Diversification at Transcription Factor Binding Sites within a Species and the Implications for Environmental Adaptation

PublishedEvolution of new cellular functions can be achieved both by changes in protein coding sequences and by alteration of expression patterns. Variation of expression may lead to changes in cellular function with relatively little change in genomic sequence. We therefore hypothesize that one of the first signals of functional divergence should be evolution of transcription factor–binding sites (TFBSs). This adaptation should be detectable as substantial variation in the TFBSs of alleles. New data sets allow the first analyses of intraspecies variation from large number of whole-genome sequences. Using data from the Saccharomyces Genome Resequencing Project, we have analyzed variation in TFBSs. We find a large degree of variation both between these closely related strains and between pairs of duplicated genes. There is a correlation between changes in promoter regions and changes in coding sequences, indicating a coupling of changes in expression and function. We show that 1) the types genes with diverged promoters vary between strains from different environments and 2) that patterns of divergence in promoters consistent with positive selection are detectable in alleles between strains and on duplicate promoters. This variation is likely to reflect adaptation to each strain's natural environment. We conclude that, even within a species, we detect signs of selection acting on promoter regions that may act to alter expression patterns. These changes may indicate functional innovation in multiple genes and across the whole genome. Change in function could represent adaptation to the environment and be a precursor to speciation.This work was funded by Biotechnology and Biological Sciences Research Council grant BB/F007620/1

Inferring Gene Family Histories in Yeast Identifies Lineage Specific Expansions

PublishedThe complement of genes found in the genome is a balance between gene gain and gene loss. Knowledge of the specific genes that are gained and lost over evolutionary time allows an understanding of the evolution of biological functions. Here we use new evolutionary models to infer gene family histories across complete yeast genomes; these models allow us to estimate the relative genome-wide rates of gene birth, death, innovation and extinction (loss of an entire family) for the first time. We show that the rates of gene family evolution vary both between gene families and between species. We are also able to identify those families that have experienced rapid lineage specific expansion/contraction and show that these families are enriched for specific functions. Moreover, we find that families with specific functions are repeatedly expanded in multiple species, suggesting the presence of common adaptations and that these family expansions/contractions are not random. Additionally, we identify potential specialisations, unique to specific species, in the functions of lineage specific expanded families. These results suggest that an important mechanism in the evolution of genome content is the presence of lineage-specific gene family changes.This work is funded by BBSRC grant BB/I020489/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Adaptive HIV-1 evolutionary trajectories are constrained by protein stability

Despite the use of combination antiretroviral drugs for the treatment of HIV-1 infection, the emergence of drug resistance remains a problem. Resistance may be conferred either by a single mutation or a concerted set of mutations. The involvement of multiple mutations can arise due to interactions between sites in the amino acid sequence as a consequence of the need to maintain protein structure. To better understand the nature of such epistatic interactions, we reconstructed the ancestral sequences of HIV-1’s Pol protein, and traced the evolutionary trajectories leading to mutations associated with drug resistance. Using contemporary and ancestral sequences we modelled the effects of mutations (i.e. amino acid replacements) on protein structure to understand the functional effects of residue changes. Although the majority of resistance-associated sequences tend to destabilise the protein structure, we find there is a general tendency for protein stability to decrease across HIV-1’s evolutionary history. That a similar pattern is observed in the non-drug resistance lineages indicates that non-resistant mutations, for example, associated with escape from the immune response, also impacts on protein stability. Maintenance of optimal protein structure therefore represents a major constraining factor to the evolution of HIV-1

Reliable Generation of Native-Like Decoys Limits Predictive Ability in Fragment-Based Protein Structure Prediction

Our previous work with fragment-assembly methods has demonstrated specific deficiencies in conformational sampling behaviour that, when addressed through improved sampling algorithms, can lead to more reliable prediction of tertiary protein structure when good fragments are available, and when score values can be relied upon to guide the search to the native basin. In this paper, we present preliminary investigations into two important questions arising from more difficult prediction problems. First, we investigated the extent to which native-like conformational states are generated during multiple runs of our search protocols. We determined that, in cases of difficult prediction, native-like decoys are rarely or never generated. Second, we developed a scheme for decoy retention that balances the objectives of retaining low-scoring structures and retaining conformationally diverse structures sampled during the course of the search. Our method succeeds at retaining more diverse sets of structures, and, for a few targets, more native-like solutions are retained as compared to our original, energy-based retention scheme. However, in general, we found that the rate at which native-like structural states are generated has a much stronger effect on eventual distributions of predictive accuracy in the decoy sets, as compared to the specific decoy retention strategy used. We found that our protocols show differences in their ability to access native-like states for some targets, and this may explain some of the differences in predictive performance seen between these methods. There appears to be an interaction between fragment sets and move operators, which influences the accessibility of native-like structures for given targets. Our results point to clear directions for further improvements in fragment-based methods, which are likely to enable higher accuracy predictions

Modular Biological Function Is Most Effectively Captured by Combining Molecular Interaction Data Types

PublishedLarge-scale molecular interaction data sets have the potential to provide a comprehensive, system-wide understanding of biological function. Although individual molecules can be promiscuous in terms of their contribution to function, molecular functions emerge from the specific interactions of molecules giving rise to modular organisation. As functions often derive from a range of mechanisms, we demonstrate that they are best studied using networks derived from different sources. Implementing a graph partitioning algorithm we identify subnetworks in yeast protein-protein interaction (PPI), genetic interaction and gene co-regulation networks. Among these subnetworks we identify cohesive subgraphs that we expect to represent functional modules in the different data types. We demonstrate significant overlap between the subgraphs generated from the different data types and show these overlaps can represent related functions as represented by the Gene Ontology (GO). Next, we investigate the correspondence between our subgraphs and the Gene Ontology. This revealed varying degrees of coverage of the biological process, molecular function and cellular component ontologies, dependent on the data type. For example, subgraphs from the PPI show enrichment for 84%, 58% and 93% of annotated GO terms, respectively. Integrating the interaction data into a combined network increases the coverage of GO. Furthermore, the different annotation types of GO are not predominantly associated with one of the interaction data types. Collectively our results demonstrate that successful capture of functional relationships by network data depends on both the specific biological function being characterised and the type of network data being used. We identify functions that require integrated information to be accurately represented, demonstrating the limitations of individual data types. Combining interaction subnetworks across data types is therefore essential for fully understanding the complex and emergent nature of biological function.JIM was funded by a Biotechnology and Biological Sciences Research Council (BBSRC) CASE studentship with industry partner Pfizer and RMA by a BBSRC studentship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Determining the evolutionary history of gene families

PublishedMotivation: Recent large-scale studies of individuals within a population have demonstrated that there is widespread variation in copy number in many gene families. In addition, there is increasing evidence that the variation in gene copy number can give rise to substantial phenotypic effects. In some cases, these variations have been shown to be adaptive. These observations show that a full understanding of the evolution of biological function requires an understanding of gene gain and gene loss. Accurate, robust evolutionary models of gain and loss events are, therefore, required. Results: We have developed weighted parsimony and maximum likelihood methods for inferring gain and loss events. To test these methods, we have used Markov models of gain and loss to simulate data with known properties. We examine three models: a simple birth–death model, a single rate model and a birth–death innovation model with parameters estimated from Drosophila genome data. We find that for all simulations maximum likelihood-based methods are very accurate for reconstructing the number of duplication events on the phylogenetic tree, and that maximum likelihood and weighted parsimony have similar accuracy for reconstructing the ancestral state. Our implementations are robust to different model parameters and provide accurate inferences of ancestral states and the number of gain and loss events. For ancestral reconstruction, we recommend weighted parsimony because it has similar accuracy to maximum likelihood, but is much faster. For inferring the number of individual gene loss or gain events, maximum likelihood is noticeably more accurate, albeit at greater computational cost.Biotechnology and Biological Sciences Research Council, UK

Gene Duplication and Environmental Adaptation within Yeast Populations

PublishedPopulation-level differences in the number of copies of genes resulting from gene duplication and loss have recently been recognized as an important source of variation in eukaryotes. However, except for a small number of cases, the phenotypic effects of this variation are unknown. Data from the Saccharomyces Genome Resequencing Project permit the study of duplication in genome sequences from a set of individuals within the same population. These sequences can be correlated with available information on the environments from which these yeast strains were isolated. We find that yeast show an abundance of duplicate genes that are lineage specific, leading to a large degree of variation in gene content between individual strains. There is a detectable bias for specific functions, indicating that selection is acting to preferentially retain certain duplicates. Most strikingly, we find that sets of over- and underrepresented duplicates correlate with the environment from which they were isolated. Together, these observations indicate that gene duplication can give rise to substantial phenotypic differences within populations that in turn can offer a shortcut to evolutionary adaptation.This work was funded by BBSRC grant BB/F007620/1

Binding interface change and cryptic variation in the evolution of protein-protein interactions

Background:Physical interactions between proteins are essential for almost all biological functions and systems. To understand the evolution of function it is therefore important to understand the evolution of molecular interactions. Of key importance is the evolution of binding specificity, the set of interactions made by a protein, since change in specificity can lead to “rewiring” of interaction networks. Unfortunately, the interfaces through which proteins interact are complex, typically containing many amino-acid residues that collectively must contribute to binding specificity as well as binding affinity, structural integrity of the interface and solubility in the unbound state. Results: In order to study the relationship between interface composition and binding specificity, we make use of paralogous pairs of yeast proteins. Immediately after duplication these paralogues will have identical sequences and protein products that make an identical set of interactions. As the sequences diverge, we can correlate amino-acid change in the interface with any change in the specificity of binding. We show that change in interface regions correlates only weakly with change in specificity, and many variants in interfaces are functionally equivalent. We show that many of the residue replacements within interfaces are silent with respect to their contribution to binding specificity. Conclusions: We conclude that such functionally-equivalent change has the potential to contribute to evolutionary plasticity in interfaces by creating cryptic variation, which in turn may provide the raw material for functional innovation and coevolution.BBSRCWellcome Trust Institutional Strategic Support Awar

On heuristic bias in fragment-Assembly methods for protein structure prediction

We discuss the issue of heuristic bias in fragment-Assembly methods for protein structure prediction. We explain the importance of this issue, which has been paid insufficient a.ention by evolutionary computation researchers engaging with the structural biology community. We proceed by describing preliminary data that illustrates the signi.cant (and expectable) impact that fragment library composition has on search performance, and discuss the challenges this poses for the development of improved fragment libraries

Improved fragment-based protein structure prediction by redesign of search heuristics

Difficulty in sampling large and complex conformational spaces remains a key limitation in fragment-based de novo prediction of protein structure. Our previous work has shown that even for small-to-medium-sized proteins, some current methods inadequately sample alternative structures. We have developed two new conformational sampling techniques, one employing a bilevel optimisation framework and the other employing iterated local search. We combine strategies of forced structural perturbation (where some fragment insertions are accepted regardless of their impact on scores) and greedy local optimisation, allowing greater exploration of the available conformational space. Comparisons against the Rosetta Abinitio method indicate that our protocols more frequently generate native-like predictions for many targets, even following the low-resolution phase, using a given set of fragment libraries. By contrasting results across two different fragment sets, we show that our methods are able to better take advantage of high-quality fragments. These improvements can also translate into more reliable identification of near-native structures in a simple clustering-based model selection procedure. We show that when fragment libraries are sufficiently well-constructed, improved breadth of exploration within runs improves prediction accuracy. Our results also suggest that in benchmarking scenarios, a total exclusion of fragments drawn from homologous templates can make performance differences between methods appear less pronounced