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Abstract:
Transport tunnels in enzymes with buried active sites are critical gatekeepers of enzymatic function, controlling substrate access, product release, and catalytic efficiency. Despite their importance, the transient nature of these tunnels makes them difficult to study using conventional simulation methods. In this study, we systematically evaluate three coarse-grained (CG) molecular dynamics approaches─Martini with Elastic network restraints, Martini with Go̅-model restraints, and SIRAH─for their ability to characterize tunnel structure and dynamics across diverse enzyme classes. Using haloalkane dehalogenase LinB and its engineered variants as model systems, we show that CG methods accurately reproduce the geometry of tunnel ensembles observed in all-atom (AA) simulations while providing notable computational speedups. The Martini-Go̅ model performed particularly well, capturing subtle mutation-induced changes in tunnel dynamics, such as the closure of a main tunnel and the de novo opening of a transient auxiliary tunnel in LinB variants. In contrast, Martini with Elastic network restraints was limited in capturing tunnel dynamics due to the structural bias introduced by the restraints. We further validated these findings across nine enzymes from the oxidoreductase, transferase, and hydrolase classes with diverse structural folds. Although all CG methods reliably identified functionally relevant tunnels and provided fairly accurate estimates of their ensemble geometry and key bottleneck residues, they differed in their ability to replicate tunnel dynamics, with tunnel occurrences and ranking showing moderate to good correspondence with AA results. This comprehensive evaluation highlights the strengths and weaknesses of CG simulations, establishing them as powerful tools for high-throughput analysis of enzyme tunnels, which enables more efficient enzyme engineering and drug design efforts targeting these critical structural features.

Keywords:
GoMartini 3, Enzymes, Protein tunnels, MD, SIRAH, Elastic Network, Martini 3, Coarse grained MD

Abstract:
Coarse-grained modeling has become an important tool to supplement experimental measurements, allowing access to spatio-temporal scales beyond all-atom based approaches. The GōMartini model combines structure- and physics-based coarse-grained approaches, balancing computational efficiency and accurate representation of protein dynamics with the capabilities of studying proteins in different biological environments. This paper introduces an enhanced GōMartini model, which combines a virtual-site implementation of Gō models with Martini 3. The implementation has been extensively tested by the community since the release of the reparametrized version of Martini. This work demonstrates the capabilities of the model in diverse case studies, ranging from protein-membrane binding to protein-ligand interactions and AFM force profile calculations. The model is also versatile, as it can address recent inaccuracies reported in the Martini protein model. Lastly, the paper discusses the advantages, limitations, and future perspectives of the Martini 3 protein model and its combination with Gō models.

Keywords:
GōMartini 3, Martini 3, Coarse graining, Proteins, IDP, membranes, Molecular Dynamics, Nanomechanics

Abstract:
Rational design of novel antibody therapeutics against viral infections such as coronavirus relies on surface complementarity and high affinity for their effectiveness. Here, we explore an additional property of protein complexes, the intrinsic mechanical stability, in SARS-CoV-2 variants when complexed with a potent antibody. In this study, we utilized a recent implementation of the GōMartini 3 approach to investigate large conformational changes in protein complexes with a focus on the mechanostability of the receptor-binding domain (RBD) from WT, Alpha, Delta, and XBB.1.5 variants in complex with the H11-H4 nanobody. The analysis revealed moderate differences in mechanical stability among these variants. Also, we identified crucial residues in both the RBD and certain protein segments in the nanobody that contribute to this property. By performing pulling simulations and monitoring the presence of specific native and non-native contacts across the protein complex interface, we provided mechanistic insights into the dissociation process. Force-displacement profiles indicate a tensile force clamp mechanism associated with the type of protein complex. Our computational approach not only highlights the key mechanostable interactions that are necessary to maintain overall stability, but it also paves the way for the rational design of potent antibodies that are mechanostable and effective against emergent SARS-CoV-2 variants.

Keywords:
SARS-CoV-2, GōMartini 3, Nanomechanics, Protein complexes, protein engineering, MD, native contacts