Dataset Information

Identifying sequence determinants of reduction potentials of metalloproteins.

ABSTRACT: The reduction potential of an electron transfer protein is one of its most important functional characteristics. Although the type of redox site and the protein fold are the major determinants of the reduction potential of a redox-active protein, its amino acid sequence may tune the reduction potential as well. Thus, homologous proteins can often be divided into different classes, with each class characterized by a biological function and a reduction potential. Site-specific mutagenesis of the sequence determinants of the differences in the reduction potential between classes should change the reduction potential of a protein in one class to that of the other class. Here, a procedure is presented that combines energetic and bioinformatic analysis of homologous proteins to identify sequence determinants that are also good candidates for site-specific mutations, using the [4Fe-4S] ferredoxins and the [4Fe-4S] high-potential iron-sulfur proteins as examples. This procedure is designed to guide site-specific mutations or more computationally expensive studies, such as molecular dynamics simulations. To make the procedure more accessible to the general scientific community, it is being implemented into CHARMMing, a Web-based portal, with a library of density functional theory results for the redox site that are used in the setting up of Poisson-Boltzmann continuum electrostatics calculations for the protein energetics.

SUBMITTER: Perrin BS

PROVIDER: S-EPMC3723707 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Identifying sequence determinants of reduction potentials of metalloproteins.

Perrin Bradley Scott BS Ichiye Toshiko T

Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry 20130521 6

The reduction potential of an electron transfer protein is one of its most important functional characteristics. Although the type of redox site and the protein fold are the major determinants of the reduction potential of a redox-active protein, its amino acid sequence may tune the reduction potential as well. Thus, homologous proteins can often be divided into different classes, with each class characterized by a biological function and a reduction potential. Site-specific mutagenesis of the s ...[more]

PMID: 23690205

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Project description:BackgroundA critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of complex phenotypes are harder to predict. While traditional approaches rely on genome-wide association studies based on Single Nucleotide Polymorphism data, the ability of machine learning algorithms to find these determinants in whole proteome data is still not well known.ResultsTo better understand the applicability of machine learning in this case, we implemented two such algorithms, adaptive boosting (AB) and repeated random forest (RRF), and developed a chunking layer that facilitates the analysis of whole proteome data. We first assessed the performance of these algorithms and tuned them on an influenza data set, for which the determinants of three complex phenotypes (infectivity, transmissibility, and pathogenicity) are known based on experimental evidence. This allowed us to show that chunking improves runtimes by an order of magnitude. Based on simulations, we showed that chunking also increases sensitivity of the predictions, reaching 100% with as few as 20 sequences in a small proteome as in the influenza case (5k sites), but may require at least 30 sequences to reach 90% on larger alignments (500k sites). While RRF has less specificity than random forest, it was never <50%, and RRF sensitivity was significantly higher at smaller chunk sizes. We then used these algorithms to predict the determinants of three types of drug resistance (to Ciprofloxacin, Ceftazidime, and Gentamicin) in a bacterium, Pseudomonas aeruginosa. While both algorithms performed well in the case of the influenza data, results were more nuanced in the bacterial case, with RRF making more sensible predictions, with smaller errors rates, than AB.ConclusionsAltogether, we demonstrated that ML algorithms can be used to identify genetic determinants in small proteomes (viruses), even when trained on small numbers of individuals. We further showed that our RRF algorithm may deserve more scrutiny, which should be facilitated by the decreasing costs of both sequencing and phenotyping of large cohorts of individuals.

Dataset Information

Identifying sequence determinants of reduction potentials of metalloproteins.

Publications

Identifying sequence determinants of reduction potentials of metalloproteins.

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