Rate My Professor Arun Konagurthu

His research centers on computational biology and bioinformatics, employing statistical inference, information theory, algorithms, data structures, and combinatorial optimization to tackle challenges in biological data, particularly protein three-dimensional structures and amino acid sequences. Konagurthu leads the Laboratory for Computational Biology (LCB), which has produced open-source tools such as MUSTANG for multiple protein structural alignments, MMLigner and seqMMLigner for probabilistic alignments of protein structures and sequences using minimum message length inference, PhiSiCal for modeling joint distributions of backbone and sidechain dihedral angles, PhiSiCal-Checkup for validating protein structures, SST for secondary structure assignment, Proçodic for topological protein architectures, Super for screening oligopeptide fragments, Superpose3D for least-squares superposition, and MMLSUM for stochastic models of amino acid evolution. Key publications include "PhiSiCal-Checkup: A Bayesian framework to validate amino acid conformations within experimental protein structures" (Proceedings of the National Academy of Sciences, 2025), "Getting 'ϕψχal' with proteins: minimum message length inference of joint distributions of backbone and sidechain dihedral angles" (Bioinformatics, 2023), "Sequence and structure alignments in post-AlphaFold era" (Current Opinion in Structural Biology, 2023), "Bridging the gaps in statistical models of protein alignment" (Bioinformatics, 2022), and "On the reliability and the limits of inference of amino acid sequence alignments" (Bioinformatics, 2022). He supervises PhD students, several of whom have received awards and fellowships.