Classification and Automatic Annotation of Tandem Repeat Proteins in RepeatsDB

Protein tandem repeats are crucial structural elements in various biological processes, playing essential roles in cell adhesion, protein-protein interactions, and molecular recognition. These repetitive regions have sparked considerable interest in structural biology and bioinformatics, leading to the development of specialized resources like RepeatsDB. RepeatsDB is a comprehensive, curated database of annotated tandem repeat protein structures, offering a valuable resource for researchers. In this study, we systematically analyzed protein tandem repeats in RepeatsDB, with a primary focus on Alpha-Solenoids and Beta-Propellers, to enhance the existing classification system and provide a more profound understanding of protein tandem repeats. Our investigation commenced with an initial statistical analysis to elucidate the diversity and population status of distinct repeat groups within the database, as well as their respective degree of annotation. This approach proved instrumental in addressing the challenges associated with numerous entries that had a missing annotation. We conducted a structural analysis using pairwise structural alignment and explored dimensionality reduction and visualization techniques to uncover novel structural relationships. These findings improved our understanding of protein structural comparisons and informed a refined classification system. We utilized the density-based clustering algorithm, DBSCAN, to establish structural similarity ranges for Clan members and provide computational support for defining Clan boundaries. This method proved effective in detecting outlier entries and refining existing clans, leading to the proposal of new repeat groups. Additionally, we implemented a supervised classification experiment using the K-Nearest Neighbors (KNN) algorithm, which facilitated the automatic annotation of previously unannotated entries. This study introduces an automatic annotation methodology that significantly improves the performance of RepeatsDB curators and can be extended to other bioinformatics applications. The findings contribute to a more comprehensive understanding of protein tandem repeats and offer valuable insights for future research in structural biology and bioinformatics.Abstract Protein tandem repeats are crucial structural elements in various biological processes, playing essential roles in cell adhesion, protein-protein interactions, and molecular recognition. These repetitive regions have sparked considerable interest in structural biology and bioinformatics, leading to the development of specialized resources like RepeatsDB. RepeatsDB is a comprehensive, curated database of annotated tandem repeat protein structures, offering a valuable resource for researchers. In this study, we systematically analyzed protein tandem repeats in RepeatsDB, with a primary focus on Alpha-Solenoids and Beta-Propellers, to enhance the existing classification system and provide a more profound understanding of protein tandem repeats. Our investigation commenced with an initial statistical analysis to elucidate the diversity and population status of distinct repeat groups within the database, as well as their respective degree of annotation. This approach proved instrumental in addressing the challenges associated with numerous entries that had a missing annotation. We conducted a structural analysis using pairwise structural alignment and explored dimensionality reduction and visualization techniques to uncover novel structural relationships. These findings improved our understanding of protein structural comparisons and informed a refined classification system. We utilized the density-based clustering algorithm, DBSCAN, to establish structural similarity ranges for Clan members and provide computational support for defining Clan boundaries. This method proved effective in detecting outlier entries and refining existing clans, leading to the proposal of new repeat groups. Additionally, we implemented a supervised classification experiment using the K-Nearest Neighbors (KNN) algorithm, which facilitated the automatic annotation of previously unannotated entries. This study introduces an automatic annotation methodology that significantly improves the performance of RepeatsDB curators and can be extended to other bioinformatics applications. The findings contribute to a more comprehensive understanding of protein tandem repeats and offer valuable insights for future research in structural biology and bioinformatics

Caracterización de las interacciones de la fasina PhaF en la bacteria modelo acumuladora de polihidroxialcanoatos, Pseudomonas putida KT2440

Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Químicas, Departamento de Bioquímica y Biología Molecular, leída el 01/04/2019The discovery of bacterial polyhydroxyalkanoates (PHA) in the cytoplasm of Bacillus megaterium by Lemoigne (Lemoigne, 1926) unveiled a new class of in vivo synthesized polymers with adjustable properties and different applications (as plastics, in medical devices, etc.); it also provided impetus to research on proteins involved in PHA accumulation.Interest in phasins emerged through two major observations: i) that they bind to PHA, potentially establishing a network-like protein layer on the surface of intracellular PHA granules; and ii) that their concentration can reach 5% (wt/wt) of the total protein content of PHA-accumulating cells (Wieczorek et al., 1995; Maestro and Sanz, 2017). Soon after it was found that these proteins are almost exclusively produced when PHA is synthesized (York et al., 2002), and that they play important roles in its biogenesis, helping to ensure optimal cell fitness. For example, phasins have been found to engage in the physical stabilization of PHA granules at the polymer-cytoplasm interface, and to participate in the control of the number and size of these granules, their segregation into daughter cells, and their mobilization (reviewed in (Mezzina and Pettinari, 2016; Maestro and Sanz, 2017))...El descubrimiento de polihidroxialcanoatos de origen bacteriano (PHA) en el citoplasma de Bacillus megaterium por Lemoigne (Lemoigne, 1926), no solo reveló una nueva clase de polímeros sintetizados in vivo con propiedades modulables y diferentes aplicaciones (como plásticos, en dispositivos médicos, etc.); sino que también impulsó la investigación sobre proteínas involucradas en la acumulación de PHA.El interés en las fasinas (phasins en inglés) surgió principalmente por dos observaciones: i) son proteínas que se unen al PHA, posiblemente estableciendo una capa proteica en forma de red en la superficie de los gránulos de PHA; y ii) su concentración puede alcanzar un 5% en peso del contenido total de proteína en las células que acumulan PHA (Wieczorek et al., 1995; Maestro and Sanz, 2017). Poco después, se descubrió que estas proteínas se producen casi exclusivamente en función de la síntesis de PHA (York et al., 2002), y que desempeñan un papel importante en su biogénesis, ayudando además a garantizar el estado óptimo de las células. Por ejemplo, se ha descrito que en la interfaz polímero-citoplasma las fasinas participan en la estabilización física de los gránulos de PHA, y que participan en el control del número y tamaño de los gránulos, la segregación de los mismos a las células hijas durante la división celular y su posterior movilización o degradación (revisado en (Mezzina y Pettinari, 2016; Maestro y Sanz, 2017)...Depto. de Bioquímica y Biología MolecularFac. de Ciencias QuímicasTRUEunpu

Characterization of Metastasis-Associated Cell Surface Glycoproteins in Prostate Cancer

Prostate cancer (PCa) is a major health problem in males in the United States. Its lethality is mostly attributed to the primary tumor metastasizing to distant sites that are highly resistant to conventional therapies. Serum Prostate Specific Antigen (PSA) is the only protein biomarker used in clinic for prediction of prostate cancer recurrence following local therapies. Nonetheless, PSA lacks the ability to predict the behavior of an individual tumor in an individual patient. Therefore, development of reliable biomarkers for detection of metastatic potential in primary tumors, as well as discovery of new therapeutic targets, is in a great need for improved disease survival and management. Tumor metastasis is a multistep process involving extravasation of a cancer cell subsequent invasion and expression at a site distal to the primary tumors. Cell surface glycoproteins play pivotal roles as recognition molecules in a range of cell communication and adhesion events. Aberrant cell surface glycosylation has been reported in various cancers including PCa, and strongly correlated with prognosis and metastasis. However, the staggering complexity of glycans renders their analysis extraordinarily difficult. This research project aims to develop a mass spectrometry-based glycoproteomic approach for the selective isolation and identification of cell surface glycoproteins from cellular samples, and apply this technology to the discovery of new glycoprotein biomarkers which are indicative of prostate cancer progression and metastasis. To this end, cell surface glycosylation patterns were characterized by lectin flow cytometry and lectin cytochemistry on a human syngeneic PCa cell metastatic model, PC3 and its two variants with different metastatic potentials. It was found that metastatic potentials of PC3 variants were inversely correlated with cell surface α2-6 sialic acid levels. Targeted to cell surface sialoglycoproteins, a new glycoproteomic approach was successfully developed, which combined selective metabolic labeling of cell surface sialyl glycans, chemically probing the labeled sugar with a biotin tag, affinity purification of sialylated proteins, SDS-PAGE separation, and subsequent LC-MS/MS for protein identification. Application of this methodology in our prostate cancer model system resulted in unique identification of a total of 80 putative cell surface sialoglycoproteins differentially expressed between PC3 variants. After prioritization of the candidate biomarkers, one cell-based prioritized biomarker CUB-domain-containing protein 1 (CDCP1) was verified in prostate cancer cell lines and clinical samples, including tissues and body fluids, by immunoassays. Results indicated that expression of CDCP1 protein is dysregulated in prostate cancer and it has potential utility as a therapeutic target and a diagnostic marker for PCa progression. Overall, the data from this research project provided the proof-of-principle evidence for our targeted glycoproteomic approach, which we believe will help expedite the discovery of new cancer biomarkers and therapeutic targets in diseases and delineation of signal transduction pathways on a global scale

Application of Signal Processing and Soft Computing To Genomics

A major challenge for genomic research is to establish a relationship among sequences,structures and function of genes. In addition processing and analyzing this information are of prime importance. Basically genes are repositories for protein coding information and proteins in turn are responsible for most of the important biological functions in all cells. These in turn gives rise to analysis of DNA sequences in proteins, designing of various drugs for genetic diseases. This thesis deals with the applications of signal processing and soft computing algorithms to the field of genomics and proteinomics. Diseases like SARS and Migraine have been modeled using these tools and potential druggable compounds have been proposed which are better than the previous available drugs. Protein structural classes have been identified more accurately based on Genetic Algorithm and Particle Swarm Optimization.Better and efficient methods like Sliding-DFT and Adaptive AR Modeling were proposed to identify Protein coding regions in genes. The proposed methods showed better results as compared to existing methods

Structural Characterization of Beta Carbonic Anhydrases From Higher Plants.

It is the goal of this dissertation research to reveal some aspects of the physical nature of spinach carbonic anhydrase as a representative $\beta$CA using the techniques of sequence comparison, molecular biology, and biophysics. Though both $\alpha$ and $\beta$ carbonic anhydrases are zinc dependent metalloenzymes, it is clear that the two isoforms do not adopt the same mechanism for coordinating the active site metal. While $\alpha$CA binds zinc through three histidine ligands, $\beta$CA cannot due to a lack of evolutionarily conserved histidines. Instead, the $\beta$ family has adopted a ligand scheme incorporating a single histidine and two cysteines. This has been determined by systematically mutating possible zinc ligands in the spinach enzyme and then assaying the resulting variants for stoichiometric metal binding. Additionally, this conclusion is corroborated by inspection of the wild type enzyme\u27s extended X-ray spectrum. This analysis indicates the metal is surrounded by two sulfur atoms and two nitrogen or oxygen species. Secondly, it has been long established that not only do the $\beta$ isoforms differ from their $\alpha$ cousins in their multimeric assembly, but subtypes exist within the $\beta$ family in which monocot forms assemble into lower molecular weight oligomers while dicot forms assemble into higher order structures. In an attempt to gain insight into the differences between monocot and dicot CAs, the CA cDNA from barley, a monocot, was sequenced. Analysis of the open reading frame revealed that the barley enzyme lacked ten amino acids at the carboxyl terminus which are conserved in the dicot isozymes. It is here demonstrated that this extension contributes to the difference in multimeric organization between monocots and dicots. When this extension is deleted from the spinach enzyme, the resulting mutant displays an apparent deficit in its ability to form higher order multimers. Furthermore, this carboxyl extension will interact with the CA holoenzyme in the yeast two-hybrid system showing that the observed characteristics of the deletion mutant do not arise from secondary disruptions, but rather the carboxyl terminus does participate in intermolecular interactions

Interaction of the spliced Oskar localization element of Oskar mRNA with the protein PYM

mRNAs and the process of mRNA localization are the fundamental and pivotal parts of cellular functions. mRNA localization encompasses an important role in cellular differentiation and site-specific cellular functions, from the basic cellular biochemical mechanism to advanced abdomen formation. The study of mRNA, its localization mechanism along its binding partners have always been the main focus of study for several years. As they define life, in terms of cellular and sub-cellular mechanisms. Our study also involves one of the binding partners of the localization complex, which is Pym protein. Pym protein and exon junction complex are the common localization binding partners to many mRNA localization and Oskar mRNA is one of them. Pym being one of the recycling factors of the Exon Junction Complex shows binding interactions with many components, such as RNAs, Exon junction Complex, and Ribosomes. Our results show interesting structural and binding features of the protein Pym. NMR studies reveal that Pym160, the shorter construct of Pym is structurally unfolded, with the general characteristic of an intrinsically disordered protein. It has the long helical structural element in the middle part of the protein, while both N-terminal and C-terminal ends remain highly flexible with the structurally unfolded regions. The C-terminal part of the protein is not showing any direct involvement in the interaction with the SOLE RNA. However, it is structurally a very important part of the protein, as it stabilizes the ionic and hydrophobic interactions of the protein, so that protein could able to be a stable soluble protein. We have studied the binding motifs of the protein Pym160 with SOLE RNA and its isomers. Pym160 has binding motifs in the N-terminal region and in the middle helical region. Studies have confirmed that the N-terminal part of the protein binds to the Y14-Mago heterodimer, which is an essential part of the exon junction complex. In the absence of an Exon Junction Complex, the N-terminal part of the protein binds to the RNA. So, the study of the protein Pym160 is very much interesting and essential as it is a common protein for the wide range of mRNA localization mechanisms. Our studies explain the widespread binding nature of the Pym160, which might be due to its functional significance of being a structurally unfolded protein