Energetics-based discovery of protein-ligand interactions on a proteomic scale

Biochemical functions of proteins in cells frequently involve interactions with various ligands. Proteomic methods for the identification of proteins that interact with specific ligands such as metabolites, signaling molecules, and drugs are valuable in investigating the regulatory mechanisms of cellular metabolism, annotating proteins with unknown functions, and elucidating pharmacological mechanisms. Here we report an energetics-based target identification method in which target proteins in a cell lysate are identified by exploiting the effect of ligand binding on their stabilities. Urea-induced unfolding of proteins in cell lysates is probed by a short pulse of proteolysis, and the effect of a ligand on the amount of folded protein remaining is monitored on a proteomic scale. As proof of principle, we identified proteins that interact with ATP in the Escherichia coli proteome. Literature and database mining confirmed that a majority of the identified proteins are indeed ATP-binding proteins. Four identified proteins that were previously not known to interact with ATP were cloned and expressed to validate the result. Except for one protein, the effects of ATP on urea-induced unfolding were confirmed. Analyses of the protein sequences and structure models were also employed to predict potential ATP binding sites in the identified proteins. Our results demonstrate that this energetics-based target identification approach is a facile method to identify proteins that interact with specific ligands on a proteomic scale.     

Mass spectrometry-based Cellular Thermal Shift Assay (CETSA®) for target deconvolution in phenotypic drug discovery

The recent renewed interest in phenotypic drug discovery has concomitantly put a focus on target deconvolution in order to achieve drug-target identification. Even though there are prescribed therapies whose mode of action is not fully understood, knowledge of the primary target will inevitably facilitate the discovery and translation of efficacy from bench to bedside. Elucidating targets and subsequent pathways engaged will also facilitate safety studies and overall development of novel drug candidates. Today, there are several techniques available for identifying the primary target, many of which rely on mass spectrometry (MS) to identify compound – target protein interactions. The Cellular Thermal Shift Assay (CETSA®) is well suited for identifying target engagement between ligands and their protein targets. Several studies have shown that CETSA combined with MS is a powerful technique that allows unlabeled target deconvolution in complex samples such as intact cells and tissues in addition to cell lysates and other protein suspensions. The applicability of CETSA MS for target deconvolution purposes will be discussed and exemplified in this mini review.     

Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-K_i was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-K_i server is freely available via: http://sdd.whu.edu.cn/dpiki.     

The human peripheral subunit-binding domain folds rapidly while overcoming repulsive Coulomb forces

Peripheral subunit binding domains (PSBDs) are integral parts of large multienzyme complexes involved in carbohydrate metabolism. PSBDs facilitate shuttling of prosthetic groups between different catalytic subunits. Their protein surface is characterized by a high density of positive charges required for binding to subunits within the complex. Here, we investigated folding thermodynamics and kinetics of the human PSBD (HSBD) using circular dichroism and tryptophan fluorescence experiments. HSBD was only marginally stable under physiological solvent conditions but folded within microseconds via a barrier‐limited apparent two‐state transition, analogous to its bacterial homologues. The high positive surface‐charge density of HSBD leads to repulsive Coulomb forces that modulate protein stability and folding kinetics, and appear to even induce native‐state movement. The electrostatic strain was alleviated at high solution‐ionic‐strength by Debye‐Hückel screening. Differences in ionic‐strength dependent characteristics among PSBD homologues could be explained by differences in their surface charge distributions. The findings highlight the trade‐off between protein function and stability during protein evolution.     

Simplified proteomics approach to discover protein-ligand interactions

Identifying targets of biologically active small molecules is an essential but still challenging task in drug research and chemical genetics. Energetics-based target identification is an approach that utilizes the change in the conformational stabilities of proteins upon ligand binding in order to identify target proteins. Different from traditional affinity-based capture approaches, energetics-based methods do not require any labeling or immobilization of the test molecule. Here, we report a surprisingly simple version of energetics-based target identification, which only requires ion exchange chromatography, SDS PAGE, and minimal use of mass spectrometry. The complexity of a proteome is reduced through fractionation by ion exchange chromatography. Urea-induced unfolding of proteins in each fraction is then monitored by the significant increase in proteolytic susceptibility upon unfolding in the presence and the absence of a ligand. Proteins showing a different degree of unfolding with the ligand are identified by SDS PAGE followed by mass spectrometry. Using this approach, we identified ATP-binding proteins in the Escherichia coli proteome. In addition to known ATP-binding proteins, we also identified a number of proteins that were not previously known to interact with ATP. To validate one such finding, we cloned and purified phosphoglyceromutase, which was not previously known to bind ATP, and confirmed that ATP indeed stabilizes this protein. The combination of fractionation and pulse proteolysis offers an opportunity to investigate protein-drug or protein-metabolite interactions on a proteomic scale with minimal instrumentation and without modification of a molecule of interest.     

Chip-based nanoelectrospray mass spectrometry for protein characterization

In the last several years, significant progress has been made in the development of microfluidic-based analytical technologies for proteomic and drug discovery applications. Chip-based nanoelectrospray coupled to a mass spectrometer detector is one of the recently developed analytical microscale technologies. This technology offers unique advantages for automated nanoelectrospray including reduced sample consumption, improved detection sensitivity and enhanced data quality for proteomic studies. This review presents an overview and introduction of recent developments in chip devices coupled to electrospray mass spectrometers including the development of the automated nanoelectrospray ionization chip device for protein characterization. Applications using automated chip-based nanoelectrospray ionization technology in proteomic and bioanalytical studies are also extensively reviewed in the fields of high-throughput protein identification, protein post-translational modification studies, top-down proteomics, biomarker screening by pattern recognition, noncovalent protein–ligand binding for drug discovery and lipid analysis. Additionally, future trends in chip-based nanoelectrospray technology are discussed.     

Chip-based nanoelectrospray mass spectrometry for protein characterization

In the last several years, significant progress has been made in the development of microfluidic-based analytical technologies for proteomic and drug discovery applications. Chip-based nanoelectrospray coupled to a mass spectrometer detector is one of the recently developed analytical microscale technologies. This technology offers unique advantages for automated nanoelectrospray including reduced sample consumption, improved detection sensitivity and enhanced data quality for proteomic studies. This review presents an overview and introduction of recent developments in chip devices coupled to electrospray mass spectrometers including the development of the automated nanoelectrospray ionization chip device for protein characterization. Applications using automated chip-based nanoelectrospray ionization technology in proteomic and bioanalytical studies are also extensively reviewed in the fields of high-throughput protein identification, protein post-translational modification studies, top-down proteomics, biomarker screening by pattern recognition, noncovalent protein-ligand binding for drug discovery and lipid analysis. Additionally, future trends in chip-based nanoelectrospray technology are discussed.     

CETSA in integrated proteomics studies of cellular processes

The Cellular Thermal Shift Assay (CETSA) has recently emerged as a promising method to directly monitor functional modulations of protein interaction states in intact cells and tissue samples. Recent data support that the mass spectrometry–coupled proteome-wide implementation of CETSA (MS-CETSA) generates stringent information on a wide range of different interaction classes and is uniquely well suited to study the modulation of protein interaction states in cellular processes and during drug action. To expand the mechanistic insight of CETSA shifts, and to complement information from CETSA experiments, we outline how the integration of MS-CETSA with other proteomics techniques can provide a new platform for detailed, comprehensive, and interactive studies of the functional modulations of proteomes in situ.     

The E-box DNA binding protein Sgc1p suppresses the gcr2 mutation, which is involved in transcriptional activation of glycolytic genes in Saccharomyces cerevisiae

Maltose binding protein (MBP) exhibits a slow phase of folding at pH 7.4, 298 K. The kinetics of this phase has been characterized as a function of denaturant concentration and temperature. Denaturant double-jump experiments and the activation energy for folding indicate that the slow phase involves processes other than proline isomerization. Although the first five N-terminal residues are disordered in the MBP crystal structure, mutations in this region slow down folding and destabilize the native structure. This is the first report showing that disordered N-terminal residues can affect folding kinetics and stability.     

Target identification by chromatographic co-elution: monitoring of drug-protein interactions without immobilization or chemical derivatization

Bioactive molecules typically mediate their biological effects through direct physical association with one or more cellular proteins. The detection of drug-target interactions is therefore essential for the characterization of compound mechanism of action and off-target effects, but generic label-free approaches for detecting binding events in biological mixtures have remained elusive. Here, we report a method termed target identification by chromatographic co-elution (TICC) for routinely monitoring the interaction of drugs with cellular proteins under nearly physiological conditions in vitro based on simple liquid chromatographic separations of cell-free lysates. Correlative proteomic analysis of drug-bound protein fractions by shotgun sequencing is then performed to identify candidate target(s). The method is highly reproducible, does not require immobilization or derivatization of drug or protein, and is applicable to diverse natural products and synthetic compounds. The capability of TICC to detect known drug-protein target physical interactions (K(d) range: micromolar to nanomolar) is demonstrated both qualitatively and quantitatively. We subsequently used TICC to uncover the sterol biosynthetic enzyme Erg6p as a novel putative anti-fungal target. Furthermore, TICC identified Asc1 and Dak1, a core 40 S ribosomal protein that represses gene expression, and dihydroxyacetone kinase involved in stress adaptation, respectively, as novel yeast targets of a dopamine receptor agonist.     

Quantifying kinetic paths of protein folding

We propose a new approach to activated protein folding dynamics via a diffusive path integral framework. The important issues of kinetic paths in this situation can be directly addressed. This leads to the identification of the kinetic paths of the activated folding process, and provides a direct tool and language for the theoretical and experimental community to understand the problem better. The kinetic paths giving the dominant contributions to the long-time folding activation dynamics can be quantitatively determined. These are shown to be the instanton paths. The contributions of these instanton paths to the kinetics lead to the "bell-like" shape folding rate dependence on temperature, which is in good agreement with folding kinetic experiments and simulations. The connections to other approaches as well as the experiments of the protein folding kinetics are discussed.     

GroEL walks the fine line: the subtle balance of substrate and co-chaperonin binding by GroEL. A combinatorial investigation by design, selection and screening

While support in protein folding by molecular chaperones is extremely efficient for endogenous polypeptides, it often fails for recombinant proteins in a bacterial host, thus constituting a major hurdle for protein research and biotechnology. To understand the reasons for this difference and to answer the question of whether it is feasible to design tailor-made chaperones, we investigated one of the most prominent bacterial chaperones, the GroEL/ES ring complex. On the basis of structural data, we designed and constructed a combinatorial GroEL library, where the substrate-binding site was randomized. Screening and selection experiments with this library demonstrated that substrate binding and release is supported by many variants, but the majority of the library members failed to assist in chaperonin-mediated protein folding under conditions where spontaneous folding is suppressed. These findings revealed a conflict between binding of substrate and binding of the co-chaperonin GroES. As a consequence, the window of mutational freedom in that region of GroEL is very small. In screening experiments, we could identify GroEL variants slightly improved for a given substrate, which were still promiscuous. As the substrate-binding site of the GroEL molecule overlaps strongly with the site of cofactor binding, the outcome of our experiments suggests that maintenance of cofactor binding affinity is more critical for chaperonin-mediated protein folding than energetically optimized substrate recognition.     

Target deconvolution from phenotype-based drug discovery by using chemical proteomics approaches

Phenotype-based drug discovery is a key strategy for small molecule drug screening, and the molecular target identification of small molecules, termed “target deconvolution,” is critical albeit challenging. In this review, we classify approaches for target deconvolution, including both direct and indirect approaches, summarize their underlying principles, and provide examples of current chemical proteomics strategies including affinity purification using compound-immobilized beads, photoaffinity labeling (PAL), cellular thermal shift assay (CETSA), and activity-based protein profiling (ABPP). Because there is no single best target deconvolution strategy, it is important to carefully select a strategy on the basis of the test compound characteristics.     

Folding and Misfolding of a PDZ Tandem Repeat

Although the vast majority of the human proteome is represented by multi-domain proteins, the study of multi-domain folding and misfolding is a relatively poorly explored field. The protein Whirlin is a multi-domain scaffolding protein expressed in the inner ear. It is characterized by the presence of tandem repeats of PDZ domains. The first two PDZ domains of Whirlin (PDZ1 and PDZ2 – namely P1P2) are structurally close and separated by a disordered short linker. We recently described the folding mechanism of the P1P2 tandem. The difference in thermodynamic stability of the two domains allowed us to selectively unfold one or both PDZ domains and to pinpoint the accumulation of a misfolded intermediate, which we demonstrated to retain physiological binding activity. In this work, we provide an extensive characterization of the folding and unfolding of P1P2. Based on the observed data, we describe an integrated kinetic analysis that satisfactorily fits the experiments and provides a valuable model to interpret multi-domain folding. The experimental and analytical approaches described in this study may be of general interest for the interpretation of complex multi-domain protein folding kinetics.     

Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.     

Mechanism of induced folding: Both folding before binding and binding before folding can be realized in staphylococcal nuclease mutants

A considerable number of functional proteins are unstructured under physiological condition. These "intrinsically disordered" proteins exhibit induced folding when they bind their targets. The induced folding comprises two elementary processes: folding and binding. Two mechanisms are possible for the induced folding: either folding before binding or binding before folding. We found that these two mechanisms can be distinguished by the target-concentration dependence of folding kinetics. We also created two types of mutants of staphylococcal nuclease showing the different inhibitor-concentration dependence of induced folding kinetics. One mutant obeys the scheme of binding before folding, while the other the folding before binding. This is the first experimental evidence demonstrating that both mechanisms are realized for a single protein. Binding before folding is possible, when the protein lacks essential nonlocal interaction to stabilize the native conformation. The results cast light on the protein folding mechanism involved in the intrinsically disordered proteins.     

Expanding the DNA-encoded library toolbox: identifying small molecules targeting RNA

DNA-encoded library (DEL) technology is a powerful tool for small molecule identification in drug discovery, yet the reported DEL selection strategies were applied primarily on protein targets in either purified form or in cellular context. To expand the application of this technology, we employed DEL selection on an RNA target HIV-1 TAR (trans-acting responsive region), but found that the majority of signals were resulted from false positive DNA–RNA binding. We thus developed an optimized selection strategy utilizing RNA patches and competitive elution to minimize unwanted DNA binding, followed by k-mer analysis and motif search to differentiate false positive signal. This optimized strategy resulted in a very clean background in a DEL selection against Escherichia coli FMN Riboswitch, and the enriched compounds were determined with double digit nanomolar binding affinity, as well as similar potency in functional FMN competition assay. These results demonstrated the feasibility of small molecule identification against RNA targets using DEL selection. The developed experimental and computational strategy provided a promising opportunity for RNA ligand screening and expanded the application of DEL selection to a much wider context in drug discovery.     

Mass spectrometry approaches to monitor protein-drug interactions

Recent advances in mass spectrometry-based approaches have enabled the investigation of drug-protein interactions in various ways including the direct detection of drug-target complexes, the examination of drug-induced changes in the target protein structure, and the monitoring of enzymatic target activity. Mass spectrometry-based proteomics methods also permit the unbiased analysis of changes in protein abundance and post-translational modifications induced by drug action. Finally, chemoproteomic affinity enrichment studies enable the deconvolution of drug targets under close to physiological conditions. This review provides an overview of current methods for the characterization of drug-target interactions by mass spectrometry and describes a protocol for chemoproteomic target binding studies using immobilized bioactive molecules.     

Interpretation of protein folding psi values

The structural characterization of transition states is essential for understanding the mechanism of protein folding. Analyzing the effect of mutations on protein stability and folding kinetics in phi-value analysis is commonly used to gain information about the presence of side-chain interactions in transition states. Recently, specific binding of ligands to engineered binding sites was applied to monitor the formation of local structures in transition states (psi analysis). A surprising result from psi analysis was the presence of parallel folding pathways in all reported studies and a major discrepancy between phi and psi values measured in the same protein. Here, we show that psi values cannot be analyzed in the same way as other rate-equilibrium free energy relationships due to the involvement of bimolecular reactions that may have different dissociation constants for the native, unfolded and transition state. As a consequence, psi values reflect the relative binding energy (kappa) of the transition state only for the extreme values of kappa=0 or kappa=1. In all other cases, non-linear rate-equilibrium free-energy relationships (Leffler plots) are observed. This apparently indicates the presence of parallel folding pathways even if folding occurs over a single homogeneous transition state. Consequently, the results from Leffler plots do not yield information about the structural properties of the transition state. This explains the lack of agreement between results from psi analysis and other methods used to characterize protein folding transition states. We further show that the same considerations apply for the analysis of the effect of pH on protein folding.