Computational Structural Biology Lab, IIT Kharagpur

03/12/2024

Exciting news from RNA-Puzzle Round V: Blind Predictions! 🎉 Three of our former lab members participated, and we're proud to share that Sunandan Mukherjee's group ranked in the top 5! 🏆 Shoutout to Nithin Chandran and Smita Priyadarshini Pilla for their remarkable contributions as well. Big congratulations to all for showcasing their talent in this challenging competition!

This incredible work is now published! 🖊️ Check it out here: https://doi.org/10.1038/s41592-024-02543-9

Big congratulations to everyone involved! 👏

The results of the Fifth RNA-Puzzles contest highlights advances in RNA three-dimensional structure prediction and uncovers new insights into RNA folding and structure.

01/06/2023

Take a look at our new article in Proteins: Structure, Function, and Bioinformatics.

"Efficient mapping of RNA-binding residues in RNA-binding proteins using local sequence features of binding site residues in protein-RNA complexes"

https://doi.org/10.1002/prot.26528

Ankita Agarwal, Shri Kant, Ranjit Prasad Bahadur

Abstract

Protein-RNA interactions play vital roles in a plethora of biological processes such as the regulation of gene expression, protein synthesis, mRNA processing, and biogenesis. Identification of RNA-binding residues (RBRs) in proteins is essential to understand RNA-mediated protein functioning, to perform site-directed mutagenesis, and to develop novel targeted drug therapies. Moreover, the extensive gap between sequence and structural data restricts the identification of binding sites in unsolved structures. However, efficient use of computational methods demanding only sequence to identify binding residues can bridge this huge sequence-structure gap. In this study, we have extensively studied the protein-RNA interface in known RNA-binding proteins (RBPs). We find that the interface is highly enriched in basic and polar residues, with Gly being the most common interface neighbor. We investigated several amino acid features and developed a method to predict putative RBRs from amino acid sequence. We have implemented a balanced random forest (BRF) classifier with local residue features of protein sequences for prediction. With 5-fold cross-validations, the sequence pattern derived dipeptide composition-based BRF model (DCP-BRF) resulted in an accuracy of 87.9%, specificity of 88.8%, the sensitivity of 82.2%, Mathew's correlation coefficient of 0.60 and AUC of 0.93, performing better than few existing methods. We further validated our prediction model on known human RBPs through RBR prediction and could map ~54% of them. Further, knowledge of binding site preferences obtained from computational predictions combined with experimental validations of potential RNA binding sites can enhance our understanding of protein-RNA interactions. This may serve to accelerate investigations on the functional roles of many novel RBPs.

https://onlinelibrary.wiley.com/share/HRSARDPSE7BQVS43FPC6?target=10.1002/prot.26528

10/03/2023

Take a look at the new article in the Biochimie Journal from Computational Structural Biology Lab.

Ankita Agarwal and Ranjit Prasad Bahadur.
https://doi.org/10.1016/j.biochi.2023.01.017

RNA-binding proteins (RBPs) are structurally and functionally diverse macromolecules with significant involvement in several post-transcriptional gene regulatory processes and human diseases. RNA recognition motif (RRM) is one of the most abundant RNA-binding domains in human RBPs. The unique modular architecture of each RBP containing RRM is crucial for its diverse target recognition and function. Genome-wide study of these structurally conserved and functionally diverse domains can enhance our understanding of their functional implications. In this study, modular architecture of RRM containing RBPs in human proteome is identified and systematically analysed. We observe that 30% of human RBPs with RNA-binding function contain RRM in single or multiple repeats or with other domains with maximum of six repeats. Zinc-fingers are the most frequently co-occurring domain partner of RRMs. Human RRM containing RBPs mostly belong to RNA metabolism class of proteins and are significantly enriched in two functional pathways including spliceosome and mRNA surveillance. Various human diseases are associated with 18% of the RRM containing RBPs. Single RRM containing RBPs are highly enriched in disorder regions. Gene ontology (GO) molecular functions including poly(A), poly(U) and miRNA binding are highly depleted in RBPs with single RRM, indicating the significance of modular nature of RRMs in specific function. The current study reports all the possible domain architectures of RRM containing human RBPs and their functional enrichment. The idea of domain architecture, and how they confer specificity and new functionalities to RBPs, can help in re-designing of modular RRM containing RBPs with re-engineered function.

20/09/2022

Ramachandran Lecture Series, 18 October 2022

Bioinformatics Centre (BIC), Department of Biotechnology, IIT Kharagpur

Bioinformatics Centre Organizes "GN Ramachandran Lecture Series on Bioinformatics and Computational Biology"

For more details please visit BIC webpage:
http://www.csb.iitkgp.ac.in/bioinformatics_centre/announcement.html

30/06/2022

Take a look at our new article in the Journal of Computational and Structural Biotechnology.

"A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences.

Ankita Agarwal , Kunal S, Shri Kant Kaushik & Bahadur RP

RNA-protein interactions play vital roles in driving the cellular machineries. Despite significant involvement in several biological processes, the underlying molecular mechanism of RNA-protein interactions is still elusive. This may be due to the experimental difficulties in solving co-crystallized RNA-protein complexes. Inherent flexibility of RNA molecules to adopt different conformations makes them functionally diverse. Their interactions with protein have implications in RNA disease biology. Thus, study of binding interfaces can provide a mechanistic insight of the molecular functioning and aberrations caused due to altered interactions. Moreover, high-throughput sequencing technologies have generated huge sequence data compared to available structural data of RNA-protein complexes. In such a scenario, efficient computational algorithms are required for identification of protein-binding interfaces of RNA in the absence of known structures. We have investigated several machine learning classifiers and various features derived from nucleotide sequences to identify protein-binding nucleotides in RNA. We achieve best performance with nucleotide-triplet and nucleotide-quartet feature-based random forest models. An overall accuracy of 84.8%, sensitivity of 83.2%, specificity of 86.1%, MCC of 0.70 and AUC of 0.93 is achieved. We have further implemented the developed models in a user-friendly webserver “Nucpred”, which is freely accessible at “http://www.csb.iitkgp.ac.in/applications/Nucpred/index”.

01/06/2022

Have a look at our new publication in Journal of biomolecular Structure & Dynamics "Molecular insights into binding dynamics of tandem RNA recognition motifs (tRRMs) of human antigen R (HuR) with mRNA and the effect of point mutations in impaired HuR-mRNA recognition".

Authors: Ankita Agarwal, Alagar Suresh, Shri Kant Kaushik and Ranjit Prasad Bahadur
https://doi.org/10.1080/07391102.2022.2073270

Abstract

Human antigen R (HuR) is a key regulatory protein with prominent roles in RNA metabolism and post-transcriptional gene regulation. Many studies have shown the involvement of HuR in plethora of human diseases, which are often manifestations of impaired HuR-RNA interactions. However, the inherent complexities of highly flexible protein-RNA interactions have limited our understanding of the structural basis of HuR-RNA recognition. In this study, we dissect the underlying molecular mechanism of interaction between N-terminal tandem RNA-recognition motifs (tRRMs) of HuR and mRNA using molecular dynamics simulation. We have also explored the effect of point mutations (T90A, R97A and R136A) of three reported critical residues in HuR-mRNA binding specificity. Our findings show that N-terminal tRRMs exhibit conformational stability upon RNA binding. We further show that R136A and R97A mutants significantly lose their binding affinity owing to the loss of critical interactions with mRNA. This may be attributed to the larger domain rearrangements in the mutant complexes, especially the β2β3 loops in both the tRRMs, leading to unfavourable conformations and loss of binding affinity. We have identified critical binding residues in tRRMs of HuR, contributing favourable binding energy in mRNA recognition. This study contributes significantly to understand the molecular mechanism of RNA recognition by tandem RRMs and provides a platform to modulate binding affinities through mutations. This may further guide in future structure-based drug-therapies targeting impaired HuR-RNA interactions.

(2022). Molecular insights into binding dynamics of tandem RNA recognition motifs (tRRMs) of human antigen R (HuR) with mRNA and the effect of point mutations in impaired HuR-mRNA recognition. Journal of Biomolecular Structure and Dynamics. Ahead of Print.

01/06/2022

❗️Congratulations❗️ 💐💐 Dr. Sunandan Mukharjee 💐💐
Alumni from Computational Structural Biology Lab, IIT Kharagpur, Dr. Sunandan Mukharjee has received funding under the scheme, organized by the Narodowe Centrum Nauki.
https://www.ncn.gov.pl/

Dr. Sunandan Mukherjee among the laureates of SONATA 17 scheme.
https://www.iimcb.gov.pl/en/press-office/news/highlights/1461-dr-sunandan-mukherjee-among-the-laureates-of-sonata-17-scheme

Dr. Sunandan Mukherjee has been awarded for the project entitled: "A framework for de novo modeling of RNA structures using restraints derived from experimental data "

A full list of awarded scientists can be found on the National Science Centre (NCN) website: https://bit.ly/3O2H5kL

11/03/2021

Have a look at our new publication in Biophysical Journal "Unusual RNA binding of FUS RRM studied by molecular dynamics simulation and enhanced sampling method"
Authors: Sushmita Basu, Suresh Alagar, Ranjit Prasad Bahadur.

https://doi.org/10.1016/j.bpj.2021.03.001

https://www.cell.com/biophysj/pdf/S0006-3495(21)00205-8.pdf

07/10/2020

Have a look at our new article "DSS1 allosterically regulates the conformation of the tower domain of BRCA2 that has dsDNA binding specificity for homologous recombination by Suresh Alagar and Ranjit Prasad Bahadur

Abstract

DSS1 is an evolutionary conserved, small intrinsically disordered protein that regulates various cellular functions. Although several studies have elucidated the role of DSS1 in stabilizing BRCA2 and its importance in homologous recombination repair (HRR), yet the structural mechanism behind the stability and HRR remains elusive. In this study, through molecular dynamics simulation, we show that DSS1 stabilizes linearly arranged DNA/DSS1 binding domains of BRCA2 with many native contacts. These contacts are absent in the complexes with two missense DSS1 mutants associated with germline breast cancer and somatic mouth carcinoma. Most importantly, our protein energy-based network models show DSS1 allosterically regulates the conformation of the distant tower domain of BRCA2 that has dsDNA binding specificity for HRR. We further postulate that the unique conformation of the tower domain with kinked-helices might be responsible for DNA strand invasion and initiation of HRR. Induced conformation of the tower domain by the kinked-helices is absent in the unbound BRCA2, as well as in the two mutant DSS1-BRCA2 complexes. This suggests that DSS1 allosterically regulates the tower domain conformations of BRCA2 that affects dsDNA binding, essential for HRR. Our results add a new dimension to the function of DSS1 and its role in regulating HRR.

DSS1 is an evolutionary conserved, small intrinsically disordered protein that regulates various cellular functions. Although several studies have elu…

20/08/2019

Have a peek at our new article
Residue conservation elucidates the evolution of r-proteins in ribosomal assembly and function
Smita P Pilla and Ranjit Prasad Bahadur

Ribosomes are the translational machineries having two unequal subunits, small subunit (SSU) and large subunit (LSU) across all the domains of life. Origin and evolution of ribosome are encoded in its structure, and the core of the ribosome is highly conserved. Here, we have used Shannon entropy to analyze the evolution of ribosomal proteins (r-proteins) across the three domains of life. Moreover, we have analyzed the residue conservation at protein-protein (PP) and protein-RNA (PR) interfaces in SSU and LSU. Furthermore, we have studied the evolution of early, intermediate and late binding r-proteins. We show that the r-proteins of Thermus thermophilus are better conserved during the evolution. Furthermore, we find the late binders are better conserved than the early and the intermediate binders. The residues at the interior of the r-proteins are the most conserved followed by those at the interface and the solvent accessible surface. Additionally, we show that the residues at the PP interfaces are better conserved than those at the PR interfaces. However, between PR and PP interfaces, the multi-interface residues at the former are better conserved than those at latter ones. Our findings may provide insights into the evolution of r-proteins in ribosomal assembly and its function.