Debswapna Bhattacharya

ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures

ORIGAMI improves assessment of multimeric protein complex structures by incorporating orientation-aware geometric features into graph neural networks, achieving better performance than existing methods.

RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation

RNAbpFlow generates RNA 3D structures using base pair conditioning and SE(3)-equivariant flow matching, improving topology sampling in benchmarks.

PARSEbp: Pairwise Agreement-based RNA Scoring with Emphasis on Base Pairings

PARSEbp improves RNA structure scoring by integrating pairwise agreement and base pairing consistency, outperforming existing methods on CASP16 targets.

Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models

ProRNA3D-single predicts protein-RNA complex structures from single-sequence inputs, outperforming existing methods with limited evolutionary data.

NEFFy: A Versatile Tool for Computing the Number of Effective Sequences

NEFFy computes the Number of Effective Sequences (NEFF) for multiple sequence alignments, offering integrated functionality and support for diverse formats and sequence types.

To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era

The paper benchmarks PSCP methods on CASP datasets, showing they work well with experimental data but struggle with AlphaFold-generated structures, and explores confidence-aware integration with limited success.

lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures

lociPARSE is a new model that scores RNA 3D structures using a locality-aware attention mechanism, outperforming existing methods on multiple datasets.

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks

EquiPNAS improves protein-nucleic acid binding site prediction using a pLM-informed equivariant graph neural network, outperforming existing methods on multiple datasets.

E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction

EquiPPIS predicts protein-protein interaction sites using E(3) equivariant graph neural networks, outperforming existing methods with predicted AlphaFold2 structures.

PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries

PIQLE estimates protein-protein interface quality using deep graph learning, outperforming existing methods by incorporating multimeric geometries and evolutionary information.

rrQNet: protein contact map quality estimation by deep evolutionary reconciliation

rrQNet estimates contact map quality using deep learning by reconciling evolutionary context with predicted residue contacts, showing accurate quality assessment across contact predictors.

DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins

DisCovER improves protein threading by integrating inter-residue distance, orientation, and neighborhood effects in template alignment.

Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins

This paper introduces DConStruct, a new hierarchical structure modeling method for ab initio protein folding that outperforms CNS in accuracy using distance and contact maps for soluble and membrane proteins.

QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks