Functional differentiation of proteins: implications for structural genomics

Biochemical functional predictions for protein structures of unknown or uncertain function

With the exponential growth in the determination of protein sequences and structures via genome sequencing and structural genomics efforts, there is a growing need for reliable computational methods to determine the biochemical function of these proteins. This paper reviews the efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins. While sequence- and three-dimensional-structure-based methods for protein function prediction have been reviewed previously, the recent trends in local structure-based methods have received less attention. These local structure-based methods are the primary focus of this review. Computational methods have been developed to predict the residues important for catalysis and the local spatial arrangements of these residues can be used to identify protein function. In addition, the combination of different types of methods can help obtain more information and better predictions of function for proteins of unknown function. Global initiatives, including the Enzyme Function Initiative (EFI), COMputational BRidges to EXperiments (COMBREX), and the Critical Assessment of Function Annotation (CAFA), are evaluating and testing the different approaches to predicting the function of proteins of unknown function. These initiatives and global collaborations will increase the capability and reliability of methods to predict biochemical function computationally and will add substantial value to the current volume of structural genomics data by reducing the number of absent or inaccurate functional annotations.

The use of evolutionary patterns in protein annotation

With genomic data skyrocketing, their biological interpretation remains a serious challenge. Diverse computational methods address this problem by pointing to the existence of recurrent patterns among sequence, structure, and function. These patterns emerge naturally from evolutionary variation, natural selection, and divergence--the defining features of biological systems--and they identify molecular events and shapes that underlie specificity of function and allosteric communication. Here we review these methods, and the patterns they identify in case studies and in proteome-wide applications, to infer and rationally redesign function.

Protein function prediction: towards integration of similarity metrics

Genomic centers discover increasingly many protein sequences and structures, but not necessarily their full biological functions. Thus, currently, less than one percent of proteins have experimentally verified biochemical activities. To fill this gap, function prediction algorithms apply metrics of similarity between proteins on the premise that those sufficiently alike in sequence, or structure, will perform identical functions. Although high sensitivity is elusive, network analyses that integrate these metrics together hold the promise of rapid gains in function prediction specificity.

GeMMA: functional subfamily classification within superfamilies of predicted protein structural domains

GeMMA (Genome Modelling and Model Annotation) is a new approach to automatic functional subfamily classification within families and superfamilies of protein sequences. A major advantage of GeMMA is its ability to subclassify very large and diverse superfamilies with tens of thousands of members, without the need for an initial multiple sequence alignment. Its performance is shown to be comparable to the established high-performance method SCI-PHY. GeMMA follows an agglomerative clustering protocol that uses existing software for sensitive and accurate multiple sequence alignment and profile–profile comparison. The produced subfamilies are shown to be equivalent in quality whether whole protein sequences are used or just the sequences of component predicted structural domains. A faster, heuristic version of GeMMA that also uses distributed computing is shown to maintain the performance levels of the original implementation. The use of GeMMA to increase the functional annotation coverage of functionally diverse Pfam families is demonstrated. It is further shown how GeMMA clusters can help to predict the impact of experimentally determining a protein domain structure on comparative protein modelling coverage, in the context of structural genomics.

The sequence-structure relationship and protein function prediction

An incomplete understanding of protein sequence/structure/function relationships causes many difficulties for prediction methods. The highly complex nature of these relationships is a consequence of the interplay between physics and evolution that has been studied using a wide array of experimental and theoretical techniques. We review recent findings relating to conservation of sequence, structure and function and discuss their use in developing improved prediction methods.

Exploring the structure and function paradigm

Advances in protein structure determination, led by the structural genomics initiatives have increased the proportion of novel folds deposited in the Protein Data Bank. However, these structures are often not accompanied by functional annotations with experimental confirmation. In this review, we reassess the meaning of structural novelty and examine its relevance to the complexity of the structure-function paradigm. Recent advances in the prediction of protein function from structure are discussed, as well as new sequence-based methods for partitioning large, diverse superfamilies into biologically meaningful clusters. Obtaining structural data for these functionally coherent groups of proteins will allow us to better understand the relationship between structure and function.

The histidine phosphatase superfamily: structure and function

The histidine phosphatase superfamily is a large functionally diverse group of proteins. They share a conserved catalytic core centred on a histidine which becomes phosphorylated during the course of the reaction. Although the superfamily is overwhelmingly composed of phosphatases, the earliest known and arguably best-studied member is dPGM (cofactor-dependent phosphoglycerate mutase). The superfamily contains two branches sharing very limited sequence similarity: the first containing dPGM, fructose-2,6-bisphosphatase, PhoE, SixA, TIGAR [TP53 (tumour protein 53)-induced glycolysis and apoptosis regulator], Sts-1 and many other activities, and the second, smaller, branch composed mainly of acid phosphatases and phytases. Human representatives of both branches are of considerable medical interest, and various parasites contain superfamily members whose inhibition might have therapeutic value. Additionally, several phosphatases, notably the phytases, have current or potential applications in agriculture. The present review aims to draw together what is known about structure and function in the superfamily. With the benefit of an expanding set of histidine phosphatase superfamily structures, a clearer picture of the conserved elements is obtained, along with, conversely, a view of the sometimes surprising variation in substrate-binding and proton donor residues across the superfamily. This analysis should contribute to correcting a history of over- and mis-annotation in the superfamily, but also suggests that structural knowledge, from models or experimental structures, in conjunction with experimental assays, will prove vital for the future description of function in the superfamily.