AIMC Topic: Protein Conformation, beta-Strand

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Harnessing protein folding neural networks for peptide-protein docking.

Nature communications Jan 10, 2022
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been develop...
Peptides Molecular Docking Simulation Protein Binding Proteins Neural Networks, Computer Software Protein Folding Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Models, Molecular Protein Conformation, beta-Strand Amino Acid Sequence Binding Sites Protein Interaction Mapping
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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...
Crystallization Protein Structure, Tertiary Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Deep Learning Proteins Molecular Dynamics Simulation Nuclear Energy Temperature
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MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.

Biomolecules Jul 29, 2021
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. Howeve...
Protein Conformation, beta-Strand Drugs, Investigational Protein Binding Proteins Kinetics High-Throughput Screening Assays Humans Datasets as Topic Binding Sites Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Molecular Docking Simulation Drug Discovery Deep Learning
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Protein structure search to support the development of protein structure prediction methods.

Proteins Feb 2, 2021
Protein structure prediction is a long-standing unsolved problem in molecular biology that has seen renewed interest with the recent success of deep learning with AlphaFold at CASP13. While developing and evaluating protein structure prediction metho...
Proteins Benchmarking Sequence Alignment Amino Acid Motifs Humans Research Design Deep Learning Structural Homology, Protein Protein Conformation, alpha-Helical Protein Conformation, beta-Strand
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DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures.

Proteins Sep 16, 2020
Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network...
Databases, Protein Amino Acid Sequence Markov Chains Sequence Alignment Sequence Homology, Amino Acid Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Neural Networks, Computer Software Deep Learning Proteins
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Energy-based graph convolutional networks for scoring protein docking models.

Proteins Mar 16, 2020
Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for su...
Humans Structural Homology, Protein Software Protein Conformation, alpha-Helical Amino Acid Sequence Research Design Deep Learning Protein Conformation, beta-Strand Ligands Computer Graphics Peptides Protein Binding Binding Sites Protein Interaction Mapping Proteins Thermodynamics Protein Multimerization Molecular Docking Simulation Benchmarking Protein Interaction Domains and Motifs
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High-accuracy protein structures by combining machine-learning with physics-based refinement.

Proteins Nov 15, 2019
Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolution...
Animals Computational Biology Proteins Protein Folding Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Humans Machine Learning
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A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model.

Genomics Nov 15, 2019
In this paper, a step-by-step classification algorithm based on double-layer SVM model is constructed to predict the secondary structure of proteins. The most important feature of this algorithm is to improve the prediction accuracy of α+β and α/β cl...
Humans Protein Conformation, alpha-Helical Animals Protein Conformation, beta-Strand Support Vector Machine Sequence Analysis, Protein
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BetaDL: A protein beta-sheet predictor utilizing a deep learning model and independent set solution.

Computers in biology and medicine Dec 2, 2018
The sequence-based prediction of beta-residue contacts and beta-sheet structures contain key information for protein structure prediction. However, the determination of beta-sheet structures poses numerous challenges due to long-range beta-residue in...
Databases, Protein Software Models, Molecular Deep Learning Protein Conformation, beta-Strand Computational Biology Proteins
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Computational design of thermostabilizing point mutations for G protein-coupled receptors.

eLife Jun 21, 2018
Engineering of GPCR constructs with improved thermostability is a key for successful structural and biochemical studies of this transmembrane protein family, targeted by 40% of all therapeutic drugs. Here we introduce a comprehensive computational ap...
Protein Conformation, beta-Strand Amino Acid Sequence Point Mutation Sequence Homology, Amino Acid Protein Stability Protein Binding Serotonin 5-HT2 Receptor Agonists Humans Protein Interaction Domains and Motifs Receptor, Serotonin, 5-HT2C Binding Sites Models, Molecular Serotonin 5-HT2 Receptor Antagonists Protein Conformation, alpha-Helical Machine Learning Crystallography, X-Ray Protein Engineering Gene Expression Animals Kinetics Thermodynamics Sequence Alignment
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