AIMC Topic: Proteins

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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 Development Cluster Analysis Proteins Machine Learning Computer Simulation Drug Discovery
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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...
ROC Curve Deep Learning Proteins User-Computer Interface Pharmaceutical Preparations Area Under Curve Databases, Chemical
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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...
Models, Molecular Amino Acid Sequence Caspases Datasets as Topic Software Protein Conformation Deep Learning Protein Folding Proteins
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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...
Saccharomyces cerevisiae Proteins Sequence Analysis, Protein Evolution, Molecular Proteins Animals Databases, Protein Protein Interaction Maps Humans Helicobacter pylori Computational Biology Machine Learning Amino Acid Sequence 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...
Neural Networks, Computer Deep Learning User-Computer Interface Sequence Alignment Protein Structure, Tertiary Proteins Amino Acid Sequence Protein Structure, Secondary
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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...
Protein Engineering Protein Structure, Secondary Sequence Alignment Databases, Protein Amino Acid Sequence Neural Networks, Computer Datasets as Topic Software Proteins
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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 ...
Humans Proteins Biophysical Phenomena src Homology Domains Peptides Protein Binding Signal Transduction Machine Learning Reproducibility of Results
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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...
Thermodynamics Point Mutation Neural Networks, Computer Amino Acids Machine Learning Protein Stability Proteins Databases, Protein
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A generative model for constructing nucleic acid sequences binding to a protein.

BMC genomics Dec 27, 2019
BACKGROUND: Interactions between protein and nucleic acid molecules are essential to a variety of cellular processes. A large amount of interaction data generated by high-throughput technologies have triggered the development of several computational...
Nucleic Acid Conformation DNA Base Sequence Humans Protein Binding Neural Networks, Computer Transcription Factors Algorithms Proteins Aptamers, Nucleotide
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Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter.

BMC genomics Dec 27, 2019
BACKGROUND: Identification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical protein...
Protein Interaction Maps Support Vector Machine Proteins Databases, Protein Saccharomyces cerevisiae Principal Component Analysis Saccharomyces cerevisiae Proteins Area Under Curve Humans ROC Curve
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