AI Medical Compendium Topic:
Protein Conformation

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Accurate refinement of docked protein complexes using evolutionary information and deep learning.

Journal of bioinformatics and computational biology
One of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes a...

PCOSKB: A KnowledgeBase on genes, diseases, ontology terms and biochemical pathways associated with PolyCystic Ovary Syndrome.

Nucleic acids research
Polycystic ovary syndrome (PCOS) is one of the major causes of female subfertility worldwide and ≈ 7-10% of women in reproductive age are affected by it. The affected individuals exhibit varying types and levels of comorbid conditions, along with the...

Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

The Journal of membrane biology
Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have ...

Protein folds recognized by an intelligent predictor based-on evolutionary and structural information.

Journal of computational chemistry
Protein fold recognition is an important and essential step in determining tertiary structure of a protein in biological science. In this study, a model termed NiRecor is developed for recognizing protein folds based on artificial neural networks inc...

APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction.

Computational biology and chemistry
Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino a...

Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein contact prediction is important for protein structure and functional study. Both evolutionary coupling (EC) analysis and supervised machine learning methods have been developed, making use of different information sources. However...

LoopIng: a template-based tool for predicting the structure of protein loops.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are n...

idDock+: Integrating Machine Learning in Probabilistic Search for Protein-Protein Docking.

Journal of computational biology : a journal of computational molecular cell biology
Predicting the three-dimensional native structures of protein dimers, a problem known as protein-protein docking, is key to understanding molecular interactions. Docking is a computationally challenging problem due to the diversity of interactions an...

Binding Activity Prediction of Cyclin-Dependent Inhibitors.

Journal of chemical information and modeling
The Cyclin-Dependent Kinases (CDKs) are the core components coordinating eukaryotic cell division cycle. Generally the crystal structure of CDKs provides information on possible molecular mechanisms of ligand binding. However, reliable and robust est...

Using support vector machines to improve elemental ion identification in macromolecular crystal structures.

Acta crystallographica. Section D, Biological crystallography
In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific know...