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Molecular Dynamics (MD) is becoming an essential method in the research toolkit of academic and applied science. Common practice is to combine MD with other in silico modelling, simulation and data analysis techniques into functional workflows. These have become complex entities over the years with significant software and hardware requirements and domain-level expertise to be handled correctly, preventing routine use by novice and expert users alike. Our Molecular and Computational Toxicology research group at the Vrije Universiteit Amsterdam develops MD-based workflows with a practical focus on drug development. To ensure user-friendliness of our workflows and promote wide-spread application of MD-based methods we have recently launched the MDStudio software platform. MDStudio uses the modern concept of “software as microservices” as the basis of constructing workflows with a number of major benefits: Open platform approach: any method can become a microservice through a thin language agnostic container exposing a standardized API to talk to others via a router over a network regardless of the database, hardware and software environment. Interoperability guaranteed! Flexible workflows: Users can interact with microservices and implement workflows at several levels of abstraction in a near language agnostic way: script based, re-computable interactive notebooks or a full graphical user interface. Fast workflow based prototyping and development making the impact of MDStudio truly greater than the sum of its parts! (Multi) user-friendly: Research is teamwork! MDStudio is multi-user ready enabling research teams to collaborate on projects through workflows, anywhere, anytime and anyplace. Heterogeneous scalability: MDStudio uses a self-contained, containerized, client-server architecture that can be deployed on many different platforms. Microservices natively scale from multi-core desktops to HPC clusters and cloud environments. Requiring only a network connection to communicate, they can be deployed on physically isolated hardware tailored to the requirements of a research team.

Poster presented at the National eScience Symposium 2016.

Poster presented at EuroQSAR 2016.

International audience; The integration of biophysical data from multiple sources is critical for developing accurate structural models of large multiprotein systems and their regulators. Mass spectrometry (MS) can be used to measure the insertion location for a wide range of topographically sensitive chemical probes, and such insertion data provide a rich, but disparate set of modeling restraints. We have developed a software platform that integrates the analysis of label-based MS and tandem MS (MS(2)) data with protein modeling activities (Mass Spec Studio). Analysis packages can mine any labeling data from any mass spectrometer in a proteomics-grade manner, and link labeling methods with data-directed protein interaction modeling using HADDOCK. Support is provided for hydrogen/deuterium exchange (HX) and covalent labeling chemistries, including novel acquisition strategies such as targeted HX-MS(2) and data-independent HX-MS(2). The latter permits the modeling of highly complex systems, which we demonstrate by the analysis of microtubule interactions.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 angstrom, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes. Proteins 2014; 82:620-632. (c) 2013 Wiley Periodicals, Inc.