Biology subjects

Gromiha, M. M.

Publications and source records attributed to Gromiha, M. M..

3 recordsLinked to original sources

bioRxiv2026

Amylo-Pipe: an integrated web server for mechanistic and kinetic prediction of protein and peptide aggregation

Protein aggregation is central to amyloid-related disorders and remains a major developability challenge for protein therapeutics. Over the past two decades, significant advances have been made to predict aggregation-prone regions (APRs) and estimate aggregation propensity in proteins and peptides. In contrast, the prediction of aggregation kinetics has received relatively less attention due to the limited availability and heterogeneity of experimental data. Consequently, aggregation propensities from APR prediction algorithms were widely accepted as a means to predict relative changes in the aggregation kinetics of proteins and mutants. Previous studies have demonstrated, using large-scale datasets, that aggregation propensity shows a weak or inconsistent correlation with aggregation kinetics. In the present study, we have integrated complementary state-of-the-art mechanistic and kinetic prediction tools for protein aggregation into a unified, user-friendly web framework entitled "Amylo-Pipe". Amylo-Pipe also implements practical features that are especially useful for protein engineering, such as gatekeeper-residue mutational scanning to support the design of aggregation-resistant variants. By consolidating multiple prediction tasks in a single interface, Amylo-Pipe enables a more comprehensive assessment of aggregation behavior than APR-only workflows. The web server is freely accessible at: https://web.iitm.ac.in/bioinfo2/amylopipe/.

bioinformatics↗

bioRxiv2025

Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16

Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 [A] resolution, collected through target R1260 in the CASP16 competition. 26 groups participated in this solvation cryo-ensemble prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions and other previously hidden components of biomolecular systems.

biophysics↗

bioRxiv2023

In Vitro Validation of Computationally Predicted Oncogenic Driver Mutations in EGFR Tyrosine Kinase Domain

Epidermal Growth Factor Receptor (EGFR) signaling is known to play essential roles in growth and development; nevertheless, overexpression and mutation of EGFR have been reported in several cancers. Non-small cell lung cancer (NSCLC), the most observed type of lung cancer, harbors the highest number of EGFR tyrosine kinase mutations and therefore, EGFR has become an important therapeutic target for treatment of these tumors. Tyrosine Kinase Inhibitors (TKIs) are found to be effective in patients whose tumors contain activating mutations in the tyrosine kinase region of the receptor. This would seem to be beneficial in the treatment of EGFR mutation-positive NSCLC patients but the activating mutations should be sensitive to TKIs. Earlier, a machine learning approach was developed to classify single amino acid polymorphisms (SAPs) in EGFR into driver (cancer-causing) and passenger (neutral) mutations using structural and functional features (Anoosha et al., 2015). This study screened all possible point mutations in EGFR and predicted a list of mutations with high probability of being a driver or a passenger. From this list, we selected 2 mutations (G729E and G719F) with high evolutionary conservation score for in vitro validation. If proven to be oncogenic drivers and sensitive to EGFR TKIs, these mutations can aid in the early diagnosis and successful therapy of EGFR mutation-positive NSCLC.

cancer biology↗