AIMC Topic: Proteins

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Navigating protein landscapes with a machine-learned transferable coarse-grained model.

Nature chemistry
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar pr...

Assisting and accelerating NMR assignment with restrained structure prediction.

Communications biology
Accurate dynamic protein structures are essential for drug design. NMR experiments can detect protein structures and potential dynamics, but the spectrum assignment and structure determination requires expertise and is time-consuming, while deep-lear...

Computational protein design: Advancing biotechnology through in silico engineering.

Progress in biophysics and molecular biology
Currently, computational protein design (CPD) is a disruptive force in biotechnology, changing the paradigm by which proteins are engineered for many applications. In this article, the evolution of CPD has been tracked from its initial forays in the ...

AF3Score: A Score-Only Adaptation of AlphaFold3 for Biomolecular Structure Evaluation.

Journal of chemical information and modeling
Scoring biomolecular complexes remains central to structural modeling efforts. Recent studies suggest that AlphaFold (AF) - a revolutionary deep learning model for biomolecular structure prediction - has implicitly learned an approximate biophysical ...

Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification.

Journal of chemical theory and computation
In this study, we propose a Kernel-PCA model designed to capture structure-function relationships in a protein. This model also enables the ranking of reaction coordinates according to their impact on protein properties. By leveraging machine learnin...

CoBdock-2: enhancing blind docking performance through hybrid feature selection combining ensemble and multimodel feature selection approaches.

Journal of computer-aided molecular design
Identifying orthosteric binding sites and predicting small molecule affinities remains a key challenge in virtual screening. While blind docking explores the entire protein surface, its precision is hindered by the vast search space. Cavity detection...

Rprot-Vec: a deep learning approach for fast protein structure similarity calculation.

BMC bioinformatics
BACKGROUND: Predicting protein structural similarity and detecting homologous sequences remain fundamental and challenging tasks in computational biology. Accurate identification of structural homologs enables function inference for newly discovered ...

End looms for protein contest?

Science (New York, N.Y.)
NIH has not committed to continuing support for contest that inspired AI tools for predicting how proteins would fold.

Integrating Protein Language Models and Geometric Deep Learning for Peptide Toxicity Prediction.

Journal of chemical information and modeling
Peptide toxicity prediction is a critical task in biomedical research, influencing drug safety and therapeutic development. Traditional methods, relying on sequence similarity or handcrafted features, struggle to capture the complex relationship betw...

Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies.

Genome biology
BACKGROUND: Proteins self-organize in dynamic cellular environments by assembling into reversible biomolecular condensates through liquid-liquid phase separation (LLPS). These condensates can comprise single or multiple proteins, with different roles...