic amino acids (tryptophan or tyrosine).

Data Files - About CMI Data Files

Users are responsible for storage of all raw and processed data collected at the CMI.

Users should have a plan to copy or transfer all raw and process data to their own local or cloud storage system.

While the CMI allows temporary local storage of CMI User data on the instrument computer, we make no guarantees on the security or long-term availability of any data at the CMI.

For most (but not all) CMI technologies, the raw data files and recommended readable exports are relatively small and can be readily transferred electronically. 

See specific instruments for exceptions and for details about the software, data file types and recommended data exports. 

Data Sharing:

Currently, a Generalist Repository is the recommended data repository for most CMI data types, as stable specialist data repositories have not been established.

Data Files - DSF - QuantStudio 6/7

Technology

Differential Scanning Fluorimetry (DSF)

Instrument

Life Technologies Quant Studio 6/7

Recommended Repository

Generalist Repository

Software Type

Data Collection

Current Version

QS Real-Time PCR Software, version 1.7.1

Data Files (Type, ~size)

experiment file

.eds

2-10 MB/plate

Software Type

Data Analysis

Current Version

Applied Biosystems Protein Thermal Shift, version 1.2

Data Files (Type, ~size)

experiment file

.eds

2-10 MB/plate

Readable Exports

raw data

.csv

2 MB/plate

analyzed data

.csv

12 KB/project

analyzed data

.txt

29 KB/project

Data Files - DSF - Prometheus

Technology

Nano Differential Scanning Fluorimetry (nanoDSF)

Instrument

NanoTemper Prometheus NT.Plex

Recommended Repository

Generalist Repository

Software Type

Data Collection (Thermal Denaturation)

Current Version

PR.ThermControl, Version 2.3.1

Data Files (Type, ~size)

experiment file

.prc

10-30 MB/project

raw data

.xslx

2 MB/project

Software Type

Data Analysis

Current Version

PR.Stability Analysis, Version 1.1

Data Files (Type, ~size)

analysis file

.pra

2-6 MB/project

Readable Exports

processed data

.xslx

~500 KB/sample

results table

.xslx

~30 KB/project

Software Type

Data Collection (Chemical Denaturation)

Current Version

PR.ChemControl, Version 1.4.3

Data Files (Type, ~size)

experiment file

.prcc

10 MB/project

Readable Exports

raw data

.xslx

6 KB/sample

Software Type

Data Collection (Time Control)

Current Version

PR.TimeControl, Version 1.0.2

Data Files (Type, ~size)

experiment file

.prtime

2 MB/project

Readable Exports

raw data

.xslx

60 KB/project

DSF Data Collection Services

DSF Service Overview

In addition to instrument training, the CMI is now offering basic protein Differential Scanning Fluorimetry services, including protein thermal shift analysis and buffer optimization.

Differential Scanning Fluorimetry (DSF) with fluorescent dye or using intrinsic protein fluorescence

Life Technologies Quant Studio 6/7

Conventional DSF, protein thermal stability using hydrophobic dye Sypro Orange

Buffer optimization

NanoTemper Technologies Prometheus NT.Plex

NanoDSF, protein thermal stability using intrinsic protein fluorescence

Chemical denaturation

Buffer optimization

Data Collection Fees Summary

Data Collection

Limited Data Collection Services are offered.

Service fees are based on labor and supplies costs, and will be charged for all completed services, regardless of experimental outcome. 

Before submitting samples for data collection, users must approve the estimated charges and be given a date and time for sample delivery.

External Users will also be required to submit a PO and a signed CMI User Agreement.

Most CMI Data Collection Services include a setup fee plus a per-sample data collection fee.

Some services include replicate measurements by default in the per-sample fee. For others, there is a reduced-price replicate measurement fee, if collected in the same dataset.

Nanobody services not available to commercial users at this time.

Current Harvard Life Lab commercial users are offered a 25% discount off the standard commercial rates.

All Data Collection Fees

DSF Resources (Conventional DSF)Quant Studio DSF SuppliesQuant Studio qPCR Resources

CMI QuantStudio DSF Getting Started Guide

Protein Thermal Shift Studies manual from Applied Biosystems by Life Technologies

All Experiments:96-well FAST-block optical plate, eg.: LifeTechnologies MicroAmp FAST optical 96-well reaction plate, 0.1 ml, 4346907optical adhesive film, eg.: LifeTechnologies MicroAmp Optical Adhesive Film, 4360954DSF/Protein Thermal Shift ExperimentsDSF compatible dye, eg.: LifeTechnologies Protein Thermal Shift Dye Kit, 4461146 (Sypro Orange)samples, ligands, buffers qPCR ExperimentsqPCR reagents (eg. LifeTechnologies PowerUp SYBR Green Master Mix, A25742)primers and templates 

qPCR Resources

CMI QuantStudio qPCR Getting Started Guide

Quant Studio 6/7 Quick Reference Guide from Applied Biosystems by Life Technologies

NanoDSF ResourcesPrometheus NT.Plex Supplies

NanoDSF Resources

CMI Prometheus NanoDSF Getting Started Guide

NanoDSF Technology Page NanoTemper Technologies

NanoDSF Supplies

NT.Plex Capillary Chips

2x 8 Standard 24-Capillary Chips, NanoTemper Catalog # PR-AC002

2x 8 High Sensitivity 24-Capillary Chips, NanoTemper Catalog # PR-AC006

384-well plates for loading capillaries

Protein samples, ligands, buffers

See also: molecular properties, Differential Scanning Fluorimetry, DSF

TechnologiesInstrumentation OverviewSurface Plasmon Resonance (SPR)Biolayer Interferometry (BLI)MicroScale Thermophoresis (MST)Isothermal Titration Calorimetry (ITC)Circular Dichroism (CD)Differential Scanning Fluorimetry (DSF)Light Scattering

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dsf职业年金什么意思啊？ - 知乎

dsf职业年金什么意思啊？ - 知乎首页知乎知学堂发现等你来答​切换模式登录/注册养老保险企业年金保险投资万能险年金险dsf职业年金什么意思啊？关注者5被浏览2,079关注问题​写回答​邀请回答​好问题​添加评论​分享​2 个回答默认排序麦尼财经​CFA 特许金融分析师资格证持证人​ 关注关于社保，企业年金，个人养老金账户，很多人都分不清楚，只知道是和养老有关。前面已经有文章详细讲解了社保(参考旧文《退休后，社保能领多少钱》)，和个人养老金账户（参考旧文《个人养老金账户》），这里就企业年金做一个详细说明，建议收藏＋关注，就算你现在没有企业年金，说不定过几年就有了呢。01 什么是企业年金？企业年金是公司为员工养老提供的一种福利计划。我们都知道，现在一般公司都会给员工上社保，给员工提供一个基本保障。有条件的公司，除了社保之外，还给员工多交一部分钱，也就是企业年金，经常听到有人说的五险二金，或者六险二金。这“二金”就是住房公积金和企业年金。和社保不同的是，社保比较普遍，企业年金只有一些大公司，或者盈利状况比较好的公司才有。因为老龄化越来越严重，政府也鼓励企业设立企业年金账户，给员工做养老补充，所以近几年，也有越来越多的企业开始设立企业年金。和企业年金类似的，还有职业年金。职业年金是机关事业单位的养老补充计划，其性质及运营模式和企业年金基本一致。02 哪些人享有？企业年金根据人社部披露的数据，截止2022年底，共有12.8万家企业设有企业年金，参加的职工共有3010.29万。对于个人来说，能否享受企业年金的福利，完全取决于自己所在的公司是否设有企业年金。企业年金对于企业来说，是一笔不小的成本，但是也有越来越多的企业设立企业年金，以此来吸引人才并且留住员工。职业年金职业年金是强制性的，不是单位想设立就设立，不想设立就不设立的。所有的机关事业单位都有职业年金，比如医生、老师、公务员，都享有职业年金。03 现在交多少钱？谁来交钱？企业年金企业年金在缴费上，和社保类似，都是由企业和员工共同承担。按照最初提出企业年金时候的规定：双方缴费金额合计不能超过员工工资的12%。其中企业缴费不能超过8%，各自缴费的比例，也可以由企业和员工共同协商决定。但是现在，这个比例也有所调整，现在企业缴纳的比例是不设上限的。职业年金职业年金是强制性的，其缴费标准也是强制性的，没有什么弹性空间。就是单位缴纳员工工资的8%，个人缴纳4%。个人缴纳部分通过企业代扣代缴，也就是发工资之前，先把这一部分扣除了，从个人的税前收入里扣，扣除完这部分之后的工资所得，再扣个税。企业（单位）缴纳部分，8%以下部分，也是从税前扣，也就是给员工缴纳企业年金（职业年金）的这部分钱，是不用交税的。8%以上部分，需要从企业税后所得里扣除。所以，一般情况下，企业缴纳金额都在8%。总的来说，就是工资越高，企业年金越多。04 这些钱谁来管理？很多人都担心：如果企业活不到我退休呢？就算公司有企业年金，但是还没等到我退休，公司就破产了，那企业年金还有用吗？我退休以后还能领到钱吗？这个问题不用担心，企业年金的钱是专门设立账户的，这些钱由专门的资管机构做投资管理。截止2022年底，全国企业年金累计总规模为2.87万亿元。不同的企业，企业年金可能会托管在不同的资管机构：看到这，可能又有一个问题：这些资管机构管理，会不会亏损呢？客观来说，所有的投资都有可能亏损。企业年金也不例外，比如去年，也就是2022年，企业年金收益率为-1.83%。企业年金的投资管理风险还是相对比较低的，其投资标的和投资范围都有严格的限制，大部分比例都是投资风险等级低的金融产品，所以长期看来，不太可能出现大幅亏损，偶尔出现小幅亏损也在所难免。05 中途离职，这些钱还能拿到吗？这也是很高频的问题，如果离职了，企业年金还能拿到吗？企业年金的钱分为两部分，公司交的和自己交的，分别在企业账户和个人账户。个人账户的钱，一直都是自己的，即使离职了，这部分也在自己的账户里。等到退休的时候，可以领取个人账户里的钱。企业账户里的钱到底能不能领取，这个比较复杂，具体要看企业的规定。比如有的企业规定，要工作满5年，才兑现一年的企业年金，5年以后，每多一年，就多兑现一年的钱。如果5年之内离职，就一点也享受不到企业缴纳的那部分钱。也有比较人性化的企业，不管工作几年，企业缴纳部分全部转给员工让其带走。但是为了留住员工，一般企业都有年限限制。当然，企业设置这个年限，也受约束，最长不能超过8年。当然，也有的企业，没等你离职，也没等你退休，因为效益不好，就先暂停企业年金了。06 什么时候开始领取？最后，就是大家最关心的问题，企业交的这笔钱，什么时候能领？从一开始就说了，这就是个补充养老的福利，所以得等到退休养老的时候才能领。退休之后，有两种领取方式：可以一次性领出来，也可以按月领取。企业账户和个人账户的钱是随时能查看的，所以退休的时候，能看到一共有多少钱。一次性领取就是账户当时的累计金额一笔领出。按月领取的话，先计算出领取月数，可能按照企业预估的员工平均寿命，退休之后还剩余多少个月，就把账户累计金额均摊到这些月发放；也可能按照企业约定的固定年限，比如发放二十年，就把账户累计金额均摊到这二十年的每个月发放。在领取上，和社保有两点不同：第一，社保是交满15年才能领取，企业年金没有这个强制要求，对这个年限要求比较灵活，各企业规定都不一样。第二，社保养老金是领取至终身，企业年金是把账户里的钱领完为止。什么时候领完，就停止不再发放了。最后，还有一个问题，如果企业年金账户里的钱还没领完，人就不在了，那剩余的钱怎么办呢？账户里剩余的金额是可以继承的，如果是这种情况，剩余的钱就作为遗产交给继承人。发布于 2023-04-17 22:04​赞同​​添加评论​分享​收藏​喜欢收起​牛肉西红柿交通银行客户经理​ 关注d+sf养老金是dsf职业年金哦！指事业单位和行政单位为雇员或公职人员提供补充养老保险的总称。在国家统一监督、指导下，由公职人员和事业单位共同缴费组成养老保险费。为公职人员提供退休收入保障的养老金保险制度。亲，dsf养老金是每个月发一次，而不是一年一次。我国的职业年金是社保之外的另一个补充xing养老保险，在职时按月按比例交费，由单位和个人共同承担，建立个人的专门帐户，办理退休后按月按照计发月数以固定金额的形式予以发放。按单位8%和个人4%存入养老保险个人账户。退休后有两种方式领取1.是全部购买商业保险，按约定每月领取。2.是按退休法对应的计发月数发，发完为止。社保局还会对某些自主创业或者灵活就业人员进行鼓励性补贴。发布于 2023-04-14 13:02​赞同​​添加评论​分享​收藏​喜欢收起​​

上海交大何亚文团队联合全球相关科研人员发表“群体感应信号DSF介导的种内、种间和跨界信号交流”综述文章_交大智慧_上海交通大学新闻学术网

上海交大何亚文团队联合全球相关科研人员发表“群体感应信号DSF介导的种内、种间和跨界信号交流”综述文章_交大智慧_上海交通大学新闻学术网

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上海交大何亚文团队联合全球相关科研人员发表“群体感应信号DSF介导的种内、种间和跨界信号交流”综述文章

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近日，国际微生物学权威综述期刊Trends in Microbiology在线发表了上海交通大学生命科学技术学院何亚文教授团队的综述文章“DSF家族群体感应信号介导的种内、种间和跨界信号交流 (DSF-family quorum sensing signal-mediated intraspecies, interspecies, and inter-kingdom communication）”。何亚文教授为综述第一作者和通讯作者，中山大学邓音乐教授、新加坡南洋理工大学缪岩松教授(Prof. Yansong Miao)为综述共同第一作者，西北大学田静教授、印度DNA指纹识别和诊断中心(Centre for DNA Fingerprinting & Diagnostics) Subhadeep Chatterjee研究员、美国加州大学伯克利分校(University of California, Berkeley)Steven Lindow院士和南阿拉巴马(University of South Alabama) Tuan Minh Tran助理教授参与了该综述文章的写作。DSF (Diffusible signaling factor)信号分子是多种革兰氏阴性细菌产生的一类中链顺式不饱和脂肪酸(图1)。这些微生物通过识别自身合成与分泌的DSF，感应自身群体细胞密度，诱导相关基因的表达，相应调整细胞代谢与生理状态。根据信号接收与传导机制，DSF介导的种内交流主要分为三类：(I)植物病原黄单胞菌为代表，DSF由RpfC/RpfG双组分系统接收与传导。上海交通大学何亚文教授团队一直引领这一研究领域，先后鉴定了黄单胞菌中DSF家族信号分子的化学结构、信号传导途径和调控的生物学功能，阐明了DSF生物合成途径与DSF翻转(turnover)的分子机制, 进一步发现寄主植物免疫系统与黄单胞菌DSF群体感应系统之间存在着精细的相互作用(图2)。(II)动植物病原伯克氏菌为代表，DSF由调控蛋白RpfR接收与传导。中山大学邓音乐教授团队一直引领这一研究领域(图3)。(III)以条件致病绿脓杆菌为代表，DSF接收与传导机理尚待研究。图1 三种DSF-介导的种内交流系统及其所依赖的信号分子图2 野油菜黄单胞菌中DSF生物合成与信号通路图3 伯克氏菌感应BDSF信号及其传导通路DSF产生菌也能够利用DSF信号与其它细菌之间开展种间交流(图4)，这些细菌包括伯克氏菌(Burkholderia spp.)、嗜麦芽寡养单胞菌(Stenotrophomonas maltophilia)、绿脓杆菌(Pseudomonas aeruginosa)、弗郎西斯菌(Francisella novicida)、芽孢杆菌(Bacillus)、噬菌蛭弧菌(Bdellovibrio bacteriovorus)、沙门氏菌(Salmonella)等。在目前发表的文献中，多数DSF产生菌均利用分泌的DSF信号分子干扰其它细菌的生长或发育，限制其发展，保持自身在局部环境的竞争力。DSF-产生菌还能与真核生物之间开展跨界信号交流(图5)。DSF家族信号分子显著影响白色念珠菌（Candida albicans）菌丝体发育和生物膜形成，影响其致病性。加州大学伯克利分校Steven Lindow教授团队系统阐明了苛养木杆菌利用DSF信号分子调控其在利器叶蝉(sharp shooter leaf hoppers)前肠中的定殖能力，有利于苛养木杆菌通过这种刺吸式昆虫转移到葡萄等寄主植物维管束中。南洋理工大学缪岩松教授团队首先发现DSF可以诱导植物细胞膜甾醇富集，高度诱导植物细胞壁纤维素合成，以及影响细胞膜上免疫功能分子的生化特性，比如植物鞭毛蛋白受体聚集和内吞，从而抑制植物病程相关分子模式激发的免疫反应(PTI)；进一步研究发现DSF 影响植物细胞壁-质膜-微丝骨架复合体[plant cell wall-plasma membrane-actin cytoskeleton (CW-PM-AC) continuum]的生物物理特性, 扰乱细胞膜表面上蛋白分子的识别与大分子组装，导致植物免疫系统信号失调 （图5）。西北大学田静教授团队发现DSF信号可以有效抑制斑马鱼中脂多糖(LPS)诱导的炎症反应，其作用机理可能是干扰Toll-样受体通路抑制下游炎症因子的表达以及影响caspase 家族级联反应激活，抑制溶酶体组织蛋白酶基因表达从而减轻炎症导致的细胞凋亡。阐明DSF介导的种内、种间与跨界交流现象与机制进一步深化了微生物社会学的内涵，同时也有助于研发新型抗感染策略图4 DSF-介导的种间与跨界信号交流图5 DSF在拟南芥和斑马鱼中的信号途径本研究得到了国家重点研发计划、国家自然科学基金和上海农乐生物制品股份有限公司资助。感谢张炼辉（Lian-Hui Zhang）教授的指导、支持与鼓励。论文链接：https://www.cell.com/action/showPdf?pii=S0966-842X%2822%2900188-3

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DSF是什么？ - 知乎首发于DSF——全球首个DeFi社交平台切换模式写文章登录/注册DSF是什么？得道社区 DSF是全球首个已经落地使用的去中心化社交金融区块链平台，也是革新现有MakerDAO和DeFi，以及承载互联网金融行业转型的项目中，公认的最受瞩目最为可行的实现方案。 DSF网络具有自主扩展和极速裂变的特点。在DSF网络中，每个节点用户都可向下开发，构建起一个巨大的无穷尽的社交网络，从而有能力去承载丰富多样的区块链金融产品。 相对于现有区块链金融平台，DSF为链上社交金融垂直领域带来了一个极高可扩展性，且极低成本的分布式应用运行、开发、维护和裂变平台，给诸多挣扎在高昂开发成本，技术难关和流量封锁的区块链金融产品团队，或正在艰难转型的互联网金融企业带来曙光。 DeChat则是DSF的首个落地杀手级DAPP，携1200万用户城池部署上链，正是由于DeChat如此超凡的竞争力，不少人认为DSF很有可能在2020成为巨无霸项目。DSF正是DeChat平台通证DE的映射生态母币。 除此之外，从最近的情况来分析，DSF链上社交金融平台开发中最困难的环节已经接近完成，在5月发布的Q3 Roadmap中，我们可以看到DSF的三大重要更新，体现了项目核心团队过去两年对区块链技术落地演进的高度思考，分别是： 1）去中心化社交网络，具备无限扩展和激励机制的社交关系上链； 2）去中心化金融钱包，通过智能合约调用用户社交网络，并提供社交金融类服务； 3）不同类型的智能合约，用于社交关系确认，基于社交关系的系统挖矿奖励，以及区块链金融产品对社交关系的使用。 正如IPFS的出现颠覆了分布式储存和共享文件的网络传输逻辑，我们认为DSF的三大核心功能更新也将改变“链上·社交·金融”现有的运转轨迹： 社交关系上链→形成去中心化社交网络→通过智能合约调度去中心化社交网络服务于区块链/互联网金融产品的开发、运营、传播→DSF Token全流程、全生态嵌入/治理/激励→社交推进金融，金融促进社交。 至此，一个更安全、便捷、极具滚雪球效应的，可无穷延展的链上社交金融生态网络闭环形成。风口之下，DSF Token的价值预期在于其顶层商业设计、资本实力、用户城池壁垒，以及团队在垂直领域的多年深耕，要知道设计出一款具有变革意义，并且自传播裂变效果很好的产品绝非易事，这是团队在社交、金融、区块链行业经验、渠道、资源质变的结果。 DSF就像是一把开启链上社交金融2.0时代新大门的钥匙，正以我们看不见的速度飞快发展，为行业打开全新的局面，越来越丰富的产品矩阵也会随之出现。 届时，DSF Token的流动性，DeChat日均UV和社交网络的裂变效应也将愈发明显，而这些最终都将表现在其通证的筑底价格稳固和币价上涨速度上。 一句话总结，DSF有打造出一个超量级去中心化社交金融网络的势能，有落地产品，有钱包入口，有盈利模型，有用户基础，并处于爆发增长赛道。 总而言之，无论是区块链社交还是Defi，DeChat团队都展现出了其强大的底蕴，特别是1200万用户这一必杀技，更是秒杀了行业内同类型的项目，使那些项目瞬间相形见绌，根本无法与DeChat相提并论。 实际上，行业内的反响也正是这样，无论是DeChat在朋友圈的火爆，还是ZB交易所的提前预定，都从侧面证明了DeChat的实力。 2020年转眼已经过半，如果说今年真的会出现一个现象级的项目的话，我认为DeChat绝对有这样的实力。 如果在你的投资布局中，有一部分是准备配比给一些新兴潜力领域的，DSF应该在其中。2014年那波熊市中诞生的以太坊，不就是这么一路走过来的千倍币吗？发布于 2020-06-28 15:01去中心化区块链技术token​赞同 1​​1 条评论​分享​喜欢​收藏​申请转载​文章被以下专栏收录DSF——全球首个DeFi社

Theory and applications of differential scanning fluorimetry in early-stage drug discovery | Biophysical Reviews

Theory and applications of differential scanning fluorimetry in early-stage drug discovery | Biophysical Reviews

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Biophysical Reviews

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Theory and applications of differential scanning fluorimetry in early-stage drug discovery

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Published: 31 January 2020

Volume 12, pages 85–104, (2020)

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Theory and applications of differential scanning fluorimetry in early-stage drug discovery

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Kai Gao1, Rick Oerlemans1 & Matthew R. Groves 

ORCID: orcid.org/0000-0001-9859-51771 

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AbstractDifferential scanning fluorimetry (DSF) is an accessible, rapid, and economical biophysical technique that has seen many applications over the years, ranging from protein folding state detection to the identification of ligands that bind to the target protein. In this review, we discuss the theory, applications, and limitations of DSF, including the latest applications of DSF by ourselves and other researchers. We show that DSF is a powerful high-throughput tool in early drug discovery efforts. We place DSF in the context of other biophysical methods frequently used in drug discovery and highlight their benefits and downsides. We illustrate the uses of DSF in protein buffer optimization for stability, refolding, and crystallization purposes and provide several examples of each. We also show the use of DSF in a more downstream application, where it is used as an in vivo validation tool of ligand-target interaction in cell assays. Although DSF is a potent tool in buffer optimization and large chemical library screens when it comes to ligand-binding validation and optimization, orthogonal techniques are recommended as DSF is prone to false positives and negatives.

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IntroductionBiophysics drives modern drug discovery efforts, allowing rapid and high-throughput data acquisition to screen through large compound libraries in an effort to identify new bioactive molecules. An important component of this biophysics armory is the thermal shift assay, also commonly known as differential scanning fluorimetry (DSF) (Semisotnov et al. 1991). DSF is a cost-effective, parallelizable, practical, and accessible biophysical technique widely used as a method to track both protein folding state and thermal stability. It provides a reliable tool to examine protein unfolding by slowly heating it up in a controlled environment. By measuring the corresponding changes in fluorescence emission upon temperature increase, the process of protein denaturation can be monitored. Since changes in sample behavior through complex formation with even weakly binding ligands affect protein thermal stability, the technique has seen many successful applications and has been used in different ways over recent years. It has been utilized primarily as a drug discovery method to identify promising lead compounds for a number of target proteins for decades (Pantoliano et al. 2001). Another major application for DSF is in protein buffer optimization, identifying optimal conditions for storage, assay screening, and crystallization. By screening sparse matrix conditions, encompassing different buffer systems that cover a wide range of pH, additives, and salt concentrations, optimal buffer components can be identified for each individual protein. This has been shown to increase the success rates of protein crystallization in past decades (Huynh and Partch 2015). More recently, DSF has also been applied to the challenge of sample preparation, with two publications demonstrating that suitable screening approaches can be used to identify and optimize sample refolding buffers—allowing significantly cheaper access to the amounts of protein sample required to support high-throughput screening campaigns (Biter et al. 2016; Wang et al. 2017). Finally, a very recent development has shown that DSF is able to provide reliable data in complex solutions, such as unpurified chemical reactions. This is an exciting development, as the production and purification of chemical entities are a major bottleneck in any screening campaign.While the robustness of the DSF method and its broad applicability in both sample preparation and screening has led it to become an important biophysical tool in drug discovery, it is important to bear its limitations in mind. This is particularly true when designing a screening campaign, as such a campaign should contain orthogonal screening options that are not susceptible to similar limitations—in order to minimize both false positives and false negatives.In this review, we will provide a theoretical background of DSF as well as examples of its use in the various aspects of drug discovery introduced above—including the latest applications of DSF by ourselves and other researchers. We will also attempt to place DSF within the variety of biophysical methods currently used in screening campaigns and highlight areas of overlap or mutual limitations.Theory of differential scanning fluorimetryIn 1997, Pantoliano et al. (1997) introduced a new thermal shift assay system used in the screening of combinatorial libraries against different receptor proteins. Compared with conventional methods of the time, such as those based on calorimetry and spectral technologies (Bouvier and Wiley 1994; Weber et al. 1994), the newly developed system could implement high-throughput screening instead of assaying a single condition at a time. The custom-designed 96- or 384-well plates and fluorescence readout apparatus could easily monitor protein unfolding in multiple conditions, with different ligands and/or at different ligand concentrations in a single experiment. This helped researchers overcome a lot of cumbersome, slow, and labor-intensive work required by traditional methods. Rather than the need for a dedicated device, many labs already possess (or have access to) real-time polymerase chain reaction (RT-PCR) equipment that allows for fluorescence measurements over a controlled temperature range. Access to such equipment, the development of more sensitive dyes, and improved protocol design drove the use of DSF (Niesen et al. 2007).Proteins are in a thermodynamic equilibrium between folded and unfolded states (Bowling et al. 2016). An increase in energy of the environment (i.e., increase in temperature) pushes a protein toward the unfolded state which, when quantified, allows for the determination of the melting temperature (Tm), defined as the temperature at which 50% of a protein sample is in folded and 50% is in an unfolded state (Lo et al. 2004) (Fig. 1a). A change in the protein environment (including pH, ionic strength, or the presence of specific anions or cations) and/or complex formation with other molecules can stabilize a protein through a reduction of the Gibbs free energy of the complex, resulting from the creation of new molecular interactions (hydrogen bonds, van der Waals interactions, etc.) or conformational reordering of the target protein. This increase in the Gibbs free energy results in an increase in thermal stability and thereby an increase in the melting temperature (Tm). Measurements of the Tm of a protein in the presence and absence of environment changes or ligands result in an estimate of the thermal shift (ΔTm) deriving from these differences (Scott et al. 2016) (Fig. 1b). This shift is typically an indicator of complex formation and/or thermal stabilization. However, it should be borne in mind that while the resulting temperature shift is directly related to the change in the Gibbs free energy, it is a measurement deriving from both binding interactions and any resulting conformational changes in the target protein, and as the thermal stability profile is generated over a temperature range, it is difficult to generate a reliable room temperature dissociation constant (kd = exp −∆G/kT; k = Boltzmann’s constant and T = thermodynamic temperature) directly from ΔTm. However, solely concentrating on Tm may mean that other systemic and thermodynamic information about protein stability can be lost. The propensity of the protein to aggregate in certain conditions is one such factor. An environmental change could result in a difference in aggregation behavior but leaves the Tm unchanged. For an in-depth review on this topic, please see Wakayama et al. (2019).Fig. 1a Typical thermal denaturation profile of a protein sample. Fluorescence emission changes with the temperature. The sigmoidal curve indicates the cooperative unfolding status of the protein from trace amounts of SYPRO Orange (yellow) bound to the native protein (green). The peak indicates that all proteins are unfolded to linear peptides or that the hydrophobic core is exposed to SYPRO Orange. Multiple mechanisms exist for the reduction in fluorescence after the peak, including temperature-driven decrease in the binding constant of the dye (so less dye is bound to the protein), the pocket binding the dye being more mobile (allowing for more quenching by solvent); the dye itself is more mobile such that the degree of planarity required for electron conjugation/aromatic character is lessened and protein aggregation and dye dissociation through the exclusion of the dye from hydrophobic cores. The midpoint of the transition curve is the melting temperature (Tm). b DSF curve showing the unfolding status of a target protein in the absence (blue) and presence (orange) of a ligand. The difference in the melting temperature indicated as ΔTm. c Sample with high background fluorescence at the beginning at lower temperature (red) compared with a typical well-folded sample (blue) in the DSF assay. Improperly folded, aggregated, denatured protein or hydrophobic area such as a lipid bilayer exposed to the dye will cause high background at low temperatures. d Multiple transitions appearing during the heating process can be caused by different domains, aggregation increasing with temperature, or ligands that stabilize a portion of the protein sample (orange); typically one Tm similar to the native protein is accompanied by one or more Tm at a higher temperature during the denaturation. e–g Overview of NanoDSF. e Intrinsic fluorescence of tryptophan is measured at both 330- and 350-nm wavelengths and plotted versus temperature from 20 to 60 °C during unfolding. f F330/350 fluorescence ratio intensity of tryptophan plotted against temperature. g The melting temperature is calculated by the first derivative of the F330/350 plots, with the sample given here showing a Tm of 48 °C. All the figures above represent thermal unfolding curves of the menin protein and are obtained from DSF experiments conducted in our lab. The experiments were performed by using either the Bio-Rad CFX96 Real-Time PCR system or the NanoTemper Prometheus NT.48 system. Curves were plotted from the fluorescence data using ExcelFull size imageIn order to monitor the thermal unfolding transition of target protein in a suitably sensitive but precise way, fluorescence has been used as the response signal. There are two main sources of this fluorescence in use today that may be broadly classed as (i) extrinsic fluorescence and (ii) intrinsic fluorescence.Extrinsic fluorescenceThe fluorescence of extrinsic fluorescent dyes is sensitive to their environment. Typically such dyes are quenched in aqueous solutions with proteins in their native folded state and provide a fluorescence signal only when the target protein begins to unfold. This unfolding allows the freely diffusing dye to interact with the exposed residues of the hydrophobic core (Fig. 1a). This approach relies on the following assumptions (in rough order of frequency as experienced by the authors):

a.

The target proteins do not possess significant hydrophobic patches on their exposed surfaces, the presence of which would lead to increased background in fluorescence (Fig. 1c).

b.

The protein is in a stable state at the beginning of the experiment, and DSF experiments using extrinsic dyes are typically performed at concentrations of 0.1–0.5 mg/ml (0.01–0.1 μM). Aggregation and/or sample instability may lead to the presence of multiple species of target protein within the experiment, both leading to increased fluorescence background from any conformational variability and resulting in variable thermal stability profiles of the different order oligomers (Fig. 1c).

c.

The target protein shows no significant binding interaction(s) with the dye in use—resulting in the shielding of the dye from the aqueous environment prior to protein unfolding and a resulting increase in fluorescence background.

d.

The target protein is composed of a single domain, as the unfolding of distinct domains is likely to occur with different Tm values resulting in a complex thermal stability profile (Fig. 1d). However, while the profile might be more complex, it is often easier to differentiate between the signals from multiple domains and this can provide valuable information as seeing a Tm shift more strongly in a specific domain can provide information about a potential binding site.

e.

No major structural rearrangements of the target protein are provoked by an increased temperature prior to its unfolding, although in such cases, deconvolution of the thermal stability profile may still be possible.

f.

The sample and dye do not chemically react with other components present in the experiment over the temperature range used.

Dyes in common usageThere are many commercial dyes available (Hawe et al. 2008). Dyes such as bis-ANS and Nile Red have been used for decades; the extrinsic dyes are summarized in Table 1. However, these dyes all possess a significant background in the presence of folded proteins. To date, the most favored dye for DSF is SYPRO Orange, mainly owing to its high signal-to-noise ratio (Niesen et al. 2007), as well as its relatively long excitation wavelength (near 500 nm). This minimizes the interference of most small molecules as these typically have absorption maxima at shorter wavelengths.Table 1 Overview of extrinsic fluorescence dyes applied in protein characterizationFull size tableIntrinsic fluorescenceAnother source of fluorescence is from the protein sample itself. In 2010, Schaeffer’s team reported a new method, using green fluorescent protein (GFP) to quantify the stability of a target protein (Moreau et al. 2010). In these experiments, a GFP tag was fused to a protein of interest through a peptide linker and used as a reporter system for protein unfolding and aggregation. The fluorescence signal from the GFP changes based on its proximal environment, meaning its signal can be used to monitor the unfolding of the protein it was linked to. Since GFP only starts losing fluorescence around 75 °C, this approach suits a large number of proteins which are significantly less stable than GFP (Moreau et al. 2010). While this is potentially an elegant solution to remove reliance on a fluorescent dye reporter, there do remain a number of limitations:

a.

The potential for interaction between GFP and the target of interest influencing the target protein conformation, thereby introducing a bias into the measured interactions with ligands.

b.

The potential for a GFP-linked domain to influence the oligomeric state of the target protein—either promoting or inhibiting assembly—with a similar effect on the target protein conformation.

c.

This approach is unsuitable for protein targets that have a similar Tm to that of GFP—in which case the unfolding signal of the target protein will be masked by that of GFP

d.

Ligands that may result in a significant elevation of the target-to-ligand complex Tm will not be clearly observed due to a similar masking effect.

e.

This approach is unable to directly distinguish between compounds that interact with GFP and those that interact with the target protein, although this can be addressed through the use of a GFP only control.

In 2014, a label-free DSF technique marketed as nanoDSF was developed (Alexander et al. 2014). This approach removes the requirement for an extrinsic dye or fusion tag, instead of relying on the change of intrinsic tryptophan fluorescence at 330 nm and 350 nm (Fig. 1e). Unfolding/denaturation results in a change in the microenvironment polarity around tryptophan residues, leading to a redshift of fluorescence (Ghisaidoobe and Chung 2014). In this approach, the Tm can be determined by measuring the ratio of the fluorescence at 330 nm and 350 nm against temperature (Fig. 1f, g). The commercial instrument Prometheus NT.48 (NanoTemper Technologies, Munich) allows a rapid analysis for both ligand screening and buffer composition optimization and, unlike the previous approaches, allows for measurements to be made in detergent-containing solutions—a prerequisite for DSF application to membrane proteins. Due to the nature of extrinsic dyes, which can bind (and fluoresce) in the presence of lipid bilayers and detergent micelles, conventional DSF cannot handle the detergent selection for membrane protein solubilization. The dye-free nanoDSF avoids this problem by using intrinsic fluorescence. Another benefit to intrinsic fluorescence is the ability to observe the transition both from folded to unfolded states and from unfolded to folded states. This allows for the detection of hysteresis (Andrews et al. 2013). The presence of hysteresis can provide information about protein stability (Mizuno et al. 2010). Due to the presence of dye, this is not possible when using an extrinsic fluorescence approach. However, the intrinsic fluorescence method also has several key limitations:

a.

The number of tryptophan residues in the target protein amino acid sequence needs to be considered before adopting this approach, since at least one tryptophan has to be present and the ratio of tryptophan present in the target protein sequence is the limiting factor to detect an unfolding signal.

b.

Experiments that result in complex populations in the thermal profile (e.g., presence of both bound and unbound states—see below) may not be successfully identified due to signal sensitivity.

c.

This approach requires a significantly larger investment for the associated equipment.

Finally, it should be clearly borne in mind that all DSF approaches are sensitive to the intrinsic fluorescence properties of the molecules present in the screen under examination, which can result in a wide variation in the background of thermal profiles—resulting in false negatives. While the use of extrinsic dyes alleviates this to some extent, as the role of the dyes in use is to significantly amplify the unfolding signal, there still remains the potential for screening components to interact with the reporting dye.Recent applications of DSFLigand screening in drug discoveryDetermining the interaction between receptors and members of a small molecule library is addressed by detecting and measuring changes in the physicochemical properties of any ligand-to-target complexes that are formed. Quantitative information arising from receptor-ligand complex formation can then drive the development process through structure-activity relationships (SAR). In the last few years, great efforts have been expended to find a general and universally applicable approach to detect binding (and ideally estimate binding affinity, Kd) between biomolecule receptors and small molecule ligands. As a result, many new biophysical technologies have emerge, briefly:

a.

Differential scanning calorimetry (DSC), which monitors the change in heat capacity of protein samples undergoing temperature-induced melting transitions in the presence and absence of small molecule ligands (Pantoliano et al. 1989).

b.

Isothermal titration calorimetry (ITC), which compares the temperature differences between a reference and receptor solution to quantify the kinetic parameters of binding (Herrera and Winnik 2016).

c.

Surface plasmon resonance (SPR), which records the angular shift of polarized light reflected from a metal film, containing a surface-immobilized target leading to changes in refractive indices upon association and dissociation of ligand (Navratilova and Hopkins 2010).

d.

Microscale thermophoresis (MST), which detects the thermophoretic behaviors of receptors in the presence of ligands under heating in capillaries (Wienken et al. 2010).

e.

NMR-based chemical shift screening, ligand-based or protein-based NMR monitors chemical shift perturbation induced by ligands; thereby, both Kd and the structural conformation of complexes can be determined.

f.

X-ray crystallography–driven fragment optimization based on the electron density of ligands, providing interaction details at atomic resolution.

g.

Mass spectrometry–based approaches, protein samples, and bound ligands are ionized preserving non-covalent interactions. Subsequently, the mass of protein and ligands can be acquired with high accuracy (multiple instances are provided in the table below).

h.

Biolayer interferometry (Wartchow et al. 2011) provides similar binding information to that obtained by SPR, with advantages in signal stability arising from the use of interferometry patterns.

MethodPrincipleAdvantagesLimitationsRefLigand-observed NMRShift change in magnetic state of ligand due to bindingMany fragments can be tested simultaneouslyUses a lot of protein. Limited to fragments with fast exchange with targetKrimm (2017)Protein-observed NMRProtein NMR peak shift induced by bindingAble to determine binding site. Titration possible to determine KDRequires large amounts of protein. Limited throughputKrimm (2017)X-ray crystallographyX-ray diffraction of cocrystallized protein-ligand complex or soaked apo-crystalProvides structural information of ligand-binding mode and interactions with the target. Enables use of computational methods of hit optimizationNeeds good-quality crystals. Not all the ligands can acquire cocrystal structures with protein target. Needs synchrotrons to obtain x-ray diffraction data. Requires large amounts of ligandBadger (2012); Patel et al. (2014)SPRRefractive index change due to ligand binding to immobilized target on sensorAble to easily obtain KD and other kinetic data. Uses very little proteinProtein needs to be able to be immobilizedNeumann et al. (2007); Chavanieu and Pugnière (2016); Huber et al. (2017)DSFThermal stability of protein is increased due to fragment bindingHigh throughput, cheap materials, equipment easy to use and widely availableMany false positives and negatives. Typically only provides a yes/no answer. Requires a dye or intrinsic fluorescenceLo et al. (2004); Douse et al. (2015); Bai et al. (2019)Isothermal titration calorimetry (ITC)Heat of the system changes upon binding eventThermodynamic and binding properties of protein—fragment interaction can directly be obtained. Label-freeUses large amount of protein; low throughputChaires (2008); Ladbury et al. (2010); Renaud et al. (2016)Differential scanning calorimetry (DSC)Amount of heat required to increase temperature of sample changes upon bindingHighly sensitive method. Label-freeUses a lot of protein. Low throughputCooper (2003); Bruylants et al. (2005); Erlanson et al. (2016)Native mass spectroscopy (MS)Mass detection of protein-ligand complex in gas phaseHighly sensitive method. Uses very little protein. Label-free. Provides large amount of information, binding affinity, stoichiometryProtein has to be stable in ESI bufferQin et al. (2015); Pedro and Quinn (2016); Ren et al. (2019)Size exclusion chromatography (SEC) MSIncubation of protein in fragment mixture then separation of bound from unbound molecules by SEC, followed by MS detectionVery high throughput. Easy to perform technique requiring simple LC-MSPotential for false negatives for low affinity binders; these can easily get lost during the SEC stepQin et al. (2015); Chan et al. (2017); Ren et al. (2019)Weak affinity chromatography (WAC) MSSeparation of molecules by affinity to immobilized receptor on the WAC column followed by MS detectionEasy method to use. High throughput possible by using fragment mixturesProtein needs to be immobilized on the column(Duong-Thi et al. 2011; Chan et al. 2017; Ohlson and Duong-Thi 2018)Hydrogen-deuterium exchange (HDX) MSLigand binding affects deuteration rate of protein residues. Which is detectable by massBinding site can directly be elucidated and gives information about protein conformational changesLow throughput and expensiveChan et al. (2017); Marciano et al. (2014)Microscale thermophoresis (MST)Change in the molecular motion of the target in a temperature gradient due to ligand bindingMeasurements can be performed in native buffers. Allows for KD determinationTarget needs to be labeled or have sufficient intrinsic fluorescence. Relatively low throughputLinke et al. (2016); Rainard et al. (2018)Affinity capillary electrophoresis (ACE)Change in electrophoretic mobility of the ligand due to binding to target (in solution)High throughput. Sensitive method. Uses small amounts of protein and ligand. Both target and ligand are free in solutionRequires detectable probe molecule or detectable fragmentsXu et al. (2016); Austin et al. (2012); Farcaş et al. (2017)Biolayer interferometry (BLI)Interference pattern change due to ligand binding to immobilized target on biolayerCan obtain KD and other kinetic parameters. Uses a small amount of proteinImmobilization of protein is requiredWartchow et al. (2011)With the advent of modern advances in bioinformatics and proteomics, many new disease targets have been identified (Lippolis and Angelis 2016). In parallel chemical synthesis, methods are more advanced and refined, being capable of rapidly producing large libraries of diverse compounds. A particularly important subgroup of these methods is those that are compatible with multicomponent reaction (MCR) chemistry (e.g., the UGI reaction) which can generate large libraries of highly specific compounds in a short amount of time. However, the pace at which chemical libraries could be screened using conventional techniques such as NMR and ITC often could not keep up with the speed that the libraries were being created, or the numbers of discrete molecules contained in these libraries.Modern DSF is well placed to address these large and diverse libraries, as it utilizes a real-time PCR machine to rapidly screen multiple molecules at once against the target protein, meaning it can handle the high throughput of compounds much better than many other technologies. With relatively low consumption of protein sample, 96, 384, or 1536 ligands can be analyzed in a single screen that takes ~ 1 h and provides qualitative binding information; it is well-suited for high-throughput library screening. This efficient workflow makes it possible to judge and rank potential binding affinity.In 2001, Pantoliano introduced a DSF-based high-throughput methodology for a variety of therapeutic target proteins (human α-estrogen receptor (ESR), bacteriorhodopsin, human α-thrombin, bovine liver dihydrofolate (DHFR), the extracellular domains of the fibroblast growth factor receptor-1 (D(II)-D(III)FGFR), and the enzyme PilD; Pantoliano et al. 2001). These targets were screened against various small molecules from combinatorial libraries, including known binding ligands. Experiments showed that the Kd calculated from Eq. (1) based on the Tm values obtained experimentally gives very similar values to those previously acquired by other techniques. For example, tamoxifen inhibits the ESR antagonist with an IC50 value reported as 0.42 μM (Bolger et al. 1998), whereas the miniaturized thermal shift assay provided an affinity of 1.1 μM. The known ligand pentosane polysulfate is reported to have a Kd of 11 μM with FGFR-1, as measured by ITC titration (Pantoliano et al. 1994), while the thermal shift assay, i.e., DSF, shows a similar binding ability of 5.5 μM. Thus, the reported thermal shift assay supports a reliable alternative for determining the interactions between proteins and small molecules.$$ {K}_L^{Tm}=\frac{\mathit{\exp}\left\{-\Delta {H}_u^{T0}/R\left[1/{T}_m-1/{T}_0\right]|+\Delta {C}_{pu}^{T0}/R\left[ In\left({T}_m/{T}_0+{T}_0/{T}_m-1\right)\right]\right\}}{\left[{L}_{Tm}\right]}\kern0.5em $$

(1)

where\( {K}_L^{Tm} \):

ligand association constant at Tm

Tm:

midpoint for the protein unfolding transition in the presence of ligand

T0:

midpoint for the unfolding transition in the absence of ligand

\( \Delta {H}_u^{T0} \):

enthalpy of protein unfolding in the absence of ligand at T0

\( \Delta {C}_{pu}^{T0} \):

change in heat capacity on protein unfolding in the absence of ligand

[LTm]:

free ligand concentration at Tm ([LTm]≅ [L]total when [L]total >>[Protein]total)

R:

universal gas constant

DSF has a direct application in fragment-based ligand design (FBLD) due to the ease of use in high-throughput screening. In this approach, small molecule building blocks (100–150 Da) are potentially pooled (3–5 molecules per pool) and screened (Elkin et al. 2015; Valenti et al. 2019). Although these small molecular mass compounds are unlikely to possess high affinity by themselves, this pooled approach allows for a significant reduction in the number of experiments that need to be performed to screen a large library. Successful “hit” pools identified on the basis of a shift in Tm can then be examined in more detail to uniquely identify fragments of interest and hits can be grouped to provide a primary metric for lead compound optimization. This strategy also provides a high probability of adding blocks to the final scaffold of lead compounds (Mashalidis et al. 2013), and two recent examples of the use of DSF in lead discovery are provided below.DSF as a simple and robust mechanism to probe fragment-binding modes and suggests linking strategiesTuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains one of the top 10 causes of death, and Mtb is the leading infectious agent (above HIV/AIDS) worldwide. In 2017, 10 million people developed TB resulting in 1.6 million deaths (World Health Organization 2018). Drug-resistant TB continues to be a public health crisis, and we still lack robust therapies to combat this burden. Consequently, new antitubercular agents that target TB with novel mechanisms are urgently needed. Biotin, also known as vitamin B7, is an essential cofactor for Mtb (Hayakawa and Oizumi, 1987). As Mtb produces biotin in order to support growth and proliferation, but this vitamin is present at very low concentration in human blood (Sassetti and Rubin 2003), therefore, targeting the biotin biosynthesis route intermediate by PLP-dependent transaminase (BioA) turns out to be a promising strategy (Mann and Ploux 2006). Dai and colleagues screened a Maybridge Ro3 fragment library with approximately 1000 compounds against BioA using DSF and discovered 21 “hit” compounds—identified as those that increased the Tm more than 2° (Dai et al. 2015). Subsequent X-ray diffraction data of cocrystals confirmed 6 fragment hits binding within the active site. The binding affinity and ligand efficiencies were cross-validated by ITC, giving a range between 7 and 42 μM in affinity and between 0.43 and 0.55 in ligand efficiencies, respectively. Comparison of all the available hits provided the basis for understanding the interaction mode of residues involved in the active pocket, leaving sufficient guidance for a lead sketch optimization consistent with the active site conformational states. Moreover, the scaffold of the small fragments found by DSF and crystallography also matched existing potent inhibitors previously reported (Park et al. 2015), further demonstrating that this strategy can be a reliable method for ligand screening.The same strategy was implemented by Hung’s team, targeting pantothenate synthetase (PS) of TB (Hung et al. 2009). Pantothenic acid (vitamin B5) plays an important role in fatty acid metabolism. It is formed through condensation of pantoate with β-alanine by pantothenate synthetase (PS), and blocking this pathway will likely impact the growth of Mtb (Sambandamurthy et al. 2002). In fragment screening via DSF, ligand 2 was identified from 1300 fragments with a ΔTm of 1.6 °C (Fig. 2). This was further confirmed by WaterLOGSY NMR spectroscopy and ITC (Kd = 1 mM). The associated X-ray structure showed that 2 binds across the pantoate-binding pocket P1, extending further along the surface of PS, to a point 3.1 Å away from another binding site of ligand 1 in the same pocket. A test with both ligands soaked into crystals showed the presence of both fragments in the active site without clashes, in conformations similar to their individual binding modes (Fig. 2). Therefore, fragment linking and optimization were recruited to enhance binding properties, with different linkers based on the adjacent structures inside the pocket. Subsequently, lead compound 3, which links fragments 1 and 2 by an acyl sulfonamide, showed a 500-fold stronger binding affinity than the individual fragments (Fig. 2).Fig. 2Fragments 1 and 2 soaked as a cocktail into the crystal of pantothenate synthetase. The two fragments are found to bind in distinct positions. Overlay of the linked lead compound 3 with fragments 1 and 2 in the active site of P1 of pantothenate synthetase. Fragments 1 and 2 shown as sticks in green. The benzofuran group is slightly rotated relative to fragment 2, indicating that the stereochemical constraints of the linker do not allow this moiety to adopt its optimum conformation. Figures created by using PyMol, based on PDB entry 3IMG and 3IVX (Hung et al. 2009)Full size imageDSF combined with limited proteolysis in the identification of tankyrase inhibitorsA fragment-based study performed by Larsson in 2013 gives a clear example of how DSF can be used to identify high-quality fragments followed by guiding the construction of a lead compound (Larsson et al. 2013). In this assay, the poly-ADP-ribosylating enzyme tankyrase was screened against a 500-compound fragment library (each present at 1 mM). To avoid oddly behaving compounds and minimize false-positive rates (i.e., pan-assay interfering compounds, PAINS) (Baell and Nissink 2018), identified hits are further validated to genuine “hits” by checking for a dose-dependent DSF response over a range of concentration (from 5 to 4000 μM). In the DSF screening of tankyrase 2, a “hit” melting profile was interpreted as those showing a two/multiple-state transition, which significantly complicated the fitting of Tm for weakly binding fragments (Fig. 3a). After adding chymotrypsin to perform an in situ digestion and remove less-ordered contaminants, they succeeded in simplifying the sigmoidal melting cure (Fig. 3b). Dose-response experiments then validated initial “hits” through an apparent increase in Tm upon elevated concentrations of an initial “hit” (Fig. 3c). Based on the cocrystal structure of TNKS2 with validated hits, various modifications of the hit fragment were proposed and evaluated. The 4-position methyl group was maintained as it protrudes down toward the catalytic glutamate, whereas changes in the 7-position group, which points toward the extended pocket responsible for adenosine binding, showed distinct differences when ligated to different functional groups. Starting with an initial fragment of 12-μM affinity, multiple rounds of modification and validation by DSF, SPR, enzymatic activity (IC50), and X-ray crystallography yielded a lead compound with an inhibition activity (IC50) of 9 nM and binding affinity (Kd) of 16 nM against TNKS2. The elegant approach of limited proteolysis of the less stable (i.e., unbound) form of the target directly addresses one limitation of DSF—incomplete binding leading to multiple transitions in the thermal profile—amplifying weak binding. However, it is likely that such an approach will be highly dependent on the target under examination and may not be generally applicable.Fig. 3a Tankyrase 2 melting curves without chymotrypsination in the absence (black) and presence (red) of a stabilizing fragment. b Tankyrase 2 melting curves treated with chymotrypsin in the absence (black) and presence (red) of the same stabilizing fragment. c Concentration-dependent response for the stabilizing fragment with chymotrypsin-digested tankyrase. d The workflow of the final lead compound optimization from the initial hit to the end was guided by DSF. This figure was adapted with permission from Larsson et al. (2013). Copyright 2013 American Chemical SocietyFull size imageIn summary, the examples above both show that fragment-based drug discovery (FBDD) has become a mainstream choice for high-throughput screening for lead discovery of therapeutic interest (Congreve et al. 2008; Murray and Rees 2009) and that DSF has been validated as a robust option in preliminary screening in FBDD for more than 2 decades (Pantoliano et al. 1997). The use of DSF in fragment screening is facilitated by its low sample consumption—both in proteins and in chemicals—as well as the rapid determination of experimental ΔTm determination—reducing labor-intensive work and providing simplified screening protocols.The use of DSF in buffer screening and optimization of protein stability and crystallizationIn proteomics studies, inter-related biochemical, cellular, and physiological information is essential to reveal protein mechanisms. A major source of information is the use of structural, functional, and chemical genomics to characterize target proteins (Christendat et al. 2000). However, the common first step for all these approaches is the purification of the target protein, which remains challenging in many cases. On average, only 50–70% soluble protein and 30% membrane proteins from prokaryotes can be expressed in a recombinant form, and among those successfully expressed, only 30–50% can be purified in a homogeneous state (Christendat et al. 2000; Norin and Sundström 2002; Dobrovetsky et al. 2005). Eukaryotic proteins—including many biomedically interesting targets from humans—seem even more challenging (Banci et al. 2006).Traditional solutions for protein production and purification mainly rely on the screening of recombinant hosts, encoding construct sequences, expression conditions, and then purification conditions (Gräslund et al. 2008; Rosano and Ceccarelli 2014; Wingfield 2015). In the last two steps, the addition of specific additives or changing buffer composition can significantly increase the solubility of recombinant proteins, as well as improving the thermal stability of the target to prevent protein unfolding or aggregation—even at a low temperature. There have been many reports (Sarciaux et al. 1999; Vedadi et al. 2006; Reinhard et al. 2013) showing that optimization of the purification conditions results in enhanced protein stability or solubility and it is not unreasonable to propose that buffer optimization should be seen as an integral part of any research project that relies on isolated protein samples. Even minor gains in protein stability can be significant in the context of process engineering, for example in the mass production of antibodies for therapeutic purposes.One remarkable case is that of the recombinant protein dnaB, produced in E. coli. Initially, it was shown to be highly unstable in the purification buffer—even when stored at 0 °C, 90% enzymatic activity was lost within 30 min. In a stepwise screening process where specific chemical reagents (Mg2+, ADP, (NH4)2SO4, and glycerol) were added, 90% activity was retained after extensive storage at 60 °C in the optimal buffer. Furthermore, the new buffer helped the isolation of soluble dnaB at increased yields and subsequent crystallization (Arai et al. 1981). While this is undoubtedly an extreme example, this clearly shows the value of buffer optimization.In the early years of structural genomics, a generally applied strategy was to use a default purification buffer for the majority of protein targets, with detailed optimization of sample buffer performed only to address pathological issues (aggregation, loss of activity, change in oligomeric state, etc.) (Mezzasalma et al. 2007). As shown below, this likely impacted the ultimate success of structural genomics projects, in which the growth of high-quality crystals from purified samples represented the major bottleneck. To address the issue of buffer optimization, Ericsson and coworkers developed a DSF-based screening system (comprised of different pH buffers, additives, heavy atoms, etc.) to test 25 different proteins expressed in Escherichia coli (Ericsson et al. 2006). The buffers consisted of a set of 23 different buffering agents at a concentration of 100 mM with a pH range from 4.5 to 9.0. Because each pH step is only 0.2 to 0.5 pH unit, it makes the screen wide enough for the majority of proteins investigated currently.In some cases, protein Tm was dramatically influenced by a single pH buffer, correlated with a preference for specific ionic effects. For example, at pH 7, the Tm of protein AC07 in K-phosphate is 37 °C, whereas it is 46 °C in the presence of Na-phosphate (Fig. 4a). In order to decouple the influence of the choice of buffer and the final pH, a three-component buffer system (Newman 2004) was implemented, which allowed a wide range pH without altering the composition of buffer chemicals. The citric acid-Hepes-Ches (CHC) buffer, which covers the pH range from 4 to 10, can quickly identify the most favorable pH of target proteins. This work showed that the Tm of the targets examined followed a typical bell-shaped curve. For example, AD28 demonstrated lower temperature stability values at both low and high pH (pH = 4 and 10), with a maximum stability close to pH 6.4.Fig. 4a Unfolding temperature of AC07 in various pH buffers of different compositions. Na-phosphate (red bar) and K-phosphate (blue bar) at a pH close to 7.4 showed a significant difference in Tm. b Melting temperature curves of the protein AD21 screened against different additives. As an essential chemical needed in the proline biosynthetic pathway, NAD(P)H (yellow) showed a visible increase in thermal stability when incubated with the target protein. The figures are adapted from Ericsson et al. (2006). Copyright 2006 with permission from ElsevierFull size imageCombinations of the above buffer optimization with additives, such as heavy metals, or substrates/cofactors like NADH at optimal pH can further enhance protein thermal stability. For example, the addition of NADH was found to increase the melting temperature of AD21 significantly (ΔTm ≈ 20 °C; Fig. 4b), which correlated with the previously known fact that it is an essential cofactor of AD21 in the catalysis of the last step in proline biosynthesis.In summary, DSF screening of additives provided data to optimize the buffer conditions for crystallization screening (Reinhard et al. 2013). Additives that gave a positive thermal shift (Tm) compared with control samples increased the protein crystallizing rate by 70%, while additives that showed destabilizing effects reduced the chance of getting crystals by around 50% compared with the control buffer. This observation strongly suggests a correlation between protein stability/solubility and crystallogenesis. For excellent in-depth reviews into the use of DSF to optimize crystallization buffers, the reader is referred to Boivin et al. (2013) and Reinhard et al. (2013).Structural biology plays an important role in early-stage drug discovery, as the elucidation of the binding modes of “hit” compounds can provide essential information to drive downstream, lead compound development (de Kloe et al. 2009; Wang et al. 2019). While crystallization of proteins relies on a number of sample properties, with sample purity and homogeneity generally agreed to be the key determining factors (Giegi et al. 1994; Dale et al. 2003; Ericsson et al. 2006), thermal stability has also been shown to be a critical parameter in a successful outcome during crystallogenesis. In a study carried out by Dupeux et al. (2011), 657 different proteins were screened by DSF, then subjected to automated vapor-diffusion crystallization. Based on an analysis of the protein melting point (Tm) and visually determined crystallization hits, the authors were able to draw clear inferences on the importance of thermal stability on the crystallization process. In this study, 437 of the 657 samples unfolded show clear and sharp temperature transitions. This behavior may be interpreted as the result of a sample population consisting of a single overall conformation, with relatively little conformational fluctuation around the “mean” fold—a scenario which is likely to be more conducive to crystallization than a sample with a high degree of conformational variation due to thermal mobility of its component elements. The average Tm for the ensemble of samples was 51.5 °C over a range of 25 to 95 °C (Fig. 5). Notably, proteins with a Tm of 45 °C or higher displayed a greater tendency to crystallize when incubated at 20 °C, with successful crystallization outcomes of 49.1%. For proteins with a Tm below 45 °C, the likelihood of crystal growth chance at 20 °C dropped to 26.8%. Additionally, a number of proteins with a Tm between 25 and 45 °C produced crystals at the lower temperature of 5 °C, where crystallization was initially unsuccessful at 20 °C. The study confirmed a previous observation that thermophilic proteins have higher rates of crystallization than those from mesophilic organisms, despite similar Tm values. In addition, a report from Szilágyi also implied that thermophilic proteins have a lower proportion of unstructured regions (Szilágyi and Závodszky 2000), with the inference that the disordered regions will hamper crystallization.Fig. 5Tm and success rate in crystallization: all the samples were incubated for crystallization at 20 °C; the numbers above the bars indicate the success rate in crystallization of each class. The samples from extremophilic organisms consist of 12 proteins with Tm between 70 and 95 °C. The figure is adapted from Dupeux et al. (2011). Reproduced with permission of the International Union of CrystallographyFull size imageAs the thermal stability of a sample may influence its chances of crystallizing, it becomes clear that optimizing the sample buffer in which the protein is finally purified and concentrated prior to crystallization can provide benefit to structural biologists, and structure-based drug design in particular. In a typical DSF buffer screening experiment, the conditions (buffering agent, pH, additives, etc.) that result in the largest thermal shifts are often combined and the resulting buffer is then used for purification and crystallization. However, this process can be complicated when multiphasic unfolding behavior is encountered as it makes accurate Tm determination more difficult. A multiphasic unfolding curve typically indicates either the presence of multiple, independently folding, domains (Ionescu et al. 2008) or a heterogeneous state of the protein sample in solution (Choudhary et al. 2017), or ligand binding is not fully saturated with protein targets (Shrake and Ross 1992; Matulis et al. 2005), which may disrupt crystallogenesis and hinder protein functional characterization. Here, DSF can also be applied to guide sample preparation buffer screening for crystallization by replacing the buffer ingredients or ligands stepwise. Geders et al. reported a multiphasic unfolding behavior when his team attempted to crystallize pyridoxal 5-phosphate (PLP)–dependent transaminase BioA from Mycobacterium tuberculosis (Geders et al. 2012). During buffer optimization for crystallization, BioA displayed a multiphasic unfolding behavior without PLP; subsaturation of cofactors in the protein-cofactor system also yields a biphasic melting curve. The protein heterogeneity resulting from insufficient levels of cofactor PLP could potentially impact crystallization. To avoid the competition for PLP binding by other factors and to induce PLP saturation of BioA, DSF was used to study PLP binding. The initial buffers used in both lysis and purification (Dey et al. 2010) were Tris-based—generating a tri-phasic melting temperature curve with transitions at 45, 68, and 86 °C (corresponding to misfolded, apo, and PLP-bound BioA, respectively (Fig. 6a)). The sample also displayed significant precipitation at higher concentration levels. The electron density from a crystal grown from a Tris buffer showed no interpretable density for a bound PLP molecule. Replacing the Tris buffer with Hepes within the purification (both lysis buffer and final purification buffer) resulted in a decreased tendency for multiphasic melting curves, especially while Hepes completely replaced Tris in both lysis and purification buffer (Fig. 6b). This result suggested that the Tris buffer partially degraded the PLP, resulting in unsaturated PLP binding to BioA partially. This partial degradation was further supported by a UV-Vis spectroscopy assay, in which PLP in Tris buffer showed an absorbance maximum near 420 nm, similar to that shown by PLP in the Schiff base form instead of a free aldehyde (Fig. 6d). PLP in Hepes buffer showed absorbance at 390 nm, similar to that of PLP in water. By replacing Tris with Hepes in all purification buffers and adding increased concentrations of PLP, the multiphasic melting curves were replaced with a single, sharp transition curve with a Tm at 88 °C. These optimizations also improved the size and quality of the crystals obtained and also resulted in clear electron density for a bound PLP molecule. Thus, the DSF analysis correlated with heterogeneity and suboptimal crystallization outcomes. This example also highlights two complications in small molecule screening: firstly, the use of Tris (or primary amines which can form Schiff base with aldehydes) should be avoided with PLP-dependent proteins—and researchers should be aware of the potential for similar effects in other protein cofactors. Secondly, care should be taken when analyzing multiphasic DSF profiles, as they may be due to molecular interactions of the screen with the buffer, rather than the protein target.Fig. 6a DSF melting curves of BioA with PLP and Tris in both lysis and storage buffer, which shows multiple peaks during denaturing. b A sharp DSF melting curve of BioA with subsaturation of PLP; misfolded and apo peaks were eliminated after BioA was saturated with PLP, resulting in enhanced stability of BioA, with a Tm at 88 °C. c First derivative overlap of the corresponding melting curves. The red line indicates BioA in Tris buffer, with multiple transitions at 45, 68, and 86 °C, representing the misfolded, apo, PLP-bound BioA, respectively. The blue line represents BioA saturated with PLP for which the Tm was enhanced dramatically to 88 °C. d UV-Vis spectroscopy of PLP or PLP-BioA(holo) at various conditions; 400 μM PLP in water (cyan) has the same absorbance as in Hepes buffer (brown); PLP-bound BioA(holo) (purple) showed the same absorbance close to 420 nm as PLP in Tris buffer (black). The figures are adapted from Geders et al. (2012). Reproduced with permission of the International Union of CrystallographyFull size imageIn biochemical or biomedical research, a well-folded protein structure with the correct activity is one of the critical factors for in vitro experiments. While numerous recombinant technologies exist to express proteins, greatly facilitating the understanding of proteomics in both prokaryotic and eukaryotic cells, the lack of suitable chaperones in E. coli (the most commonly used recombinant source) results in ~ 80% of these proteins misfolding into insoluble inclusion body without a defined fold or biological activity (Carrió and Villaverde 2002; Sørensen and Mortensen 2005; Gräslund et al. 2008; Rosano and Ceccarelli 2014). Moreover, refolding of proteins from inclusion bodies is an empirical art, with functionally related proteins of different construct designs or from different sources requiring significantly different conditions to support refolding. Thus, systematic and high-throughput compatible assays are needed to address this. In 2016, Biter and colleagues established a DSF-guided refolding method (DGR) to rapidly screen for the refolding of inclusion bodies, including proteins that contain disulfide bonds and novel structures with no preexisting model (Biter et al. 2016). The refolding trials used a PACT (pH, anion, cation testing) sparse matrix crystallization, leveraging the sparse matrix search of buffers to examine the large chemical space of biologically compatible buffers. Inclusion bodies were purified by centrifugation prior to solubilization in chaotropes (urea or guanidine) and the addition of a fluorescent dye (SYPRO Orange). Precipitants were excluded from the screen (Fig. 7a). The solubilized targets were incubated with components of the PACT screen for 2 h, centrifuged to remove any resultant precipitation/aggregation, and directly analyzed using DSF. Fluorescence data showing protein unfolding under DSF conditions was interpreted as corresponding to a condition that supported protein refolding. Due to the wide range in pH, cations, and anions, the PACT screen provided clear hints for pepsin refolding (Fig. 7c, d). For disulfide-containing proteins, such as lysozyme, the PACT screen conditions were supplemented with oxidized and reduced glutathione. The resulting thermal melting profile of the refolded lysozyme showed a clear Tm at 65 at pH 9 in the presence of equimolar GSH and GSSH.Fig. 7a The modified PACT screen in use in a refolding assay; three main parts consist of pH screen, cations, and anions in different combinations; the color indicates the Tm found in certain conditions. b Thermal melting profiles of pepsin in native, denatured, refolded, and misfolded states. c Peak height Tm in the PACK screen profile; the color indicates that under acid conditions, pepsin has a higher Tm. d First derivatives of pepsin from the guanidine-solubilized dilution; populations in red correspond to the misfolded state, and blue is natively a folded state. The figures are adapted from Biter et al. (2016)Full size imageAttempts to refold the novel proteins from inclusion bodies also succeeded in generating an improved yield of fibroblast growth factors 19 and 21, leading to crystals. When DGR was applied to the hormone Irisin, the success in refolding helped to generate an eight-dimer crystal form (Schumacher et al. 2013).One year later, colleagues in our group expanded the DGR approach by investigating the refolding agent arginine and other additives in systematic buffer screens (Wang et al. 2017). Arginine has been widely used to suppress protein aggregation in refolding, and it can slow or prevent protein association reactions via weak interactions with the targets (Baynes et al. 2005; Arakawa et al. 2007), distinct from chaotropes such as urea or guanidine. Therefore, we designed two sequential screening kits to provide a general screening strategy. The primary screen is a combination of various pH buffers in the presence or absence of arginine at a concentration of 0.4 M. This can rapidly identify a suitable refolding pH while also screening for the effect of arginine in refolding. A secondary screen is then explored, by adding different sugars, detergents, osmolytes, PEGs, amino acids, concentration gradients of salt, and reducing agents, expanding on the PACT screen which mainly focuses on pH, anions, and cations (Fig. 8). This approach identified optimal refolding buffers for four different therapeutic target proteins from inclusion bodies expressed in E. coli, as well as identifying a final gel filtration buffer for storage or crystallization. A number of factors that affect protein refolding were revealed during this study, including the chemical composition of the buffer, refolding time, redox state, and the use of arginine as an inhibitor of aggregation. For example, DGR analysis of the refolding of interleukin-17A (IL-17A) gave obvious melting transition signals at pH 9.5 in CHC and CHES buffer—but not the MMT or MIB buffer system at the same pH—indicating that the compositions of the buffer have a significant effect. In the presence of arginine, the Tm increased from 40 to 60 °C, suggesting a more stable final product of the refolding process (Fig. 9). Refolding time also plays an essential role in all the assays, as data showed for all the proteins tested that the maximum efficiency appeared at a defined refolding time. The receptor-binding domain of hemagglutinin (HA-RBD) showed a clear melting curve when refolding was limited to 1 h, whereas the melting transition signal disappeared after 6-h incubation in refolding buffer. IL-17A needed extensive refolding time, requiring 15 h for an optimal DGR signal. Additionally, this data demonstrated that buffers optimized from the refolding process are not necessarily ideal for subsequent storage or crystallization—potentially as they stabilize an intermediate in the refolding process, rather than the final folded form.Fig. 8The composition of the secondary additive screen covers a wide range of sugars, detergents, salts, buffers, and reducing agents. This figure is adapted from Wang et al. (2017)Full size imageFig. 9Melting transition of IL-17A in CHC buffer system at pH 9–10 in the absence (a) and presence (b) of arginine; both showed a typical sigmoidal melting curve at pH 9.5. The figures are adapted from Wang et al. (2017)Full size imageDSF applications for in vivo ligand: target interaction validationA common issue in monitoring drug binding and efficacy during therapy is that the interactions between target proteins and drugs cannot be measured directly in cells and tissues. Validation methods normally study downstream cellular responses after multiple doses. Furthermore, some drugs tested may have good binding activity when incubated with target proteins but fail in clinical trials, with later research showing them to not act on the predicted target within cells (Auld et al. 2009; Schmidt 2010; Guha 2011). In 2013, Molina et al. (2013) introduced a new way to monitor the drug interactions inside cells by performing thermal shift assays on cells, lysates, or tissues, which is also based on ligand-induced thermal stabilization of target proteins, but no protein purification steps are needed. The cellular thermal shift assay (CETSA) functions by heating cells, whereby the proteins inside also unfold and precipitate—similarly to the in vitro approaches described above. After extract and centrifugation, the remaining soluble proteins were separated from the precipitate and quantified by Western blotting. Plotting the amount of soluble protein based on the Western blot signal strength provides the CETSA melting curve. In the preliminary study, dihydrofolate reductase (DHFR) and thymidylate synthase (TS) were selected as targets for the antifolate cancer drugs methotrexate and raltitrexed. Samples were exposed to either of the two drugs either as intact cells or as lysates. The result showed a distinct thermal shift increase for DHFR- or TS-treated cells compared with controls. To investigate drug concentration effects, an isothermal dose-response (ITDR) method has also been developed to assess binding of compounds. In this approach, cell lysate is aliquoted and exposed to different serial concentrations of the drug, while keeping the temperature and heating time constant. Following Western blotting, the signal strength can indicate when a higher drug concentration is needed for saturation, which is potentially more useful than commonly used half-saturation points (i.e., IC50, Kd) which are related to affinity. Further research validated that the CETSA method can be applied as a reliable biophysical technique for studies of ligand binding to proteins in cells and lysates. In a recent report, Maji’s group screened a library with more than 2000 small molecules in order to identify inhibitors of CRISPR-Cas9, which could then be used for the precise control of CRISPR-Cas9 in genome engineering. CETSA was used to confirm a hit compound that disrupted the SpCas9:DNA interaction and decreased the Tm of SpCas9 by ~ 2.5 °C in compound-treated cells (Maji et al. 2019). In another structure-based design of a small molecule to target the interaction of menin-MLL in leukemia, an irreversible, highly potent chemical M-525 was also confirmed by CETSA in a cellular assay (Xu et al. 2018). The covalent-binding compound enhanced the thermal stability of menin in both MV4;11 and MOLM-13 cells; the concentration of M-525 used here was as low as 0.4–1.2 nM. Furthermore, CETSA also showed that the compound specifically targeted menin, and no effect was detected on another MLL-binding protein WDR5.ConclusionDSF constitutes a robust biophysical technique for studying protein stability in a particular environment, either within selected buffer conditions or when (partially) saturated with ligands of interest. The protein unfolding thermodynamic parameter ΔTm is monitored as the primary indicator to justify stability changes of the target protein, no matter whether targets were in a purified form, in lysate, cells, or even tissues. Newly emerged label-free nanoDSF approaches especially obviate the need for dyes, allowing the same approach to be applied to membrane protein research, simultaneously addressing problems caused by the interaction between dye and the hydrophobic surface of proteins, or the detergent additives applied and interactions between the dye and other molecules in a screen. Over the almost two decades since it first appeared, the DSF technique has been used to characterize the thermal properties of numerous proteins, aided by low sample consumption and high throughput—making DSF suitable for optimizing buffer ingredients in crystallization, as well as screening large ligand libraries. In terms of ligand-binding validation, although many successful cases have been reported in the literature, it is still important to be aware that this correlation typically occurs for similarly structured compounds within a series, and stubbornly pursuing fragment hits on the basis of significant thermal shifts may mislead further optimization. It should also be borne in mind that ligands can interplay with both the folded and unfolded states of target proteins, and a negative shift in melting temperature does not exclude binding to the native state. Unlike titration-based techniques such as ITC, MST, and SPR in which interaction behaviors of receptors rely on different serial concentrations of ligands and end-point measurements, DSF is sensitive to all stages along a binding pathway, complicating its use to determine the affinity of molecules toward mobile protein receptors. Nevertheless, the robustness and applicability of DSF to address various problems across such a wide range of sample types should ensure its status as a central technology of modern drug discovery.

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Download referencesAcknowledgmentsWe would like to thank the kind support and access to sample application from NanoTemper (München, Germany).Author informationAuthors and AffiliationsStructure Biology in Drug Design, Drug Design Group XB20, Departments of Pharmacy, University of Groningen, Groningen, The NetherlandsKai Gao, Rick Oerlemans & Matthew R. GrovesAuthorsKai GaoView author publicationsYou can also search for this author in

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Reprints and permissionsAbout this articleCite this articleGao, K., Oerlemans, R. & Groves, M.R. Theory and applications of differential scanning fluorimetry in early-stage drug discovery.

Biophys Rev 12, 85–104 (2020). https://doi.org/10.1007/s12551-020-00619-2Download citationReceived: 04 December 2019Accepted: 08 January 2020Published: 31 January 2020Issue Date: February 2020DOI: https://doi.org/10.1007/s12551-020-00619-2Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard

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KeywordsThermal stabilityFoldingUnfoldingRefoldingFluorimetryLigands screeningCrystallizationBuffer optimization

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