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Cardiomyopathies are a severe and chronic cardiovascular burden worldwide, affecting a large cohort in the general population. Cysteine and glycine-rich protein 3 (CSRP3) is one of key proteins implicated in dominant dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). In this study, we device a rapid in silico screening protocol that creates a mutational landscape map for all possible allowed and disallowed substitutions in the protein of interest. This map provides the structural and functional insights on the stability of LIM domains of CSRP3. Further, the sequence analysis delineates the eukaryotic CSRP3 protein orthologs which complements the mutational map, but provide limited information of amino acid exchanges. Next, we also evaluated the effect of HCM/DCM mutations on these domains. One of highly destabilising mutations—L44P (also disease causing) and a neutral mutation—L44M were further subjected to molecular dynamics (MD) simulations. The results establish that L44P substitution affects the LIM domain structure by altering secondary structure and due to loss of hydrophobic interaction with Phenylananine 35. The present study provides a useful perspective to our understanding of the role of mutations in the CSRP3 LIM domains and their evolution. This study provides a novel computational screening method for quick identification of key mutation sites for specific protein structures that can reduce the burden on experimental research.

Abstract Non-structural protein (NS1) is a 350 amino acid long conserved protein in the dengue virus. Conservation of NS1 is expected due to its importance in dengue pathogenesis. The protein is known to exist in dimeric and hexameric states. The dimeric state is involved in its interaction with host proteins and viral replication, and the hexameric state is involved in viral invasion. In this work, we performed extensive structure and sequence analysis of NS1 protein, and uncovered the role of NS1 quaternary states in its evolution. A three-dimensional modeling of unresolved loop regions in NS1 structure is performed. “Conserved” and “Variable” regions within NS1 protein were identified from sequences obtained from patient samples and the role of compensatory mutations in selecting destabilizing mutations were identified. Molecular dynamics (MD) simulations were performed to extensively study the effect of a few mutations on NS1 structure stability and compensatory mutations. Virtual saturation mutagenesis, predicting the effect of every individual amino acid substitution on NS1 stability sequentially, revealed virtual-conserved and variable sites. The increase in number of observed and virtual-conserved regions across NS1 quaternary states suggest the role of higher order structure formation in its evolutionary conservation. Our sequence and structure analysis could enable in identifying possible protein–protein interfaces and druggable sites. Virtual screening of nearly 10,000 small molecules, including FDA-approved drugs, permitted us to recognize six drug-like molecules targeting the dimeric sites. These molecules could be promising due to their stable interactions with NS1 throughout the simulation.

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

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.

Toll-like receptors 4 (TLR4) recognize lipopolysaccharides (LPS) from bacteria as their conventional ligands and undergo downstream signaling to produce cytokines. They mediate the signaling either by the TIRAP-MyD88 complex or by the TRAM-TRIF complex. The MyD88 pathway is common to all other TLRs, whereas the TRAM-TRIF complex is largely exclusive to TLR4. Here we study the TIR domain of TRAM and TRIF ortholog proteins that are crucial for downstream signaling. Our previous work on pan-genome-wide survey, indicates to be the ancestral organism with both TRAM and TRIF proteins. To gain a deeper insight into the protein function and to compare them with adaptor proteins, we modeled the docking of the TRAM-TRIF complex of representative organisms across various taxa. These modeling experiments provide insights to ascertain a possible interaction surface and calculate the energetics and electrostatic potential of the complex. Furthermore, this enables us to employ normal mode analysis (NMA) to examine fluctuating, interacting, and other specific residue clusters that could have a role in protein functioning in both and . We also performed molecular dynamics simulations of these complexes and cross-validated the functionally important residues using network parameters. We compared the stoichiometry of TRAM-TRIF complexes and found that the tetrameric models (TRAM and TRIF dimer) were more stable than the trimeric model (TRAM dimer and TRIF monomer). While the critical residues of TIRAP, TRIF, and MyD88 were preserved, we also found that the important residues of TRAM signaling were not conserved in . This suggests the presence of functional TIRAP-MyD88-mediated TLR4 signaling and TRIF-mediated TLR3 signaling in the ancestral species. The overall biological function of this signaling domain appears to be gradually acquired through the orchestration of several motifs through an evolutionary scale.

Cardiomyopathies are progressive disease conditions that give rise to an abnormal heart phenotype and are a leading cause of heart failures in the general population. These are complex diseases that show co-morbidity with other diseases. The molecular interaction network in the localised disease neighbourhood is an important step toward deciphering molecular mechanisms underlying these complex conditions. In this pursuit, we employed network medicine techniques to systematically investigate cardiomyopathy’s genetic interplay with other diseases and uncover the molecular players underlying these associations. We predicted a set of candidate genes in cardiomyopathy by exploring the DIAMOnD algorithm on the human interactome. We next revealed how these candidate genes form association across different diseases and highlighted the predominant association with brain, cancer and metabolic diseases. Through integrative systems analysis of molecular pathways, heart-specific mouse knockout data and disease tissue-specific transcriptomic data, we screened and ascertained prominent candidates that show abnormal heart phenotype, including NOS3, MMP2 and SIRT1 . Our computational analysis broadens the understanding of the genetic associations of cardiomyopathies with other diseases and holds great potential in cardiomyopathy research.

RNA polymerase-binding protein A (RbpA) is an actinomycetes-specific protein crucial for the growth and survival of the pathogen Mycobacterium tuberculosis. Its role is essential and influences the transcription and antibiotic responses. However, the regulatory mechanisms underlying RbpA-mediated transcription remain unknown. In this study, we employed various computational techniques to investigate the role of RbpA in the formation and dynamics of the RNA polymerase complex. Our analysis reveals significant structural rearrangements in RNA polymerase happen upon interaction with RbpA. Hotspot residues, crucial amino acids in the RbpA-mediated transcriptional regulation, were identified through our examination. The study elucidates the dynamic behavior within the complex, providing insights into the flexibility and functional dynamics of the RbpA-RNA polymerase interaction. Notably, potential allosteric mechanisms, involving the interface of subunits α1 and α2 were uncovered, shedding light on how RbpA modulates transcriptional activity. Finally, potential ligands meant for the α1-α2 binding site were identified through virtual screening. The outcomes of our computational study serve as a foundation for experimental investigations into inhibitors targeting the RbpA-regulated dynamics in RNA polymerase. Overall, this research contributes valuable information for understanding the intricate regulatory networks of RbpA in the context of transcription and suggests potential avenues for the development of RbpA-targeted therapeutics.

Toll/Interleukin-1 receptor (TIR) domains are cytoplasmic domain that mediates receptor signalling. These domains are present in proteins like Toll-like receptors (TLR), its signaling adaptors and Interleukins, that form a major part of the immune system. These TIR domain containing signaling adaptors binds to the TLRs and interacts with their TIR domains for downstream signaling. We have examined the evolutionary divergence across the tree of life of two of these TIR domain containing adaptor molecules (TICAM) i.e., TIR domain-containing adapter-inducing interferon-β (TRIF/TICAM1) and TIR domain containing adaptor molecule2 (TRAM/TICAM2), by using computational approaches. We studied their orthologs, domain architecture, conserved motifs, and amino acid variations. Our study also adds a timeframe to infer the duplication of TICAM protein from Leptocardii and later divergence into TICAM1/TRIF and TICAM2/TRAM. More evidence of TRIF proteins was seen, but the absence of conserved co-existing domains such as TRIF-NTD, TIR, and RHIM domains in distant relatives hints on diversification and adaptation to different biological functions. TRAM was lost in Actinopteri and has conserved domain architecture of TIR across species except in Aves . An additional isoform of TRAM, TAG (TRAM adaptor with the GOLD domain), could be identified in species in the Mesozoic era. Finally, the Hypothesis based Likelihood ratio test was applied to look for selection pressure amongst orthologues of TRIF and TRAM to search for positively selected sites. These residues were mostly seen in the non-structural region of the proteins. Overall, this study unravels evolutionary information on the adaptors TRAM and TRIF and how well they had duplicated to perform diverse functions by changes in their domain architecture across lineages.

Celiac disease (CD) is a common autoimmune disorder in which the patients are unable to digest gluten, which is present in foods made up of wheat, barley and rye. Whilst diagnosis happens late in 80% of the cases, avoidance of such foods appears to be the common solution. Alternative management strategies are required for the patients and their families since CD is also genetically carried over. Probiotic therapeutics and the consumption of appropriate enzymes, such as prolyloligopeptidases (POPs), from gut-friendly bacteria could reduce the disease burden and provide a better lifestyle for CD patients. We have examined around 5000 gut bacterial genomes and identified nearly 4000 non-redundant putative POPs. A select set of 10 gut bacterial POP sequences were subject to three-dimensional modelling, ligand docking and molecular dynamics simulations where stable interactions were observed between the POPs and gluten peptides. Our study provides sequence and structural analysis of potential POP enzymes in gut bacterial genomes, which form a strong basis to offer probiotic solutions to CD patients. In particular, these enzymes could be lead future therapeutics for this disease.