AIMC Topic: Amino Acid Sequence

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Uncovering new families and folds in the natural protein universe.

Nature Sep 13, 2023
We are now entering a new era in protein sequence and structure annotation, with hundreds of millions of predicted protein structures made available through the AlphaFold database. These models cover nearly all proteins that are known, including thos...
Databases, Protein Internet Deep Learning Proteins Protein Folding Structural Homology, Protein Amino Acid Sequence Molecular Sequence Annotation
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A novel hybrid CNN and BiGRU-Attention based deep learning model for protein function prediction.

Statistical applications in genetics and molecular biology Sep 4, 2023
Proteins are the building blocks of all living things. Protein function must be ascertained if the molecular mechanism of life is to be understood. While CNN is good at capturing short-term relationships, GRU and LSTM can capture long-term dependenci...
Amino Acid Sequence Language Saccharomyces cerevisiae Humans Deep Learning Virion
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AggBERT: Best in Class Prediction of Hexapeptide Amyloidogenesis with a Semi-Supervised ProtBERT Model.

Journal of chemical information and modeling Aug 8, 2023
The prediction of peptide amyloidogenesis is a challenging problem in the field of protein folding. Large language models, such as the ProtBERT model, have recently emerged as powerful tools in analyzing protein sequences for applications, such as pr...
Machine Learning Peptides Heuristics Amyloid Supervised Machine Learning Amino Acid Sequence
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Interpretable Machine Learning of Amino Acid Patterns in Proteins: A Statistical Ensemble Approach.

Journal of chemical theory and computation Aug 8, 2023
Explainable and interpretable unsupervised machine learning helps one to understand the underlying structure of data. We introduce an ensemble analysis of machine learning models to consolidate their interpretation. Its application shows that restric...
Trypsin Machine Learning Peptide Fragments Amino Acids Amino Acid Sequence
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Cross-protein transfer learning substantially improves disease variant prediction.

Genome biology Aug 7, 2023
BACKGROUND: Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation m...
Proteome Mutation Amino Acid Sequence Mutation, Missense Machine Learning Computational Biology Humans
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Linear-Scaling Kernels for Protein Sequences and Small Molecules Outperform Deep Learning While Providing Uncertainty Quantitation and Improved Interpretability.

Journal of chemical information and modeling Jul 27, 2023
Gaussian process (GP) is a Bayesian model which provides several advantages for regression tasks in machine learning such as reliable quantitation of uncertainty and improved interpretability. Their adoption has been precluded by their excessive comp...
Deep Learning Bayes Theorem Algorithms Amino Acid Sequence Uncertainty Humans
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LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model.

Journal of proteome research Jul 17, 2023
Phosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harne...
Deep Learning Amino Acid Sequence Proteins Protein Processing, Post-Translational Phosphorylation
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DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning.

Analytical chemistry Jul 17, 2023
Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the p...
Amino Acid Sequence Tandem Mass Spectrometry Peptides Proteins Deep Learning
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Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning.

Nature communications Jul 12, 2023
The turnover number k, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental k estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate comp...
Amino Acid Sequence Deep Learning Proteome Machine Learning Electric Power Supplies
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A Highly Sensitive Model Based on Graph Neural Networks for Enzyme Key Catalytic Residue Prediction.

Journal of chemical information and modeling Jul 3, 2023
Determining the catalytic site of enzymes is a great help for understanding the relationship between protein sequence, structure, and function, which provides the basis and targets for designing, modifying, and enhancing enzyme activity. The unique l...
Amino Acid Sequence Catalytic Domain Amino Acids Neural Networks, Computer Proteins
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