Dataset Information

Do natural proteins differ from random sequences polypeptides? Natural vs. random proteins classification using an evolutionary neural network.

ABSTRACT: Are extant proteins the exquisite result of natural selection or are they random sequences slightly edited by evolution? This question has puzzled biochemists for long time and several groups have addressed this issue comparing natural protein sequences to completely random ones coming to contradicting conclusions. Previous works in literature focused on the analysis of primary structure in an attempt to identify possible signature of evolutionary editing. Conversely, in this work we compare a set of 762 natural proteins with an average length of 70 amino acids and an equal number of completely random ones of comparable length on the basis of their structural features. We use an ad hoc Evolutionary Neural Network Algorithm (ENNA) in order to assess whether and to what extent natural proteins are edited from random polypeptides employing 11 different structure-related variables (i.e. net charge, volume, surface area, coil, alpha helix, beta sheet, percentage of coil, percentage of alpha helix, percentage of beta sheet, percentage of secondary structure and surface hydrophobicity). The ENNA algorithm is capable to correctly distinguish natural proteins from random ones with an accuracy of 94.36%. Furthermore, we study the structural features of 32 random polypeptides misclassified as natural ones to unveil any structural similarity to natural proteins. Results show that random proteins misclassified by the ENNA algorithm exhibit a significant fold similarity to portions or subdomains of extant proteins at atomic resolution. Altogether, our results suggest that natural proteins are significantly edited from random polypeptides and evolutionary editing can be readily detected analyzing structural features. Furthermore, we also show that the ENNA, employing simple structural descriptors, can predict whether a protein chain is natural or random.

SUBMITTER: De Lucrezia D

PROVIDER: S-EPMC3353917 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Do natural proteins differ from random sequences polypeptides? Natural vs. random proteins classification using an evolutionary neural network.

De Lucrezia Davide D Slanzi Debora D Poli Irene I Polticelli Fabio F Minervini Giovanni G

PloS one 20120516 5

Are extant proteins the exquisite result of natural selection or are they random sequences slightly edited by evolution? This question has puzzled biochemists for long time and several groups have addressed this issue comparing natural protein sequences to completely random ones coming to contradicting conclusions. Previous works in literature focused on the analysis of primary structure in an attempt to identify possible signature of evolutionary editing. Conversely, in this work we compare a s ...[more]

PMID: 22615786

Similar Datasets

Project description:BACKGROUND: Sequencing the genomes of multiple, taxonomically diverse eukaryotes enables in-depth comparative-genomic analysis which is expected to help in reconstructing ancestral eukaryotic genomes and major events in eukaryotic evolution and in making functional predictions for currently uncharacterized conserved genes. RESULTS: We examined functional and evolutionary patterns in the recently constructed set of 5,873 clusters of predicted orthologs (eukaryotic orthologous groups or KOGs) from seven eukaryotic genomes: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Encephalitozoon cuniculi. Conservation of KOGs through the phyletic range of eukaryotes strongly correlates with their functions and with the effect of gene knockout on the organism's viability. The approximately 40% of KOGs that are represented in six or seven species are enriched in proteins responsible for housekeeping functions, particularly translation and RNA processing. These conserved KOGs are often essential for survival and might approximate the minimal set of essential eukaryotic genes. The 131 single-member, pan-eukaryotic KOGs we identified were examined in detail. For around 20 that remained uncharacterized, functions were predicted by in-depth sequence analysis and examination of genomic context. Nearly all these proteins are subunits of known or predicted multiprotein complexes, in agreement with the balance hypothesis of evolution of gene copy number. Other KOGs show a variety of phyletic patterns, which points to major contributions of lineage-specific gene loss and the 'invention' of genes new to eukaryotic evolution. Examination of the sets of KOGs lost in individual lineages reveals co-elimination of functionally connected genes. Parsimonious scenarios of eukaryotic genome evolution and gene sets for ancestral eukaryotic forms were reconstructed. The gene set of the last common ancestor of the crown group consists of 3,413 KOGs and largely includes proteins involved in genome replication and expression, and central metabolism. Only 44% of the KOGs, mostly from the reconstructed gene set of the last common ancestor of the crown group, have detectable homologs in prokaryotes; the remainder apparently evolved via duplication with divergence and invention of new genes. CONCLUSIONS: The KOG analysis reveals a conserved core of largely essential eukaryotic genes as well as major diversification and innovation associated with evolution of eukaryotic genomes. The results provide quantitative support for major trends of eukaryotic evolution noticed previously at the qualitative level and a basis for detailed reconstruction of evolution of eukaryotic genomes and biology of ancestral forms.

Dataset Information

Do natural proteins differ from random sequences polypeptides? Natural vs. random proteins classification using an evolutionary neural network.

Publications

Do natural proteins differ from random sequences polypeptides? Natural vs. random proteins classification using an evolutionary neural network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets