AIMC Topic: Proteins

Clear Filters Showing 1781 to 1790 of 2080 articles

SSpro/ACCpro 6: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, deep learning and structural similarity.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and an early-stage component of typical protein 3D structure prediction pipelines.

BACPI: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction.

Bioinformatics (Oxford, England)
MOTIVATION: The identification of compound-protein interactions (CPIs) is an essential step in the process of drug discovery. The experimental determination of CPIs is known for a large amount of funds and time it consumes. Computational model has th...

Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities.

Briefings in bioinformatics
Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods are more and more popular in recent y...

ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.

Briefings in bioinformatics
Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secr...

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.

Briefings in bioinformatics
MOTIVATION: Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds...

EGRET: edge aggregated graph attention networks and transfer learning improve protein-protein interaction site prediction.

Briefings in bioinformatics
MOTIVATION: Protein-protein interactions (PPIs) are central to most biological processes. However, reliable identification of PPI sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computa...

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds.

Briefings in bioinformatics
Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force f...

Learning spatial structures of proteins improves protein-protein interaction prediction.

Briefings in bioinformatics
Spatial structures of proteins are closely related to protein functions. Integrating protein structures improves the performance of protein-protein interaction (PPI) prediction. However, the limited quantity of known protein structures restricts the ...

Comprehensive Prediction of Lipocalin Proteins Using Artificial Intelligence Strategy.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Lipocalin belongs to the calcyin family, and its sequence length is generally between 165 and 200 residues. They are mainly stable and multifunctional extracellular proteins. Lipocalin plays an important role in several stress responses a...

ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.

Bioinformatics (Oxford, England)
MOTIVATION: Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from ther...