AIMC Topic: Proteins

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Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach.

Journal of chemical information and modeling Mar 3, 2020
We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard...
Proteins Ligands Deep Learning Protein Binding Molecular Docking Simulation
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Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

Journal of chemical information and modeling Mar 3, 2020
In recent years, protein-ligand interaction scoring functions derived through machine-learning are repeatedly reported to outperform conventional scoring functions. However, several published studies have questioned that the superior performance of m...
Neural Networks, Computer Proteins Bayes Theorem Ligands Machine Learning Algorithms
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Drug-target interaction prediction with tree-ensemble learning and output space reconstruction.

BMC bioinformatics Feb 7, 2020
BACKGROUND: Computational prediction of drug-target interactions (DTI) is vital for drug discovery. The experimental identification of interactions between drugs and target proteins is very onerous. Modern technologies have mitigated the problem, lev...
Drug Discovery Machine Learning Drug Development Proteins Computer Simulation Cluster Analysis
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DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening.

Genomics, proteomics & bioinformatics Feb 6, 2020
Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-base...
Pharmaceutical Preparations ROC Curve Databases, Chemical Proteins Deep Learning Area Under Curve User-Computer Interface
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Improved protein structure prediction using potentials from deep learning.

Nature Jan 15, 2020
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence. This problem is of fundamental importance as the structure of a protein largely determines its function; however, protein str...
Protein Conformation Protein Folding Caspases Proteins Software Datasets as Topic Deep Learning Models, Molecular Amino Acid Sequence
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Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information.

IEEE/ACM transactions on computational biology and bioinformatics Jan 10, 2020
Protein-Protein Interactions (PPIs) play an irreplaceable role in biological activities of organisms. Although many high-throughput methods are used to identify PPIs from different kinds of organisms, they have some shortcomings, such as high cost an...
Machine Learning Animals Computational Biology Evolution, Molecular Protein Interaction Maps Humans Saccharomyces cerevisiae Proteins Proteins Sequence Analysis, Protein Amino Acid Sequence Databases, Protein Helicobacter pylori Protein Interaction Mapping
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DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment.

BMC bioinformatics Jan 9, 2020
BACKGROUND: Recently developed methods of protein contact prediction, a crucially important step for protein structure prediction, depend heavily on deep neural networks (DNNs) and multiple sequence alignments (MSAs) of target proteins. Protein seque...
Protein Structure, Secondary Deep Learning Sequence Alignment Proteins Protein Structure, Tertiary Neural Networks, Computer User-Computer Interface Amino Acid Sequence
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ProDCoNN: Protein design using a convolutional neural network.

Proteins Jan 6, 2020
Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this...
Neural Networks, Computer Databases, Protein Amino Acid Sequence Datasets as Topic Software Proteins Sequence Alignment Protein Engineering Protein Structure, Secondary
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Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.

Nature methods Jan 6, 2020
In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 ...
Biophysical Phenomena src Homology Domains Signal Transduction Proteins Humans Machine Learning Peptides Reproducibility of Results Protein Binding
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Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes.

Biomolecules Dec 31, 2019
Accurate prediction of protein stability changes resulting from amino acid substitutions is of utmost importance in medicine to better understand which mutations are deleterious, leading to diseases, and which are neutral. Since conducting wet lab ex...
Protein Stability Thermodynamics Machine Learning Point Mutation Neural Networks, Computer Proteins Databases, Protein Amino Acids
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