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.

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Prediction of protein function from protein sequence and structure.

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the AllosMod server for modeling ligand-induced protein dynamics (http://salilab.org/allosmod), the AllosMod-FoXS server for predicting a structural ensemble that fits an SAXS profile (http://salilab.org/allosmod-foxs), the FoXSDock server for protein-protein docking filtered by an SAXS profile (http://salilab.org/foxsdock), the SAXS Merge server for automatic merging of SAXS profiles (http://salilab.org/saxsmerge) and the Pose & Rank server for scoring protein-ligand complexes (http://salilab.org/poseandrank). In this update, we also highlight two applications of ModBase: a PSI:Biology initiative to maximize the structural coverage of the human alpha-helical transmembrane proteome and a determination of structural determinants of human immunodeficiency virus-1 protease specificity.

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MODBASE, a database of annotated comparative protein structure models, and associated resources

As a base for human transcriptome and functional genomics, we created the "full-length long Japan" (FLJ) collection of sequenced human cDNAs. We determined the entire sequence of 21,243 selected clones and found that 14,490 cDNAs (10,897 clusters) were unique to the FLJ collection. About half of them (5,416) seemed to be protein-coding. Of those, 1,999 clusters had not been predicted by computational methods. The distribution of GC content of nonpredicted cDNAs had a peak at approximately 58% compared with a peak at approximately 42%for predicted cDNAs. Thus, there seems to be a slight bias against GC-rich transcripts in current gene prediction procedures. The rest of the cDNAs unique to the FLJ collection (5,481) contained no obvious open reading frames (ORFs) and thus are candidate noncoding RNAs. About one-fourth of them (1,378) showed a clear pattern of splicing. The distribution of GC content of noncoding cDNAs was narrow and had a peak at approximately 42%, relatively low compared with that of protein-coding cDNAs.

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Complete sequencing and characterization of 21,243 full-length human cDNAs

Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed light on their evolution. These observations are illustrated using several protein families, including phosphatases, proteases, ultraviolet-irradiation DNA damage repair enzymes, glutamine synthetase, and RuBisCO. The diversity added by GOS data has implications for choosing targets for experimental structure characterization as part of structural genomics efforts. Our analysis indicates that new families are being discovered at a rate that is linear or almost linear with the addition of new sequences, implying that we are still far from discovering all protein families in nature.

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The Sorcerer II Global Ocean Sampling expedition: expanding the universe of protein families.

FeII/alpha-ketoglutarate (alphaKG)-dependent hydroxylases catalyze an amazing diversity of reactions that result in protein side-chain modifications, repair of alkylated DNA/RNA, biosynthesis of antibiotics and plant products, metabolism related to lipids, and biodegradation of a variety of compounds. These enzymes possess a beta-strand "jellyroll" structural fold that contains three metal-binding ligands found in a His1-X-Asp/Glu-Xn-His2 motif. The cosubstrate, alphaKG, chelates FeII using its C-2 keto group (binding opposite the Asp/Glu residue) and C-1 carboxylate (coordinating opposite either His1 or His2). Oxidative decomposition of alphaKG forms CO2 plus succinate and leads to the generation of an FeIV-oxo or other activated oxygen species that hydroxylate the primary substrate. The reactive oxygen species displays alternate reactivity in related enzymes that catalyze desaturations, ring expansions, or ring closures. Other enzymes resemble the FeII/alphaKG-dependent hydroxylases in terms of protein structure or chemical mechanism but do not utilize alphaKG as a substrate. This review describes the reactions catalyzed by this superfamily of enzymes, highlights key active site features revealed by structural studies, and summarizes results from spectroscopic and other approaches that provide insights into the chemical mechanisms.

FeII/alpha-ketoglutarate-dependent hydroxylases and related enzymes.

Progress in understanding the organization and sequences of genes in model organisms and humans is rapidly accelerating. Although genome sequences from prokaryotes have been available for some time, only recently have the genome sequences of several eukaryotic organisms been reported, including Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, and humans (Green 2001). A logical continuation of this line of scientific inquiry is to understand the structure and function of all genes in simple and complex organisms, including the pathways leading to the organization and biochemical function of macromolecular assemblies, organelles, cells, organs, and whole life forms. Such investigations have been variously called integrative or systems biology and -omics or high-throughput biology (Ideker et al. 2001, Greenbaum et al. 2001, Vidal 2001). These studies have blossomed because of advances in technologies that allow highly parallel examination of multiple genes and gene products as well as a vision of biology that is not purely reductionist. Although a unified understanding of biological organisms is still far in the future, new high-throughput biological approaches are having a drastic impact on the scientific mainstream.

One offshoot of the high-throughput approach, which directly leverages the accumulating gene sequence information, involves mining the sequence data to detect important evolutionary relationships, to identify the basic set of genes necessary for independent life, and to reveal important metabolic processes in humans and clinically relevant pathogens. Programs such as MAGPIE (www.genomes.rockefeller.edu/magpie/magpie.html) compare organisms at a whole genome level (Gaasterland and Sensen 1996; Gaasterland and Ragan 1998) and ask what functions are conferred by the new genes that have evolved in higher organisms (Gaasterland and Oprea 2001). Concurrent with computational annotations of gene structure and function, thousands of full-length ORFs from yeast and higher eukaryotes have become available because of advances in cloning and other molecular biology techniques (Walhout et al. 2000a). Structural biologists have embraced high-throughput biology by developing and implementing technologies that will enable the structures of hundreds of protein domains to be solved in a relatively short time. Although thousands of structures are deposited annually in the Protein Data Bank (PDB), most are identical or very similar in sequence to a structure previously existing in the data bank, representing structures of mutants or different ligand bound states (Brenner et al. 1997). Providing structural information for a broader range of sequences requires a focused effort on determining structure for sequences that are divergent from those already in the database. Although structure does not always elucidate function, in many instances (including the structures of two proteins reported here) the atomic structure readily provides insight into the function of a protein whose function was previously unknown. Typically, such functional annotations are based on homologies that are not recognizable at the sequence level but that are clearly revealed on inspection of the protein fold, identification of a conserved constellation of side-chain functionalities, or by the observation of cofactors associated with function (Burley et al. 1999; Shi et al. 2001; Bonanno et al. 2002).

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Guoshun He

Papers

Structural genomics: A pipeline for providing structures for the biologist

Cocrystal Structures of Diaminopimelate Decarboxylase: Mechanism, Evolution, and Inhibition of an Antibiotic Resistance Accessory Factor