Pralay Mitra

@iitkgp.ac.in

Associate Professor in Computer Science and Engineering
Indian Institute of Technology Kharagpur



                    

https://researchid.co/pralaymitra

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science Applications, Software, Biotechnology, Molecular Biology

39

Scopus Publications

526

Scholar Citations

13

Scholar h-index

19

Scholar i10-index

Scopus Publications

  • The molecular prognostic score, a classifier for risk stratification of high-grade serous ovarian cancer
    Siddik Sarkar, Sarbar Ali Saha, Abhishek Swarnakar, Arnab Chakrabarty, Avipsa Dey, Poulomi Sarkar, Sarthak Banerjee, and Pralay Mitra
    Springer Science and Business Media LLC

  • Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence
    Suvendu Nandi, Soumyadeep Bhaduri, Debraj Das, Priya Ghosh, Mahitosh Mandal, and Pralay Mitra
    American Chemical Society (ACS)
    Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.



  • Genome surveillance of SARS-CoV-2 variants and their role in pathogenesis focusing on second wave of COVID-19 in India
    Poulomi Sarkar, Sarthak Banerjee, Sarbar Ali Saha, Pralay Mitra, and Siddik Sarkar
    Springer Science and Business Media LLC
    AbstractIndia had witnessed unprecedented surge in SARS-CoV-2 infections and its dire consequences during the second wave of COVID-19, but the detailed report of the epidemiological based spatiotemporal incidences of the disease is missing. In the manuscript, we have applied various statistical approaches (correlation, hierarchical clustering) to decipher the pattern of pathogenesis of the circulating VoCs responsible for surge in the incidences. B.1.617.1 (Kappa) was the predominant VoC during the early phase of the second wave, whereas, Delta (B.1.617.2) or Delta-like (AY.x) VoC constitutes majority ($$>90.17$$ > 90.17 %) of the cases during the peak of the second wave. The correlation plot of Delta/Delta-like lineage demonstrates inverse correlation with other lineages including B.1.617.1, B.1.1.7, B.1, B.1.36.29 and B.1.36. The spatiotemporal analysis shows that most of the Indian states were affected during the peak of the second wave due to the Delta surge, and fall under the same cluster. The second cluster populated mostly by north-eastern states and the islands of India were minimally affected. The presence of signature mutations (T478K, D950N, E156G) along with L452K, D614G and P681R within the spike protein of Delta or Delta-like might cause elevation in the host cell attachment, increased transmission and altered antigenicity which in due course of time has replaced the other circulating variants.The timely assessment of new VoCs including Delta-like will provide a rationale for updating the diagnostic, vaccine development by medical industries and decision making by various agencies including government, educational institutions, and corporate industries.

  • A novel computational predictive biological approach distinguishes Integrin β1 as a salient biomarker for breast cancer chemoresistance
    Subhayan Das, Moumita Kundu, Atif Hassan, Aditya Parekh, Bikash Ch. Jena, Swati Mundre, Indranil Banerjee, Rajesh Yetirajam, Chandan K. Das, Anjan K. Pradhan,et al.
    Elsevier BV


  • A sequence space search engine for computational protein design to modulate molecular functionality
    Ayush Malik, Anupam Banerjee, Abantika Pal, and Pralay Mitra
    Informa UK Limited
    Abstract De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds. Communicated by Ramaswamy H. Sarma

  • Therapeutic targeting of RBPJ, an upstream regulator of ETV6 gene, abrogates ETV6-NTRK3 fusion gene transformations in glioblastoma
    Angana Biswas, Yetirajam Rajesh, Subhayan Das, Indranil Banerjee, Neelkamal Kapoor, Pralay Mitra, and Mahitosh Mandal
    Elsevier BV


  • Modularity-based parallel protein design algorithm with an implementation using shared memory programming
    Abantika Pal, Rohith Mulumudy, and Pralay Mitra
    Wiley
    AbstractGiven a target protein structure, the prime objective of protein design is to find amino acid sequences that will fold/acquire to the given three‐dimensional structure. The protein design problem belongs to the non‐deterministic polynomial‐time‐hard class as sequence search space increases exponentially with protein length. To ensure better search space exploration and faster convergence, we propose a protein modularity–based parallel protein design algorithm. The modular architecture of the protein structure is exploited by considering an intermediate structural organization between secondary structure and domain defined as protein unit (PU). Here, we have incorporated a divide‐and‐conquer approach where a protein is split into PUs and each PU region is explored in a parallel fashion. It has been further analyzed that our shared memory implementation of modularity‐based parallel sequence search leads to better search space exploration compared to the case of traditional full protein design. Sequence‐based analysis on design sequences depicts an average of 39.7% sequence similarity on the benchmark data set. Structure‐based comparison of the modeled structures of the design protein with the target structure exhibited an average root‐mean‐square deviation of 1.17 Å and an average template modeling score of 0.89. The selected modeled structures of the design protein sequences are validated using 100 ns molecular dynamics simulations where 80% of the proteins have shown better or similar stability to the respective target proteins. Our study informs that our modularity‐based protein design algorithm can be extended to protein interaction design as well.

  • Human prion protein: exploring the thermodynamic stability and structural dynamics of its pathogenic mutants
    Puspita Halder and Pralay Mitra
    Informa UK Limited
    Abstract Human familial prion diseases are known to be associated with different single-point mutants of the gene coding for prion protein with a primary focus at several locations of the globular domain. We have identified 12 different single-point pathogenic mutants of human prion protein (HuPrP) with the help of extensive perturbations/mutation technique at multiple locations of HuPrP sequence related to potentiality towards conformational disorders. Among these, some of the mutants include pathogenic variants that corroborate well with the literature reported proteins while majority include some unique single-point mutants that are either not explicitly studied early or studied for variants with different residues at the specific position. Primarily, our study sheds light on the unfolding mechanism of the above mentioned mutants in depth. Besides, we could identify some mutants under investigation that demonstrates not only unfolding of the helical structures but also extension and generation of the β-sheet structures and or simultaneously have highly exposed hydrophobic surface which is assumed to be linked with the production of aggregate/fibril structures of the prion protein. Among the identified mutants, Q212E needs special attention due to its maximum exposure of hydrophobic core towards solvent and E200Q is found to be important due to its maximum extent of β-content. We are also able to identify different respective structural conformations of the proteins according to their degree of structural unfolding and those conformations can be extracted and further studied in detail. Communicated by Ramaswamy H. Sarma

  • A computational framework for modeling functional protein-protein interactions
    Abantika Pal, Debnath Pal, and Pralay Mitra
    Wiley
    AbstractProtein interactions and their assemblies assist in understanding the cellular mechanisms through the knowledge of interactome. Despite recent advances, a vast number of interacting protein complexes is not annotated by three‐dimensional structures. Therefore, a computational framework is a suitable alternative to fill the large gap between identified interactions and the interactions with known structures. In this work, we develop an automated computational framework for modeling functionally related protein‐complex structures utilizing GO‐based semantic similarity technique and co‐evolutionary information of the interaction sites. The framework can consider protein sequence and structure information as input and employ both rigid‐body docking and template‐based modeling exploiting the existing structural templates and sequence homology information from the PDB. Our framework combines geometric as well as physicochemical features for re‐ranking the docking decoys. The proposed framework has an 83% success rate when tested on a benchmark dataset while considering Top1 models for template‐based modeling and Top10 models for the docking pipeline. We believe that our computational framework can be used for any pair of proteins with higher confidence to identify the functional protein‐protein interactions.


  • High-Performance Whole-Cell Simulation Exploiting Modular Cell Biology Principles
    Barnali Das and Pralay Mitra
    American Chemical Society (ACS)
    One of the grand challenges of this century is modeling and simulating a whole cell. Extreme regulation of an extensive quantity of model and simulation data during whole-cell modeling and simulation renders it a computationally expensive research problem in systems biology. In this article, we present a high-performance whole-cell simulation exploiting modular cell biology principles. We prepare the simulation by dividing the unicellular bacterium, Escherichia coli (E. coli), into subcells utilizing the spatially localized densely connected protein clusters/modules. We set up a Brownian dynamics-based parallel whole-cell simulation framework by utilizing the Hamiltonian mechanics-based equations of motion. Though the velocity Verlet integration algorithm possesses the capability of solving the equations of motion, it lacks the ability to capture and deal with particle-collision scenarios. Hence, we propose an algorithm for detecting and resolving both elastic and inelastic collisions and subsequently modify the velocity Verlet integrator by incorporating our algorithm into it. Also, we address the boundary conditions to arrest the molecules' motion outside the subcell. For efficiency, we define one hashing-based data structure called the cellular dictionary to store all of the subcell-related information. A benchmark analysis of our CUDA C/C++ simulation code when tested on E. coli using the CPU-GPU cluster indicates that the computational time requirement decreases with the increase in the number of computing cores and becomes stable at around 128 cores. Additional testing on higher organisms such as rats and humans informs us that our proposed work can be extended to any organism and is scalable for high-end CPU-GPU clusters.

  • An Evolutionary Profile Guided Greedy Parallel Replica-Exchange Monte Carlo Search Algorithm for Rapid Convergence in Protein Design
    Anupam Banerjee, Kuntal Pal, and Pralay Mitra
    Institute of Electrical and Electronics Engineers (IEEE)
    Protein design, also known as the inverse protein folding problem, is the identification of a protein sequence that folds into a target protein structure. Protein design is proved as an NP-hard problem. While researchers are working on designing heuristics with an emphasis on new scoring functions, we propose a replica-exchange Monte Carlo (REMC) search algorithm that ensures faster convergence using a greedy strategy. Using biological insights, we construct an evolutionary profile to encode the amino acid variability in different positions of the target protein from its structural homologs. The evolutionary profile guides the REMC search, and the greedy approach confirms appreciable exploration and exploitation of the sequence-structure fitness surface. We allow termination of a simulation trajectory once stagnant situation is detected. A series of sequence and structure level validations establish the goodness of our design. On a benchmark dataset, our algorithm reports an average root-mean-square deviation of 1.21Å between the target and the design proteins when modeled with an existing protein folding software. Besides, our algorithm assures 6.16 times overall speedup. In Molecular Dynamics simulations, we observe that four out of selected five design proteins report better to comparable stability to the corresponding target proteins.

  • Estimating Change in Foldability Due to Multipoint Deletions in Protein Structures
    Anupam Banerjee, Amit Kumar, Kushal Kanti Ghosh, and Pralay Mitra
    American Chemical Society (ACS)
    Insertions/deletions of amino acids in the protein backbone potentially result in altered structural/functional specifications. They can either contribute positively to the evolutionary process or can result in disease conditions. Despite being the second most prevalent form of protein modification, there are no databases or computational frameworks that delineate harmful multipoint deletions (MPD) from beneficial ones. We introduce a positive unlabeled learning-based prediction framework (PROFOUND) that utilizes fold-level attributes, environment-specific properties, and deletion site-specific properties to predict the change in foldability arising from such MPDs, both in the non-loop and loop regions of protein structures. In the absence of any protein structure dataset to study MPDs, we introduce a dataset with 153 MPD instances that lead to native-like folded structures and 7650 unlabeled MPD instances whose effect on the foldability of the corresponding proteins is unknown. PROFOUND on 10-fold cross-validation on our newly introduced dataset reports a recall of 82.2% (86.6%) and a fall out rate (FR) of 14.2% (20.6%), corresponding to MPDs in the protein loop (non-loop) region. The low FR suggests that the foldability in proteins subject to MPDs is not random and necessitates unique specifications of the deleted region. In addition, we find that additional evolutionary attributes contribute to higher recall and lower FR. The first of a kind foldability prediction system owing to MPD instances and the newly introduced dataset will potentially aid in novel protein engineering endeavors.

  • Ebola Virus VP35 Protein: Modeling of the Tetrameric Structure and an Analysis of Its Interaction with Human PKR
    Anupam Banerjee and Pralay Mitra
    American Chemical Society (ACS)
    The Viral Protein 35 (VP35), a crucial protein of the Zaire Ebolavirus (EBOV), interacts with a plethora of human proteins to cripple the human immune system. Despite its importance, the entire structure of the tetrameric assembly of EBOV VP35 and the means by which it antagonizes the autophosphorylation of the kinase domain of human protein kinase R (PKRK) is still elusive. We consult existing structural information to model a tetrameric assembly of the VP35 protein where 93% of the protein is modeled using crystal structure templates. We analyze our modeled tetrameric structure to identify interchain bonding networks and use molecular dynamics simulations and normal-mode analysis to unravel the flexibility and deformability of the different regions of the VP35 protein. We establish that the C-terminal of VP35 (VP35C) directly interacts with PKRK to prevent it from autophosphorylation. Further, we identify three plausible VP35C–PKRK complexes with better affinity than the PKRK dimer formed during autophosphorylation and use protein design to establish a new stretch in VP35C that interacts with PKRK. The proposed tetrameric assembly will aid in better understanding of the VP35 protein, and the reported VP35C–PKRK complexes along with their interacting sites will help in the shortlisting of small molecule inhibitors.


  • Estimating the Effect of Single-Point Mutations on Protein Thermodynamic Stability and Analyzing the Mutation Landscape of the p53 Protein
    Anupam Banerjee and Pralay Mitra
    American Chemical Society (ACS)
    Nonsynonymous Single Nucleotide Polymorphisms often result in altered protein stability while playing crucial roles both in the evolution process and in the development of human diseases. Prediction of change in thermodynamic stability due to such missense mutations will help in protein engineering endeavors and will contribute to a better understanding of different disease conditions. Here, we develop a machine learning-based framework viz., ProTSPoM to estimate the change in protein thermodynamic stability arising out of single point mutations (SPMs). ProTSPoM outperforms existing methods on the S2698 and S1925 databases and reports a Pearson correlation coefficient of 0.82 (0.88) and a root-mean-squared-error of 0.92 (1.06) kcal/mol between the predicted and experimental ∆∆G values for the long-established S350 (tumor suppressor p53 protein) dataset. Further, we estimate the change in thermodynamic stability for all possible SPMs in the DNA binding domain of the p53 protein. We identify SNPs in p53 which are plausibly detrimental to its structural integrity and interaction affinity with the DNA molecule. ProTSPoM with its reliable estimates and time-efficient prediction is well suited to be integrated with existing protein engineering techniques. The ProTSPoM web server is accessible at http://cosmos.iitkgp.ac.in/ProTSPoM/.

  • Boosting phosphorylation site prediction with sequence feature-based machine learning
    Shyantani Maiti, Atif Hassan, and Pralay Mitra
    Wiley
    AbstractProtein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM‐based computational approach that uses evolutionary, geometric, sequence environment, and amino acid‐specific features to decipher phosphate binding sites from a protein sequence. Our method, while compared with other existing methods on 2429 protein sequences taken from standard Phospho.ELM (P.ELM) benchmark data set featuring 11 organisms reports a higher F1 score = 0.504 (harmonic mean of the precision and recall) and ROC AUC = 0.836 (area under the curve of the receiver operating characteristics). The computation time of our proposed approach is much less than that of the recently developed deep learning‐based framework. Structural analysis on selected protein sequences informs that our prediction is the superset of the phosphorylation sites, as mentioned in P.ELM data set. The foundation of our scheme is manual feature engineering and a decision tree‐based classification. Hence, it is intuitive, and one can interpret the final tree as a set of rules resulting in a deeper understanding of the relationships between biophysical features and phosphorylation sites. Our innovative problem transformation method permits more control over precision and recall as is demonstrated by the fact that if we incorporate output probability of the existing deep learning framework as an additional feature, then our prediction improves (F1 score = 0.546; ROC AUC = 0.849). The implementation of our method can be accessed at http://cse.iitkgp.ac.in/~pralay/resources/PPSBoost/ and is mirrored at https://cosmos.iitkgp.ac.in/PPSBoost.

  • Delineation of crosstalk between HSP27 and MMP-2/MMP-9: A synergistic therapeutic avenue for glioblastoma management
    Y. Rajesh, Anupam Banerjee, Ipsita Pal, Angana Biswas, Subhayan Das, Kaushik Kumar Dey, Neelkamal Kapoor, Ananta Kumar Ghosh, Pralay Mitra, and Mahitosh Mandal
    Elsevier BV

  • Analyzing Change in Protein Stability Associated with Single Point Deletions in a Newly Defined Protein Structure Database
    Anupam Banerjee, Yaakov Levy, and Pralay Mitra
    American Chemical Society (ACS)
    Protein backbone alternation due to insertion/deletion or mutation operation often results in a change of fundamental biophysical properties of proteins. The proposed work intends to encode the protein stability changes associated with single point deletions (SPDs) of amino acids in proteins. The encoding will help in the primary screening of detrimental backbone modifications before opting for expensive in vitro experimentations. In the absence of any benchmark database documenting SPDs, we curate a data set containing SPDs that lead to both folded conformations and unfolded state. We differentiate these SPD instances with the help of simple structural and physicochemical features and eventually classify the foldability resulting out of SPDs using a Random Forest classifier and an Elliptic Envelope based outlier detector. Adhering to leave one out cross validation, the accuracy of the Random Forest classifier and the Elliptic Envelope is of 99.4% and 98.1%, respectively. The newly defined database and the delineation of SPD instances based on its resulting foldability provide a head start toward finding a solution to the given problem.

  • Changing the Apoptosis Pathway through Evolutionary Protein Design
    David Shultis, Pralay Mitra, Xiaoqiang Huang, Jarrett Johnson, Naureen Aslam Khattak, Felicia Gray, Clint Piper, Jeff Czajka, Logan Hansen, Bingbing Wan,et al.
    Elsevier BV

  • A network-based zoning for parallel whole-cell simulation
    Barnali Das, Abhijeet Rajendra Patil, and Pralay Mitra
    Oxford University Press (OUP)
    Abstract Motivation In Computational Cell Biology, whole-cell modeling and simulation is an absolute requirement to analyze and explore the cell of an organism. Despite few individual efforts on modeling, the prime obstacle hindering its development and progress is its compute-intensive nature. Towards this end, little knowledge is available on how to reduce the enormous computational overhead and which computational systems will be of use. Results In this article, we present a network-based zoning approach that could potentially be utilized in the parallelization of whole-cell simulations. Firstly, we construct the protein–protein interaction graph of the whole-cell of an organism using experimental data from various sources. Based on protein interaction information, we predict protein locality and allocate confidence score to the interactions accordingly. We then identify the modules of strictly localized interacting proteins by performing interaction graph clustering based on the confidence score of the interactions. By applying this method to Escherichia coli K12, we identified 188 spatially localized clusters. After a thorough Gene Ontology-based analysis, we proved that the clusters are also in functional proximity. We then conducted Principal Coordinates Analysis to predict the spatial distribution of the clusters in the simulation space. Our automated computational techniques can partition the entire simulation space (cell) into simulation sub-cells. Each of these sub-cells can be simulated on separate computing units of the High-Performance Computing (HPC) systems. We benchmarked our method using proteins. However, our method can be extended easily to add other cellular components like DNA, RNA and metabolites. Availability and implementation   Supplementary information Supplementary data are available at Bioinformatics online.

RECENT SCHOLAR PUBLICATIONS

  • DeepPROTECTNeo: A Deep learning-based Personalized and RV-guided Optimization tool for TCR Epitope interaction using Context-aware Transformers
    D Das, S Bhaduri, A Pramanick, P Mitra
    bioRxiv, 2025.01. 04.631301 2025

  • E (Q) AGNN-PPIS: Attention Enhanced Equivariant Graph Neural Network for Protein-Protein Interaction Site Prediction
    Animesh, R Suvvada, PK Bhowmick, P Mitra
    bioRxiv, 2024.10. 06.616807 2024

  • rpcFold: residual parallel convolutional neural network to decipher RNA folding from RNA sequence
    N Sharma, P Mitra
    bioRxiv, 2024.08. 26.609824 2024

  • The molecular prognostic score, a classifier for risk stratification of high-grade serous ovarian cancer
    S Sarkar, SA Saha, A Swarnakar, A Chakrabarty, A Dey, P Sarkar, ...
    Journal of Ovarian Research 17 (1), 159 2024

  • Deciphering the lexicon of protein targets: a review on multifaceted drug discovery in the era of artificial intelligence
    S Nandi, S Bhaduri, D Das, P Ghosh, M Mandal, P Mitra
    Molecular Pharmaceutics 21 (4), 1563-1590 2024

  • MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction
    S Ghosh, P Mitra
    Computer Methods and Programs in Biomedicine 244, 107955 2024

  • Parsers, Data Structures, and Algorithms for Macromolecular Analysis Toolkit (MAT): Design and Implementation
    G Kalyan, V Junghare, P Mitra, A Chattopadhyay, S Hazra
    ESS Open Archive eprints 542, 54234804 2024

  • Deep Reinforcement Learning in Healthcare and Biomedical Research
    S Agrawal, P Mitra
    Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real‐World 2024

  • A novel computational predictive biological approach distinguishes Integrin β1 as a salient biomarker for breast cancer chemoresistance
    S Das, M Kundu, A Hassan, A Parekh, BC Jena, S Mundre, I Banerjee, ...
    Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 1869 (6), 166702 2023

  • A sequence space search engine for computational protein design to modulate molecular functionality
    A Malik, A Banerjee, A Pal, P Mitra
    Journal of Biomolecular Structure and Dynamics 41 (7), 2937-2946 2023

  • ProFuMCell and ProModb: Web services for analyzing interaction-based functionally localized protein modules in a cell (vol 28, 167, 2022)
    B Das, P Mitra
    JOURNAL OF MOLECULAR MODELING 29 (5) 2023

  • Correction to: ProFuMCell and ProModb: Web services for analyzing interaction‑based functionally localized protein modules in a cell
    B Das, P Mitra
    Journal of Molecular Modeling 29 (5), 148 2023

  • Genome surveillance of SARS-CoV-2 variants and their role in pathogenesis focusing on second wave of COVID-19 in India
    P Sarkar, S Banerjee, SA Saha, P Mitra, S Sarkar
    Scientific Reports 13 (1), 4692 2023

  • Human prion protein: exploring the thermodynamic stability and structural dynamics of its pathogenic mutants
    P Halder, P Mitra
    Journal of Biomolecular Structure and Dynamics 40 (21), 11274-11290 2022

  • Therapeutic targeting of RBPJ, an upstream regulator of ETV6 gene, abrogates ETV6-NTRK3 fusion gene transformations in glioblastoma
    A Biswas, Y Rajesh, S Das, I Banerjee, N Kapoor, P Mitra, M Mandal
    Cancer Letters 544, 215811 2022

  • ProFuMCell and ProModb: web services for analyzing interaction-based functionally localized protein modules in a cell
    B Das, P Mitra
    Journal of Molecular Modeling 28 (6), 167 2022

  • Modularity‐based parallel protein design algorithm with an implementation using shared memory programming
    A Pal, R Mulumudy, P Mitra
    Proteins: Structure, Function, and Bioinformatics 90 (3), 658-669 2022

  • A computational framework for modeling functional protein‐protein interactions
    A Pal, D Pal, P Mitra
    Proteins: Structure, Function, and Bioinformatics 89 (10), 1353-1364 2021

  • Deciphering biological evolution exploiting the topology of Protein Locality Graph
    B Das, P Mitra
    bioRxiv, 2021.06. 03.446976 2021

  • High-performance whole-cell simulation exploiting modular cell biology principles
    B Das, P Mitra
    Journal of Chemical Information and Modeling 61 (3), 1481-1492 2021

MOST CITED SCHOLAR PUBLICATIONS

  • EvoDesign: de novo protein design based on structural and evolutionary profiles
    P Mitra, D Shultis, Y Zhang
    Nucleic acids research 41 (W1), W273-W280 2013
    Citations: 61

  • An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis
    P Mitra, D Shultis, JR Brender, J Czajka, D Marsh, F Gray, T Cierpicki, ...
    PLoS computational biology 9 (10), e1003298 2013
    Citations: 50

  • How many protein-protein interactions types exist in nature?
    L Garma, S Mukherjee, P Mitra, Y Zhang
    PLoS One 7 (6), e38913 2012
    Citations: 40

  • Combining Bayes classification and point group symmetry under Boolean framework for enhanced protein quaternary structure inference
    P Mitra, D Pal
    Structure 19 (3), 304-312 2011
    Citations: 37

  • Delineation of crosstalk between HSP27 and MMP-2/MMP-9: A synergistic therapeutic avenue for glioblastoma management
    Y Rajesh, A Banerjee, I Pal, A Biswas, S Das, KK Dey, N Kapoor, ...
    Biochimica et Biophysica Acta (BBA)-General Subjects 1863 (7), 1196-1209 2019
    Citations: 36

  • New measures for estimating surface complementarity and packing at protein–protein interfaces
    P Mitra, D Pal
    FEBS letters 584 (6), 1163-1168 2010
    Citations: 31

  • ETV6 gene aberrations in non-haematological malignancies: a review highlighting ETV6 associated fusion genes in solid tumors
    A Biswas, Y Rajesh, P Mitra, M Mandal
    Biochimica et Biophysica Acta (BBA)-Reviews on Cancer 1874 (1), 188389 2020
    Citations: 27

  • Changing the apoptosis pathway through evolutionary protein design
    D Shultis, P Mitra, X Huang, J Johnson, NA Khattak, F Gray, C Piper, ...
    Journal of molecular biology 431 (4), 825-841 2019
    Citations: 21

  • Estimating the effect of single-point mutations on protein thermodynamic stability and analyzing the mutation landscape of the p53 protein
    A Banerjee, P Mitra
    Journal of chemical information and modeling 60 (6), 3315-3323 2020
    Citations: 20

  • PRUNE and PROBE—two modular web services for protein–protein docking
    P Mitra, D Pal
    Nucleic acids research 39 (suppl_2), W229-W234 2011
    Citations: 18

  • Using correlated parameters for improved ranking of protein–protein docking decoys
    P Mitra, D Pal
    Journal of Computational Chemistry 32 (5), 787-796 2011
    Citations: 17

  • Boosting phosphorylation site prediction with sequence feature‐based machine learning
    S Maiti, A Hassan, P Mitra
    Proteins: Structure, Function, and Bioinformatics 88 (2), 284-291 2020
    Citations: 16

  • Genome surveillance of SARS-CoV-2 variants and their role in pathogenesis focusing on second wave of COVID-19 in India
    P Sarkar, S Banerjee, SA Saha, P Mitra, S Sarkar
    Scientific Reports 13 (1), 4692 2023
    Citations: 13

  • Analyzing change in protein stability associated with single point deletions in a newly defined protein structure database
    A Banerjee, Y Levy, P Mitra
    Journal of proteome research 18 (3), 1402-1410 2019
    Citations: 13

  • MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction
    S Ghosh, P Mitra
    Computer Methods and Programs in Biomedicine 244, 107955 2024
    Citations: 11

  • Deciphering the lexicon of protein targets: a review on multifaceted drug discovery in the era of artificial intelligence
    S Nandi, S Bhaduri, D Das, P Ghosh, M Mandal, P Mitra
    Molecular Pharmaceutics 21 (4), 1563-1590 2024
    Citations: 10

  • High-performance whole-cell simulation exploiting modular cell biology principles
    B Das, P Mitra
    Journal of Chemical Information and Modeling 61 (3), 1481-1492 2021
    Citations: 10

  • Ebola virus VP35 protein: modeling of the tetrameric structure and an analysis of its interaction with human PKR
    A Banerjee, P Mitra
    Journal of Proteome Research 19 (11), 4533-4542 2020
    Citations: 10

  • Ebolavirus interferon antagonists—protein interaction perspectives to combat pathogenesis
    A Banerjee, A Pal, D Pal, P Mitra
    Briefings in Functional Genomics 17 (6), 392-401 2018
    Citations: 10

  • A novel computational predictive biological approach distinguishes Integrin β1 as a salient biomarker for breast cancer chemoresistance
    S Das, M Kundu, A Hassan, A Parekh, BC Jena, S Mundre, I Banerjee, ...
    Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 1869 (6), 166702 2023
    Citations: 9