Stevens, J. A. | Search bioRxiv

bioRxiv2025

TS2CG as a membrane builder

Molecular dynamics (MD) simulations excel at capturing biological processes at the molecular scale but rely on a well-defined initial structure. As MD simulations now extend to whole-cell-level modeling, new tools are needed to efficiently build initial structures. Here, we introduce TS2CG version 2, designed to construct coarse-grained membrane structures with any desired shape and lateral organization. This version enables precise placement of lipids and proteins based on curvature preference, facilitating the creation of large, near-equilibrium membranes. Additional features include controlled pore generation and the placement of specific lipids at membrane edges for stabilization. Moreover, a Python interface allows users to extend functionality while maintaining the high performance of the C++ core. To demonstrate its capabilities, we showcase challenging simulations, including a Mobius strip membrane, a vesicle with lipid domain as continental plates (Martini globe), and entire mitochondrial membranes exhibiting lipid heterogeneity due to curvature, along with a comprehensive set of tutorials.

biophysics↗

bioRxiv2024

An integrative modelling approach to the mitochondrial cristae

Mitochondria are implicated in many cellular functions such as energy production and apoptosis but also disease pathogenesis. To effectively perform these roles, the mitochondrial inner membrane has invaginations known as cristae that dramatically increase the surface area. This works to provide more space for membrane proteins that are essential to the roles of mitochondria. While separate components of this have been studied computationally, it remains a challenge to combine elements into an overall model. Here we present a workflow to create a comprehensive model of a crista junction from a human mitochondrion. Our coarse-grained representation of a crista shows how various experimentally determined features of organelles can be combined with molecular modelling to give insights into the interactions and dynamics of complicated biological systems. This work is presented as an initial living model for this system, intended to be built upon and improved as our understanding, methodology and resources develop.

biochemistry↗

bioRxiv2024

Benchmarking coarse-grained simulation methods for investigation of transport tunnels in enzymes

Enzymes are pivotal to numerous biological processes, often featuring buried active sites linked to the surrounding solvent through intricate and dynamic tunnels. These tunnels are vital for facilitating substrate access, enabling product release, and regulating solvent exchange, which collectively influence enzymatic function and efficiency. Consequently, knowledge of tunnels is key for a holistic understanding of the effect of mutations as well as predicting drug residence times. Unfortunately, most transport tunnels are transient, i.e., equipped by molecular gates, rendering their opening a rare event that is often notoriously hard to study with conventional molecular dynamics simulations. To overcome the sampling limitation of such simulations, this study investigated the efficacy of three different coarse-grained (CG) molecular dynamics simulation methods for inferring enzyme tunnel structure and dynamics. Here, we covered the Martini and SIRAH models with different restraint protocols providing stability to CG proteins while to some extent biasing the sampling towards a reference structure. By contrasting CG results with all-atom simulations, we benchmarked the ability of CG methods to replicate ensemble characteristics of complex tunnel networks in haloalkane dehalogenase LinB and two of its mutants with engineered tunnel networks. The assessed tunnel parameters are essential for prioritizing functionally relevant tunnels and delineating the effect of mutations on transport tunnels. Our findings reveal that while CG methods significantly enhance the efficiency of tunnel analyses, some of them, like Martini with Elastic network restraints, were limited in recapitulating all-atom tunnel dynamics due to the structural bias applied. In contrast, the Martini G[o] model even captured the intricate details of mutation perturbing tunnel dynamics. All studied CG methods performed well in capturing the geometry of tunnel ensembles in line with all-atom simulations. Additionally, the wider applicability of CG methods was verified by analyzing tunnel networks of nine enzymes from different combinations of structural and functional classes, demonstrating their potential to uncover new tunnel phenomena and validate their utility in broader biological and functional contexts. This comprehensive evaluation underscores the strengths and constraints of CG simulations in capturing enzyme tunnels and benefiting from their computational speed for studying huge datasets of enzymes. These insights are valuable for enzyme engineering, drug design, and understanding enzyme function while benefitting from the efficiency of coarse-grained models.

bioinformatics↗