Analysis of transcription factor CREM dependent gene expression during mouse spermatogenesis

Computational methods are getting increasingly important for the analysis of large data sets in molecular biology. The data sets analyzed in this thesis are derived from experiments measuring the changes of expression levels in response to the transcription factor CREM (cAMP Responsive Element Modulator) during mouse spermatogenesis. In the course of this analysis new computational methods were developed and used that will also be of value in other projects in Bioinformatics. CREM belongs to a family of cAMP-responsive nuclear factors. The activator splice-isoform CREM is exclusively expressed at high levels in post-meiotic germ cells during mouse spermiogenesis. Mutant male mice lacking CREM expression are sterile due to lack of maturation of the germ cells. In order to find CREM target genes the mRNA expression levels in testes of CREM-deficient mice and wild-type mice were compared using the suppression subtractive hybridization (SSH) technique as well as oligonucleotide DNA microarrays. SSH was used to selectively amplify the differentially expressed genes. 12,000 clones, which contain sequence fragments of genes expressed stronger in wild-type as in the CREM (-/-) mutant, were analyzed by a combination of sequencing and hybridization. Sequence analysis methods were used to characterize 956 unique sequences. Homologies to 158 known mouse genes and 99 known genes from other organisms were detected. 296 sequences show homologies to sequences of expressed sequence tags (ESTs). 199 novel sequences have been found. The sequences not corresponding to full length genes of known function were characterized using publicly available EST data. To make EST databases useful for data analysis all of the publicly available ESTs have been grouped into clusters and methods to analyze and visualize EST data were developed. Nylon cDNA microarrays containing the unique sequences from the CREM SSH library were constructed to determine expression levels of those sequences. Most of the sequences from the CREM SSH library are shown to be expressed in wild-type but are down-regulated in CREM deficient mice. Statistical methods to standardize microarray expression data were developed and software was implemented to perform comparisons. Further CREM dependent genes were detected comparing the mRNA expression levels in testes of CREM deficient mice and wild-type mice using Affymetrix oligonucleotide microarrays containing 10,000 mouse sequences. Comparison of the different techniques (SSH, nylon cDNA arrays and Affymetrix oligonucleotide microarrays) shows that the results are complementing each other. The unique sequences from the CREM SSH library were further analyzed by determining the spermatogenic stage specific expression profiles. cDNA from prepubertal mice at certain stages of spermatogenesis were hybridized on nylon cDNA arrays. Several important functional groups of genes like transcription factors, signal transduction proteins and metabolic enzymes are shown to be coexpressed at the latest stages of spermatogenesis. Expression profiles were arranged to find similar profile shapes and co-regulation of functionally related genes. An algorithm to arrange the profiles in an optimal linear order was developed. The linear order is constructed in a way that similar expression profiles end up close together in the linear order, i.e. the sum over all distances of neighboring profiles is minimized. This corresponds to the solution of a traveling salesman problem (TSP), which is well known in computer science. A fast algorithm that computes a heuristic solution to a TSP was adapted to be used in expression profile analysis

Development and application of distributed computing tools for virtual screening of large compound libraries

Im derzeitigen Drug Discovery Prozess ist die Identifikation eines neuen Targetproteins und dessen potenziellen Liganden langwierig, teuer und zeitintensiv. Die Verwendung von in silico Methoden gewinnt hier zunehmend an Bedeutung und hat sich als wertvolle Strategie zur Erkennung komplexer Zusammenhänge sowohl im Bereich der Struktur von Proteinen wie auch bei Bioaktivitäten erwiesen. Die zunehmende Nachfrage nach Rechenleistung im wissenschaftlichen Bereich sowie eine detaillierte Analyse der generierten Datenmengen benötigen innovative Strategien für die effiziente Verwendung von verteilten Computerressourcen, wie z.B. Computergrids. Diese Grids ergänzen bestehende Technologien um einen neuen Aspekt, indem sie heterogene Ressourcen zur Verfügung stellen und koordinieren. Diese Ressourcen beinhalten verschiedene Organisationen, Personen, Datenverarbeitung, Speicherungs- und Netzwerkeinrichtungen, sowie Daten, Wissen, Software und Arbeitsabläufe. Das Ziel dieser Arbeit war die Entwicklung einer universitätsweit anwendbaren Grid-Infrastruktur - UVieCo (University of Vienna Condor pool) -, welche für die Implementierung von akademisch frei verfügbaren struktur- und ligandenbasierten Drug Discovery Anwendungen verwendet werden kann. Firewall- und Sicherheitsprobleme wurden mittels eines virtuellen privaten Netzwerkes gelöst, wohingegen die Virtualisierung der Computerhardware über das CoLinux Konzept ermöglicht wurde. Dieses ermöglicht, dass unter Linux auszuführende Aufträge auf Windows Maschinen laufen können. Die Effektivität des Grids wurde durch Leistungsmessungen anhand sequenzieller und paralleler Aufgaben ermittelt. Als Anwendungsbeispiel wurde die Assoziation der Expression bzw. der Sensitivitätsprofile von ABC-Transportern mit den Aktivitätsprofilen von Antikrebswirkstoffen durch Data-Mining des NCI (National Cancer Institute) Datensatzes analysiert. Die dabei generierten Datensätze wurden für liganden-basierte Computermethoden wie Shape-Similarity und Klassifikationsalgorithmen mit dem Ziel verwendet, P-glycoprotein (P-gp) Substrate zu identifizieren und sie von Nichtsubstraten zu trennen. Beim Erstellen vorhersagekräftiger Klassifikationsmodelle konnte das Problem der extrem unausgeglichenen Klassenverteilung durch Verwendung der „Cost-Sensitive Bagging“ Methode gelöst werden. Applicability Domain Studien ergaben, dass unser Modell nicht nur die NCI Substanzen gut vorhersagen kann, sondern auch für wirkstoffähnliche Moleküle verwendet werden kann. Die entwickelten Modelle waren relativ einfach, aber doch präzise genug um für virtuelles Screening einer großen chemischen Bibliothek verwendet werden zu können. Dadurch könnten P-gp Substrate schon frühzeitig erkannt werden, was möglicherweise nützlich sein kann zur Entfernung von Substanzen mit schlechten ADMET-Eigenschaften bereits in einer frühen Phase der Arzneistoffentwicklung. Zusätzlich wurden Shape-Similarity und Self-organizing Map Techniken verwendet um neue Substanzen in einer hauseigenen sowie einer großen kommerziellen Datenbank zu identifizieren, die ähnlich zu selektiven Serotonin-Reuptake-Inhibitoren (SSRI) sind und Apoptose induzieren können. Die erhaltenen Treffer besitzen neue chemische Grundkörper und können als Startpunkte für Leitstruktur-Optimierung in Betracht gezogen werden. Die in dieser Arbeit beschriebenen Studien werden nützlich sein um eine verteilte Computerumgebung zu kreieren die vorhandene Ressourcen in einer Organisation nutzt, und die für verschiedene Anwendungen geeignet ist, wie etwa die effiziente Handhabung der Klassifizierung von unausgeglichenen Datensätzen, oder mehrstufiges virtuelles Screening.In the current drug discovery process, the identification of new target proteins and potential ligands is very tedious, expensive and time-consuming. Thus, use of in silico techniques is of utmost importance and proved to be a valuable strategy in detecting complex structural and bioactivity relationships. Increased demands of computational power for tremendous calculations in scientific fields and timely analysis of generated piles of data require innovative strategies for efficient utilization of distributed computing resources in the form of computational grids. Such grids add a new aspect to the emerging information technology paradigm by providing and coordinating the heterogeneous resources such as various organizations, people, computing, storage and networking facilities as well as data, knowledge, software and workflows. The aim of this study was to develop a university-wide applicable grid infrastructure, UVieCo (University of Vienna Condor pool) which can be used for implementation of standard structure- and ligand-based drug discovery applications using freely available academic software. Firewall and security issues were resolved with a virtual private network setup whereas virtualization of computer hardware was done using the CoLinux concept in a way to run Linux-executable jobs inside Windows machines. The effectiveness of the grid was assessed by performance measurement experiments using sequential and parallel tasks. Subsequently, the association of expression/sensitivity profiles of ABC transporters with activity profiles of anticancer compounds was analyzed by mining the data from NCI (National Cancer Institute). The datasets generated in this analysis were utilized with ligand-based computational methods such as shape similarity and classification algorithms to identify and separate P-gp substrates from non-substrates. While developing predictive classification models, the problem of imbalanced class distribution was proficiently addressed using the cost-sensitive bagging approach. Applicability domain experiment revealed that our model not only predicts NCI compounds well, but it can also be applied to drug-like molecules. The developed models were relatively simple but precise enough to be applicable for virtual screening of large chemical libraries for the early identification of P-gp substrates which can potentially be useful to remove compounds of poor ADMET properties in an early phase of drug discovery. Additionally, shape-similarity and self-organizing maps techniques were used to screen in-house as well as a large vendor database for identification of novel selective serotonin reuptake inhibitor (SSRI) like compounds to induce apoptosis. The retrieved hits possess novel chemical scaffolds and can be considered as a starting point for lead optimization studies. The work described in this thesis will be useful to create distributed computing environment using available resources within an organization and can be applied to various applications such as efficient handling of imbalanced data classification problems or multistep virtual screening approach

In silico studies of the effect of phenolic compounds from grape seed extracts on the activity of phosphoinositide 3-kinase (PI3K) and the farnesoid x receptor (FXR)

In silico studies of the effect of phenolic compounds from grape seed extracts on the activity of phosphoinositide 3-kinase (PI3K) and farnesoid X receptor (FXR)Montserrat Vaqué Marquès En aquesta tesis es pretén aplicar metodologies computacionals (generació de farmacòfors i docking proteïna lligand) en l'àmbit de la nutigenòmica (ciència que pretén entendre, a nivell molecular, com els nutrients afecten la salut). S'aplicaran metodologies in silico per entendre a nivell molecular com productes naturals com els compostos fenòlics presents en la nostra dieta, poden modular la funció d'una diana comportant un efect en la salut. Aquest efecte es creu que podria ser degut a la seva interacció directa amb proteïnes de vies de senyalització molecular o bé a la modificació indirecta de l'expressió gènica. Donat que el coneixement de l'estructura del complex lligand-receptor és bàsic per entendre el mecanisme d'acció d'aquests lligands s'aplica la metodologia docking per predir l'estructura tridimensional del complex. En aquest sentit, un dels programes de docking és AutoGrid/AutoDock (un dels més citats). No obstant, l'automatització d'AutoGrid/AutoDock no és trivial tan per (a) la cerca virtual en una llibreria de lligands contra un grup de possibles receptors, (b) l'ús de flexibilitat, i (c) realitzar un docking a cegues utilitzant tota la superfície del receptor. Per aquest motiu, es dissenya una interfície gràfica de fàcil ús per utilitzar AutoGrid/AutoDock. Blind Docking Tester (BDT) és una aplicació gràfica que s'executa sobre quatre programes escrits en Fortran i que controla les condicions de les execucions d'AutoGrid i AutoDock. BDT pot ser utilitzat per equips d'investigadors en el camp de la química i de ciències de la vida interessats en dur a terme aquest tipus d'experiments però que no tenen suficient habilitats en programació. En la modulació del metabolisme de la glucosa, treballs in vivio i in vitro en el nostre grup de recerca s'han atribuït els efectes beneficiosos de l'extracte de pinyol de raïm en induir captació de glucosa (punt crític pel manteniment de l'homeostasis de la glucosa). No obstant alguns compostos fenòlics no tenen efecte en la captació de la glucosa, d'altres l'inhibeixen reversiblement. En alguns casos aquesta inhibició és el resultat de la competició dels compostos fenòlics amb ATP pel lloc d'unió de l'ATP de la subunitat catalítica de la fosfatidil inositol 3-kinasa (PI3K). Estudis recents amb inhibidors específics d'isoforma han identificat la p110α (la subunitat catalítica de PI3Kα) com la isoforma crucial per la captació de glucosa estimulada per insulina en algunes línies cel·lulars. Els programes computacionals han estat aplicats per tal de correlacionar l'activitat biològica dels compostos fenòlics amb informació estructural per obtenir una relació quantitativa estructura-activitat (3D-QSAR) i obtenir informació dels requeriments estructura-lligand per augmentar l'afinitat i/o selectivitat amb la diana (proteïna). Tot hi haver-se demostrat que l'adició d'extractes de compostos fenòlics en l'aliment pot tenir en general un benefici per la salut, s'ha de tenir en compte que l'estudi 3D-QSAR (construït a partir d'inhibidors sintètics de p110α) prediu que algunes d'aquestes molècules poden agreujar la resistència a la insulina en individus susceptibles dificultant la capatació de glucosa en múscul i teixit adipós i, per tant, produir un efecte secundari indesitjat. Resultats en el nostre grup de recerca han demostrat que compostos fenòlics presents en extractes de llavor de raïm incrementen l'activitat del receptor "farnesoid x receptor" (FXR) de manera dosi depenent quan el lligand natural de FXR (CDCA) és present. Les metodologies in silico, docking i 3D-QSAR, han estat aplicades juntament amb dades biològiques d'agonistes no esteroidals de FXR que s'uneixen a un lloc d'unió proper però diferent al lligand esteroidal 6CDCA. Els resultats han mostrat que els compostos fenòlics no són capaços d'activar FXR per ells mateixos però poden afegir noves interaccions que estabilitzarien la conformació activa de FXR en presència del lligand natural CDCA. Els compostos fenòlics podrien induir canvis conformacionals específics que augmentarien l'activitat de FXR. In silico studies of the effect of phenolic compounds from grape seed extracts on the activity of phosphoinositide 3-kinase (PI3K) and farnesoid X receptor (FXR)Montserrat Vaqué Marquès This thesis was written with the aim of applying computational methods that have already been developed for molecular design and simulation (i.e. pharmacophore generation and protein-ligand docking) to nutrigenomics. So, in silico tools that are routinely used by the pharmaceutical industry to develop drugs have been used to understand, at the molecular level, how natural products such as phenolic compounds (i.e. molecules that are commonly found in fruits and vegetables) can improve health and prevent diseases. Therefore, we first focused on predicting the structure of protein-ligand complexes. The docking algorithms can use the individual structures from receptor and ligand to predict (1) whether they can form a complex and (2) if so, the structure of the resulting complex. This prediction can be made, for instance, with AutoGrid/AutoDock, the most cited docking software in the literature. The automation of AutoGrid/AutoDock is not trivial for tasks such as (1) the virtual screening of a library of ligands against a set of possible receptors; (2) the use of receptor flexibility and (3) making a blind-docking experiment with the whole receptor surface. Therefore, in order to circumvent these limitations, we have designed BDT (i.e. blind-docking tester; http://www.quimica.urv.cat/~pujadas/BDT), an easy-to-use graphic interface for using AutoGrid/AutoDock. BDT is a Tcl/Tk graphic front-end application that runs on top of four Fortran programs and which controls the conditions of the AutoGrid and AutoDock runs. As far as the modulation of the glucose metabolism is concerned, several in vivo and in vitro results obtained by our group have shown that grape seed procyanidin extracts (GSPE) stimulate glucose uptake in 3T3-L1 adipocytes and thus help to maintain their glucose homeostasis. In contrast, it is also well known that although some phenolic compounds do not affect glucose uptake, others reversibly inhibit it in several cell lines. Moreover, for at least some of these phenolic compounds, this inhibition is the result of their competition with ATP for the ATP-binding site in p110α (i.e. the α isoform of the catalytic subunit of phosphoinositide 3-kinase or PI3Kα). Furthermore, recent studies with isoform-specific inhibitors have identified p110α as the crucial isoform for insulin-stimulated glucose-uptake in some cell lines. Therefore, although it has been proved that the addition of phenolic compound extracts to food can have an overall benefit on health, it should be taken into account that some of these molecules may exacerbate insulin resistance in susceptible individuals via impaired glucose uptake in muscle and adipose tissues and, therefore, produce an undesirable side effect. In this context, we have applied computational approaches (i.e. protein-ligand docking and 3D-QSAR) to predict the IC50 (i.e. the concentration that reduces the p110α activity to 50%). Our results agree with previous experimental results and predict that some compounds are potential inhibitors of this enzyme. Recent results in our research group have demonstrated that the phenolic compounds in GSPE increase the activity of the farnesoid X receptor (i.e. FXR) in a dose-dependent way when the natural ligand of FXR (i.e. CDCA) is also present. The phenolic compounds might induce specific conformational changes that increase FXR activity and then contribute to cardioprotection through mechanisms that are independent of their intrinsic antioxidant capacities but that involve direct interaction with FXR to modulate gene expression. Taking into account this hypothesis a 3D-QSAR analysis was made in an attempt to understand how phenolic compounds activate FXR. So, our results explain why phenolic compounds cannot activate FXR by themselves and how they can add new interactions to stabilize the active conformation of FXR when its natural ligand (i.e. CDCA) is present. Therefore, we proposed a mechanism of FXR activation by dietary phenolic compounds in which they may enhance bile acid-bound FXR activity

Investigation of Atypical Transformations During the Biosynthesis of Marine Cyanobacterial Natural Products.

Naturally occurring chemicals isolated from plants and fungi have been used as medicines for millennia. Biochemical and genetic analysis of these chemically fertile organisms has led to the elucidation of protein-megasynthases responsible for the stepwise condensation of simple chemical subunits in the assembly of biologically-significant small molecules with intriguing chemical architecture. The breadth of biological activity is directly related to the vast sampling of chemical space, achieved through this combinatorial biosynthetic approach. Indeed, structural heterogeneity can be introduced through the incorporation of enzymes catalyzing distinctive biochemical transformations during initiation, elongation and termination steps of biosynthesis. Within the context of the marine cyanobacterial natural products, the linear lipopeptide curacin A is striking not only for its incredibly potent anticancer properties, but also the unique mode by which is constructed. The 2-methyl-cyclopropyl moiety, critical for pharmacological activity, is installed via a rare six-domain beta-branching insertional protein cassette. A portion of this work is focused on parsing the mechanism by which non-catalytic proteins are involved in enhancing the biosynthetic flux through this pathway. Herein we have demonstrated that a C-terminal flanking domain (Cd) serves to not only enhance intramodular interaction, by also stabilizes intermodular protein engagement. The unusual activated-elimination chain termination strategy, carried out by the curacin sulfotransferase (ST) and thioesterase (TE), is also examined for potential development as a novel bio-fuels platform. Homologous proteins in olefin synthase gene cluster from a related cyanobacterium are also characterized. Additionally, chemical and biochemical activation of fatty acid substrates for processing by the tandem functioning ST-TE enzyme pair are studied. Finally, the biosynthetic origins and preliminary biochemical characterization of proteins responsible for the antimalarial compound carmabin A are also examined.PHDChemical BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102313/1/eleisman_1.pd

Database development and machine learning prediction of pharmaceutical agents

Ph.DDOCTOR OF PHILOSOPH

An in-silico investigation of Morita-Baylis-Hillman accessible heterocyclic analogues for applications as novel HIV-1 C protease inhibitors

Cheminformatic approaches have been employed to optimize the bis-coumarin scaffold identified by Onywera et al. (2012) as a potential hit against the protease HIV-1 protein. The Open Babel library of commands was used to access functions that were incorporated into a markov chain recursive program that generated 17750 analogues of the bis-coumarin scaffold. The Morita-Baylis-Hillman accessible heterocycles were used to introduce structural diversity within the virtual library. In silico high through-put virtual screening using AutoDock Vina was used to rapidly screen the virtual library ligand set against 61 protease models built by Onywera et al. (2012). CheS-Mapper computed a principle component analysis of the compounds based on 13 selected chemical descriptors. The compounds were plotted against the principle component analysis within a 3 dimensional chemical space in order to inspect the diversity of the virtual library. The physicochemical properties and binding affinities were used to identify the top 3 performing ligands. ACPYPE was used to inspect the constitutional properties and eliminated virtual compounds that possessed open valences. Chromene based ligand 805 and ligand 6610 were selected as the lead candidates from the high-throughput virtual screening procedure we employed. Molecular dynamic simulations of the lead candidates performed for 5 ns allowed the stability of the ligand protein complexes with protease model 305152. The free energy of binding of the leads with protease model 305152 was computed over the first 50 ps of simulation using the molecular mechanics Poisson-Boltzmann method. Analysis structural features and energy profiles from molecular dynamic simulations of the protein–ligand complexes indicated that although ligand 805 had a weaker binding affinity in terms of docking, it outperformed ligand 6610 in terms of complex stability and free energy of binding. Medicinal chemistry approaches will be used to optimize the lead candidates before their analogues will be synthesized and assayed for in vivo protease activity

Application of machine learning, molecular modelling and structural data mining against antiretroviral drug resistance in HIV-1

Millions are affected with the Human Immunodeficiency Virus (HIV) world wide, even though the death toll is on the decline. Antiretrovirals (ARVs), more specifically protease inhibitors have shown tremendous success since their introduction into therapy since the mid 1990’s by slowing down progression to the Acquired Immune Deficiency Syndrome (AIDS). However, Drug Resistance Mutations (DRMs) are constantly selected for due to viral adaptation, making drugs less effective over time. The current challenge is to manage the infection optimally with a limited set of drugs, with differing associated levels of toxicities in the face of a virus that (1) exists as a quasispecies, (2) may transmit acquired DRMs to drug-naive individuals and (3) that can manifest class-wide resistance due to similarities in design. The presence of latent reservoirs, unawareness of infection status, education and various socio-economic factors make the problem even more complex. Adequate timing and choice of drug prescription together with treatment adherence are very important as drug toxicities, drug failure and sub-optimal treatment regimens leave room for further development of drug resistance. While CD4 cell count and the determination of viral load from patients in resource-limited settings are very helpful to track how well a patient’s immune system is able to keep the virus in check, they can be lengthy in determining whether an ARV is effective. Phenosense assay kits answer this problem using viruses engineered to contain the patient sequences and evaluating their growth in the presence of different ARVs, but this can be expensive and too involved for routine checks. As a cheaper and faster alternative, genotypic assays provide similar information from HIV pol sequences obtained from blood samples, inferring ARV efficacy on the basis of drug resistance mutation patterns. However, these are inherently complex and the various methods of in silico prediction, such as Geno2pheno, REGA and Stanford HIVdb do not always agree in every case, even though this gap decreases as the list of resistance mutations is updated. A major gap in HIV treatment is that the information used for predicting drug resistance is mainly computed from data containing an overwhelming majority of B subtype HIV, when these only comprise about 12% of the worldwide HIV infections. In addition to growing evidence that drug resistance is subtype-related, it is intuitive to hypothesize that as subtyping is a phylogenetic classification, the more divergent a subtype is from the strains used in training prediction models, the less their resistance profiles would correlate. For the aforementioned reasons, we used a multi-faceted approach to attack the virus in multiple ways. This research aimed to (1) improve resistance prediction methods by focusing solely on the available subtype, (2) mine structural information pertaining to resistance in order to find any exploitable weak points and increase knowledge of the mechanistic processes of drug resistance in HIV protease. Finally, (3) we screen for protease inhibitors amongst a database of natural compounds [the South African natural compound database (SANCDB)] to find molecules or molecular properties usable to come up with improved inhibition against the drug target. In this work, structural information was mined using the Anisotropic Network Model, Dynamics Cross-Correlation, Perturbation Response Scanning, residue contact network analysis and the radius of gyration. These methods failed to give any resistance-associated patterns in terms of natural movement, internal correlated motions, residue perturbation response, relational behaviour and global compaction respectively. Applications of drug docking, homology-modelling and energy minimization for generating features suitable for machine-learning were not very promising, and rather suggest that the value of binding energies by themselves from Vina may not be very reliable quantitatively. All these failures lead to a refinement that resulted in a highly sensitive statistically-guided network construction and analysis, which leads to key findings in the early dynamics associated with resistance across all PI drugs. The latter experiment unravelled a conserved lateral expansion motion occurring at the flap elbows, and an associated contraction that drives the base of the dimerization domain towards the catalytic site’s floor in the case of drug resistance. Interestingly, we found that despite the conserved movement, bond angles were degenerate. Alongside, 16 Artificial Neural Network models were optimised for HIV proteases and reverse transcriptase inhibitors, with performances on par with Stanford HIVdb. Finally, we prioritised 9 compounds with potential protease inhibitory activity using virtual screening and molecular dynamics (MD) to additionally suggest a promising modification to one of the compounds. This yielded another molecule inhibiting equally well both opened and closed receptor target conformations, whereby each of the compounds had been selected against an array of multi-drug-resistant receptor variants. While a main hurdle was a lack of non-B subtype data, our findings, especially from the statistically-guided network analysis, may extrapolate to a certain extent to them as the level of conservation was very high within subtype B, despite all the present variations. This network construction method lays down a sensitive approach for analysing a pair of alternate phenotypes for which complex patterns prevail, given a sufficient number of experimental units. During the course of research a weighted contact mapping tool was developed to compare renin-angiotensinogen variants and packaged as part of the MD-TASK tool suite. Finally the functionality, compatibility and performance of the MODE-TASK tool were evaluated and confirmed for both Python2.7.x and Python3.x, for the analysis of normals modes from single protein structures and essential modes from MD trajectories. These techniques and tools collectively add onto the conventional means of MD analysis