protein structure prediction

DaliScope: Distilling Biological Insights from Deep Structural Alignments

DaliScope analyzes DALI structural alignments to uncover protein relationships and structural patterns, revealing unannotated domains and structurally anchored sequence signatures.

LOca-1: A minimal calcium-activated arginyl-specific orthocaspase from the Loki's Castle hydrothermal vent field

The paper characterizes LOca-1, a calcium-activated arginyl-specific orthocaspase from Loki's Castle, showing its thermoactivity and structural conservation with plant metacaspases.

Membrane Geometry Reveals Strengths and Limitations of AlphaFold-Predicted Transmembrane Protein Complexes

The paper evaluates AlphaFold's predictions of transmembrane protein complexes, finding that while most show plausible membrane geometry, some have inconsistencies and limitations in predicting lipid interactions.

OmniT2I: a unified table-to-image framework for survival prediction and cross-modal inference with incomplete multimodal cancer data

OmniT2I converts molecular profiles into images and predicts survival and missing modalities from incomplete cancer data, achieving higher accuracy than separate models.

Realism and resolution: How AI-predicted protein structures differ from experimental structures

The paper introduces WhatRes, a tool to evaluate AI-predicted protein structures by comparing their geometry to experimental structures across resolution and realism.

Bacterial major vault protein homologs form divergent vault structures

The paper identifies bacterial MVP homologs and reports cryoEM structures of vaults from Corallococcus praedator and a Deltaproteobacteria bacterium, revealing novel symmetries.

A cohesin acetylation complex that recognises nicked DNA

The paper predicts a cohesin acetylation complex in budding yeast that interacts with nicked DNA and confirms its function through experiments.

StableProt: Structure-Aware Deep Learning for Protein Thermostability (Tm) and Environmental Adaptation (OGT) Prediction

StableProt predicts protein thermostability and organismal optimal growth temperature using a structure-aware deep learning framework, achieving high accuracy and robustness across temperature regimes.

Cryo-EM structures of an Acinetobacter baumannii podovirus reveal a canonical capsid maturation

The paper reports the cryo-EM structures of an Acinetobacter baumannii podovirus, revealing its capsid maturation mechanism and providing a reference for phage engineering.

Generative Models for Novel Enzymatic Reactions: How Far Do They Go?

This paper evaluates generative models for predicting novel enzymatic reactions, finding they can generate correct reactions for 25% of test cases and improve over existing methods.

RPFace: From biased structural benchmarks to generalizable RNA-protein interface prediction

RPFace predicts RNA-protein interfaces directly from sequence, outperforming existing methods on RNA interfaces and generalizing to in vivo data without pretraining.

Joint protein-DNA language modeling enables prediction and engineering of DNA recognition specificity

This paper introduces PDLMs, protein-DNA language models that jointly represent protein and DNA sequences to predict and engineer DNA recognition specificity.

RPI-Zero: Zero-shot RNA-Protein Interaction Prediction

RPI-Zero predicts RNA-protein interactions without prior data by using a sequence-only model trained on a large benchmark of CLIP experiments.

TRPP Ion Channels Function in the π-Conformation Rather Than the α-Conformation

This study uses molecular dynamics simulations to show that the π-conformation of PKD2 supports ion conduction, challenging the previous model that the α-conformation is the conductive state.

NanoPAMDA enables rapid PAM characterization of metagenome-derived Cas9 effectors

The paper identifies novel Cas9 orthologs from metagenomic data and develops NanoPAMDA to rapidly characterize their PAM preferences.

A protein language model for domain, functional site, and variant effect prediction

PALM, a protein language model, predicts domain, functional site, and variant effects with high accuracy using curated sequence data and outperforms existing models in multiple benchmarks.

Clinical-Anchored Incremental Survival Learning: A Stability-Constrained Framework for Molecularly Enhanced Survival Prediction in Lung Adenocarcinoma

This paper introduces CAISL, a framework for improving survival prediction in lung adenocarcinoma by integrating clinical and molecular data with stability constraints, achieving better performance than clinical and molecular-only models.

FoldAbS: Repurposing the Protein Folding Model as a Foundation Encoder for Antibody Screening

FoldAbS repurposes protein folding models as foundation encoders to improve antibody screening by evaluating specificity, affinity, and developability in a unified framework.

AlphaFold 3 Prediction Accuracy and Reliability across RNA Structures and Protein-RNA Interfaces

This study evaluates AlphaFold 3's accuracy and reliability in predicting RNA structures and protein-RNA interfaces, finding that confidence scores do not always correlate with correct predictions.

Finely tuned DDA-PASEF-based immunopeptidomics workflow enhances HLA class I coverage

This study optimizes a DDA-PASEF-based immunopeptidomics workflow to enhance HLA class I peptide coverage in low-input samples.

All-Atom Structure Tokenizer: AtomLM-9b for Biomolecular Structure Prediction

The paper introduces AtomLM-9b, a language model using an all-atom structure tokenizer to predict protein structures with performance comparable to AlphaFold 3.

Structural and evolutionary insights into FcαμR recognition of polymeric immunoglobulins

This study determines cryo-EM structures of FcR bound to IgM and IgA, revealing its binding mechanism and evolutionary insights into pIg receptor recognition.

Peptorama: Structure creation and exploratory data analysis for non-ribosomal peptides

Peptorama provides tools for creating and analyzing non-ribosomal peptide structures, enabling efficient comparison of known and predicted peptides to aid in biodiscovery.

CARTnet: Template-guided B-cell Epitope Prediction with Contact-aware Attention

CARTnet improves B-cell epitope prediction using a contact-aware deep learning framework, outperforming existing methods on internal and external datasets.

Representation-aware calibration enables statistically reliable protein structure search

Struct-Caliper improves protein structure search reliability by providing accurate p-values, increasing homolog recovery and reducing false discoveries compared to native E-values.

CryoID2: deep learning-enhanced identification of protein and RNA from cryoEM maps

CryoID2 uses deep learning and sequence analysis to identify proteins and RNA from cryoEM maps, achieving high accuracy in molecular identification.

Structural Basis for Inhibition of Human Anion Transporter SLC26A6/PAT-1

This study reports cryo-EM structures of human PAT-1 with inhibitors to elucidate their inhibition mechanisms and domain interactions.

FoXSight: Facilitating Computational Hit Identification in Crystallographic Fragment Screening Experiments

FoXSight identifies weak binding events in crystallographic fragment screening by detecting target-specific density features in noisy electron density maps.

Resonant Recognition Model analysis of cGAS: conserved spectral frequencies and a divergent naked mole-rat phase signature

This paper uses the Resonant Recognition Model to analyze cGAS and FANCI sequences across species, identifying conserved spectral features and divergent phase signatures in naked mole-rats.

A validation approach for RNA structures based on deep-learning 3D-structure predictions

This paper extends a protein structure validation method to RNA, showing it can detect errors in low-resolution structures where map-based methods fail.