@article {164791, title = {Emerging strategies for computational identification of protein-protein interaction hotspots.}, journal = {Curr Opin Struct Biol}, volume = {98}, year = {2026}, month = {2026 Mar 10}, pages = {103241}, abstract = {

A small number of residues at protein-protein interfaces, commonly referred to as hotspots, dominate binding free energy and play a decisive role in stabilizing protein complexes. Identifying these residues is central to understanding the energetic architecture of protein-protein interactions and to developing strategies for therapeutic intervention. Although experimental approaches such as alanine scanning have provided critical insights, they are often impractical for large or dynamic systems. This has positioned computational approaches at the forefront of hotspot analysis. This review highlights recent developments in molecular dynamics simulations and machine-learning-based predictors for hotspot identification, discusses current challenges, and outlines emerging directions in the field. Finally, we suggest that combining these complementary approaches could offer a powerful framework for capturing the dynamic and energetic complexity of protein interfaces, making hotspot predictions more robust and interpretable.

}, issn = {1879-033X}, doi = {10.1016/j.sbi.2026.103241}, url = {https://www.sciencedirect.com/science/article/pii/S0959440X26000230?via\%3Dihub}, author = {Pathak, Aditi and Tiwari, Vikas and Sowdhamini, Ramanathan} } @article {160476, title = {Exploration of structural space of Cus17 lectin with glycans using molecular docking and simulation studies.}, journal = {Comput Struct Biotechnol J}, volume = {27}, year = {2025}, month = {2025}, pages = {3319-3327}, abstract = {

The Cus17 phloem protein, in the case of species, plays an important role in the phloem-based defense of the plant. Cus17 can bind to various carbohydrates present on insect exoskeletons or fungal cells. The recent experimental structure of chitotriose bound Cus17 elucidates the carbohydrate interacting residues of Cus17. Higher chito-oligosaccharides are also known to interact with Cus17, but the lack of experimental structure impedes our understanding of their interaction. In this study, we have employed methods to explore the binding interactions of higher chito-oligosaccharides with Cus17. Chitoheptaose forms stable interactions with canonical binding site residues Trp48 and Asp50. Smaller chito-oligosaccharides were observed to be relatively unstable at the canonical binding site of Cus17. Further, the chito-oligosaccharides were inspected for interactions with predicted ligand binding sites. We also generated different tetramers of Cus17 and docked the chito-oligosaccharides to the tetrameric Cus17. All chito-oligosaccharides were found to make persistent interactions with Cus17 tetramer. Interestingly, chitotriose shows the best binding affinity and maintains stable interactions with Cus17 tetramer upon extended simulations with the canonical site.

}, keywords = {Non-canonical binding; Oligosaccharides; Protein-ligand interactions}, issn = {2001-0370}, doi = {10.1016/j.csbj.2025.07.028}, author = {Tiwari, Vikas and Pathak, Aditi and Poojary, Tejaswini and Sowdhamini, Ramanathan and Surolia, Avadhesha} } @article {126669, title = {Structural modelling and dynamics of full-length of TLR10 sheds light on possible modes of dimerization, ligand binding and mechanism of action.}, journal = {Curr Res Struct Biol}, volume = {5}, year = {2023}, month = {2023}, pages = {100097}, abstract = {

Toll like receptors (TLRs) play a pivotal role in innate and adaptive immunity. There are 10 TLRs in the human genome, of which TLR10 is the least characterized. Genetic polymorphism of TLR10 has been shown to be associated with multiple diseases including tuberculosis and rheumatoid arthritis. TLR10 consists of an extracellular domain (ECD), a single-pass transmembrane (TM) helix and intracellular TIR (Toll/Interleukin-1 receptor) domain. ECD is employed for ligand recognition and the intracellular domain interacts with other TIR domain-containing adapter proteins for signal transduction. Experimental structure of ECD or TM domain is not available for TLR10. In this study, we have modelled multiple forms of TLR10-ECD dimers, such as closed and open forms, starting from available structures of homologues. Subsequently, multiple full-length TLR10 homodimer models were generated by utilizing homology modelling and protein-protein docking. The dynamics of these models in membrane-aqueous environment revealed the global motion of ECD and TIR domain towards membrane bilayer. The TIR domain residues exhibited high root mean square fluctuation compared to ECD. The {\textquoteright}closed form{\textquoteright} model was observed to be energetically more favorable than {\textquoteright}open form{\textquoteright} model. The evaluation of persistent interchain interactions, along with their conservation score, unveiled critical residues for each model. Further, the binding of dsRNA to TLR10 was modelled by defined and blind docking approaches. Differential binding of dsRNA to the protomers of TLR10 was observed upon simulation that could provide clues on ligand disassociation. Dynamic network analysis revealed that the {\textquoteright}open form{\textquoteright} model can be the functional form while {\textquoteright}closed form{\textquoteright} model can be the apo form of TLR10.

}, issn = {2665-928X}, doi = {10.1016/j.crstbi.2023.100097}, author = {Tiwari, Vikas and Sowdhamini, R} } @article {126684, title = {Structure modelling of odorant receptor from and identification of potential repellent molecules.}, journal = {Comput Struct Biotechnol J}, volume = {21}, year = {2023}, month = {2023}, pages = {2204-2214}, abstract = {

Odorant receptors (ORs) are important class of proteins involved in olfactory behaviour of insects. These are GPCR-like heptahelical transmembrane proteins with inverted topology compared to GPCR and require a co-receptor (ORco) for their function. OR function can be modulated through small molecules and negative modulation can be beneficial in case of disease vectors like . OR4 of is implicated in host recognition through human odour. is a vector for viruses that spread diseases like dengue, Zika and Chikungunya. In this study, we have attempted to model the full-length structure of OR4 and the ORco of due to lack of experimental structure. Further, we have screened a library of natural compounds (\>0.3 million) along with known repellent molecules against ORco and OR4. Many natural compounds, including those from plants like (Holy Basil) and (Black pepper) were found to have better binding affinity towards ORco compared to known repellents like DEET providing an alternative to existing repellent molecules. For specific inhibitor of OR4, several natural compounds (including those from plant like Mulberry) were identified. Further, we have utilized multiple docking approaches and conservation analysis to understand the interaction between OR4 and ORco. It was observed that the residues from the seventh transmembrane helix of OR4 and pore forming helix of ORco could play an important role along with known intracellular loop 3 residues in mediating the heteromer formation of OR and ORco.

}, issn = {2001-0370}, doi = {10.1016/j.csbj.2023.03.005}, author = {Tiwari, Vikas and Sowdhamini, R} } @article {117966, title = {Identification of potential modulators of IFITM3 by in-silico modeling and virtual screening.}, journal = {Sci Rep}, volume = {12}, year = {2022}, month = {2022 Sep 24}, pages = {15952}, abstract = {

IFITM3 is a transmembrane protein that confers innate immunity. It has been established to restrict entry of multiple viruses. Overexpression of IFITM3 has been shown to be associated with multiple cancers, implying IFITM3 to be good therapeutic target. The regulation of IFITM3 activity is mediated by multiple post-translational modifications (PTM). In this study, we have modelled the structure of IFITM3, consistent with experimental predictions on its membrane topology. MD simulation in membrane-aqueous environment revealed the stability of the model. Ligand binding sites on the IFITM3 surface were predicted and it was observed that the best site includes important residues involved in PTM and has good druggable score. Molecular docking was performed using FDA approved ligands and natural ligands from Super Natural II database. The ligands were re-ranked by calculating binding free energy. Select docking complexes were simulated again to substantiate the binding between ligand and IFITM3. We observed that known drugs like Eluxadoline and natural products like SN00224572 and Parishin A have good binding affinity against IFITM3. These ligands form persistent interactions with key lysine residues (Lys83, Lys104) and hence can potentially alter the activity of IFITM3. The results of this computational study can provide a starting point for experimental investigations on IFITM3 modulators.

}, keywords = {Biological Products, Ligands, Lysine, Molecular Docking Simulation, RNA-Binding Proteins}, issn = {2045-2322}, doi = {10.1038/s41598-022-20259-8}, author = {Tiwari, Vikas and Viswanath, Shruthi} } @article {88160, title = {InsectOR-Webserver for sensitive identification of insect olfactory receptor genes from non-model genomes.}, journal = {PLoS One}, volume = {16}, year = {2021}, month = {2021}, pages = {e0245324}, abstract = {

Insect Olfactory Receptors (ORs) are diverse family of membrane protein receptors responsible for most of the insect olfactory perception and communication, and hence they are of utmost importance for developing repellents or pesticides. Accurate gene prediction of insect ORs from newly sequenced genomes is an important but challenging task. We have developed a dedicated webserver, {\textquoteright}insectOR{\textquoteright}, to predict and validate insect OR genes using multiple gene prediction algorithms, accompanied by relevant validations. It is possible to employ this server nearly automatically and perform rapid prediction of the OR gene loci from thousands of OR-protein-to-genome alignments, resolve gene boundaries for tandem OR genes and refine them further to provide more complete OR gene models. InsectOR outperformed the popular genome annotation pipelines (MAKER and NCBI eukaryotic genome annotation) in terms of overall sensitivity at base, exon and locus level, when tested on two distantly related insect genomes. It displayed more than 95\% nucleotide level precision in both tests. Finally, given the same input data and parameters, InsectOR missed less than 2\% gene loci, in contrast to 55\% loci missed by MAKER for Drosophila melanogaster. The webserver is freely available on the web at http://caps.ncbs.res.in/insectOR/ and the basic package can be downloaded from https://github.com/sdk15/insectOR for local use. This tool will allow biologists to perform quick preliminary identification of insect olfactory receptor genes from newly sequenced genomes and also assist in their further detailed annotation. Its usage can also be extended to other divergent gene families.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0245324}, author = {Karpe, Snehal Dilip and Tiwari, Vikas and Ramanathan, Sowdhamini} } @article {102047, title = {Ligand Docking Methods to Recognize Allosteric Inhibitors for G-Protein-Coupled Receptors.}, journal = {Bioinform Biol Insights}, volume = {15}, year = {2021}, month = {2021}, pages = {11779322211037769}, abstract = {

G-protein-coupled receptors (GPCRs) are membrane proteins which play an important role in many cellular processes and are excellent drug targets. Despite the existence of several US Food and Drug Administration (FDA)-approved GPCR-targeting drugs, there is a continuing challenge of side effects owing to the nonspecific nature of drug binding. We have investigated the diversity of the ligand binding site for this class of proteins against their cognate ligands using computational docking, even if their structures are known already in the ligand-complexed form. The cognate ligand of some of these receptors dock at allosteric binding site with better score than the binding at the conservative site. Interestingly, amino acid residues at such allosteric binding site are not conserved across GPCR subfamilies. Such a computational approach can assist in the prediction of specific allosteric binders for GPCRs.

}, issn = {1177-9322}, doi = {10.1177/11779322211037769}, author = {Harini, K and Jayashree, S and Tiwari, Vikas and Vishwanath, Sneha and Sowdhamini, Ramanathan} } @article {79804, title = {Computational search for potential COVID-19 drugs from FDAapproved drugs and small molecules of natural origin identifies several anti-virals and plant products.}, journal = {J Biosci}, volume = {45}, year = {2020}, month = {2020}, abstract = {

The world is currently facing the COVID-19 pandemic, for which mild symptoms include fever and dry cough. In severe cases, it could lead to pneumonia and ultimately death in some instances. Moreover, the causative pathogen is highly contagious and there are no drugs or vaccines for it yet. The pathogen, SARS-CoV-2, is one of the human coronaviruses which was identified to infect humans first in December 2019. SARS-CoV-2 shares evolutionary relationship to other highly pathogenic viruses such as Severe Acute Respiratory Syndrome (SARS) and Middle East respiratory syndrome (MERS). We have exploited this similarity to model a target non-structural protein, NSP1, since it is implicated in the regulation of host gene expression by the virus and hijacking of host machinery. We next interrogated the capacity to repurpose around 2300 FDA-approved drugs and more than 3,00,000 small molecules of natural origin towards drug identification through virtual screening and molecular dynamics. Interestingly, we observed simple molecules like lactose, previously known anti-virals and few secondary metabolites of plants as promising hits. These herbal plants are already practiced in Ayurveda over centuries to treat respiratory problems and inflammation. Disclaimer: we would not like to recommend uptake of these small molecules for suspect COVID patients until it is approved by competent national or international authorities.

}, issn = {0973-7138}, author = {Sharma, Abhishek and Tiwari, Vikas and Sowdhamini, Ramanathan} } @article {60326, title = {Topology prediction of insect olfactory receptors.}, journal = {Curr Opin Struct Biol}, volume = {55}, year = {2019}, month = {2019 Jun 21}, pages = {194-203}, abstract = {

Olfactory receptors are important transmembrane proteins that enable organisms to perceive odours and react to them. Structural understanding of insect olfactory receptors is scarce. In this review, we discuss different transmembrane helix prediction methods, consensus methods, topology prediction methods which can enable topology prediction of these proteins. We discuss the current success rates by applying the algorithms on few G-protein coupled receptors of known structure and olfactory receptor sequences and outstanding challenges. Finally, we discuss the impact of topology prediction on biology and modeling of ORs.

}, issn = {1879-033X}, doi = {10.1016/j.sbi.2019.05.014}, author = {Tiwari, Vikas and Karpe, Snehal D and Sowdhamini, Ramanathan} }