AIMC Topic: Protein Binding

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A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.

Nucleic acids research Aug 21, 2017
Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-s...
Algorithms Reproducibility of Results Protein Binding Computational Biology RNA-Binding Proteins Humans Machine Learning Base Sequence Binding Sites Nucleotide Motifs Binding, Competitive 3' Untranslated Regions Mutation Internet High-Throughput Nucleotide Sequencing Gene Expression Regulation
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NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.

Nucleic acids research Jul 3, 2017
Peptides are extensively used to characterize functional or (linear) structural aspects of receptor-ligand interactions in biological systems, e.g. SH2, SH3, PDZ peptide-recognition domains, the MHC membrane receptors and enzymes such as kinases and ...
HLA-B8 Antigen Forkhead Transcription Factors Peptides Humans Amino Acid Sequence Neural Networks, Computer HLA-DRB1 Chains Protein Binding Ligands Software Databases, Protein Cell Cycle Proteins Algorithms Binding Sites Basic Helix-Loop-Helix Leucine Zipper Transcription Factors Internet Trans-Activators Sequence Alignment HLA-A1 Antigen HLA-B7 Antigen
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Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network.

Molecular bioSystems Jun 27, 2017
Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. Although high-throughput technologies provide the possibi...
Algorithms Protein Binding Computational Biology Support Vector Machine Protein Interaction Mapping Neural Networks, Computer
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Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.

Oncotarget Apr 4, 2017
Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has...
Computational Biology Support Vector Machine Databases, Protein Bacterial Proteins Helicobacter pylori Humans Algorithms Reproducibility of Results Protein Binding Saccharomyces cerevisiae Protein Interaction Mapping Saccharomyces cerevisiae Proteins
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ACTIVITY OF BLACKCURRANT AND CHOKEBERRY EXTRACTS AND TWO MAJOR CYANIDIN GLYCOSIDES AGAINST LIPID MEMBRANE OXIDATION AND THEIR BINDING PROPERTIES TO ALBUMIN.

Acta poloniae pharmaceutica Mar 1, 2017
The purpose of this study was to explain how extracts from chokeberry and blackcurrant interact with the lipid phase of biological membrane and with human albumin - the main protein of blood. Aiming at better understanding of the observed biological ...
Fruit Membrane Fluidity Prunus Plant Extracts Plants, Medicinal Humans Ribes Cell Membrane Liposomes Dose-Response Relationship, Drug Time Factors Serum Albumin, Human Phosphatidylcholines Oxidation-Reduction Protein Binding Phytotherapy Anthocyanins Antioxidants Galactosides
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Design of Knowledge Bases for Plant Gene Regulatory Networks.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2017
Developing a knowledge base that contains all the information necessary for the researcher studying gene regulation in a particular organism can be accomplished in four stages. This begins with defining the data scope. We describe here the necessary ...
Computational Biology Transcription Factors Databases, Genetic Plants Chromosome Mapping Knowledge Bases Software Web Browser Binding Sites Transcription Initiation Site User-Computer Interface Gene Expression Regulation, Plant Gene Regulatory Networks Search Engine Protein Binding Database Management Systems
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An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2017
Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to ...
Signal Transduction Databases, Protein Workflow Supervised Machine Learning Protein Interaction Mapping Phosphotyrosine Web Browser Protein Interaction Domains and Motifs src Homology Domains Algorithms Reproducibility of Results Protein Binding Humans Computational Biology
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Continuous Hemodiafiltration with an AN69ST Hemofilter (AN69ST-CHDF) as FGF-23-Lowering Therapy.

Clinical laboratory Dec 1, 2016
BACKGROUND: Recent studies have shown that fibroblast growth factor-23 (FGF-23) is elevated not only in chronic kidney disease (CKD), but also in acute illnesses such as acute kidney injury, septic shock, and acute heart failure. FGF-23 would be not ...
Surface Properties Female Acrylonitrile Aged Equipment Design Membranes, Artificial Protein Binding Male Fibroblast Growth Factor-23 Hemodiafiltration Treatment Outcome Humans Middle Aged Fibroblast Growth Factors Adsorption Biomarkers Acrylic Resins Acute Disease Time Factors Down-Regulation
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Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase.

Molecular bioSystems Nov 15, 2016
N-Myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking substrate proteins to membrane locations, stabilization and protein-protei...
Machine Learning Algorithms Molecular Docking Simulation Protein Binding Workflow Molecular Dynamics Simulation Enzyme Inhibitors Datasets as Topic Catalytic Domain Parasitic Sensitivity Tests Quantitative Structure-Activity Relationship Binding Sites Trypanocidal Agents Acyltransferases
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Convolutional neural network architectures for predicting DNA-protein binding.

Bioinformatics (Oxford, England) Jun 15, 2016
MOTIVATION: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA-protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth...
Neural Networks, Computer Algorithms Protein Binding Proteins DNA
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