AIMC Topic: Protein Binding

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Editorial-Sequences and topology.

Current opinion in structural biology Jun 4, 2017
Protein Binding Computational Biology Proteins Machine Learning Animals Molecular Docking Simulation Protein Multimerization Protein Interaction Domains and Motifs Humans Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Binding Sites Thermodynamics Protein Interaction Mapping
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Protein-Protein Interaction Interface Residue Pair Prediction Based on Deep Learning Architecture.

IEEE/ACM transactions on computational biology and bioinformatics May 19, 2017
MOTIVATION: Proteins usually fulfill their biological functions by interacting with other proteins. Although some methods have been developed to predict the binding sites of a monomer protein, these are not sufficient for prediction of the interactio...
Protein Binding Computational Biology Proteins Molecular Docking Simulation Deep Learning Binding Sites Surface Properties Protein Conformation
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Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

Scientific reports Apr 18, 2017
Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large ...
Machine Learning Monte Carlo Method Multiprotein Complexes Protein Binding Predictive Value of Tests Models, Chemical
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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...
Machine Learning Protein Binding Ligands Computational Biology Proteins Amino Acid Sequence
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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...
Data Accuracy Protein Conformation Models, Statistical Proteins Models, Molecular Humans Machine Learning Animals Protein Binding
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Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences.

BMC genomics Mar 14, 2017
BACKGROUND: The broad heterogeneity of antigen-antibody interactions brings tremendous challenges to the design of a widely applicable learning algorithm to identify conformational B-cell epitopes. Besides the intrinsic heterogeneity introduced by di...
Protein Conformation, beta-Strand Epitope Mapping Humans Protein Interaction Domains and Motifs B-Lymphocytes Epitopes, B-Lymphocyte Protein Binding Supervised Machine Learning Protein Conformation, alpha-Helical Antigens Models, Statistical Binding Sites Antigen-Antibody Complex
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Study of Structure-active Relationship for Inhibitors of HIV-1 Integrase LEDGF/p75 Interaction by Machine Learning Methods.

Molecular informatics Feb 28, 2017
HIV-1 integrase (IN) is a promising target for anti-AIDS therapy, and LEDGF/p75 is proved to enhance the HIV-1 integrase strand transfer activity in vitro. Blocking the interaction between IN and LEDGF/p75 is an effective way to inhibit HIV replicati...
Protein Binding Structure-Activity Relationship Models, Molecular Humans Computer Simulation Molecular Conformation Machine Learning Molecular Structure Reproducibility of Results HIV Integrase ROC Curve Intercellular Signaling Peptides and Proteins HIV Integrase Inhibitors
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RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

BMC bioinformatics Feb 28, 2017
BACKGROUND: RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recog...
Protein Binding Neural Networks, Computer Data Mining Nucleotide Motifs Machine Learning RNA-Binding Proteins RNA Algorithms Sequence Analysis, RNA Binding Sites
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A Hybrid Knowledge-Based and Empirical Scoring Function for Protein-Ligand Interaction: SMoG2016.

Journal of chemical information and modeling Feb 27, 2017
We present the third generation of our scoring function for the prediction of protein-ligand binding free energy. This function is now a hybrid between a knowledge-based potential and an empirical function. We constructed a diversified set of ∼1000 c...
Protein Binding Protein Conformation Entropy Knowledge Bases Ligands Proteins Probability Databases, Protein Models, Molecular
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Imputation for transcription factor binding predictions based on deep learning.

PLoS computational biology Feb 24, 2017
Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard....
High-Throughput Nucleotide Sequencing K562 Cells Binding Sites Protein Interaction Mapping Chromatin Immunoprecipitation Humans Neural Networks, Computer Machine Learning Protein Binding Reproducibility of Results Transcription Factors Sensitivity and Specificity Hep G2 Cells
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