AI Medical Compendium Topic:
Amino Acid Sequence

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Learning the molecular grammar of protein condensates from sequence determinants and embeddings.

Proceedings of the National Academy of Sciences of the United States of America
Intracellular phase separation of proteins into biomolecular condensates is increasingly recognized as a process with a key role in cellular compartmentalization and regulation. Different hypotheses about the parameters that determine the tendency of...

Sequence representation approaches for sequence-based protein prediction tasks that use deep learning.

Briefings in functional genomics
Deep learning has been increasingly used in bioinformatics, especially in sequence-based protein prediction tasks, as large amounts of biological data are available and deep learning techniques have been developed rapidly in recent years. For sequenc...

Towards peptide-based tunable multistate memristive materials.

Physical chemistry chemical physics : PCCP
Development of new memristive hardware is a technological requirement towards widespread neuromorphic computing. Molecular spintronics seems to be a fertile field for the design and preparation of this hardware. Within molecular spintronics, recent r...

Machine and Deep Learning for Prediction of Subcellular Localization.

Methods in molecular biology (Clifton, N.J.)
Protein subcellular localization prediction (PSLP), which plays an important role in the field of computational biology, identifies the position and function of proteins in cells without expensive cost and laborious effort. In the past few decades, v...

Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives.

Current medicinal chemistry
Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming...

Improved Prediction of Protein-Protein Interaction Mapping on by Using Amino Acid Sequence Features in a Supervised Learning Framework.

Protein and peptide letters
BACKGROUND: Protein-Protein Interaction (PPI) has emerged as a key role in the control of many biological processes including protein function, disease incidence, and therapy design. However, the identification of PPI by wet lab experiment is a chall...

Predicting bacterial virulence factors - evaluation of machine learning and negative data strategies.

Briefings in bioinformatics
Bacterial proteins dubbed virulence factors (VFs) are a highly diverse group of sequences, whose only obvious commonality is the very property of being, more or less directly, involved in virulence. It is therefore tempting to speculate whether their...

TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying compound-protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires develo...

Protein-protein interaction site prediction through combining local and global features with deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPIs) play important roles in many biological processes. Conventional biological experiments for identifying PPI sites are costly and time-consuming. Thus, many computational approaches have been proposed to ...

MUFold-SSW: a new web server for predicting protein secondary structures, torsion angles and turns.

Bioinformatics (Oxford, England)
MOTIVATION: Protein secondary structure and backbone torsion angle prediction can provide important information for predicting protein 3D structures and protein functions. Our new methods MUFold-SS, MUFold-Angle, MUFold-BetaTurn and MUFold-GammaTurn,...