AIMC Topic: Databases, Protein

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AGL-Score: Algebraic Graph Learning Score for Protein-Ligand Binding Scoring, Ranking, Docking, and Screening.

Journal of chemical information and modeling
Although algebraic graph theory-based models have been widely applied in physical modeling and molecular studies, they are typically incompetent in the analysis and prediction of biomolecular properties, confirming the common belief that "one cannot ...

Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites.

Proteins
Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites throu...

Conservation region finding for influenza A viruses by machine learning methods of N-linked glycosylation sites and B-cell epitopes.

Mathematical biosciences
Influenza type A, a serious infectious disease of the human respiratory tract, poses an enormous threat to human health worldwide. It leads to high mortality rates in poultry, pigs, and humans. The primary target identity regions for the human immune...

Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.

BMC bioinformatics
BACKGROUND: Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in protein...

Prediction of Enzyme Function Based on Three Parallel Deep CNN and Amino Acid Mutation.

International journal of molecular sciences
During the past decade, due to the number of proteins in PDB database being increased gradually, traditional methods cannot better understand the function of newly discovered enzymes in chemical reactions. Computational models and protein feature rep...

ProteinNet: a standardized data set for machine learning of protein structure.

BMC bioinformatics
BACKGROUND: Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like computer vision, progress has been driven by standardized ...

An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies.

BMC bioinformatics
BACKGROUND: Antimicrobial peptides (AMPs) are essential components of the innate immune system and can protect the host from various pathogenic bacteria. The marine environment is known to be one of the richest sources for AMPs. Effective usage of AM...

A novel matrix of sequence descriptors for predicting protein-protein interactions from amino acid sequences.

PloS one
Protein-protein interactions (PPIs) play an important role in the life activities of organisms. With the availability of large amounts of protein sequence data, PPIs prediction methods have attracted increasing attention. A variety of protein sequenc...

MHCSeqNet: a deep neural network model for universal MHC binding prediction.

BMC bioinformatics
BACKGROUND: Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells expressing unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synt...

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.

Nature methods
In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impair...