International journal of molecular sciences
Mar 28, 2018
Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the...
Designing protein sequences that can fold into a given structure is a well-known inverse protein-folding problem. One important characteristic to attain for a protein design program is the ability to recover wild-type sequences given their native bac...
Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neura...
Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO,...
BACKGROUND: Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of bio...
The receptor-associated protein (RAP) is an inhibitor of endocytic receptors that belong to the lipoprotein receptor gene family. In this study, a computational approach was tried to find the evolutionarily related fold of the RAP proteins. Through t...
Journal of chemical information and modeling
Feb 22, 2018
Fast generation of plausible molecular conformations is central to molecular modeling. This paper presents an approach to conformer generation that makes extensive use of the information available in the Cambridge Structural Database. By using geomet...
Current opinion in structural biology
Feb 21, 2018
Classical molecular dynamics (MD) simulations will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy. Notwithstanding this, MD will still be in the regime of lo...
Journal of chemical information and modeling
Feb 15, 2018
To develop a new ensemble learning method and construct highly predictive regression models in chemoinformatics and chemometrics, applicability domains (ADs) are introduced into the ensemble learning process of prediction. When estimating values of a...
Quantitative structure-activity relationships (QSARs) built using machine learning methods, such as artificial neural networks (ANNs) are powerful in prediction of (antioxidant) activity from quantum mechanical (QM) parameters describing the molecula...
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