Sergei Grudinin

Constructed Negatives Shape the Evaluation of Sequence-Based Protein--Protein Interaction Models

The paper evaluates how different constructed negative examples affect sequence-based protein-protein interaction models, showing significant variability in performance metrics based on negative construction methods.

CryoARC: Atomic-resolution conformational landscapes of protein assemblies from cryo-EM single particles with evolutionary priors

CryoARC reconstructs atomic-resolution conformational landscapes of protein assemblies from cryo-EM data using evolutionary sequence priors and deep learning.

SimHS-AFMfit-MD: An Integrative Approach to Deciphering Alpha-Actinin Atomic Conformational Dynamics

This paper introduces SimHS-AFMfit-MD, a framework combining HS-AFM, MD simulations, and AFMfit to reconstruct atomic-scale conformational dynamics of alpha-actinin.

DynamicGT: a dynamic-aware geometric transformer model to predict protein binding interfaces in flexible and disordered regions

DynamicGT improves protein binding interface prediction by incorporating dynamic features and conformational changes, outperforming static models on flexible and disordered regions.

Structural basis for alpha-actinin inhibition of cooperative cofilin binding to actin filaments

This study investigates how alpha-actinin stabilizes actin filament structure, preventing cofilin-induced helical shortening and cooperative binding.

AFMfit : Deciphering conformational dynamics in AFM data using fast nonlinear NMA and FFT-based search

AFMfit fits atomic models to AFM data to infer conformational dynamics of single molecules using fast nonlinear NMA and FFT-based search.

SeaMoon: from protein language models to continuous structural heterogeneity

SeaMoon predicts continuous protein motions from sequences using pLM embeddings and a lightweight CNN, achieving reasonable accuracy for 40% of test proteins.

Voronota-LT: efficient, flexible and solvent-aware tessellation-based analysis of atomic interactions

Voronota-LT efficiently computes atom-atom contacts within solvent-accessible surfaces using tessellation-based methods, offering speed and flexibility for molecular interaction analysis.

Explaining Conformational Diversity in Protein Families through Molecular Motions

This paper introduces DANCE, a method to analyze conformational variability in protein families, and evaluates dimensionality reduction techniques for sampling unseen protein states.

Annotating Macromolecular Complexes in the Protein Data Bank: Improving the FAIRness of Structure Data

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

Convex-PLR - Revisiting affinity predictions and virtual screening using physics-informed machine learning

The paper introduces Convex-PLR, a physics-informed machine learning model for virtual screening that improves scoring function performance and interpretability.

HOPMA: Boosting protein functional dynamics with colored contact maps

HOPMA predicts protein functional states and transitions using a modified elastic network model and contact maps, improving conformational space exploration.

VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures

The paper introduces VoroCNN, a deep convolutional neural network for predicting local qualities of 3D protein folds using Voronoi tessellation, achieving competitive results compared to existing methods.

Predicting protein functional motions: an old recipe with a new twist

The paper introduces a method to predict protein conformational transitions using nonlinear twists from normal mode analysis, enabling access to previously inaccessible motions while preserving structure.

Protein model quality assessment using 3D oriented convolutional neural networks

The paper introduces a 3D convolutional neural network for protein model quality assessment, achieving state-of-the-art performance on established benchmarks.