Jitin Singla

The paper shows a sequence-only pipeline using TabPFN achieves high accuracy in multi-activity antimicrobial peptide profiling, outperforming complex deep models.

This study uses 3D morphometry to show how mitochondrial structure, density, and location change in response to metabolic stimuli in INS-1E cells.

The paper introduces MitoXRNet, a deep learning model for 3D segmentation of mitochondria and nuclei from soft x-ray tomography data, showing improved performance and enabling analysis of mitochondrial morphology changes under different conditions.

This study demonstrates EstS1 esterase's ability to degrade PET plastic and its intermediate BHET, supported by kinetic, structural, and morphological analyses.

This study identifies and characterizes the bifunctional PFP enzyme in CLas, showing its ability to catalyze both F6P to FBP and S7P to SBP reactions.

This paper develops an in silico affinity maturation pipeline to enhance nanobody binding to the SARS-CoV-2 Omicron RBD, achieving three-fold improved affinity through computational and experimental validation.

This study characterizes the structural and mechanistic properties of EstS1 esterase, revealing its role in degrading phthalate diesters through a bi-bi ping-pong mechanism and covalent substrate binding.

This study enhances the affinity of SARS-CoV-2 neutralizing nanobodies through structure-guided mutations in CDRs, resulting in improved neutralization activity against pseudovirus.

This study evaluates 15 scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography, identifying two robust methods that work across varying signal-to-noise ratios.