Current state-of-the-art of the research conducted in mapping protein cavities – binding sites of bioactive compounds, peptides or other proteins

Ο σκοπός της διπλωματικής εργασίας είναι η διερεύνηση και αποτύπωση των ερευνητικών μελετών που αφορούν στον χαρακτηρισμό μιας πρωτεϊνικής κοιλότητας – κέντρου πρόσδεσης βιοδραστικών ενώσεων, πεπτιδίων ή άλλων πρωτεϊνών. Στην παρούσα εργασία χρησιμοποιήθηκε η μέθοδος της βιβλιογραφικής επισκόπησης. Παρουσιάζονται τα κυριότερα ευρήματα προηγούμενων ερευνών που σχετίζονται με τη διαδικασία σχεδιασμού φαρμάκων και τον εντοπισμό φαρμακοφόρων με βάση ένα σύνολο προσδετών. Στη συνέχεια συγκρίνονται διαδικασίες επεξεργασίας και ανάλυσης της πρωτεϊνικής κοιλότητας προγενέστερων ερευνών με τη προσέγγιση που προτάθηκε από τους Παπαθανασίου και Φωτόπουλου το 2015. Αναδεικνύονται βασικά πλεονεκτήματα της προσέγγισης αυτής, όπως η εφαρμογή του αλγορίθμου πολυδιάστατη k-means ομαδοποίηση (multidimensional k-means clustering). Η εύρεση βιβλιογραφίας βασίστηκε σε αναζήτηση επιστημονικών άρθρων σε ξενόγλωσσα επιστημονικά περιοδικά, σε κεφάλαια βιβλίων και σε διάφορα άρθρα σε ηλεκτρονικούς ιστότοπους σχετικά με τον σχεδιασμό φαρμάκων και τις κοιλότητες που απαντώνται στις πρωτεΐνες. Στην παρούσα εργασία παρουσιάζονται εν συντομία εργαλεία που εντοπίστηκαν χρησιμοποιώντας λέξεις κλειδιά όπως για παράδειγμα δυναμική πρωτεϊνικής κοιλότητας, καταλυτικό κέντρο ενός ενζύμου, πρόσδεση, πρωτεϊνική θήκη κλπ. Στη συνέχεια συγκροτήθηκε κατάλογος με τα εργαλεία βιοπληροφορικής ανάλυσης που βρέθηκαν και ακολούθησε εκτενής αναφορά επιλεκτικά σε κάποια από αυτά. Κριτήριο επιλογής αυτών των εργαλείων αποτέλεσε η ημερομηνία δημοσίευσής τους, οι αλγόριθμοι και η μεθοδολογία που χρησιμοποιούν. Τα εργαλεία αυτά κατηγοριοποιήθηκαν με βάση τις λέξεις κλειδιά που χρησιμοποιήθηκαν για την εξόρυξη των δεδομένων από την βιβλιογραφία. Τέλος πραγματοποιήθηκε συγκριτική μελέτη αυτών αναδεικνύοντας τα πλεονεκτήματα και εστιάζοντας στην περαιτέρω αξιοποίησή τους.The aim of this thesis was to report on the current state-of-the-art of the research conducted concerning mapping of protein cavities with a potential function role as binding sites of bioactive compounds, peptides or other proteins. A literature review was performed with emphasis on the relevant tools developed during the last decade. In addition, the main research findings regarding drug design and druggable targets based on binding sites are presented. Processes performed in protein cavity detection and analysis, of previous research articles, are compared with the approach described by Anaxagoras Fotopoulos and Athanasios Papathanasiou (2015). The results showed that a competitive advantage of their approach is the multidimensional k-means algorithm for clustering. For the bibliographic review the scientific knowledgebase has been used, which includes international articles and journals, book chapters, as well as online articles regarding drug design and protein cavity. Search keywords such as protein cavity dynamics, catalytic sites of enzymes, protein pocket etc. were used to identify bioinformatics tools with text mining. A catalogue of the most recently developed tools is presented followed by a brief description of selected tools. The selection criteria imposed for preparing the catalogue and the detailed description included the publication date, as well as the algorithms and the methods they use. The tools were then classified according to the search keywords. The findings of this research are discussed, and the algorithms and methods they use are compared, highlighting the advantages of protein cavity detection

Virtual Human Project

This paper describes the development of a comprehensive human modeling environment, the Virtual Human, which will be used initially to model the human respiratory system for purposes of predicting pulmonary disease or injury using lung sounds. The details of the computational environment, including the development of a Virtual Human Thorax, a database for storing models, model parameters, and experimental data, and a Virtual Human web interface are outlined. Preliminary progress in developing this environment will be presented. A separate paper at the conference describes the modeling of sound generation using computational fluid dynamics and the modeling of sound propagation in the human respiratory system

Modeling cellular processes with PATİKA

Ankara : The Department of Molecular Biology and Genetics and the Institute of Engineering and Science of Bilkent University, 2001.Thesis (Master's) -- Bilkent University, 2001.Includes bibliographical references leaves 49-51Availability of the sequences of entire genomes shifts the scientific curiosity toward the identification of function of the genomes in large scale as in genome studies. In the near future data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively. We define an ontology for a comprehensive representation of cellular events. The model presented here enables integration of fragmented or incomplete pathway information and supports manipulation and incorporations of the stored data, as well as multiple levels of abstraction. Based on this model, we present an integrated environment named PATIKA (Pathway Analysis Tool for Integration and Knowledge Acquisition). PATIKA is composed of a server-side, scalable, object-oriented database and client-side editors to provide an integrated, multi-user environment for visualizing and manipulating network of cellular events. This tool features automated pathway layout, functional computation support, advanced querying and a user-friendly graphical interface. We expect that PATIKA will be a valuable tool for rapid knowledge acquisition; micro array generated large-scale data interpretation; disease gene identification and drug developmentDemir, EmekM.S

The Rational Design of LRRK2 Inhibitors for Parkinson's Disease

Parkinson’s disease is a chronic neurodegenerative disorder that affects 1-2% of the world’s population over the age of 65. Current treatments that reduce the severity of symptoms cause numerous side-effects and lose efficacy over the course of disease progression. Leucine-rich repeat kinase 2 (LRRK2) is a novel drug target for the development of disease modifying therapeutics for Parkinson’s disease. LRRK2 mutants have elevated kinase activity and, as such, chemical inhibitors have therapeutic potential. The physiological benefits that arise from chemically inhibiting LRRK2 have been proven through the use of generic kinase inhibitors and more recently the selective benzodiazepinone compound LRRK2IN1. LRRK2IN1 is a highly potent inhibitor, exhibiting a half-maximal inhibitory concentration (IC50) of 9 nM in cellular assays. However, LRRK2IN1 is not biologically available in the brain because it has poor physicochemical and pharmacokinetic properties. In previous research we rationally designed a LRRK2IN1 analogue (IN1_G) that was predicted to have improved metabolic stability and blood-brain barrier permeability. Preliminary biological analysis indicated that both LRRK2IN1 and IN1_G potently inhibited LRRK2-associated neuro-inflammation in vitro. However, the high molecular weight, topological polar surface area and lipophilicity of LRRK2IN1 and IN1_G were predicted to be incompatible with functional activity in vivo. Structural modifications were thus required to optimise compounds as neuro-protective treatments for Parkinson’s disease. Biological evaluation of the structural components of LRRK2IN1 and IN1_G indicated that the aniline-bipiperidine 1 motif was a moderately potent inhibitor of neuro-inflammation, whilst the tricyclic diazepinone motif IN1_H had no anti-inflammatory efficacy. In the current research a series of truncated LRRK2IN1/IN1_G analogues were rationally designed to determine if the diazepinone motif could be replaced with low molecular weight bioisosteres without affecting functional potency. In silico property predictions and scoring functions were used to guide the design of truncated analogues. The Schrödinger suite programs LigPrep, QikProp and Marvin were used to predict the physicochemical and pharmacokinetic properties of analogues. The recently described central nervous system multi-parameter optimisation score was used to select analogues that were likely to possess favourable pharmacokinetic and safety profiles. Analogues were docked in a homology model of the LRRK2 kinase domain that was developed in our previous research. Analogues that conformed to the binding mode of known kinase inhibitors and were predicted by GLIDE to bind to the LRRK2 homology model with high affinity were prioritised for synthesis. Twenty analogues were synthesised using methods known in the literature. The substrate scope of Buchwald-Hartwig chemistry was explored. Novel “all-water” chemistry was employed to synthesise N-benzyl aniline analogues. Methodology recently developed in our group was used to synthesise diazepine and oxazepine analogues of IN1_H. Analogues were assessed for anti-inflammatory efficacy in two cell-based assays. Four truncated analogues — 25, 30, 31 and 39 — had equivalent functional efficacy to LRRK2IN1/IN1_G, inhibiting the secretion of pro-inflammatory cytokines from stimulated primary human microglia by more than 43% at concentrations of 1 µM. These analogues were all predicted to have improved pharmacokinetic properties compared to LRRK2IN1/IN1_G and are excellent candidates for further development. The synthetic intermediate 63 was found to be highly potent (57% inhibition of cytokine secretion at 1 µM), which has suggested options for the development of future analogues. The potency of analogues 25, 30, 31 and 39 indicated that the tricyclic diazepinone motif was not essential for anti-inflammatory efficacy. Analogues from this research have been used to identify a role for LRRK2 in the pathology of severe brain cancer glioblastoma. Although their mechanisms of action have not yet been determined, it is clear that analogues developed in this research have potential applications in the treatment of numerous disorders driven by an inflammatory microenvironment. Further optimisation of the analogues developed in this research will provide the first disease-modifying therapeutics for Parkinson’s disease

An application of genetic algorithms to chemotherapy treatment.

The present work investigates methods for optimising cancer chemotherapy within the bounds of clinical acceptability and making this optimisation easily accessible to oncologists. Clinical oncologists wish to be able to improve existing treatment regimens in a systematic, effective and reliable way. In order to satisfy these requirements a novel approach to chemotherapy optimisation has been developed, which utilises Genetic Algorithms in an intelligent search process for good chemotherapy treatments. The following chapters consequently address various issues related to this approach. Chapter 1 gives some biomedical background to the problem of cancer and its treatment. The complexity of the cancer phenomenon, as well as the multi-variable and multi-constrained nature of chemotherapy treatment, strongly support the use of mathematical modelling for predicting and controlling the development of cancer. Some existing mathematical models, which describe the proliferation process of cancerous cells and the effect of anti-cancer drugs on this process, are presented in Chapter 2. Having mentioned the control of cancer development, the relevance of optimisation and optimal control theory becomes evident for achieving the optimal treatment outcome subject to the constraints of cancer chemotherapy. A survey of traditional optimisation methods applicable to the problem under investigation is given in Chapter 3 with the conclusion that the constraints imposed on cancer chemotherapy and general non-linearity of the optimisation functionals associated with the objectives of cancer treatment often make these methods of optimisation ineffective. Contrariwise, Genetic Algorithms (GAs), featuring the methods of evolutionary search and optimisation, have recently demonstrated in many practical situations an ability to quickly discover useful solutions to highly-constrained, irregular and discontinuous problems that have been difficult to solve by traditional optimisation methods. Chapter 4 presents the essence of Genetic Algorithms, as well as their salient features and properties, and prepares the ground for the utilisation of Genetic Algorithms for optimising cancer chemotherapy treatment. The particulars of chemotherapy optimisation using Genetic Algorithms are given in Chapter 5 and Chapter 6, which present the original work of this thesis. In Chapter 5 the optimisation problem of single-drug chemotherapy is formulated as a search task and solved by several numerical methods. The results obtained from different optimisation methods are used to assess the quality of the GA solution and the effectiveness of Genetic Algorithms as a whole. Also, in Chapter 5 a new approach to tuning GA factors is developed, whereby the optimisation performance of Genetic Algorithms can be significantly improved. This approach is based on statistical inference about the significance of GA factors and on regression analysis of the GA performance. Being less computationally intensive compared to the existing methods of GA factor adjusting, the newly developed approach often gives better tuning results. Chapter 6 deals with the optimisation of multi-drug chemotherapy, which is a more practical and challenging problem. Its practicality can be explained by oncologists' preferences to administer anti-cancer drugs in various combinations in order to better cope with the occurrence of drug resistant cells. However, the imposition of strict toxicity constraints on combining various anticancer drugs together, makes the optimisation problem of multi-drug chemotherapy very difficult to solve, especially when complex treatment objectives are considered. Nevertheless, the experimental results of Chapter 6 demonstrate that this problem is tractable to Genetic Algorithms, which are capable of finding good chemotherapeutic regimens in different treatment situations. On the basis of these results a decision has been made to encapsulate Genetic Algorithms into an independent optimisation module and to embed this module into a more general and user-oriented environment - the Oncology Workbench. The particulars of this encapsulation and embedding are also given in Chapter 6. Finally, Chapter 7 concludes the present work by summarising the contributions made to the knowledge of the subject treated and by outlining the directions for further investigations. The main contributions are: (1) a novel application of the Genetic Algorithm technique in the field of cancer chemotherapy optimisation, (2) the development of a statistical method for tuning the values of GA factors, and (3) the development of a robust and versatile optimisation utility for a clinically usable decision support system. The latter contribution of this thesis creates an opportunity to widen the application domain of Genetic Algorithms within the field of drug treatments and to allow more clinicians to benefit from utilising the GA optimisation

Design, Synthesis and Pharmacological Evaluation of Some Novel Heterocyclic Antihyperlipidemic Agents

In-silico approach was used to select thirty molecules which are predicted to be effective against the target enzyme HMG -Co-A and the protein was downloaded from Protein Bank (PDB id-1t02). This was done by molecular docking studies against the target enzyme and the ligands. • In-silico ADME assessment and In-silico toxicity predictions were carried out to find the drug likeness property and toxicity nature of the selected 30 molecules after docking. • 30 molecules which were selected from docking score were synthesized. • The synthesised molecules are 1,2,3 triazole derivatives of Coumarin, Quinoline and Pthalimide. Thirty new molecules comprises of 14 number of 1,2,3 – triazole derivatives of Coumarin (5a-5g and 6a-6g). 14 number of 1,2,3 – triazole derivatives of Quinoline (9a-9g and 10a-10g). 2 number of 1,2,3 – triazole derivatives of Pthalimide (13a-13b). • The thirty synthesised compounds were purified by chromatography using ethyl acetate and hexane (1:2) as eluting agent. • Melting point was determined by open capillary method and are presented uncorrected. All the molecules were characterized by FT-IR,1H-NMR,13C-NMR and Mass spectra. • Based upon the docking score top four molecules were chosen for anti hyperlipidemic activity. (QMB (9b), QEB (10a), QEB (10b) and CEH(6e)). • Anti-hyperlipidemic activity was carried out by feeding high fat diet to 7 groups of six animals each and at the end of nine weeks, animals were sacrificed. • The tissues were taken for in-vivo antioxidant study. • All the synthesized compounds showed increase in HDL level of the animals as compared to the group which was administered with standard drug. • The compound reduced the body weight of the animals which are fed with high fat diet at a lower dose and also decreased the level of LDL in the blood. • The in- vivo antioxidant showed good increase in the levels of Superoxide Dismutase (SOD), Catalase (CAT), Glutathione peroxidase (GPx) and Glutathione reductase (GR) compared with that of the control group. • The stability of the ligand receptor complexes were analysed by molecular dynamic stimulation. This was performed with the top glide score ligand. The study confirmed that the ligand receptor complex was stable without any notable conformational changes during the simulation run. • At the end of the MD simulation, changes in the position and orientation of ligands in the introduced binding site was observed which indicates the usefulness of the MD simulation for optimization of the ligands into the target binding site. • In the present work, simple and efficient practical methods were adopted for the synthesis of the heterocyclics which resulted from the in-silico and the compunds were obtained in good yield. • The compounds with 1,2,3 triazoles showed good anti-hyperlipidemic activity at lower dose as compared to that of the standard. • The good profile of the molecules with the In-silico toxicity and In-silico ADME properties shows it can be taken for further studies. • The Molecular simulation method also concludes the stability of ligand -receptor is significant for the compound with 1,2,3 triazole containing quinoline nucleus QEP(10b). • The above findings have demonstrated that the compound is possibly a future drug moiety for treating hyperlipidemia

Influence of non-synonymous sequence mutations on the architecture of HIV-1 clade C protease receptor site : docking and molecular dynamics studies

Despite the current interventions to avert contagions and AIDS-related deaths, sub-Saharan Africa is still the region most severely affected by the HIV/AIDS pandemic, where clade C is the dominant circulating HIV-1 strain. The pol-encoded HIV-1 protease enzyme has been extensively exploited as a drug target. Protease inhibitors have been engineered within the framework of clade B, the commonest in America, Europe and Australia. Recent studies have attested the existence of sequence and catalytic disparities between clades B and C proteases that could upset drug susceptibilities. Emergence of drug-resistant associated mutations and combinatorial explosions due to recombination thwarts the attempt to stabilize the current highly active antiretroviral therapy (HAART) baseline. The project aimed at identifying the structural and molecular mechanisms hired by mutants to affect the efficacies of both FDA approved and Rhodes University (RU)-synthesized inhibitors, in order to define how current and or future drugs ought to be modified or synthesized with the intent of combating drug resistance. The rationale involved the generation of homology models of the HIV-1 sequences from the South African infants failing treatment with two protease inhibitors: lopinavir and ritonavir (as monitored by alterations in surrogate markers: CD4 cell count decline and viral load upsurge). Consistent with previous studies, we established nine polymorphisms: 12S, 15V, 19I, 36I, 41K, 63P, 69K, 89M, and 93L, linked to subtype C wild-type; some of which are associated with protease treatment in clade B. Even though we predicted two occurrence patterns of M46I, I54V and V82A mutations as V82A→I54V→M46I and I54V→V82A→M46V, other possibilities might exist. Mutations either caused a protracted or contracted active site cleft, which enforced differential drug responses. The in silico docking indicated susceptibility discordances between clades B and C in certain polymorphisms and non-polymorphisms. The RU-synthesized ligands displayed varied efficacies that were below those of the FDA approved protease inhibitors. The flaps underwent a wide range of structural motions to accommodate and stabilize the ligands. Computational analyses unravelled the need for these potential drugs to be restructured by (de novo) drug engineers to improve their binding fits, affinities, energies and interactions with multiple key protease residues in order to target resilient HIV-1 assemblages. Accumulating evidences on contrasting drug-choice interpretations from the Stanford HIVdb should act as an impetus for the customization of a HIVdb for the sub-Saharan subcontinent

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference