Structural genomics

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An example of a protein structure from Protein Data Bank. Argonne's Midwest Center for Structural Genomics deposits 1,000th protein structure.jpg
An example of a protein structure from Protein Data Bank.

Structural genomics seeks to describe the 3-dimensional structure of every protein encoded by a given genome. This genome-based approach allows for a high-throughput method of structure determination by a combination of experimental and modeling approaches. The principal difference between structural genomics and traditional structural prediction is that structural genomics attempts to determine the structure of every protein encoded by the genome, rather than focusing on one particular protein. With full-genome sequences available, structure prediction can be done more quickly through a combination of experimental and modeling approaches, especially because the availability of large number of sequenced genomes and previously solved protein structures allows scientists to model protein structure on the structures of previously solved homologs.

Contents

Because protein structure is closely linked with protein function, the structural genomics has the potential to inform knowledge of protein function. In addition to elucidating protein functions, structural genomics can be used to identify novel protein folds and potential targets for drug discovery. Structural genomics involves taking a large number of approaches to structure determination, including experimental methods using genomic sequences or modeling-based approaches based on sequence or structural homology to a protein of known structure or based on chemical and physical principles for a protein with no homology to any known structure.

As opposed to traditional structural biology, the determination of a protein structure through a structural genomics effort often (but not always) comes before anything is known regarding the protein function. This raises new challenges in structural bioinformatics, i.e. determining protein function from its 3D structure.

Structural genomics emphasizes high throughput determination of protein structures. This is performed in dedicated centers of structural genomics.

While most structural biologists pursue structures of individual proteins or protein groups, specialists in structural genomics pursue structures of proteins on a genome wide scale. This implies large-scale cloning, expression and purification. One main advantage of this approach is economy of scale. On the other hand, the scientific value of some resultant structures is at times questioned. A Science article from January 2006 analyzes the structural genomics field. [1]

One advantage of structural genomics, such as the Protein Structure Initiative, is that the scientific community gets immediate access to new structures, as well as to reagents such as clones and protein. A disadvantage is that many of these structures are of proteins of unknown function and do not have corresponding publications. This requires new ways of communicating this structural information to the broader research community. The Bioinformatics core of the Joint center for structural genomics (JCSG) has recently developed a wiki-based approach namely Open protein structure annotation network (TOPSAN) for annotating protein structures emerging from high-throughput structural genomics centers.

Goals

One goal of structural genomics is to identify novel protein folds. Experimental methods of protein structure determination require proteins that express and/or crystallize well, which may inherently bias the kinds of proteins folds that this experimental data elucidate. A genomic, modeling-based approach such as ab initio modeling may be better able to identify novel protein folds than the experimental approaches because they are not limited by experimental constraints.

Protein function depends on 3-D structure and these 3-D structures are more highly conserved than sequences. Thus, the high-throughput structure determination methods of structural genomics have the potential to inform our understanding of protein functions. This also has potential implications for drug discovery and protein engineering. [2] Furthermore, every protein that is added to the structural database increases the likelihood that the database will include homologous sequences of other unknown proteins. The Protein Structure Initiative (PSI) is a multifaceted effort funded by the National Institutes of Health with various academic and industrial partners that aims to increase knowledge of protein structure using a structural genomics approach and to improve structure-determination methodology.

Methods

Structural genomics takes advantage of completed genome sequences in several ways in order to determine protein structures. The gene sequence of the target protein can also be compared to a known sequence and structural information can then be inferred from the known protein's structure. Structural genomics can be used to predict novel protein folds based on other structural data. Structural genomics can also take modeling-based approach that relies on homology between the unknown protein and a solved protein structure.

de novo methods

Completed genome sequences allow every open reading frame (ORF), the part of a gene that is likely to contain the sequence for the messenger RNA and protein, to be cloned and expressed as protein. These proteins are then purified and crystallized, and then subjected to one of two types of structure determination: X-ray crystallography and nuclear magnetic resonance (NMR). The whole genome sequence allows for the design of every primer required in order to amplify all of the ORFs, clone them into bacteria, and then express them. By using a whole-genome approach to this traditional method of protein structure determination, all of the proteins encoded by the genome can be expressed at once. This approach allows for the structural determination of every protein that is encoded by the genome.

Modelling-based methods

ab initio modeling

This approach uses protein sequence data and the chemical and physical interactions of the encoded amino acids to predict the 3-D structures of proteins with no homology to solved protein structures. One highly successful method for ab initio modeling is the Rosetta program, which divides the protein into short segments and arranges short polypeptide chain into a low-energy local conformation. Rosetta is available for commercial use and for non-commercial use through its public program, Robetta.

Sequence-based modeling

This modeling technique compares the gene sequence of an unknown protein with sequences of proteins with known structures. Depending on the degree of similarity between the sequences, the structure of the known protein can be used as a model for solving the structure of the unknown protein. Highly accurate modeling is considered to require at least 50% amino acid sequence identity between the unknown protein and the solved structure. 30-50% sequence identity gives a model of intermediate-accuracy, and sequence identity below 30% gives low-accuracy models. It has been predicted that at least 16,000 protein structures will need to be determined in order for all structural motifs to be represented at least once and thus allowing the structure of any unknown protein to be solved accurately through modeling. [3] One disadvantage of this method, however, is that structure is more conserved than sequence and thus sequence-based modeling may not be the most accurate way to predict protein structures.

Threading

Threading bases structural modeling on fold similarities rather than sequence identity. This method may help identify distantly related proteins and can be used to infer molecular functions.

Examples of structural genomics

There are currently a number of on-going efforts to solve the structures for every protein in a given proteome.

Thermotogo maritima proteome

One current goal of the Joint Center for Structural Genomics (JCSG), a part of the Protein Structure Initiative (PSI) is to solve the structures for all the proteins in Thermotogo maritima , a thermophillic bacterium. T. maritima was selected as a structural genomics target based on its relatively small genome consisting of 1,877 genes and the hypothesis that the proteins expressed by a thermophilic bacterium would be easier to crystallize.

Lesley et al used Escherichia coli to express all the open-reading frames (ORFs) of T. martima. These proteins were then crystallized and structures were determined for successfully crystallized proteins using X-ray crystallography. Among other structures, this structural genomics approach allowed for the determination of the structure of the TM0449 protein, which was found to exhibit a novel fold as it did not share structural homology with any known protein. [4]

Mycobacterium tuberculosis proteome

The goal of the TB Structural Genomics Consortium is to determine the structures of potential drug targets in Mycobacterium tuberculosis , the bacterium that causes tuberculosis. The development of novel drug therapies against tuberculosis are particularly important given the growing problem of multi-drug-resistant tuberculosis.

The fully sequenced genome of M. tuberculosis has allowed scientists to clone many of these protein targets into expression vectors for purification and structure determination by X-ray crystallography. Studies have identified a number of target proteins for structure determination, including extracellular proteins that may be involved in pathogenesis, iron-regulatory proteins, current drug targets, and proteins predicted to have novel folds. So far, structures have been determined for 708 of the proteins encoded by M. tuberculosis.

Protein structure databases and classifications

See also

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

<span class="mw-page-title-main">Protein</span> Biomolecule consisting of chains of amino acid residues

Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, providing structure to cells and organisms, and transporting molecules from one location to another. Proteins differ from one another primarily in their sequence of amino acids, which is dictated by the nucleotide sequence of their genes, and which usually results in protein folding into a specific 3D structure that determines its activity.

<span class="mw-page-title-main">Structural biology</span> Study of molecular structures in biology

Structural biology is a field that is many centuries old which, as defined by the Journal of Structural Biology, deals with structural analysis of living material at every level of organization. Early structural biologists throughout the 19th and early 20th centuries were primarily only able to study structures to the limit of the naked eye's visual acuity and through magnifying glasses and light microscopes.

<span class="mw-page-title-main">Genomics</span> Discipline in genetics

Genomics is an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes. A genome is an organism's complete set of DNA, including all of its genes as well as its hierarchical, three-dimensional structural configuration. In contrast to genetics, which refers to the study of individual genes and their roles in inheritance, genomics aims at the collective characterization and quantification of all of an organism's genes, their interrelations and influence on the organism. Genes may direct the production of proteins with the assistance of enzymes and messenger molecules. In turn, proteins make up body structures such as organs and tissues as well as control chemical reactions and carry signals between cells. Genomics also involves the sequencing and analysis of genomes through uses of high throughput DNA sequencing and bioinformatics to assemble and analyze the function and structure of entire genomes. Advances in genomics have triggered a revolution in discovery-based research and systems biology to facilitate understanding of even the most complex biological systems such as the brain.

Protein engineering is the process of developing useful or valuable proteins through the design and production of unnatural polypeptides, often by altering amino acid sequences found in nature. It is a young discipline, with much research taking place into the understanding of protein folding and recognition for protein design principles. It has been used to improve the function of many enzymes for industrial catalysis. It is also a product and services market, with an estimated value of $168 billion by 2017.

<span class="mw-page-title-main">Protein structure prediction</span> Type of biological prediction

Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology; and it is important in medicine and biotechnology.

<span class="mw-page-title-main">Structural bioinformatics</span> Bioinformatics subfield

Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.

<span class="mw-page-title-main">Protein structure</span> Three-dimensional arrangement of atoms in an amino acid-chain molecule

Protein structure is the three-dimensional arrangement of atoms in an amino acid-chain molecule. Proteins are polymers – specifically polypeptides – formed from sequences of amino acids, which are the monomers of the polymer. A single amino acid monomer may also be called a residue, which indicates a repeating unit of a polymer. Proteins form by amino acids undergoing condensation reactions, in which the amino acids lose one water molecule per reaction in order to attach to one another with a peptide bond. By convention, a chain under 30 amino acids is often identified as a peptide, rather than a protein. To be able to perform their biological function, proteins fold into one or more specific spatial conformations driven by a number of non-covalent interactions, such as hydrogen bonding, ionic interactions, Van der Waals forces, and hydrophobic packing. To understand the functions of proteins at a molecular level, it is often necessary to determine their three-dimensional structure. This is the topic of the scientific field of structural biology, which employs techniques such as X-ray crystallography, NMR spectroscopy, cryo-electron microscopy (cryo-EM) and dual polarisation interferometry, to determine the structure of proteins.

In molecular biology, protein threading, also known as fold recognition, is a method of protein modeling which is used to model those proteins which have the same fold as proteins of known structures, but do not have homologous proteins with known structure. It differs from the homology modeling method of structure prediction as it is used for proteins which do not have their homologous protein structures deposited in the Protein Data Bank (PDB), whereas homology modeling is used for those proteins which do. Threading works by using statistical knowledge of the relationship between the structures deposited in the PDB and the sequence of the protein which one wishes to model.

In biochemistry, a hypothetical protein is a protein whose existence has been predicted, but for which there is a lack of experimental evidence that it is expressed in vivo. Sequencing of several genomes has resulted in numerous predicted open reading frames to which functions cannot be readily assigned. These proteins, either orphan or conserved hypothetical proteins, make up ~ 20% to 40% of proteins encoded in each newly sequenced genome. The real evidences for the hypothetical protein functioning in the metabolism of the organism can be predicted by comparing its sequence or structure homology by considering the conserved domain analysis. Even when there is enough evidence that the product of the gene is expressed, by techniques such as microarray and mass-spectrometry, it is difficult to assign a function to it given its lack of identity to protein sequences with annotated biochemical function. Nowadays, most protein sequences are inferred from computational analysis of genomic DNA sequence. Hypothetical proteins are created by gene prediction software during genome analysis. When the bioinformatic tool used for the gene identification finds a large open reading frame without a characterised homologue in the protein database, it returns "hypothetical protein" as an annotation remark.

The TB Structural Genomics Consortium (TBSGC) is a worldwide consortium of scientists developing a foundation for tuberculosis diagnosis and treatment by determining the three-dimensional structures of proteins from M. tuberculosis founded in 2000 as a part of the Protein Structure Initiative. The consortium seeks to solve structures of proteins that are of great interest to the TB biology community. A major goal of the consortium is to have a putative function for every ORF in the TB genome.

<span class="mw-page-title-main">Homology modeling</span> Method of protein structure prediction using other known proteins

Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein. Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence. It has been seen that protein structures are more conserved than protein sequences amongst homologues, but sequences falling below a 20% sequence identity can have very different structure.

Loop modeling is a problem in protein structure prediction requiring the prediction of the conformations of loop regions in proteins with or without the use of a structural template. Computer programs that solve these problems have been used to research a broad range of scientific topics from ADP to breast cancer. Because protein function is determined by its shape and the physiochemical properties of its exposed surface, it is important to create an accurate model for protein/ligand interaction studies. The problem arises often in homology modeling, where the tertiary structure of an amino acid sequence is predicted based on a sequence alignment to a template, or a second sequence whose structure is known. Because loops have highly variable sequences even within a given structural motif or protein fold, they often correspond to unaligned regions in sequence alignments; they also tend to be located at the solvent-exposed surface of globular proteins and thus are more conformationally flexible. Consequently, they often cannot be modeled using standard homology modeling techniques. More constrained versions of loop modeling are also used in the data fitting stages of solving a protein structure by X-ray crystallography, because loops can correspond to regions of low electron density and are therefore difficult to resolve.

In computational biology, de novo protein structure prediction refers to an algorithmic process by which protein tertiary structure is predicted from its amino acid primary sequence. The problem itself has occupied leading scientists for decades while still remaining unsolved. According to Science, the problem remains one of the top 125 outstanding issues in modern science. At present, some of the most successful methods have a reasonable probability of predicting the folds of small, single-domain proteins within 1.5 angstroms over the entire structure.

<span class="mw-page-title-main">Protein Structure Initiative</span>

The Protein Structure Initiative (PSI) was a USA based project that aimed at accelerating discovery in structural genomics and contribute to understanding biological function. Funded by the U.S. National Institute of General Medical Sciences (NIGMS) between 2000 and 2015, its aim was to reduce the cost and time required to determine three-dimensional protein structures and to develop techniques for solving challenging problems in structural biology, including membrane proteins. Over a dozen research centers have been supported by the PSI for work in building and maintaining high-throughput structural genomics pipelines, developing computational protein structure prediction methods, organizing and disseminating information generated by the PSI, and applying high-throughput structure determination to study a broad range of important biological and biomedical problems.

In biology, a protein structure database is a database that is modeled around the various experimentally determined protein structures. The aim of most protein structure databases is to organize and annotate the protein structures, providing the biological community access to the experimental data in a useful way. Data included in protein structure databases often includes three-dimensional coordinates as well as experimental information, such as unit cell dimensions and angles for x-ray crystallography determined structures. Though most instances, in this case either proteins or a specific structure determinations of a protein, also contain sequence information and some databases even provide means for performing sequence based queries, the primary attribute of a structure database is structural information, whereas sequence databases focus on sequence information, and contain no structural information for the majority of entries. Protein structure databases are critical for many efforts in computational biology such as structure based drug design, both in developing the computational methods used and in providing a large experimental dataset used by some methods to provide insights about the function of a protein.

Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.

<span class="mw-page-title-main">DNA annotation</span> The process of describing the structure and function of a genome

In molecular biology and genetics, DNA annotation or genome annotation is the process of describing the structure and function of the components of a genome, by analyzing and interpreting them in order to extract their biological significance and understand the biological processes in which they participate. Among other things, it identifies the locations of genes and all the coding regions in a genome and determines what those genes do.

Phyre and Phyre2 are free web-based services for protein structure prediction. Phyre is among the most popular methods for protein structure prediction having been cited over 1500 times. Like other remote homology recognition techniques, it is able to regularly generate reliable protein models when other widely used methods such as PSI-BLAST cannot. Phyre2 has been designed to ensure a user-friendly interface for users inexpert in protein structure prediction methods. Its development is funded by the Biotechnology and Biological Sciences Research Council.

<span class="mw-page-title-main">Enzyme Function Initiative</span> Collaborative project to determine enzyme function

The Enzyme Function Initiative (EFI) is a large-scale collaborative project aiming to develop and disseminate a robust strategy to determine enzyme function through an integrated sequence–structure-based approach. The project was funded in May 2010 by the National Institute of General Medical Sciences as a Glue Grant which supports the research of complex biological problems that cannot be solved by a single research group. The EFI was largely spurred by the need to develop methods to identify the functions of the enormous number proteins discovered through genomic sequencing projects.

References

  1. ↑ Chandonia JM, Brenner SE (January 2006). "The impact of structural genomics: expectations and outcomes". Science. 311 (5759): 347–51. Bibcode:2006Sci...311..347C. doi:10.1126/science.1121018. PMID   16424331. S2CID   800902.
  2. ↑ Kuhn P, Wilson K, Patch MG, Stevens RC (October 2002). "The genesis of high-throughput structure-based drug discovery using protein crystallography". Curr Opin Chem Biol. 6 (5): 704–10. doi:10.1016/S1367-5931(02)00361-7. PMID   12413557.
  3. ↑ Baker D, Sali A (October 2001). "Protein structure prediction and structural genomics". Science. 294 (5540): 93–6. Bibcode:2001Sci...294...93B. doi:10.1126/science.1065659. PMID   11588250. S2CID   7193705.
  4. ↑ Lesley SA, Kuhn P, Godzik A, et al. (September 2002). "Structural genomics of the Thermotoga maritima proteome implemented in a high-throughput structure determination pipeline". Proc. Natl. Acad. Sci. U.S.A. 99 (18): 11664–9. Bibcode:2002PNAS...9911664L. doi: 10.1073/pnas.142413399 . PMC   129326 . PMID   12193646.

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