Conceptual Framework and Methodology for Analysing Previous Molecular Docking Results

Modern drug discovery relies on in-silico computational simulations such as molecular docking. Molecular docking models biochemical interactions to predict where and how two molecules would bind. The results of large-scale molecular docking simulations can provide valuable insight into the relationship between two molecules. This is useful to a biomedical scientist before conducting in-vitro or in-vivo wet-lab experiments. Although this ˝eld has seen great advancements, feedback from biomedical scientists shows that there is a need for storage and further analysis of molecular docking results. To meet this need, biomedical scientists need to have access to computing, data, and network resources, and require speci˝c knowledge or skills they might lack. Therefore, a conceptual framework speci˝cally tailored to enable biomedical scientists to reuse molecular docking results, and a methodology which uses regular input from scientists, has been proposed. The framework is composed of 5 types of elements and 13 interfaces. The methodology is light and relies on frequent communication between biomedical sciences and computer science experts, speci˝ed by particular roles. It shows how developers can bene˝t from using the framework which allows them to determine whether a scenario ˝ts the framework, whether an already implemented element can be reused, or whether a newly proposed tool can be used as an element. Three scenarios that show the versatility of this new framework and the methodology based on it, have been identi˝ed and implemented. A methodical planning and design approach was used and it was shown that the implementations are at least as usable as existing solutions. To eliminate the need for access to expensive computing infrastructure, state-of-the-art cloud computing techniques are used. The implementations enable faster identi˝cation of new molecules for use in docking, direct querying of existing databases, and simpler learning of good molecular docking practice without the need to manually run multiple tools. Thus, the framework and methodol-ogy enable more user-friendly implementations, and less error-prone use of computational methods in drug discovery. Their use could lead to more e˙ective discovery of new drugs

Specifying timing requirements in domain specific languages for modeling

Complex Real-Time Embedded Systems (RTESs) can be developed using model-based engineering. The problem is choosing a modeling language that has capabilities to model the most important characteristic of RTESs: timing. This paper shows an analysis of the most popular modeling languages and their capabilities to model timing constraints in RTESs. It includes UML, SysML, AADL, MARTE and EAST-ADL. A brief comparison between MARTE and EAST-ADL, based on the case study from the automotive industry, is also included

Model-based engineering in real-time embedded systems: specifying timing constraints

This paper presents the results from a research project on development of Real-Time Embedded Systems (RTESs) using a Model-Based Engineering (MBE) approach. A review of the state-of-the-art modelling languageswas done in order to assess their capabilities to model time. A chosen case study,a Brake-By-Wire (BBW) system, was taken from the automotive industry.The case study focuses on the use of EAST-ADL to model the RTES and TADL to specify timing constraints. A different approach using MARTE to model the BBW system was developed within our project. This approach is used to compare MARTE (and OCL) with EAST-ADL (and TADL). The results show that MARTE can be used to model an RTES from the automotive industry but lacks some important semantic expressions for the timing constraints which are present in TADL

A Generic Framework and Methodology for Implementing Science Gateways for Analysing Molecular Docking Results

Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. The framework is modular enabling the reuse of already existing components. The methodology is agile and encourages the input and participation of end-users. A prototype implementation, based on the framework and methodology, of a science-gateway-based molecular docking environment for recommending a ligand-protein pair for next docking experiment is also presented and evaluated

Molecular docking with Raccoon2 on clouds: extending desktop applications with cloud computing

Molecular docking is a computer simulation that predicts the binding affinity between two molecules, a ligand and a receptor. Large-scale docking simulations, using one receptor and many ligands, are known as structure-based virtual screening. Often used in drug discovery, virtual screening can be very computationally demanding. This is why user-friendly domain-specific web or desktop applications that enable running simulations on powerful computing infrastructures have been created. Cloud computing provides on-demand availability, pay-per-use pricing, and great scalability which can improve the performance and efficiency of scientific applications. This paper investigates how domain-specific desktop applications can be extended to run scientific simulations on various clouds. A generic approach based on scientific workflows is proposed, and a proof of concept is implemented using the Raccoon2 desktop application for virtual screening, WS-PGRADE workflows, and gUSE services with the CloudBroker platform. The presented analysis illustrates that this approach of extending a domain-specific desktop application can run workflows on different types of clouds, and indeed makes use of the on-demand scalability provided by cloud computing. It also facilitates the execution of virtual screening simulations by life scientists without requiring them to abandon their favourite desktop environment and providing them resources without major capital investment

Extending Molecular Docking Desktop Applications with Cloud Computing Support and Analysis of Results

Structure-based virtual screening simulations, which are often used in drug discovery, can be very computationally demanding. This is why user-friendly domain-specific web or desktop applications that enable running simulations on powerful computing infrastructures have been created. This article investigates how domain-specific desktop applications can be extended to use cloud computing and how they can be part of scenarios that require sharing and analysing previous molecular docking results. A generic approach based on interviews with scientists and analysis of existing systems is proposed. A proof of concept is implemented using the Raccoon2 desktop application for virtual screening, WS-PGRADE workflows, gUSE services with the CloudBroker Platform, the structural alignment tool DeepAlign, and the ligand similarity tool LIGSIFT. The presented analysis illustrates that this approach of extending a domainspecific desktop application can use different types of clouds, thus facilitating the execution of virtual screening simulations by life scientists without requiring them to abandon their favourite desktop environment and providing them resources without major capital investment. It also shows that storing and sharing molecular docking results can produce additional conclusions such as viewing similar docking input files for verification or learning

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma.

The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy

Building Science Gateways for Analysing Molecular Docking Results Using a Generic Framework and Methodology

Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. The framework is modular enabling the reuse of already existing components. The methodology, which proposes three techniques that the development team can use, is agile and encourages active participation of end-users. Based on the framework and methodology, two prototype implementations of science-gateway-based docking environments are presented and evaluated. The first system recommends a receptor-ligand pair for the next docking experiment, and the second filters docking results based on ligand properties

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy