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Predicting and Experimentally Validating Hot-spot Residues at Protein-Protein Interfaces

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Predicting and Experimentally Validating Hot-spot Residues at Protein-Protein Interfaces. / Ibarra, Amaurys A; Bartlett, Gail J; Hegedüs, Zsöfia; Dutt, Som; Hobor, Fruzsina; Horner, Katherine A; Hetherington, Kristina; Spence, Kirstin; Nelson, Adam; Edwards, Thomas A; Woolfson, Derek N; Sessions, Richard B; Wilson, Andrew J.

In: ACS Chemical Biology, 16.09.2019.

Research output: Contribution to journalArticle

Harvard

Ibarra, AA, Bartlett, GJ, Hegedüs, Z, Dutt, S, Hobor, F, Horner, KA, Hetherington, K, Spence, K, Nelson, A, Edwards, TA, Woolfson, DN, Sessions, RB & Wilson, AJ 2019, 'Predicting and Experimentally Validating Hot-spot Residues at Protein-Protein Interfaces', ACS Chemical Biology. https://doi.org/10.1021/acschembio.9b00560

APA

Ibarra, A. A., Bartlett, G. J., Hegedüs, Z., Dutt, S., Hobor, F., Horner, K. A., ... Wilson, A. J. (2019). Predicting and Experimentally Validating Hot-spot Residues at Protein-Protein Interfaces. ACS Chemical Biology. https://doi.org/10.1021/acschembio.9b00560

Vancouver

Ibarra AA, Bartlett GJ, Hegedüs Z, Dutt S, Hobor F, Horner KA et al. Predicting and Experimentally Validating Hot-spot Residues at Protein-Protein Interfaces. ACS Chemical Biology. 2019 Sep 16. https://doi.org/10.1021/acschembio.9b00560

Author

Ibarra, Amaurys A ; Bartlett, Gail J ; Hegedüs, Zsöfia ; Dutt, Som ; Hobor, Fruzsina ; Horner, Katherine A ; Hetherington, Kristina ; Spence, Kirstin ; Nelson, Adam ; Edwards, Thomas A ; Woolfson, Derek N ; Sessions, Richard B ; Wilson, Andrew J. / Predicting and Experimentally Validating Hot-spot Residues at Protein-Protein Interfaces. In: ACS Chemical Biology. 2019.

Bibtex

@article{33ee3ec5ba194304bd76bbd41bede9e4,
title = "Predicting and Experimentally Validating Hot-spot Residues at Protein-Protein Interfaces",
abstract = "Protein-protein interactions (PPIs) are vital to all biological processes. These interactions are often dynamic, sometimes transient, typically occur over large topographically shallow protein surfaces, and can exhibit a broad range of affinities. Considerable progress has been made in determining PPI structures. However, given the above properties, understanding the key determinants of their thermodynamic stability remains a challenge in chemical biology. An improved ability to identify and engineer PPIs would advance understanding of biological mechanisms and mutant phenotypes, and also, provide a firmer foundation for inhibitor design. In silico prediction of PPI hot-spot amino acids using computational alanine scanning (CAS) offers a rapid approach for predicting key residues that drive protein-protein association. This can be applied to all known PPI structures, however there is a trade-off between throughput and accuracy. Here we describe a comparative analysis of multiple CAS methods, which highlights effective approaches to improve the accuracy of predicting hot-spot residues. Alongside this, we introduce a new method, BUDE Alanine Scanning, which can be applied to single structures from crystallography, and to structural ensembles from NMR or molecular dynamics data. The comparative analyses facilitate accurate prediction of hot-spots that we validate experimentally with three diverse targets: NOXA-B/MCL-1 (an α helix-mediated PPI), SIMS/SUMO and GKAP/SHANK-PDZ (both β strand-mediated interactions). Finally, the approach is applied to the accurate prediction of hot-residues at a topographically novel Affimer/BCL-xL protein-protein interface.",
keywords = "SYNTHETIC BIOLOGY",
author = "Ibarra, {Amaurys A} and Bartlett, {Gail J} and Zs{\"o}fia Heged{\"u}s and Som Dutt and Fruzsina Hobor and Horner, {Katherine A} and Kristina Hetherington and Kirstin Spence and Adam Nelson and Edwards, {Thomas A} and Woolfson, {Derek N} and Sessions, {Richard B} and Wilson, {Andrew J}",
note = "The acceptance date for this record is provisional and based upon the month of publication for the article.",
year = "2019",
month = "9",
day = "16",
doi = "10.1021/acschembio.9b00560",
language = "English",
journal = "ACS Chemical Biology",
issn = "1554-8929",
publisher = "American Chemical Society",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Predicting and Experimentally Validating Hot-spot Residues at Protein-Protein Interfaces

AU - Ibarra, Amaurys A

AU - Bartlett, Gail J

AU - Hegedüs, Zsöfia

AU - Dutt, Som

AU - Hobor, Fruzsina

AU - Horner, Katherine A

AU - Hetherington, Kristina

AU - Spence, Kirstin

AU - Nelson, Adam

AU - Edwards, Thomas A

AU - Woolfson, Derek N

AU - Sessions, Richard B

AU - Wilson, Andrew J

N1 - The acceptance date for this record is provisional and based upon the month of publication for the article.

PY - 2019/9/16

Y1 - 2019/9/16

N2 - Protein-protein interactions (PPIs) are vital to all biological processes. These interactions are often dynamic, sometimes transient, typically occur over large topographically shallow protein surfaces, and can exhibit a broad range of affinities. Considerable progress has been made in determining PPI structures. However, given the above properties, understanding the key determinants of their thermodynamic stability remains a challenge in chemical biology. An improved ability to identify and engineer PPIs would advance understanding of biological mechanisms and mutant phenotypes, and also, provide a firmer foundation for inhibitor design. In silico prediction of PPI hot-spot amino acids using computational alanine scanning (CAS) offers a rapid approach for predicting key residues that drive protein-protein association. This can be applied to all known PPI structures, however there is a trade-off between throughput and accuracy. Here we describe a comparative analysis of multiple CAS methods, which highlights effective approaches to improve the accuracy of predicting hot-spot residues. Alongside this, we introduce a new method, BUDE Alanine Scanning, which can be applied to single structures from crystallography, and to structural ensembles from NMR or molecular dynamics data. The comparative analyses facilitate accurate prediction of hot-spots that we validate experimentally with three diverse targets: NOXA-B/MCL-1 (an α helix-mediated PPI), SIMS/SUMO and GKAP/SHANK-PDZ (both β strand-mediated interactions). Finally, the approach is applied to the accurate prediction of hot-residues at a topographically novel Affimer/BCL-xL protein-protein interface.

AB - Protein-protein interactions (PPIs) are vital to all biological processes. These interactions are often dynamic, sometimes transient, typically occur over large topographically shallow protein surfaces, and can exhibit a broad range of affinities. Considerable progress has been made in determining PPI structures. However, given the above properties, understanding the key determinants of their thermodynamic stability remains a challenge in chemical biology. An improved ability to identify and engineer PPIs would advance understanding of biological mechanisms and mutant phenotypes, and also, provide a firmer foundation for inhibitor design. In silico prediction of PPI hot-spot amino acids using computational alanine scanning (CAS) offers a rapid approach for predicting key residues that drive protein-protein association. This can be applied to all known PPI structures, however there is a trade-off between throughput and accuracy. Here we describe a comparative analysis of multiple CAS methods, which highlights effective approaches to improve the accuracy of predicting hot-spot residues. Alongside this, we introduce a new method, BUDE Alanine Scanning, which can be applied to single structures from crystallography, and to structural ensembles from NMR or molecular dynamics data. The comparative analyses facilitate accurate prediction of hot-spots that we validate experimentally with three diverse targets: NOXA-B/MCL-1 (an α helix-mediated PPI), SIMS/SUMO and GKAP/SHANK-PDZ (both β strand-mediated interactions). Finally, the approach is applied to the accurate prediction of hot-residues at a topographically novel Affimer/BCL-xL protein-protein interface.

KW - SYNTHETIC BIOLOGY

U2 - 10.1021/acschembio.9b00560

DO - 10.1021/acschembio.9b00560

M3 - Article

C2 - 31525028

JO - ACS Chemical Biology

JF - ACS Chemical Biology

SN - 1554-8929

ER -