AIMC Topic: Protein Structure, Secondary

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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 Binding Molecular Dynamics Simulation Peptides Deep Learning Protein Structure, Secondary Proto-Oncogene Proteins c-mdm2 Hydrogen Bonding Protein Structure, Tertiary Binding Sites
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Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model.

Proceedings of the National Academy of Sciences of the United States of America Jun 25, 2024
Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral charact...
Protein Conformation Spectrophotometry, Infrared Protein Structure, Secondary Protein Folding Machine Learning Proteins
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Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis.

Journal of chemical theory and computation Mar 19, 2024
The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau protein is a promising avenue for tauopathies therapy. To dissect the underlying aggregation mechanisms ...
Humans tau Proteins Protein Structure, Secondary Tauopathies Deep Learning Amino Acid Sequence gamma-Globulins Melphalan
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ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.

Journal of chemical information and modeling Jan 2, 2024
Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for p...
Protein Structure, Secondary Mutation Protein Stability Neural Networks, Computer Proteins
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De novo design of high-affinity binders of bioactive helical peptides.

Nature Dec 18, 2023
Many peptide hormones form an α-helix on binding their receptors, and sensitive methods for their detection could contribute to better clinical management of disease. De novo protein design can now generate binders with high affinity and specificity ...
Protein Structure, Secondary Glucagon Parathyroid Hormone Substrate Specificity Luminescent Measurements Diffusion Deep Learning Biosensing Techniques Proteins Models, Molecular Mass Spectrometry Peptides Computer-Aided Design
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Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information.

International journal of molecular sciences Oct 27, 2023
Protein structure prediction continues to pose multiple challenges despite outstanding progress that is largely attributable to the use of novel machine learning techniques. One of the widely used representations of local 3D structure-protein blocks ...
Protein Structure, Secondary Amino Acids Amino Acid Sequence Neural Networks, Computer Algorithms Proteins
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Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.

Molecules (Basel, Switzerland) Oct 12, 2023
Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network re...
Neural Networks, Computer Deep Learning Proteins Amino Acid Sequence Protein Structure, Secondary
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More than just pattern recognition: Prediction of uncommon protein structure features by AI methods.

Proceedings of the National Academy of Sciences of the United States of America Jul 3, 2023
The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the...
Neural Networks, Computer Protein Conformation Protein Structure, Secondary Proteins Amino Acid Sequence Artificial Intelligence
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Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Coordinates.

Biomolecules May 31, 2023
Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information ...
Hydrogen Bonding Protein Structure, Secondary Machine Learning Algorithms Proteins
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Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction.

PLoS computational biology Apr 17, 2023
Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raise...
Protein Structure, Secondary Deep Learning Machine Learning RNA Algorithms
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