AIMC Topic: Databases, Protein

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Structure-based protein function prediction using graph convolutional networks.

Nature communications
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting pro...

Deep Learning-Based Advances in Protein Structure Prediction.

International journal of molecular sciences
Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine prot...

An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties.

BMC bioinformatics
BACKGROUND: DNA-binding hot spots are dominant and fundamental residues that contribute most of the binding free energy yet accounting for a small portion of protein-DNA interfaces. As experimental methods for identifying hot spots are time-consuming...

Bingham deep neural and oppositional fish swarm optimized protein structure prediction.

Journal of biomolecular structure & dynamics
It is familiar that essential proteins take part in managing cellular activities in living organisms. Moreover, protein structure prediction from its amino acid sequence is advantageous to the comprehending of cellular functions. Formerly, several es...

Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolver.

STAR protocols
Computational generation of new proteins with a predetermined three-dimensional shape and computational optimization of existing proteins while maintaining their shape are challenging problems in structural biology. Here, we present a protocol that u...

Convolutional neural networks with image representation of amino acid sequences for protein function prediction.

Computational biology and chemistry
Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approache...

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.

BioMed research international
Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be e...

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data.

Nature methods
Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein ...

A Cascade Graph Convolutional Network for Predicting Protein-Ligand Binding Affinity.

International journal of molecular sciences
Accurate prediction of binding affinity between protein and ligand is a very important step in the field of drug discovery. Although there are many methods based on different assumptions and rules do exist, prediction performance of protein-ligand bi...

Harnessing machine learning to guide phylogenetic-tree search algorithms.

Nature communications
Inferring a phylogenetic tree is a fundamental challenge in evolutionary studies. Current paradigms for phylogenetic tree reconstruction rely on performing costly likelihood optimizations. With the aim of making tree inference feasible for problems i...