Protein Structure and Dynamics

This paper introduces a method using two neural networks to design drug-binding proteins with high affinity, surpassing current state-of-the-art methods.

The paper uses entanglement measures from knot theory to analyze sequence and structural features of intrinsically disordered proteins and identifies functionally enriched regions in entanglement space.

The paper investigates the energy landscape of coupled folding and binding in intrinsically disordered proteins, finding that electrostatic interactions compensate for energetic frustration.

This paper develops a deep learning framework to predict protein flexibility from static structural data, achieving moderate accuracy and identifying key structural features associated with flexibility.

The paper introduces CNR, a framework to quantify how protein contact networks respond to stabilizing interaction changes, using human AlphaFold models to analyze 22,167 structures.

The paper introduces a docking-based simulation method that combines rigid-body approximations with flexible protein structures to model macromolecular interactions in crowded cellular environments over long timescales.

kontakteUR transforms structural coordinates into a chemically informed contact space to analyze biomolecular interactions and their changes, enabling systematic comparison and functional insights.

The paper investigates how sequence differences in CITED CTADs affect their conformational ensembles and TAZ1 binding, showing that minor sequence changes impact IDP structure and function.

Ovo provides an open-source platform for de novo protein design, integrating tools, workflows, and visualization to simplify and scale the design process.

The paper uses molecular dynamics simulations to show that phosphate release from actin filaments is limited by dissociation from Mg2+ and varies with subunit position and water molecule count.

AFM-Fold reconstructs 3D protein conformations from AFM images using a generative AI framework, outperforming rigid-body fitting and capturing conformational dynamics.

SteerAF improves AlphaFold2's ability to predict alternative protein conformations by using distogram information from MSAs, achieving better results than existing methods.

The paper shows that a spontaneous post-translational succinimide modification in a bacterial solute-binding protein enhances its thermostability and binding affinity for 5-oxoproline.

The paper identifies Ki-67-like proteins in non-vertebrate eukaryotes, showing conserved biophysical and functional properties despite low sequence homology.

This study shows that crowder shape affects Sup35NM conformation and phase separation, with rod-like crowders promoting phase separation while spherical ones do not.

This paper introduces dEVA, a framework for de novo metalloenzyme design, achieving high catalytic activity without natural templates.

This paper introduces HomoDSP, a method for DNA-binding specificity prediction using homologous protein-DNA structures, showing improved accuracy and generalization over existing methods.

The paper identifies a histidine switch that regulates pH-responsive self-assembly of a helical protein filament through electrostatic interactions.

The paper shows that blocking protein quality control degradation stabilizes DHFR indel variants, suggesting a therapeutic approach for protein misfolding diseases.

MartiniSurf automates the setup of surface-immobilized biomolecular systems for Martini simulations, enabling systematic in silico studies of immobilization strategies.

PatchMAN2 improves peptide-protein docking efficiency by filtering fragments and focusing sampling on relevant regions, reducing computational cost while maintaining accuracy.

This study evaluates AlphaFold2-Multimer and AlphaFold3's ability to predict oligomeric states of proteins, showing their utility but also highlighting limitations for proteins without structural representatives in the training set.

The paper designs de novo TIM barrels with structural lids to create active sites for enzymatic function, achieving high catalytic efficiency.

The paper shows that machine learning models and large datasets fail to accurately predict extremophile enzyme melting and optimum temperatures, due to biased and error-laden training data.

The paper uses molecular dynamics simulations to identify a unifying mechanism for excipient-mediated protein thermal stability based on shape complementarity between proteins and solvents.

The paper identifies transposable elements as a source of intrinsic disorder in human proteins, showing that TE insertions are enriched in disordered regions and their conformational fate depends on their age and TE class.

The paper evaluates how protein fitness models predict and design variants with altered specificity, finding that models biased against such changes can be leveraged by using weighted differences in model scores.

The paper tests AlphaFold2 and ColabFold's reliability by using false protein sequences and finds a low false positive rate, while also discovering new pseudogenes in the human genome.

This study investigates the membrane-dependent structure of CPXV012 and its interaction with TAP, revealing a two-helical organization and flexible ER-luminal region critical for TAP recognition.

MoRFchibi 2.0 predicts Molecular Recognition Features in protein sequences, outperforming existing tools and models including AlphaFold and protein language models.