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bioRxiv · 10.1101/2024.02.24.581358

Decoding Stability and Epistasis in Human Myoglobin by Deep Mutational Scanning and Codon-level Machine Learning

Kueng, C.·Protsenko, O.·Vanella, R.·Nash, M.
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Abstract

Engineering protein stability is a critical challenge in biotechnology. Here, we used massively parallel deep mutational scanning (DMS) to comprehensively explore the mutational stability landscape of human myoglobin (hMb) and identify key mutations that enhance stability. Our DMS approach involved screening over 10,000 hMb variants by yeast surface display, single-cell sorting and high-throughput DNA sequencing. We show how surface display levels serve as a proxy for thermostability of soluble hMb variants, and report strong correlations between DMS-derived display levels and top-performing machine learning stability prediction algorithms. This approach led to the discovery of a variant with a de novo disulfide bond between residues R32C and C111, which increased thermostability by >12 {degrees}C compared to wild-type hMb. By combining single stabilizing mutations with R32C, we engineered combinatorial variants that exhibited predominantly additive effects on stability with minimal epistasis. The most stable combinatorial variant exhibited a denaturation temperature exceeding 89 {degrees}C, representing a >17 {degrees}C improvement over wild-type hMb. Our findings demonstrate the capabilities in DMS-assisted combinatorial protein engineering to guide the discovery of thermostable variants, and highlight the potential of massively parallel mutational analysis for the development of proteins for industrial and biomedical applications.

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Kueng, C., Protsenko, O., Vanella, R., Nash, M.. 2024-02-26. Decoding Stability and Epistasis in Human Myoglobin by Deep Mutational Scanning and Codon-level Machine Learning. https://doi.org/10.1101/2024.02.24.581358

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