AIMC Topic: Proteins

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FFENCODER-PL: Pair Wise Energy Descriptors for Protein-Ligand Pose Selection.

Journal of chemical theory and computation Sep 23, 2021
Scoring functions are the essential component in molecular docking methods. An accurate scoring function is expected to distinguish the native ligand pose from decoy poses. Our previous experience (Pei et al. 2019, 59 (7), 3305-3315) proved that com...
Proteins Molecular Docking Simulation Ligands Protein Binding Machine Learning Algorithms
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SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation.

The journal of physical chemistry. B Sep 21, 2021
Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protei...
Neural Networks, Computer Thermodynamics Mutation Proteins Protein Stability
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Structure-based protein design with deep learning.

Current opinion in chemical biology Sep 20, 2021
Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to u...
Deep Learning Proteins
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Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences.

Medical & biological engineering & computing Sep 20, 2021
Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localiz...
Bayes Theorem Algorithms Support Vector Machine Amino Acid Sequence Neural Networks, Computer Proteins
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PARROT is a flexible recurrent neural network framework for analysis of large protein datasets.

eLife Sep 17, 2021
The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to i...
Software Deep Learning Computational Biology Proteins Humans Neural Networks, Computer High-Throughput Nucleotide Sequencing Sequence Analysis, Protein Databases, Protein Phosphorylation
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Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine.

Scientific reports Sep 15, 2021
The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubilit...
Molecular Conformation Protein Biosynthesis Machine Learning Models, Molecular Structure-Activity Relationship Proteins Amino Acids
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A deep-learning framework for multi-level peptide-protein interaction prediction.

Nature communications Sep 15, 2021
Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide...
Protein Domains Binding Sites Proteins Models, Molecular Peptides Deep Learning Protein Binding Computational Biology Algorithms Reproducibility of Results
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FALCON2: a web server for high-quality prediction of protein tertiary structures.

BMC bioinformatics Sep 15, 2021
BACKGROUND: Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a ...
Computers Proteins Amino Acid Sequence Neural Networks, Computer Software Protein Conformation
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Improved 3-D Protein Structure Predictions using Deep ResNet Model.

The protein journal Sep 12, 2021
Protein Structure Prediction (PSP) is considered to be a complicated problem in computational biology. In spite of, the remarkable progress made by the co-evolution-based method in PSP, it is still a challenging and unresolved problem. Recently, alon...
Protein Folding Proteins Databases, Protein Models, Molecular Protein Conformation Software Neural Networks, Computer Computational Biology
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New Frontiers for Machine Learning in Protein Science.

Journal of molecular biology Sep 6, 2021
Protein function is fundamentally reliant on inter-molecular interactions that underpin the ability of proteins to form complexes driving biological processes in living cells. Increasingly, such interactions are recognised as being formed between pro...
Machine Learning Animals Protein Aggregates Humans Phase Transition Protein Interaction Maps Proteins Protein Folding
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