Mapping Structural Drivers of Insulin Analogs Using Molecular Dynamics and Free Energy Calculations at Insulin Receptor
Abstract A century on from the discovery of insulin, a complete understanding of insulin interactions with the insulin receptor (IR) at atomic level remains elusive. In this work, we have leveraged recent advancements in structural biology that have resulted in multiple high-resolution structures of the insulin-IR complex. As a first step, we employed molecular dynamics (MD) simulations to unravel atomic insights into the interactions between insulin-IR complexes in order to better understand ligand recognition at the receptor. The MD simulations were followed up with free energy perturbation (FEP) calculations to discriminate between and elucidate the drivers for ligand association for various natural and man-made insulin analogs. As an example, these calculations were utilized to understand the molecular mechanisms that characterized the loss-of-function seen in disease-associated insulin mutations seen in different populations. Further, multiple man-made insulin analogs spanning a range of potencies, mutations, and sequence lengths were studied using FEP and a comprehensive molecular level map of potency determinants were established. ∼85% of FEP calculations captured the direction of shift of potency, and in ∼53% of cases the predictions were within 1 kcal/mol of experiment. The impressive accuracy of FEP in recapitulating functional profiles across such a span of insulin analogs and potency profiles provided clear evidence of its utility in computational mutagenesis. In addition to the impressive accuracy, the ability of FEP to provide a dissected understanding of protein residue, solvent and solvent-mediated contributions to binding energy clearly establishes its utility in the design of novel insulins and peptides in general..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 30. Nov. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sena, Mohan Maruthi [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2022.05.27.493461 |
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XBI036149098 |
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| 520 | |a Abstract A century on from the discovery of insulin, a complete understanding of insulin interactions with the insulin receptor (IR) at atomic level remains elusive. In this work, we have leveraged recent advancements in structural biology that have resulted in multiple high-resolution structures of the insulin-IR complex. As a first step, we employed molecular dynamics (MD) simulations to unravel atomic insights into the interactions between insulin-IR complexes in order to better understand ligand recognition at the receptor. The MD simulations were followed up with free energy perturbation (FEP) calculations to discriminate between and elucidate the drivers for ligand association for various natural and man-made insulin analogs. As an example, these calculations were utilized to understand the molecular mechanisms that characterized the loss-of-function seen in disease-associated insulin mutations seen in different populations. Further, multiple man-made insulin analogs spanning a range of potencies, mutations, and sequence lengths were studied using FEP and a comprehensive molecular level map of potency determinants were established. ∼85% of FEP calculations captured the direction of shift of potency, and in ∼53% of cases the predictions were within 1 kcal/mol of experiment. The impressive accuracy of FEP in recapitulating functional profiles across such a span of insulin analogs and potency profiles provided clear evidence of its utility in computational mutagenesis. In addition to the impressive accuracy, the ability of FEP to provide a dissected understanding of protein residue, solvent and solvent-mediated contributions to binding energy clearly establishes its utility in the design of novel insulins and peptides in general. | ||
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| 700 | 1 | |a Gromiha, M. Michael |4 aut | |
| 700 | 1 | |a Chatterji, Monalisa |4 aut | |
| 700 | 1 | |a Khedkar, Anand |4 aut | |
| 700 | 1 | |a Ranganathan, Anirudh |4 aut | |
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