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MmCSM-NA: accurately predicting effects of single and multiple mutations on protein-nucleic acid binding affinity.

ABSTRACT: While protein-nucleic acid interactions are pivotal for many crucial biological processes, limited experimental data has made the development of computational approaches to characterise these interactions a challenge. Consequently, most approaches to understand the effects of missense mutations on protein-nucleic acid affinity have focused on single-point mutations and have presented a limited performance on independent data sets. To overcome this, we have curated the largest dataset of experimentally measured effects of mutations on nucleic acid binding affinity to date, encompassing 856 single-point mutations and 141 multiple-point mutations across 155 experimentally solved complexes. This was used in combination with an optimized version of our graph-based signatures to develop mmCSM-NA (http://biosig.unimelb.edu.au/mmcsm_na), the first scalable method capable of quantitatively and accurately predicting the effects of multiple-point mutations on nucleic acid binding affinities. mmCSM-NA obtained a Pearson's correlation of up to 0.67 (RMSE of 1.06 Kcal/mol) on single-point mutations under cross-validation, and up to 0.65 on independent non-redundant datasets of multiple-point mutations (RMSE of 1.12 kcal/mol), outperforming similar tools. mmCSM-NA is freely available as an easy-to-use web-server and API. We believe it will be an invaluable tool to shed light on the role of mutations affecting protein-nucleic acid interactions in diseases.

SUBMITTER: Nguyen TB

PROVIDER: S-EPMC8600011 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Project description:Coronaviruses pose serious health threats to humans and other animals. Understanding the mechanisms of their replication has important implications for global health and economic stability. Nonstructural protein 9 (nsp9) is an essential RNA binding protein for coronavirus replication. However, the mechanisms of the dimerization and nucleic acid binding of nsp9 remain elusive. Here, we report four crystal structures, including wild-type porcine delta coronavirus (PDCoV) nsp9, PDCoV nsp9-ΔN7 (N-terminal 7 amino acids deleted), wild-type porcine epidemic diarrhea virus (PEDV) nsp9, and PEDV nsp9-C59A mutant. These structures reveal the diverse dimerization forms of coronavirus nsp9. We first found that the N-finger of nsp9 from PDCoV plays a critical role in dimerization. Meanwhile, PEDV nsp9 is distinguished by the presence of a disulfide bond in the dimer interface. Interestingly, size exclusion chromatography and analytical ultracentrifugation analyses indicate that the PDCoV nsp9-ΔN7 and PEDV nsp9-C59A mutants are monomeric in solution. In addition, electrophoretic mobility shift assays and microscale thermophoresis analysis indicate that the monomeric forms of PDCoV nsp9 and PEDV nsp9 still have nucleic acid binding affinity, although it is lower than that of the wild type. Our results show that the diverse dimerization forms of coronavirus nsp9 proteins enhance their nucleic acid binding affinity.IMPORTANCE Coronaviruses cause widespread respiratory, gastrointestinal, and central nervous system diseases in humans and other animals, threatening human health and causing economic loss. Coronavirus nsp9, a member of the replication complex, is an important RNA binding subunit in the RNA-synthesizing machinery of all coronaviruses. However, the mechanisms of the dimerization and nucleic acid binding of nsp9 remain elusive. In this study we determined the nsp9 crystal structures of PDCoV and PEDV. We first found that the N-finger of nsp9 from PDCoV plays a critical role in dimerization. Meanwhile, PEDV nsp9 is distinguished by the presence of a disulfide bond in the dimer interface. This study provides a structural and functional basis for understanding the mechanism of dimerization and shows that the diverse dimerization modes of coronavirus nsp9 proteins enhance their nucleic acid binding affinity. Importantly, these findings may provide a new insight for antiviral drug development.

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MmCSM-NA: accurately predicting effects of single and multiple mutations on protein-nucleic acid binding affinity.

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