A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissionsoup.com..
Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen's protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein-drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
|---|---|
| Enthalten in: |
Briefings in functional genomics - 22(2023), 2 vom: 13. Apr., Seite 195-203 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Padhi, Aditya K [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Adaptable mutations |
|---|
| Anmerkungen: |
Date Completed 17.04.2023 Date Revised 12.05.2023 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1093/bfgp/elac020 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM343718022 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM343718022 | ||
| 003 | DE-627 | ||
| 005 | 20231226021113.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1093/bfgp/elac020 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1145.xml |
| 035 | |a (DE-627)NLM343718022 | ||
| 035 | |a (NLM)35851634 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Padhi, Aditya K |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 17.04.2023 | ||
| 500 | |a Date Revised 12.05.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissionsoup.com. | ||
| 520 | |a Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen's protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein-drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a SARS-CoV-2 | |
| 650 | 4 | |a adaptable mutations | |
| 650 | 4 | |a adaptation signatures | |
| 650 | 4 | |a binding affinity | |
| 650 | 4 | |a computational protocol | |
| 650 | 4 | |a drug resistance | |
| 650 | 4 | |a main protease | |
| 650 | 4 | |a protein design | |
| 650 | 7 | |a Antiviral Agents |2 NLM | |
| 700 | 1 | |a Tripathi, Timir |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Briefings in functional genomics |d 2010 |g 22(2023), 2 vom: 13. Apr., Seite 195-203 |w (DE-627)NLM193189364 |x 2041-2657 |7 nnns |
| 773 | 1 | 8 | |g volume:22 |g year:2023 |g number:2 |g day:13 |g month:04 |g pages:195-203 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1093/bfgp/elac020 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 22 |j 2023 |e 2 |b 13 |c 04 |h 195-203 | ||