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Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions

Award Number: R01GM147635
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/20/2022
PERIOD OF PERFORMANCE END DATE: 08/31/2026

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Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions - Summary Advanced sequencing technologies provide ever-increasing quantities of data about human genetic variation and viral evolution. However, predicting the outcomes of missense mutations in protein coding regions remains a challenge, creating a bottleneck in discriminating biomedically-relevant variants from neutral ones (with little or no effect on phenotype). In particular, outcome predictions are very poor when a missense mutation alters amino acids that are located far from a protein’s functional/binding sites. These shortcomings also impair protein design. We propose to ameliorate these needs by developing quantitative, computational models that predict the effects of long-distance substitutions on binding interactions. To that end, we have developed an approach in which (1) a protein’s collective motions are first revealed by molecular dynamics simulations and then (2) force perturbation is used to disrupt the protein’s equilibrium, thereby approximating the effects of ligand binding. We have used this approach in published studies and preliminary data to illuminate the propagation of dynamical changes through a protein’s anisotropic network of interactions. Results suggest that changes in these dynamic networks have crucial effects on protein function, thereby leading to our central hypothesis: The effects of long- distance substitutions on ligand binding are emergent properties of changes in the protein’s dynamically-coupled, anisotropic network. The goal of the current proposal is to extend this computational approach to develop models that predict: (Aim 1) the magnitudes of binding affinity changes arising from long-distance, modulating substitutions; (Aim 2) which pairs of non-contact substitutions have non-additive effects on binding affinities (“epistasis”); and (Aim 3) which long-distance positions contribute to ligand specificity. To that end, we have a well-established collaboration that allows us to iterate between computational predictions and experimental testing, enabling development of quantitative models with computed accuracies. Our preliminary studies used the well-characterized E. coli lactose repressor protein (LacI), for which experimental results validate our preliminary computational models and provide specific hypotheses for Aims 1-3. Additional model proteins will be used to show the generality of our approach and will include the LacI homolog PurR, the cAMP receptor protein, and a viral protease SARS-Cov2-Mpro. Results will be used to provide novel computational tools for predicting functional outcomes of long-distance substitutions. The success of this project will catalyze research at the interface of protein structural biology, molecular genetics, evolution and medicine by advancing the mechanistic understanding of how substitutions distal from functional sites alter ligand binding.


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 = $416,686 )
2025	2025	ARIZONA STATE UNIVERSITY	660 S MILL AVENUE STE 204	TEMPE	AZ	85281	MARICOPA	USA	Biomedical Research and Research Training	000	4	9/8/2025	NON-COMPETING CONTINUATION	$416,686
														Subtotal = $416,686

Issue Date FY: 2024 ( Subtotal = $410,129 )
2024	2024	ARIZONA STATE UNIVERSITY	660 S MILL AVENUE STE 204	TEMPE	AZ	85281	MARICOPA	USA	Biomedical Research and Research Training	000	3	8/28/2024	NON-COMPETING CONTINUATION	$410,129
														Subtotal = $410,129

Issue Date FY: 2023 ( Subtotal = $504,104 )
2023	2023	ARIZONA STATE UNIVERSITY	660 S MILL AVENUE STE 204	TEMPE	AZ	85281	MARICOPA	USA	Biomedical Research and Research Training	001	2	9/1/2023	SUPPLEMENT FOR EXPANSION	$93,975
2023	2023	ARIZONA STATE UNIVERSITY	660 S MILL AVENUE STE 204	TEMPE	AZ	85281	MARICOPA	USA	Biomedical Research and Research Training	000	2	8/16/2023	NON-COMPETING CONTINUATION	$410,129
														Subtotal = $504,104

Issue Date FY: 2022 ( Subtotal = $424,111 )
2022	2022	ARIZONA STATE UNIVERSITY	660 S MILL AVE STE 312	TEMPE	AZ	85281	MARICOPA	USA	Biomedical Research and Research Training	000	1	9/20/2022	NEW	$424,111
														Subtotal = $424,111

Grand Total All Awards = $1,755,030

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Using dynamic network models to quantitatively predict changes in binding affinity/specificity that arise from long-range amino acid substitutions

Award Number: R01GM147635

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/20/2022

PERIOD OF PERFORMANCE END DATE: 08/31/2026

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