Local Function Conservation in Sequence and Structure Space

We assess the variability of protein function in protein sequence and structure space. Various regions in this space exhibit considerable difference in the local conservation of molecular function. We analyze and capture local function conservation by means of logistic curves. Based on this analysis, we propose a method for predicting molecular function of a query protein with known structure but unknown function. The prediction method is rigorously assessed and compared with a previously published function predictor. Furthermore, we apply the method to 500 functionally unannotated PDB structures and discuss selected examples. The proposed approach provides a simple yet consistent statistical model for the complex relations between protein sequence, structure, and function. The GOdot method is available online (http://godot.bioinf.mpi-inf.mpg.de)

Investigating the role of BPLF1 in the EBV life cycle

The Epstein-Barr Virus (EBV) is a γ-herpesvirus that establishes a lifelong infection in human hosts. This infection can manifest into cancer. This affliction is attributed to the latent encoded EBV proteins. However, lytic proteins have recently been demonstrated to contribute to tumour development, with notable examples being tegument proteins BNRF1 and BPLF1. In my thesis, I studied BPLF1 in its full form to identify novel regions involved in the EBV life cycle and in carcinogenesis. These goals are achieved through expression in vitro expression studies and in the context of EBV virions that infect primary B cells ex vivo. I used co-immunoprecipitation in tandem with mass spectrometric analysis to identify novel host BPLF1 binding partner SENP6, a deSUMOylase responsible for maintaining genomic integrity. I proceeded to study the effects that BPLF1 has on SENP6 activity and the physiological consequences. I produced domain knockouts of the BPLF1 protein to map the region responsible for interaction and activity of BPLF1 on SENP6. I found that not only does BPLF1 bind to SENP6; it also effectively suppresses SENP6 activity. Downstream effects on SENP6 inhibition are the reduction of Centromeric Protein A (CENP-A) constituency at the centromeres, leading to improper chromosomal segregation during anaphase. This leads to the accumulation of genomic abnormalities such as increased rates of aneuploidy and polyploidy. I found this phenotype occurs independently from the catalytic region of BPLF1 and is mapped to the BPLF1765-1327 stretch of amino acids. B cells exposed to virus particles devoid of BPLF1, showed reduced nuclear abnormalities when compared to virus particles containing BPLF1. I observed increased SUMO2/3 conjugation and loss of CENP-A at the centromeric regions in B cells exposed to virus particles possessing BPLF1 in contrast to virus devoid of BPLF1, showing BPLF1’s interference in chromosomal stability

An account of conserved functions and how biologists use them to integrate cell and evolutionary biology

In this paper, we characterize a type of functional explanation that addresses why a homologous trait that originated deep in the evolutionary history of a clade is observed to have remained widespread and largely unchanged across many lineages in the clade. We argue this type of explanation is provided when evolutionary biologists attribute conserved functions to traits, both phenotypic and genetic. The concept of conserved function applies broadly to many biological domains, but we illustrate its importance in particular using examples at the intersection of evolution and cell biology. We also show how the study of conserved functions serves to integrate knowledge of both a trait’s evolutionary history of natural selection and its causal effects on fitness, but in an overlooked way that does not rely on positive selection. Moreover, we show how conserved function provides a novel basis for addressing several objections against evolutionary functions raised by Robert Cummins

Dissecting protein loops with a statistical scalpel suggests a functional implication of some structural motifs

<p>Abstract</p> <p>Background</p> <p>One of the strategies for protein function annotation is to search particular structural motifs that are known to be shared by proteins with a given function.</p> <p>Results</p> <p>Here, we present a systematic extraction of structural motifs of seven residues from protein loops and we explore their correspondence with functional sites. Our approach is based on the structural alphabet HMM-SA (Hidden Markov Model - Structural Alphabet), which allows simplification of protein structures into uni-dimensional sequences, and advanced pattern statistics adapted to short sequences. Structural motifs of interest are selected by looking for structural motifs significantly over-represented in SCOP superfamilies in protein loops. We discovered two types of structural motifs significantly over-represented in SCOP superfamilies: (i) ubiquitous motifs, shared by several superfamilies and (ii) superfamily-specific motifs, over-represented in few superfamilies. A comparison of ubiquitous words with known small structural motifs shows that they contain well-described motifs as turn, niche or nest motifs. A comparison between superfamily-specific motifs and biological annotations of Swiss-Prot reveals that some of them actually correspond to functional sites involved in the binding sites of small ligands, such as ATP/GTP, NAD(P) and SAH/SAM.</p> <p>Conclusions</p> <p>Our findings show that statistical over-representation in SCOP superfamilies is linked to functional features. The detection of over-represented motifs within structures simplified by HMM-SA is therefore a promising approach for prediction of functional sites and annotation of uncharacterized proteins.</p

Novel techniques for protein structure characterization using graph representation of proteins

Proteins exhibit an infinite variety of structures. Around 50K 3D structures of proteins exist in PDB database among unlimited possibilities. The three dimensional structure of a protein is crucial to its function. Even within a common structure family, proteins vary in length, size, and sequence. This variation is the reflection of evolution on protein sequences. The intrinsic information in protein structures can be captured by their graph representations. The structural similarities between protein families can be deduced using their structural features such as connectivity, betweenness, and cliquishness. Most of the structure comparison and alignment methods use all atom coordinates that’s why they need reliable full atom representation of proteins which is difficult to obtain using experimental methods. These methods can be used for variety of problems in bioinformatics such as protein fold prediction, function annotation, domain prediction, and fold classification. Our approach can capture the same knowledge by using much less information from the actual structure. In this thesis, we used graph representations of proteins and graph theoretical properties to discriminate native and non-native proteins. Then we used these methods to find out overall and local similarity of protein structures by using dynamic programming. Afterward, local alignment using dynamic programming is used to determine the function of a protein. Moreover, sub graph matching algorithms was employed for domain prediction. In order to find the correct fold we also developed a genetic algorithm based threading approach. All these applications gave better or comparable results to state of the art