Dataset Information

The limits of protein sequence comparison?

ABSTRACT: Modern sequence alignment algorithms are used routinely to identify homologous proteins, proteins that share a common ancestor. Homologous proteins always share similar structures and often have similar functions. Over the past 20 years, sequence comparison has become both more sensitive, largely because of profile-based methods, and more reliable, because of more accurate statistical estimates. As sequence and structure databases become larger, and comparison methods become more powerful, reliable statistical estimates will become even more important for distinguishing similarities that are due to homology from those that are due to analogy (convergence). The newest sequence alignment methods are more sensitive than older methods, but more accurate statistical estimates are needed for their full power to be realized.

SUBMITTER: Pearson WR

PROVIDER: S-EPMC2845305 | biostudies-literature | 2005 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The limits of protein sequence comparison?

Pearson William R WR Sierk Michael L ML

Current opinion in structural biology 20050601 3

Modern sequence alignment algorithms are used routinely to identify homologous proteins, proteins that share a common ancestor. Homologous proteins always share similar structures and often have similar functions. Over the past 20 years, sequence comparison has become both more sensitive, largely because of profile-based methods, and more reliable, because of more accurate statistical estimates. As sequence and structure databases become larger, and comparison methods become more powerful, relia ...[more]

PMID: 15919194

Similar Datasets

Project description:BACKGROUND: Many proposed statistical measures can efficiently compare protein sequence to further infer protein structure, function and evolutionary information. They share the same idea of using k-word frequencies of protein sequences. Given a protein sequence, the information on its related protein sequences hasn't been used for protein sequence comparison until now. This paper proposed a scheme to construct protein 'sequence space' which was associated with protein sequences related to the given protein, and the performances of statistical measures were compared when they explored the information on protein 'sequence space' or not. This paper also presented two statistical measures for protein: gre.k (generalized relative entropy) and gsm.k (gapped similarity measure). RESULTS: We tested statistical measures based on protein 'sequence space' or not with three data sets. This not only offers the systematic and quantitative experimental assessment of these statistical measures, but also naturally complements the available comparison of statistical measures based on protein sequence. Moreover, we compared our statistical measures with alignment-based measures and the existing statistical measures. The experiments were grouped into two sets. The first one, performed via ROC (Receiver Operating Curve) analysis, aims at assessing the intrinsic ability of the statistical measures to discriminate and classify protein sequences. The second set of the experiments aims at assessing how well our measure does in phylogenetic analysis. Based on the experiments, several conclusions can be drawn and, from them, novel valuable guidelines for the use of protein 'sequence space' and statistical measures were obtained. CONCLUSION: Alignment-based measures have a clear advantage when the data is high redundant. The more efficient statistical measure is the novel gsm.k introduced by this article, the cos.k followed. When the data becomes less redundant, gre.k proposed by us achieves a better performance, but all the other measures perform poorly on classification tasks. Almost all the statistical measures achieve improvement by exploring the information on 'sequence space' as word's length increases, especially for less redundant data. The reasonable results of phylogenetic analysis confirm that Gdis.k based on 'sequence space' is a reliable measure for phylogenetic analysis. In summary, our quantitative analysis verifies that exploring the information on 'sequence space' is a promising way to improve the abilities of statistical measures for protein comparison.

Project description:BackgroundThe structure and function of bacterial nucleoid are controlled by histone-like proteins of HU/IHF family, omnipresent in bacteria and also founding archaea and some eukaryotes.HU protein binds dsDNA without sequence specificity and avidly binds DNA structures with propensity to be inclined such as forks, three/four-way junctions, nicks, overhangs and DNA bulges. Sequence comparison of thousands of known histone-like proteins from diverse bacteria phyla reveals relation between HU/IHF sequence, DNA-binding properties and other protein features.Methodology and principal findingsPerformed alignment and clusterization of the protein sequences show that HU/IHF family proteins can be unambiguously divided into three groups, HU proteins, IHF_A and IHF_B proteins. HU proteins, IHF_A and IHF_B proteins are further partitioned into several clades for IHF and HU; such a subdivision is in good agreement with bacterial taxonomy. We also analyzed a hundred of 3D fold comparative models built for HU sequences from all revealed HU clades. It appears that HU fold remains similar in spite of the HU sequence variations. We studied DNA-binding properties of HU from N. gonorrhoeae, which sequence is similar to one of E.coli HU, and HU from M. gallisepticum and S. melliferum which sequences are distant from E.coli protein. We found that in respect to dsDNA binding, only S. melliferum HU essentially differs from E.coli HU. In respect to binding of distorted DNA structures, S. melliferum HU and E.coli HU have similar properties but essentially different from M. gallisepticum HU and N. gonorrhea HU. We found that in respect to dsDNA binding, only S. melliferum HU binds DNA in non-cooperative manner and both mycoplasma HU bend dsDNA stronger than E.coli and N. gonorrhoeae. In respect to binding to distorted DNA structures, each HU protein has its individual profile of affinities to various DNA-structures with the increased specificity to DNA junction.Conclusions and significanceHU/IHF family proteins sequence alignment and classification are updated. Comparative modeling demonstrates that HU protein 3D folding's even more conservative than HU sequence. For the first time, DNA binding characteristics of HU from N. gonorrhoeae, M. gallisepticum and S. melliferum are studied. Here we provide detailed analysis of the similarity and variability of DNA-recognizing and bending of four HU proteins from closely and distantly related HU clades.

Dataset Information

The limits of protein sequence comparison?

Publications

The limits of protein sequence comparison?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets