Work

Chemistry-informed Machine Learning Explains Calcium-binding Proteins Fuzzy Shape for Communicating Changes in the Atomic States of Calcium Ions

Year: 2024

Type: preprint

Abstract: electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. With our designs, we provided a framework of explainable machine learning model to annotate atomic charges of calcium ions in calcium-binding proteins with domain knowledge in response to the chemical changes in an environment based on the limited size of scientific data in a genome space.

Source: PubMed

DOI: 10.48550/arxiv.2407.17017

Authors: Zhang, Pengzhi, Nde, Jules, Eliaz, Yossi, Jennings, Nathaniel, Cieplak, Piotr, Cheung, Margaret. S.

Institutions: Houston Methodist Research Institute, University of Washington, University of Washington Applied Physics Laboratory, Holon Institute of Technology, University of Houston, Sanford Burnham Prebys Medical Discovery Institute

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, University of Houston, National Institutes of Health, National Institute of General Medical Sciences

Awards: Emergent Protein Assemblies in Cytoplasm, Principles for Tuning Target Selectivity in Signaling Proteins

OpenAlex ID: W6929600966

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Cited by: 1

Related to: 10