Protein family clustering for structural genomics

A major goal of structural genomics is the provision of a structural template for a large fraction of protein domains. The magnitude of this task depends on the number and nature of protein sequence families. With a large number of bacterial genomes now fully sequenced, it is possible to obtain improved estimates of the number and diversity of families in that kingdom. We have used an automated clustering procedure to group all sequences in a set of genomes into protein families. Bench-marking shows the clustering method is sensitive at detecting remote family members, and has a low level of false positives. This comprehensive protein family set has been used to address the following questions. (1) What is the structure coverage for currently known families? (2) How will the number of known apparent families grow as more genomes are sequenced? (3) What is a practical strategy for maximizing structure coverage in future? Our study indicates that approximately 20% of known families with three or more members currently have a representative structure. The study indicates also that the number of apparent protein families will be considerably larger than previously thought: We estimate that, by the criteria of this work, there will be about 250,000 protein families when 1000 microbial genomes have been sequenced. However, the vast majority of these families will be small, and it will be possible to obtain structural templates for 70-80% of protein domains with an achievable number of representative structures, by systematically sampling the larger families.     

Clustering tendency in the protein fold space

Several non-redundant ensembles of protein three-dimensional structures were analyzed in order to estimate their natural clustering tendency by means of the Cox-Lewis coefficient. It was observed that, despite proteins tend to aggregate into different and well separated groups, some overlap between different clusters occurs. This suggests that classifications bases only on structural data cannot allow a systematic classification of proteins. Additional information are in particular needed in order to monitor completely the complex evolutionary relationships between proteins.     

A procedure for detecting structural domains in proteins.

A procedure is described for detecting domains in proteins of known structure. The method is based on the intuitively simple idea that each domain should contain an identifiable hydrophobic core. By applying the algorithm described in the companion paper (Swindells MB, 1995, Protein Sci 4:93-102) to identify distinct cores in multi-domain proteins, one can use this information to determine both the number and the location of the constituent domains. Tests have shown the procedure to be effective on a number of examples, even when the domains are discontinuous along the sequence. However, deficiencies also occur when hydrophobic cores from different domains continue through the interface region and join one another.     

Will my protein crystallize? A sequence‐based predictor

We propose a machine-learning approach to sequence-based prediction of protein crystallizability in which we exploit subtle differences between proteins whose structures were solved by X-ray analysis [or by both X-ray and nuclear magnetic resonance (NMR) spectroscopy] and those proteins whose structures were solved by NMR spectroscopy alone. Because the NMR technique is usually applied on relatively small proteins, sequence length distributions of the X-ray and NMR datasets were adjusted to avoid predictions biased by protein size. As feature space for classification, we used frequencies of mono-, di-, and tripeptides represented by the original 20-letter amino acid alphabet as well as by several reduced alphabets in which amino acids were grouped by their physicochemical and structural properties. The classification algorithm was constructed as a two-layered structure in which the output of primary support vector machine classifiers operating on peptide frequencies was combined by a second-level Naive Bayes classifier. Due to the application of metamethods for cost sensitivity, our method is able to handle real datasets with unbalanced class representation. An overall prediction accuracy of 67% [65% on the positive (crystallizable) and 69% on the negative (noncrystallizable) class] was achieved in a 10-fold cross-validation experiment, indicating that the proposed algorithm may be a valuable tool for more efficient target selection in structural genomics. A Web server for protein crystallizability prediction called SECRET is available at http://webclu.bio.wzw.tum.de:8080/secret.     

从结构基因组学到功能基因组学

基因组学研究随着模式生物基因组全序列测定的完成由结构基因组学阶段发展到功能基因组学阶段,基因组学成为当今最为活跃、最有影响的前沿学科.以结构基因组学的研究成果为基础,功能基因组学中各学科因其原理不同及其关键技术的特点和优势,具有各自的应用范畴和发展趋势.功能基因组学不断渗透入现代科学的各领域,促成了适用于不同研究目的新兴学科的诞生.     

结构基因组学研究与核磁共振

各种生物的基因组DNA测序计划的完成,将结构生物学带入了结构基因组学时代.结构基因组学是对所有基因组产物结构的系统性测定,它运用高通量的选择、表达、纯化以及结构测定和计算分析手段,为基因组的每个蛋白质产物提供实验测定的结构或较好的理论模型,这将加速生命科学各个领域的研究.生物信息学、基因工程、结构测定技术等的发展为结构基因组学研究提供了保证.近年来核磁共振在技术方法上的进展,使其成为结构基因组学高通量结构分析中的一个关键方法.     

In silico evidence for functional specialization after genome duplication in yeast

A fairly recent whole-genome duplication (WGD) event in yeast enables the effects of gene duplication and subsequent functional divergence to be characterized. We examined 15 ohnolog pairs (i.e. paralogs from a WGD) out of c . 500 Saccharomyces cerevisiae ohnolog pairs that have persisted over an estimated 100 million years of evolution. These 15 pairs were chosen for their high levels of asymmetry, i.e. within the pair, one ohnolog had evolved much faster than the other. Sequence comparisons of the 15 pairs revealed that the faster evolving duplicated genes typically appear to have experienced partially – but not fully – relaxed negative selection as evidenced by an average nonsynonymous/synonymous substitution ratio (d N /d S _avg=0.44) that is higher than the slow-evolving genes' ratio (d N /d S _avg=0.14) but still <1. Increased number of insertions and deletions in the fast-evolving genes also indicated loosened structural constraints. Sequence and structural comparisons indicated that a subset of these pairs had significant differences in their catalytically important residues and active or cofactor-binding sites. A literature survey revealed that several of the fast-evolving genes have gained a specialized function. Our results indicate that subfunctionalization and even neofunctionalization has occurred along with degenerative evolution, in which unneeded functions were destroyed by mutations.     

Clustering of protein domains in the human genome

We present a systematic study of the clustering of genes within the human genome based on homology inferred from both sequence and structural similarity. The 3D-Genomics automated proteome annotation pipeline () was utilised to infer homology for each protein domain in the genome, for the 26 superfamilies most highly represented in the Structural Classification Of Proteins (SCOP) database. This approach enabled us to identify homologues that could not be detected by sequence-based methods alone. For each superfamily, we investigated the distribution, both within and among chromosomes, of genes encoding at least one domain within the superfamily. The results indicate a diversity of clustering behaviours: some superfamilies showed no evidence of any clustering, and others displayed significant clustering either within or among chromosomes, or both. Removal of tandem repeats reduced the levels of clustering observed, but some superfamilies still displayed highly significant clustering. Thus, our study suggests that either the process of gene duplication, or the evolution of the resulting clusters, differs between structural superfamilies.     

氨基酸组成聚类、蛋白质结构型和结构型的预测

用信息聚类方法对蛋白质的氨基酸组成进行聚类,发现存在梯级成团（大集团分解成小集团）现象,６４５个蛋白质可分成１５个小集团,每一个小集团与蛋白质二级结构含量决定的结构型有一定相关性,但与蛋白质五大结构型相关性不明显。指出了由氨基酸成分和二级结构含量预测结构型的方案中存在的问题。提出了由蛋白质二级结构序列预测蛋白质结构型的新方法,并给出了预测蛋白质结构型的简明预测规则