AIMC Topic: Protein Conformation

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Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2023
Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The thres...
Proteins Databases, Protein Protein Structure, Tertiary Protein Conformation Sequence Analysis, Protein Protein Folding Machine Learning Computational Biology
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Challenges in antibody structure prediction.

mAbs Jan 1, 2023
Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. I...
Artificial Intelligence Computational Biology Antibodies Protein Conformation Proteins Databases, Protein
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GeoPacker: A novel deep learning framework for protein side-chain modeling.

Protein science : a publication of the Protein Society Dec 1, 2022
Atomic interactions play essential roles in protein folding, structure stabilization, and function performance. Recent advances in deep learning-based methods have achieved impressive success not only in protein structure prediction, but also in prot...
Proteins Amino Acid Sequence Algorithms Deep Learning Protein Conformation Protein Structure, Secondary
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'The entire protein universe': AI predicts shape of nearly every known protein.

Nature Aug 1, 2022
Computational Biology Proteins Protein Conformation Artificial Intelligence
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AlphaFold predicts the most complex protein knot and composite protein knots.

Protein science : a publication of the Protein Society Aug 1, 2022
The computer artificial intelligence system AlphaFold has recently predicted previously unknown three-dimensional structures of thousands of proteins. Focusing on the subset with high-confidence scores, we algorithmically analyze these predictions fo...
Protein Conformation Protein Folding Proteins Models, Molecular Artificial Intelligence
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CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning.

Nucleic acids research Jul 5, 2022
Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational appro...
Binding Sites Membrane Proteins Software Protein Interaction Mapping Ligands Deep Learning Protein Conformation
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DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction.

Nucleic acids research Jul 5, 2022
Most proteins in nature contain multiple folding units (or domains). The revolutionary success of AlphaFold2 in single-domain structure prediction showed potential to extend deep-learning techniques for multi-domain structure modeling. This work pres...
Software Algorithms Deep Learning Computational Biology Proteins Protein Conformation
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LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation.

Nucleic acids research Jul 5, 2022
Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function...
Sequence Analysis, Protein Sequence Alignment Deep Learning Software Proteins Algorithms Models, Chemical Protein Conformation
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AlphaFold accurately predicts distinct conformations based on the oligomeric state of a de novo designed protein.

Protein science : a publication of the Protein Society Jul 1, 2022
Using the molecular modeling program Rosetta, we designed a de novo protein, called SEWN0.1, which binds the heterotrimeric G protein Gα The design is helical, well-folded, and primarily monomeric in solution at a concentration of 10 μM. However, whe...
Amino Acid Sequence Artificial Intelligence Protein Conformation Proteins Models, Molecular
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InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol.

Bioinformatics (Oxford, England) Jun 13, 2022
MOTIVATION: Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide-protein ...
Peptides Computer Simulation Proteins Monte Carlo Method Protein Conformation Deep Learning
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