AIMC Topic: Protein Structure, Tertiary

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A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.

Scientific reports Apr 23, 2025
Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein's native structure from its amino acid sequence, the focus on a singl...
Neural Networks, Computer Algorithms Models, Molecular Protein Structure, Tertiary Autoencoder Computational Biology Proteins Deep Learning
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Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning.

Molecules (Basel, Switzerland) Jul 18, 2024
Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and binding free energy calculations were combined together to probe the bi...
Protein Structure, Tertiary Protein Structure, Secondary Proto-Oncogene Proteins c-mdm2 Deep Learning Hydrogen Bonding Peptides Molecular Dynamics Simulation Protein Binding Binding Sites
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Structure, dynamics, and stability of the smallest and most complex 7 protein knot.

The Journal of biological chemistry Dec 10, 2023
Proteins can spontaneously tie a variety of intricate topological knots through twisting and threading of the polypeptide chains. Recently developed artificial intelligence algorithms have predicted several new classes of topological knotted proteins...
Artificial Intelligence Peptides Models, Molecular Protein Structure, Tertiary Bacterial Proteins Ureaplasma urealyticum Protein Folding
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Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles.

Journal of chemical theory and computation May 26, 2023
Cyclic peptides have emerged as a promising class of therapeutics. However, their design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide ...
Machine Learning Models, Molecular Molecular Dynamics Simulation Neural Networks, Computer Peptides, Cyclic Protein Structure, Tertiary
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Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network.

Scientific reports Oct 26, 2021
The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of protei...
Protein Structure, Tertiary Evolution, Molecular Proteins Predictive Value of Tests Protein Structure, Secondary Databases, Protein Neural Networks, Computer Software
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Mapping the glycosyltransferase fold landscape using interpretable deep learning.

Nature communications Sep 27, 2021
Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small molecule substrates. The extensive structural and functional diversifi...
Glycosyltransferases Sequence Alignment Protein Structure, Tertiary Databases, Genetic Amino Acid Sequence Glycosylation Protein Structure, Secondary Datasets as Topic Deep Learning Protein Folding Computational Biology
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Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.

PloS one Sep 2, 2021
Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simula...
Protein Structure, Tertiary Protein Conformation, alpha-Helical Nuclear Energy Proteins Protein Conformation, beta-Strand Temperature Molecular Dynamics Simulation Deep Learning Crystallization
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Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14.

Proteins Aug 17, 2021
The trRosetta structure prediction method employs deep learning to generate predicted residue-residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to se...
Humans Software Computational Biology Proteins Deep Learning Metagenome Protein Structure, Tertiary Sequence Analysis, Protein
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Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14.

Proteins Jul 27, 2021
Substantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by inc...
Sequence Alignment Protein Structure, Tertiary Sequence Analysis, Protein Proteins Models, Molecular Software Algorithms Humans Deep Learning
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MULTICOM2 open-source protein structure prediction system powered by deep learning and distance prediction.

Scientific reports Jun 23, 2021
Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we p...
Models, Molecular Amino Acid Sequence Sequence Alignment Deep Learning Structure-Activity Relationship Protein Structure, Tertiary Software
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