AIMC Topic: Protein Conformation

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Computed structures of core eukaryotic protein complexes.

Science (New York, N.Y.) Dec 10, 2021
Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coe...
Protein Interaction Mapping Protein Interaction Maps Saccharomyces cerevisiae Proteins Computational Biology DNA Repair Proteome Protein Conformation Saccharomyces cerevisiae Membrane Proteins Ubiquitin Evolution, Molecular Acyltransferases Models, Molecular Homologous Recombination Protein Biosynthesis Chromosome Segregation Ribosomes Ligases Multiprotein Complexes Computer Simulation Deep Learning
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De novo protein design by deep network hallucination.

Nature Dec 1, 2021
There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences. Here we investigate whether the information captured by such networks is sufficiently...
Humans Neural Networks, Computer Proteins Amino Acid Sequence Hallucinations Crystallography, X-Ray Protein Conformation
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Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets.

International journal of molecular sciences Nov 27, 2021
The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures; however, it has a prohibitively long inference ...
Sequence Alignment Software Design Protein Structural Elements Humans Neural Networks, Computer Computational Biology Protein Conformation Protein Folding Proteins Amino Acid Sequence
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MemDis: Predicting Disordered Regions in Transmembrane Proteins.

International journal of molecular sciences Nov 12, 2021
Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a wel...
Intrinsically Disordered Proteins Protein Conformation Membrane Proteins Databases, Protein Models, Molecular Internet Amino Acid Sequence Data Mining Neural Networks, Computer Reproducibility of Results Computational Biology
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AI reveals structures of protein complexes.

Science (New York, N.Y.) Nov 11, 2021
Software Deep Learning Proteins Artificial Intelligence Eukaryota Fungal Proteins Protein Conformation Protein Interaction Mapping
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Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Oct 31, 2021
The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure predicti...
Deep Learning Datasets as Topic Neural Networks, Computer Protein Conformation Computational Biology Sequence Analysis, Protein Models, Molecular
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The AlphaFold Database of Protein Structures: A Biologist's Guide.

Journal of molecular biology Oct 29, 2021
AlphaFold, the deep learning algorithm developed by DeepMind, recently released the three-dimensional models of the whole human proteome to the scientific community. Here we discuss the advantages, limitations and the still unsolved challenges of the...
Proteome Models, Molecular Protein Conformation Databases, Factual Deep Learning Humans Computational Biology Protein Folding Algorithms Molecular Biology
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Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

Journal of chemical information and modeling Oct 15, 2021
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to inclu...
Proteins Ligands Protein Conformation Machine Learning Molecular Docking Simulation Protein Binding
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Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.

Nature methods Oct 4, 2021
Deep learning using neural networks relies on a class of machine-learnable models constructed using 'differentiable programs'. These programs can combine mathematical equations specific to a particular domain of natural science with general-purpose, ...
Neural Networks, Computer Deep Learning Computational Biology Proteins Protein Conformation Models, Chemical Biophysics Pattern Recognition, Automated Computational Chemistry
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XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers.

PLoS computational biology Sep 27, 2021
Graph representations are traditionally used to represent protein structures in sequence design protocols in which the protein backbone conformation is known. This infrequently extends to machine learning projects: existing graph convolution algorith...
Proteins Neural Networks, Computer Computer Graphics Models, Molecular Machine Learning Computers Protein Conformation Algorithms Computer-Aided Design Computational Biology
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