Resolving the fine structure in the energy landscapes of repeat proteins.

Ankyrin (ANK) repeat proteins are coded by tandem occurrences of patterns with around 33 amino acids. They often mediate protein-protein interactions in a diversity of biological systems. These proteins have an elongated non-globular shape and often display complex folding mechanisms. This work investigates the energy landscape of representative proteins of this class made up of 3, 4 and 6 ANK repeats using the energy-landscape visualisation method (ELViM). By combining biased and unbiased coarse-grained molecular dynamics AWSEM simulations that sample conformations along the folding trajectories with the ELViM structure-based phase space, one finds a three-dimensional representation of the globally funnelled energy surface. In this representation, it is possible to delineate distinct folding pathways. We show that ELViMs can project, in a natural way, the intricacies of the highly dimensional energy landscapes encoded by the highly symmetric ankyrin repeat proteins into useful low-dimensional representations. These projections can discriminate between multiplicities of specific parallel folding mechanisms that otherwise can be hidden in oversimplified depictions.

Examining the Ensembles of Amyloid-ß Monomer Variants and Their Propensities to Form Fibers Using an Energy Landscape Visualization Method.

The amyloid-ß (Aß) monomer, an intrinsically disordered peptide, is produced by the cleavage of the amyloid precursor protein, leading to Aß-40 and Aß-42 as major products. These two isoforms generate pathological aggregates, whose accumulation correlates with Alzheimer's disease (AD). Experiments have shown that even though the natural abundance of Aß-42 is smaller than that for Aß-40, the Aß-42 is more aggregation-prone compared to Aß-40. Moreover, several single-point mutations are associated with early onset forms of AD. This work analyzes coarse-grained associative-memory, water-mediated, structure and energy model (AWSEM) simulations of normal Aß-40 and Aß-42 monomers, along with six single-point mutations associated with early onset disease. We analyzed the simulations using the energy landscape visualization method (ELViM), a reaction-coordinate-free approach suited to explore the frustrated energy landscapes of intrinsically disordered proteins. ELViM is shown to distinguish the monomer ensembles of variants that rapidly form fibers from those that do not form fibers as readily. It also delineates the amino acid contacts characterizing each ensemble. The results shed light on the potential of ELViM to probe intrinsically disordered proteins.

SMOG 2 and OpenSMOG: Extending the limits of structure-based models.

Applying simulations with structure-based Go¯-like models has proven to be an effective strategy for investigating the factors that control biomolecular dynamics. The common element of these models is that some (or all) of the intra/inter-molecular interactions are explicitly defined to stabilize an experimentally determined structure. To facilitate the development and application of this broad class of models, we previously released the SMOG 2 software package. This suite allows one to easily customize and distribute structure-based (i.e., SMOG) models for any type of polymer-ligand system. The force fields generated by SMOG 2 may then be used to perform simulations in highly optimized MD packages, such as Gromacs, NAMD, LAMMPS, and OpenMM. Here, we describe extensions to the software and demonstrate the capabilities of the most recent version (SMOG v2.4.2). Changes include new tools that aid user-defined customization of force fields, as well as an interface with the OpenMM simulation libraries (OpenSMOG v1.1.0). The OpenSMOG module allows for arbitrary user-defined contact potentials and non-bonded potentials to be employed in SMOG models, without source-code modifications. To illustrate the utility of these advances, we present applications to systems with millions of atoms, long polymers and explicit ions, as well as models that include non-structure-based (e.g., AMBER-based) energetic terms. Examples include large-scale rearrangements of the SARS-CoV-2 Spike protein, the HIV-1 capsid with explicit ions, and crystallographic lattices of ribosomes and proteins. In summary, SMOG 2 and OpenSMOG provide robust support for researchers who seek to develop and apply structure-based models to large and/or intricate biomolecular systems.

Computational study on the allosteric mechanism of Leishmania major IF4E-1 by 4E-interacting protein-1: Unravelling the determinants of m7GTP cap recognition.

During their life cycle, Leishmania parasites display a fine-tuned regulation of the mRNA translation through the differential expression of isoforms of eukaryotic translation initiation factor 4E (LeishIF4Es). The interaction between allosteric modulators such as 4E-interacting proteins (4E-IPs) and LeishIF4E affects the affinity of this initiation factor for the mRNA cap. Here, several computational approaches were employed to elucidate the molecular bases of the previously-reported allosteric modulation in L. major exerted by 4E-IP1 (Lm4E-IP1) on eukaryotic translation initiation factor 4E 1 (LmIF4E-1). Molecular dynamics (MD) simulations and accurate binding free energy calculations (ΔGbind ) were combined with network-based modeling of residue-residue correlations. We also describe the differences in internal motions of LmIF4E-1 apo form, cap-bound, and Lm4E-IP1-bound systems. Through community network calculations, the differences in the allosteric pathways of allosterically-inhibited and active forms of LmIF4E-1 were revealed. The ΔGbind values show significant differences between the active and inhibited systems, which are in agreement with the available experimental data. Our study thoroughly describes the dynamical perturbations of LmIF4E-1 cap-binding site triggered by Lm4E-IP1. These findings are not only essential for the understanding of a critical process of trypanosomatids' gene expression but also for gaining insight into the allostery of eukaryotic IF4Es, which could be useful for structure-based design of drugs against this protein family.

pH and Charged Mutations Modulate Cold Shock Protein Folding and Stability: A Constant pH Monte Carlo Study.

The folding and stability of proteins is a fundamental problem in several research fields. In the present paper, we have used different computational approaches to study the effects caused by changes in pH and for charged mutations in cold shock proteins from Bacillus subtilis (Bs-CspB). First, we have investigated the contribution of each ionizable residue for these proteins to their thermal stability using the TKSA-MC, a Web server for rational mutation via optimizing the protein charge interactions. Based on these results, we have proposed a new mutation in an already optimized Bs-CspB variant. We have evaluated the effects of this new mutation in the folding energy landscape using structure-based models in Monte Carlo simulation at constant pH, SBM-CpHMC. Our results using this approach have indicated that the charge rearrangements already in the unfolded state are critical to the thermal stability of Bs-CspB. Furthermore, the conjunction of these simplified methods was able not only to predict stabilizing mutations in different pHs but also to provide essential information about their effects in each stage of protein folding.

Distinguishing Biomolecular Pathways and Metastable States.

Protein folding occurs in a high dimensional phase space, and the representation of the associated energy landscape is nontrivial. A widely applied approach to studying folding landscapes is to describe the dynamics along a small number of reaction coordinates. However, other strategies involve more elaborate analysis of the complex phase space. There have been many attempts to obtain a more detailed representation of all available conformations for a given system. In this work, we address this problem using a metric based on internal distances between amino acids to describe the differences between any two conformations. Using an effective projection method, we are able to go beyond the typical one-dimensional representation and provide intuitive two dimensional visualizations of the landscape. We refer to this method as the energy landscape visualization method (ELViM). We have applied this methodology using a Cα structure-based model to study the folding of two well-known proteins: SH3 domain and protein-A. Our visualization method yields a detailed description of the folding process, making possible the identification of transition state regions, and establishing the paths that lead to the native state. For SH3, we have analyzed structural differences in the distribution of folding routes. The competition between the native and mirror structures in protein A is also discussed. Finally, the method is applied to study conformational changes in the protein elongation factor thermally unstable. Distinct features of ELViM are that it does not require or assume a reaction coordinate, and it does not require analysis of kinetic aspects of the system.

Visualization of protein folding funnels in lattice models.

Protein folding occurs in a very high dimensional phase space with an exponentially large number of states, and according to the energy landscape theory it exhibits a topology resembling a funnel. In this statistical approach, the folding mechanism is unveiled by describing the local minima in an effective one-dimensional representation. Other approaches based on potential energy landscapes address the hierarchical structure of local energy minima through disconnectivity graphs. In this paper, we introduce a metric to describe the distance between any two conformations, which also allows us to go beyond the one-dimensional representation and visualize the folding funnel in 2D and 3D. In this way it is possible to assess the folding process in detail, e.g., by identifying the connectivity between conformations and establishing the paths to reach the native state, in addition to regions where trapping may occur. Unlike the disconnectivity maps method, which is based on the kinetic connections between states, our methodology is based on structural similarities inferred from the new metric. The method was developed in a 27-mer protein lattice model, folded into a 3×3×3 cube. Five sequences were studied and distinct funnels were generated in an analysis restricted to conformations from the transition-state to the native configuration. Consistent with the expected results from the energy landscape theory, folding routes can be visualized to probe different regions of the phase space, as well as determine the difficulty in folding of the distinct sequences. Changes in the landscape due to mutations were visualized, with the comparison between wild and mutated local minima in a single map, which serves to identify different trapping regions. The extension of this approach to more realistic models and its use in combination with other approaches are discussed.