AIMC Topic: Protein Folding

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Deep learning techniques have significantly impacted protein structure prediction and protein design.

Current opinion in structural biology Feb 24, 2021
Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constrai...
Neural Networks, Computer Protein Folding Deep Learning Proteins
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[A giant step towards elucidating the function of proteins].

Medecine sciences : M/S Feb 16, 2021
Deep Learning Proteins Caspases Artificial Intelligence Algorithms Protein Structure, Tertiary X-Ray Diffraction Myoglobin Caspase 1 Crystallography Protein Folding
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Structural protein fold recognition based on secondary structure and evolutionary information using machine learning algorithms.

Computational biology and chemistry Feb 12, 2021
Understanding the function of protein is conducive to research in advanced fields such as gene therapy of diseases, the development and design of new drugs, etc. The prerequisite for understanding the function of a protein is to determine its tertiar...
Algorithms Databases, Protein Amino Acid Sequence Protein Structure, Secondary Protein Folding Humans Machine Learning
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ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations.

Journal of molecular biology Jan 13, 2021
The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated f...
Language Reproducibility of Results User-Computer Interface Software Algorithms Internet Protein Folding Computational Biology Databases, Protein Mutation Neural Networks, Computer
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Energy-dependent protein folding: modeling how a protein folding machine may work.

F1000Research Jan 5, 2021
Proteins fold robustly and reproducibly , but many cannot fold in isolation from cellular components. Despite the remarkable progress that has been achieved by the artificial intelligence approaches in predicting the protein native conformations, t...
Peptides Proteins Protein Conformation Protein Folding Artificial Intelligence
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A protein folding robot driven by a self-taught agent.

Bio Systems Dec 29, 2020
This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemagglutinin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to sol...
Hemagglutinins, Viral Viral Fusion Proteins Systems Analysis Humans Coronavirus Robotics Algorithms Models, Molecular Computer Simulation Protein Conformation Amino Acid Sequence Systems Biology Viral Proteins Neural Networks, Computer Protein Folding Machine Learning
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DeepFrag-k: a fragment-based deep learning approach for protein fold recognition.

BMC bioinformatics Nov 18, 2020
BACKGROUND: One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment leve...
Computational Biology Proteins Protein Folding Neural Networks, Computer Deep Learning
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Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.

Chembiochem : a European journal of chemical biology Nov 17, 2020
Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved h...
Protein Engineering Protein Folding Protein Multimerization Epoxide Hydrolases Molecular Dynamics Simulation Rhodococcus Limonene Machine Learning Mutation
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ReFold-MAP: Protein remote homology detection and fold recognition based on features extracted from profiles.

Analytical biochemistry Nov 6, 2020
Protein remote homology detection and protein fold recognition are two important tasks in protein structure and function prediction. There are three kinds of methods in this field, including the discriminative methods, the alignment methods, and the ...
Protein Folding Sequence Alignment Sequence Analysis, Protein Artificial Intelligence Proteins Databases, Protein
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A Machine Learning Protocol for Predicting Protein Infrared Spectra.

Journal of the American Chemical Society Oct 30, 2020
Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations i...
Quantum Theory Ubiquitin Spectrophotometry, Infrared Peptides Machine Learning Protein Structure, Secondary Proteins Protein Folding Temperature Amides
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