Improvisation in evolution of genes and genomes: whose structure is it anyway?

Significant progress has been made in recent years in a variety of seemingly unrelated fields such as sequencing, protein structure prediction, and high-throughput transcriptomics and metabolomics. At the same time new microscopic models were developed that made it possible to analyze evolution of genes and genomes from first principles. The results from these efforts enable, for the first time, a comprehensive insight into the evolution of complex systems and organisms on all scales – from sequences to organisms and populations. Every newly sequenced genome uncovers new genes, families, and folds. Where do these new genes come from? How does gene duplication and subsequent divergence of sequence and structure affect the fitness of the organism? What role does regulation play in the evolution of proteins and folds? Emerging synergism between data and modeling provide first robust answers to these questions.Chemistry and Chemical Biolog

ELISA: Structure-Function Inferences Based On Statistically Significant and Evolutionarily Inspired Observations

The problem of functional annotation based on homology modeling is primary to current bioinformatics research. Researchers have noted regularities in sequence, structure and even chromosome organization that allow valid functional cross-annotation. However, these methods provide a lot of false negatives due to limited specificity inherent in the system. We want to create an evolutionarily inspired organization of data that would approach the issue of structure-function correlation from a new, probabilistic perspective. Such organization has possible applications in phylogeny, modeling of functional evolution and structural determination. ELISA (Evolutionary Lineage Inferred from Structural Analysis, ) is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function "neighborhoods". The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). We introduce a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind. Availability: The database is available at http://romi.bu.edu/elisa.Chemistry and Chemical Biolog

ELISA: Structure-Function Inferences based on statistically significant and evolutionarily inspired observations

The problem of functional annotation based on homology modeling is primary to current bioinformatics research. Researchers have noted regularities in sequence, structure and even chromosome organization that allow valid functional cross-annotation. However, these methods provide a lot of false negatives due to limited specificity inherent in the system. We want to create an evolutionarily inspired organization of data that would approach the issue of structure-function correlation from a new, probabilistic perspective. Such organization has possible applications in phylogeny, modeling of functional evolution and structural determination. ELISA (Evolutionary Lineage Inferred from Structural Analysis, ) is an online database that combines functional annotation with structure and sequence homology modeling to place proteins into sequence-structure-function "neighborhoods". The atomic unit of the database is a set of sequences and structural templates that those sequences encode. A graph that is built from the structural comparison of these templates is called PDUG (protein domain universe graph). We introduce a method of functional inference through a probabilistic calculation done on an arbitrary set of PDUG nodes. Further, all PDUG structures are mapped onto all fully sequenced proteomes allowing an easy interface for evolutionary analysis and research into comparative proteomics. ELISA is the first database with applicability to evolutionary structural genomics explicitly in mind. Availability: The database is available at

Binding Site Graphs: A New Graph Theoretical Framework for Prediction of Transcription Factor Binding Sites

Computational prediction of nucleotide binding specificity for transcription factors remains a fundamental and largely unsolved problem. Determination of binding positions is a prerequisite for research in gene regulation, a major mechanism controlling phenotypic diversity. Furthermore, an accurate determination of binding specificities from high-throughput data sources is necessary to realize the full potential of systems biology. Unfortunately, recently performed independent evaluation showed that more than half the predictions from most widely used algorithms are false. We introduce a graph-theoretical framework to describe local sequence similarity as the pair-wise distances between nucleotides in promoter sequences, and hypothesize that densely connected subgraphs are indicative of transcription factor binding sites. Using a well-established sampling algorithm coupled with simple clustering and scoring schemes, we identify sets of closely related nucleotides and test those for known TF binding activity. Using an independent benchmark, we find our algorithm predicts yeast binding motifs considerably better than currently available techniques and without manual curation. Importantly, we reduce the number of false positive predictions in yeast to less than 30%. We also develop a framework to evaluate the statistical significance of our motif predictions. We show that our approach is robust to the choice of input promoters, and thus can be used in the context of predicting binding positions from noisy experimental data. We apply our method to identify binding sites using data from genome scale ChIP–chip experiments. Results from these experiments are publicly available at http://cagt10.bu.edu/BSG. The graphical framework developed here may be useful when combining predictions from numerous computational and experimental measures. Finally, we discuss how our algorithm can be used to improve the sensitivity of computational predictions of transcription factor binding specificities

Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABA(A) receptor subunit genes

Understanding transcription factor (TF) mediated control of gene expression remains a major challenge at the interface of computational and experimental biology. Computational techniques predicting TF-binding site specificity are frequently unreliable. On the other hand, comprehensive experimental validation is difficult and time consuming. We introduce a simple strategy that dramatically improves robustness and accuracy of computational binding site prediction. First, we evaluate the rate of recurrence of computational TFBS predictions by commonly used sampling procedures. We find that the vast majority of results are biologically meaningless. However clustering results based on nucleotide position improves predictive power. Additionally, we find that positional clustering increases robustness to long or imperfectly selected input sequences. Positional clustering can also be used as a mechanism to integrate results from multiple sampling approaches for improvements in accuracy over each one alone. Finally, we predict and validate regulatory sequences partially responsible for transcriptional control of the mammalian type A γ-aminobutyric acid receptor (GABA(A)R) subunit genes. Positional clustering is useful for improving computational binding site predictions, with potential application to improving our understanding of mammalian gene expression. In particular, predicted regulatory mechanisms in the mammalian GABA(A)R subunit gene family may open new avenues of research towards understanding this pharmacologically important neurotransmitter receptor system

A graph-theoretical treatment of protein domain evolution

Thesis (Ph.D.)--Boston University. PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at [email protected]. Thank you.Understanding the mechanisms and driving forces behind molecular evolution is the defining challenge ofcomputational biology. However, a comprehensive, quantitative theory ofmolecular evolution remains elusive. We evaluate a new graph-theoretic treatment ofthis problem. We start by defining a multi-dimensional protein domain universe graph (PDUG). The nodes in this graph are the atomic units of evolution - structures ofrecurring domains and sequences that fold into those structures. Each ofthe three dimensions in PDUG-structure, function and phylogeny represents a potential constraint from evolutionary pressure. We go on to characterize graph-theoretic properties such as phase transitions, power-law degree distributions, and correlations between the three dimensions. We compare the observed properties with those expected from random graphs. The comparison enables us to identify the likely contours of sets of co-evolved proteins. We further our understanding by assessing several computationally tractable models of evolution that recapitulate some fundamental characteristics of PDUG. We go on to define fitness characteristics derived from simple physical properties of structure and function that serve to clarify the uneven relationship between fold and sequence space topology. However, we also find that evolutionary history plays a crucial role since structural fitness is only the potential for sequence entropy, while variable time of evolutionary search determines the fulfillment of that potential. Armed with our new understanding of protein fitness we describe its progression over time. We establish that eukaryotic domains enjoy a faster exploration of sequence and function space than prokaryotic ones. We further note that biological phenomena such as thermophilic adaptation and duplication success may be explained in light of our newly found understanding ofprotein fitness. Finally, we employ the newly developed PDUG paradigm to quantify the structure-function relationship. We show through modeling of divergent evolution that functions coalesce non-randomly as sfructural clusters grow. We fmd that the widely held hierarchical description of structure space has theoretical underpinnings in the natural clustering of the PDUG. We finish by calculating the theoretical lower limit of uncertainty inherent in structure function correlation of protein domains.2031-01-0

The Correlations between Z, F, and P Scores

<div><p>(A) The correlation between structural comparison Z-score and functional distance F-score. (Pearson's r = 0.96 and slope = 0.007.) Each bin contains at least 200 observations. It is worth noting that the average functional distance (F-score) falls from 0.48 to 0.30, only by a third during two decades of structural similarity [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0010009#pcbi-0010009-b14" target="_blank">14</a>].</p> <p>(B) The correspondence between phylogenetic profile distances calculated using mutual information and F-score. Slope of the linear fit is 0.36, with Pearson's r = 0.96. The correlation is averaged, i.e., each data point represents a bin containing 150–200 domains, and the functional distances are averaged inside the bin [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0010009#pcbi-0010009-b14" target="_blank">14</a>].</p> <p>(C) The landscape of functional distance with respect to Z and P scores. An average F-score is calculated for each of the 36 bins; each bin contains 100–200 observations. Since F-score is a distance metric, hotter colors represent domains that are farther away and cooler colors represent those that are closer.</p></div