AIMC Topic: Proteins

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Integrating Bonded and Nonbonded Potentials in the Knowledge-Based Scoring Function for Protein Structure Prediction.

Journal of chemical information and modeling May 13, 2019
An accurate energy scoring function is crucial for protein structure prediction. Given the increasing number of experimentally determined structures, knowledge-based approaches have been widely used to develop scoring functions for protein structure ...
Proteins Knowledge Bases Thermodynamics Computational Biology
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MCP: A multi-component learning machine to predict protein secondary structure.

Computers in biology and medicine May 12, 2019
The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through the translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightl...
Proteins Protein Structure, Secondary Machine Learning Models, Molecular
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PiPred - a deep-learning method for prediction of π-helices in protein sequences.

Scientific reports May 3, 2019
Canonical π-helices are short, relatively unstable secondary structure elements found in proteins. They comprise seven or more residues and are present in 15% of all known protein structures, often in functionally important regions such as ligand- an...
Deep Learning Models, Molecular Amino Acid Sequence Computational Biology Proteins Protein Conformation, alpha-Helical
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Extracting chemical-protein interactions from biomedical literature via granular attention based recurrent neural networks.

Computer methods and programs in biomedicine Apr 30, 2019
BACKGROUND AND OBJECTIVE: The extraction of interactions between chemicals and proteins from biomedical literature is important for many biomedical tasks such as drug discovery and precision medicine. In the existing systems, the methods achieving co...
Probability Deep Learning Humans Databases, Protein Software PubMed Proteins Publications Data Mining Reproducibility of Results Neural Networks, Computer
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Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space.

Structure (London, England : 1993) Apr 25, 2019
Flexibility is often a key determinant of protein function. To elucidate the link between their molecular structure and role in an organism, computational techniques such as molecular dynamics can be leveraged to characterize their conformational spa...
Protein Binding Proteins Molecular Dynamics Simulation Protein Conformation Molecular Docking Simulation Deep Learning Algorithms
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Reporting and connecting cell type names and gating definitions through ontologies.

BMC bioinformatics Apr 25, 2019
BACKGROUND: Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a p...
Proteins Immune System Humans Biological Ontologies Protein Subunits Databases, Factual
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Deep Robust Framework for Protein Function Prediction Using Variable-Length Protein Sequences.

IEEE/ACM transactions on computational biology and bioinformatics Apr 16, 2019
The order of amino acids in a protein sequence enables the protein to acquire a conformation suitable for performing functions, thereby motivating the need to analyze these sequences for predicting functions. Although machine learning based approache...
Discriminant Analysis Protein Conformation Computational Biology Amino Acid Sequence Algorithms Proteins Sequence Analysis, Protein Deep Learning
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Computational advances in combating colloidal aggregation in drug discovery.

Nature chemistry Apr 15, 2019
Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assa...
Protein Binding Organic Chemicals Machine Learning Drug Discovery Small Molecule Libraries Proteins Colloids
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Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.

Methods (San Diego, Calif.) Apr 13, 2019
Polyadenylation signals (PAS) are found in most protein-coding and some non-coding genes in eukaryotes. Their accurate recognition improves understanding gene regulation mechanisms and recognition of the 3'-end of transcribed gene regions where prema...
Proteins Genome, Human Poly A Polyadenylation Software Genomics Humans Neural Networks, Computer
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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...
Deep Learning Binding Sites Metals Proteins
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