Theory and application of differential scanning fluorimetry in... 2/24/2020 DSF and examples of their use in the various aspects of drug discovery presented above - including - [PDF Document] (2023)

REVIEW

Theory and application of differential scanning fluorometry in early drug discovery

Kai Gao1 and Rick Oerlemans1 and Matthew R. Groves1

Received: 4 December 2019 / Accepted: 8 January 2020 # Author(s) 2020

Abstract Differential scanning fluorimetry (DSF) is an accessible, rapid and economical biophysical technique that has found many applications over the years, ranging from the detection of protein folding states to the identification of protein-binding target ligands. In this Review, we discuss the theory, applications, and limitations of DSF, including recent applications of DSF by myself and other researchers. We demonstrate that DSF is a powerful high-throughput tool for early drug discovery. We place DSF in the context of other biophysical methods commonly used in drug discovery and highlight their advantages and disadvantages. We present the use of DSF in the optimization of protein buffers for stability, folding, and crystallization and provide several examples for each. We also show the use of DSF in a downstream application where it is used as an in vivo tool to verify ligand-target interactions in cellular assays. Although DSF is a powerful tool for buffer optimization and screening of large chemical libraries when confirming and optimizing ligand binding, orthogonal techniques are recommended because DSF is prone to false positives and negatives.

Keywords Thermal stability. Collapsible. development of wrinkles .Fluorimetry . ligature control. crystallization. buffer optimization

Introduction

Biophysics drives modern drug discovery efforts, enabling rapid, high-throughput data collection to search large compound libraries to identify novel bioactive molecules. An important part of this biophysical armamentarium is the thermoshift assay, also known as differential scanning fluorimetry (DSFnovetS al.1991). DSF is an inexpensive, parallel, practical and accessible biophysical technique widely used as a method for monitoring protein folding states and thermal stability. It provides a reliable tool to study protein unfolding by slow heating in a controlled environment. By measuring the corresponding changes in fluorescence emission with increasing temperature, the protein denaturation process can be monitored. Since compds change the behavior of the sample

The formation of even weakly binding substituents affects the thermostability of the protein. The technique has seen many successful applications and has been used in a variety of ways in recent years. For decades, it has been used primarily as a drug development method to identify promising lead compounds for a variety of target proteins (Pantoliano et al. 2001). Another important application of DSF is protein buffer optimization, determination of optimal storage conditions, assay validation and crystallization. By controlling the rare matrix conditions, which include different buffer systems covering a wide range of pH values, additive and salt concentrations, the optimal buffer components for each individual protein can be identified. This has been shown to increase the success rate of protein crystallization in recent decades (Huynh and Partch 2015). More recently, DSF has also been applied to the challenge of sample preparation, with two papers showing that appropriate screening approaches can be used to identify and optimize sample folding buffers—providing significantly less expensive access to the amounts of protein sample needed to support high molecular weight samples. . Throughput screening campaigns (Biter et al. 2016; Wang et al. 2017). Finally, recent developments have shown that DSFs are capable of providing reliable data for complex solutions, such as crude chemical reactions. This is one

* Matthew R.[emailprotected]

1 Structural Biology in Drug Design, Drug Design Group XB20, Faculty of Pharmacy, University of Groningen, Groningen, The Netherlands

https://doi.org/10.1007/s12551-020-00619-2Biophysical Reviews (2020) 12: –10485

Published online: January 31, 2020/

http://crossmark.crossref.org/dialog/?doi=10.1007/s12551-020-00619-2&domain=pdfhttp://orcid.org/0000-0001-9859-5177mailto:[emailprotected]

an exciting development since the production and purification of chemical substances is a major hurdle in any screening campaign.

Although the robustness of the DSF method and its wide application in sample preparation and screening have made it an important biophysical tool in drug discovery, it is important to consider its limitations. This is especially true when designing a Display Network campaign, as the campaign should include orthogonal viewing options that are not similarly restricted to reduce false positives and false negatives.

In this review, we will provide the theoretical background of DSF as well as examples of its use in the various aspects of drug development presented above—including recent applications of DSF by us and other researchers. We will also attempt to classify DSF in the variety of biophysical methods currently used in screening campaigns and to indicate areas of overlap or mutual restriction.

Differential scanning fluorescence theory

In 1997, Pantoliano et al. (1997) presented a new thermoshift assay system used to screen combinatorial libraries against different receptor proteins. Compared to traditional 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 identifying only one disease at a time. With custom 96- or 384-well plates and a fluorescence reader, protein unfolding can be easily monitored under multiple conditions, with different ligands and/or at different ligand concentrations in a single experiment. This has helped researchers to complete many tedious, slow and intensive tasks that traditional methods require. Rather than requiring special equipment, many laboratories already have (or have access to) real-time polymerase chain reaction (RT-PCR) equipment that allows fluorescence measurements over a controlled temperature range. Access to such devices, the development of more sensitive dyes, and improved protocol design have led to the use of DSF (Niesen et al. 2007).

Proteins are in thermodynamic equilibrium between the folded and unfolded states (Bowling et al. 2016). An increase in environmental energy (i.e. an increase in temperature) brings the protein to the unfolded state, which when measured quantitatively allows the determination of the melting temperature (Tm), defined as the temperature at which 50% of the protein is in the folded sample and 50% is in the unfolded state (Lo et al. 2004) (Fig. 1a). A change in the protein's environment (including pH, ionic strength, or the presence of specific anions or cations) and/or the formation of complexes with other molecules can stabilize the protein

Reduction of the Gibbs free energy of the complex as a result of the appearance of new molecular interactions (hydrogen bonds, van der Waals interactions, etc.) or conformational rearrangement of the target protein. This increase in Gibbs free energy leads to an increase in thermal stability and thus an increase in the melting temperature (Tm). Measurements of protein Tm in the presence and absence of environmental or ligand changes result in an invaluable thermal shift (ΔTm) arising from these differences (Scott et al. 2016) (Figure 1b). This shift typically indicates complex formation and/or orthothermostabilization. However, it should be noted that, although the resulting temperature shift is directly related to the change in Gibbs free energy, it is a measurement that depends both on the binding interactions and any conformational changes that occur in the target protein, as well as on the stability of the thermal profile generated over the temperature range. generate a reliable room temperature dimensional constant (kd = exp −ΔG/kT, k = Boltzmann constant and T = thermodynamic temperature) directly from ΔTm. However, focusing only on Tm may imply that other systemic and thermodynamic information is present

Figure 1 a Typical thermal denaturation profile of a protein sample. Fluorescence emission changes with temperature. The sigmoid curve shows the cooperative unfolding state of the protein using SYPRO Orange traces (yellow) bound to the downstream protein (green). The peak indicates that all proteins are unfolded into a linear peptide or that the hydrophobic core is exposed to SYPRO Orange. There are several mechanisms to reduce post-peak fluorescence, including a temperature-related decrease in the dye-binding constant (therefore less dye binds to the protein), while the dye-binding pocket is more mobile (allowing for earlier quenching). Solvent). The dye itself is more mobile, so the degree of planarity required for electron/aromatic coupling is reduced, and protein aggregation and dye dissociation occur by excluding the dye from the hydrophobic cores. The midpoint of the transition curve is the melting temperature (Tm).bDSF curves showing the unfolded state of the target protein in the absence (blue) and presence (orange) of ligand. The difference in melting temperature is expressed as ΔTm. c A sample with high background fluorescence initially at a lower temperature (red) compared to a typical well-folded sample (blue) in a DSF assay. Improperly folded, aggregated, denatured proteins or hydrophobic regions such as the lipid bilayer exposed to the dye result in low temperatures. d Several transitions that occur during the heating process can cause different domains, aggregation that increases with temperature, or ligands that stabilize part of the protein pattern (orange). Typically, the native protein-like Tm is followed by one or more Tms at a higher temperature during denaturation. eg B. Overview of NanoDSF. e Intrinsic fluorescence of tryptophan is measured at wavelengths of 330 and 350 nm and plotted as a function of temperature from 20 to 60 °C during unfolding. f F330/350 Tryptophan fluorescence ratio intensity versus temperature. g The melting temperature is calculated from the first derivative of the F330/350 diagram. The sample listed here has a Tm of 48 °C. All figures above represent thermal evolution curves of menin proteins and are derived from DSF experiments performed in our laboratory. Experiments were performed using a Bio-Rad CFX96 real-time PCR system or a NanoTemper Prometheus NT.48 system. Curves were plotted from the fluorescence data using Excel

Biophys Rev (2020) 12: –1048586

Protein stability can be lost. One such factor is the tendency of proteins to aggregate under certain conditions. Changes in the environment can lead to different aggregation behavior but leave Tm unchanged. For a detailed review of this topic, see Wakayama et al. (2019).

Fluorescence was used as a response signal to monitor the thermal unfolding transition of the target protein in a suitably sensitive yet accurate manner. Today, there are two main sources of this fluorescence, which can be roughly classified into (i) exogenous fluorescence and (ii) endogenous fluorescence.

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External fluorescence

The fluorescence of exogenous fluorescent dyes is sensitive to the environment. Typically, such dyes in aqueous solutions quench proteins in their native folded state, providing a fluorescent 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 is based on the following assumptions (in rough order of frequency encountered by the authors):

one. The target proteins lack significant hydrophobic sites on their exposed surfaces, the presence of which would result in increased background fluorescence (Figure 1c).

vas The protein is at steady state at the start of the experiment, and DSF experiments with exogenous dyes are typically performed at concentrations of 0.1-0.5 mg/mL (0.01-0.1 µM). Aggregation and/or instability of the sample can lead to the presence of multiple target protein species in the experiment, which simultaneously results in increased background fluorescence due to any conformational variability and variable thermostability profiles of differently aligned oligomers (Figure 1c).

That. The target protein does not exhibit any significant binding interactions with the dye used—resulting in protection of the dye from the aqueous environment prior to unfolding of the protein and subsequent increase in background fluorescence.

hej The target protein consists of a single domain, since different domains likely fold at different Tm values, resulting in a complex thermal stability profile (Figure 1d). Although the profile can be more complex, it is often easier to distinguish signals from multiple domains and this can provide valuable information since a more pronounced Tm shift in a particular domain can provide information about the location of the absence of commitment.

M. Elevated temperature prior to unfolding does not induce significant structural rearrangement of the target protein, although in such cases unfolding of the thermal stability profile may still be possible.

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

Colors for general use

There are many commercially available dyes (Hawe et al. 2008). Dyes such as Bis-ANS and Nile Red have been used for decades. Exogenous dyes are summarized in Table 1.

All these pigments have a significant background in the presence of folded proteins. So far, SYPROOrange is the most popular dye for DSFs, mainly due to its high signal-to-noise ratio (Niesen et al. 2007) as well as its relatively long excitation wavelength (close to 500 nm). This reduces the interference of most small molecules, which usually have the highest absorption at shorter wavelengths.

autofluorescence

Another source of fluorescence is the protein sample itself. In 2010, Schaeffer's group reported a new method using green fluorescent protein (GFP) to quantify the stability of target proteins (Moreau et al. 2010). In these experiments, the aGFP tag was fused to the protein of interest via a peptide linker, and the asa reporter system was used to unfold and aggregate the protein. GFP's fluorescence signal changes depending on its immediate environment, meaning that its signal can be used to monitor the unfolding of the protein to which it is bound. Since GFP begins to lose its fluorescence only at 75 °C, this approach is suitable for different proteins that are significantly less reliable than GFP (Moreau et al. 2010). Although this can be an elegant solution to remove the dependency on a fluorescent dye reporter, there are still some limitations:

one. The possibility of interaction between GFP and the target of interest affects the conformation of the target protein and thus leads to a bias in the measured ligand interactions.

vas The potential of the GFP-related domain to affect the oligomeric state of the target protein—regardless of whether it promotes inhibitory assembly—with a similar effect on target protein conformation.

Tuna. This approach is not suitable for target proteins that have a similar Tm to GFP—in which case the unfolded signal of the target protein is masked by the signal of GFP

hey Ligands that can lead to a significant increase in the target ligand-Tm complex are not clearly observed due to a similar masking effect.

M. This approach cannot directly distinguish between compounds that interact with GFP and those that interact with the target protein, although this can be remedied by using a single GFP control.

In 2014, a label-free DSF technique was developed and commercialized as nanoDSF (Alexander et al. 2014). This approach eliminates the need for an external dye or fusion label and instead relies on the change in endogenous tryptophan fluorescence at 330 nm and 350 nm (Figure 1e). Unfolding/denaturation leads to a change in the polarity of the microenvironment around the tryptophan residues, resulting in a fluorescence shift (Ghisaidoobe and Chung 2014). With this approach,

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Tm can be determined by measuring the ratio of the fluorine centers at 330 nm and 350 nm with respect to temperature (Fig. 1f,g). The commercial instrument Prometheus NT.48 (NanoTemper Technologies, Munich) enables rapid analysis for the optimization of both ligands and regulatory sites. Unlike previous approaches, it enables measurements in solutions containing detergent - which is a prerequisite for the application of DSF in membrane proteins. Due to the nature of exogenous dyes that can bind (and fluoresce) in the presence of lipid bilayers and detergent micelles, conventional DSF is unable to tailor detergent selection to solubilize membrane proteins. Dye-free NanoDSF avoids this problem by using intrinsic fluorescence. Another advantage of endofluorescence is the ability to observe folded-to-folded and unfolded-to-folded transitions. This allows detection of delays (Andrews et al.2013). The presence of a delay can provide information about protein stability (Mizuno et al. 2010). Due to the presence of dyes, this is not possible when using the exogenous fluorescence approach. However, the endogenous fluorescence method also has some significant limitations:

one. Before applying this approach, the number of tryptophan residues in the acidic sequence of the target protein must be considered, since at least one tryptophan must be present, and the proportion of tryptophan present in the target protein sequence is a limiting factor in detecting the signal that occurs. .

vas Experiments resulting in complex populations in the thermal profile (eg presence of bound and unbound states - see below) may not be successfully identified due to signal sensitivity.

Do it. This approach requires significantly higher investments in the associated equipment.

Finally, it should be clearly considered that all DSF approaches are sensitive to intrinsic fluorescence

Properties of the molecules present in the screen sub-assay that can cause large fluctuations in the background of thermal profiles, leading to false negative results. Although the use of external dyes mitigates this to some extent, since the role of the used dyes significantly improves signal development, there is still the possibility of interaction of screening components with the reference dye.

Current DSF applications

Screening of ligands in drug development

Determining the interaction between receptors and members of the small molecule library is achieved by detecting and measuring changes in the physicochemical properties of all formed ligand-target complexes. Quantitative information derived from receptor-ligand complex formation can then guide the developmental process through structure-activity relationships (SARs). In recent years, many efforts have been made to find a general and universally applicable approach to detect binding (and ideally to estimate the binding affinity, Kd) between biomolecule receptors and small molecule ligands. As a result, many new biophysical technologies have emerged, in short:

one. Differential scanning calorimetry (DSC) that 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).

vas Isothermal titration calorimetry (ITC), which compares the temperature differences between a reference solution and the receptor to quantify the kinetic parameters of binding (Herrera and Winnik2016).

Tuna. surface plasmon resonance (SPR), which records the angular displacement of polarized light reflected from a metal film,

Table 1. Overview of exogenous fluorescent dyes used for protein characterization

Dye molecular formula Application Excitation (nm) Emission (nm) Ref

bis-ANS C32H22K2N2O6S2 hydrophobic unfolding/folding aggregation 395 470–530 Grillo et al. (2001)

Nil Red C20H18N2O2 aggregation unwinding/winding hydrophobic450 590–665 Greenspan et al. (1985)

SYPRO Orange C28H42N2O3S hydrophobic unfolding/folding aggregation 488 500–610 Lo et al. (2004)

DCVJ C16H15N3 Ambient stiffness protein viscosity 433480–530 Menzen and Friess (2013)

CCVJ C16H16N2O2 ambient stiffness protein viscosity 435480-505 Rumble et al. (2012)

ThT C17H19ClN2S fibrillationAggregation

450 460-600 Nielsen et al. (2001)

ProteoStat C45H62I2N4a Proteinagregacija 488 600 McClure et al. (2018)

CPM C16H14N2O4 hydrophobicity in relation to cysteine

387 463 Alexandrov et al. (2008)

abstract from patent (Patton et al. 2013)

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contains a surface-immobilized target that results in refractive index changes upon ligand binding and cleavage (Navratilova and Hopkins 2010).

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

M. NMR chemical shift monitoring based on NMR, ligands or proteins monitor chemical shift perturbations induced by a substituent. Therefore, both the Kd and the conformation of the complex can be determined.

essen optimization of fragments based on X-ray crystallography based on the electron density of the ligands, which provides detailed analysis of interactions with atoms.

G. In mass spectrometry-based approaches, protein samples and binding ligands are ionized while maintaining noncovalent interactions. The mass of proteins and ligands can then be determined with great accuracy (a few cases are listed in the table below).

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

Method Principle Advantages Limitations Ref

Ligand observed by NMR

Change in the shift in the magnetic state of the ligand due to binding

Multiple fragments can be tested simultaneously

Consumes a lot of protein. Limited to frags with quick target exchange

cream (2017)

NMR observed with the protein

Binding-induced protein NMR peak shift

The ability to determine the binding position. Possibility of titration to determine KD

It requires large amounts of protein. Limited power

cream (2017)

X-ray crystallography

X-ray diffraction of co-crystallized protein-ligand complex or soaked decrystal.

It provides structural information about how a ligand binds to and interacts with a target. It enables the use of computational methods to optimize success

Good quality crystals are required. Not all ligands can obtain co-crystal structures with the protein target. Synchrotrons are needed to obtain X-ray diffraction data. It requires large amounts of ligands

Jazavac (2012) Patel et al. (2014)

SPR Refractive index change due to ligand binding to the immobilized target on the sensor

Ability to easily download KD and other kinetic data. Consumes very little protein

The protein must be able to be immobilized

Neumann i on. (2007.); Chavanieu i Pugniere (2016.); Huberet al. (2017)

DSF The thermal stability of proteins is increased by linking fragments

High efficiency, cheap materials, easy to use and widely used equipment

Lots of false positives and negatives. It usually only gives a yes/no answer. Requires dye or endofluorescence

Lo i sure. (2004.); Douse i on. (2015.); Bayet al. (2019)

Isothermal Titration Calorimetry (ITC)

The heat of the system changes during the bonding process

The thermodynamic and binding properties of the protein-fragment interaction can be determined directly. There is no tag

It consumes a large amount of protein. poor performance

Stolice (2008) Ladbury et al. (2010.); Renaud i on. (2016)

Differential scanning calorimetry (DSC)

The amount of heat required to raise the temperature of the sample depends on the binder

High sensitivity method. There is no tag

Consumes a lot of protein. Poor performance

Cooper (2003), Bruylants et al. (2005), Erlanson i on. (2016)

Native Mass Spectroscopy (MS)

Mass detection of protein-ligand complexes in the gas phase

High sensitivity method. It consumes very little protein. There is no tag. It provides a large amount of information, binding affinity and stoichiometry

More proteins were ESI purified by Qin et al. bit stable. (2015.); Peter and Quinn (2016.); Rain i sur. (2019)

Size Exclusion Chromatography (SEC) MS

Incubation of a mixture of protein residues and subsequent separation of bound from unbound molecules by SEC followed by MS detection

Very high performance. A simple technique to perform that requires simple LC-MS

There is a risk of false negative results with low affinity binders. These can easily be lost during the SEC step

Qin i on. (2015.); Chan i on. (2017.); Renet al. (2019)

Weak Affinity Chromatography (WAC) MS

Separation of receptor-immobilized affinity molecules in WAC

An easy-to-use method. High yield potential through debris mixes

Protein must be immobilized in the colony

(Duong-Thi i sur. 2011.; Chanet sur. 2017.;

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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, the methods are more advanced and sophisticated, they can quickly produce large libraries of different compounds. A particularly important subset of these methods are those that are compatible with multicomponent reaction (MCR) chemistry (eg, the UGI reaction) and can generate large libraries of highly specific compounds in a short time. However, the speed at which chemical libraries can be screened using traditional techniques such as NMR and ITC has often not matched the speed at which libraries are generated or the number of different molecules contained in those libraries.

Modern DSF is well placed to handle these large and diverse libraries as it uses a real-time PCR engine to rapidly screen multiple molecules simultaneously against a target protein, meaning it is much better at handling high-throughput compounds than many other low-protein technologies sample consumption, 96, 384 or 1536 ligands can be analyzed in a simple display that takes about an hour and provides qualitative binding information. It is suitable for high-throughput library screening. This efficient workflow enables the assessment and ranking of potential engagement affinity.

In 2001, Pantoliano introduced a method based on high-throughput DSF for various therapeutic protein targets (human estrogen receptor (ESR), bacteriorhodopsin, human α-thrombin, bovine liver dihydrofolate (DHFR), extracellular-luminal domains of growth factors). receptor -1(D(II)-D(III)FGFR) and enzyme PilD; Pantoliano et al. 2001). These targets were screened against various combinatorial libraries of small molecules, including known compounds

ligands. Experiments have shown that Kd calculated from equation (1) based on experimentally obtained Tm values ​​gives very similar values ​​to values ​​previously obtained by other techniques. For example, tamoxifen inhibits the ESR antagonist with an IC50 value of 0.42 µM (Bolger et al. 1998), while the thermal shift micrographic assay gave an affinity of 1.1 µM. The known ligand pentosan polysulfate has been reported to have a Kd of 11 µM with FGFR-1 measured by ITC titration (Pantoliano et al. 1994) while a heat shift assay, i.e. H. DSF, has been reported to show a similar binding capacity of 5 .5 µM. Istiska supports a reliable alternative for determining protein-small molecule interactions.

KTmL 1⁄4exp −ΔHT0u =R 1=Tm−1=T01⁄2 j þ ΔCT0pu =R Σε Tm=T0 þT0=Tm−1ð Þ1⁄2

NO

LTm½ �ð1Þ

Wo

Association constants of KTmL ligands in the TmTm medium for the unfolding transition in the protein

The presence of a T0 ligand center for the unfolding transition is absent

ΔHT0u ligand enthalpy of unfolded protein in the absence

The ligand in T0ΔCT0pu contributes to the heat capacity change in the unfolded protein

in the absence of free ligand concentration [LTm] at Tm ([LTm]≅[L]total if

[L]total >>[Protein]total)

(to be continued)

Method Principle Advantages Limitations Ref

Column followed by MSdetection

Ohlson in Duong-Thi 2018.)

Hydrogen and deuterium exchange (HDX)MS

Ligand binding affects the rate of deuteration of protein residues. What can be seen in the crowd

The binding site can be elucidated directly and provide information on conformational changes of the protein

Low performance and expensive Chan et al. (2017); Marciano et al. (2014)

Microscale thermophoresis (MST)

A change in the movement of a target molecule across a temperature gradient due to ligand binding

Measurements can be performed at native intermediate positions. Allows determination of KD

The target must be labeled or have sufficient autofluorescence. Relatively low performance

Link i on. (2016.); Rainard i on. (2018)

Capillary Affinity Electrophoresis (ACE)

Change in electrophoretic mobility of the ligand due to target binding (dissociation)

high performance. sensitive method. Small amounts of proteins and ligands are used. Both target and ligand are free in solution

Requires detectable probe molecules or detectable fragments

Xu and Sr. (2016) · Austin et al. (2012); Farcaset al. (2017)

Biostratum signalometrija (BLI)

Change in the interference pattern due to ligand binding to the immobilized target coating

Can determine KD and other kinetic parameters. Consumes a small amount of protein

Protein immobilization is required. Wartchow et al. (2011)

Biophys Rev (2020) 12: –10485 91

R universal gas constant

DSF finds direct application in fragment-based ligand dissociation (FBLD) due to its ease of use in high-throughput screening. In this approach, building blocks of small molecules (100-150 Da) are potentially pooled (3-5 molecules per pool) and screened (Elkin et al. 2015; Valenti et al. 2019). Although these small molecules are unlikely to exhibit high affinity alone, this pooled approach allows for a significant reduction in the number of experiments that need to be performed to screen a large library. Successful groups of "hits" identified based on Tm shifts can then be further examined to identify unique segments of interest, and visits can be grouped to provide a primary metric for lead optimization. This strategy also offers a high probability of adding blocks to the final framework of lead compounds (Mashalidis et al. 2013), and two recent examples of the use of DSF in lead discovery are listed below.

DSF as a simple and powerful mechanism for fragment detection, joining modes and proposed joining strategies

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains one of the ten leading causes of death, and Mtb is the leading infectious agent worldwide (above HIV/AIDS). In 2017, 10 million people contracted tuberculosis, resulting in 1.6 million deaths (World Health Organization 2018). Drug-resistant tuberculosis remains a public health crisis and we still lack effective treatments to combat this burden. Therefore, new anti-tuberculosis drugs that fight tuberculosis through novel mechanisms are urgently needed. Biotin, also known as vitamin B7, is an essential cofactor for Mtb (Hayakawa and Oizumi, 1987). Since Mtb produces biotin to support growth and proliferation, but this vitamin is present in human blood at very low concentrations (Sassetti and Rubin 2003), targeting the intermediate pathway of biotin biosynthesis with PLP-dependent transaminase (BioA) holds promise. strategy (MannandPloux 2006). Dai and colleagues used DSF to screen a Maybridge Ro3 fragment library of about 1000 compounds against BioA and discovered 21 “hit” compounds—identified as those that increased Tm by more than 2° (Daiet al. 2015). Subsequent crystal X-ray diffraction data confirmed that 6 fragments bind within the active site. Ligand binding affinity and efficiency were cross-validated by ITC, resulting in a range between 7 and 42 μM in affinity and between 0.43 and 0.55 in ligand yield. The comparison of all available hits provided a basis for understanding a sufficiently active mode of interaction in an interaction situation. to optimize command design compatible with active page configuration states. In addition, the scaffolding of the small fragments found by DSF and crystallography also closely matches the existing ones

Previously reported inhibitors (Park et al. 2015), providing further evidence that this strategy can be a reliable method for ligand screening.

The same strategy was used by Hung's group and targeted TB pantothene synthase (PS) (Hung et al. 2009). Pantothenic acid (vitamin B5) plays an important role in the metabolism of fatty acids. It is formed by the condensation of pantoate with β-alanine by pantothene synthetase (PS) and blocking this pathway is likely to compromise Mtb growth (Sambandamurthy et al. 2002). In fragment screening by DSF, ligand 2 was identified from 1300 fragments with a ΔTm of 1.6 °C (Figure 2). This was further confirmed by WaterLOGSY NMR and ITC spectroscopy (Kd = 1 mM). The related X-ray structure showed that 2 binds to the pantoate binding pocket of P1 and extends further along the surface of PS, at a point 3.1 Å away from the second binding site of ligand 1 in the same pocket. The test in which both substituents were doped into the crystals revealed the presence of both fragments in the active site without collision, in conformations similar to the individual binding modes (Figure 2). Therefore, fragment docking and optimization were used to improve binding properties, using different ligands based on adjacent structures within the pocket. Then, the lead compound 3, which connects fragments 1 and 2 with anacylsulfonamide, showed a 500-fold stronger binding affinity than the individual fragments (Figure 2).

DSF combined with limited proteolysis to identify tankyrase inhibitors

A fragment-based study by Larsson 2013 clearly demonstrates how DSF can be used to identify high-value fragments and then direct them to construct a lead compound (Larsson et al. 2013). In this assay, the poly-ADP-ribosylating enzyme metakanirase was screened against a fragment library of 500 compounds (each present at 1 mM). To avoid compounds with strange behavior and to reduce the false positive rate (e.g. pan-test interfering compounds, PAINS) (Baell and Nissink 2018), the identified hits are further validated into real "hits" by using a cross-case DSF-dependent about the dose, the response is checked by concentrations (5 to 3). 4000 uM). In the DSF study of deposit 2, the "bump" melting profile was interpreted as a melting profile with two/multiple state transitions, which made the fitting of weakly binding Tm fragments much more difficult (Fig. 3a). By adding chymotrypsin, which performs in situ digestion and removes less ordered impurities, they were able to simplify the sigma fusion treatment (Fig. 3b). Subsequently, dose-response experiments confirmed the initial "hits" with an apparent increase in Tm after increasing concentrations of the initial "hit" (Figure 3c). Based on the crystal structure of TNKS2 with confirmed hits, Misc

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Hit Fragment changes are suggested and rated. The methyl group at position 4 is retained on its way down to the catalytic glutamate, while the methyl group at position 7, pointing toward the extended glutamate, changes

The pocket responsible for adenosine binding showed significant differences when associated with different functional groups. Starting with an initial 12 µM affinity fragment, multiple rounds of modification and validation using DSF, SPR and enzyme activity

Figure 3 a Melting curves of tankyrase 2 in the absence of chymotrypsin (black) and in the presence (red) of the stabilizing fragment. Melting curves of chymotrypsin-treated bTankyrase2 in the absence (black) and presence (red) of the same stabilizing fragment. c Concentration-dependent response for the chymotrypsin-fixed fragment

digested tankiraza. d The work process of optimizing the final water formulation from initial visit to completion was managed by DSF. This image is reproduced with permission from Larsson et al. adjusted. (2013). Copyright 2013 American Chemical Society

Figure 2 Fragments 1 and 2 impregnate the cocktail with crystalline phantothenate synthetase. There are two fragments in bound positions. Overlay of the linked junction of lead 3 with fragments 1 and 2 in the active site of P1 pantotheatosynthetase. Fragments 1 and 2 are shown as green sticks. The benzofuran group is slightly rotated relative to fragment 2, suggesting that steric constraints of the ligand do not allow this fragment to adopt its optimal conformation. Figures were generated with PyMol, based on PDB entries 3IMG and 3IV9 al.200ung)

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(IC50) and X-ray crystallography revealed a lead compound with inhibitory activity (IC50) of 9 nM and binding affinity (Kd) of 16 nM against TNKS2. The elegant approach of limited proteolysis of the less stable (ie, unbound) form of the target directly addresses the limitation in DSF—incomplete binding leading to multiple transitions in the thermal profile—by strengthening weak binding. However, such an approach is likely to be highly target dependent and may not be universally applicable.

In summary, the above examples show that fragment-based drug discovery (FBDD) has evolved into a first-line high-throughput screening for the discovery of therapeutic lead structures (Congreve et al. 2008; Murray and Rees 2009) and that DSF has been validated as a powerful pre-screening option in FBDD for more than two decades (Pantoliano et al. 1997). The use of DSF substrate screening facilitates low sample consumption – both protein and chemical – as well as rapid determination of experimental determination of ΔTm, which reduces workload and enables simplified screening protocols.

Use of DSF in buffer screening and to optimize protein stability and crystallization

In proteomics studies, interrelated biochemical, cellular, and physiological information is crucial for elucidating protein mechanisms. An important 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 in all these approaches is the purification of the target protein, which is challenging in many cases. On average, only 50-70% of soluble proteins and 30% of membrane proteins of prokaryotes can be expressed in recombinant form, and of those that are successfully expressed, only 30-50% can be purified to homogeneity (Christendatet al 2000, Norin and Sundstrom 2002, Dobrovetsky et al 2005). Eukaryotic proteins—including many biomedically interesting human targets—appear to be an even greater challenge (Banciet al. 2006).

Traditional solutions for protein production and purification are mainly based on screening recombinant hosts, coding construct sequences, expression conditions and subsequent purification conditions (Gräslund et al. 2008; Rosano and Ceccarelli 2014; Wingfield 2015). In the last two steps, adding specific additives or changing the buffer composition can significantly increase the solubility of recombinant proteins and improve the thermal stability of the target to prevent protein unfolding or aggregation—even at low temperatures. There are many reports (Sarciaux et al. 1999; Vedadi et al. 2006; Reinhard et al. 2013) showing that optimization of purification conditions leads to improved protein stability or solubility, and it is not unreasonable to suggest that buffer optimization should be considered as

an integral part of any research project based on isolated protein samples. Even small improvements in protein stability can be important in the engineering process, for example in the mass production of antibodies for therapeutic purposes.

A notable case is that of the recombinant naB protein produced in E. coli. First, it was shown to be extremely unstable in the purification buffer - even when stored at 0 °C, 90% of the enzyme activity was lost within 30 minutes. In a gradual screening process, by adding specific chemical reagents (Mg2+, ADP, (NH4)2SO4 and glycerol), 90% of the activity was retained after longer storage at 60°C in the optimal buffer. Additionally, the new buffer contributed to the isolation of soluble dnaB in higher yields and subsequent crystallization (Arai et al. 1981). While this is undoubtedly an extreme example, it clearly demonstrates the value of buffer optimization.

In the early years of structural genomics, a commonly used strategy was to use a standard purification buffer for most protein targets, with detailed sample buffer optimization performed only to treat pathological problems (aggregation, loss of activity, change in oligomeric state, etc.). .) (Mezzasalma et al. 2007). As shown below, this potential influence ultimately led to the success of structural genomics projects where growing high-quality crystals from purified samples was a major obstacle. 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 buffers with a concentration of 100 mM and a pH range of 4.5 to 9.0. Since each pH step is only 0.2 to 0.5 pH units, the screen is wide enough for most proteins currently used for research.

In some cases, a single pH buffer dramatically affected the Tm protein, correlating with a preference for specific ionic effects. For example, the Tm of AC07 protein in K-phosphate at pH 7 is 37 °C, while in the presence of sodium phosphate it is 46 °C (Figure 4a). To disentangle the influence of buffer choice and final pH, a three-component buffer system was implemented (Newman 2004), which allowed for a wide range of pH without changing the composition of the buffer chemicals. Hepes-Ches (CHC) citric acid buffer, which covers the pH range of 4 to 10, can quickly determine the most favorable pH of target proteins. This work showed that the Tm of the investigated targets follows a typical bell-shaped curve. For example, AD28 showed lower values ​​of temperature stability at low and high pH (pH = 4 and 10), with maximum stability near pH 6.4.

Combinations of the above buffer optimization with additives such as heavy metals or substrates/cofactors such as NADH at optimal pH can further improve protein thermal stability. For example, the addition of NADH was found to significantly increase the melting temperature of AD21 (ΔTm).

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≈ 20 °C; Figure 4b), which correlates with the previously known fact that it is an essential cofactor of AD21 in catalyzing the last step of proline biosynthesis.

In summary, DSF screening of additives provided data to optimize buffer conditions to control crystallization (Reinhard et al. 2013). Additives that gave a positive thermal shift (Tm) compared to control samples increased the rate of protein crystallization by 70%, while additives that showed destabilizing effects reduced the probability of crystal formation by about 50% compared to the control buffer. This observation strongly suggests a link between protein stability/solubility and crystallogenesis. For excellent detailed reviews of the use of DSF to optimize crystallization buffers, the reader is referred to Boivin et al. send. (2013) and Reinhard et al. (2013).

Structural biology plays an important role in early drug discovery, as elucidating the binding mode of "hit" compounds can provide important driving information

downstream, the development of lead compounds (de Kloe et al. 2009; Wang et al. 2019). While protein crystallization depends on a number of sample properties, with sample purity and homogeneity generally being key factors (Giegi et al. 1994; Dale et al. 2003; Ericsson et al. 2006), thermal stability has also been shown to be a critical parameter for successful results in crystallization. A study by Dupeux et al. (2011) 657 different proteins were screened using DSF and then subjected to automated vapor diffusion crystallization. Based on the analysis of protein melting point (Tm) and optically determined crystallization peaks, the authors were able to draw clear conclusions about the importance of thermal stability in the crystallization process. In this study, 437 out of 657 unfolded samples show clear and sharp temperature transitions. This behavior can be interpreted as the result of a population sample consisting of a single overall conformation with relatively little conformational variation around the "mean" fold—a scenario that

Figure 4 a Application temperature of AC07 in different pH buffers with different compositions. Sodium phosphate (red bar) and K-phosphate (blue bar) showed a significant difference in Tm at pH near 7.4. b Melting temperature curves of AD21 protein are compared with different addn. A key chemical required in the proline biosynthetic pathway, NAD(P)H (yellow) showed a marked increase in thermal stability when incubated with the target protein. Figures adapted from Ericsson et al. (2006). Copyright 2006 under license from Elsevier

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is likely to favor crystallization more than a sample with a high degree of conformational variation due to the thermomobility of its constituents. The mean value of Tm of all samples was 51.5 °C in the range from 25 to 95 °C (Figure 5). In particular, proteins with a Tm of 45 °C or higher showed a greater tendency to crystallize when incubated at 20 °C, with successful crystallization results of 49.1%. For proteins with a temperature below 45 °C, the probability of crystal growth is reduced to 26.8% at 20 °C. In addition, a range of proteins with temperatures between 25 and 45 °C formed crystals at a temperature lower than 5 °C, while crystallization at 20 °C was initially unsuccessful. The study confirmed the previous observation that thermophilic proteins have higher crystallization rates than those from mesophilic organisms, despite similar Tm values. Furthermore, Szilágyi's report also implies that thermophilic proteins have a lower proportion of unstructured regions (Szilágyi and Závodszky 2000), implying that disordered regions would hinder crystallization.

Since the thermal stability of a sample can affect its prospects for crystallization, it becomes clear that optimizing the sample buffer in which the protein is finally purified and concentrated prior to crystallization can offer advantages for structural biologists and especially for structural drug design. In a typical DSF buffer screening experiment, the conditions (buffering agents, 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 behavior occurs. makes it difficult to determine accurately. A polyphasic unfolding curve usually indicates either the presence of multiple, independently folding domains (Ionescu et al. 2008) or the heterogeneous state of the protein sample in solution (Choudhary et al.

2017) or ligand binding is not fully saturated with target proteins (Shrake and Ross 1992; Matulis et al. 2005), which can disrupt crystallogenesis and make protein characterization difficult. Here, DSF can also be used to control sample preparation buffer screening for crystallization by stepwise exchange of buffer components or ligands. Geders et al. reported multiphasic unfolding behavior when his group attempted to crystallize the pyridoxal-5-phosphate (PLP)-dependent transaminase BioA from Mycobacterium tuberculosis (Geders et al. 2012). When optimizing the crystallization buffer, BioA without PLP showed polyphasic unfolding behavior. Cofactor undersaturation in the protein-cofactor system also leads to a biphasic melting curve. Protein heterogeneity due to insufficient amounts of PLP cofactors could potentially affect crystallization. Dey et al. 2010) were based on Tris and generated a three-phase melting temperature curve with transitions at 45, 68 and 86 °C (folded, apo and PLP-bound BioA, respectively (Fig. 6a)). The sample also showed clear precipitates at higher concentrations. Electron density of a crystal grown from Tris buffer showed no interpretable density for the bound PLP molecule. Replacing Tris buffer with Hepes during purification (both lysis buffer and final purification buffer) resulted in a reduced tendency for multiphasic melting curves, particularly as Hepes completely replaced Tris in both lysis buffer and purification buffer (Figure 6b). This result suggests that Trisbuffer partially degraded PLP, resulting in partial binding of unsaturated PLP to BioA. This partial degradation was further supported by a UV-Vis spectroscopic assay using PLPin

Figure 5 Tm and crystallization success rate: All samples were incubated at 20°C for crystallization. The numbers above the lines indicate the crystallization of the success rate of each class. The extremophile samples consist of 12 proteins with Tm between 70 and 95 °C. Figure based on Dupeux et al. (2011). Reproduced with permission from the International Union of Crystallography

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Tris buffer showed an absorption maximum near 420 nm, similar to that observed for PLP in the Schiff base form instead of the free aldehyde (Figure 6d). PLP in Hepes buffer showed an absorbance at 390 nm similar to that of PLP in water. By substituting Hepes for Tris in the purification buffers and adding increasing concentrations of PLP, the multiphase melting curves were replaced by a single sharp transition curve with a Tm at 88 °C. These optimizations also improved the size and quality of the obtained crystals and also resulted in unclear electron density for the bound PLP molecule. Therefore, DSF analysis correlated with heterogeneity and suboptimal crystallization results. This example also highlights two complications in small molecule screening: First, the use of Tris (or primary amines that can form a Schiff base with aldehydes) should be avoided with PLP-dependent compounds.

Proteins - and researchers should be aware of possible similar effects on other protein cofactors. Second, should they be considered when analyzing multiphase DSF profiles since they may be due to molecular interactions of the screen with the buffer rather than the target protein.

In biochemical or biomedical research, a well-folded protein structure with appropriate activity is one of the key factors for in vitro experiments. Although there are numerous recombinant protein expression technologies that greatly aid the understanding of proteomics in prokaryotic and eukaryotic cells, the lack of appropriate chaperones in E. coli (the most commonly used recombinant source) results in approximately 80% of these proteins folding into a fold in the body or in insoluble body. (Carrió and Villaverde 2002; Sørensen and Mortensen 2005; Gräslund et al. 2008; Rosano and Ceccarelli 2014).

Figure 6 aDSF melting curves of BioA with PLP and Tris in lysis and storage buffer showing multiple peaks during denaturation. b Steep DSF melting curve of PLP desaturation by BioA. after saturating BioA with PLP, misfolds and peaks were eliminated, resulting in increased stability of BioA at aTm at 88 °C. c The first derivative overlaps the corresponding melting curves. The red line shows BioA in Tris buffer with multiple transitions at 45, 68, and 86 °C, representing misfolded, apo- and PLP-bound BioA, respectively. The blue line represents

BioA saturated PLP whose Tm increased dramatically to 88 °C. d UV-Vis spectroscopy of PLP or PLP-BioA(Holo) under different conditions. 400 µM PLP in water (cyan) has the same absorbance as Hepes buffer (brown). PLP-bound BioA(holo) (purple) showed the same absorbance at 420 nm as PLP in Tris buffer (black). Elements are from Geders et al. accepted. (2012). Reproduced with permission from the International Union of Crystallography

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Furthermore, protein folding from inclusion bodies is an empirical art, as functionally related proteins with different structural designs or from different sources require significantly different conditions to support folding. Therefore, systematic and compatible high-throughput tests are needed to solve this problem. In 2016, Biter and colleagues developed the DSF-directed folding (DGR) method to rapidly search for the folding of inclusion bodies, including proteins with disulfide bonds and new structures without a previously existing model. 2016). The assays then used sparse lattice PACT crystallization (pH, anion, cation) and used a dilute matrix buffer search to screen a large chemical space of biologically compatible buffers. Inclusion bodies were removed by centrifugation before dissolution in chaotrope (organidine urea) and addition of fluorescent dye (SYPROOrange). The sediment is excluded from the sieve (Figure 7a). Solubilized targets were incubated with PACT screening components for 2 hours, centrifuged to remove any precipitates/aggregates that formed, and analyzed directly with DSF. Fluorescence data showing protein unfolding under DSF conditions were interpreted as

which corresponds to a state that supports protein folding. Due to the wide range of pH, cations and anions, the PACT screen provided clear evidence of pepsin folding (Fig. 7c,d). For disulfide-containing proteins such as lysozyme, the PACT screening conditions were supplemented with oxidized and reduced glutathione. The resulting thermal melting profile of the folded cozyme showed a clear Tm of 65 at pH 9 in the presence of equimolar GSH and GSSH.

Attempts to fold new proteins from inclusion bodies also resulted in improved yields of fibroblast growth factors 19 and 21, resulting in crystn. When DGR was applied to the hormone irisin, the folding success helped generate the crystal form of the octadimer (Schumacher et al. 2013).

A year later, colleagues from our group extended the DGR approach by examining arginine folding factors and other additional non-systematic buffer screens (Wang et al. 2017). Arginine is often used to suppress protein aggregation during folding and, in contrast, can slow or prevent protein association reactions through weak interactions with targets (Baynes et al. 2005; Arakawa et al. 2007).

Figure 7a Modified PACT screen used in bending test. The three parts consist of a pH check, cations and anions in different combinations. Color indicates Tm found under certain conditions. b Melting heat profiles of pepsin in native, denatured, folded and misfolded state. c Peak height Tm in the PACK screen profile. The

The color shows that pepsin has a higher Tm under acidic conditions. dPrimary pepsin derivatives from guanidine-solubilized dilutions. Populations marked in red correspond to the misfolded state, and blue to the natively folded state. Numbers are from Biter et al. (2016)

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Chaotropics such as urea or guanidine. For this reason, we developed two sequential screening kits to provide a general screening strategy. A primary screening is a combination of different pH buffers in the presence or absence of arginine at a concentration of 0.4 M. This allows rapid determination of the appropriate pH for folding and at the same time the effect of arginine on folding can be examined. A secondary screen is then tested, adding various sugars, detergents, osmolytes, PEGs, amino acids, salt concentration levels and reducing agents, expanding the PACT screen, which mainly focuses on pH, anions and cations (Figure 8). This approach identified optimal folding buffers for four different therapeutic target proteins from inclusion bodies expressed in E. coli as well as identifying the final gel filtration buffer for storage or crystallization. During this study, several factors affecting protein folding were discovered, including buffer chemistry, folding time, redox status, and the use of arginine as an inhibitor of anine aggregation. For example, DGR analysis of interleukin-17A (IL-17A) folding revealed obvious melting transition signals at pH 9.5 in CHC and CHES buffers—but not in MMT or MIB buffers at the same pH—indicating that buffer bonds did so with significant effect. IN

in the presence of arginine, the Tm increased from 40 to 60 °C, indicating a more stable end product of the folding process (Figure 9). The refolding time also plays a significant role in all tests, as the data for all proteins tested showed that the maximum force occurs at a certain refolding time. The hemagglutinin receptor binding domain (HA-RBD) showed a clear melting curve when refolding was limited to 1 hour, while the melting transition signal disappeared after 6 hours of incubation in refolding buffer. IL-17A requires a longer folding time, requiring 15 hours for an optimal DGR signal. Furthermore, these data show that buffers optimized by the refolding process are not necessarily ideal for subsequent storage or crystallization—they can be stabilized as an intermediate in the refolding process.

Application of DSF for ligands in vivo: Validation of target interaction

A common problem in monitoring drug binding and efficacy during therapy is that interactions between target proteins and drugs cannot be measured directly in cells and tissues.

Figure 8. The composition of the secondary network of additives includes a wide range of sugars, detergents, salts, buffers and reducing agents. This figure was determined by Wang et al. accepted. (2017)

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Validation methods typically examine subsequent cellular responses after multiple doses. In addition, some tested drugs may show good binding activity when incubated with target proteins, but fail in clinical trials, and subsequent studies show that they do not act on the desired target in cells (Auld et al. 2009; Schmidt 2010; Guha2011). ). In 2013, Molina et al. (2013) presented a new method for monitoring intracellular drug interactions by performing thermal shift assays in cells, lysates or tissues, which is also based on ligand-induced thermal stabilization of target proteins, but requires purification steps for non-proteins. In the cellular thermal shift assay (CETSA), cells are heated so that the protein on the plate also unfolds and precipitates—similar to the in vitro approaches described above. After extraction and centrifugation, the remaining soluble proteins were separated from the precipitate and quantified by Western blotting. A CETSA melting curve is obtained by plotting the amount of soluble protein against the strength of the Western blot signal. In a preliminary study, dihydrofolate reductase (DHFR) and thymidylate synthase (TS) were selected as targets for the antifolate anticancer drugs methotrexate and titrexide. The samples were exposed to both drugs either as intact cells or as lysates. The result showed a significant increase in thermal shift in cells treated with DHFR or TS compared to controls. To study the effects of drug concentration, an anisothermal dose-response method (ITDR) was developed to evaluate the binding of compounds. In this approach, the cell product is aliquoted and exposed to different serial drug concentrations while keeping temperature and heating time constant. After Western blotting, signal strength can indicate when a higher saturating drug concentration is required, which can be more useful than commonly used affinity-related half-saturation points (eg, IC50, Kd). Further research confirmed that the CETSA method can be used as a reliable biophysical technique for protein-ligand binding studies in cells and wastewater. In a recent report, Maji's team screened a library of more than 2,000 small molecules to identify CRISPR-Cas9 inhibitors that could then be used to precisely control

CRISPR-Cas9 in genome engineering. CETSA was used to confirm that the compound disrupted the SpCas9:DNA interaction and reduced the Tm of SpCas9 by ~2.5 °C in cells treated without the compound (Maji et al. 2019). In another design of a structure-based small molecule targeting the menin-MLL interaction in leukemia, the irreversible, highly potent chemical M-525 was also validated by CETSA in an acellular assay (Xu et al. 2018). Covalent binding of pounds increased the thermal stability of menin in MV4, 11 and MOLM-13 cells. The concentration of M-525 used here was only 0.4-1.2 nM. In addition, CETSA also showed that the compound specifically targets menin and that no effect was observed on another MLL-binding protein, WDR5.

Diploma

DSF is a powerful biophysical technique for studying protein stability in a specific environment, either within selected buffer conditions or at (partial) saturation with ligands of interest. Protein unfolding by a thermodynamic ΔTm meter is monitored as a primary indicator for justifying changes in target protein stability, regardless of whether the targets are in pure form, in lysate, cells, or events. New label-free nanoDSF approaches explicitly eliminate the need for dyes and allow the same approach to be applied to the study of membrane proteins. At the same time, the problems caused by the interaction between the dye and the hydrophobic surface of the protein or the detergent additives used, as well as the interaction between the dye and other molecules on the screen, are solved. In almost two decades since its first appearance, the DSF technique has been used to characterize the thermal properties of many proteins, thanks to its low sample consumption and high throughput. This makes DSF suitable for buffer optimization in crystallization and for screening large ligand libraries. In terms of confirming ligand binding, there are indeed many successful cases

Figure 9 Melting transition of IL-17A in CHC buffer system at pH 9-10 in the absence (a) and presence (b) of arginine. both showed an atypical sigmoidal melting curve at pH 9.5. The elements were determined by Wang et al. accepted. (2017)

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Although this correlation has already been reported in the literature, it is still important to note that this correlation usually occurs in sequence for similarly structured compounds and that continuous monitoring of fragments based on significant thermal shifts can lead to further optimization. It should also be taken into account that ligands can act with both folded and unfolded states of target proteins, and a negative shift in the melting temperature does not preclude binding to the native state. Unlike titration-based techniques such as ITC, MST, and SPR, in which receptor interaction behavior is based on varying serial ligand concentrations and endpoint measurements, DSF is sensitive to all steps along the entry pathway, complicating its use in determining the affinity of molecules for non-protein mobile receptors. The robustness and applicability of DSF to solve a variety of problems in such a wide range of sample types should ensure its status as a central technology of modern drug discovery.

Acknowledgments We would like to thank NanoTemper (Munich, Germany) for their kind support and access to a sample application.

Compliance with ethical standards

Conflict of interest The authors declare that there is no conflict of interest.

Ethics approval This article does not include human or animal studies conducted by any of the authors.

Open Access This article is licensed under the CreativeCommonsAttribution 4.0 International License, which permits it to be used, shared, adapted, distributed, and reproduced in any medium or format, provided that you properly credit the original author(s) and source with the Creative License link Commons and indicate whether changes have been made. Images or third-party materials in this article are licensed under the Creative Commons Article License, unless otherwise noted in the material attribution. If the material is not subject to a Creative Commons subject license and the intended use of the material is illegal or exceeds the law, you must seek permission directly from the copyright owner. To view a copy of this license, visit http://creativecommons.org/licenses/from/4.0/.

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