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Updated: Aug 13, 2026
Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
Published on: March 22, 2019
Statistical and visual morph movie analysis of crystallographic mutant selection bias in protein mutation resource
Werner G Krebs1, Philip E Bourne
1Department of Pharmacology, University of California at San Diego, La Jolla, 92093-0505, USA. wkrebs@sdsc.edu
Abstract:
The relationship between protein mutations and conformational change can potentially decipher the language relating sequence to structure. Elsewhere, we presented the Protein Mutant Resource (PMR), an online tool that systematically identified related mutants in the Protein DataBank (PDB), inferred mutant Gene Ontology classifications using data-mining, and allowed intuitive exploration of relationships between mutant structures. Here, we perform a comprehensive statistical analysis of PMR mutants. Although the PMR contains spectacular conformational changes, generally there is a counter-intuitive inverse relationship between conformational change and the number of mutations. That is, PDB mutations contrast naturally evolved mutations. We compare the frequencies of mutations in the PMR/PDB datasets against the PAM250 natural mutation frequencies to confirm this. We make available morph movies from PMR structure pairs, allowing visual analysis of conformational change and the ability to distinguish visually between conformational change due to motions (e.g., ligand binding)and mutations. The PMR is at http://pmr.sdsc.edu.
Insights
The Protein Mutant Resource (PMR) reveals that protein conformational changes often decrease with more mutations, unlike natural evolution. Visual tools help distinguish mutation-induced changes from other protein motions.
Area of Science:
- Structural Biology
- Bioinformatics
- Computational Biology
Background:
- Understanding protein mutations and their impact on structure is crucial for deciphering sequence-structure relationships.
- The Protein Mutant Resource (PMR) was previously developed to systematically identify protein mutants in the Protein DataBank (PDB).
Purpose of the Study:
- To perform a comprehensive statistical analysis of mutants within the PMR.
- To investigate the relationship between the number of mutations and the degree of conformational change.
- To compare mutation patterns in the PDB with naturally evolved mutations.
Main Methods:
- Statistical analysis of PMR mutant data.
- Comparison of mutation frequencies in PMR/PDB datasets with PAM250 natural mutation frequencies.
- Generation of morph movies for visual analysis of conformational changes.
Main Results:
- A generally inverse relationship was observed between conformational change and the number of mutations in the PDB.
- PDB mutations exhibit different frequency patterns compared to naturally evolved mutations.
- Visualizations allow differentiation between conformational changes caused by mutations versus other biological processes.
Conclusions:
- The PMR provides valuable insights into protein mutation effects.
- Protein DataBank mutations may not fully represent natural evolutionary processes regarding conformational change.
- Visual tools enhance the understanding of mutation-induced structural dynamics.
