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Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high‐throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease‐related variants that can benefit from analyses with structure‐based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high‐throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high‐throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication‐ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction. [ABSTRACT FROM AUTHOR]

Environmental effects on the transmission of many high burden infectious diseases are increasingly recognized, especially for transmission routes through water and vectors, or intermediate hosts, that live in the environment. Control of such diseases, which is currently largely based on treatment, is increasingly challenging due to climate-induced changes in infection patterns and the rapid emergence of drug resistance. Therefore, prevention through simulation of future disease risk and the devising of more comprehensive strategies, rather than exclusively relying on treatment, are becoming key. This requires mechanistic understanding of the link between disease transmission and underlying drivers, including heterogeneous on-the-ground environmental characteristics, which are those it may be possible to manipulate locally to reduce disease burdens. However, currently assumed drivers on the variability of disease risk are often only climatic, and environmental interventions to supplement drug treatment still under-recognised. In this work, we use the widespread parasitic disease of fasciolosis, and emerging zoonosis, as a case study. First, we apply the previously developed hydro-epidemiological model for liver fluke to investigate the variability of climatic and environmental drivers on disease risk across the UK. This allows exploring opportunities for environmental control strategies to complement treatment. Second, we evaluate how future risk of infection might respond to climate change across the country, including potential shifts in disease risk and its drivers, using climate projection data. Our results show shifts from modal to bimodal seasonality across the UK going forwards, and that environmental management may provide benefits in risk reduction in certain areas, offering a more sustainable and cost-effective approach in the long run. [ABSTRACT FROM AUTHOR]

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction., (© 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)