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Proteins

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ProGen2: Exploring the boundaries of protein language models.

Cell systems
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-...

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.)
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...

Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction.

ACS synthetic biology
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...

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
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 ...

MLapRVFL: Protein sequence prediction based on Multi-Laplacian Regularized Random Vector Functional Link.

Computers in biology and medicine
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...

Integrated Molecular Modeling and Machine Learning for Drug Design.

Journal of chemical theory and computation
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...

CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.

Proteins
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...

Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction.

Computational biology and chemistry
Accurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been s...

A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.

Genomics, proteomics & bioinformatics
Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that...

Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.

Journal of biomolecular structure & dynamics
The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can...