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Published on: July 5, 2024
Computed structures of point deletion mutants and their enzymatic activities
Monica Berrondo1, Jeffrey J Gray
1Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Abstract:
Point deletions in enzymes can vary in effect from negligible to complete loss of activity; however, these effects are not generally predictable. Deletions are widely observed in nature and often result in diseases such as cancer, cystic fibrosis, or osteogenesis imperfecta. Here, we have developed an algorithm to model the perturbed structures of deletion mutants with the ultimate goal of predicting their activities. The algorithm works by deleting the specified residue from the wild-type structure, creating a gap that is closed using a combination of local and global moves that change the backbone torsion angles of the protein structure. On a set of five proteins for which both wild-type and deletion mutant x-ray crystal structures are available, the algorithm produces deep, narrow energy funnels within 1.5 Å of the crystal structure for the deletion mutants. To assess the ability of our algorithm to predict activity from the predicted structures, we tested the correlation of experimental activity with several measures of the predicted structure ensemble using a set of 45 point deletions from ricin. Estimates incorporating likely prevalence of active and inactive deletion sites suggest that activity can be predicted correctly over 60% of the time from the active site root-mean squared deviation of the lowest energy predicted structures. The predictions are stronger than simple sequence organization measures, but more fundamental work is required in structure prediction and enzyme activity determination to allow consistent prediction of activity.
Insights
Scientists developed an algorithm to predict enzyme activity after point deletions. This computational tool models structural changes, aiding in understanding disease-related mutations and protein function.
Area of Science:
- Biochemistry
- Structural Biology
- Computational Biology
Background:
- Point deletions in enzymes can lead to unpredictable effects on activity, ranging from minor changes to complete loss.
- Such deletions are implicated in various diseases, including cancer, cystic fibrosis, and osteogenesis imperfecta.
- Predicting the functional consequences of these mutations is crucial for understanding disease mechanisms.
Purpose of the Study:
- To develop and validate an algorithm for modeling the structural perturbations caused by point deletions in proteins.
- To assess the algorithm's ability to predict enzyme activity based on the modeled structures of deletion mutants.
- To provide a computational approach for understanding the impact of deletions on protein function.
Main Methods:
- An algorithm was developed to model deletion mutants by removing residues and closing structural gaps using local and global moves.
- The algorithm's accuracy in predicting protein structures was validated against available x-ray crystal structures of wild-type and deletion mutant proteins.
- The correlation between predicted structural ensembles and experimental enzyme activity was assessed using a dataset of ricin point deletions.
Main Results:
- The algorithm successfully modeled deletion mutants, producing structures within 1.5 Å of experimental crystal structures.
- Activity predictions based on the lowest energy structures showed over 60% accuracy.
- The predictive power of the algorithm surpassed that of simple sequence-based measures.
Conclusions:
- The developed algorithm provides a promising tool for predicting the activity of enzyme deletion mutants.
- Accurate structure prediction is key to understanding and predicting the functional impact of protein deletions.
- Further research in protein structure prediction and activity assays is needed for consistent prediction of enzyme activity.
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