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Leveraging machine learning and evolution to navigate sequence-function landscapes in multidomain proteins

Award Number: K99GM152766
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 12/01/2023
PERIOD OF PERFORMANCE END DATE: 11/30/2025

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Leveraging machine learning and evolution to navigate sequence-function landscapes in multidomain proteins - Project Summary/Abstract Allostery, the phenomenon describing how the state of one site in a protein is coupled to the state of a distal site, is a fundamental driver of functional evolution in protein families. It is especially impactful in multimeric and multidomain proteins – those that arise from the recombination of protein domains that are structurally and functionally distinct. The goal of this proposal is to develop methods that combine computational and experimental approaches to understand the role of allostery in establishing new functions by coupling enzymatic activity to biological processes at the membrane. Insights gained in this work will enable us to better understand how domain recombination has expanded the functional repertoires of protein families, and will enable more efficient engineering of synthetic proteins. In Aim 1 of this proposal, I will leverage recent advances in machine learning and computational geometry to develop more accurate generative models of protein families that implicitly account for evolutionary processes that act upon them. In Aim 2, I will conduct a systematic investigation into sequence-function landscape of a dimeric bacterial bicarbonate transporter that couples proton transport across the membrane to enzymatic production of bicarbonate. Using deep mutational scans in the context of a suppressor screen, I will identify sequence positions that decouple enzymatic activity from proton transport and will use this knowledge to test structure-function hypotheses related to allostery in this protein system. In Aim 3, I will use machine learning models fit to protein families to rationally design focused deep mutational scans to explore allostery in human atrial natriuretic peptide receptors. These receptors directly couple ligand binding to secondary messenger production in a single polypeptide chain containing multiple distinct domains. Using information from evolution will help me make more effective use of an experimentally limited mutational budget and will allow me to interrogate the higher order interactions that are a hallmark of allosteric networks. My background in structural biology and subsequent training in biological machine learning give me a unique perspective and skillset to tackle these challenging problems. The engaging scientific environment at UC Berkeley, and the strong support of my mentors Dr. Yun Song and Dr. David Savage will enable me to more seamlessly operate at the interface of computation and experimentation in biology as I launch my independent research career.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $113,368 )
2025	2025	REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE	1608 4TH ST STE 201	BERKELEY	CA	94710	ALAMEDA	USA	Biomedical Research and Research Training	000	2	12/3/2024	NON-COMPETING CONTINUATION	$113,368
														Subtotal = $113,368

Issue Date FY: 2024 ( Subtotal = $113,368 )
2024	2024	REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE	1608 4TH ST STE 201	BERKELEY	CA	94710	ALAMEDA	USA	Biomedical Research and Research Training	000	1	12/1/2023	NEW	$113,368
														Subtotal = $113,368

Grand Total All Awards = $226,736

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Leveraging machine learning and evolution to navigate sequence-function landscapes in multidomain proteins

Award Number: K99GM152766

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 12/01/2023

PERIOD OF PERFORMANCE END DATE: 11/30/2025

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