Stereochemical Criteria for Prediction of the Effects of Proline Mutations on Protein Stability
| Autor: | C. Ramakrishnan, Kanika Bajaj, Mallur S. Madhusudhan, Bharat V. Adkar, Andrej Sali, Purbani Chakrabarti, Raghavan Varadarajan |
|---|---|
| Přispěvatelé: | Bourne, Philip E |
| Rok vydání: | 2007 |
| Předmět: |
Models
Molecular Protein Conformation Mathematical Sciences 0302 clinical medicine Protein structure Sequence Analysis Protein Models Protein methods Site-Directed lcsh:QH301-705.5 Peptide sequence Protein secondary structure 0303 health sciences Ecology Chemistry 030305 genetics & heredity Stereoisomerism MODELLER Biological Sciences Protein structure prediction Computational Theory and Mathematics Biochemistry Modeling and Simulation Patient Safety Sequence Analysis Research Article Bioinformatics Bacterial Toxins Molecular Sequence Data Biophysics Chemical Dihedral angle Structure-Activity Relationship 03 medical and health sciences Cellular and Molecular Neuroscience Bacterial Proteins Information and Computing Sciences Genetics Computer Simulation Amino Acid Sequence Molecular Biology Ecology Evolution Behavior and Systematics 030304 developmental biology Protein Computational Biology Molecular Protein engineering Eubacteria Models Chemical lcsh:Biology (General) Amino Acid Substitution Mutagenesis Mutation Mutagenesis Site-Directed Generic health relevance 030217 neurology & neurosurgery |
| Zdroj: | PLoS computational biology, vol 3, iss 12 PLoS Computational Biology, Vol 3, Iss 12, p e241 (2007) PLoS Computational Biology |
| ISSN: | 1553-7358 |
| Popis: | When incorporated into a polypeptide chain, proline (Pro) differs from all other naturally occurring amino acid residues in two important respects. The φ dihedral angle of Pro is constrained to values close to −65° and Pro lacks an amide hydrogen. Consequently, mutations which result in introduction of Pro can significantly affect protein stability. In the present work, we describe a procedure to accurately predict the effect of Pro introduction on protein thermodynamic stability. Seventy-seven of the 97 non-Pro amino acid residues in the model protein, CcdB, were individually mutated to Pro, and the in vivo activity of each mutant was characterized. A decision tree to classify the mutation as perturbing or nonperturbing was created by correlating stereochemical properties of mutants to activity data. The stereochemical properties including main chain dihedral angle φ and main chain amide H-bonds (hydrogen bonds) were determined from 3D models of the mutant proteins built using MODELLER. We assessed the performance of the decision tree on a large dataset of 163 single-site Pro mutations of T4 lysozyme, 74 nsSNPs, and 52 other Pro substitutions from the literature. The overall accuracy of this algorithm was found to be 81% in the case of CcdB, 77% in the case of lysozyme, 76% in the case of nsSNPs, and 71% in the case of other Pro substitution data. The accuracy of Pro scanning mutagenesis for secondary structure assignment was also assessed and found to be at best 69%. Our prediction procedure will be useful in annotating uncharacterized nsSNPs of disease-associated proteins and for protein engineering and design. Author Summary Unlike other amino acids that constitute proteins, Proline is missing a vital hydrogen atom and also bestows local structural rigidity to the three-dimensional (3D) structure of proteins. In some locations, proline can be introduced with little or no detrimental effect to protein function, while at others it is destabilizing and can result in significant degradation or aggregation of the protein. To determine the features of protein 3D structure that tolerate the introduction of prolines, each of the 101 amino acid residues of the protein CcdB were replaced with Proline, and the functional consequence of the mutations were observed. On correlating these data to features of protein 3D structure, a decision tree was generated to predict the functional consequences of proline mutations in proteins of known (or accurately modeled) 3D structure. The performance of the tree was assessed on three different datasets that contained a total of 289 proline mutants in 37 different proteins. The average accuracy of prediction was 75%. The decision tree will be useful in predicting if known but uncharacterized proline mutations in disease-related proteins are likely to have adverse effects. It will also be useful in engineering and designing new proteins and peptides. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|