AIMC Topic: Databases, Protein

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Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics.

Bioinformatics (Oxford, England)
MOTIVATION: The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geo...

Improving protein fold recognition using triplet network and ensemble deep learning.

Briefings in bioinformatics
Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive adva...

PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture.

Briefings in bioinformatics
The prediction of peptide secondary structures is fundamentally important to reveal the functional mechanisms of peptides with potential applications as therapeutic molecules. In this study, we propose a multi-view deep learning method named Peptide ...

Machine learning builds full-QM precision protein force fields in seconds.

Briefings in bioinformatics
Full-quantum mechanics (QM) calculations are extraordinarily precise but difficult to apply to large systems, such as biomolecules. Motivated by the massive demand for efficient calculations for large systems at the full-QM level and by the significa...

nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.

Briefings in bioinformatics
Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identificatio...

Forman persistent Ricci curvature (FPRC)-based machine learning models for protein-ligand binding affinity prediction.

Briefings in bioinformatics
Artificial intelligence (AI) techniques have already been gradually applied to the entire drug design process, from target discovery, lead discovery, lead optimization and preclinical development to the final three phases of clinical trials. Currentl...

The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity.

Current medicinal chemistry
BACKGROUND: One of the main challenges in the early stages of drug discovery is the computational assessment of protein-ligand binding affinity. Machine learning techniques can contribute to predicting this type of interaction. We may apply these tec...

Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction.

Briefings in bioinformatics
Molecular descriptors are essential to not only quantitative structure activity/property relationship (QSAR/QSPR) models, but also machine learning based chemical and biological data analysis. In this paper, we propose persistent spectral hypergraph ...

A comprehensive review of the imbalance classification of protein post-translational modifications.

Briefings in bioinformatics
Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-dept...