AIMC Topic: Protein Conformation, alpha-Helical

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Deep learning tools predict variants in disordered regions with lower sensitivity.

BMC genomics Apr 12, 2025
BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined ...
Humans Deep Learning Mutation Computational Biology Methionine Intrinsically Disordered Proteins Proteome Protein Conformation, alpha-Helical Models, Molecular
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Structure-aware machine learning strategies for antimicrobial peptide discovery.

Scientific reports May 25, 2024
Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a p...
Protein Conformation, alpha-Helical Drug Discovery Models, Molecular Antimicrobial Peptides Machine Learning
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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...
Binding Sites Protein Interaction Mapping Protein Folding Protein Conformation, beta-Strand Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Proteins Models, Molecular Amino Acid Sequence Peptides Molecular Docking Simulation Protein Binding Neural Networks, Computer Software
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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 Conformation, beta-Strand Nuclear Energy Molecular Dynamics Simulation Temperature Protein Structure, Tertiary Crystallization Deep Learning Protein Conformation, alpha-Helical Proteins
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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...
Kinetics High-Throughput Screening Assays Datasets as Topic Deep Learning Protein Binding Molecular Docking Simulation Proteins Drug Discovery Protein Conformation, beta-Strand Humans Drugs, Investigational Binding Sites Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical
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Deep Learning for Novel Antimicrobial Peptide Design.

Biomolecules Mar 22, 2021
Antimicrobial resistance is an increasing issue in healthcare as the overuse of antibacterial agents rises during the COVID-19 pandemic. The need for new antibiotics is high, while the arsenal of available agents is decreasing, especially for the tre...
Molecular Conformation Area Under Curve Protein Conformation, alpha-Helical Deep Learning Bayes Theorem Pore Forming Cytotoxic Proteins ROC Curve Amino Acid Sequence Escherichia coli Machine Learning
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Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design.

Communications biology Mar 19, 2021
Microbial rhodopsins are photoreceptive membrane proteins, which are used as molecular tools in optogenetics. Here, a machine learning (ML)-based experimental design method is introduced for screening rhodopsins that are likely to be red-shifted from...
Ion Channels Optogenetics Protein Conformation, alpha-Helical Rhodopsins, Microbial Amino Acid Sequence Proof of Concept Study Databases, Protein Sequence Analysis, Protein Bayes Theorem Color Machine Learning Hydrogen-Ion Concentration Escherichia coli Light
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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...
Benchmarking Sequence Alignment Amino Acid Motifs Protein Conformation, beta-Strand Structural Homology, Protein Protein Conformation, alpha-Helical Research Design Deep Learning Proteins Humans
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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...
Sequence Homology, Amino Acid Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Amino Acid Sequence Markov Chains Sequence Alignment Neural Networks, Computer Software Deep Learning Proteins Databases, Protein
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A deep attention network for predicting amino acid signals in the formation of [Formula: see text]-helices.

Journal of bioinformatics and computational biology Aug 6, 2020
The secondary and tertiary structure of a protein has a primary role in determining its function. Even though many folding prediction algorithms have been developed in the past decades - mainly based on the assumption that folding instructions are en...
Protein Conformation, alpha-Helical Models, Molecular Machine Learning Amino Acids Software Protein Structure, Secondary Databases, Protein
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