Protein Structure and Dynamics

PySteMoDA: An Open Source Python Package for the Analysis of Steered Molecular Dynamics Simulations Data

PySteMoDA provides an automated tool for analyzing steered molecular dynamics simulations, improving accuracy and accessibility in computational biochemistry and biophysics.

GYDE: A collaborative drug discovery platform for AI-powered protein design and engineering

GYDE provides an open-source platform for bench scientists to access AI tools for protein design and drug discovery through a collaborative interface.

Holographic fingerprinting reveals oligomer-driven phase separation in Bovine Serum Albumin

This study uses holographic imaging to show that Bovine Serum Albumin phase separation depends on higher-order oligomers and is tunable by pH.

Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning

ProTact improves protein-protein interaction prediction using a geometric graph neural network with physics-informed constraints, outperforming existing methods on benchmark datasets.

Is metabolism spatially optimized? Structural modeling of consecutive enzyme pairs reveals no evidence for spatial optimization of catalytic site proximity.

The paper investigates whether consecutive enzymes in metabolic pathways are spatially optimized for catalytic site proximity, finding no evidence for such optimization.

Residue-level mapping of crowding effects on protein phase separation

This study investigates how different crowding agents affect protein phase separation using NMR, revealing residue-level environmental influences and highlighting the importance of crowder identity.

Co-folding of Membrane Proteins and Lipid Molecules Improves Membrane-Protein Structure Prediction Accuracy

The paper introduces CoMPLip, a method that improves membrane-protein structure prediction by co-folding proteins with lipid molecules during AlphaFold 3 predictions, enhancing ligand-pose prediction and conformational sampling.

AI-guided design of candidate BMPR1A-binding peptides for cartilage regeneration: a multi-tool computational benchmarking study

The paper benchmarks four AI tools for designing BMPR1A-binding peptides, identifies top candidates, and highlights differences in binding site targeting.

ProteinMCP: An Agentic AI Framework for Autonomous Protein Engineering

ProteinMCP accelerates and democratizes protein engineering by automating workflows and integrating 38 tools, achieving a comprehensive fitness modeling task in 11 minutes.

CombinGym: a benchmark platform for machine learning-assisted design of combinatorial protein variants

CombinGym benchmarks machine learning for combinatorial protein variant design using curated datasets and evaluates model performance across multiple tasks and data splits.

Disordered but Different: The Unique Characteristics of Intrinsically Disordered Regions in Human Transcription Factors

The paper identifies and analyzes human intrinsically disordered regions (IDRs) in transcription factors, revealing their unique functional, phenotypic, and evolutionary characteristics compared to non-TF IDRs.

NEAT-DNA: A Chemically Accurate, Sequence-Dependent Coarse-Grained Model for Large-Scale DNA Simulations

NEAT-DNA is a coarse-grained DNA model that improves physical realism and sequence specificity for large-scale simulations of genome structure.

Decoding Allosteric Grammar with Explainable AI Integrating Protein Language Models and Energy Landscape Analysis: Neutral Frustration at Allosteric Binding Sites Encodes Regulatory Versatility in Protein Kinases

This study uses explainable AI to analyze the energy landscape of protein kinases, revealing that allosteric sites are in neutrally frustrated regions, explaining their detection challenges.

Mutational Robustness Predicts Protein Dynamics Across Natural and Designed Proteins

The paper shows that mutational robustness correlates with protein dynamics across natural and designed proteins, outperforming existing structural confidence scores.

Evaluating Deep Learning Based Structure Prediction Methods on Antibody-Antigen Complexes

This study benchmarks deep learning methods for predicting antibody-antigen complex structures, finding that increased sampling improves accuracy but model selection remains challenging, with AlphaFold3 performing best overall.

Dynamic, yet well-defined organization of the FUS RGG3 dense phase

This study uses molecular dynamics simulations to show that the FUS RGG3 IDR forms a dynamically organized dense phase with defined interaction motifs and multi-scale topology.

Structure-Based Calculation of Excipient Effects on the Viscosity ofConcentrated Antibody Solutions

This paper introduces a coarse-grained simulation method to predict the viscosity of antibody solutions and their response to excipients.

ST-PARM: Pareto-Complete Inference-Time Alignment for Multi-Objective Protein Design

ST-PARM improves multi-objective protein design by aligning a frozen language model with user-specified trade-offs using a reward model that handles uncertainty and achieves broader Pareto coverage.

Integrating NMR restraints into coarse-grained simulations: toward accurate conformational ensembles of complex protein systems

This paper introduces Martini3-NMR, a framework that integrates NMR data into coarse-grained simulations to improve the accuracy of protein conformational ensembles.

evedesign: accessible biosequence design with a unified framework

evedesign provides a unified framework for conditional biosequence design, enabling multiobjective workflows and experimental integration across various applications.

StrucTTY: An Interactive, Terminal-Native Protein Structure Viewer

StrucTTY provides real-time, interactive visualization of protein structures in text-only terminals using ASCII graphics and supports multi-structure comparison and Foldseek alignments.

DynMoCo: a Novel AI Framework to Reveal Modular Substructures of Protein From Molecular Dynamics

DynMoCo identifies modular substructures in protein dynamics from molecular simulations by detecting time-evolving communities of residues.

On the Mechanism of Ezrin Activation

This study reveals that ezrin activation involves spontaneous FERM-CTD dissociation triggered by PI(4,5)P2 binding, with phosphorylation stabilizing the open conformation.

Protein disorder controls allostery in DNA

The paper shows that intrinsically disordered regions in proteins amplify DNA structural fluctuations, enabling allostery in transcription factor binding.

drFrankenstein: An Automated Pipeline for the Parameterisation of Non-Canonical Amino Acids

drFrankenstein automates the generation of AMBER force field parameters for non-canonical amino acids, improving the efficiency and accuracy of molecular dynamics simulations.

Protein Diffusion Models as Statistical Potentials

This paper introduces ProteinEBM, a protein conformational energy model that outperforms existing methods in predicting mutation effects and protein structures.

Evolutionary turnover of key amino acids explains conservation of function without conservation of sequence in transcriptional activation domains

The paper shows that function can be conserved in transcriptional activation domains without sequence conservation through evolutionary turnover of key residues.

Efficient protein structure prediction fromcompact computers to datacenters withOpenFold-TRT

The paper accelerates protein structure prediction using OpenFold with TensorRT and MMseqs2-GPU, achieving 131x faster inference than AlphaFold2 on specific hardware.

Experimental Data Driven AI Framework for Flexible Protein Conformational Reconstruction

AlphaSAXS uses SAXS data to guide AI in reconstructing accurate protein conformations, improving upon sequence-only models by incorporating experimental constraints.

BiHiTo: Biomolecular Hierarchy-inspired Tokenization

BiHiTo introduces a tokenizer inspired by biomolecular hierarchies to improve structure reconstruction and dynamic conformation exploration, achieving better performance than existing methods.