Accurate protein stability predictions from homology models
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Accurate protein stability predictions from homology models. / Valanciute, Audrone; Nygaard, Lasse; Zschach, Henrike; Maglegaard Jepsen, Michael; Lindorff-Larsen, Kresten; Stein, Amelie.
In: Computational and Structural Biotechnology Journal, Vol. 21, 2023, p. 66-73.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Accurate protein stability predictions from homology models
AU - Valanciute, Audrone
AU - Nygaard, Lasse
AU - Zschach, Henrike
AU - Maglegaard Jepsen, Michael
AU - Lindorff-Larsen, Kresten
AU - Stein, Amelie
N1 - Publisher Copyright: © 2022
PY - 2023
Y1 - 2023
N2 - Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.
AB - Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.
KW - Mutation
KW - Protein stability
KW - Protein variant
KW - ΔΔG
U2 - 10.1016/j.csbj.2022.11.048
DO - 10.1016/j.csbj.2022.11.048
M3 - Journal article
C2 - 36514339
AN - SCOPUS:85144041215
VL - 21
SP - 66
EP - 73
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
SN - 2001-0370
ER -
ID: 331317936