AIMC Topic: Proteins

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MM-StackEns: A new deep multimodal stacked generalization approach for protein-protein interaction prediction.

Computers in biology and medicine
Accurate in-silico identification of protein-protein interactions (PPIs) is a long-standing problem in biology, with important implications in protein function prediction and drug design. Current computational approaches predominantly use a single da...

Prediction of lysine HMGylation sites using multiple feature extraction and fuzzy support vector machine.

Analytical biochemistry
Protein 3-hydroxyl-3-methylglutarylation (HMGylation) is newly discovered lysine acylation modification in mitochondrion. The accurate identification of HMGylation sites is the premise and key to further explore the molecular mechanisms of HMGylation...

Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods.

Current opinion in structural biology
Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in,...

NCSP-PLM: An ensemble learning framework for predicting non-classical secreted proteins based on protein language models and deep learning.

Mathematical biosciences and engineering : MBE
Non-classical secreted proteins (NCSPs) refer to a group of proteins that are located in the extracellular environment despite the absence of signal peptides and motifs. They usually play different roles in intercellular communication. Therefore, the...

DeepBSRPred: deep learning-based binding site residue prediction for proteins.

Amino acids
MOTIVATION: Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand t...

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction.

Computers in biology and medicine
New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance o...

Application of machine learning on understanding biomolecule interactions in cellular machinery.

Bioresource technology
Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecul...

Water irradiation devoid pulses enhance the sensitivity of H,H nuclear Overhauser effects.

Journal of biomolecular NMR
The nuclear Overhauser effect (NOE) is one of NMR spectroscopy's most important and versatile parameters. NOE is routinely utilized to determine the structures of medium-to-large size biomolecules and characterize protein-protein, protein-RNA, protei...

A systematic review on the state-of-the-art strategies for protein representation.

Computers in biology and medicine
The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected effi...

Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings.

BMC bioinformatics
BACKGROUND: Compound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound-protein interacti...