AIMC Topic: Protein Folding

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Highly accurate protein structure prediction with AlphaFold.

Nature Jul 15, 2021
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort, the structures of around 100,000 unique proteins have been determined, but this r...
Deep Learning Computational Biology Proteins Neural Networks, Computer Reproducibility of Results Protein Conformation Protein Folding Sequence Alignment Databases, Protein Models, Molecular Amino Acid Sequence
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Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.

Molecular diversity Jun 10, 2021
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data...
Big Data Protein Binding Models, Theoretical Algorithms Drug Design Drug Discovery Workflow Data Mining Protein Interaction Mapping Proteins Structure-Activity Relationship Models, Molecular Protein Folding Databases, Pharmaceutical Humans Artificial Intelligence
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AlphaFold - A Personal Perspective on the Impact of Machine Learning.

Journal of molecular biology Jun 2, 2021
I outline how over my career as a protein scientist Machine Learning has impacted my area of science and one of my pastimes, chess, where there are some interesting parallels. In 1968, modelling of three-dimensional structures was initiated based on ...
Protein Folding Animals Humans Proteins Protein Conformation Machine Learning Models, Molecular Trypsin Software Chymotrypsin
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The whole is greater than its parts: ensembling improves protein contact prediction.

Scientific reports Apr 13, 2021
The prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, c...
Models, Molecular Protein Conformation Protein Folding Sequence Analysis, Protein Deep Learning Computational Biology Proteins Software Algorithms Neural Networks, Computer
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Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.

PLoS computational biology Mar 26, 2021
The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by en...
Neural Networks, Computer Protein Conformation Protein Folding Sequence Analysis, Protein Reproducibility of Results Computational Biology Proteins
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Stereoelectronic effects in stabilizing protein-N-glycan interactions revealed by experiment and machine learning.

Nature chemistry Mar 15, 2021
The energetics of protein-carbohydrate interactions, central to many life processes, cannot yet be manipulated predictably. This is mostly due to an incomplete quantitative understanding of the enthalpic and entropic basis of these interactions in aq...
Humans Machine Learning Proteins Thermodynamics 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...
Protein Folding Neural Networks, Computer Deep Learning Proteins
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[A giant step towards elucidating the function of proteins].

Medecine sciences : M/S Feb 16, 2021
Artificial Intelligence Caspase 1 Proteins Protein Folding Protein Structure, Tertiary Caspases X-Ray Diffraction Algorithms Myoglobin Deep Learning Crystallography
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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...
Humans Machine Learning Protein Structure, Secondary Algorithms Protein Folding Databases, Protein Amino Acid Sequence
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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...
User-Computer Interface Computational Biology Mutation Neural Networks, Computer Internet Software Algorithms Protein Folding Language Databases, Protein Reproducibility of Results
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