protein design

This study shows directed evolution improves a designed enzyme by enhancing its transition-state-like conformation, reducing reaction barriers through desolvation and electric field organization.

Inverse FoldDir designs protein sequences compatible with a given backbone structure, achieving high accuracy and experimental validation in nanobody redesign.

GenCDR uses LLaMa-based models to generate antibody CDRs with framework context, achieving high recovery and developable designs through alignment-driven training.

The paper develops a closed-loop framework for designing receptor-selective cell-penetrating peptides using multi-objective optimization and evaluates its effectiveness through experimental validation.

This study designs allosterically controlled enzymes using denoising diffusion models, showing that activity can be activated or repressed by designed effectors.

This paper evaluates a self-architecting transformer for protein representation, showing it achieves better performance than existing models with a smaller dataset.

EukaUTR is a foundation model for eukaryotic 3' UTRs that predicts function and generates new sequences with regulatory properties.

This study identifies evolutionary differences in PINK1 orthologues that affect stability and activity, revealing the role of NTE, CTE, and TOM20 in kinase function and pathogenic variants.

PKProbDesign optimizes thermodynamic folding probability for pseudoknotted RNA targets, achieving high pseudo-joint folding probability on most tested targets.

Odin-Multi is a binder design framework that optimizes sequences against multiple targets simultaneously, improving cross-reactivity and specificity compared to single-target methods.

This paper designs novel Rubisco variants using protein language models, identifying active candidates with improved CO2/O2 specificity and structural validity.

This paper introduces a new cyclisation strategy using a bromomethyl picolinaldehyde linker to expand macrocyclic peptide topologies, leading to more potent and diverse inhibitors.

The paper uses protein language models to predict ChrimsonR mutations that enhance photocurrent response, validating two mutations that significantly improve function.

PDV provides a lightweight, browser-based molecular visualization tool with validated quantitative structural analysis and antibody-specific features.

The paper designs and validates self-assembling protein cages using helical fusion and machine learning, achieving precise structures with high resolution and molecular mass accuracy.

This study uses ESM3 to generate knotted proteins, finding high success rates with guided decoding and robustness in real knotted proteins.

The paper designs small protein binders targeting specific MAP4K4 sites using computational tools and evaluates their binding using AlphaFold3, finding high-confidence candidates with novel sequence space solutions.

This paper identifies the MBD-ID module as a unified DNA/RNA binding interface in MECP2, showing it is disrupted in Rett syndrome due to mutations in the intervening domain.

The paper screens variants of a Form II Rubisco to improve oxygen tolerance, identifies key mutations, and uses a transformer model to guide engineering.

The paper develops methods to improve yeast surface display of FLS2 for detecting bacterial ligands, identifies glycosylation as a limiting factor, and shows how modifying glycosylation sites enhances binding specificity.

The paper designs mutational paths for WW domains to switch specificity using computational methods and validates them experimentally, showing high success rates for structured paths over random ones.

CatESO optimizes enzyme sequences to improve catalytic turnover by integrating kcat prediction with differentiable design, achieving higher predicted activity and structural confidence than existing methods.

The paper introduces a sparse feature circuit framework to identify causal features in ESM-2 for zero-shot mutation effect prediction, showing that shared circuits explain mutation effects through 3D contact residues.

GRASP enables transformation-independent CRISPR-based chromatin isolation in plants by using dCas9-gRNA complexes to capture specific genomic regions from purified nuclei.

This paper demonstrates that large language models can generate de novo protein sequences that bind specific ligands without copying existing sequences, with a 25% hit rate in experimental validation.

This paper introduces SLIDE, a method to estimate landscape ruggedness in directed evolution using population-level phenotypic data and mutation rate, improving selection strategies for protein design.

This paper introduces cSAGE, a conjugation-based genome engineering platform that enables site-specific integration in transformation-resistant bacteria, demonstrating its utility in bioproduction pathway prototyping.

This paper uses deep learning to design bacteriophage receptor binding proteins with multiobjective specificity, showing high tunability and success rates in targeting diverse strains.

This study identifies a selective effect of the D620N VPS35 mutation on the brain-specific protein interactome in mammalian cells and brain tissue.

The paper structures and engineers deep-sea halophilic PET hydrolases to improve their salt tolerance and catalytic efficiency for industrial applications.