Structural Investigation of MscL Gating Using Experimental Data and Coarse Grained MD Simulations

The mechanosensitive channel of large conductance (MscL) has become a model system in which to understand mechanosensation, a process involved in osmoregulation and many other physiological functions. While a high resolution closed state structure is available, details of the open structure and the gating mechanism remain unknown. In this study we combine coarse grained simulations with restraints from EPR and FRET experiments to study the structural changes involved in gating with much greater level of conformational sampling than has previously been possible. We generated a set of plausible open pore structures that agree well with existing open pore structures and gating models. Most interestingly, we found that membrane thinning induces a kink in the upper part of TM1 that causes an outward motion of the periplasmic loop away from the pore centre. This previously unobserved structural change might present a new mechanism of tension sensing and might be related to a functional role in osmoregulation.The study was supported by a grant from the Australian Research Council. The simulations were carried out using computer time from iVEC and a Merit Allocation Scheme on the NCI National Facility at the Australian National University. ED was supported by a Jean Rogerson Postgraduate scholarship and the Beryl Henderson Memorial Grant by the Australian Federation of University Women ACT. Websites of funding agencies: http://nci.org.au/access/merit-allocationscheme/, http://www.ivec.org/ http://www.arc.gov.au/ncgp/default.htm, http://spe.publishing.uwa.edu.au/latest/scholarships/postgraduate/rogerson, http://www. afgw.org.au/what-we-do/scholarships-2/ The authors hereby confirm that the funding agencies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Lipid-mediated interactions tune the association of glycophorin A helix and its disruptive mutants in membranes

The specific and non-specific driving forces of helix association within membranes are still poorly understood. Here, we use coarse-grain molecular dynamics simulations to study the association behavior of glycophorin A and two disruptive mutants, T87F and a triple mutant of the GxxxG motif (G79LG83LG86L), embedded in a lipid membrane. Self-assembly simulations and the association free-energy profile confirm an energetically-favorable dimerized state for both the wild type and the mutants. The reduced association of the mutants compared to the wild type, as observed in experiments, can be justified from comparisons of the free energy profiles. Less-favorable protein-protein interactions as well as disruption of lipid packing around the mutant dimers is responsible for their reduced association. The role of the non-specific "lipid-phobic'' contribution appears to be as important as the specific "helix-helix'' contribution. However, the differences between the wild type and mutants are subtle and our simulations predict a dimerization state not only for the wild-type glycophorin A, but also for these 'disruptive' mutants. Our results highlight the importance of both specific as well as non-specific driving forces in the association of transmembrane helices, and point to the need of more careful interpretation of experimental measurements

Reactive Martini:Chemical Reactions in Coarse-Grained Molecular Dynamics Simulations

Chemical reactions are ubiquitous in both materials and the biophysical sciences. While coarse-grained (CG) molecular dynamics simulations are often needed to study the spatiotemporal scales present in these fields, chemical reactivity has not been explored thoroughly in CG models. In this work, a new approach to model chemical reactivity is presented for the widely used Martini CG Martini model. Employing tabulated potentials with a single extra particle for the angle dependence, the model provides a generic framework for capturing bonded topology changes using nonbonded interactions. As a first example application, the reactive model is used to study the macrocycle formation of benzene-1,3-dithiol molecules through the formation of disulfide bonds. We show that starting from monomers, macrocycles with sizes in agreement with experimental results are obtained using reactive Martini. Overall, our reactive Martini framework is general and can be easily extended to other systems. All of the required scripts and tutorials to explain its use are provided online.</p

Martini 3 Coarse-Grained Model for Second-Generation Unidirectional Molecular Motors and Switches

[Image: see text] Artificial molecular motors (MMs) and switches (MSs), capable of undergoing unidirectional rotation or switching under the appropriate stimuli, are being utilized in multiple complex and chemically diverse environments. Although thorough theoretical work utilizing QM and QM/MM methods have mapped out many of the critical properties of MSs and MMs, as the experimental setups become more complex and ambitious, there is an ever increasing need to study the behavior and dynamics of these molecules as they interact with their environment. To this end, we have parametrized two coarse-grained (CG) models of commonly used MMs and a model for an oxindole-based MS, which can be used to study the ground state behavior of MMs and MSs in large simulations for significantly longer periods of time. We also propose methods to perturb these systems which can allow users to approximate how such systems would respond to MMs rotating or the MSs switching

Curvature effects on lipid packing and dynamics in liposomes revealed by coarse grained molecular dynamics simulations

The molecular packing details of lipids in planar bilayers are well characterized. For curved bilayers, however, little data is available. In this paper we study the effect of temperature and membrane composition on the structural and dynamical properties of a liposomal membrane in the limit of high curvature (liposomal diameter of 15-20 nm), using coarse grained molecular dynamics simulations. Both pure dipalmitoyl phosphatidylcholine (DPPC) liposomes and binary mixtures of DPPC and either dipalmitoyl phosphatidylethanolamine (DPPE) or polyunsaturated dilinoleylphosphatidylcholine (DLiPC) lipids are modeled. We take special care in the equilibration of the liposomes requiring lipid flip-flopping, which can be facilitated by the temporary insertion of artificial pores. The equilibrated liposomes show some remarkable properties. Curvature induces membrane thinning and reduces the thermal expansivity of the membrane. In the inner monolayer the lipid head groups are very closely packed and dehydrated, and the lipids tails relatively disordered. The opposite packing effects are seen in the outer monolayer. In addition, we noticed an increased tendency of the lipid tails to backfold toward the interface in the outer monolayer. The distribution of lipids over the monolayers was found to be strongly temperature dependent. Higher temperatures favor more equally populated monolayers. Relaxation times of the lipid tails were found to increase with increasing curvature, with the lipid tails in the outer monolayer showing a significant slower dynamics compared to the lipid tails in the inner monolayer. In the binary systems there is a clear tendency toward partial transversal demixing of the two components, with especially DPPE enriched in the inner monolayer. This observation is in line with a static shape concept which dictates that inverted-cone shaped lipids such as DPPE and DLiPC would prefer the concave volume of the inner monolayer. However, our results for DLiPC show that another effect comes into play that is almost equally strong and provides a counter-acting driving force toward the outer, rather than the inner monolayer. This effect is the ability of the polyunsaturated tails of DLiPC to backfold, which is advantageous in the outer monolayer. We speculate that polyunsaturated lipids in biological membranes may play an important role in stabilizing both positive and negative regions of curvature.</p

Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks

Finding new enzyme variants with the desired substrate scope requires screening through a large number of potential variants. In a typical in silico enzyme engineering workflow, it is possible to scan a few thousands of variants, and gather several candidates for further screening or experimental verification. In this work, we show that a Graph Convolutional Neural Network (GCN) can be trained to predict the binding energy of combinatorial libraries of enzyme complexes using only sequence information. The GCN model uses a stack of message-passing and graph pooling layers to extract information from the protein input graph and yield a prediction. The GCN model is agnostic to the identity of the ligand, which is kept constant within the mutant libraries. Using a miniscule subset of the total combinatorial space (20 4-20 8 mutants) as training data, the proposed GCN model achieves a high accuracy in predicting the binding energy of unseen variants. The network's accuracy was further improved by injecting feature embeddings obtained from a language module pretrained on 10 million protein sequences. Since no structural information is needed to evaluate new variants, the deep learning algorithm is capable of scoring an enzyme variant in under 1 ms, allowing the search of billions of candidates on a single GPU. </p

Computational prediction of ω-transaminase selectivity by deep learning analysis of molecular dynamics trajectories

We previously presented a computational protocol to predict the enzymatic (enantio)selectivity of an ω-transaminase towards a set of ligands (Ramírez-Palacios et al. (2021) Journal of Chemical Information and Modeling 61(11), 5569-5580) by counting the number of binding poses present in molecular dynamics (MD) simulations that met a defined set of geometric criteria. The geometric criteria consisted of a hand-crafted set of distances, angles and dihedrals deemed to be important for the enzymatic reaction to take place. In this work, the MD trajectories are reanalysed using a deep-learning approach to predict the enantiopreference of the enzyme without the need for hand-crafted criteria. We show that a convolutional neural network is capable of classifying the trajectories as belonging to the 'reactive' or 'non-reactive' enantiomer (binary classification) with a good accuracy (>0.90). The new method reduces the computational cost of the methodology, because it does not necessitate the sampling approach from the previous work. We also show that analysing how neural networks reach specific decisions can aid hand-crafted approaches (e.g. definition of near-attack conformations, or binding poses)

SWINGER:A clustering algorithm for concurrent coupling of atomistic and supramolecular liquids

In this contribution, we review recent developments and applications of a dynamic clustering algorithm SWINGER tailored for the multiscale molecular simulations of biomolecular systems. The algorithm on-the-fly redistributes solvent molecules among supramolecular clusters. In particular, we focus on its applications in combination with the adaptive resolution scheme, which concurrently couples atomistic and coarse-grained molecular representations. We showcase the versatility of our multiscale approach on a few applications to biomolecular systems coupling atomistic and supramolecular water models such as the well-established MARTINI and dissipative particle dynamics models and provide an outlook for future work

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3