AIMC Topic: Protein Folding

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On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins.

Journal of molecular biology Aug 12, 2021
Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no ...
Protein Folding Amino Acid Sequence Intrinsically Disordered Proteins Protein Conformation Machine Learning Animals Humans
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Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.

Proteins Aug 7, 2021
In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed com...
Software Deep Learning Computational Biology Hydrogen Bonding Protein Folding Sequence Alignment Sequence Analysis, Protein Proteins Models, Molecular Protein Conformation
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Highly accurate protein structure prediction for the human proteome.

Nature Jul 22, 2021
Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues i...
Protein Folding Glucose-6-Phosphatase Datasets as Topic Protein Conformation Diacylglycerol O-Acyltransferase Proteome Deep Learning Membrane Proteins Models, Molecular Computational Biology Humans Reproducibility of Results
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Accurate prediction of protein structures and interactions using a three-track neural network.

Science (New York, N.Y.) Jul 15, 2021
DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track ...
Amino Acid Sequence ADAM Proteins Crystallography, X-Ray Databases, Protein Receptors, G-Protein-Coupled Models, Molecular Multiprotein Complexes Protein Subunits Sphingosine N-Acyltransferase Protein Conformation Cryoelectron Microscopy Protein Folding Neural Networks, Computer Membrane Proteins Proteins Computer Simulation Deep Learning
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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...
Neural Networks, Computer Amino Acid Sequence Protein Conformation Protein Folding Sequence Alignment Proteins Reproducibility of Results Databases, Protein Deep Learning Models, Molecular Computational Biology
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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...
Models, Molecular Workflow Data Mining Databases, Pharmaceutical Protein Interaction Mapping Protein Folding Models, Theoretical Proteins Structure-Activity Relationship Drug Discovery Humans Big Data Artificial Intelligence Protein Binding Algorithms Drug Design
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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 ...
Humans Proteins Machine Learning Models, Molecular Trypsin Software Protein Conformation Chymotrypsin Animals Protein Folding
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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...
Computational Biology Software Proteins Algorithms Neural Networks, Computer Models, Molecular Deep Learning Protein Conformation Protein Folding Sequence Analysis, Protein
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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...
Sequence Analysis, Protein Proteins Protein Conformation Protein Folding Neural Networks, Computer Reproducibility of Results Computational Biology
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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 Protein Folding Proteins Machine Learning Thermodynamics
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