ProBiS-CHARMMing: Web Interface for Prediction and Optimization of Ligands in Protein Binding Sites

An in-silico approach to unravel the structure of 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase (DAHPS): a critical enzyme for sennoside biosynthesis in Cassia angustifolia Vahl

The laxative properties of senna are attributed to the presence of sennosides produced in the plant. The low production level of sennosides in the plant is an important impediment to their growing demand and utilization. Understanding biosynthetic pathways helps to engineer them in terms of enhanced production. The biosynthetic pathways of sennoside production in plants are not completely known yet. However, attempts to get information on genes and proteins engaged in it have been made which decode involvement of various pathways including shikimate pathway. 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase (DAHPS) is a key enzyme involved in sennosides production through the shikimate pathway. Unfortunately, there is no information available on proteomic characterization of DAHPS enzyme of senna (caDAHPS) resulting in lack of knowledge about its role. We for the first time characterized DAHPS enzyme of senna using in-silico analysis. To the best of our knowledge this is the first attempt to identify the coding sequence of caDAHPS by cloning and sequencing. We found Gln179, Arg175, Glu462, Glu302, Lys357 and His420 amino acids in the active site of caDAHPS through molecular docking. followed by molecular dynamic simulation. The amino acid residues, Lys182, Cys136, His460, Leu304, Gly333, Glu334, Pro183, Asp492 and Arg433 at the surface interact with PEP by van der Waals bonds imparting stability to the enzyme-substrate complex. Docking results were further validated by molecular dynamics. The presented in-silico analysis of caDAHPS will generate opportunities to engineer the sennoside biosynthesis in plants.Communicated by Ramaswamy H. Sarma.

Mce1R of Mycobacterium tuberculosis prefers long-chain fatty acids as specific ligands: a computational study

The mce1 operon of Mycobacterium tuberculosis, which codes the Mce1 transporter, facilitates the transport of fatty acids. Fatty acids are one of the major sources for carbon and energy for the pathogen during its intracellular survival and pathogenicity. The mce1 operon is transcriptionally regulated by Mce1R, a VanR-type regulator, which could bind specific ligands and control the expression of the mce1 operon accordingly. This work reports computational identification of Mce1R-specific ligands. Initially by employing cavity similarity search algorithm by the ProBis server, the cavities of the proteins similar to that of Mce1R and the bound ligands were identified from which fatty acids were selected as the potential ligands. From the earlier-generated monomeric structure, the dimeric structure of Mce1R was then modeled by the GalaxyHomomer server and validated computationally to use in molecular docking and molecular dynamics simulation analysis. The fatty acid ligands were found to dock within the cavity of Mce1R and the docked complexes were subjected to molecular dynamics simulation to explore their stabilities and other dynamic properties. The data suggest that Mce1R preferably binds to long-chain fatty acids and undergoes distinct structural changes upon binding.

Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach

In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.

BMaps: A Web Application for Fragment-Based Drug Design and Compound Binding Evaluation

Fragment-based drug design uses data about where, and how strongly, small chemical fragments bind to proteins, to assemble new drug molecules. Over the past decade, we have been successfully using fragment data, derived from thermodynamically rigorous Monte Carlo fragment-protein binding simulations, in dozens of preclinical drug programs. However, this approach has not been available to the broader research community because of the cost and complexity of doing simulations and using design tools. We have developed a web application, called BMaps, to make fragment-based drug design widely available with greatly simplified user interfaces. BMaps provides access to a large repository (>550) of proteins with 100s of precomputed fragment maps, druggable hot spots, and high-quality water maps. Users can also employ their own structures or those from the Protein Data Bank and AlphaFold DB. Multigigabyte data sets are searched to find fragments in bondable orientations, ranked by a binding-free energy metric. The designers use this to select modifications that improve affinity and other properties. BMaps is unique in combining conventional tools such as docking and energy minimization with fragment-based design, in a very easy to use and automated web application. The service is available at https://www.boltzmannmaps.com.

Protein binding sites for drug design

Drug development is a lengthy and challenging process that can be accelerated at early stages by new mathematical approaches and modern computers. To address this important issue, we are developing new mathematical solutions for the detection and characterization of protein binding sites that are important for new drug development. In this review, we present algorithms based on graph theory combined with molecular dynamics simulations that we have developed for studying biological target proteins to provide important data for optimizing the early stages of new drug development. A particular focus is the development of new protein binding site prediction algorithms (ProBiS) and new web tools for modeling pharmaceutically interesting molecules-ProBiS Tools (algorithm, database, web server), which have evolved into a full-fledged graphical tool for studying proteins in the proteome. ProBiS differs from other structural algorithms in that it can align proteins with different folds without prior knowledge of the binding sites. It allows detection of similar binding sites and can predict molecular ligands of various types of pharmaceutical interest that could be advanced to drugs to treat a disease, based on the entire Protein Data Bank (PDB) and AlphaFold database, including proteins not yet in the PDB. All ProBiS Tools are freely available to the academic community at http://insilab.org and https://probis.nih.gov.

ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites

ProBiS (Protein Binding Sites), a local structure-based comparison algorithm, is used in the new ProBiS-Fold web server to annotate human structures from the AlphaFold database without a corresponding structure in the Protein Data Bank (PDB) to discover new druggable binding sites. The ProBiS algorithm is used to compare each query protein structure predicted by the AlphaFold approach with the protein structures from the PDB to identify similarities between known binding sites found in the PDB and yet unknown binding sites in the AlphaFold database. Ligands bound in these identified similar PDB sites are then transferred to each query protein from the AlphaFold database, and binding sites are identified as ligand clusters on an AlphaFold protein. Small molecule binding sites are assigned druggability scores based on the similarity of their ligands to known drugs, allowing them to be ranked according to their perceived and actual potential for drug development. ProBiS-Fold provides interactive and downloadable binding sites for the entire human structural proteome, including more than 3000 new druggable binding sites that have no corresponding structure in the PDB, taking into account AlphaFold's model quality, to enable protein structure-function relationship studies and pharmaceutical drug discovery research. The web server is freely accessible to academic users at http://probis-fold.insilab.org.

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning

Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein-ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.

Molecular Modeling is an Enabling Approach to Complement and Enhance Channelopathy Research

Hundreds of human membrane proteins form channels that transport necessary ions and compounds, including drugs and metabolites, yet details of their normal function or how function is altered by genetic variants to cause diseases are often unknown. Without this knowledge, researchers are less equipped to develop approaches to diagnose and treat channelopathies. High-resolution computational approaches such as molecular modeling enable researchers to investigate channelopathy protein function, facilitate detailed hypothesis generation, and produce data that is difficult to gather experimentally. Molecular modeling can be tailored to each physiologic context that a protein may act within, some of which may currently be difficult or impossible to assay experimentally. Because many genomic variants are observed in channelopathy proteins from high-throughput sequencing studies, methods with mechanistic value are needed to interpret their effects. The eminent field of structural bioinformatics integrates techniques from multiple disciplines including molecular modeling, computational chemistry, biophysics, and biochemistry, to develop mechanistic hypotheses and enhance the information available for understanding function. Molecular modeling and simulation access 3D and time-dependent information, not currently predictable from sequence. Thus, molecular modeling is valuable for increasing the resolution with which the natural function of protein channels can be investigated, and for interpreting how genomic variants alter them to produce physiologic changes that manifest as channelopathies. © 2022 American Physiological Society. Compr Physiol 12:3141-3166, 2022.

ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison

The protein data bank (PDB) is a rich source of protein ligand structures, but ligands are not explicitly used in current docking algorithms. We have developed ProBiS-Dock, a docking algorithm complementary to the ProBiS-Dock Database (J. Chem. Inf. Model. 2021, 61, 4097-4107) that treats small molecules and proteins as fully flexible entities and allows conformational changes in both after ligand binding. A new scoring function is described that consists of a binding site-specific scoring function (ProBiS-Score) and a general statistical scoring function. ProBiS-Dock enables rapid docking of small molecules to proteins and has been successfully validated in silico against standard benchmarks. It enables rapid search for new active ligands by leveraging existing knowledge in the PDB. The potential of the software for drug development has been confirmed in vitro by the discovery of new inhibitors of human indoleamine 2,3-dioxygenase 1, an enzyme that is an attractive target for cancer therapy and catalyzes the first rate-determining step of l-tryptophan metabolism via the kynurenine pathway. The software is freely available to academic users at http://insilab.org/probisdock.

Comparative Metabolic Pathways Analysis and Subtractive Genomics Profiling to Prioritize Potential Drug Targets Against Streptococcus pneumoniae

Streptococcus pneumoniae is a notorious pathogen that affects ∼450 million people worldwide and causes up to four million deaths per annum. Despite availability of antibiotics (i.e., penicillin, doxycycline, or clarithromycin) and conjugate vaccines (e.g., PCVs), it is still challenging to treat because of its drug resistance ability. The rise of antibiotic resistance in S. pneumoniae is a major source of concern across the world. Computational subtractive genomics is one of the most applied techniques in which the whole proteome of the bacterial pathogen is gradually reduced to a limited number of potential therapeutic targets. Whole-genome sequencing has greatly reduced the time required and provides more opportunities for drug target identification. The goal of this work is to evaluate and analyze metabolic pathways in serotype 14 of S. pneumonia to identify potential drug targets. In the present study, 47 potent drug targets were identified against S. pneumonia by employing the computational subtractive genomics approach. Among these, two proteins are prioritized (i.e., 4-oxalocrotonate tautomerase and Sensor histidine kinase uniquely present in S. pneumonia) as novel drug targets and selected for further structure-based studies. The identified proteins may provide a platform for the discovery of a lead drug candidate that may be capable of inhibiting these proteins and, therefore, could be helpful in minimizing the associated risk related to the drug-resistant S. pneumoniae. Finally, these enzymatic proteins could be of prime interest against S. pneumoniae to design rational targeted therapy.

CIFDock: A novel CHARMM-based flexible receptor-flexible ligand docking protocol

Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co-factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM-based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all-atom force fields coupled to enhanced sampling molecular dynamics procedures. Self-guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex-the protein, ligand, and waters-are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross-docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM-based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data.

Mechanistic Insights into Biological Activities of Polyphenolic Compounds from Rosemary Obtained by Inverse Molecular Docking

Rosemary (Rosmarinus officinalis L.) represents a medicinal plant known for its various health-promoting properties. Its extracts and essential oils exhibit antioxidative, anti-inflammatory, anticarcinogenic, and antimicrobial activities. The main compounds responsible for these effects are the diterpenes carnosic acid, carnosol, and rosmanol, as well as the phenolic acid ester rosmarinic acid. However, surprisingly little is known about the molecular mechanisms responsible for the pharmacological activities of rosemary and its compounds. To discern these mechanisms, we performed a large-scale inverse molecular docking study to identify their potential protein targets. Listed compounds were separately docked into predicted binding sites of all non-redundant holo proteins from the Protein Data Bank and those with the top scores were further examined. We focused on proteins directly related to human health, including human and mammalian proteins as well as proteins from pathogenic bacteria, viruses, and parasites. The observed interactions of rosemary compounds indeed confirm the beforementioned activities, whereas we also identified their potential for anticoagulant and antiparasitic actions. The obtained results were carefully checked against the existing experimental findings from the scientific literature as well as further validated using both redocking procedures and retrospective metrics.

Identification of a Novel Therapeutic Target against XDR Salmonella Typhi H58 Using Genomics Driven Approach Followed Up by Natural Products Virtual Screening

Typhoid fever is caused by a pathogenic, rod-shaped, flagellated, and Gram-negative bacterium known as Salmonella Typhi. It features a polysaccharide capsule that acts as a virulence factor and deceives the host immune system by protecting phagocytosis. Typhoid fever remains a major health concern in low and middle-income countries, with an estimated death rate of ~200,000 per annum. However, the situation is exacerbated by the emergence of the extensively drug-resistant (XDR) strain designated as H58 of S. Typhi. The emergence of the XDR strain is alarming, and it poses serious threats to public health due to the failure of the current therapeutic regimen. A relatively newer computational method called subtractive genomics analyses has been widely applied to discover novel and new drug targets against pathogens, particularly drug-resistant ones. The method involves the gradual reduction of the complete proteome of the pathogen, leading to few potential and novel drug targets. Thus, in the current study, a subtractive genomics approach was applied against the Salmonella XDR strain to identify potential drug targets. The current study predicted four prioritized proteins (i.e., Colanic acid biosynthesis acetyltransferase wcaB, Shikimate dehydrogenase aroE, multidrug efflux RND transporter permease subunit MdtC, and pantothenate synthetase panC) as potential drug targets. Though few of the prioritized proteins are treated in the literature as the established drug targets against other pathogenic bacteria, these drug targets are identified here for the first time against S. Typhi (i.e., S. Typhi XDR). The current study aimed at drawing attention to new drug targets against S. Typhi that remain largely unexplored. One of the prioritized drug targets, i.e., Colanic acid biosynthesis acetyltransferase, was predicted as a unique, new drug target against S. Typhi XDR. Therefore, the Colanic acid was further explored using structure-based techniques. Additionally, ~1000 natural compounds were docked with Colanic acid biosynthesis acetyltransferase, resulting in the prediction of seven compounds as potential lead candidates against the S. Typhi XDR strain. The ADMET properties and binding energies via the docking program of these seven compounds characterized them as novel drug candidates. They may potentially be used for the development of future drugs in the treatment of Typhoid fever.

ProBiS-Dock Database: A Web Server and Interactive Web Repository of Small Ligand-Protein Binding Sites for Drug Design

We have developed a new system, ProBiS-Dock, which can be used to determine the different types of protein binding sites for small ligands. The binding sites identified this way are then used to construct a new binding site database, the ProBiS-Dock Database, that allows for the ranking of binding sites according to their utility for drug development. The newly constructed database currently has more than 1.4 million binding sites and offers the possibility to investigate potential drug targets originating from different biological species. The interactive ProBiS-Dock Database, a web server and repository that consists of all small-molecule ligand binding sites in all of the protein structures in the Protein Data Bank, is freely available at http://probis-dock-database.insilab.org. The ProBiS-Dock Database will be regularly updated to keep pace with the growth of the Protein Data Bank, and our anticipation is that it will be useful in drug discovery.

Ligand Binding Site Comparison - LiBiSCo - a web-based tool for analyzing interactions between proteins and ligands to explore amino acid specificity within active sites

Interaction between protein and ligands are ubiquitous in a biological cell, and understanding these interactions at the atom level in protein-ligand complexes is crucial for structural bioinformatics and drug discovery. Here, we present a web-based protein-ligand interaction application named Ligand Binding Site Comparison (LiBiSCo) for comparing the amino acid residues interacting with atoms of a ligand molecule between different protein-ligand complexes available in the Protein Data Bank (PDB) database. The comparison is performed at the ligand atom level irrespectively of having binding site similarity or not between the protein structures of interest. The input used in LiBiSCo is one or several PDB IDs of protein-ligand complex(es) and the tool returns a list of identified interactions at ligand atom level including both bonded and non-bonded interactions. A sequence profile for the interaction for each ligand atoms is provided as a WebLogo. The LiBiSco is useful in understanding ligand binding specificity and structural promiscuity among families that are structurally unrelated. The LiBiSCo tool can be accessed through https://albiorix.bioenv.gu.se/LiBiSCo/HomePage.py.

LmjF.36.3850, a novel hypothetical Leishmania major protein, contributes to the infection

Leishmania is a protozoan parasite that resides in mammalian macrophages and inflicts the disease known as leishmaniasis. Although prevalent in 88 countries, an anti-leishmanial vaccine remains elusive. While comparing the virulent and avirulent L. major transcriptomes by microarray, PCR and functional analyses for identifying a novel virulence-associated gene, we identified LmjF.36.3850, a hypothetical protein significantly less expressed in the avirulent parasite and without any known function. Motif search revealed that LmjF.36.3850 protein shared phosphorylation sites and other structural features with sucrose non-fermenting protein (Snf7) that shuttles virulence factors. LmjF.36.3850 was predicted to bind diacylglycerol (DAG) with energy value similar to PKCα and PKCβ, to which DAG is a cofactor. Indeed, 1-oleoyl-2-acetyl-sn-glycerol (OAG), a DAG analogue, enhanced the phosphorylation of PKCα and PKCβI. We cloned LmjF.36.3850 gene in a mammalian expression vector and primed susceptible BALB/c mice followed by challenge infection. We observed a higher parasite load, comparable antibody response and higher anti-inflammatory cytokines such as IL-4 and IL-10, while expression of major anti-leishmanial cytokine, IFN-γ, remained unchanged in LmjF.36.3850-vaccinated mice. CSA restimulated LN cells from vaccinated mice after challenge infection secreted comparable IL-4 and IL-10 but reduced IFN-γ, as compared to controls. These observations suggest a skewed Th2 response, diminished IFN-γ secreting Th1-T_EM cells and increased central and effector memory subtype of Th2, Th17 and Treg cells in the vaccinated mice. These data indicate that LmjF.36.3850 is a plausible virulence factor that enhances disease-promoting response, possibly by interfering with PKC activation and by eliciting disease-promoting T cells.

Kinome analyses of Candida albicans, C. parapsilosis and C. tropicalis enable novel kinases as therapeutic drug targets in candidiasis

Candida spp. have attracted considerable attention as they cause serious human diseases in immunocompromised individuals. The genomes of the pathogenic Candida spp. have been sequenced, but systemic characterizations of their kinomes are yet to be reported. As in various eukaryotes, the protein kinases play crucial regulatory roles in pathogenicity of Candida. Increased frequency of antifungal resistance in Candida spp. requires significant attention to explore novel therapeutic molecules for their control. The present in-silico study involves novel bioinformatics strategies to identify the kinase proteins and their potential drug targets with the purpose to combat fungal infections. The study reports 103, 107 and 106 kinase proteins from 3 Candida spp., C. albicans, C. parapsilosis and C. tropicalis, respectively. Moreover, 79 common kinase proteins were identified, of which 54 proteins play essential roles in Candida spp. and 42 proteins were human non-homologues. Among the essential and human non-homologous protein kinases, 9 were found to be common essential human non-homologues, of which 6 are uniquely present in Candida. These 6 protein kinases namely, Hsl1, Npr1, Ptk2, Kin2, Ksp1 and orf19.3854 (CAALFM_CR06040WA) are involved in various molecular and cellular processes regulating virulence or pathogenicity. Further, these 6 kinases are prioritized as potential drug targets and explored for discovering new lead compounds against candidiasis. The drug repurposing approach for these 6 kinases show 13 approved drugs and investigational compounds that might play substantial inhibitory roles during combating candidiasis.

Methods for Molecular Modelling of Protein Complexes

Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.

Chemogenomics and bioinformatics approaches for prioritizing kinases as drug targets for neglected tropical diseases

Neglected tropical diseases (NTDs) are a group of twenty-one diseases classified by the World Health Organization that prevail in regions with tropical and subtropical climate and affect more than one billion people. There is an urgent need to develop new and safer drugs for these diseases. Protein kinases are a potential class of targets for developing new drugs against NTDs, since they play crucial role in many biological processes, such as signaling pathways, regulating cellular communication, division, metabolism and death. Bioinformatics is a field that aims to organize large amounts of biological data as well as develop and use tools for understanding and analyze them in order to produce meaningful information in a biological manner. In combination with chemogenomics, which analyzes chemical-biological interactions to screen ligands against selected targets families, these approaches can be used to stablish a rational strategy for prioritizing new drug targets for NTDs. Here, we describe how bioinformatics and chemogenomics tools can help to identify protein kinases and their potential inhibitors for the development of new drugs for NTDs. We present a review of bioinformatics tools and techniques that can be used to define an organisms kinome for drug prioritization, drug and target repurposing, multi-quinase inhibition approachs and selectivity profiling. We also present some successful examples of the application of such approaches in recent case studies.

Ensemble Docking Coupled to Linear Interaction Energy Calculations for Identification of Coronavirus Main Protease (3CLpro) Non-Covalent Small-Molecule Inhibitors

SARS-CoV-2, or severe acute respiratory syndrome coronavirus 2, represents a new strain of Coronaviridae. In the closing 2019 to early 2020 months, the virus caused a global pandemic of COVID-19 disease. We performed a virtual screening study in order to identify potential inhibitors of the SARS-CoV-2 main viral protease (3CL^pro or M^pro). For this purpose, we developed a novel approach using ensemble docking high-throughput virtual screening directly coupled with subsequent Linear Interaction Energy (LIE) calculations to maximize the conformational space sampling and to assess the binding affinity of identified inhibitors. A large database of small commercial compounds was prepared, and top-scoring hits were identified with two compounds singled out, namely 1-[(R)-2-(1,3-benzimidazol-2-yl)-1-pyrrolidinyl]-2-(4-methyl-1,4-diazepan-1-yl)-1-ethanone and [({(S)-1-[(1H-indol-2-yl)methyl]-3-pyrrolidinyl}methyl)amino](5-methyl-2H-pyrazol-3-yl)formaldehyde. Moreover, we obtained a favorable binding free energy of the identified compounds, and using contact analysis we confirmed their stable binding modes in the 3CL^pro active site. These compounds will facilitate further 3CL^pro inhibitor design.

A new method for protein characterization and classification using geometrical features for 3D face analysis: An example of tubulin structures

This article reports on the results of research aimed to translate biometric 3D face recognition concepts and algorithms into the field of protein biophysics in order to precisely and rapidly classify morphological features of protein surfaces. Both human faces and protein surfaces are free-forms and some descriptors used in differential geometry can be used to describe them applying the principles of feature extraction developed for computer vision and pattern recognition. The first part of this study focused on building the protein dataset using a simulation tool and performing feature extraction using novel geometrical descriptors. The second part tested the method on two examples, first involved a classification of tubulin isotypes and the second compared tubulin with the FtsZ protein, which is its bacterial analog. An additional test involved several unrelated proteins. Different classification methodologies have been used: a classic approach with a support vector machine (SVM) classifier and an unsupervised learning with a k-means approach. The best result was obtained with SVM and the radial basis function kernel. The results are significant and competitive with the state-of-the-art protein classification methods. This leads to a new methodological direction in protein structure analysis.

Loop Grafting between Similar Local Environments for Fc-Silent Antibodies

Reduction of the affinity of the fragment crystallizable (Fc) region with immune receptors by substitution of one or a few amino acids, known as Fc-silencing, is an established approach to reduce the immune effector functions of monoclonal antibody therapeutics. This approach to Fc-silencing, however, is problematic as it can lead to instability and immunogenicity of the developed antibodies. We evaluated loop grafting as a novel approach to Fc-silencing in which the Fc loops responsible for immune receptor binding were replaced by loops of up to 20 amino acids from similar local environments in other human and mouse antibodies. Molecular dynamics simulations of the designed variants of an Fc region in a complex with the immune receptor FcγIIIa confirmed that loop grafting potentially leads to a significant reduction in the binding of the antibody variants to the receptor, while retaining their stability. In comparison, standard variants with less than eight substituted amino acids showed possible instability and a lower degree of Fc-silencing due to the occurrence of compensatory interactions. The presented approach to Fc-silencing is general and could be used to modulate undesirable side effects of other antibody therapeutics without affecting their stability or increasing their immunogenicity.

In Silico Laboratory: Tools for Similarity-Based Drug Discovery

Computational methods that predict and evaluate binding of ligands to receptors implicated in different pathologies have become crucial in modern drug design and discovery. Here, we describe protocols for using the recently developed package of computational tools for similarity-based drug discovery. The ProBiS stand-alone program and web server allow superimposition of protein structures against large protein databases and predict ligands based on detected binding site similarities. GenProBiS allows mapping of human somatic missense mutations related to cancer and non-synonymous single nucleotide polymorphisms and subsequent visual exploration of specific interactions in connection to these mutations. We describe protocols for using LiSiCA, a fast ligand-based virtual screening software that enables easy screening of large databases containing billions of small molecules. Finally, we show the use of BoBER, a web interface that enables user-friendly access to a large database of bioisosteric and scaffold hopping replacements.

Analysis of predicted amino acid biosynthesis in Rv3344c in Mycobacterium tuberculosis H37 Rv using bioinformatics tools

According to World Health Organization (WHO) report, Mycobacterium tuberculosis H₃₇ Rv (M. tuberculosis) affects one-third population of the world. Emergence of effective treatment/research against this disease is need of the hour. Therefore, we present some important aspects of Rv3344c, which is a PE_PGRS protein. Evidence shows that PE_PGRS proteins show fibronectin binding activity. This protein has affinity for calcium and also shows motifs of GTP-binding protein. It also shows the presence of sites for ribose-5-phosphate binding and motifs of aspartate-beta-semialdehyde dehydrogenase, both of which are involved in amino acid biosynthesis. Thus, this protein might be targeted to block the amino acid biosynthesis in M. tuberculosis. This article takes into consideration some important aspects of Rv3344c protein as its function is still unknown. This study includes retrieval of protein sequence database, multiple sequence alignment, protein-protein interaction, epitope prediction, localization, function prediction, phosphorylation site prediction, model building and its validation, ligand-binding prediction along with mutational analysis. Hence, this study might be an important step in the development of new drugs and treatment of tuberculosis.

Conformational Studies of Glucose Transporter 1 (GLUT1) as an Anticancer Drug Target

Glucose transporter 1 (GLUT1) is a facilitative glucose transporter overexpressed in various types of tumors; thus, it has been considered as an important target for cancer therapy. GLUT1 works through conformational switching from an outward-open (OOP) to an inward-open (IOP) conformation passing through an occluded conformation. It is critical to determine which conformation is preferred by bound ligands because the success of structure-based drug design depends on the appropriate starting conformation of the target protein. To find out the most favorable GLUT 1 conformation for ligand binding, we ran systemic molecular docking studies for different conformations of GLUT1 using known GLUT1 inhibitors. Our data revealed that the IOP is the preferred conformation and that residues Phe291, Phe379, Glu380, Trp388, and Trp412 may play critical roles in ligand binding to GLUT1. Our data suggests that conformational differences in these five amino acids in the different conformers of GLUT1 may be used to design ligands that inhibit GLUT1.

Binding site comparisons for target-centered drug discovery

INTRODUCTION

The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.

Computational Outlook of Marine Compounds as Anti-Cancer Representatives Targeting BCL-2 and Survivin

INTRODUCTION

The regulation of apoptosis via compounds originated from marine organisms signifies a new wave in the field of drug discovery. Marine organisms produce potent compounds as they hold the phenomenal diversity in chemical structures. The main focus of drug development is anticancer therapy.

METHODS

Expertise on manifold activities of compounds helps in the discovery of their derivatives for preclinical and clinical experiment that promotes improved activity of compounds for cancer patients.

RESULTS

These marine derived compounds stimulate apoptosis in cancer cells by targeting Bcl-2 and Survivin, highlighting the fact that instantaneous targeting of these proteins by novel derivatives results in efficacious and selective killing of cancer cells.

CONCLUSION

Our study reports the identification of Aplysin and Haterumaimide J as Bcl-2 inhibitors and Cortistatin A as an inhibitor of survivin protein, from a sequential virtual screening approach.

An Augmented Pocketome: Detection and Analysis of Small-Molecule Binding Pockets in Proteins of Known 3D Structure

Protein-ligand interactions form the basis of most cellular events. Identifying ligand binding pockets in proteins will greatly facilitate rationalizing and predicting protein function. Ligand binding sites are unknown for many proteins of known three-dimensional (3D) structure, creating a gap in our understanding of protein structure-function relationships. To bridge this gap, we detect pockets in proteins of known 3D structures, using computational techniques. This augmented pocketome (PocketDB) consists of 249,096 pockets, which is about seven times larger than what is currently known. We deduce possible ligand associations for about 46% of the newly identified pockets. The augmented pocketome, when subjected to clustering based on similarities among pockets, yielded 2,161 site types, which are associated with 1,037 ligand types, together providing fold-site-type-ligand-type associations. The PocketDB resource facilitates a structure-based function annotation, delineation of the structural basis of ligand recognition, and provides functional clues for domains of unknown functions, allosteric proteins, and druggable pockets.

Inverse Molecular Docking as a Novel Approach to Study Anticarcinogenic and Anti-Neuroinflammatory Effects of Curcumin

Research efforts are placing an ever increasing emphasis on identifying signal transduction pathways related to the chemopreventive activity of curcumin. Its anticarcinogenic effects are presumably mediated by the regulation of signaling cascades, including nuclear factor κB (NF-κB), activator protein 1 (AP-1), and mitogen-activated protein kinases (MAPK). By modulating signal transduction pathways, curcumin induces apoptosis in malignant cells, thus inhibiting cancer development and progression. Due to the lack of mechanistic insight in the scientific literature, we developed a novel inverse molecular docking protocol based on the CANDOCK algorithm. For the first time, we performed inverse molecular docking of curcumin into a collection of 13,553 available human protein structures from the Protein Data Bank resulting in prioritized target proteins of curcumin. Our predictions were in agreement with the scientific literature and confirmed that curcumin binds to folate receptor β, DNA (cytosine-5)-methyltransferase 3A, metalloproteinase-2, mitogen-activated protein kinase 9, epidermal growth factor receptor and apoptosis-inducing factor 1. We also identified new potential protein targets of curcumin, namely deoxycytidine kinase, NAD-dependent protein deacetylase sirtuin-1 and -2, ecto-5'-nucleotidase, core histone macro-H2A.1, tyrosine-protein phosphatase non-receptor type 11, macrophage colony-stimulating factor 1 receptor, GTPase HRas, aflatoxin B1 aldehyde reductase member 3, aldo-keto reductase family 1 member C3, amiloride-sensitive amine oxidase, death-associated protein kinase 2 and tryptophan-tRNA ligase, that may all play a crucial role in its observed anticancer effects. Moreover, our inverse docking results showed that curcumin potentially binds also to the proteins cAMP-specific 3',5'-cyclic phosphodiesterase 4D and 17-β-hydroxysteroid dehydrogenase type 10, which provides a new explanation for its efficiency in the treatment of Alzheimer's disease. We firmly believe that our computational results will complement and direct future experimental studies on curcumin's anticancer activity as well as on its therapeutic effects against Alzheimer's disease.

Transcriptome profiling uncovers β-galactosidases of diverse domain classes influencing hypocotyl development in jute (Corchorus capsularis L.)

Enzyme β-galactosidase (EC 3.2.1.23) is known to influence vascular differentiation during early vegetative growth of plants, but its role in hypocotyl development is not yet fully understood. We generated the hypocotyl transcriptome data of a hypocotyl-defect jute (Corchorus capsularis L.) mutant (52,393 unigenes) and its wild-type (WT) cv. JRC-212 (44,720 unigenes) by paired-end RNA-seq and identified 11 isoforms of β-galactosidase, using a combination of sequence annotation, domain identification and structural-homology modeling. Phylogenetic analysis classified the jute β-galactosidases into six subfamilies of glycoside hydrolase-35 family, which are closely related to homologs from Malvaceous species. We also report here the expression of a β-galactosidase of glycoside hydrolase-2 family that was earlier considered to be absent in higher plants. Comparative analysis of domain structure allowed us to propose a domain-centric evolution of the five classes of plant β-galactosidases. Further, we observed 1.8-12.2-fold higher expression of nine β-galactosidase isoforms in the mutant hypocotyl, which was characterized by slower growth, undulated shape and deformed cell wall. In vitro and in vivo β-galactosidase activities were also higher in the mutant hypocotyl. Phenotypic analysis supported a significant (P ≤ 0.01) positive correlation between enzyme activity and undulated hypocotyl. Taken together, our study identifies the complete set of β-galactosidases expressed in the jute hypocotyl, and provides compelling evidence that they may be involved in cell wall degradation during hypocotyl development.

Structural characterization of a Δ3, Δ2-enoyl-CoA isomerase from Pseudomonas aeruginosa: implications for its involvement in unsaturated fatty acid metabolism

Gene PA4980 from Pseudomonas aeruginosa encodes a putative enoyl-coenzyme A hydratase/isomerase that is associated with the function of the biofilm dispersion-inducing signal molecule cis-2-decenoic acid. To elucidate the role of PA4980 in cis-2-decenoic acid biosynthesis, we reported the crystal structure of its protein product at 2.39 Å. The structural analysis and substrate binding prediction suggest that it acts as a monofunctional enoyl-coenzyme A isomerase, implicating an alternative pathway of the cis-2-decenoic acid synthesis.

Analgesic Activity, Chemical Profiling and Computational Study on Chrysopogon aciculatus

Present study was undertaken to evaluate the analgesic activity of the ethanol extract of Chrysopogon aciculatus. In addition to bioassays in mice, chemical profiling was done by LC-MS and GC-MS to identify phytochemicals, which were further docked on the catalytic site of COX-2 enzymes with a view to suggest the possible role of such phytoconstituents in the observed analgesic activity. Analgesic activity of C. aciculatus was evaluated by acetic acid induced writhing reflex method and hot plate technique. Phytochemical profiling was conducted using liquid chromatography mass spectrometry (LC-MS) and gas chromatography mass spectrometry (GC-MS). In docking studies, homology model of human COX-2 enzyme was prepared using Easy Modeler 4.0 and the identified phytoconstituents were docked using Autodock Vina. Preliminary acute toxicity test of the ethanol extract of C. aciculatus showed no sign of mortality at the highest dose of 4,000 mg/kg. The whole plant extract significantly (p < 0.05) inhibited acetic acid induced writhing in mice at the doses of 500 and 750 mg/kg. The extract delayed the response time in hot plate test in a dose dependent manner. LC-MS analysis of the plant extract revealed the presence of aciculatin, nudaphantin and 5α,8α-epidioxyergosta-6,22-diene-3β-ol. Three compounds namely citronellylisobutyrate; 2,4-dihydroxy-7-methoxy-(2H)-1,4-benzoxazin-3(4H)-one and nudaphantin were identified in the n-hexane fraction by GC-MS. Among these compounds, six were found to be interacting with the binding site for arachidonic acid in COX-2 enzyme. Present study strongly supports the traditional use of C. aciculatus in the management of pain. In conclusion, compounds (tricin, campesterol, gamma oryzanol, and citronellyl isobutyrate) showing promising binding affinity in docking studies, along with previously known anti-inflammatory compound aciculatin can be held responsible for the observed activity.

Structural and functional analysis of Erwinia amylovora SrlD. The first crystal structure of a sorbitol-6-phosphate 2-dehydrogenase

Sorbitol-6-phosphate 2-dehydrogenases (S6PDH) catalyze the interconversion of d-sorbitol 6-phosphate to d-fructose 6-phosphate. In the plant pathogen Erwinia amylovora the S6PDH SrlD is used by the bacterium to utilize sorbitol, which is used for carbohydrate transport in the host plants belonging to the Amygdaloideae subfamily (e.g., apple, pear, and quince). We have determined the crystal structure of S6PDH SrlD at 1.84 Å resolution, which is the first structure of an EC 1.1.1.140 enzyme. Kinetic data show that SrlD is much faster at oxidizing d-sorbitol 6-phosphate than in reducing d-fructose 6-phosphate, however, equilibrium analysis revealed that only part of the d-sorbitol 6-phosphate present in the in vitro environment is converted into d-fructose 6-phosphate. The comparison of the structures of SrlD and Rhodobacter sphaeroides sorbitol dehydrogenase showed that the tetrameric quaternary structure, the catalytic residues and a conserved aspartate residue that confers specificity for NAD⁺ over NADP⁺ are preserved. Analysis of the SrlD cofactor and substrate binding sites identified residues important for the formation of the complex with cofactor and substrate and in particular the role of Lys42 in selectivity towards the phospho-substrate. The comparison of SrlD backbone with the backbone of 302 short-chain dehydrogenases/reductases showed the conservation of the protein core and identified the variable parts. The SrlD sequence was compared with 500 S6PDH sequences selected by homology revealing that the C-terminal part is more conserved than the N-terminal, the consensus of the catalytic tetrad (Y[SN]AGXA) and a not previously described consensus for the NAD(H) binding.

Global organization of a binding site network gives insight into evolution and structure-function relationships of proteins

The global organization of protein binding sites is analyzed by constructing a weighted network of binding sites based on their structural similarities and detecting communities of structurally similar binding sites based on the minimum description length principle. The analysis reveals that there are two central binding site communities that play the roles of the network hubs of smaller peripheral communities. The sizes of communities follow a power-law distribution, which indicates that the binding sites included in larger communities may be older and have been evolutionary structural scaffolds of more recent ones. Structurally similar binding sites in the same community bind to diverse ligands promiscuously and they are also embedded in diverse domain structures. Understanding the general principles of binding site interplay will pave the way for improved drug design and protein design.

ProBiS tools (algorithm, database, and web servers) for predicting and modeling of biologically interesting proteins

ProBiS (Protein Binding Sites) Tools consist of algorithm, database, and web servers for prediction of binding sites and protein ligands based on the detection of structurally similar binding sites in the Protein Data Bank. In this article, we review the operations that ProBiS Tools perform, provide comments on the evolution of the tools, and give some implementation details. We review some of its applications to biologically interesting proteins. ProBiS Tools are freely available at http://probis.cmm.ki.si and http://probis.nih.gov.

Elucidating a chemical defense mechanism of Antarctic sponges: A computational study

In 2000, a novel secondary metabolite (erebusinone, Ereb) was isolated from the Antarctic sea sponge, Isodictya erinacea. The bioactivity of Ereb was investigated, and it was found to inhibit molting when fed to the arthropod species Orchomene plebs. Xanthurenic acid (XA) is a known endogenous molt regulator present in arthropods. Experimental studies have confirmed that XA inhibits molting by binding to either (or both) of two P450 enzymes (CYP315a1 or CYP314a1) that are responsible for the final two hydroxylations in the production of the molt-inducing hormone, 20-hydroxyecdysone (20E). The lack of crystal structures and biochemical assays for CYP315a1 or CYP314a1, has prevented further experimental exploration of XA and Ereb's molt inhibition mechanisms. Herein, a wide array of computational techniques - homology modeling, molecular dynamics simulations, binding site bioinformatics, flexible receptor-flexible ligand docking, and molecular mechanics-generalized Born surface area calculations - have been employed to elucidate the structure-function relationships between the aforementioned P450s and the two described small molecule inhibitors (Ereb and XA). Results indicate that Ereb likely targets CYP315a1 by interacting with a network of aromatic residues in the binding site, while XA may inhibit both CYP315a1 and CYP314a1 because of its aromatic, as well as charged nature.

Comparison of in silico tools for binding site prediction applied for structure-based design of autolysin inhibitors

Autolysin E (AtlE) is a bacteriolytic enzyme which plays an important role in division and growth of bacterial cells and therefore represents a promising potential drug target. Its 3D structure has been recently elucidated. We used in silico prediction tools to study substrate or ligand (inhibitor) binding regions of AtlE. We applied several freely available tools and a commercial tool for binding site identification and compared results of the prediction. Calculation time, number of predictions and output data provided by specific software vary according to the different approaches utilized by specific method categories. Despite different approaches, binding sites in similar locations on the protein were predicted. Specific amino acid residues that form these binding sites were predicted as binding residues. The predicted residues, especially those with predicted highest conservation score, could theoretically have catalytic and binding properties. According to our results, we assume that E138, which has the highest conservation score, is the catalytic residue and F161, G162 and Y224, which are also highly conserved, are responsible for substrate binding. Ligands developed with binding specificity towards these residues could inhibit the catalysis and binding of the substrate of AtlE. The molecules with inhibitory potency could therefore represent potential new antibacterial agents.

Identification of Ecdysone Hormone Receptor Agonists as a Therapeutic Approach for Treating Filarial Infections

Background

A homologue of the ecdysone receptor has previously been identified in human filarial parasites. As the ecdysone receptor is not found in vertebrates, it and the regulatory pathways it controls represent attractive potential chemotherapeutic targets.

Methodology/ Principal Findings

Administration of 20-hydroxyecdysone to gerbils infected with B. malayi infective larvae disrupted their development to adult stage parasites. A stable mammalian cell line was created incorporating the B. malayi ecdysone receptor ligand-binding domain, its heterodimer partner and a secreted luciferase reporter in HEK293 cells. This was employed to screen a series of ecdysone agonist, identifying seven agonists active at sub-micromolar concentrations. A B. malayi ecdysone receptor ligand-binding domain was developed and used to study the ligand-receptor interactions of these agonists. An excellent correlation between the virtual screening results and the screening assay was observed. Based on both of these approaches, steroidal ecdysone agonists and the diacylhydrazine family of compounds were identified as a fruitful source of potential receptor agonists. In further confirmation of the modeling and screening results, Ponasterone A and Muristerone A, two compounds predicted to be strong ecdysone agonists stimulated expulsion of microfilaria and immature stages from adult parasites.

Conclusions

The studies validate the potential of the B. malayi ecdysone receptor as a drug target and provide a means to rapidly evaluate compounds for development of a new class of drugs against the human filarial parasites.

The human filarial parasites are the causative agents of two neglected tropical diseases targeted for elimination by the international community. The current elimination programs rely upon the mass distribution of a limited number of drugs, leaving the programs open to failure in the event that resistance develops. Thus, there is a critical need for novel chemotherapeutic agents to supplement the current arsenal. The filarial parasites are ecdysozoans, whose developmental processes are controlled by a master regulator, the ecdysone receptor. Here we validate the potential of the filarial ecdysone receptor as a chemotherapeutic target and report the development of high throughput and virtual screening assays that may be used to compounds that target it.

Mechanistic Study of Human Glucose Transport Mediated by GLUT1

The glucose transporter 1 (GLUT1) belongs to the major facilitator superfamily (MFS) and is responsible for the constant uptake of glucose. However, the molecular mechanism of sugar transport remains obscure. In this study, homology modeling and molecular dynamics (MD) simulations in lipid bilayers were performed to investigate the combination of the alternate and multisite transport mechanism of glucose with GLUT1 in atomic detail. To explore the substrate recognition mechanism, the outward-open state human GLUT1 homology model was generated based on the template of xylose transporter XylE (PDB ID: 4GBZ), which shares up to 29% sequence identity and 49% similarity with GLUT1. Through the MD simulation study of glucose across lipid bilayer with both the outward-open GLUT1 and the GLUT1 inward-open crystal structure, we investigated six different conformational states and identified four key binding sites in both exofacial and endofacial loops that are essential for glucose recognition and transport. The study further revealed that four flexible gates consisting of W65/Y292/Y293-M420/TM10b-W388 might play important roles in the transport cycle. The study showed that some side chains close to the central ligand binding site underwent larger position changes. These conformational interchanges formed gated networks within an S-shaped central channel that permitted staged ligand diffusion across the transporter. This study provides new inroads for the understanding of GLUT1 ligand recognition paradigm and configurational features which are important for molecular, structural, and physiological research of the MFS members, especially for GLUT1-targeted drug design and discovery.

Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling

Protein structure prediction and modeling provide a tool for understanding protein functions by computationally constructing protein structures from amino acid sequences and analyzing them. With help from protein prediction tools and web servers, users can obtain the three-dimensional protein structure models and gain knowledge of functions from the proteins. In this chapter, we will provide several examples of such studies. As an example, structure modeling methods were used to investigate the relation between mutation-caused misfolding of protein and human diseases including epilepsy and leukemia. Protein structure prediction and modeling were also applied in nucleotide-gated channels and their interaction interfaces to investigate their roles in brain and heart cells. In molecular mechanism studies of plants, rice salinity tolerance mechanism was studied via structure modeling on crucial proteins identified by systems biology analysis; trait-associated protein-protein interactions were modeled, which sheds some light on the roles of mutations in soybean oil/protein content. In the age of precision medicine, we believe protein structure prediction and modeling will play more and more important roles in investigating biomedical mechanism of diseases and drug design.