protein sequence

Machine learning-assisted directed evolution of plant Rubisco

This study uses machine learning to guide directed evolution of plant Rubisco, identifying variants with improved catalytic efficiency and carboxylation rates.

REZCUE: Rescuing 3D spatial omics from archived FFPE tissue

REZCUE enables 3D spatial multi-omic analysis of archived FFPE tissue by preserving nucleic acids and proteins for subsequent assays.

Evolution of membrane rotary ATP synthases: A trade-off between the net productivity and mechanistic stability?

This study investigates the evolution of rotary ATP synthases, finding that efficiency gains in F-ATPases came at the cost of mechanistic stability, while lateral gene transfers contributed to the diversification of A-ATPases.

Generalizing Protein Fitness Prediction Across Distribution Shifts via Structural and Co-evolutionary Priors

This paper develops a framework for protein fitness prediction that improves generalization across distribution shifts by integrating structural and co-evolutionary information.

PepRePs: Peptide-Retargeted Phosphatases Designed with Generative Language Models

This paper introduces PepRePs, a system using peptide-retargeted phosphatases to selectively manipulate protein phosphorylation, showing reduced phosphorylation and downstream effects on cellular processes.

EdiProPred: Prediction of Edible Plant Tissue-Associated Proteins Using Large Language Models

The paper develops EdiProPred, a model using large language models to classify plant proteins as edible or non-edible based on sequence data, achieving high accuracy.

Joint protein, mRNA and DNA sequence design and optimization with nucleotide-level Potts models

A structure-conditioned nucleotide-level Potts model designs protein and coding sequences jointly, enabling tRNA adaptation and RNA motif embedding beyond synonymous codons with designability comparable to Caliby.

Characterization of cross-family conserved fragments

The paper characterizes cross-family conserved fragments, showing their structural and biophysical properties and their role in folding and stability.

Mechanistic classification of the AAA superfamily with protein language models

This paper uses a protein language model to classify AAA family proteins by their mechanistic function, showing that sequence context can predict biochemical mechanisms.

Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo

This paper introduces AlphaProtein Novo, a machine-learning pipeline for de novo enzyme design that outperforms natural sequence mining in creating enzymes for new-to-nature chemistry and substrates.

CropStabDB: a protein stability resource and Engineering Atlas of orthologue-supported substitutions for 30 plant proteomes

CropStabDB provides a resource of protein stability scores and substitutions across 30 plant proteomes, identifying residue changes that improve thermostability.

PRIZM: Combining Low-N Data and Zero-shot Models to Design Enhanced Protein Variants

PRIZM uses low-N data and zero-shot models to rank protein variants, improving design efficiency with minimal experimental data.

AmyloDeep: pLM-based ensemble model for predicting amyloid propensity from the amino acid sequence

AmyloDeep predicts amyloid propensity from amino acid sequences using an ensemble of deep learning models, achieving high accuracy and robustness.

CHAIR an antigen-resolved antibody-reactome resource for healthy adult immune aging

CHAIR provides an antigen-resolved immune resource for healthy adult aging by integrating PhIP-seq, clinical data, and immune phenotyping across multiple age groups.

Antibody-Antigen Affinity Prediction with Chain-Aware Protein Language Modeling

AbAffinity predicts antibody-antigen affinity using a chain-aware sequence model, achieving high correlation on benchmark datasets and maintaining performance without structural data.

Auditing Protein-Protein Interaction Signals with Sparse Autoencoder Fingerprints

AuditPPI evaluates PPI predictors by transforming language model features into auditable fingerprints, revealing that predictive success does not necessarily reflect biological signal capture.

A Structure-Aware Generative Framework for Exploring Protein Sequence and Function Space

This paper introduces a structure-aware generative framework that integrates geometric encoding and coevolutionary constraints to map, cluster, and design protein sequences, improving homology detection and structure-guided sequence generation.

Design-Evolution Synergy Enables New-to-Nature Enzyme Chemistry

This study combines design and evolution to create new-to-nature enzymes capable of performing regio- and enantioselective C-H amination reactions for synthesizing chiral piperidines.

AmyloCore-ML: AI/Machine Learning Enabled Identification of Amyloid Fibril Core Regions Using Protein Language Models

AmyloCore-ML identifies amyloid-core regions in proteins using sequence-based predictors and protein language models, achieving high AUROC and AUPRC values.

A dichotomous family of Cache domain proteins evolved through c-type heme acquisition

Cache sensor domains of DUF3365/Tll0287 family bind covalent c-type heme through a CXXCH insertion; a signaling member behaves like an NO sensor, a stand-alone one like a redox protein.

Quantifying the Provenance-to-Function Gap in Antidiabetic Peptide Prediction: Homology-Aware Evaluation and the ADP-Hybrid Baseline

The paper evaluates the provenance-to-function gap in antidiabetic peptide prediction by re-evaluating benchmarks with homologous peptide grouping and introduces the ADP-Hybrid baseline classifier.

Ribosome remodeling drives translation adaptation during viral infection and cellular stress

The paper shows that ribosome remodeling enables enhanced viral protein synthesis during infection and cellular stress by altering ribosome composition and structure.

Evidence Scaling for Zero-Shot Protein Reasoning with Large Language Models

This paper shows that adding biological evidence to large language models improves zero-shot protein variant prediction without retraining the model.

pH-sensitive binder design with Proton-PottsMPNN

This paper introduces Proton-PottsMPNN, a model that designs pH-sensitive binders by explicitly modeling protonation states, resulting in pH-dependent PD-L1 binders with transition pH values between 4.0 and 5.8.

Joint enzyme-reaction retrieval and catalytic optima prediction via multimodal fusion

GERO improves enzyme-reaction retrieval and catalytic optima prediction by integrating multimodal data for condition-aware recommendations.

Systematic screen of PKR reveals genetic variants that broadly evade divergent viral pseudosubstrate inhibitors

This study screens genetic variants of PKR to identify those that resist diverse viral pseudosubstrate inhibitors, finding shared resistance mechanisms across different inhibitors.

GeoEPred: A Multimodal Structure-Aware Geometric Deep Learning Framework for Gram-Negative Bacterial Secreted Effector Prediction with Sequence Semantics

GeoEPred improves effector protein prediction by integrating sequence and structural information to better model functional motifs and spatial conformations.

Repurposing PeTriBERT for Protein Protein Interaction Prediction with Sequence Structure Fusion

The paper presents PeTriPPI2, a hybrid model for protein-protein interaction prediction that combines sequence and structural features, achieving high accuracy on the Pinder dataset.

Scaffold-Constrained Generative Design Overcomes Failure Modes in Computational Protein Engineering

The paper develops a scaffold-constrained generative model for protein design, showing it produces high-quality candidates with better ranking scores than previous methods.

Bridging the gap between omics and structural data: A framework for interpreting protein-RNA interaction specificity

This paper evaluates AlphaFold3's ability to predict protein-RNA interaction specificity using omics data and structural information, highlighting both its potential and limitations.