Identification and validation of bioactive small molecule target through phenotypic screening

For effective bioactive small molecule discovery and development into new therapeutic drug, a systematic screening and target protein identification is required. Different from the conventional screening system, herein phenotypic screening in combination with multi-omics-based target identification and validation (MOTIV) is introduced. First, phenotypic screening provides visual effect of bioactive small molecules in the cell or organism level. It is important to know the effect on the cell or organism level since small molecules affect not only a single target but the entire cellular mechanism within a cell or organism. Secondly, MOTIV provides systemic approach to discover the target protein of bioactive small molecule. With the chemical genomics and proteomics approach of target identification methods, various target protein candidates are identified. Then network analysis and validations of these candidates result in identifying the biologically relevant target protein and cellular mechanism. Overall, the combination of phenotypic screening and MOTIV will provide an effective approach to discover new bioactive small molecules and their target protein and mechanism identification.     

Small molecule induction of MSH2-dependent cell death suggests a vital role of mismatch repair proteins in cell death

Avoidance of apoptosis is one of the hallmarks of cancer development and progression. Chemotherapeutic agents aim to initiate an apoptotic response, but often fail due to dysregulation. MSH proteins are capable of recognizing cisplatin damage in DNA and participate in the initiation of cell death. We have exploited this recognition and computationally simulated a MutS homolog (MSH) "death conformation". Screening and docking experiments based on this model determined that the MSH2-dependent cell-death pathway can be induced by a small molecule without DNA damage, reserpine. Reserpine was identified via virtual screening on structures obtained from molecular dynamics as a small molecule that selectively binds a protein "death" conformation. The virtual screening predicts that this small molecule binds in the absence of DNA. Cell biology confirmed that reserpine triggers the MSH2-dependent cell-death pathway. This result supports the hypothesis that the MSH2-dependent pathway is initiated by specific protein conformational changes triggered by binding to either DNA damage or small compound molecules. These findings have multiple implications for drug discovery and cell biology. Computational modeling may be used to identify and eventually design small molecules that selectively activate particular pathways through conformational control. Molecular dynamics simulations can be used to model the biologically relevant conformations and virtual screening can then be used to select for small molecules that bind specific conformations. The ability of a small molecule to induce the cell-death pathway suggests a broader role for MMR proteins in cellular events, such as cell-death pathways, than previously suspected.     

Fragment-based virtual screening approach and molecular dynamics simulation studies for identification of BACE1 inhibitor leads

Traditional structure-based virtual screening method to identify drug-like small molecules for BACE1 is so far unsuccessful. Location of BACE1, poor Blood Brain Barrier permeability and P-glycoprotein (Pgp) susceptibility of the inhibitors make it even more difficult. Fragment-based drug design method is suitable for efficient optimization of initial hit molecules for target like BACE1. We have developed a fragment-based virtual screening approach to identify/optimize the fragment molecules as a starting point. This method combines the shape, electrostatic, and pharmacophoric features of known fragment molecules, bound to protein conjugate crystal structure, and aims to identify both chemically and energetically feasible small fragment ligands that bind to BACE1 active site. The two top-ranked fragment hits were subjected for a 53 ns MD simulation. Principle component analysis and free energy landscape analysis reveal that the new ligands show the characteristic features of established BACE1 inhibitors. The potent method employed in this study may serve for the development of potential lead molecules for BACE1-directed Alzheimer’s disease therapeutics.     

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.     

Identification of novel leads applying in silico studies for Mycobacterium multidrug resistant (MMR) protein

Multidrug efflux mechanism is the main cause of intrinsic drug resistance in bacteria. Mycobacterium multidrug resistant (MMR) protein belongs to small multidrug resistant family proteins (SMR), causing multidrug resistance to proton (H⁺)-linked lipophilic cationic drug efflux across the cell membrane. In the present work, MMR is treated as a novel target to identify new molecular entities as inhibitors for drug resistance in Mycobacterium tuberculosis. In silico techniques are applied to evaluate the 3D structure of MMR protein. The putative amino acid residues present in the active site of MMR protein are predicted. Protein–ligand interactions are studied by docking cationic ligands transported by MMR protein. Virtual screening is carried out with an in-house library of small molecules against the grid created at the predicted active site residues in the MMR protein. Absorption distribution metabolism and elimination (ADME) properties of the molecules with best docking scores are predicted. The studies with cationic ligands and those of virtual screening are analysed for identification of new lead molecules as inhibitors for drug resistance caused by the MMR protein.     

Probing small molecule-protein interactions: A new perspective for functional proteomics

The isolation of proteome subsets on the basis of the interactions of small molecules with proteins is an emerging paradigm in proteomics. Depending on the nature of the small molecule used as a bait, entire protein families can be monitored in biological samples, or new functions can be attributed to previously uncharacterized proteins. With pharmaceutical compounds as baits, drug targets and toxicity-relevant off-targets can be discovered in an unbiased proteomic screen. At the heart of this strategy are synthetic bi- or trifunctional small molecule probes. These probes carry the small molecules of interest as baits (selectivity function), as well as a sorting function for the isolation of small molecule-protein complexes or conjugates from complex protein mixtures. In some designs, a covalent linkage of the bound protein to the probe is established through a separate reactivity function or a combined selectivity/reactivity function. The covalent linkage allows for isolation or detection of probe-protein conjugates also under harsh or denaturing conditions. Ultimately, specifically isolated proteins are commonly identified by mass spectrometry. This review summarizes probe designs, workflows, and published applications of the three dominant approaches in the field, namely affinity pulldown, activity-based protein profiling, and Capture Compound Mass Spectrometry.     

Structural adaptation of extreme halophilic proteins through decrease of conserved hydrophobic contact surface

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.     

A Combinatorial approach: To design inhibitory molecules on Hemagglutinin protein of H1N1 virus (Swine Flu)

The Hemagglutinin (HA) is a protein of influenza A virus. It is present on the surface of influenza A virus and it is a glycoprotein. The HA is identified as potential drug target. H1N1 thiazolides, proved to be a potent drug in the inhibition of H1N1 replication. It is also known as inhibitor of other strains of influenza A virus. Thiazolide drug represses viral HA''s maturation at a level which exists just before the resistance from digestion of endoglycosidase-H and thereby it hampers, HA insertion in host membrane. Blocking the appropriate active site of hemagglutinin protein helps in the disease control. In the present work, we have generated diverse combinatorial library based ligands on known inhibitor thiazolides and they were used for virtual screening by Molegro virtual docker program. K-means clustering approach was used for finding new inhibitory molecules with more appropriate features. These resulted molecules are may be helpful in the treatment of swine flu and many other related diseases.     

Chemoproteomic approaches to drug target identification and drug profiling

Chemoproteomics represents a new research discipline at the interface of medicinal chemistry, biochemistry, and cell biology focused on studying the molecular mechanisms of action of drugs and other bioactive small molecules. Research strategies frequently combine phenotypic screening with subsequent target identification, and aim at a proteome-wide characterization of drug-induced changes in cellular protein expression and post-translational modifications. In recent years quantitative mass spectrometry has taken center stage in many of these approaches. This review describes experimental strategies in current chemical proteomics research, discusses recent examples of successful applications, and highlights areas in drug discovery where chemical proteomics-based assays using native endogenous proteins are expected to have substantial impact.     

Expanding the scope of site-specific protein biotinylation strategies using small molecules

We present a new approach to site-specifically biotinylate protein in a cell-free protein synthesis system with puromycin-containing small molecules. With this new method, biotinylated proteins were generated from the DNA templates in a matter of hours, making it useful for protein microarray generation. We also validated that the method is compatible with other high-throughput cloning/proteomics methods.     

Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina

The Mur E enzyme of Mur pathway of Mycobacterium tuberculosis is an attractive drug target as it is unique to bacteria and is absent in mammalian cells. The virtual screening of large libraries of drug like molecules against a protein target is a common strategy used to identify novel inhibitors. However, the method has a large number of pitfalls, with large variations in accuracy caused in part by inaccurate protocols, use of improper standards and libraries, and system dependencies such as the potential for nonspecific docking from large active-site cavities. The screening of drug-like small molecules from diversity sets can, however, be used to short-list potential fragments as building blocks to generate leads with improved specificity. We describe a protocol to implement this strategy, which involves an analysis of the active site and known inhibitors to identify orthospecific determinants, virtual screening of a drug-like diversity library to identify potential drug primitives, and inspection of the potential docked fragments for both binding potential and toxicity. The protocol is implemented on the M.tb Mur E protein which has a large active site with poor enrichment of known positives and a set of drug-like molecules that meets this criteria is presented for further analysis.

Abbreviations

MTB - Mycobacterium tuberculosis, NCI - National Cancer Institute, PDB - Protein Databank.     

Target class strategies in mass spectrometry-based proteomics

The cornerstone of proteomics resides in using traditional methods of protein chemistry, to extract and resolve complex mixtures, in concert with the powerful engines of mass spectrometry to decipher peptide and protein identities. The broad utility of proteomics technologies to map protein interactions, understand regulatory mechanisms and identify biomarkers associated with disease states and drug treatments necessitates a targeted biochemical approach tailored to the characteristics of the tissue, fluid or cellular extract being studied. The application of affinity methods in proteomic studies to focus on particular classes of molecules is being used with increasing frequency and comprises the subject of this review. An overview of successfully applied affinity methods is provided, along with speculation on the use of innovative approaches. Sample preparation and processing are critical for proteomics with affinity reagents, as only functional and active proteins can be isolated in most cases. Considerations for methods of sample preparation to optimize affinity capture and release are also discussed.     

Target class strategies in mass spectrometry-based proteomics

The cornerstone of proteomics resides in using traditional methods of protein chemistry, to extract and resolve complex mixtures, in concert with the powerful engines of mass spectrometry to decipher peptide and protein identities. The broad utility of proteomics technologies to map protein interactions, understand regulatory mechanisms and identify biomarkers associated with disease states and drug treatments necessitates a targeted biochemical approach tailored to the characteristics of the tissue, fluid or cellular extract being studied. The application of affinity methods in proteomic studies to focus on particular classes of molecules is being used with increasing frequency and comprises the subject of this review. An overview of successfully applied affinity methods is provided, along with speculation on the use of innovative approaches. Sample preparation and processing are critical for proteomics with affinity reagents, as only functional and active proteins can be isolated in most cases. Considerations for methods of sample preparation to optimize affinity capture and release are also discussed.     

Epithelial proteomics in multiple organs and tissues: similarities and variations between cells, organs, and diseases

Epithelial cells play an important role in physiological and pathophysiological situations, with organ-, tissue-, type-, and function-specific patterns. Proteome analysis has been used to study epithelial-origin diseases and identify novel prognostic, diagnostic, and therapeutic markers. The present review compares the variation of sample preparation for epithelial proteomic analysis, search similarities, and differences of epithelial proteomics between different cells, locations, and diseases. We focus on specificity of proteomic markers for epithelial-involved diseases. Proteomic alterations in epithelial cell lines were mapped to understand protein patterns, differentiation, oncogenesis, and pathogenesis of epithelial-origin diseases. Changes of proteomic patterns depend on different epithelial cell lines, challenges, and preparation. Epithelial protein profiles associated with intracellular locations and protein function. Epithelial proteomics has been greatly developed to link clinical questions, e.g., disease severity, biomarkers for disease diagnosis, and drug targets. There is an exciting and attractive start to link epithelial proteomics with histology of clinical samples. From the present review, we can find that most of disease-associated investigation of epithelial proteomics has been focused on epithelial-origin cancer. There is a significant gap of epithelial proteomics between acute and chronic organ injury, inflammation, and multiple organ dysfunction. Epithelial proteomics will provide powerful information on the relationships between biological molecules and disease mechanisms. Epithelial proteomics strategies and approaches should become more global, multidimensional, and systemic.     

JVirGel: Calculation of virtual two-dimensional protein gels

We developed JVirGel, a collection of tools for the simulation and analysis of proteomics data. The software creates and visualizes virtual two-dimensional (2D) protein gels based on the migration behaviour of proteins in dependence of their theoretical molecular weights in combination with their calculated isoelectric points. The utilization of all proteins of an organism of interest deduced from genes of the corresponding genome project in combination with the elimination of obvious membrane proteins permits the creation of an optimized calculated proteome map. The electrophoretic separation behaviour of single proteins is accessible interactively in a Java(TM) applet (small application in a web browser) by selecting a pI/MW range and an electrophoretic timescale of interest. The calculated pattern of protein spots helps to identify unknown proteins and to localize known proteins during experimental proteomics approaches. Differences between the experimentally observed and the calculated migration behaviour of certain proteins provide first indications for potential protein modification events. When possible, the protein spots are directly linked via a mouse click to the public databases SWISS-PROT and PRODORIC. Additionally, we provide tools for the serial calculation and visualization of specific protein properties like pH dependent charge curves and hydrophobicity profiles. These values are helpful for the rational establishment of protein purification procedures. The proteomics tools are available on the World Wide Web at http://prodoric.tu-bs.de/proteomics.php.     

Deep learning methods for 3D structural proteome and interactome modeling

Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in tasks ranging from protein contact map prediction to protein folding, prediction of protein–protein interaction interfaces, and characterization of protein–drug binding pockets. In particular, emergence of ab initio protein structure prediction methods including AlphaFold2 has revolutionized protein structural modeling. From a protein function perspective, numerous deep learning methods have facilitated deconvolution of the exact amino acid residues and protein surface regions responsible for binding other proteins or small molecule drugs. In this review, we provide a comprehensive overview of recent deep learning methods applied in structural proteomics.     

Modulating protein-protein interactions with small molecules: the importance of binding hotspots

The modulation of protein-protein interactions (PPIs) by small drug-like molecules is a relatively new area of research and has opened up new opportunities in drug discovery. However, the progress made in this area is limited to a handful of known cases of small molecules that target specific diseases. With the increasing availability of protein structure complexes, it is highly important to devise strategies exploiting homologous structure space on a large scale for discovering putative PPIs that could be attractive drug targets. Here, we propose a scheme that allows performing large-scale screening of all protein complexes and finding putative small-molecule and/or peptide binding sites overlapping with protein-protein binding sites (so-called "multibinding sites"). We find more than 600 nonredundant proteins from 60 protein families with multibinding sites. Moreover, we show that the multibinding sites are mostly observed in transient complexes, largely overlap with the binding hotspots and are more evolutionarily conserved than other interface sites. We investigate possible mechanisms of how small molecules may modulate protein-protein binding and discuss examples of new candidates for drug design.     

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.     

小分子药物靶标寻找方法研究进展

许多微生物的次生代谢物属于小分子活性化合物,在医疗及农业领域发挥着重要的作用。在基因组学、蛋白质组学与生物信息学等技术的推动下,一些新的小分子药物靶标寻找方法应运而生了,这些新的方法主要是基于细胞中基因或蛋白质的表达量、蛋白质的亲和性、稳定性等各种特性进行靶标寻找的。小分子药物靶标寻找方法的发展加快了阐明小分子药物作用机理的历程,也为发现新的靶标资源以便于进一步筛选活性更高的药物提供了技术保障。     

Receptor–ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.