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Challenges in structural approaches to cell modeling - PubMed

  • ️Fri Jan 01 2016

Review

Challenges in structural approaches to cell modeling

Wonpil Im et al. J Mol Biol. 2016.

Abstract

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.

Keywords: cellular membranes; chromosome modeling; macromolecular crowding; modeling of biological mesoscale; protein interactions.

Copyright © 2016 Elsevier Ltd. All rights reserved.

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Figures

Figure 1
Figure 1. A preliminary computer assembled and generated 3D model of Mycoplasma genitalium, a parasitic bacterium found in human urogenital and respiratory tracts

This pathogen has one of the smallest genomes of any free-living organism (525 genes). It was produced using the autoPACK/cellPACK software.

Figure 2
Figure 2. Structural modeling of protein interactome

(a) ~50% of protein complexes with interfaces modeled by high-throughput techniques have accuracy suitable for docking. (b) Models by template-based docking classified according to CAPRI-like criteria (clashes and contacts not considered). High accuracy: ligand (the smaller protein in the complex) RMSD < 1.0 Å or interface RMSD (measured on the interface residues Cα) < 1.0 Å. Medium accuracy: ligand RMSD < 5.0 Å or interface RMSD < 2.0 Å. Acceptable accuracy: ligand RMSD < 10.0 Å or interface RMSD < 4.0 Å. The data indicates that even inaccurate models typically dock with good success rate. (c) Availability of protein-protein structures in genomes with the largest number of known protein interactions (according to BIND [227] and DIP [228] databases). “No model” indicates no template for the interactors, not the complex (almost all structurally characterized interactors have templates for their complexes).

Figure 3
Figure 3. Ligand binding of MBP in vivo and in vitro

Left: possible shuttling of MBP in the E. coli periplasm for transport of maltose into the cytoplasm. Right: competition of Ficoll and maltose for interaction with MBP, shown by NMR spectroscopy. In buffer, apo MBP shows well-resolved 1H-15N TROSY spectra. With 200 g/l Ficoll, most of the TROSY peaks are broadened beyond detection, indicating MBP-Ficoll association. Upon further addition of 1 mM maltose, the peaks are recovered, indicating that the ligand has competed out the weakly associated Ficoll. Adapted from Miklos et al. [129].

Figure 4
Figure 4. Energy surface and encounter complexes

(a) IRMSD from the native complex vs. the energy function obtained by docking histidine-containing phosphocarrier protein (HPr) to the N-terminal domain of Enzyme I (EIN). Clusters around the three lowest energy minima are indicated. (b) Native complex formed by EIN (gray surface) and HPr (shown as yellow cartoon). Centers of HPr structures in the encounter complex ensemble are shown as small spheres. Colors indicate classification as follows: Cluster 1, overlapping with the final complex, blue; Cluster 2, encounter complex within 20Å RMSD from the final complex, but not overlapping with it; Cluster 3, encounter complex around 30Å RMSD from the final complex. A number of low energy structures that do not belong to any of these clusters are shown in pink. (c) Same as b, but after rotating 180° around the vertical axis (the bound HPr is now on the left side, almost completely hidden by EIN.

Figure 5
Figure 5. Energy surface and encounter complexes in non-specific association

(a) IRMSD vs. the energy function obtained by docking phosphocarrier protein (HPr) to another copy of HPr. The IRMSD is calculated from an arbitrary low energy structure. (b) Centers of docked structures of the second HPr molecule are shown as small spheres.

Figure 6
Figure 6. Gram-negative bacterial outer membrane molecular complexity

The image illustrates a typical E. coli outer membrane and the molecular system used to represent the complexity in molecular dynamics simulations. Molecules represent the bilayer composed of (from the top, external leaflet) glycosylated amphipathic molecules known as lipopolysaccharide consisting of an O-antigen polysaccharide, a core oligosaccharide, and lipid A and (the bottom, periplasmic leaflet) consisting of various phospholipid molecules such as phosphatidylethanolamine (PE; green), phosphatidylglycerol (PG; orange), and cardiolipin (CL; magenta) in a ratio of PE : PG : CL = 15 : 4 : 1. The cyan atoms interspersed with the core oligosaccharides are Ca2+ ions, which immobilize the membrane by mediating the cross-linking electrostatic interaction network with phosphate and carboxyl groups attached to the lipid A and core sugars. Magenta and yellow spheres represent K+ and Cl− ions, respectively.

Figure 7
Figure 7. GPI-anchored glycosylated prion protein in raft-like membranes

Prion protein, N-glycan 1, N-glycan 2, GPI-anchor, cholesterol, POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine), and PSM (N-palmitoyl sphingomyelin) are represented in cartoon (HA: orange, HB: blue, HC: red), green surface, blue surface, magenta sticks, light green spheres, light blue spheres, and light yellow spheres, respectively. The bilayer composition is about 1 : 1 : 1 of cholesterol : POPC : PSM. Water molecules and KCl ions are omitted for clarity.

Figure 8
Figure 8. Chromatin chain models and scaling properties

(a) In the C-SAC model, a chromatin fiber is a self-avoiding polymer chain with a persistence length Lp, consisting of beads with a diameter df, with blue spheres at the boundaries of Lp, cyan spheres interpolated in-between. Chromatin chains are generated by a chain growth algorithm inside a confined space of a diameter D. (b) The scaling of mean-square spatial distance R2(s) in log10 scale derived from 10,000 chains of length 1000Lp in a confinement of diameter D proportional to ~11 8#x00B5;m diameter of an average human cell. R2(s) follows a power law of ~s2ν, with ν ~ 0.34, similar to measured ν of ~ 0.33. (c) The scaling of contact probability Pc(s) follows a power law of ~1/sα, with α ~1.05, similar to the measured α of 1.08. (d) A random C-SAC chromatin chain with two interacting sub-structures that can give rise to topologically associated domains. The two domain-like substructures and their corresponding spatial distance matrices can be seen, where distances between loci are color-coded. Inter-substructure interactions are highlighted in the purple box. Details can be found in Gursoy et al. [224].

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