Sreenath Sasidharan Nair

PDBe-SIFTS improves residue-level sequence-structure mapping with faster search, better scoring, and accurate alignment, comparable to expert curation.

The paper predicts quaternary structures of viral proteins using AlphaFold2 and AlphaFold-Multimer, identifying new insights into vaccinology and protease targets.

A parallelized BindCraft pipeline generated 306,146 in silico binder candidates against 8,296 human proteins, each with sequence, predicted complex and confidence metrics, freely accessible online.

The paper expands the AlphaFold Protein Structure Database to include proteome-scale quaternary structures, predicting 1.81M high-confidence protein complexes and revealing conserved structural clusters across domains of life.

This study introduces ECIFP, a method to analyze ligand binding modes in the PDB, showing it can identify similar binding sites and detect binding-mode similarities across diverse ligands.

The paper introduces a tool that links protein residue mentions in text to their corresponding positions in protein structures, enabling integrated exploration with experimental evidence.

The paper introduces PDBe tools for analysing small molecules in the PDB, enhancing ligand data access, interaction analysis, and functional role identification.

PDBe CCDUtils processes and analyzes small molecules from the PDB using RDKit, offering tools for property computation and database cross-references.

The paper standardizes naming and annotations for macromolecular complexes in the PDB using external resources, improving FAIRness and enabling better research and education.

The paper clusters experimentally determined protein structures to identify conformational states and compares them to AlphaFold2 predictions.

The paper collects and predicts structures of ABC transmembrane proteins using AlphaFold2 and presents them via a web application.

PDB ProtVista is a sequence feature viewer that provides 2D visualization of residue-level annotations and integrates with 3D molecular viewers.

3D-Beacons provides a federated network to unify access to protein structure data from multiple sources, enabling comparative analysis of models.