AIMC Topic: Peptides

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HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

Bioinformatics (Oxford, England)
MOTIVATION: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioac...

Deep learning of protein sequence design of protein-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protei...

How to Design Peptides.

Methods in molecular biology (Clifton, N.J.)
Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial ...

HLAncPred: a method for predicting promiscuous non-classical HLA binding sites.

Briefings in bioinformatics
Human leukocyte antigens (HLA) regulate various innate and adaptive immune responses and play a crucial immunomodulatory role. Recent studies revealed that non-classical HLA-(HLA-E & HLA-G) based immunotherapies have many advantages over traditional ...

DeepSCP: utilizing deep learning to boost single-cell proteome coverage.

Briefings in bioinformatics
Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computat...

Predicting protein-peptide binding residues via interpretable deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on...

InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol.

Bioinformatics (Oxford, England)
MOTIVATION: Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide-protein ...

Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides.

Briefings in bioinformatics
Therapeutic peptides act on the skeletal system, digestive system and blood system, have antibacterial properties and help relieve inflammation. In order to reduce the resource consumption of wet experiments for the identification of therapeutic pept...

Do deep learning models make a difference in the identification of antimicrobial peptides?

Briefings in bioinformatics
In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the development of machine learning models to predict antimicrobial activities in peptides. The first genera...