AIMC Topic: Proteins

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DeepMPSF: A Deep Learning Network for Predicting General Protein Phosphorylation Sites Based on Multiple Protein Sequence Features.

Journal of chemical information and modeling Nov 6, 2023
Phosphorylation, as one of the most important post-translational modifications, plays a key role in various cellular physiological processes and disease occurrences. In recent years, computer technology has been gradually applied to the prediction of...
Deep Learning Mice Humans Animals Amino Acid Sequence Proteins Phosphorylation Rats Protein Processing, Post-Translational
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The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions.

Acta crystallographica. Section D, Structural biology Nov 3, 2023
Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Å for both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Å both the procedu...
Artificial Intelligence Peptides Proteins Models, Molecular Crystallography, X-Ray Protein Conformation Cryoelectron Microscopy
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Automation to Enable High-Throughput Chemical Proteomics.

Journal of proteome research Nov 2, 2023
Chemical proteomics utilizes small-molecule probes to covalently engage with their interacting proteins. Since chemical probes are tagged to the active or binding sites of functional proteins, chemical proteomics can be used to profile protein target...
Proteomics Proteins Automation Artificial Intelligence Drug Discovery
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ProGen2: Exploring the boundaries of protein language models.

Cell systems Oct 30, 2023
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial-intelligence-driven protein design. However, we lack a sufficient understanding of how very large-...
Artificial Intelligence Databases, Factual Amino Acid Sequence Language Proteins
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Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Food research international (Ottawa, Ont.) Oct 27, 2023
Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypert...
Humans Neural Networks, Computer Proteins Peptides Amino Acid Sequence Deep Learning
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Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction.

ACS synthetic biology Oct 27, 2023
Predicting properties of proteins is of interest for basic biological understanding and protein engineering alike. Increasingly, machine learning (ML) approaches are being used for this task. However, the accuracy of such ML models typically degrades...
Machine Learning Neural Networks, Computer Proteins Bayes Theorem
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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 ...
Amino Acid Sequence Protein Structure, Secondary Proteins Amino Acids Neural Networks, Computer Algorithms
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MLapRVFL: Protein sequence prediction based on Multi-Laplacian Regularized Random Vector Functional Link.

Computers in biology and medicine Oct 26, 2023
Protein sequence classification is a crucial research field in bioinformatics, playing a vital role in facilitating functional annotation, structure prediction, and gaining a deeper understanding of protein function and interactions. With the rapid d...
Proteins Machine Learning Amino Acid Sequence Algorithms
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Integrated Molecular Modeling and Machine Learning for Drug Design.

Journal of chemical theory and computation Oct 26, 2023
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping r...
Proteins Ligands Machine Learning Retrospective Studies Molecular Docking Simulation Drug Design
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CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.

Proteins Oct 23, 2023
Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted...
Protein Interaction Mapping Protein Interaction Domains and Motifs Protein Conformation Binding Sites Protein Binding Computational Biology Software Deep Learning Humans Models, Molecular Ligands Proteins Databases, Protein
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