CaXML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features
[1];
[5];
- Houston Methodist Research Institutes, TX (United States)
- Univ. of Washington, Seattle, WA (United States)
- Univ. of Houston, TX (United States); Holon Institute of Technology (HIT) (Israel)
- Univ. of Houston, TX (United States)
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA (United States)
- Univ. of Washington, Seattle, WA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Environmental Molecular Sciences Laboratory (EMSL)
Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio 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. Our design created a computational tool of CaXML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- National Institutes of Health (NIH); National Science Foundation (NSF); USDOE
- Grant/Contract Number:
- AC05-76RL01830
- OSTI ID:
- 2526238
- Report Number(s):
- PNNL-SA--199576
- Journal Information:
- Protein Science, Journal Name: Protein Science Journal Issue: 2 Vol. 34; ISSN 0961-8368
- Publisher:
- Wiley -- The Protein SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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