Nucleotide-Binding Sites of the Heterodimeric LmrCD ABC-Multidrug Transporter of Lactococcus lactis Are Asymmetric

LmrCD is a lactococcal, heterodimeric multidrug transporter, which belongs to the ABC superfamily. It consists of two half-transporters, LmrC and LmrD, that are necessary and sufficient for drug extrusion and ATP hydrolysis. LmrCD is asymmetric in terms of the conservation of the functional motifs of the nucleotide-binding domains (NBDs). Important residues of the nucleotide-binding site of LmrC and the C loop of LmrD are not conserved. To investigate the functional importance of the LmrC and LmrD subunits, the putative catalytic base residue adjacent to the Walker B motif of both NBDs were substituted for the respective carboxamides. Our data demonstrate that Glu587 of LmrD is essential for both drug transport and ATPase activity of the LmrCD heterodimer, whereas mutation of Asp495 of LmrC has a less severe effect on the activity of the complex. Structural and/or functional asymmetry is further demonstrated by differential labeling of both subunits by 8-azido-[α-32P]ATP, which, at 4 °C, occurs predominantly at LmrC, while aluminiumfluoride (AlFx)-induced trapping of the hydrolyzed nucleotide at 30 °C results in an almost exclusive labeling of LmrD. It is concluded that the LmrCD heterodimer contains two structurally and functionally distinct NBDs.

Integrated mining of feature spaces for bioinformatics domain discovery

One of the major challenges in the field of bioinformatics is the elucidation of protein folding for the functional annotation of proteins. The factors that govern protein folding include the chemical, physical, and environmental conditions of the protein\u27s surroundings, which can be measured and exploited for computational discovery purposes. These conditions enable the protein to transform from a sequence of amino acids to a globular three-dimensional structure. Information concerning the folded state of a protein has significant potential to explain biochemical pathways and their involvement in disorders and diseases. This information impacts the ways in which genetic diseases are characterized and cured and in which designer drugs are created. With the exponential growth of protein databases and the limitations of experimental protein structure determination, sophisticated computational methods have been developed and applied to search for, detect, and compare protein homology. Most computational tools developed for protein structure prediction are primarily based on sequence similarity searches. These approaches have improved the prediction accuracy of high sequence similarity proteins but have failed to perform well with proteins of low sequence similarity. Data mining offers unique algorithmic computational approaches that have been used widely in the development of automatic protein structure classification and prediction. In this dissertation, we present a novel approach for the integration of physico-chemical properties and effective feature extraction techniques for the classification of proteins. Our approaches overcome one of the major obstacles of data mining in protein databases, the encapsulation of different hydrophobicity residue properties into a much reduced feature space that possess high degrees of specificity and sensitivity in protein structure classification. We have developed three unique computational algorithms for coherent feature extraction on selected scale properties of the protein sequence. When plagued by the problem of the unequal cardinality of proteins, our proposed integration scheme effectively handles the varied sizes of proteins and scales well with increasing dimensionality of these sequences. We also detail a two-fold methodology for protein functional annotation. First, we exhibit our success in creating an algorithm that provides a means to integrate multiple physico-chemical properties in the form of a multi-layered abstract feature space, with each layer corresponding to a physico-chemical property. Second, we discuss a wavelet-based segmentation approach that efficiently detects regions of property conservation across all layers of the created feature space. Finally, we present a unique graph-theory based algorithmic framework for the identification of conserved hydrophobic residue interaction patterns using identified scales of hydrophobicity. We report that these discriminatory features are specific to a family of proteins, which consist of conserved hydrophobic residues that are then used for structural classification. We also present our rigorously tested validation schemes, which report significant degrees of accuracy to show that homologous proteins exhibit the conservation of physico-chemical properties along the protein backbone. We conclude our discussion by summarizing our results and contributions and by listing our goals for future research

Clustering System and Clustering Support Vector Machine for Local Protein Structure Prediction

Protein tertiary structure plays a very important role in determining its possible functional sites and chemical interactions with other related proteins. Experimental methods to determine protein structure are time consuming and expensive. As a result, the gap between protein sequence and its structure has widened substantially due to the high throughput sequencing techniques. Problems of experimental methods motivate us to develop the computational algorithms for protein structure prediction. In this work, the clustering system is used to predict local protein structure. At first, recurring sequence clusters are explored with an improved K-means clustering algorithm. Carefully constructed sequence clusters are used to predict local protein structure. After obtaining the sequence clusters and motifs, we study how sequence variation for sequence clusters may influence its structural similarity. Analysis of the relationship between sequence variation and structural similarity for sequence clusters shows that sequence clusters with tight sequence variation have high structural similarity and sequence clusters with wide sequence variation have poor structural similarity. Based on above knowledge, the established clustering system is used to predict the tertiary structure for local sequence segments. Test results indicate that highest quality clusters can give highly reliable prediction results and high quality clusters can give reliable prediction results. In order to improve the performance of the clustering system for local protein structure prediction, a novel computational model called Clustering Support Vector Machines (CSVMs) is proposed. In our previous work, the sequence-to-structure relationship with the K-means algorithm has been explored by the conventional K-means algorithm. The K-means clustering algorithm may not capture nonlinear sequence-to-structure relationship effectively. As a result, we consider using Support Vector Machine (SVM) to capture the nonlinear sequence-to-structure relationship. However, SVM is not favorable for huge datasets including millions of samples. Therefore, we propose a novel computational model called CSVMs. Taking advantage of both the theory of granular computing and advanced statistical learning methodology, CSVMs are built specifically for each information granule partitioned intelligently by the clustering algorithm. Compared with the clustering system introduced previously, our experimental results show that accuracy for local structure prediction has been improved noticeably when CSVMs are applied

A structural classification of protein-protein interactions for detection of convergently evolved motifs and for prediction of protein binding sites on sequence level

BACKGROUND: A long-standing challenge in the post-genomic era of Bioinformatics is the prediction of protein-protein interactions, and ultimately the prediction of protein functions. The problem is intrinsically harder, when only amino acid sequences are available, but a solution is more universally applicable. So far, the problem of uncovering protein-protein interactions has been addressed in a variety of ways, both experimentally and computationally. MOTIVATION: The central problem is: How can protein complexes with solved threedimensional structure be utilized to identify and classify protein binding sites and how can knowledge be inferred from this classification such that protein interactions can be predicted for proteins without solved structure? The underlying hypothesis is that protein binding sites are often restricted to a small number of residues, which additionally often are well-conserved in order to maintain an interaction. Therefore, the signal-to-noise ratio in binding sites is expected to be higher than in other parts of the surface. This enables binding site detection in unknown proteins, when homology based annotation transfer fails. APPROACH: The problem is addressed by first investigating how geometrical aspects of domain-domain associations can lead to a rigorous structural classification of the multitude of protein interface types. The interface types are explored with respect to two aspects: First, how do interface types with one-sided homology reveal convergently evolved motifs? Second, how can sequential descriptors for local structural features be derived from the interface type classification? Then, the use of sequential representations for binding sites in order to predict protein interactions is investigated. The underlying algorithms are based on machine learning techniques, in particular Hidden Markov Models. RESULTS: This work includes a novel approach to a comprehensive geometrical classification of domain interfaces. Alternative structural domain associations are found for 40% of all family-family interactions. Evaluation of the classification algorithm on a hand-curated set of interfaces yielded a precision of 83% and a recall of 95%. For the first time, a systematic screen of convergently evolved motifs in 102.000 protein-protein interactions with structural information is derived. With respect to this dataset, all cases related to viral mimicry of human interface bindings are identified. Finally, a library of 740 motif descriptors for binding site recognition - encoded as Hidden Markov Models - is generated and cross-validated. Tests for the significance of motifs are provided. The usefulness of descriptors for protein-ligand binding sites is demonstrated for the case of "ATP-binding", where a precision of 89% is achieved, thus outperforming comparable motifs from PROSITE. In particular, a novel descriptor for a P-loop variant has been used to identify ATP-binding sites in 60 protein sequences that have not been annotated before by existing motif databases

Homology inference with specific molecular constraints

Evolutionary processes can be considered at multiple levels of biological organization. The work developed in this thesis focuses on protein molecular evolution. Although proteins are linear polymers composed from a basic set of 20 amino acids, they generate an enormous variety of form and function. Proteins that have arisen by a common descent are classified into families; they often share common properties including similarities in sequence, structure, and function. Multiple methods have been developed to infer evolutionary relationships between proteins and classify them into families. Yet, those generic methods are often inaccurate, especially when specific protein properties limit their applications. In this thesis, we analyse two protein classes that are often difficult for the evolutionary analysis: the coiled-coils – repetitive protein domains defined by a simple widespread peptide motif (chapters 2 and 3) and Rab small GTPases – a large family of closely related proteins (chapters 4 and 5). In both cases, we analyse the specific properties that determine protein structure and function and use them to improve their evolutionary inference

CHARACTERIZATION OF THE INTERACTION OF THE HPV8 E2 TETHERING PROTEIN WITH HOST CHROMOSOMES

One of the mechanisms by which papillomaviruses establish persistent infection of the host is by tethering their genomes to host chromosomes during mitosis. This ensures maintenance and partitioning of the viral genomes to daughter cells after each cell division. Although studies have shown that the viral E2 protein links the viral genome to host chromosomes in several papillomaviruses, the exact molecular mechanism of this interaction has yet to be elucidated for the beta-papillomaviruses. The studies described in this dissertation aimed to characterize the interaction of the E2 protein of the human papillomavirus type 8 (HPV8), a type of beta-papillomavirus, with mitotic chromosomes. The E2 protein consists of a conserved N-terminal transactivation domain and a C-terminal DNA binding and dimerization domain that are linked by a flexible hinge. We have mapped a sixteen amino acid region in the hinge that, when linked to the DNA binding domain, is crucial and sufficient for chromosomal association. Further we have identified two residues in this region, arginine 250 (R250) and serine 253 (S253) within a highly conserved RXXS motif that are required for HPV8 E2 chromosome binding. Additionally, we have shown that the S253 residue is phosphorylated. To gain insight into the regulation of the E2 chromosome binding function, we investigated the role of phosphorylation of S253. We have shown that S253 is phosphorylated by PKA in S-phase, which increases the half-life of E2 protein and modulates its interaction with host chromatin. Since E2 is also involved in transcriptional regulation and viral genome replication, we examined if mutating residues R250 or S253 affected the transcriptional activation or replication functions of the HPV8 E2 protein. Furthermore using a domain swapping approach, we also explored the role of the C-terminal domain in the HPV8 E2 chromosome binding function. Finally to establish the mode of interaction responsible for mediating HPV8 E2 chromosome binding, we employed both a proteomics approach and ribonuclease treatment techniques, to examine whether HPV8 E2 chromosomal association is mediated through protein-protein or protein-RNA interactions, respectively. Collectively, these studies have added to our current understanding of the interaction of HPV8 E2 protein with host chromosomes

Functional Analysis of an Acid Adaptive DNA Adenine Methyltransferase from Helicobacter pylori 26695

HP0593 DNA-(N6-adenine)-methyltransferase (HP0593 MTase) is a member of a Type III restriction-modification system in Helicobacter pylori strain 26695. HP0593 MTase has been cloned, overexpressed and purified heterologously in Escherichia coli. The recognition sequence of the purified MTase was determined as 5′-GCAG-3′and the site of methylation was found to be adenine. The activity of HP0593 MTase was found to be optimal at pH 5.5. This is a unique property in context of natural adaptation of H. pylori in its acidic niche. Dot-blot assay using antibodies that react specifically with DNA containing m6A modification confirmed that HP0593 MTase is an adenine-specific MTase. HP0593 MTase occurred as both monomer and dimer in solution as determined by gel-filtration chromatography and chemical-crosslinking studies. The nonlinear dependence of methylation activity on enzyme concentration indicated that more than one molecule of enzyme was required for its activity. Analysis of initial velocity with AdoMet as a substrate showed that two molecules of AdoMet bind to HP0593 MTase, which is the first example in case of Type III MTases. Interestingly, metal ion cofactors such as Co2+, Mn2+, and also Mg2+ stimulated the HP0593 MTase activity. Preincubation and isotope partitioning analyses clearly indicated that HP0593 MTase-DNA complex is catalytically competent, and suggested that DNA binds to the MTase first followed by AdoMet. HP0593 MTase shows a distributive mechanism of methylation on DNA having more than one recognition site. Considering the occurrence of GCAG sequence in the potential promoter regions of physiologically important genes in H. pylori, our results provide impetus for exploring the role of this DNA MTase in the cellular processes of H. pylori

The La-Related Proteins, a Family with Connections to Cancer

The evolutionarily-conserved La-related protein (LARP) family currently comprises Genuine La, LARP1, LARP1b, LARP4, LARP4b, LARP6 and LARP7. Emerging evidence suggests each LARP has a distinct role in transcription and/or mRNA translation that is attributable to subtle sequence variations within their La modules and specific C-terminal domains. As emerging research uncovers the function of each LARP, it is evident that La, LARP1, LARP6, LARP7 and possibly LARP4a and 4b are dysregulated in cancer. Of these, LARP1 is the first to be demonstrated to drive oncogenesis. Here, we review the role of each LARP and the evidence linking it to malignancy. We discuss a future strategy of targeting members of this protein family as cancer therapy

Vpu Mediated Enhancement of Human Immunodeficiency Virus Pathogenesis: The Role of Conserved and Unique Domains in Protein Function

The work in this dissertation examined the biological characteristics of different HIV-1 Vpu subtypes, with an emphasis on subtypes B and C, and the potential impact of these proteins on SHIV pathogenesis. Different Vpu subtypes exhibited distinct biological properties that potentially could affect HIV-1 pathogenesis and/or transmission efficiency, including intracellular localization, efficiency in down-modulating CD4 surface expression, and the ability to enhance virion release in HeLa cells. We show for the first time that the subtype B and C Vpu proteins partitioned into detergent resistant membranes (DRMs), a property characteristic of lipid raft association. We also identified two mutants, IVV19-21AAA and W22A, that prevented this association. Additionally, we found a correlation between the ability of Vpu to stably associate with DRMs and the ability to enhance virion release in HeLa cells. Analysis of different Vpu proteins from clinical isolates identified a membrane proximal tyrosine motif that is highly conserved among all Vpu subtypes and an overlapping dileucine motif ([D/E]xxxL[L/I]) that is conserved among subtype C Vpu proteins. Substitution of the tyrosine residue in the tyrosine motif with an alanine (Y35A) significantly inhibited SHIV replication while substitution of the primary leucine residue with a glycine (L39G) in the overlapping [D/E]xxxL[L/I] motif significantly increased the amount of virus released from C8166 cells and the mean number of viral particles per cell compared to cells inoculated with the parental SHIVSCVpu. Recently, the enhanced virion release function of Vpu has been attributed to the antagonism of bone marrow stromal antigen 2 (BST-2) protein. This has been shown to involve the transmembrane domains (TMD) of both proteins. Our results indicate that the length of the BST-2 TMD is more important than the primary sequence both for the function and sensitivity of the protein. Additionally, we showed that the BST-2 protein expressed in pig-tailed macaques is not antagonized by HIV-1 Vpu, but rather by SIV Nef, thus indicating a species-specific basis for this antagonism. Based on these results we continued our analyses by examining the biological characteristics of SHIV expressing either a subtype B (SHIVKU-1bMC33) or C (SHIVSCVpu) Vpu protein. SHIVSCVpu caused a more gradual rate of CD4+ T cell loss and lower peak viral loads in infected pig-tailed macaques compared to macaques inoculated with SHIVKU-1bMC33. The identification of the TMD and the putative sorting signals proximal to the membrane as key determinants in Vpu-mediated enhanced virion release prompted the hypothesis that these two regions may dictate these differences in pathogenesis. Therefore, we constructed chimeric Vpu proteins in which the N-terminus/TMD regions of the subtype B and C Vpu proteins were exchanged (VpuBC and VpuCB). Inoculation of pig-tailed macaques with SHIVVpuCB resulted in a more gradual loss of circulating CD4+ T cells compared to SHIVVpuBC-inoculated macaques, but more rapid than resulted in macaques inoculated with parental SHIVSCVpu. Since both of these proteins down-modulated CD4 surface expression similar to the unmodified VpuSCEGFP1 protein, our results indicate that the differences observed in CD4 surface down-modulation in vitro are most likely not physiologically relevant. Finally, as pig-tailed macaques express a BST-2 protein that is not affected by the HIV-1 Vpu protein, our results suggest that the enhanced virion release function of different Vpu subtypes as observed during an intravenous inoculation may be dependent upon a different factor(s). The work presented here demonstrates a clear potential for differential signaling and functional efficiency among all HIV-1 Vpu subtypes with the ability to modify pathogenesis. Additionally, our analyses identified the TMD and membrane proximal regions as crucial components to Vpu enhancement of pathogenesis providing novel information essential for anti-retroviral therapeutic development

Molecular and Cellular Characterization of an AT-Hook Protein from Leishmania

AT-rich DNA, and the proteins that bind it (AT-hook proteins), modulate chromosome structure and function in most eukaryotes. Unlike other trypanosomatids, the genome of Leishmania species is unusually GC-rich, and the regulation of Leishmania chromosome structure, replication, partitioning is not fully understood. Because AT-hook proteins modulate these functions in other eukaryotes, we examined whether AT-hook proteins are encoded in the Leishmania genome, to test their potential functions. Several Leishmania ORFs predicted to be AT-hook proteins were identified using in silico approaches based on sequences shared between eukaryotic AT-hook proteins. We have used biochemical, molecular and cellular techniques to characterize the L. amazonensis ortholog of the L. major protein LmjF06.0720, a potential AT-hook protein that is highly conserved in Leishmania species. Using a novel fusion between the AT-hook domain encoded by LmjF06.0720 and a herpesviral protein, we have demonstrated that LmjF06.0720 functions as an AT-hook protein in mammalian cells. Further, as observed for mammalian and viral AT-hook proteins, the AT-hook domains of LmjF06.0720 bind specific regions of condensed mammalian metaphase chromosomes, and support the licensed replication of DNA in mammalian cells. LmjF06.0720 is nuclear in Leishmania, and this localization is disrupted upon exposure to drugs that displace AT-hook proteins from AT-rich DNA. Coincidentally, these drugs dramatically alter the cellular physiology of Leishmania promastigotes. Finally, we have devised a novel peptido-mimetic agent derived from the sequence of LmjF06.0720 that blocks the proliferation of Leishmania promastigotes, and lowers amastigote parasitic burden in infected macrophages. Our results indicate that AT-hook proteins are critical for the normal biology of Leishmania. In addition, we have described a simple technique to examine the function of Leishmania chromatin-binding proteins in a eukaryotic context amenable to studying chromosome structure and function. Lastly, we demonstrate the therapeutic potential of compounds directed against AT-hook proteins in Leishmania