PatchSearch: a web server for off-target protein identification

DIONYSUS: a database of protein-carbohydrate interfaces

Protein-carbohydrate interactions govern a wide variety of biological processes and play an essential role in the development of different diseases. Here, we present DIONYSUS, the first database of protein-carbohydrate interfaces annotated according to structural, chemical and functional properties of both proteins and carbohydrates. We provide exhaustive information on the nature of interactions, binding site composition, biological function and specific additional information retrieved from existing databases. The user can easily search the database using protein sequence and structure information or by carbohydrate binding site properties. Moreover, for a given interaction site, the user can perform its comparison with a representative subset of non-covalent protein-carbohydrate interactions to retrieve information on its potential function or specificity. Therefore, DIONYSUS is a source of valuable information both for a deeper understanding of general protein-carbohydrate interaction patterns, for annotation of the previously unannotated proteins and for such applications as carbohydrate-based drug design. DIONYSUS is freely available at www.dsimb.inserm.fr/DIONYSUS/.

VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.

Designing dual inhibitors against potential drug targets of Plasmodium falciparum -M17 Leucyl Aminopeptidase and Plasmepsins

Malaria is one of the major diseases of concern worldwide, especially in the African regions. According to a recent WHO report, 95% of deaths that occur due to malaria are in the African regions. Resistance to present antimalarial drugs is increasing rapidly and becoming a problem of concern. M17 Leucyl Aminopeptidase (PfM17LAP) and vacuolar Plasmepsins (PfPM) are two important enzymes involved in the haemoglobin degradation pathway of Plasmodium falciparum. PfM17LAP regulates the release of amino acids and PfPM mediates the conversion of haemoglobin proteins to oligopeptides. These enzymes thus play an essential role in the survival of malaria parasites inside the human body. In the present study, we used in-silico molecular docking, simulation and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) studies to find potential dual inhibitors of PfPM and PfM17LAP using the ChEMBL antimalarial library. Absorption, distribution, metabolism, excretion and toxicity (ADMET) profiling of the top ten ranked molecules was done using the BIOVIA Discovery Studio. The present investigation revealed that the compound CHEMBL426945 is stable in the binding site of both PfPM and PfM17LAP. In this study, we have reported novel dual-inhibitors that may act better than the present antimalarial drugs.Communicated by Ramaswamy H. Sarma.

Emerging Need of Today: Significant Utilization of Various Databases and Softwares in Drug Design and Development

In drug discovery, in silico methods have become a very important part of the process. These approaches impact the entire development process by discovering and identifying new target proteins as well as designing potential ligands with a significant reduction of time and cost. Furthermore, in silico approaches are also preferred because of reduction in the experimental use of animals as; in vivo testing for safer drug design and repositioning of known drugs. Novel software-based discovery and development such as direct/indirect drug design, molecular modelling, docking, screening, drug-receptor interaction, and molecular simulation studies are very important tools for the predictions of ligand-target interaction pattern, pharmacodynamics as well as pharmacokinetic properties of ligands. On the other part, the computational approaches can be numerous, requiring interdisciplinary studies and the application of advanced computer technology to design effective and commercially feasible drugs. This review mainly focuses on the various databases and software used in drug design and development to speed up the process.

LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds

Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at https://cbbio.online/LigTMap. The source code is released on GitHub (https://github.com/ShirleyWISiu/LigTMap) under the BSD 3-Clause License to encourage re-use and further developments.

Discerning novel drug targets for treating Mycobacterium avium ss. paratuberculosis-associated autoimmune disorders: an in silico approach

Mycobacterium avium subspecies paratuberculosis (MAP) exhibits 'molecular mimicry' with the human host resulting in several autoimmune diseases such as multiple sclerosis, type 1 diabetes mellitus (T1DM), Hashimoto's thyroiditis, Crohn's disease (CD), etc. The conventional therapy for autoimmune diseases includes immunosuppressants or immunomodulators that treat the symptoms rather than the etiology and/or causative mechanism(s). Eliminating MAP-the etiopathological agent might be a better strategy to treat MAP-associated autoimmune diseases. In this case study, we conducted a systematic in silico analysis to identify the metabolic chokepoints of MAP's mimicry proteins and their interacting partners. The probable inhibitors of chokepoint proteins were identified using DrugBank. DrugBank molecules were stringently screened and molecular interactions were analyzed by molecular docking and 'off-target' binding. Thus, we identified 18 metabolic chokepoints of MAP mimicry proteins and 13 DrugBank molecules that could inhibit three chokepoint proteins viz. katG, rpoB and narH. On the basis of molecular interaction between drug and target proteins finally eight DrugBank molecules, viz. DB00609, DB00951, DB00615, DB01220, DB08638, DB08226, DB08266 and DB07349 were selected and are proposed for treatment of three MAP-associated autoimmune diseases namely, T1DM, CD and multiple sclerosis. Because these molecules are either approved by the Food and Drug Administration or these are experimental drugs that can be easily incorporated in clinical studies or tested in vitro. The proposed strategy may be used to repurpose drugs to treat autoimmune diseases induced by other pathogens.