AIMC Topic: Amino Acids

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PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.

Molecular diversity Jul 21, 2024
Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and can methylate various substrates with DNA, RNA, protein, a...
Machine Learning Support Vector Machine Amino Acids Methyltransferases Algorithms Computational Biology
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Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.

Scientific reports Jul 3, 2024
Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehe...
Diagnosis, Differential Area Under Curve Amino Acids Liquid Chromatography-Mass Spectrometry Metabolome Mycobacterium Infections, Nontuberculous Down-Regulation Up-Regulation Random Forest Tuberculosis Male Metabolomics Phenotype Humans ROC Curve Biomarkers Middle Aged Models, Statistical Machine Learning Aged
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Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis.

International journal of molecular sciences Jun 6, 2024
Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature () of enzymes is crucial for optimizing reaction cond...
Conserved Sequence Phosphoric Monoester Hydrolases Amino Acid Sequence Enzyme Stability Catalysis Algorithms Amino Acids Temperature Machine Learning
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Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities.

Journal of chemical information and modeling Jun 3, 2024
Raman spectroscopy is an important tool in the study of vibrational properties and composition of molecules, peptides, and even proteins. Raman spectra can be simulated based on the change of the electronic polarizability with vibrations, which can n...
Amino Acids Molecular Dynamics Simulation Spectrum Analysis, Raman Density Functional Theory Machine Learning Peptides Neural Networks, Computer
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Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning.

Journal of chemical information and modeling May 13, 2024
Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from a...
Protein Conformation Models, Molecular Antimicrobial Peptides Amino Acids Deep Learning Amino Acid Sequence
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Generalized biomolecular modeling and design with RoseTTAFold All-Atom.

Science (New York, N.Y.) Apr 19, 2024
Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases w...
Proteins DNA Amino Acids Protein Engineering Models, Molecular Deep Learning Crystallography
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Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning.

Journal of chemical information and modeling Apr 16, 2024
Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical m...
Nanostructures Thermodynamics Amino Acids Molecular Dynamics Simulation Machine Learning Neural Networks, Computer Algorithms Adsorption
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xCAPT5: protein-protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model.

BMC bioinformatics Mar 10, 2024
BACKGROUND: Predicting protein-protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, wh...
Algorithms Proteins Amino Acids Neural Networks, Computer Amino Acid Sequence
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HormoNet: a deep learning approach for hormone-drug interaction prediction.

BMC bioinformatics Feb 28, 2024
Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is ...
Proteins Humans Hormones Amino Acids Deep Learning Drug Interactions
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Protein design using structure-based residue preferences.

Nature communications Feb 22, 2024
Recent developments in protein design rely on large neural networks with up to 100s of millions of parameters, yet it is unclear which residue dependencies are critical for determining protein function. Here, we show that amino acid preferences at in...
Amino Acids Proteins Neural Networks, Computer Mutation
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