Dataset Information

Binding site on human immunoglobulin G for the affinity ligand HWRGWV.

ABSTRACT: Affinity ligand HWRGWV has demonstrated the ability to isolate human immunoglobulin G (hIgG) from mammalian cell culture media. The ligand specifically binds hIgG through its Fc portion. This work shows that deglycosylation of hIgG has no influence on its binding to the HWRGWV ligand and the ligand does not compete with Protein A or Protein G in binding hIgG. It is suggested by the mass spectrometry (MS) data and docking simulation that HWRGWV binds to the pFc portion of hIgG and interacts with the amino acids in the loop Ser383-Asn389 (SNGQPEN) located in the C(H)3 domain. Subsequent modeling has suggested a possible three-dimensional minimized solution structure for the interaction of hIgG and the HWRGWV ligand. The results support the fact that a peptide as small as a hexamer can have specific interactions with large proteins such as hIgG.

SUBMITTER: Yang H

PROVIDER: S-EPMC3906645 | biostudies-literature | 2010 May-Jun

REPOSITORIES: biostudies-literature

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Binding site on human immunoglobulin G for the affinity ligand HWRGWV.

Yang Haiou H Gurgel Patrick V PV Williams D Keith DK Bobay Benjamin G BG Cavanagh John J Muddiman David C DC Carbonell Ruben G RG

Journal of molecular recognition : JMR 20100501 3

Affinity ligand HWRGWV has demonstrated the ability to isolate human immunoglobulin G (hIgG) from mammalian cell culture media. The ligand specifically binds hIgG through its Fc portion. This work shows that deglycosylation of hIgG has no influence on its binding to the HWRGWV ligand and the ligand does not compete with Protein A or Protein G in binding hIgG. It is suggested by the mass spectrometry (MS) data and docking simulation that HWRGWV binds to the pFc portion of hIgG and interacts with ...[more]

PMID: 20049844

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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.

Dataset Information

Binding site on human immunoglobulin G for the affinity ligand HWRGWV.

Publications

Binding site on human immunoglobulin G for the affinity ligand HWRGWV.

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