Functional Genomics and Structural Biology in the Definition of Gene Function

Abstract

By mid-2007, the three-dimensional (3D) structures of some 45,000 proteins have been solved, over a period where the linear structures of millions of genes have been defined. Technical challenges associated with X-ray crystallography are being overcome and high-throughput methods both for crystallization of proteins and for solving their 3D structures are under development. The question arises as to how structural biology can be integrated with and adds value to functional genomics programs. Structural biology will assist in the definition of gene function through the identification of the likely function of the protein products of genes. The 3D information allows protein sequences predicted from DNA sequences to be classified into broad groups, according to the overall 'fold', or 3D shape, of the protein. Structural information can be used to predict the preferred substrate of a protein, and thereby greatly enhance the accurate annotation of the corresponding gene. Furthermore, it will enable the effects of amino acid substitutions in enzymes to be better understood with respect to enzyme function and could thereby provide insights into natural variation in genes. If the molecular basis of transcription factor-DNA interactions were defined through precise 3D knowledge of the protein-DNA binding site, it would be possible to predict the effects of base substitutions within the motif on the specificity and/or kinetics of binding. In this chapter, we present specific examples of how structural biology can provide valuable information for functional genomics programs.

Plant High-Affinity Potassium (HKT) Transporters Involved in Salinity Tolerance: Structural Insights to Probe Differences in Ion Selectivity

“…Protein structure determines protein function [18,72,73]. Therefore, the 3D structure of a protein provides vital information as to how a protein functions [73].…”

“…In the absence of experimentally determined structure, it is possible to infer structural data from comparisons with known structures or from computational predictions, such as comparative (homology) modelling [16,72]. These methods are limited by the availability of appropriate templates.…”

Computational Challenges in Deciphering Genomic Structures of Bacteria

“…So we need to constantly remind ourselves about this when attempting to bridge the gap! In the rest of the article, I will discuss the information that is potentially derivable from genome sequence alone in the foreseeable future, which will need to be integrated, when attempting to understand the whole picture, with information derivable based on other experimental data such as (a) microarray gene expression data [76][77] for inference of the transcription subsystem and associated regulatory subsystem in a cell, (b) tiling arrays [78][79] for identification of cis regulatory elements of operons, (c) ChIP on chip data [80] for identification of interaction partners between transcription regulators and their cis regulatory elements, (d) proteomic data measuring the presence and the quantities of proteins under specific conditions typically collected using mass spectrometry techniques [81][82] , (e) metabolomic data measuring the metabolites as the results of metabolic reactions and their quantities using mass spectrometry or nuclear magnetic resonance (NMR) techniques [83][84] , (f) protein interaction data generated using techniques like yeast two-hybrid arrays [85] or pull-down approaches [86][87] , (g) protein and complex structures generated using X-ray crystallography [88][89] , NMR or electronic cryo-microscopy techniques [90][91] , which can provide detailed information such as how a protein executes a particular reaction, and (h) imaging data for tracing the movements of bio-molecules inside a cell, as well as information derived through systems level modeling and simulational studies as outlined earlier.…”

Biosynthesis of Plant Cell Wall and Related Polysaccharides by Enzymes of the GT2 and GT48 Families

“…Currently, the functions of about 50% of genes on plant genomes can be predicted from sequence similarities with proteins from other organisms. However, the evidence for function is usually circumstantial or inferred from a third source and in most cases direct biochemical evidence for function is not available (Hrmova & Fincher 2009 ). In many cases the identities of the genes and their annotated function are simply incorrect (Brown & Sj ö lander 2006 ).…”