Biology subjects

Rajagopal, G.

Publications and source records attributed to Rajagopal, G..

5 recordsLinked to original sources

bioRxiv2026

ProteinDock: A physics-informed layer to improve protein-protein docking reliability

Computational modeling provides geometric insight into protein-protein interactions without requiring the resources of experimentation. However, reliability can be hindered when modeling proteins with distinctive features, such as antibodies, that use flexible, polar-rich loops to bind antigens. We developed ProteinDock, a physics-based tool that can be used in combination with leading modeling programs to improve the reliability of protein-protein docking; this work provides a case study of antibody-antigen interfaces. ProteinDock was layered onto Rosetta for docking unbound experimentally determined structures, and when evaluated on Docking Benchmark Set 5.5, generated CAPRI acceptable-quality or better for 80.2% of targets, an improvement of 32.8 percentage points over vanilla Rosettas 47.4% on the same dataset. To improve protein-protein prediction reliability from sequence inputs, we demonstrate that a truncated version of ProteinDock can be used to choose the optimal prediction among outputs from multiple deep learning-based tools. We show that this strategy is a computationally efficient alternative to increasing the seed quantity for deep-learning predictions. A graphical user interface for layering ProteinDock has been created and is available at https://github.com/Kimmel-Lab/proteindock and https://proteindock.com/. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/739238v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1547128org.highwire.dtl.DTLVardef@d1364corg.highwire.dtl.DTLVardef@143dcedorg.highwire.dtl.DTLVardef@5d5ba4_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

CovSite: A High-Throughput Blind Covalent Screening Framework for Reactive Site Detection

Targeted covalent inhibitors are a powerful, yet underexplored, class of therapeutics, and current computational covalent screeners are constrained in early drug discovery due to the need for prior knowledge of the target site and limited throughput. We present CovSite, a blind covalent screening tool that identifies candidate reactive residues across the entire protein surface, utilizing only the protein structure and electrophile SMILES. CovSite applies a pipeline of four orthogonal physicochemical filters (nucleophile identification, solvent accessibility, environment-dependent deprotonation prediction, and semi-quantum-mechanical reactivity ranking) to identify potential small-molecule candidate inhibitors. Validated against 2,062 diverse covalent protein-ligand complexes spanning six nucleophilic residue types, CovSite achieves a 98.5% blind target site hit on a held-out benchmark set of 207 cysteine-targeted complexes while reducing the search space by 97.8%. The target-site hit detection exceeds the 53-62% accuracy of popular covalent screening tools operating under non-blind conditions on the same benchmark set. By extending nucleophilic coverage beyond cysteine to include serine, threonine, lysine, histidine, and tyrosine, and completing a screening of a 200-residue protein in two to three minutes on standard hardware, CovSite serves as a platform technology with the potential to address critical gaps in throughput, generalizability, and accuracy in this field of covalent screening. We demonstrate this capability by using CovSite as a blind, ligand-specific approach that enables iterative, machine-learning-driven covalent inhibitor generation that is impractical with existing tools, establishing a foundation for computationally guided covalent drug discovery for novel and understudied targets. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/739288v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@139335forg.highwire.dtl.DTLVardef@5bd9feorg.highwire.dtl.DTLVardef@44d6c8org.highwire.dtl.DTLVardef@1710154_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

Imagined Speech Reconstruction with 3D Neural Metabolism and Large Language Model Integration

Cognitive linguistics posits that language underpins human thought, and this principle has influenced the study and development of large language models (LLMs). In particular, several studies have investigated the metabolic costs of sentence formation using neuroimaging techniques such as positron emission tomography, functional magnetic resonance imaging, electroencephalography (EEG), and imagined speech reconstruction (ISR). In this study, EEG data corresponding to imagined English-language speech phonemes were used for ISR, in combination with an LLM trained on an abridged autobiography. The LLM-generated text responses guided the synthesis of EEG data from relevant phonemes, which were then used to estimate corresponding metabolic activity, and the changes in simulated neurometabolic and electrical parameters were visually represented. Notably, introducing pseudorandom variance significantly (p < 0.001) enhanced the models ability to reflect biological variability. Future directions include expanding the ISR system with lightweight or locally run LLMs, incorporating training data from larger and more diverse populations, and utilizing truly random variability sources. Further optimization for broader hardware compatibility and implementation--such as neural phantoms, emotional context integration, or human-computer interaction platforms--offer promising pathways for advancement. Overall, this work establishes a foundation for the next generation of biologically inspired, modular, and adaptable ISR systems for both research and practical applications.

neuroscience↗

bioRxiv2020

Precision genetic cellular models identify therapies protective against endoplasmic reticulum stress

Congenital disorders of glycosylation (CDG) and deglycosylation (CDDG) are a collection of rare pediatric disorders with symptoms that range from mild to life threatening. They typically affect multiple organ systems and usually present with neurological abnormalities including hypotonia, cognitive impairment, and intractable seizures. Several genes have been implicated in the thirty-six types of CDG, but currently NGLY1 is the only known CDDG gene. A common biological mechanism among CDG types and in CDDG is endoplasmic reticulum (ER) stress. Here, we develop two isogenic human cellular models of CDG (PMM2, the most prevalent type of CDG, and DPAGT1) and of the only CDDG (NGLY1) in an effort to identify drugs that can alleviate ER stress. Systematic phenotyping identified elevated ER stress and autophagy levels among other cellular and morphological phenotypes in each of the cellular models. We screened a complex drug library for compounds able to correct aberrant morphological phenotypes in each of the models using an agnostic phenotypic cell painting assay based on >300 cellular features. The image-based screen identified multiple candidate compounds able to correct aberrant morphology, and we show a subset of these are able to correct cellular and molecular defects in each of the models. These results provide new directions for the treatment of rare diseases of glycosylation and deglycosylation and a framework for new drug screening paradigms for more common neurodegenerative diseases characterized by ER stress. Summary sentenceNovel drug screening modality identifies compounds that correct aberrant molecular phenotypes in precision cellular models of glycosylation defects.

neuroscience↗

bioRxiv2017

Homology-based hydrogen bond information improves crystallographic structures in the PDB

Crystallographic structure models in the Protein Data Bank (PDB) are optimized against the crystal diffraction data and geometrical restraints. This process of crystallographic refinement typically ignored hydrogen bond (H-bond) distances as a source of information. However, H-bond restraints can improve structures, especially at low resolution where diffraction data are limited. To improve low-resolution structure refinement, we present methods for deriving H-bond information either globally from well-refined high-resolution structures from the PDB-REDO databank, or specifically from on-the-fly constructed sets of homologous high-resolution structures. Refinement incorporating HOmology DErived Restraints (HODER), improves geometrical quality and the fit to the diffraction data for many low-resolution structures. Using approximately 60 years of CPU-time in massively parallel computing, we constructed a new instance of the PDB-REDO databank, a novel resource to help biologists gain insight on protein families or on specific structures, as we demonstrate with examples.

biochemistry↗