Analysis of X-ray Structures of Matrix Metalloproteinases via Chaotic Map Clustering

<p>Abstract</p> <p>Background</p> <p>Matrix metalloproteinases (MMPs) are well-known biological targets implicated in tumour progression, homeostatic regulation, innate immunity, impaired delivery of pro-apoptotic ligands, and the release and cleavage of cell-surface receptors. With this in mind, the perception of the intimate relationships among diverse MMPs could be a solid basis for accelerated learning in designing new selective MMP inhibitors. In this regard, decrypting the latent molecular reasons in order to elucidate similarity among MMPs is a key challenge.</p> <p>Results</p> <p>We describe a pairwise variant of the non-parametric chaotic map clustering (CMC) algorithm and its application to 104 X-ray MMP structures. In this analysis electrostatic potentials are computed and used as input for the CMC algorithm. It was shown that differences between proteins reflect genuine variation of their electrostatic potentials. In addition, the analysis has been also extended to analyze the protein primary structures and the molecular shapes of the MMP co-crystallised ligands.</p> <p>Conclusions</p> <p>The CMC algorithm was shown to be a valuable tool in knowledge acquisition and transfer from MMP structures. Based on the variation of electrostatic potentials, CMC was successful in analysing the MMP target family landscape and different subsites. The first investigation resulted in rational figure interpretation of both domain organization as well as of substrate specificity classifications. The second made it possible to distinguish the MMP classes, demonstrating the high specificity of the S₁' pocket, to detect both the occurrence of punctual mutations of ionisable residues and different side-chain conformations that likely account for induced-fit phenomena. In addition, CMC demonstrated a potential comparable to the most popular UPGMA (Unweighted Pair Group Method with Arithmetic mean) method that, at present, represents a standard clustering bioinformatics approach. Interestingly, CMC and UPGMA resulted in closely comparable outcomes, but often CMC produced more informative and more easy interpretable dendrograms. Finally, CMC was successful for standard pairwise analysis (i.e., Smith-Waterman algorithm) of protein sequences and was used to convincingly explain the complementarity existing between the molecular shapes of the co-crystallised ligand molecules and the accessible MMP void volumes.</p

Insights into the Complex Formed by Matrix Metalloproteinase-2 and Alloxan Inhibitors: Molecular Dynamics Simulations and Free Energy Calculations

Matrix metalloproteinases (MMP) are well-known biological targets implicated in tumour progression, homeostatic regulation, innate immunity, impaired delivery of pro-apoptotic ligands, and the release and cleavage of cell-surface receptors. Hence, the development of potent and selective inhibitors targeting these enzymes continues to be eagerly sought. In this paper, a number of alloxan-based compounds, initially conceived to bias other therapeutically relevant enzymes, were rationally modified and successfully repurposed to inhibit MMP-2 (also named gelatinase A) in the nanomolar range. Importantly, the alloxan core makes its debut as zinc binding group since it ensures a stable tetrahedral coordination of the catalytic zinc ion in concert with the three histidines of the HExxHxxGxxH metzincin signature motif, further stabilized by a hydrogen bond with the glutamate residue belonging to the same motif. The molecular decoration of the alloxan core with a biphenyl privileged structure allowed to sample the deep S1′ specificity pocket of MMP-2 and to relate the high affinity towards this enzyme with the chance of forming a hydrogen bond network with the backbone of Leu116 and Asn147 and the side chains of Tyr144, Thr145 and Arg149 at the bottom of the pocket. The effect of even slight structural changes in determining the interaction at the S1′ subsite of MMP-2 as well as the nature and strength of the binding is elucidated via molecular dynamics simulations and free energy calculations. Among the herein presented compounds, the highest affinity (pIC50 = 7.06) is found for BAM, a compound exhibiting also selectivity (>20) towards MMP-2, as compared to MMP-9, the other member of the gelatinases

Collagen 1 fibers and hypoxic tumor microenvironments in breast cancer and their effect on transport of molecules within the tumor matrix

The work presented in this thesis represents a culmination of four years of research work focused on understanding the role of collagen I (Col1) fibers, a major component of the tumor extracellular matrix in breast cancer. We have investigated the relationship of Col1 fibers with metastasis, hypoxia, macromolecular transport, water diffusion and fractional anisotropy using clinical breast cancer specimens and human breast cancer xenografts genetically engineered to fluoresce under hypoxia. The thesis describes in the detail of the rationale, study design, results and discussion for our studies, which have been either published or submitted as four separate papers. The work here also presents multimodal in vivo imaging techniques which could be used clinically for non-invasive diagnoses of breast cancer progression. These techniques will help provide better patient management

The Role of Extracellular Matrix in Cancer Development and Progression

The extracellular matrix (ECM) scaffold, which surrounds and supports the cells in tissues, consists of fibrillar proteins, proteoglycans, glycosaminoglycans, signaling molecules, and enzymes involved in its remodeling. The stages of cancer progression, e.g., local invasion, intravasation, extravasation, distant invasion and immunosuppression, are obligatorily perpetrated through interactions of these tumor cells with the ECM. Cancer-related ECM changes can be exploited for the evaluation of disease progression, anticancer therapy development, and monitoring of therapy response. Thus, in breast cancer, hyaluronan-mediated wound repair mechanisms are hijacked to promote tumor development. Altered mechanical properties of the pancreatic cancer ECM are immunosuppressive and prevent the penetration of cytotoxic chemotherapy agents. The expression of the proteoglycan syndecan-4 is modulated by anticancer drugs, suggesting its potential druggabilty capacity. Another proteoglycan, lumican, is proposed as a cancer prognosis marker, chemoresistance regulator, and cancer therapy target. Due to their remodeling properties, the MMPs are vital mediators and important therapeutic targets. Treatment of breast cancer cells with sulfated hyaluronan has been shown to attenuate tumor cell growth, migration, and invasion. Extracellular vesicles (EVs), comprising exosomes, microvesicles, and apoptotic bodies, are released by all cells into the ECM and body fluids and can be utilized as diagnostic markers in malignant pleural mesothelioma. These exciting developments encourage tumor biology scientists for further creative research

Complexity in Developmental Systems: Toward an Integrated Understanding of Organ Formation

During animal development, embryonic cells assemble into intricately structured organs by working together in organized groups capable of implementing tightly coordinated collective behaviors, including patterning, morphogenesis and migration. Although many of the molecular components and basic mechanisms underlying such collective phenomena are known, the complexity emerging from their interplay still represents a major challenge for developmental biology. Here, we first clarify the nature of this challenge and outline three key strategies for addressing it: precision perturbation, synthetic developmental biology, and data-driven inference. We then present the results of our effort to develop a set of tools rooted in two of these strategies and to apply them to uncover new mechanisms and principles underlying the coordination of collective cell behaviors during organogenesis, using the zebrafish posterior lateral line primordium as a model system. To enable precision perturbation of migration and morphogenesis, we sought to adapt optogenetic tools to control chemokine and actin signaling. This endeavor proved far from trivial and we were ultimately unable to derive functional optogenetic constructs. However, our work toward this goal led to a useful new way of perturbing cortical contractility, which in turn revealed a potential role for cell surface tension in lateral line organogenesis. Independently, we hypothesized that the lateral line primordium might employ plithotaxis to coordinate organ formation with collective migration. We tested this hypothesis using a novel optical tool that allows targeted arrest of cell migration, finding that contrary to previous assumptions plithotaxis does not substantially contribute to primordium guidance. Finally, we developed a computational framework for automated single-cell segmentation, latent feature extraction and quantitative analysis of cellular architecture. We identified the key factors defining shape heterogeneity across primordium cells and went on to use this shape space as a reference for mapping the results of multiple experiments into a quantitative atlas of primordium cell architecture. We also propose a number of data-driven approaches to help bridge the gap from big data to mechanistic models. Overall, this study presents several conceptual and methodological advances toward an integrated understanding of complex multi-cellular systems

Molecular Imaging

The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world

Causal Inference from Statistical Data

The so-called kernel-based tests of independence are developed for automatic causal discovery between random variables from purely observational statistical data, i.e., without intervention. Beyond the independence relations, the complexity of conditional distriubtions is used as an additional inference principle of determining the causal ordering between variables. Experiments with simulated and real-world data show that the proposed methods surpass the state-of-the-art approaches