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Interactions in protein complexes can be predicted from evolutionary information from genomic sequences.

An example of a deep learning-based strategy to discover novel protein-protein interactions and predict their complex structures at high accuracy.

The utilization of hydrogen/deuterium exchange mass spectrometry reveals dynamic insights and interactions driving liquid-liquid demixing of the chromosome passenger complex.

A comprehensive approach to prediction of stabilizing mutations in G-protein coupled receptors yields high hit rate and crystal structures of 5HT2C in both active and inactive states.

AlphaFold-Multimer was used to investigate extracellular protein interactions involved in mammalian egg-sperm recognition, suggesting a putative pentameric complex that includes TMEM81, a sperm protein not previously involved in gamete recognition.

Big data analysis, protein structure prediction, and artificial intelligence are integrated to predict novel and highly transmissible SARS-CoV-2 variants.

The N-terminal domain of Staphylococcus aureus GpsB, a scaffolding protein, forms an atypical asymmetric dimer and binds to the C-termini of FtsZ and PBP4, influencing the localization and regulation of both the Z-ring and cell wall synthesis.

PPI-hotspot^ID, trained using an automated machine-learning framework, AutoGluon, on the largest PPI-hot spot dataset to date, detects hot spots beyond protein–protein interfaces, uncovering druggable interactions to aid design of PPI-modulating therapeutics.

A combination of cryo-electron microscopy, structure prediction, and evolutionary analyses reveal several functional states of the dynamic protein translocation machinery at the mammalian endoplasmic reticulum.

Discovery of the structural basis for recognition and uptake of a human precursor for body odour production reveals an important role for bacterial peptide transport and novel routes to prevent its production in humans.