The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.
The pH-low insertion peptide (pHLIP) is an anionic membrane-active peptide with promising potential for applications in imaging of cancer tumors and targeted delivery of chemotherapeutics. The key advantage of pHLIP lies in its acid sensitivity: in acidic cellular environments, pHLIP can insert unidirectionally into the plasma membrane. Partitioning–folding coupling is triggered by titration of the acidic residues in pHLIP, transforming pHLIP from a hydrophilic to a hydrophobic peptide. Despite this knowledge, the reverse pathway that leads to exit of the peptide from the plasma membrane is poorly understood. Our hypothesis is that sequential deprotonation of pHLIP is a prerequisite for exit of the peptide from the plasma membrane. We carried out molecular dynamics (MD) simulations to characterize the effect that deprotonation of the acidic residues of pHLIP has on the stability of the peptide when inserted into a model lipid bilayer of 1-palmitoyl-2-oleoyl-sn-3-phosphocholine (POPC). Initiation of the exit mechanism is facilitated by a complex relationship between the peptide, bulk solvent, and the membrane environment. As the N-terminal acidic residues of pHLIP are deprotonated, localized loss of helicity drives unfolding of the peptide and more pronounced interactions with the bilayer at the lipid–water interface. Deprotonation of the C-terminal acidic residues (D25, D31, D33, and E34) leads to further loss of secondary structure distal from the C-terminus, as well as formation of a water channel that stabilizes the orientation of pHLIP parallel to the membrane normal. Together, these results help explain how stabilization of intermediates between the surface-bound and inserted states of pHLIP occur and provide insights into rational design of pHLIP variants with modified abilities of insertion.
Interdependence across time and length scales is common in biology, where atomic interactions can impact largerscale phenomenon. Such dependence is especially true for a wellknown cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length-scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein−membrane interactions that identify specific lipid−protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 μm 2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein− lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.
The pH Low Insertion Peptide (pHLIP) binds, folds and inserts spontaneously into the membrane as a transmembrane helix under acidic conditions. This functionality makes pHLIP a promising tool for drug delivery and diagnostic imaging of disorders associated with acidosis, such as arthritis, cancer, and ischemia. The acidic sensitivity of pHLIP arises from the sequential protonation of its acidic residues which, after binding to the membrane, induces simultaneous folding and insertion in order to seclude its hydrophobic segment from aqueous solvent. However, the peptide‐bilayer interactions of pHLIP in the fully inserted state are poorly understood. Our aim is to characterize the effect of deprotonation of the acidic residues of pHLIP on the stability of the peptide and the bilayer in its inserted state. Our hypothesis is that increasing the degree of deprotonation of pHLIP disrupts favorable interactions between pHLIP and the bilayer. In order to test our hypothesis, we carried out molecular dynamics (MD) simulations of α‐helical pHLIP inserted into a 1‐palmitoyl‐2‐oleoyl‐sn‐3‐phosphocholine (POPC) bilayer. Our variable was the protonation state of the acidic residues in pHLIP. Our results show that in the fully protonated state, pHLIP remains stable. Interestingly, in the fully deprotonated state, the negatively‐charged residues remain embedded in the interior of the bilayer, stabilized by a defect that allows for interactions with waters from bulk solvent. In addition, deprotonation of the C‐terminal acidic residues leads to greater destabilization of pHLIP in the bilayer. Together, these results reveal several of the interactions between pHLIP and the aqueous and lipid phases that are critical in maintaining the fully inserted state of the peptide.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.