Springer Science and Business Media LLC

Metabolic profiling has been shown to be useful to improve our understanding of complex metabolic processes. Shared data are key to the analysis and validation of metabolic profiling and untargeted spectral analysis and may increase the pace of new discovery. Improving the existing portfolio of open software may increase the fraction of shared data by decreasing the amount of effort required to publish them in a manner that is useful to others. However, a weakness of open software, when compared to commercial ones, is the lack of user-friendly graphical interface that may discourage inexperienced researchers. Here, a web-browser-oriented solution is presented and demonstrated for metabolic profiling analysis that combines the power of R for back-end statistical analyses and of JavaScript for front-end visualisations and user interactivity. This unique combination of statistical programming and web-browser visualisation brings enhanced data interoperability and interactivity into the open source realm. It is exemplified by characterizing the extent to which bariatric surgery perturbs the metabolisms of rats, showing the value of the approach in iterative analysis by the end-user to establish a deeper understanding of the system perturbation. HastaLaVista is available at: (https://github.com/jwist/hastaLaVista, 10.5281/zenodo.3544800) under MIT license. The approach described in this manuscript can be extended to connect the interface to other scripting languages such as Python, and to create interfaces for other types of data analysis.

Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.

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