AIMC Topic: Proteins

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

Bioinformatics (Oxford, England) Aug 22, 2016
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...
Amino Acids Solvents Protein Structure, Secondary Computational Biology Proteins Neural Networks, Computer
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Eliciting the Functional Taxonomy from protein annotations and taxa.

Scientific reports Aug 18, 2016
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...
Gene Ontology Proteome Proteins Databases, Protein Species Specificity Humans Animals
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A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regions.

BMC bioinformatics Aug 17, 2016
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...
Machine Learning Proteins Protein Processing, Post-Translational Protein Interaction Domains and Motifs
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Sorting protein decoys by machine-learning-to-rank.

Scientific reports Aug 17, 2016
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...
Algorithms Proteins Machine Learning Models, Molecular Protein Transport
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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 Aug 16, 2016
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...
Humans Support Vector Machine Proteins Protein Interaction Mapping Datasets as Topic Saccharomyces cerevisiae
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SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

PloS one Aug 15, 2016
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...
Support Vector Machine Proteins Computational Biology Amino Acid Sequence Sequence Alignment Databases, Protein Internet
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Positive-Unlabeled Learning for Pupylation Sites Prediction.

BioMed research international Aug 7, 2016
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 ...
Proteins Ubiquitination Support Vector Machine Lysine Databases, Protein Machine Learning Binding Sites Protein Processing, Post-Translational Algorithms
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Protein function in precision medicine: deep understanding with machine learning.

FEBS letters Aug 6, 2016
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...
Humans Machine Learning Precision Medicine Computational Biology Proteins Protein Interaction Maps
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Adaptive local learning in sampling based motion planning for protein folding.

BMC systems biology Aug 1, 2016
BACKGROUND: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods ...
Protein Folding Computational Biology Proteins Thermodynamics Models, Molecular Movement Protein Conformation Machine Learning
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A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces.

International journal of molecular sciences Jul 27, 2016
Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous publ...
Proteins Databases, Protein Protein Conformation Protein Interaction Mapping Algorithms Computational Biology Protein Interaction Domains and Motifs Humans Machine Learning
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