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Amino Acid Sequence

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A CNN-CBAM-BIGRU model for protein function prediction.

Statistical applications in genetics and molecular biology
Understanding a protein's function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this challenge has proven difficult. However, recent years have witnessed the rise of deep learning as a powe...

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

Briefings in bioinformatics
The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods ...

TriplEP-CPP: Algorithm for Predicting the Properties of Peptide Sequences.

International journal of molecular sciences
Advancements in medicine and pharmacology have led to the development of systems that deliver biologically active molecules inside cells, increasing drug concentrations at target sites. This improves effectiveness and duration of action and reduces s...

APLpred: A machine learning-based tool for accurate prediction and characterization of asparagine peptide lyases using sequence-derived optimal features.

Methods (San Diego, Calif.)
Asparagine peptide lyase (APL) is among the seven groups of proteases, also known as proteolytic enzymes, which are classified according to their catalytic residue. APLs are synthesized as precursors or propeptides that undergo self-cleavage through ...

Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity.

Nature biomedical engineering
Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic use that rules governing the membrane selectivity of antimicrobial peptides be deciphered. However,...

Context-aware geometric deep learning for protein sequence design.

Nature communications
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning appr...

Uncovering substrate specificity determinants of class IIb aminoacyl-tRNA synthetases with machine learning.

Journal of molecular graphics & modelling
Specific amino acid (AA) binding by aminoacyl-tRNA synthetases (aaRSs) is necessary for correct translation of the genetic code. Sequence and structure analyses have revealed the main specificity determinants and allowed a partitioning of aaRSs into ...

Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.

International journal of molecular sciences
Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has s...

StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features.

Methods (San Diego, Calif.)
Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform ...

EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.

Protein science : a publication of the Protein Society
Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule prod...