Protein function predictions articles from across Nature Portfolio

CLC transporters exchange Cl⁻ for H⁻ with 2:1 stoichiometry. Here, authors integrate hydrogen-deuterium exchange, cryo-EM, MD simulations, ion binding analysis, and functional assays to establish a complete ion-exchange model, explaining how subtle dynamics enable reversible transport.

Protein language models capture single proteins but struggle with interactions. Here, authors present MINT, trained on large PPI datasets, which outperforms existing PLMs in predicting binding, mutations, and immune interactions, advancing biomedical discovery.

Understanding how mutations alter protein stability is essential for biology and disease research. Here, the authors develop SPURS, a model that rewires pre-trained protein generative models to accurately predict stability changes.

This work integrates large-scale ribosome display screening with deep learning computational analysis. Using the pipeline, the authors identify short peptides with high affinity to Streptactin and systematically map protein-peptide interaction plasticity.

Although it is possible to use deep learning models to predict static protein conformations from sequencing data, proteins are not static biochemical artefacts. ItsFlexible is a graph-based deep learning tool that is trained on a new dataset of experimentally captured protein motif conformations to classify the dynamic characteristics of proteins.

A new approach sheds light on the biological features learned by protein language models, promising greater interpretability for unsupervised sequence learning.

Thoughtfully designed negative training datasets may hold the key to more robust machine learning models. Ursu et al. reveal how negative training data composition shapes antibody prediction models and their generalizability. Sometimes, the best way to get better is to train harder.

A recent study introduces a series of approaches that predict protein fitness and stability after the introduction of mutations. The work focuses on combining different data and pre-training to overcome data scarcity.