AIMC Topic: Protein Conformation

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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,...
Neural Networks, Computer Algorithms Protein Conformation Static Electricity Thermodynamics Proteins Molecular Dynamics Simulation Quantum Theory
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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...
Protein Folding Amino Acid Sequence Protein Conformation Binding Sites Computer Simulation Machine Learning Zinc Software Protein Binding Algorithms Computational Biology Support Vector Machine Databases, Protein
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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...
Molecular Docking Simulation Deep Learning Protein Binding Algorithms Proteins Molecular Dynamics Simulation Protein Conformation
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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...
Protein Conformation Sequence Analysis, Protein Proteins Algorithms Deep Learning Discriminant Analysis Computational Biology Amino Acid Sequence
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DNAPred: Accurate Identification of DNA-Binding Sites from Protein Sequence by Ensembled Hyperplane-Distance-Based Support Vector Machines.

Journal of chemical information and modeling Apr 16, 2019
Accurate identification of protein-DNA binding sites is significant for both understanding protein function and drug design. Machine-learning-based methods have been extensively used for the prediction of protein-DNA binding sites. However, the data ...
Nucleic Acid Conformation Support Vector Machine DNA Models, Molecular DNA-Binding Proteins Protein Conformation
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Machine learning-assisted directed protein evolution with combinatorial libraries.

Proceedings of the National Academy of Sciences of the United States of America Apr 12, 2019
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequ...
Rhodothermus Small Molecule Libraries Combinatorial Chemistry Techniques Oxygenases Protein Conformation Directed Molecular Evolution Humans Machine Learning Models, Molecular Amino Acid Sequence
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Deep Learning and Random Forest Approach for Finding the Optimal Traditional Chinese Medicine Formula for Treatment of Alzheimer's Disease.

Journal of chemical information and modeling Apr 2, 2019
It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's disease (AD). On the basis of the world largest traditional Chinese medicine (TCM) database, a network-pharmacology-based approach was utilized to investigate TCM ...
Protein Interaction Maps Computational Biology Support Vector Machine Molecular Docking Simulation Deep Learning Drug Compounding Quantitative Structure-Activity Relationship Protein Conformation Medicine, Chinese Traditional Molecular Dynamics Simulation Alzheimer Disease Databases, Pharmaceutical Glycogen Synthase Kinase 3
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Automated discovery of GPCR bioactive ligands.

Current opinion in structural biology Mar 23, 2019
While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiolog...
Receptors, G-Protein-Coupled Molecular Dynamics Simulation Ligands Drug Design Humans Protein Conformation Drug Discovery Machine Learning Molecular Docking Simulation
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IVS2vec: A tool of Inverse Virtual Screening based on word2vec and deep learning techniques.

Methods (San Diego, Calif.) Mar 22, 2019
Inverse Virtual Screening is a powerful technique in the early stage of drug discovery process. This technique can provide important clues for biologically active molecules, which is useful in the following researches of durg discovery. In this work,...
User-Computer Interface Proteins Protein Conformation Databases, Factual Molecular Docking Simulation Software Deep Learning Algorithms
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Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.

Journal of chemical information and modeling Mar 18, 2019
Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual scr...
Thermodynamics Cheminformatics Ligands Protein Conformation User-Computer Interface Machine Learning Proteins Molecular Docking Simulation Drug Evaluation, Preclinical
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