The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …

Abstract Protein Structure Prediction is a central topic in Structural Bioinformatics. Since
the'60s statistical methods, followed by increasingly complex Machine Learning and recently …

Computational biology and bioinformatics provide vast data gold-mines from protein
sequences, ideal for Language Models (LMs) taken from Natural Language Processing …

Background Predicting protein function and structure from sequence is one important
challenge for computational biology. For 26 years, most state-of-the-art approaches …

The ability to predict local structural features of a protein from the primary sequence is of
paramount importance for unraveling its function in absence of experimental structural …

Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate
understanding of the sequence (-structure)-function paradigm for DNAs, RNAs and proteins …

INTRODUCTION Protein homeostasis in the endoplasmic reticulum (ER) is maintained by a
quality control system. When a newly synthesized ER protein misfolds, it is ultimately …

Proteomics, the study of all the proteins in biological systems, is becoming a data‐rich
science. Protein sequences and structures are comprehensively catalogued in online …

Accurately predicting peptide secondary structures remains a challenging task due to the
lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep …

The inapplicability of amino acid covariation methods to small protein families has limited
their use for structural annotation of whole genomes. Recently, deep learning has shown …