AIMC Topic: Proteins

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A D3R prospective evaluation of machine learning for protein-ligand scoring.

Journal of computer-aided molecular design
We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in which they were trained make them sufficiently diverse to...

Purely Structural Protein Scoring Functions Using Support Vector Machine and Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
The function of a protein is determined by its structure, which creates a need for efficient methods of protein structure determination to advance scientific and medical research. Because current experimental structure determination methods carry a h...

Predicting the errors of predicted local backbone angles and non-local solvent- accessibilities of proteins by deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Backbone structures and solvent accessible surface area of proteins are benefited from continuous real value prediction because it removes the arbitrariness of defining boundary between different secondary-structure and solvent-accessibil...

Eliciting the Functional Taxonomy from protein annotations and taxa.

Scientific reports
The advances of omics technologies have triggered the production of an enormous volume of data coming from thousands of species. Meanwhile, joint international efforts like the Gene Ontology (GO) consortium have worked to provide functional informati...

A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regions.

BMC bioinformatics
BACKGROUND: One very important functional domain of proteins is the protein-protein interacting region (PPIR), which forms the binding interface between interacting polypeptide chains. Post-translational modifications (PTMs) that occur in the PPIR ca...

Sorting protein decoys by machine-learning-to-rank.

Scientific reports
Much progress has been made in Protein structure prediction during the last few decades. As the predicted models can span a broad range of accuracy spectrum, the accuracy of quality estimation becomes one of the key elements of successful protein str...

Effect of Protein Repetitiveness on Protein-Protein Interaction Prediction Results Using Support Vector Machines.

Journal of computational biology : a journal of computational molecular cell biology
BACKGROUND: There are many computational approaches to predict the protein-protein interactions using support vector machines (SVMs) with high performance. In fact, performance of currently reported methods are significantly over-estimated and affect...

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

PloS one
Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine l...

Positive-Unlabeled Learning for Pupylation Sites Prediction.

BioMed research international
Pupylation plays a key role in regulating various protein functions as a crucial posttranslational modification of prokaryotes. In order to understand the molecular mechanism of pupylation, it is important to identify pupylation substrates and sites ...

Protein function in precision medicine: deep understanding with machine learning.

FEBS letters
Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and speciali...