Targeting novel folds for structural genomics

The ultimate goal of structural genomics is to obtain the structure of each protein coded by each gene within a genome to determine gene function. Because of cost and time limitations, it remains impractical to solve the structure for every gene product experimentally. Up to a point, reasonably accurate three‐dimensional structures can be deduced for proteins with homologous sequences by using comparative modeling. Beyond this, fold recognition or threading methods can be used for proteins showing little homology to any known fold, although this is relatively time‐consuming and limited by the library of template folds currently available. Therefore, it is appropriate to develop methods that can increase our knowledge base, expanding our fold libraries by earmarking potentially “novel” folds for experimental structure determination. How can we sift through proteomic data rapidly and yet reliably identify novel folds as targets for structural genomics? We have analyzed a number of simple methods that discriminate between “novel” and “known” folds. We propose that simple alignments of secondary structure elements using predicted secondary structure could potentially be a more selective method than both a simple fold recognition method (GenTHREADER) and standard sequence alignment at finding novel folds when sequences show no detectable homology to proteins with known structures. Proteins 2002;48:44–52. © 2002 Wiley‐Liss, Inc.     

Genome analysis: Assigning protein coding regions to three-dimensional structures

We describe the results of a procedure for maximizing the number of sequences that can be reliably linked to a protein of known three-dimensional structure. Unlike other methods, which try to increase sensitivity through the use of fold recognition software, we only use conventional sequence alignment tools, but apply them in a manner that significantly increases the number of relationships detected. We analyzed 11 genomes and found that, depending on the genome, between 23 and 32% of the ORFs had significant matches to proteins of known structure. In all cases, the aligned region consisted of either >100 residues or >50% of the smaller sequence. Slightly higher percentages could be attained if smaller motifs were also included. This is significantly higher than most previously reported methods, even those that have a fold-recognition component. We survey the biochemical and structural characteristics of the most frequently occurring proteins, and discuss the extent to which alignment methods can realistically assign function to gene products.     

Prediction of functional class of novel plant proteins by a statistical learning method

In plant genomes, the function of a substantial percentage of the putative protein-coding open reading frames (ORFs) is unknown. These ORFs have no significant sequence similarity to known proteins, which complicates the task of functional study of these proteins. Efforts are being made to explore methods that are complementary to, or may be used in combination with, sequence alignment and clustering methods. A web-based protein functional class prediction software, SVMProt, has shown some capability for predicting functional class of distantly related proteins. Here the usefulness of SVMProt for functional study of novel plant proteins is evaluated. To test SVMProt, 49 plant proteins (without a sequence homolog in the Swiss-Prot protein database, not in the SVMProt training set, and with functional indications provided in the literature) were selected from a comprehensive search of MEDLINE abstracts and Swiss-Prot databases in 1999-2004. These represent unique proteins the function of which, at present, cannot be confidently predicted by sequence alignment and clustering methods. The predicted functional class of 31 proteins was consistent, and that of four other proteins was weakly consistent, with published functions. Overall, the functional class of 71.4% of these proteins was consistent, or weakly consistent, with functional indications described in the literature. SVMProt shows a certain level of ability to provide useful hints about the functions of novel plant proteins with no similarity to known proteins.     

A protein sequence fitness function for identifying natural and nonnatural proteins

The infinitesimally small sequence space naturally scouted in the millions of years of evolution suggests that the natural proteins are constrained by some functional prerequisites and should differ from randomly generated sequences. We have developed a protein sequence fitness scoring function that implements sequence and corresponding secondary structural information at tripeptide levels to differentiate natural and nonnatural proteins. The proposed fitness function is extensively validated on a dataset of about 210 000 natural and nonnatural protein sequences and benchmarked with existing methods for differentiating natural and nonnatural proteins. The high sensitivity, specificity, and percentage accuracy (0.81%, 0.95%, and 91% respectively) of the fitness function demonstrates its potential application for sampling the protein sequences with higher probability of mimicking natural proteins. Moreover, the four major classes of proteins (α proteins, β proteins, α/β proteins, and α + β proteins) are separately analyzed and β proteins are found to score slightly lower as compared to other classes. Further, an analysis of about 250 designed proteins (adopted from previously reported cases) helped to define the boundaries for sampling the ideal protein sequences. The protein sequence characterization aided by the proposed fitness function could facilitate the exploration of new perspectives in the design of novel functional proteins.     

Promoter trapping identifies real genes in C. elegans

Promoter trapping involved screening uncharacterized fragments of C. elegans genomic DNA for C. elegans promoter activity. By sequencing the ends of these DNA fragments and locating their genomic origin using the available genome sequence data, promoter trapping has now been shown to identify real promoters of real genes, exactly as anticipated. Developmental expression patterns have thereby been linked to gene sequence, allowing further inferences on gene function to be drawn. Some expression patterns generated by promoter trapping include subcellular details. Localization to the surface of particular cells or even particular aspects of the cell surface was found to be consistent with the genes, now associated with these patterns, encoding membrane-spanning proteins. Data on gene expression patterns are easier to generate and characterize than mutant phenotypes and may provide the best means of interpreting the large quantity of sequence data currently being generated in genome projects. Received: 12 June 1998 / Accepted: 21 August 1998     

Prediction of protein function from protein sequence and structure

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.     

Protein identification in DNA databases by peptide mass fingerprinting.

Proteins can be identified using a set of peptide fragment weights produced by a specific digestion to search a protein database in which sequences have been replaced by fragment weights calculated for various cleavage methods. We present a method using multidimensional searches that greatly increases the confidence level for identification, allowing DNA sequence databases to be examined. This method provides a link between 2-dimensional gel electrophoresis protein databases and genome sequencing projects. Moreover, the increased confidence level allows unknown proteins to be matched to expressed sequence tags, potentially eliminating the need to obtain sequence information for cloning. Database searching from a mass profile is offered as a free service by an automatic server at the ETH, Zürich. For information, send an electronic message to the address cbrg/inf.ethz.ch with the line: help mass search, or help all.     

Functional annotation of proteomic sequences based on consensus of sequence and structural analysis

To maximise the assignment of function of the proteins encoded by a genome and to aid the search for novel drug targets, there is an emerging need for sensitive methods of predicting protein function on a genome-wide basis. GeneAtlas is an automated, high-throughput pipeline for the prediction of protein structure and function using sequence similarity detection, homology modelling and fold recognition methods. GeneAtlas is described in detail here. To test GeneAtlas, a 'virtual' genome was used, a subset of PDB structures from the SCOP database, in which the functional relationships are known. GeneAtlas detects additional relationships by building 3D models in comparison with the sequence searching method PSI-BLAST. Functionally related proteins with sequence identity below the twilight zone can be recognised correctly.     

Inferring functional relationships of proteins from local sequence and spatial surface patterns

We describe a novel approach for inferring functional relationship of proteins by detecting sequence and spatial patterns of protein surfaces. Well-formed concave surface regions in the form of pockets and voids are examined to identify similarity relationship that might be directly related to protein function. We first exhaustively identify and measure analytically all 910,379 surface pockets and interior voids on 12,177 protein structures from the Protein Data Bank. The similarity of patterns of residues forming pockets and voids are then assessed in sequence, in spatial arrangement, and in orientational arrangement. Statistical significance in the form of E and p-values is then estimated for each of the three types of similarity measurements. Our method is fully automated without human intervention and can be used without input of query patterns. It does not assume any prior knowledge of functional residues of a protein, and can detect similarity based on surface patterns small and large. It also tolerates, to some extent, conformational flexibility of functional sites. We show with examples that this method can detect functional relationship with specificity for members of the same protein family and superfamily, as well as remotely related functional surfaces from proteins of different fold structures. We envision that this method can be used for discovering novel functional relationship of protein surfaces, for functional annotation of protein structures with unknown biological roles, and for further inquiries on evolutionary origins of structural elements important for protein function.     

小麦干旱诱导蛋白及相关基因研究进展

干旱胁迫条件下,小麦相关基因受到激活并表达产生干旱胁迫蛋白,主动适应干旱环境、维持个体存活和产量形成。介绍了小麦中一些干旱诱导蛋白及相关基因的研究进展,包括不同小麦品种、胁迫程度、发育阶段的差异性反应和共性特征、对主要干旱信号物质ABA和Ca²⁺的差异应答、以及新近发现的干旱诱导蛋白及相关基因的生物学特性及主要功能等。对于干旱诱导蛋白来说,研究手段和目标从过去以单向电泳技术为主、揭示蛋白条带的表达差异转到现在以双向电泳技术为主、以揭示蛋白质组中干旱诱导蛋白结构和功能的耦合。对于干旱诱导蛋白相关基因来说,研究内容主要包括功能基因和调控基因两大类,功能基因研究主要集中在LEA蛋白基因和透物质合成酶基因等几大类型上,而调控基因研究主要集中在转录因子和蛋白激酶等相关基因及其作用。对干旱诱导蛋白及相关基因在小麦栽培管理和产量育种中的应用前景展开了讨论。     

Genome-wide validation of Magnaporthe grisea gene structures based on transcription evidence

Accurate cDNA data is useful to validate gene structures in a genome. We sequenced 35 189 expressed sequence tags (ESTs) obtained from the highly destructive rice blast fungus, Magnaporthe grisea. Our custom-made computational programs mapped these ESTs on the M. grisea genome sequence, and reconstructed gene structures as well as protein-coding regions. As a result, we predicted 4480 protein-coding sequences, which were more accurate than ab initio predictions. Moreover, cross-species comparisons suggested that our predicted proteins were nearly complete. The cDNA clones obtained in this study will be important for further experimental studies. Our genome annotation is available at http://www.mg.dna.affrc.go.jp/.     

Functional Annotation of Conserved Hypothetical Proteins from Haemophilus influenzae Rd KW20

Haemophilus influenzae is a Gram negative bacterium that belongs to the family Pasteurellaceae, causes bacteremia, pneumonia and acute bacterial meningitis in infants. The emergence of multi-drug resistance H. influenzae strain in clinical isolates demands the development of better/new drugs against this pathogen. Our study combines a number of bioinformatics tools for function predictions of previously not assigned proteins in the genome of H. influenzae. This genome was extensively analyzed and found 1,657 functional proteins in which function of 429 proteins are unknown, termed as hypothetical proteins (HPs). Amino acid sequences of all 429 HPs were extensively annotated and we successfully assigned the function to 296 HPs with high confidence. We also characterized the function of 124 HPs precisely, but with less confidence. We believed that sequence of a protein can be used as a framework to explain known functional properties. Here we have combined the latest versions of protein family databases, protein motifs, intrinsic features from the amino acid sequence, pathway and genome context methods to assign a precise function to hypothetical proteins for which no experimental information is available. We found these HPs belong to various classes of proteins such as enzymes, transporters, carriers, receptors, signal transducers, binding proteins, virulence and other proteins. The outcome of this work will be helpful for a better understanding of the mechanism of pathogenesis and in finding novel therapeutic targets for H. influenzae.     

Homology modeling in a dynamical world

A key concept in template‐based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well‐established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure‐function relationship. We show that protein families with low conformational diversity show a well‐correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.     

Analysis of the Thermotoga maritima genome combining a variety of sequence similarity and genome context tools

The proliferation of genome sequence data has led to the development of a number of tools and strategies that facilitate computational analysis. These methods include the identification of motif patterns, membership of the query sequences in family databases, metabolic pathway involvement and gene proximity. We re-examined the completely sequenced genome of Thermotoga maritima by employing the combined use of the above methods. By analyzing all 1877 proteins encoded in this genome, we identified 193 cases of conflicting annotations (10%), of which 164 are new function predictions and 29 are amendments of previously proposed assignments. These results suggest that the combined use of existing computational tools can resolve inconclusive sequence similarities and significantly improve the prediction of protein function from genome sequence.     

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.     

Structural and functional conservation profiles of novel cathepsin L-like proteins identified in the Drosophila melanogaster genome

Cathepsin L is a cysteine protease which degrades connective tissue proteins including collagen, elastin, and fibronectin. In this study, five well-characterized cathepsin L proteins from different arthropods were used as query sequences for the Drosophila genome database. The search yielded 10 cathepsin L-like sequences, of which eight putatively represent novel cathepsin L-like proteins. To understand the phylogenetic relationship among these cathepsin L-like proteins, a phylogenetic tree was constructed based on their sequences. In addition, models of the tertiary structures of cathepsin L were constructed using homology modeling methods and subjected to molecular dynamics simulations to obtain reasonable structure to understand its dynamical behavior. Our findings demonstrate that all of the potential Drosophila cathepsin L-like proteins contain at least one cathepsin propeptide inhibitor domain. Multiple sequence alignment and homology models clearly highlight the conservation of active site residues, disulfide bonds, and amino acid residues critical for inhibitor binding. Furthermore, comparative modeling indicates that the sequence/structure/function profiles and active site architectures are conserved.     

Gene content and organization of an 85-kb DNA segment from the genome of the phytopathogenic mollicute Spiroplasma kunkelii

Spiroplasma kunkelii, the causative agent of corn stunt disease in maize ( Zea mays L.), is a helical, cell wall-less prokaryote assigned to the class Mollicutes. As part of a project to sequence the entire S. kunkelii genome, we analyzed an 85-kb DNA segment from the pathogenic strain CR2-3x. This genome segment contains 101 ORFs and two tRNA genes. The majority of the ORFs code for predicted proteins that can be assigned to respective clusters of orthologous groups (COGs). These COGs cover diverse functional categories including genetic information storage and processing, cellular processes, and metabolism. The most notable gene cluster in this genome segment is a super-operon capable of encoding 24 ribosomal proteins. The organization of genes in this operon reflects the unique evolutionary position of the spiroplasma. Gene duplications, domain rearrangements, and frameshift mutations in the segment are interpreted as indicators of phase variation in the spiroplasma. To our knowledge, this is the first analysis of a large genome segment from a plant pathogenic spiroplasma.Communicated by W. Goebel