Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.
Proteins
; 92(4): 529-539, 2024 Apr.
Article
em En
| MEDLINE
| ID: mdl-37991066
ABSTRACT
Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https//github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https//rbd-ace2-mutbench.github.io/.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Benchmarking
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Enzima de Conversão de Angiotensina 2
Limite:
Humans
Idioma:
En
Revista:
Proteins
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Turquia
País de publicação:
EEUU
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ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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US
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USA