The simulation configuration files are hardcoded into the workflow, standardizing the resulting simulation for reuse and promoting the reproducibility of the computational simulations
The simulation configuration files are hardcoded into the workflow, standardizing the resulting simulation for reuse and promoting the reproducibility of the computational simulations. The run length of simulations is highly dependent on the nature of the protein under study, and can become significant for highly dynamic or large structures. composed of three impartial modules that can be used sequentially to generate the variant scaffolds of missense protein variants from your wildtype protein structure. The workflow first generates the mutant structure and configuration files required to execute molecular dynamics simulations of solvated protein variant structures. The producing trajectories are clustered based on the structural diversity of residues involved in ligand binding to produce one or more variant scaffolds of the protein structure. Finally, these unique structural conformations are bound to small molecule ligand libraries to predict variant induced changes to drug binding relative to the wildtype protein structure. Conclusions SNP2SIM provides a platform to apply molecular simulation based functional analysis of sequence variance in the protein targets of small molecule therapies. In addition to simplifying the simulation of variant specific drug interactions, the workflow enables large level computational mutagenesis by controlling the parameterization of molecular simulations across multiple users or distributed computing infrastructures. This enables the parallelization of the computationally rigorous molecular simulations to be aggregated for downstream functional analysis, and facilitates comparing various simulation options, such as the specific residues used to define structural variant clusters. The Python scripts that implement the SNP2SIM workflow are available (SNP2SIM Repository. https://github.com/mccoymd/SNP2SIM, Accessed 2019 February ), and individual SNP2SIM modules are available as apps around the Seven Bridges Malignancy Genomics Cloud (Lau et al., Malignancy Res 77(21):e3-e6, 2017; Malignancy Genomics Cloud [www.cancergenomicscloud.org; Accessed 2018 November]). strong class=”kwd-title” Keywords: Molecular dynamics, Ligand docking, Protein structure, Functional prediction Background Molecular simulation is usually a powerful tool used by computational biologists to analyze the relationship between protein structure and its functional properties. Ranging from high throughput AZM475271 drug screening to focused characterization of protein conformational dynamics, the creative analysis has several translational applications. Large libraries of drug candidates can be evaluated to produce novel targeted therapeutics, and insight into specific molecular interactions between effective drugs and their protein targets aids the design novel molecules [1, 2]. An advantage of the AZM475271 computational simulations is the ability to probe how variance in the protein sequence alters those molecular interactions, and can be extended to the development of therapies targeted at specific sequence variants [3C6]. In addition to drug discovery and design, the insight can be further extended to inform treatment AZM475271 planning when selecting an optimal targeted therapeutic strategy [7]. Due to an inherent tradeoff between resolution and computational Rabbit Polyclonal to INSL4 requirements, molecular simulations can be divided between methods which only simulate a portion of the overall molecule and those which explicitly consider all atomic interactions occurring within a solvated system. Coarse grained methods which do not explicitly consider the internal interactions occurring within the protein backbone are used to address the enormous search space that must be sampled when predicting how two molecules interact [8]. For example, predicting how well a small molecule ligand will bind to a target protein depends on the sum total of all the individual atomic interactions. Depending on the chemical nature of the ligand, the conformational diversity can be quite large due to rotation around individual bonds and limited steric constraints of a single ligand molecule. Furthermore, the protein surface represents a large area of potential interactions and exponentially increases the degrees of freedom which must be explored when identifying an optimally bound structure. In order to simplify the search for optimized protein:ligand conformations and to simulate high throughput binding of large libraries of low molecular excess weight ligands, coarse grained docking methods will AZM475271 typically only model the flexibility of the ligand and a small number of interacting protein residues within a defined area of a rigid protein structure [8]. While the liberties taken by these types of simulations allow for a greater throughput, they fail to account for internal protein.