AIMC Topic: Binding Sites

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DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.

Journal of computer-aided molecular design May 23, 2019
DNA-binding proteins (DBPs) participate in various biological processes including DNA replication, recombination, and repair. In the human genome, about 6-7% of these proteins are utilized for genes encoding. DBPs shape the DNA into a compact structu...
Animals Support Vector Machine Evolution, Molecular Humans Machine Learning Algorithms Binding Sites Position-Specific Scoring Matrices Databases, Protein DNA DNA-Binding Proteins
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Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods.

Molecular omics May 2, 2019
The zinc (Zn) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in pro...
Computer Simulation Machine Learning Zinc Amino Acid Sequence Software Protein Binding Binding Sites Protein Conformation Algorithms Computational Biology Protein Folding Support Vector Machine Databases, Protein
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MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites.

BMC bioinformatics May 1, 2019
BACKGROUND: Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these ...
Algorithms Humans Support Vector Machine Protein Binding Binding Sites Transcription Factors Nucleotide Motifs
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Prediction of Protein Metal Binding Sites Using Deep Neural Networks.

Molecular informatics Apr 12, 2019
Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which v...
Metals Proteins Binding Sites Deep Learning
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RNA-Protein Binding Sites Prediction via Multi Scale Convolutional Gated Recurrent Unit Networks.

IEEE/ACM transactions on computational biology and bioinformatics Apr 11, 2019
RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-pr...
RNA Binding Sites Neural Networks, Computer Algorithms Protein Binding Computational Biology RNA-Binding Proteins
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FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.

Methods (San Diego, Calif.) Mar 26, 2019
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, call...
Binding Sites Protein Binding Neural Networks, Computer Chromatin Transcription Factors Software Nucleotides Deep Learning
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Elucidating the druggability of the human proteome with eFindSite.

Journal of computer-aided molecular design Mar 19, 2019
Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and r...
Binding Sites 5-Aminolevulinate Synthetase Humans Proteins Software Supervised Machine Learning Drug Design Serine Proteases Drug Discovery Protein Binding Proteome
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Predicting protein-peptide interaction sites using distant protein complexes as structural templates.

Scientific reports Mar 12, 2019
Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details...
Humans Machine Learning Software Peptides Cluster Analysis Molecular Docking Simulation Datasets as Topic Proteins Protein Binding Binding Sites Amino Acid Sequence Computational Biology Protein Interaction Mapping
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A combined drug discovery strategy based on machine learning and molecular docking.

Chemical biology & drug design Mar 7, 2019
Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k-Nearest neighbor, support vector machines, random forests,...
Protein Structure, Tertiary Inhibitory Concentration 50 Crystallography, X-Ray Binding Sites ROC Curve Principal Component Analysis Area Under Curve Molecular Docking Simulation Drug Discovery Machine Learning Databases, Factual
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Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

Journal of molecular graphics & modelling Mar 5, 2019
BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has al...
Ligands Hydrophobic and Hydrophilic Interactions Humans Receptors, FSH Quantitative Structure-Activity Relationship Computational Biology Machine Learning Cell Line Drug Design Binding Sites Models, Molecular Protein Binding Computers, Molecular
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