Dataset Information

Simple and accurate estimation of ancestral protein sequences.

ABSTRACT: There are a variety of reasons to reconstruct the sequences of ancient proteins, but whatever the reason, the value of the reconstructed protein depends on the accuracy with which the ancient sequence is inferred. This study uses sequences simulated by a sequence-evolution simulation program that compares parsimony, maximum likelihood, and the Bayesian methods of inferring ancestral sequences and concludes that the Bayesian method, as implemented by MRBAYES 3.11, is preferred. Estimated ancestral sequences are of necessity the same length as the alignment on which the underlying phylogeny is based. A highly accurate method for correcting the estimated sequences is introduced, and it is shown that the correction permits inferring the sequences of ancient protein sequences with a very high degree of accuracy.

SUBMITTER: Hall BG

PROVIDER: S-EPMC1459372 | biostudies-literature | 2006 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Simple and accurate estimation of ancestral protein sequences.

Hall Barry G BG

Proceedings of the National Academy of Sciences of the United States of America 20060327 14

There are a variety of reasons to reconstruct the sequences of ancient proteins, but whatever the reason, the value of the reconstructed protein depends on the accuracy with which the ancient sequence is inferred. This study uses sequences simulated by a sequence-evolution simulation program that compares parsimony, maximum likelihood, and the Bayesian methods of inferring ancestral sequences and concludes that the Bayesian method, as implemented by MRBAYES 3.11, is preferred. Estimated ancestra ...[more]

PMID: 16567642

Similar Datasets

Project description:In any macromolecular polyprotic system-for example protein, DNA or RNA-the isoelectric point-commonly referred to as the pI-can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge-and thus the electrophoretic mobility-of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods.Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction.yperez@ebi.ac.ukThe software and data are freely available at https://github.com/ypriverol/pIRSupplementary information: Supplementary data are available at Bioinformatics online.

Dataset Information

Simple and accurate estimation of ancestral protein sequences.

Publications

Simple and accurate estimation of ancestral protein sequences.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets