AIMC Topic: Proteins

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Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.

BMC bioinformatics
BACKGROUND: Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when boun...

Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences.

Genomics
Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annota...

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique.

Journal of theoretical biology
As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimen...

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.

Computational biology and chemistry
Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (I...

Clustering molecular dynamics trajectories for optimizing docking experiments.

Computational intelligence and neuroscience
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the f...

BgN-Score and BsN-Score: bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes.

BMC bioinformatics
BACKGROUND: Accurately predicting the binding affinities of large sets of protein-ligand complexes is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. Since a scorin...

Bioinformatics approaches for the functional interpretation of protein lists: from ontology term enrichment to network analysis.

Proteomics
The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation ...

Machine learning in computational docking.

Artificial intelligence in medicine
OBJECTIVE: The objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has...

Computationally predicting protein-RNA interactions using only positive and unlabeled examples.

Journal of bioinformatics and computational biology
Protein-RNA interactions (PRIs) are considerably important in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulations of gene expression to the active defense of host against virus. With the development...

BagReg: Protein inference through machine learning.

Computational biology and chemistry
Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have ...