AIMC Topic: Proteins

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Protein-Ligand Scoring with Convolutional Neural Networks.

Journal of chemical information and modeling Apr 11, 2017
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affini...
User-Computer Interface Drug Evaluation, Preclinical Computational Biology Ligands Protein Conformation Neural Networks, Computer Proteins Models, Molecular
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Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions.

Journal of chemical information and modeling Apr 5, 2017
The prediction of protein-ligand binding affinity has recently been improved remarkably by machine-learning-based scoring functions. For example, using a set of simple descriptors representing the atomic distance counts, the RF-Score improves the Pea...
Proteins Amino Acid Sequence Protein Binding Machine Learning Computational Biology Ligands
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Assessment of Semantic Similarity between Proteins Using Information Content and Topological Properties of the Gene Ontology Graph.

IEEE/ACM transactions on computational biology and bioinformatics Mar 31, 2017
The semantic similarity between two interacting proteins can be estimated by combining the similarity scores of the GO terms associated with the proteins. Greater number of similar GO annotations between two proteins indicates greater interaction aff...
Databases, Protein Humans Saccharomyces cerevisiae Proteins Gene Ontology Protein Interaction Maps ROC Curve Semantics Computational Biology Proteins
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Protein fold recognition based on sparse representation based classification.

Artificial intelligence in medicine Mar 27, 2017
Knowledge of protein fold type is critical for determining the protein structure and function. Because of its importance, several computational methods for fold recognition have been proposed. Most of them are based on well-known machine learning tec...
Protein Folding Support Vector Machine Proteins Algorithms Image Processing, Computer-Assisted
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Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data.

BMC bioinformatics Mar 23, 2017
BACKGROUND: One goal of structural biology is to understand how a protein's 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex i...
Protein Binding Machine Learning Protein Conformation Models, Molecular Models, Statistical Data Accuracy Humans Proteins Animals
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Exploring Approaches for Detecting Protein Functional Similarity within an Orthology-based Framework.

Scientific reports Mar 23, 2017
Protein functional similarity based on gene ontology (GO) annotations serves as a powerful tool when comparing proteins on a functional level in applications such as protein-protein interaction prediction, gene prioritization, and disease gene discov...
Proteins Molecular Sequence Annotation Gene Ontology Databases, Factual Computational Biology
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InterPred: A pipeline to identify and model protein-protein interactions.

Proteins Mar 21, 2017
Protein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the numb...
Benchmarking Amino Acid Sequence Protein Interaction Mapping Proteins Internet Protein Interaction Domains and Motifs Sequence Analysis, Protein Software Computational Biology Support Vector Machine Molecular Docking Simulation
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Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment.

Proteins Mar 21, 2017
Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during t...
Computational Biology Support Vector Machine Models, Statistical Software Protein Conformation Monte Carlo Method Internet Benchmarking Proteins Molecular Dynamics Simulation Double-Blind Method
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Multitask Protein Function Prediction through Task Dissimilarity.

IEEE/ACM transactions on computational biology and bioinformatics Mar 17, 2017
Automated protein function prediction is a challenging problem with distinctive features, such as the hierarchical organization of protein functions and the scarcity of annotated proteins for most biological functions. We propose a multitask learning...
Humans Machine Learning Gene Ontology Models, Statistical Drosophila Proteins Algorithms Proteins Computational Biology Escherichia coli Proteins
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FFLUX: Transferability of polarizable machine-learned electrostatics in peptide chains.

Journal of computational chemistry Mar 10, 2017
The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a krigi...
Physical Phenomena Protein Conformation Static Electricity Thermodynamics Machine Learning Models, Molecular Amino Acids Models, Theoretical Proteins Peptides
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