The automatic discovery of structural principles describing protein fold space

The study of protein structure has been driven largely by the careful inspection of experimental data by human experts. However, the rapid determination of protein structures from structural-genomics projects will make it increasingly difficult to analyse (and determine the principles responsible for) the distribution of proteins in fold space by inspection alone. Here, we demonstrate a machine-learning strategy that automatically determines the structural principles describing 45 folds. The rules learnt were shown to be both statistically significant and meaningful to protein experts. With the increasing emphasis on high-throughput experimental initiatives, machine-learning and other automated methods of analysis will become increasingly important for many biological problems.     

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.     

基于集成分类器的蛋白质折叠模式识别

蛋白质折叠问题被列为"21世纪的生物物理学"的重要课题,他是分子生物学中心法则尚未解决的一个重大生物学问题,因此预测蛋白质折叠模式是一个复杂、困难、和有挑战性的工作。为了解决该问题,我们引入了分类器集成,本文所采用的是三种分类器(LMT、RandomForest、SMO)进行集成以及188维组合理化特征来对蛋白质类别进行预测。实验证明,该方法可以有效表征蛋白质折叠模式的特性,对蛋白质序列数据实现精确分类;交叉验证和独立测试均证明本文预测准确率超过70%,比前人工作提高近10个百分点。     

Quantifying the similarities within fold space

We have used GRATH, a graph-based structure comparison algorithm, to map the similarities between the different folds observed in the CATH domain structure database. Statistical analysis of the distributions of the fold similarities has allowed us to assess the significance for any similarity. Therefore we have examined whether it is best to represent folds as discrete entities or whether, in fact, a more accurate model would be a continuum wherein folds overlap via common motifs. To do this we have introduced a new statistical measure of fold similarity, termed gregariousness. For a particular fold, gregariousness measures how many other folds have a significant structural overlap with that fold, typically comprising 40% or more of the larger structure. Gregarious folds often contain commonly occurring super-secondary structural motifs, such as beta-meanders, greek keys, alpha-beta plait motifs or alpha-hairpins, which are matching similar motifs in other folds. Apart from one example, all the most gregarious folds matching 20% or more of the other folds in the database, are alpha-beta proteins. They also occur in highly populated architectural regions of fold space, adopting sandwich-like arrangements containing two or more layers of alpha-helices and beta-strands.Domains that exhibit a low gregariousness, are those that have very distinctive folds, with few common motifs or motifs that are packed in unusual arrangements. Most of the superhelices exhibit low gregariousness despite containing some commonly occurring super-secondary structural motifs. In these folds, these common motifs are combined in an unusual way and represent a small proportion of the fold (<10%). Our results suggest that fold space may be considered as continuous for some architectural arrangements (e.g. alpha-beta sandwiches), in that super-secondary motifs can be used to link neighbouring fold groups. However, in other regions of fold space much more discrete topologies are observed with little similarity between folds.     

Structure determination of archaea-specific ribosomal protein L46a reveals a novel protein fold

Three archaea-specific ribosomal proteins recently identified show no sequence homology with other known proteins. Here we determined the structure of L46a, the most conserved one among the three proteins, from Sulfolobus solfataricus P2 using NMR spectroscopy. The structure presents a twisted β-sheet formed by the N-terminal part and two helices at the C-terminus. The L46a structure has a positively charged surface which is conserved in the L46a protein family and is the potential rRNA-binding site. Searching homologous structures in Protein Data Bank revealed that the structure of L46a represents a novel protein fold. The backbone dynamics identified by NMR relaxation experiments reveal significant flexibility at the rRNA binding surface. The potential position of L46a on the ribosome was proposed by fitting the structure into a previous electron microscopy map of the ribosomal 50S subunit, which indicated that L46a contacts to domain I of 23S rRNA near a multifunctional ribosomal protein L7ae.     

Protein fold similarity estimated by a probabilistic approach based on C(alpha)-C(alpha) distance comparison.

The distribution of the C(alpha)-C(alpha) distances between residues separated by three to 30 amino acid residues is highly characteristic of protein folds and makes it possible to identify them from a straightforward comparison of the distance histograms. The comparison is carried out by contingency table analysis and yields a probability of identity (PRIDE score), with values between zero and 1. For closely related structures, PRIDE is highly correlated with the root-mean-square distance between C(alpha) atoms, but it provides a correct classification even for unrelated structures for which a structural alignment is not meaningful. For example, an analysis of the CATH database of fold structures showed that 98.8% of the folds fall into the correct CATH homologous superfamily category, based on the highest PRIDE score obtained. Structural alignment and secondary-structure assignment are not necessary for the calculation of PRIDE, which is fast enough to allow the scanning of large databases.     

A contact energy function considering residue hydrophobic environment and its application in protein fold recognition

The three-dimensional （3D） structure prediction of proteins ：is an important task in bioinformatics. Finding energy functions that can better represent residue-residue and residue-solvent interactions is a crucial way to improve the prediction accu- racy. The widely used contact energy functions mostly only consider the contact frequency between different types of residues; however, we find that the contact frequency also relates to the residue hydrophobic environment. Accordingly, we present an improved contact energy function to integrate the two factors, which can reflect the influence of hydrophobic interaction on the stabilization of protein 3D structure more effectively. Furthermore, a fold recognition （threading） approach based on this energy function is developed. The testing results obtained with 20 randomly selected proteins demonstrate that, compared with common contact energy functions, the proposed energy function can improve the accuracy of the fold template prediction from 20% to 50%, and can also improve the accuracy of the sequence-template alignment from 35% to 65%.     

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.     

Improving fold recognition without folds

The most reliable way to align two proteins of unknown structure is through sequence-profile and profile-profile alignment methods. If the structure for one of the two is known, fold recognition methods outperform purely sequence-based alignments. Here, we introduced a novel method that aligns generalised sequence and predicted structure profiles. Using predicted 1D structure (secondary structure and solvent accessibility) significantly improved over sequence-only methods, both in terms of correctly recognising pairs of proteins with different sequences and similar structures and in terms of correctly aligning the pairs. The scores obtained by our generalised scoring matrix followed an extreme value distribution; this yielded accurate estimates of the statistical significance of our alignments. We found that mistakes in 1D structure predictions correlated between proteins from different sequence-structure families. The impact of this surprising result was that our method succeeded in significantly out-performing sequence-only methods even without explicitly using structural information from any of the two. Since AGAPE also outperformed established methods that rely on 3D information, we made it available through. If we solved the problem of CPU-time required to apply AGAPE on millions of proteins, our results could also impact everyday database searches.     

A procedure for the automatic determination of hydrophobic cores in protein structures.

An algorithm is described for automatically detecting hydrophobic cores in proteins of known structure. Three pieces of information are considered in order to achieve this goal. These are: secondary structure, side-chain accessibility, and side-chain-side-chain contacts. Residues are considered to contribute to a core when they occur in regular secondary structure and have buried side chains that form predominantly nonpolar contacts with one another. This paper describes the algorithm's application to families of proteins with conserved topologies but low sequence similarities. The aim of this investigation is to determine the efficacy of the algorithm as well as to study the extent to which similar cores are identified within a common topology.     

Pfam: A comprehensive database of protein domain families based on seed alignments

Databases of multiple sequence alignments are a valuable aid to protein sequence classification and analysis. One of the main challenges when constructing such a database is to simultaneously satisfy the conflicting demands of completeness on the one hand and quality of alignment and domain definitions on the other. The latter properties are best dealt with by manual approaches, whereas completeness in practice is only amenable to automatic methods. Herein we present a database based on hidden Markov model profiles (HMMs), which combines high quality and completeness. Our database, Pfam, consists of parts A and B. Pfam-A is curated and contains well-characterized protein domain families with high quality alignments, which are maintained by using manually checked seed alignments and HMMs to find and align all members. Pfam-B contains sequence families that were generated automatically by applying the Domainer algorithm to cluster and align the remaining protein sequences after removal of Pfam-A domains. By using Pfam, a large number of previously unannotated proteins from the Caenorhabditis elegans genome project were classified. We have also identified many novel family memberships in known proteins, including new kazal, Fibronectin type III, and response regulator receiver domains. Pfam-A families have permanent accession numbers and form a library of HMMs available for searching and automatic annotation of new protein sequences. Proteins: 28:405–420, 1997. © 1997 Wiley-Liss, Inc.     

Crystal structure of a conserved hypothetical protein from Escherichia coli

The crystal structure of a conserved hypothetical protein from Escherichia coli has been determined using X-ray crystallography. The protein belongs to the Cluster of Orthologous Group COG1553 (National Center for Biotechnology Information database, NLM, NIH), for which there was no structural information available until now. Structural homology search with DALI algorism indicated that this protein has a new fold with no obvious similarity to those of other proteins with known three-dimensional structures. The protein quaternary structure consists of a dimer of trimers, which makes a characteristic cylinder shape. There is a large closed cavity with approximate dimensions of 16 Å × 16 Å × 20 Å in the center of the hexameric structure. Six putative active sites are positioned along the equatorial surface of the hexamer. There are several highly conserved residues including two possible functional cysteines in the putative active site. The possible molecular function of the protein is discussed.