Dr Arun Prasad Pandurangan is a computational structural biologist whose research over more than two decades has advanced computational approaches for understanding protein structure, molecular recognition, and the functional consequences of genetic variation.
Dr Pandurangan is a Teaching Fellow at Gonville and Caius College, University of Cambridge, and a former Research Assistant Professor at the University of Cambridge. He currently serves as Visiting Faculty at the Institute for Protein Research, Osaka University. Over the course of his career, he has held research positions at several leading institutions, including Birkbeck, University of London / University College London (UCL), the MRC Laboratory of Molecular Biology (LMB) in Cambridge, and the European Bioinformatics Institute (EMBL-EBI).
Dr Pandurangan’s early research introduced innovative sampling strategies based on Mutually Orthogonal Latin Squares, enabling efficient ab initio protein–ligand docking at a time when exhaustive conformational exploration represented a major computational challenge. These approaches helped lay conceptual foundations for later ensemble-based docking strategies widely used in structural modelling and structure-based drug discovery. A major strand of his work focuses on cryo-electron microscopy (cryo-EM) modelling. Beginning well before the cryo-EM resolution revolution, when structural interpretation from low-resolution density maps posed substantial challenges, he developed several computational tools that facilitated the modelling of large macromolecular assemblies. These include RIBFIND for identifying rigid-body domains, flexible-fitting approaches for multi-subunit complexes, and γ-TEMPy, a genetic-algorithm framework that enables the simultaneous fitting of multiple protein subunits into cryo-EM density maps. Together, these contributions have helped advance integrative modelling strategies for complex biomolecular systems.
Dr Pandurangan leads the development of SDM, a mutation-stability prediction method based on statistical potentials that evaluates the effects of amino acid substitutions on protein stability. As a framework independent of machine-learning-based predictors, SDM provides a complementary and widely used approach for interpreting genetic variation in studies of disease-associated mutations, protein engineering, and molecular evolution.
In addition to method development, he has contributed extensively to proteome-scale structural annotation. His work on SUPERFAMILY 2.0 and subsequent InterPro releases improved the integration of structural domain information and functional classification across genomes, supporting large-scale comparative genomics and evolutionary analyses.
More recently, Dr Pandurangan has applied structure-informed computational approaches to antimicrobial resistance, integrating comparative genomics, mutation-effect modelling, and structural analysis to elucidate mechanisms of resistance in Mycobacterium and other clinically important pathogens. He has developed several freely available computational tools and structural annotation resources, including large-scale databases for proteome-wide domain analysis, and leads interdisciplinary collaborations that combine structural bioinformatics with emerging artificial intelligence and machine learning approaches to advance understanding of molecular mechanisms underlying health and disease.
In recognition of his scientific contributions and engagement with the wider scientific community, Dr Pandurangan has been elected a Fellow of the Royal Society of Biology and a Fellow of the Linnean Society of London.