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

NTPase and 5′-RNA Triphosphatase Activities of Chikungunya Virus nsP2 Protein

Chikungunya virus (CHIKV) is an insect borne virus (genus: Alphavirus) which causes acute febrile illness in humans followed by a prolonged arthralgic disease that affects the joints of the extremities. Re-emergence of the virus in the form of outbreaks in last 6–7 years has posed a serious public health problem. CHIKV has a positive sense single stranded RNA genome of about 12,000 nt. Open reading frame 1 of the viral genome encodes a polyprotein precursor, nsP1234, which is processed further into different non structural proteins (nsP1, nsP2, nsP3 and nsP4). Sequence based analyses have shown helicase domain at the N-terminus and protease domain at C-terminus of nsP2. A detailed biochemical analysis of NTPase/RNA helicase and 5′-RNA phosphatase activities of recombinant CHIKV-nsP2T protein (containing conserved NTPase/helicase motifs in the N-terminus and partial papain like protease domain at the C-terminus) was carried out. The protein could hydrolyze all NTPs except dTTP and showed better efficiency for ATP, dATP, GTP and dGTP hydrolysis. ATP was the most preferred substrate by the enzyme. CHIKV-nsP2T also showed 5′-triphosphatase (RTPase) activity that specifically removes the γ-phosphate from the 5′ end of RNA. Both NTPase and RTPase activities of the protein were completely dependent on Mg2+ ions. RTPase activity was inhibited by ATP showing sharing of the binding motif by NTP and RNA. Both enzymatic activities were drastically reduced by mutations in the NTP binding motif (GKT) and co-factor, Mg2+ ion binding motif (DEXX) suggesting that they have a common catalytic site

A Novel Heterocyclic Compound CE-104 Enhances Spatial Working Memory in the Radial Arm Maze in Rats and Modulates the Dopaminergic System

Various psychostimulants targeting monoamine neurotransmitter transporters (MAT) have been shown to rescue cognition in patients with neurological disorders and improve cognitive abilities in healthy subjects at low doses. Here, we examined the effects upon cognition of a chemically synthetized novel MAT inhibiting compound 2-(benzhydrylsulfinylmethyl)-4-methylthiazole (named as CE-104). The efficacy of CE-104 in blocking MAT (DAT – dopamine transporter, SERT – serotonin transporter and NET – norepinephrine transporter) was determined using in vitro neurotransmitter uptake assay. The effect of the drug at low doses (1 and 10mg/kg) on spatial memory was studied in male rats in the radial arm maze (RAM). Furthermore, the dopamine receptor and transporter complex levels of frontal cortex (FC) tissue of trained and untrained animals treated either with the drug or vehicle were quantified on blue native PAGE (BN-PAGE). The drug inhibited dopamine (IC50: 27.88µM) and norepinephrine uptake (IC50: 160.40µM), but had a negligible effect on SERT. In the RAM, both drug-dose groups improved spatial working memory during the performance phase of RAM as compared to vehicle. BN-PAGE western blot quantification of dopamine receptor and transporter complexes revealed that D1, D2, D3 and DAT complexes were modulated due to training and by drug effects. The drug’s ability to block DAT and its influence on dopamine transporter and receptor complex levels in the FC is proposed as a possible mechanism for the observed learning and memory enhancement in the RAM

ATPase activity of CHIKV-nsP2T.

<p>At different enzyme concentrations: CHIKV-nsP2T protein was incubated at different concentrations (5 ng to 50 ng) in a 50 µl reaction containing 50 mM MOPS at pH 7.25, 1 mM ATP, 1 mM MgCl_2, at 37°C for 30 min. Released phosphate was quantitated as described in the experimental procedures. Effect of pH on ATPase activity: CHIKV-nsP2T protein was incubated in a 50 µl reaction containing 50 mM MOPS at pH 6.25–8.0, 1 mM ATP, 1 mM MgCl_2, at 37°C for 30 min. Released phosphate was quantitated as described in the experimental procedures. Effect of MgCl₂ concentration on ATPase activity: CHIKV-nsP2T protein was incubated in a 50 µl reaction containing 50 mM MOPS at pH 7.25, 0–5 mM MgCl₂, 1 mM ATP, at 37°C for 30 min. Released phosphate was quantitated as described in the experimental procedures. Analysis of released phosphate on TLC: Reaction was carried out in 20 µl containing 1 nM CHIKV-nsP2T, 50 mM MOPS (pH 7.25), 1 mM MgCl₂, 1 mM ATP, 1 µCi of [γ-³²P] ATP, incubated at 37°C for 30 min and 1 µl of the mixture was analyzed by TLC and processed for autoradiography.</p

Strand displacement activity of CHIKV-nsP2T.

<p>With different RNA substrates: Unwinding activity of the protein was checked using different RNA substrates. CHIKV-nsP2 protein was incubated with RNA duplexes with 5′ overhang (lanes 1, 2, 3); with 3′ overhang (lanes 4, 5, 6) and with blunt ends (7, 8, and 9). With different protein concentrations: Unwinding activity was carried out in presence of increasing concentrations of CHIKV-nsP2T using RNA substrate with both 5′ and 3′ overhangs, Lanes (1) control, (2) heat denatured substrate RNA, and (3 to 8) different CHIKV-nsP2T concentrations (1, 10, 50, 100, 500 and 1000 ng).</p

NTPase activity of CHIKV-nsP2T.

<p>Effect of poly (U) RNA on ATPase activity: CHIKV-nsP2T protein was incubated in a 50 µl reaction containing 50 mM MOPS at pH 7.25, 1 mM ATP, 1 mM MgCl₂, poly (U) RNA (0–5000 ng), at 37°C for 30 min. Released phosphate was quantitated as described in the experimental procedures. Effect of different nucleic acid oligonucleotides on ATPase activity: CHIKV-nsP2T protein was incubated in a 50 µl reaction containing 50 mM MOPS at pH 7.25, 1 mM ATP, 1 mM MgCl₂, and different homopolynucleotides (25 ng/µl of the reaction), at 37°C for 30 min. Released phosphate was quantitated as described in the experimental procedures. ATPase activity of CHIKV-nsP2T mutant proteins (mut I and mut II): CHIKV-nsP2T mutant proteins were incubated in a 50 µl reaction containing 50 mM MOPS at pH 7.25, 1 mM MgCl₂, 1 mM ATP, at 37°C for 30 min. Released phosphate was quantitated as given in the experimental procedures. NTPase activity of CHIKV-nsP2T: CHIKV-nsP2T protein was incubated in a 50 µl reaction containing 50mM MOPS at pH 7.25, 1 mM MgCl₂, and increasing concentrations of different NTPs, at 37°C for 30 min. Released phosphate was quantitated as described in the experimental procedures.</p

Expression of truncated CHIKV nsP2.

<p>Schematic representation of CHIKV nsP2: N- terminal helicase domain (white) and C-terminal protease domain (gray) are indicated in the figure. The two proteins expressed were, CHIKV-Hel (166–441 a.a.) and CHIKV-nsP2T (166–630 a.a.). Both this proteins spanned all seven signature motifs of SF1 helicases which are conserved among Alphaviruses. SDS-PAGE analysis: HPLC purified proteins were analyzed on 10% SDS-PAGE. Lanes are: (1) Wild type CHIKV-nsP2T, (2) nsP2T-mut I, (3) nsP2T-mut II, (MW) Molecular weight marker.</p

Effect of different conditions on the RNA 5′-triphosphatase activity of CHIKV-nsP2T.

<p>Effect of AMP, ADP and ATP on RTPase activity: CHIKV-nsP2T was incubated with 5′-[γ-³²P]-RNA at 37°C for 30 min in presence of different concentrations of AMP/ADP/ATP independently and products were analyzed by TLC. Activity of CHIKV-nsP2T without AMP/ADP/ATP was taken as 100% and the percent activity of each reaction was calculated separately for each reaction. The effect of MgCl₂ on RTPase activity: CHIKV-nsP2T was incubated with 5′-[γ-³²P]-RNA at 37°C for 30 min in presence of different concentrations of MgCl₂ (0 -5.0 mM). Released radiolabel [³²Pi] was quantitated for three independent experiments and mean values were plotted. RTPase activity of nsP2 mutants: CHIKV-nsP2T wild type, mut I and mut II proteins were incubated with 5′-[γ-³²P]-RNA at 37°C. Aliquots were removed at different time points (5, 10, 15, 20, 25, 30 and 40 min) and analyzed. Released radiolabel [³²Pi] was quantitated for three independent experiments and mean values were plotted.</p

Helicase sequence motifs of the CHIKV-nsP2 identified by using amino acid alignments.

<p>Helicase sequence motifs of the CHIKV-nsP2 identified by using amino acid alignments.</p