Simulation-Based Methods for Model Building and Refinement in Cryoelectron Microscopy.

Two structure determination methods, based on the molecular dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5 Å cryo-electron microscopy (EM) maps with either single structures or ensembles of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution. Application of sequential re-refinement enables MDFF to achieve a radius of convergence of ~25 Å demonstrated with the accurate modeling of β-galactosidase and TRPV1 proteins at 3.2 Å and 3.4 Å resolution, respectively. The MDFF refinements uniquely offer map-model validation and B-factor determination criteria based on the inherent dynamics of the macromolecules studied, captured by means of local root mean square fluctuations. The MDFF tools described are available to researchers through an easy-to-use and cost-effective cloud computing resource on Amazon Web Services.DOI: http://dx.doi.org/10.7554/eLife.16105.001

Striated muscle contraction is regulated in a calcium-dependent manner through dynamic motions of the tropomyosin/troponin polymer, a multicomponent complex wrapped around actin-containing thin filaments. Tropomyosin/troponin sterically blocks myosin-binding at low-calcium concentrations but moves to expose myosin-binding sites at high-calcium concentrations leading to force development. Understanding the key intermolecular interactions that define these dynamic motions will promote our understanding of mutation-induced contractile dysfunction that eventually leads to hypertrophic cardiomyopathy, dilated cardiomyopathy, and skeletal myopathies. Advancements in cryoelectron microscopy (cryoEM) have resulted in a partial elucidation of structures of the thin filament, revealing many atomic-level interactions between the component proteins and critical calcium-dependent conformational alterations. However, building models at the resolutions achieved can be challenging since landmarks in the maps are often missing or ambiguous. Therefore, current computational analyses including de novo structure prediction, protein-protein docking, molecular dynamics flexible fitting, and molecular dynamics simulations are needed to ensure good quality models. We review here our efforts to model the troponin T domain spanning the head-to-tail overlap domain of tropomyosin, improving previous models. Next, we refined the published cryoEM modeled structures, which had mistakenly compressed alpha helices, with a model that has expected helical parameters while matching densities in the cryoEM volume. Lastly, we used this model to reinterpret the interactions between tropomyosin and troponin I showing key features that hold the tropomyosin cable in its low-calcium, sterically blocking position. These revised thin filament models show improved intermolecular interactions in the key low- and high-calcium regulatory states, providing novel insights into function.

Advances in the molecular dynamics flexible fitting method for cryo-EM modeling

Hybrid structure fitting methods combine data from cryo-electron microscopy and X-ray crystallography with molecular dynamics simulations for the determination of all-atom structures of large biomolecular complexes. Evaluating the quality-of-fit obtained from hybrid fitting is computationally demanding, particularly in the context of a multiplicity of structural conformations that must be evaluated. Existing tools for quality-of-fit analysis and visualization have previously targeted small structures and are too slow to be used interactively for large biomolecular complexes of particular interest today such as viruses or for long molecular dynamics trajectories as they arise in protein folding. We present new data-parallel and GPU-accelerated algorithms for rapid interactive computation of quality-of-fit metrics linking all-atom structures and molecular dynamics trajectories to experimentally-determined density maps obtained from cryo-electron microscopy or X-ray crystallography. We evaluate the performance and accuracy of the new quality-of-fit analysis algorithms vis-à-vis existing tools, examine algorithm performance on GPU-accelerated desktop workstations and supercomputers, and describe new visualization techniques for results of hybrid structure fitting methods.

Structural modeling of proteins from cryo-electron microscopy (cryo-EM) density maps is one of the challenging issues in structural biology. De novo modeling combined with flexible fitting refinement (FFR) has been widely used to build a structure of new proteins. In de novo prediction, artificial conformations containing local structural errors such as chirality errors, cis peptide bonds, and ring penetrations are frequently generated and cannot be easily removed in the subsequent FFR. Moreover, refinement can be significantly suppressed due to the low mobility of atoms inside the protein. To overcome these problems, we propose an efficient scheme for FFR, in which the local structural errors are fixed first, followed by FFR using an iterative simulated annealing (SA) molecular dynamics protocol with the united atom (UA) model in an implicit solvent model; we call this scheme “SAUA-FFR”. The best model is selected from multiple flexible fitting runs with various biasing force constants to reduce overfitting. We apply our scheme to the decoys obtained from MAINMAST and demonstrate an improvement of the best model of eight selected proteins in terms of the root-mean-square deviation, MolProbity score, and RWplus score compared to the original scheme of MAINMAST. Fixing the local structural errors can enhance the formation of secondary structures, and the UA model enables progressive refinement compared to the all-atom model owing to its high mobility in the implicit solvent. The SAUA-FFR scheme realizes efficient and accurate protein structure modeling from medium-resolution maps with less overfitting.

"Everything that living things do can be understood in terms of jigglings and wigglings of atoms." Richard Feynman's remarks in the early 1960's summarize what is today widely accepted, namely, that biological processes can be described by the dynamics of biomolecules. Molecular dynamics (MD) simulation, in this regard, is the main methodology employed in structural biology to explore the dynamical behavior of macromolecules at a microscopic level. Aided by MD, researchers have been able, for instance, to resolve atomic structures of multi-protein complexes from cryo-EM densities, thus unveiling the atomistic details of enzymatic mechanisms and characterize the binding of small molecules to proteins. To achieve all this, the capabilities of MD packages are constantly evolving, providing a multitude of complex simulation and analysis techniques, e.g., enhanced sampling and free energy calculations. Although applicable to a wide variety of research problems, a broader usage of MD is hindered by a steep initial learning curve imposed by nearly every MD software. To reduce this initial barrier and make the methodology more accessible to the general community of biomolecular researchers, we developed an intuitive tool named QwikMD (1), which assists the users in the preparation, execution, and analysis of biomolecular MD simulations. Among many other features, QwikMD automatically checks the initial structure for structural inconsistencies, facilitates structure manipulations such as point mutations and partial deletions, simplifies the protein insertion in lipid membranes and enables the visualization and analysis of MD simulations on the fly. The user-friendly graphical interface of QwiKMD allows the preparation of MD simulations in a point-and-click fashion, offering the user multiple MD protocols, such as unbiased MD simulations, Steered MD, MD Flexible Fitting (MDFF), and, most recently, hybrid QM/MM simulations. The latter exploits the recently developed VMD and NAMD interface to common quantum mechanics software packages. QwikMD facilitates performing MD simulations for nearly any user, novice or expert. While assisting the user, QwikMD ensures reproducibility of the results by recording all parameters and steps into two log files, one in a script-like format and another in a "methods section" format. QwikMD also serves as a learning tool, providing the theoretical background of the different MD protocols and options in many "info buttons". J. V Ribeiro et al., QwikMD — Integrative Molecular Dynamics Toolkit for Novices and Experts. Sci. Rep. 6, 26536 (2016).

The rigid-body fitting of predicted structural models into cryo-electron microscopy (cryo-EM) density maps is a necessary procedure for density map-guided protein structure determination and prediction. We proposed a novel multiobjective optimization protocol, MOFIT, which performs a rigid-body density-map fitting based on particle swarm optimization (PSO). MOFIT was tested on a large set of 292 nonhomologous single-domain proteins. Starting from structural models predicted by I-TASSER, MOFIT achieved an average coordinate root-mean-square deviation of 2.46 Å, which was 1.57, 2.79, and 3.95 Å lower than three leading single-objective function-based methods, where the differences were statistically significant with p-values of 1.65 × 10-6, 6.36 × 10-8, and 6.44 × 10-11 calculated using two-tail Student's t tests. Detailed analyses showed that the major advantages of MOFIT lie in the multiobjective protocol and the extensive PSO search simulations guided by the composite objective functions, which integrates complementary correlation coefficients from the global structure, local fragments, and individual residues with the cryo-EM density maps.

Scorpion venom is a rich source of toxins which have great potential to develop new therapeutic agents. Scorpion chloride channel toxins (ClTxs), such as Chlorotoxin selectively inhibit human Matrix Methaloproteinase-2 (hMMP-2). The inhibitors of hMMP-2 have potential use in cancer therapy. Three new ClTxs, meuCl14, meuCl15 and meuCl16, derived from the venom transcriptome of Iranian scorpion, M. eupeus (Buthidea family), show high sequence identity (71.4%) with Chlorotoxin. Here, 3-D homology model of new ClTxs were constructed. The models were optimized by Molecular Dynamics simulation based on MDFF (molecular dynamics flexible fitting) method. New ClTxs indicate the presence of CSαβ folding of other scorpion toxins. A docking followed by steered molecular dynamics (SMD) simulations to investigate the interactions of meuCl14, meuCl15, and meuCl16 with hMMP-2 was applied. The current study creates a correlation between the unbinding force and the inhibition activities of meuCl14, meuCl15 and meuCl16 to shed some insights as to which toxin may be used as a drug deliverer. To this aim, SMD simulations using Constant Force Pulling method were carried out. The SMD provided useful details related to the changes of electrostatic, van de Waals (vdW), and hydrogen-bonding (H-bonding) interactions between ligands and receptor during the pathway of unbinding. According to SMD results, the interaction of hMMP-2 with meuCl14 is more stable. In addition, Arginine residue was found to contribute significantly in interaction of ClTxs with hMMP-2. All in all, the present study is a dynamical approach whose results are capable of being implemented in structure-based drug design.

The two major challenges in protein structure prediction problems are (1) the lack of an accurate energy function and (2) the lack of an efficient search algorithm. A protein energy function accurately describing the interaction between residues is able to supervise the optimization of a protein conformation, as well as select native or native-like structures from numerous possible conformations. An efficient search algorithm must be able to reduce a conformational space to a reasonable size without missing the native conformation. My PhD research studies focused on these two directions. A protein energy function—the distance and orientation dependent energy function of amino acid key blocks (DOKB), containing a distance term, an orientation term, and a highly packed term—was proposed to evaluate the stability of proteins. In this energy function, key blocks of each amino acids were used to represent each residue; a novel reference state was used to normalize block distributions. The dependent relationship between the orientation term and the distance term was revealed, representing the preference of different orientations at different distances between key blocks. Compared with four widely used energy functions using six general benchmark decoy sets, the DOKB appeared to perform very well in recognizing native conformations. Additionally, the highly packed term in the DOKB played its important role in stabilizing protein structures containing highly packed residues. The cluster potential adjusted the reference state of highly packed areas and significantly improved the recognition of the native conformations in the ig_structal data set. The DOKB is not only an alternative protein energy function for protein structure prediction, but it also provides a different view of the interaction between residues. The top-k search algorithm was optimized to be used for proteins containing both α-helices and β-sheets. Secondary structure elements (SSEs) are visible in cryo-electron microscopy (cryo-EM) density maps. Combined with the SSEs predicted in a protein sequence, it is feasible to determine the topologies referring to the order and direction of the SSEs in the cryo-EM density map with respect to the SSEs in the protein sequence. Our group member Dr. Al Nasr proposed the top-k search algorithm, searching the top-k possible topologies for a target protein. It was the most effective algorithm so far. However, this algorithm only works well for pure a-helix proteins due to the complexity of the topologies of β-sheets. Based on the known protein structures in the Protein Data Bank (PDB), we noticed that some topologies in β-sheets had a high preference; on the contrary, some topologies never appeared. The preference of different topologies of β-sheets was introduced into the optimized top-k search algorithm to adjust the edge weight between nodes. Compared with the previous results, this optimization significantly improved the performance of the top-k algorithm in the proteins containing both α-helices and…

Molecular Dynamics Simulations of the AAA Protein P97

Human α-defensins are proteins of the innate immune system that suppress viral and bacterial infections by multiple mechanisms including membrane disruption. For viruses that lack envelopes, such as human adenovirus (HAdV), other, less well defined, mechanisms must be involved. A previous structural study on the interaction of an α-defensin, human α-defensin 5 (HD5), with HAdV led to a proposed mechanism in which HD5 stabilizes the vertex region of the capsid and blocks uncoating steps required for infectivity. Studies with virus chimeras comprised of capsid proteins from sensitive and resistant serotypes supported this model. To further characterize the critical binding site, we determined subnanometer resolution cryo-electron microscopy (cryoEM) structures of HD5 complexed with both neutralization-sensitive and -resistant HAdV chimeras. Models were built for the vertex regions of these chimeras with monomeric and dimeric forms of HD5 in various initial orientations. CryoEM guided molecular dynamics flexible fitting (MDFF) was used to restrain the majority of the vertex model in well-defined cryoEM density. The RGD-containing penton base loops of both the sensitive and resistant virus chimeras are predicted to be intrinsically disordered, and little cryoEM density is observed for them. In simulations these loops from the sensitive virus chimera, interact with HD5, bridge the penton base and fiber proteins, and provides significant stabilization with a three-fold increase in the intermolecular nonbonded interactions of the vertex complex. In the case of the resistant virus chimera, simulations revealed fewer bridging interactions and reduced stabilization by HD5. This study implicates a key dynamic region in mediating a stabilizing interaction between a viral capsid and a protein of the innate immune system with potent anti-viral activity.

Mechanisms of Membrane-Protein Insertion at the Inner and Outer Membranes

The challenge of modelling the spatial conformation of chromatin remains an open problem. While multiple data-driven approaches have been proposed, each has limitations. This work introduces two image-driven modelling methods based on the Molecular Dynamics Flexible Fitting (MDFF) approach: the force method and the correlational method. Both methods have already been used successfully in protein modelling. We propose a novel way to employ them for building chromatin models directly from 3D images. This approach is termed image-driven modelling. Additionally, we introduce the initial structure generator, a tool designed to generate optimal starting structures for the proposed algorithms. The methods are versatile and can be applied to various data types, with minor modifications to accommodate new generation imaging techniques.

Cryo_fit: Democratization of flexible fitting for cryo-EM

Cryo-electron microscopy (cryo-EM) has become a leading approach for protein structure determination, but it remains challenging to accurately model atomic structures with cryo-EM density maps. We propose a hybrid method, CR-I-TASSER, which integrates deep neural-network learning with I-TASSER assembly simulations for automated cryo-EM structure determination. The method is benchmarked on 778 proteins with simulated and experimental density maps, where CR-I-TASSER constructs models with a correct fold (TM-score>0.5) for 643 targets that is 64% higher than the best of other de novo and refinement-based approaches on high-resolution data samples. Detailed data analyses showed that the major advantage of CR-I-TASSER lies in the deep-learning based position prediction, which significantly improves the threading template quality and therefore boosts the accuracy of final models through optimized fragment assembly simulations. These results demonstrate a new avenue to determine cryo-EM protein structures with high accuracy and robustness covering various target types and density-map resolutions.