patterns and network topology into a combined distance measure that is used for hierar-chical clustering (Hanischet al., 2002). Network alignment can also be used to identify functional modules through evolutionary conserved modules. These methods combine the interaction topology and protein similarity to detect protein complexes and path-ways that are evolutionarily conserved across different species (Jaeger and Leser, 2007;
Kalaevet al., 2008).
The detected functional modules can be validated experimentally or by comparison to well-known manually curated protein complexes and modules (Mewes et al., 2002;
Ruepp et al., 2010). However, it is difficult to assess to which extent extracted clusters reflect true modules within an organism.
2.4 Evolution of protein interaction networks
The evolution of biological networks contributed largely to the diversification of living organisms. On an evolutionary time-scale protein interaction networks evolved through two fundamental mechanisms: (i) gene duplications and (ii) gain and loss of interactions through mutations (Berg et al., 2004). The first mechanism contributes primarily to network growth while the latter one accounts for functional divergence.
The duplication of a single gene generates a pair of genes whose products have initially identical binding partners. Duplication events are followed either by gene silencing in which one of the duplicates is immediately inactivated upon formation or by functional divergence of the duplicates (Berg et al., 2004). About 90% of the duplicated genes in yeast are silenced directly after duplication indicating that gene duplication itself does not govern network evolution (Wagner, 2003). Yet, gene duplications occur frequently in eukaryotes at high rates, which accounts for the fact that up to 50% of a eukaryotic genome may consist of duplicate genes (Lynch and Conery, 2000).
Functional divergence after duplication, i.e., acquiring (partially) new function, re-sults from changes in the interaction patterns of the duplicated proteins. Point mu-tations in their genes affect the interface of the interacting proteins leading to gain and loss of protein interactions. Although duplicated proteins may share interaction partners, the fraction of duplicates without common interaction partners is significantly higher (Makino and Gojobori, 2007). Empirical studies in yeast show that evolutionary rates of duplicates are considerably accelerated shortly after duplication due to their dif-ferentiation (Lynch, 2007). In consequence, the number of shared interactions between duplicates decreases according to their evolutionary distance (Wagner, 2001). Different studies indicated that the prevalence of degenerative mutations, i.e., mutational loss of interactions after gene duplications contributes most to the diversification (Wagner, 2003). Moreover, interactions are often lost asymmetrically, where one of the duplicates loses most of its original interactions while the other retains them.
The evolutionary rate of proteins depends on their interaction strength, i.e., transient and stable interactions (see Section 2.2). Proteins involved in the formation of stable complexes have been shown to evolve at similar rates (Fraser et al., 2002). Residues in
their interfaces evolve at slower rate, and appear to co-evolve. This means, substitutions in one protein induce complementary alterations in its interaction partner to preserve the functionality of the interaction (Mintseris and Weng, 2005). In contrast, proteins participating in transient interactions show little evidence of co-evolution and thus are presumed to evolve at different rates.
Overall, gain and loss of protein interactions is the primary evolutionary force which shapes the structure of interaction networks while gene duplications affect, in first place, its size (Berget al., 2004).
3 Approaches to Protein Function Prediction
The following chapter provides a comprehensive overview on approaches to protein func-tion predicfunc-tion. We start with a general introducfunc-tion on protein funcfunc-tion emphasizing its importance for the post-genomic era. Subsequently, we briefly discuss traditional experimental methods for elucidating protein function and highlight their limitations for characterizing human proteins. Section 3.2 surveys established approaches that have been developed to circumvent technical and ethical drawbacks of experimental meth-ods by computational means. We explain the most important concepts behind common sequence-, structure and genome-based function prediction methods. Section 3.3 focuses on the principles of network-based function prediction which is a central theme in this thesis. We summarize the benefits and limitations of the distinct methodologies to cat-egorize our proposed protein function prediction approach (see Chapter 4) within the scope of network-based prediction methods.
3.1 Protein function
The large number of genome sequencing projects provide a wealth of knowledge on hundreds of organisms. The interpretation of this wealth of data is a fundamental challenge of the post-genomic era. Completing a new genome is commonly followed by a process known as genome annotation to predict, among others, its protein coding regions and to associate biological information to them (Stein, 2001). Elucidating the functional role of each individual gene product is one of the major challenges in molecular biology and bioinformatics, fundamental to understand biological processes, cellular mechanisms, evolutionary changes and the onset of diseases (Eisenberg et al., 2000; Frishman, 2007).
Traditionally, protein function has been determined for single proteins, one at a time, using classical biochemical and molecular biological experiments. Function derived from, e.g., knock-out experiments, targeted mutations and functional assays (Whisstock and Lesk, 2003), has been commonly reported in the biomedical literature, which in turn is assessed by database curators. Manual curation of such experimental data provides comprehensive and accurate knowledge for genes/proteins (Dimmer et al., 2008) which is widely considered as gold standard for functional annotation.
However, experimental characterization of protein function cannot compete with the pace at which genomic data is being produced (Frishman, 2007). Performing functional assays for each uncharacterized gene in every genome is technically and ethically impos-sible. This has several reasons:
• Even detailed biochemical studies often cannot identify the full repertoire of func-tional activities (Whisstock and Lesk, 2003).
• Conclusions from in vitro experiments might be limited as particularly eukaryotic proteins cannot be studied in conditions close to their natural environment.
• Knock-out experiments in human beings are prohibited for the obvious ethical reasons.
Annotation of protein function becomes more and more a bottleneck in the progress of biomolecular sciences. The gap between available sequence data and functionally characterized proteins is widening (Frishman, 2007). Even for the best-studied model organisms, such as yeast and fly, a substantial fraction of proteins is still uncharacter-ized (Sharan et al., 2007). In attempt to close that gap, numerous high-throughput methods have been developed to study the basic properties of gene products system-atically. Techniques such as DNA microarrays (Schena et al., 1995; Lockhart et al., 1996), yeast two-hybrid systems (Fields and Song, 1989), RNA interference (RNAi) (Fire et al., 1998; Kamath and Ahringer, 2003) and large-scale systematic deletions (Que and Winzeler, 2002) generated a variety of data sets. However, the huge amount of data, accumulated over the last years, rendered biological discovery via manual analysis im-possible (Baumgartneret al., 2007; Dimmeret al., 2008).
Facing these circumstances, scientists turn increasingly toward advanced in silico methods for annotating the vast amount of biological data. Numerous approaches have been developed exploiting the different biological data for assigning functions to unchar-acterized proteins. Note that today, functional annotation of newly sequenced genomes relies primarily on computational methods (Friedberg, 2006; Pandeyet al., 2006; Frish-man, 2007; Sleator and Walsh, 2010).
In the following sections, we present distinct computational methodologies for predict-ing protein function from various types of input data. Before we introduce the different approaches, we will first define biological function and the means of describing it by using standardized machine readable ontologies, such as the Gene Ontology.
Definition of protein function
Function is a highly context-sensitive concept covering all functional activities a gene product may be involved in (Sleator and Walsh, 2010). When speaking of function, one might refer to the molecular, biochemical, cellular, developmental or physiological char-acteristics of a protein. For instance, the function of a protein kinase, in a biochemical aspect, involves the phosphorylation of the hydroxyl group of specific substrates. In a physiological aspect, the kinase is part of distinct signaling pathways, where proteins both phosphorylate, and are phosphorylated by, interaction partners. A mutation in this kinase might implicate a disease, so yet another functional aspect is a phenotypic one. Clearly, the exact meaning of function depends on the biological context in which it is used (Rostet al., 2003; Friedberg, 2006).
Because of its various facets, the “functions” under study need to be clearly defined to be subject of computational studies. Specifying function in a concise manner is
3.1 Protein function difficult as it should reflect the complexity of the concept. In first place, functional information is typically not available in machine-readable format but described in the scientific literature using natural language. However, for studying and inferring function computationally, function needs to be presented in a controlled and well-defined format.
To this end, different vocabularies and annotation schemes have been devised to stan-dardize the description of protein function, typically in a hierarchical fashion starting with generic function and progressing toward more specific function. The first system-atic scheme, the Enzyme Classification (EC), was proposed in 1992 to classify enzymes based on their enzymatic activity using a four-level hierarchy (Nomenclature Committee of the International Union of Biochemistry and Molecular Biology, 1992). Several other functional classification systems emerged (Rueppet al., 2004; Keseleret al., 2009), often in context with individual species or protein families (see Risonet al. (2000); Ouzounis et al. (2003) for an overview).
Gene Ontology
In this work, protein function is defined by the Gene Ontology (GO) (Ashburneret al., 2000), the most widely adopted vocabulary for representing function in a systematic manner. GO consists of two components: the ontology itself, defined by concepts and relationships between concepts (GO ontology); and the associations between gene prod-ucts and concepts (GO annotations). GO covers three major aspects of function, each structured as an independent subontology:
• Molecular function describes the fundamental biochemical activities of a gene pro-duct at the molecular level.
• Biological process describes the series of molecular events or functions that are crucial for the functioning of cells, tissues, organs, and organisms.
• Cellular component characterizes the compartments of a cell or its extracellular environment.
Currently, there are about 32,000 concepts defined in GO but more will be included as the ontology continues to mature, see Table 3.1.
Table 3.1: Gene Ontology statistics for its three categories, molecular function (MF), biological process (BP), and cellular component (CC).Data have been retrieved from the Gene Ontology website (January 2011) and its archives (January 2005 and April 2008).
Date Molecular Function Biological Process Cellular Component
2005 (Jan) 6,962 8,924 1,397
2008 (Apr) 8,260 14,659 2,064
2011 (Jan) 8,933 20,188 2,796
Each subontology is modeled as a directed acyclic graph (DAG) where nodes represent GO terms and edges denote the different relationships between them (see Figure 3.1).
Initially, two relationship types have been used to link terms: is a and part of. GOA
is a
Figure 3.1: Example of the Gene Ontology. Visualization of a small excerpt of the GO subontology biological process, i.e., showing the GO term cell cycle, including its parent terms, children and the different types of relationships between them.
is a GOB means that GOA is a subtype of GOB, e.g., mitotic cell cycle is a subtype of cell cyclewhich in turn is a subtype ofcellular process (see Figure 3.1). The transitivity of this relation implies thatmitotic cell cycle is also a subtype of cellular process. Part of indicates part-whole relationships where a relation is only added if a concept GOB is necessarily part of another concept GOA. For instance, whenevercell cycle processexists, it is part ofcell cycle. Hence, the presence of the first term implies the presence of latter one. Thepart of relation has been recently extended by three other types of relationships to distinguish gene products that play more regulatory than direct roles in biological processes (Gene Ontology Consortium, 2010). Regulates and its sub-relationspositively regulates and negatively regulates are similarly used to specifically mean necessarily-regulates.
Associating gene products with GO annotations can be performed either manually by database curators or automatically through prediction methods. Each association includes an evidence code referencing the type of information the annotation is based upon (Rheeet al., 2008). Such evidence codes can be broadly divided into four categories:
experimental, computational analysis, author statements, and curatorial statements6. Out of the many different codes, only one is not assigned by curators but automated methods. Annotations without curatorial judgment are associated with the ‘inferred from electronic annotation’ (IEA) evidence code.
Annotations derived manually from direct experimental evidence are generally thought to be of higher quality than those inferred from computational or indirect evidence. How-ever, over 98% (September 2009) of the annotations in GO are automatically assigned and have not been curated yet (Gene Ontology Consortium, 2010). To ensure a high
6http://www.geneontology.org/GO.evidence.shtml
3.2 Computational approaches for protein function prediction