AIMC Topic: Proteins

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Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites.

Proteins Jun 22, 2019
Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites throu...
Algorithms Protein Binding Evolution, Molecular Humans Artificial Intelligence Machine Learning Protein Conformation Binding Sites Allosteric Site Allosteric Regulation Proteins Databases, Protein Molecular Dynamics Simulation
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DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction.

BMC bioinformatics Jun 17, 2019
BACKGROUND: Protein secondary structure (PSS) is critical to further predict the tertiary structure, understand protein function and design drugs. However, experimental techniques of PSS are time consuming and expensive, and thus it's very urgent to ...
Neural Networks, Computer Protein Structure, Secondary Models, Theoretical Algorithms Proteins Deep Learning Protein Domains
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Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.

BMC bioinformatics Jun 17, 2019
BACKGROUND: Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in protein...
Lysine Molecular Sequence Annotation Gene Ontology Protein Structure, Secondary Position-Specific Scoring Matrices Databases, Protein Amino Acid Sequence Acetylation Support Vector Machine Protein Processing, Post-Translational Proteins Humans Animals Mice Computational Biology Rats
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DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.

PLoS computational biology Jun 14, 2019
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In severa...
Proteins Models, Molecular Amino Acid Sequence Drug Discovery Deep Learning Computational Biology Ligands Binding Sites Sequence Analysis, Protein Computer Simulation
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ProteinNet: a standardized data set for machine learning of protein structure.

BMC bioinformatics Jun 11, 2019
BACKGROUND: Rapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like computer vision, progress has been driven by standardized ...
Machine Learning Proteins Databases, Protein Amino Acid Sequence Reference Standards Sequence Alignment
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Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index.

Nature communications Jun 7, 2019
Chemical shifts (CS) are determined from NMR experiments and represent the resonance frequency of the spin of atoms in a magnetic field. They contain a mixture of information, encompassing the in-solution conformations a protein adopts, as well as th...
Models, Molecular Neural Networks, Computer Amino Acids Nuclear Magnetic Resonance, Biomolecular Magnetic Resonance Imaging Biophysical Phenomena Magnetic Resonance Spectroscopy Proteins Protein Structure, Secondary
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Deep Learning to Therapeutically Target Unreported Complexes.

Trends in pharmacological sciences May 21, 2019
The disruption of large protein-protein (PP) interfaces remains a challenge in targeted therapy. Designing drugs that compete with binding partners is daunting, especially when the structure of the protein complex is unknown. To address the problem w...
Molecular Targeted Therapy Deep Learning Protein Interaction Maps Humans Proteins Databases, Factual Drug Discovery
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Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures.

Amino acids May 16, 2019
Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be obtained from known protein structures. The pairwise potential between ato...
Entropy Knowledge Bases Hydrophobic and Hydrophilic Interactions Protein Conformation Proteins Molecular Structure Models, Molecular Amino Acids Amino Acid Sequence
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PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges.

Journal of chemical theory and computation May 14, 2019
In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry. After being trained on appropriate ab initio reference data, these methods allow for accurately predicting the properties of chemical systems,...
Proteins Molecular Dynamics Simulation Quantum Theory Protein Conformation Static Electricity Thermodynamics Neural Networks, Computer Algorithms
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DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks.

Scientific reports May 14, 2019
Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conventional algorithms in comput...
Humans Neural Networks, Computer Proteins Pseudomonas Infections Bacterial Proteins Pseudomonas aeruginosa Models, Biological Data Mining Software Gene Ontology Deep Learning Biofilms
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