AIMC Topic: Proteins

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EnsembleFam: towards more accurate protein family prediction in the twilight zone.

BMC bioinformatics Mar 14, 2022
BACKGROUND: Current protein family modeling methods like profile Hidden Markov Model (pHMM), k-mer based methods, and deep learning-based methods do not provide very accurate protein function prediction for proteins in the twilight zone, due to low s...
Proteins Humans Support Vector Machine
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PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information.

PeerJ Mar 14, 2022
Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based o...
Deep Learning Proteins Machine Learning Humans Amino Acid Sequence Algorithms
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Large-scale design and refinement of stable proteins using sequence-only models.

PloS one Mar 14, 2022
Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be ...
Amino Acid Sequence Protein Stability Proteins Neural Networks, Computer Amino Acids
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EvoRator: Prediction of Residue-level Evolutionary Rates from Protein Structures Using Machine Learning.

Journal of molecular biology Mar 11, 2022
Measuring evolutionary rates at the residue level is indispensable for gaining structural and functional insights into proteins. State-of-the-art tools for estimating rates take as input a large set of homologous proteins, a probabilistic model of ev...
Software Algorithms Phylogeny Proteins Humans Machine Learning Internet Use Protein Conformation Sequence Alignment
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Data Mining Meets Machine Learning: A Novel ANN-based Multi-body Interaction Docking Scoring Function (MBI-score) Based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-ligand Complexes.

Molecular informatics Mar 9, 2022
Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. I...
Machine Learning Protein Binding Artificial Intelligence Ligands Binding Sites Proteins Data Mining
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BioS2Net: Holistic Structural and Sequential Analysis of Biomolecules Using a Deep Neural Network.

International journal of molecular sciences Mar 9, 2022
BACKGROUND: For decades, the rate of solving new biomolecular structures has been exceeding that at which their manual classification and feature characterisation can be carried out efficiently. Therefore, a new comprehensive and holistic tool for th...
Algorithms Proteins Neural Networks, Computer
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Accurate positioning of functional residues with robotics-inspired computational protein design.

Proceedings of the National Academy of Sciences of the United States of America Mar 7, 2022
SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geom...
Amino Acids Protein Conformation Robotics Models, Molecular Protein Engineering Isomerases Computer-Aided Design Computational Biology Proteins Structure-Activity Relationship Algorithms Reproducibility of Results
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Compound-protein interaction prediction by deep learning: Databases, descriptors and models.

Drug discovery today Mar 3, 2022
The screening of compound-protein interactions (CPIs) is one of the most crucial steps in finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address intrinsic limitations of traditional HTS and virtual screening with th...
Databases, Factual Drug Discovery Deep Learning Proteins
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Researchers turn to deep learning to decode protein structures.

Proceedings of the National Academy of Sciences of the United States of America Mar 2, 2022
Problem Solving Proteins Models, Molecular Research Personnel Protein Conformation Deep Learning Computational Biology
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DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions.

Methods (San Diego, Calif.) Feb 24, 2022
Predicting drug-target interactions (DTIs) is essential for both drug discovery and drug repositioning. Recently, deep learning methods have achieved relatively significant performance in predicting DTIs. Generally, it needs a large amount of approve...
Drug Repositioning Neural Networks, Computer Drug Discovery Deep Learning Proteins
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