Protein Structure and Dynamics

BioPipelines: Accessible Computational Protein and Ligand Design for Chemical Biologists

BioPipelines provides an open-source Python framework for computational protein and ligand design, enabling multi-step workflows with minimal code and integration of over 30 tools.

Lubricin's Mucin Domain Has Strong Polyproline Type-II Helical Character

This paper uses molecular dynamics simulations and circular dichroism experiments to show that lubricin's mucin domain has significant polyproline type II helical structure, stabilized by glycosylation.

ProNA3D: Distance-Based Analysis of Nucleic Acid-Containing Interfaces

ProNA3D analyzes nucleic acid-containing interfaces in protein-nucleic acid and nucleic acid-only complexes, providing tools for visualization, scoring, and dynamic complex evaluation.

NMR reveals specific remodelling of protein folding landscapes in ionic liquids

This paper shows ionic liquids modulate protein folding by stabilizing or destabilizing folded/unfolded states through distinct mechanisms, challenging the conventional view of protein stabilization.

Large-scale simulations reveal evolutionary constraints on intrinsically disordered regions imposed by full-length protein architecture

The paper uses large-scale simulations to show that intrinsically disordered regions in proteins are structurally and evolutionarily constrained by their full-length protein architecture.

SlytheRINs: using graph parameters and residue interaction networks to analyze protein dynamics and structural ensembles

SlytheRINs compares conformational changes in proteins by analyzing residue interaction networks across multiple structures to identify dynamic shifts linked to functional impacts.

Oligomerization enables the selective targeting of intrinsically disordered regions by small molecules

The paper shows a small molecule selectively targets an oligomeric form of a protein with an intrinsically disordered region, altering its interaction network and condensation properties.

ProChoreo: de novo Binder Design from Conformational Ensembles with Generative Deep Learning

ProChoreo designs protein binders using conformational ensembles and generative deep learning, showing improved performance in binding and structure quality.

Integrating Segmental Deuteration iCM-SANS with SAXS and MD for Dynamical Analysis of Multi-domain Proteins

The paper develops a protocol combining segmental deuteration and multi-step protein ligation with SAXS and iCM-SANS to improve discrimination of conformational ensembles in multi-domain proteins.

Decoding the Allosteric Paradox: A Dual Framework Integrating AI Cofolding Models with Landscape-Guided Interpretable AI Framework of Ligand-Protein Binding

This study identifies a universal failure in AI models to predict allosteric ligand binding, linking it to biophysical constraints in energy landscapes.

Mechanism of MEK1 activation by phosphorylation

This study uses molecular dynamics simulations to show that double phosphorylation of MEK1 unwinds its helix, increases solvent exposure of catalytic residues, and induces correlated motion at the active site, enhancing enzymatic activity.

Deconvolving mutation effects on protein stability and function with disentangled protein language models

This paper introduces DETANGO, a deep learning framework that disentangles mutation effects on protein stability and function, identifying stable-but-inactive variants and functionally critical residues.

Improving conformational ensembles of folded proteins in GoMartini

The paper improves the conformational ensembles of folded proteins in GoMartini by optimizing G[o] networks using a perturbation-based approach against atomistic simulations.

Rapid sequence-based screening of structure-disrupting protein mutations

The paper develops a sequence-based method to identify structure-disrupting mutations using protein language models, reducing the need for expensive structure prediction.

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.

Cracking the Capsid Code: A Computationally-Feasible Approach for Investigating Virus-Excipient Interactions in Biologics Design

This paper introduces CapSACIN, a computational framework to study virus-excipient interactions, showing it effectively models PPV stability and excipient effects.

Conditional Diffusion with Locality-Aware Modal Alignment for Generating Diverse Protein Conformational Ensembles

This paper introduces Mac-Diff, a diffusion model that generates diverse protein conformations by aligning sequence and geometry views, showing improved recovery of conformational distributions and meta-stable states.

Intermediate heterogeneity modulates coupling between chain compaction and structure formation during protein folding

This study investigates how intermediate-state heterogeneity affects the coupling between chain compaction and structure formation during protein folding using monellin as a model system.

RevelioPlots: An Interactive Web Application for Fast AI-Based Protein Models Quality Assessment

RevelioPlots provides an interactive web tool for assessing AI-predicted protein structure quality using pLDDT scores and Ramachandran plots.

Coarse-grained Martini 3 model of chondroitin sulfate A

The paper develops a coarse-grained Martini 3 model of chondroitin sulfate A to enable efficient molecular dynamics simulations of its structural and electrostatic properties.

A Comprehensive Atlas and Machine-Learning Framework for Predicting IDR-Protein Binding Affinity

The paper introduces a dataset and a graph-transformer model to predict IDR-protein binding affinity, achieving high accuracy and generalizing to designed binders.

Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction

Protenix-v1 is an open-source biomolecular structure prediction model that outperforms AlphaFold3 with the same training data cutoff, offering improved accuracy and new features for real-world applications.

The Untangle Challenge for accurate ensemble models

The paper identifies density misfit barrier traps that limit macromolecular model accuracy and introduces a synthetic dataset to challenge and improve ensemble refinement algorithms.

Defining the Active Conformation of Typical Protein Kinase Domains from Substrate-Bound PDB Structures Enables Active-State AlphaFold2 Models for All 437 Human Catalytic Protein Kinases

The paper defines structural criteria for the active form of human protein kinases using substrate-bound structures and uses AlphaFold2 to generate active-state models for all 437 human catalytic kinases.

Topological Investigation of Protein Folding and Intrinsic Disorder

This study uses circuit topology to analyze protein folding, showing that ordered and disordered proteins differ in topological organization and that topology predicts folding state and kinetics.

ProteinConformers: large-scale and energetically profiled descriptions of protein conformational landscapes

ProteinConformers provides a large-scale dataset of protein conformations with energy profiles and similarity annotations, offering a benchmarking framework and analysis platform.

Prediction of ligand-dependent conformational sampling of ABC transporters by AlphaFold3 and correlation to experimental structures and energetics

The paper uses AlphaFold3 to predict ligand-dependent conformational changes in ABC transporters and shows these predictions correlate with experimental data and energetics.

Engineering nanocondensate formation through sequence composition and patterning

This paper designs peptides that form stable nanocondensates by combining computational methods with experimental validation, revealing molecular mechanisms that control condensate size.

Controllable Protein Design via Autoregressive Direct Coupling Analysis Conditioned on Principal Components

FeatureDCA improves protein sequence generation by conditioning DCA on principal components, achieving accurate structural and functional results across multiple families.

Influence of molecular representation and charge on protein-ligand structural predictions by popular co-folding methods

This study evaluates how input format and charge affect protein-ligand structure predictions using four co-folding methods, finding format has a stronger impact than charge.