InterPro as a new tool for complete genome analysis: An example of comparative analysis

InterPro, an integrated documentation resource for protein families, protein domains, and functional sites, was developed to amalgamate the individual efforts of the PROSITE, PRINTS, Pfam, and ProDom databases. InterPro can be used for the computational functional classification of newly determined amino acid sequences that lack biochemical characterization and for comparative genome analysis. InterPro contains over 3500 entries for more than 1 000 000 hits in SWISS-PROT and TrEMBL. The database is accessible for text-and sequence-based searches at http://www.ebi.ac.uk/interpro/. InterPro was used for the complete analysis of the proteome of the pathogenic microorganism Mycobacterium tuberculosis and the comparison with the predicted protein-coding sequences of the complete genomes of Bacillus subtilis and Escherichia coli. It was found that 64.8% of proteins in the proteome of M. tuberculosis matched InterPro entries and can be classified by their functions. The comparison with B. subtilis and E. coli provided information on the most common protein families and domains and on the most highly represented protein families in each organism. Thus, InterPro is a useful tool for general comparison of complete proteomes and their compositions.     

ProDom: automated clustering of homologous domains

The ProDom database is a comprehensive set of protein domain families automatically generated from the SWISS-PROT and TrEMBL sequence databases. An associated database, ProDom-CG, has been derived as a restriction of ProDom to completely sequenced genomes. The ProDom construction method is based on iterative PSI-BLAST searches and multiple alignments are generated for each domain family. The ProDom web server provides the user with a set of tools to visualise multiple alignments, phylogenetic trees and domain architectures of proteins, as well as a BLAST-based server to analyse new sequences for homologous domains. The comprehensive nature of ProDom makes it particularly useful to help sustain the growth of InterPro.     

构建适用于蛋白质家族分类的相似性网络

图聚类用于蛋白质分类问题可以获得较好结果,其前提是将蛋白质之间复杂的相互关系转化为适当的相似性网络作为图聚类分类的输入数据。本文提出一种基于BLAST检索的相似性网络构建方法,从目标蛋白质序列出发,通过若干轮次的BLAST检索逐步从数据库中提取与目标蛋白质直接或间接相关的序列,构成关联集。关联集中序列之间的相似性关系即相似性网络,可作为图聚类算法的分类依据。对Pfam数据库中依直接相似关系难以正确分类的蛋白质的计算表明,按本文方法构建的相似性网络取得了比较满意的结果。     

植物蛋白激酶研究进展

近年来,在分子生物学技术不断完善和在酵母与动物蛋白激酶研究的基础上,植物蛋白激酶的研究已取得了很大的进展。就近十年来国内外学者对植物蛋白激酶的发现,家族分类,磷酸化过程及其生理功能等方面的研究进行综述。最后分析了存在的问题并对今后的研究提出了展望。     

Protein classification artificial neural system.

A neural network classification method is developed as an alternative approach to the large database search/organization problem. The system, termed Protein Classification Artificial Neural System (ProCANS), has been implemented on a Cray supercomputer for rapid superfamily classification of unknown proteins based on the information content of the neural interconnections. The system employs an n-gram hashing function that is similar to the k-tuple method for sequence encoding. A collection of modular back-propagation networks is used to store the large amount of sequence patterns. The system has been trained and tested with the first 2,148 of the 8,309 entries of the annotated Protein Identification Resource protein sequence database (release 29). The entries included the electron transfer proteins and the six enzyme groups (oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases), with a total of 620 superfamilies. After a total training time of seven Cray central processing unit (CPU) hours, the system has reached a predictive accuracy of 90%. The classification is fast (i.e., 0.1 Cray CPU second per sequence), as it only involves a forward-feeding through the networks. The classification time on a full-scale system embedded with all known superfamilies is estimated to be within 1 CPU second. Although the training time will grow linearly with the number of entries, the classification time is expected to remain low even if there is a 10-100-fold increase of sequence entries. The neural database, which consists of a set of weight matrices of the networks, together with the ProCANS software, can be ported to other computers and made available to the genome community. The rapid and accurate superfamily classification would be valuable to the organization of protein sequence databases and to the gene recognition in large sequencing projects.     

Mimicking cellular sorting improves prediction of subcellular localization

Predicting the native subcellular compartment of a protein is an important step toward elucidating its function. Here we introduce LOCtree, a hierarchical system combining support vector machines (SVMs) and other prediction methods. LOCtree predicts the subcellular compartment of a protein by mimicking the mechanism of cellular sorting and exploiting a variety of sequence and predicted structural features in its input. Currently LOCtree does not predict localization for membrane proteins, since the compositional properties of membrane proteins significantly differ from those of non-membrane proteins. While any information about function can be used by the system, we present estimates of performance that are valid when only the amino acid sequence of a protein is known. When evaluated on a non-redundant test set, LOCtree achieved sustained levels of 74% accuracy for non-plant eukaryotes, 70% for plants, and 84% for prokaryotes. We rigorously benchmarked LOCtree in comparison to the best alternative methods for localization prediction. LOCtree outperformed all other methods in nearly all benchmarks. Localization assignments using LOCtree agreed quite well with data from recent large-scale experiments. Our preliminary analysis of a few entirely sequenced organisms, namely human (Homo sapiens), yeast (Saccharomyces cerevisiae), and weed (Arabidopsis thaliana) suggested that over 35% of all non-membrane proteins are nuclear, about 20% are retained in the cytosol, and that every fifth protein in the weed resides in the chloroplast.     

Novel structural tree of (α + β)-proteins containing abCd units

A database of 926 (α + β)-proteins and (α + β)-domains containing abCd units, including 401 non-homologous, was compiled from the Protein Data Bank (total of 2636 PDB entries). A novel structural tree of this structural class of proteins was constructed to include 286 possible polypeptide chain folds. A structural classification of (α + β)-proteins containing abCd unit was developed on the basis of the structural tree. The database and the structural tree are available at http://strees.protres.ru/.     

Structure-based combinatorial protein engineering (SCOPE)

Presented here is the development a semi-rational protein engineering approach that uses information from protein structure coupled with established DNA manipulation techniques to design and create multiple crossover libraries from non-homologous genes. The utility of structure-based combinatorial protein engineering (SCOPE) was demonstrated by its application to two distantly related members of the X-family of DNA polymerases: rat DNA polymerase beta (Pol beta) and African swine fever virus DNA polymerase X (Pol X). These proteins share similar folds but have low sequence identity, and differ greatly in both size and activity. "Equivalent" subdomain elements of structure were designed on the basis of the tertiary structure of Pol beta and the corresponding regions of Pol X were inferred from homology modeling and sequence alignment analysis. Libraries of chimeric genes with up to five crossovers were synthesized in a series of PCR reactions by employing hybrid oligonucleotides that code for variable connections between structural elements. Genetic complementation in Escherichia coli enabled identification of several novel DNA polymerases with enhanced phenotypes. Both the composition of structural elements and the manner in which they were linked were shown to be essential for this property, indicating the importance of these aspects of design.     

Viral adaptation to host: a proteome‐based analysis of codon usage and amino acid preferences

Viruses differ markedly in their specificity toward host organisms. Here, we test the level of general sequence adaptation that viruses display toward their hosts. We compiled a representative data set of viruses that infect hosts ranging from bacteria to humans. We consider their respective amino acid and codon usages and compare them among the viruses and their hosts. We show that bacteria‐infecting viruses are strongly adapted to their specific hosts, but that they differ from other unrelated bacterial hosts. Viruses that infect humans, but not those that infect other mammals or aves, show a strong resemblance to most mammalian and avian hosts, in terms of both amino acid and codon preferences. In groups of viruses that infect humans or other mammals, the highest observed level of adaptation of viral proteins to host codon usages is for those proteins that appear abundantly in the virion. In contrast, proteins that are known to participate in host‐specific recognition do not necessarily adapt to their respective hosts. The implication for the potential of viral infectivity is discussed.