Computational Methodologies for Real-Space Structural Refinement of Large Macromolecular Complexes

Abstract

The rise of the computer as a powerful tool for model building and refinement has revolutionized the field of structure determination for large biomolecular systems. Despite the wide availability of robust experimental methods capable of resolving structural details across a range of spatiotemporal resolutions, computational hybrid methods have the unique ability to integrate the diverse data from multimodal techniques such as X-ray crystallography and electron microscopy into consistent, fully atomistic structures. Here, commonly employed strategies for computational real-space structural refinement are reviewed, and their specific applications are illustrated for several large macromolecular complexes: ribosome, virus capsids, chemosensory array, and photosynthetic chromatophore. The increasingly important role of computational methods in large-scale structural refinement, along with current and future challenges, is discussed.

Keywords: cryo-EM; flexible fitting; hybrid methods; integrative modeling; molecular dynamics; simulation.

Figure 1

Evolution of modeling tools used for structure determination. From manual construction of physical models, such as the forest of rods, in the 1950s (left), to computer-aided model building using visualization software in the 1980s and 1990s (middle), to today’s state-of-the-art computational structural refinement approaches such as MDFF (right), technological advancements have enabled scientists to solve the atomistic structures of increasingly large biomolecular systems. The image of John Kendrew building the all-atom structure of myoglobin (left) is reproduced here with permission from MRC Laboratory of Molecular Biology, (c) 1958 by MRC Laboratory of Molecular Biology.

Figure 2

General strategy for structural refinement of biomolecules using modern hybrid methods. Primary sources of structural information include experimental methods, such as X-ray crystallography, NMR spectroscopy, cryo-EM, and SAXS, as well as in silico structure prediction tools. All-atom structures are generated by integrating experimental data from a range of accessible resolutions using computational approaches, such as those implemented in MDFF and xMDFF. Complete structural models are further refined through MD simulations, employing restraints based on experimental data or enhanced sampling techniques. Final models are evaluated with theoretical checking tools and validated on the basis of data from additional experimental studies.

Figure 3

The energy landscape of a protein in different conformations. During MD simulations, including MDFF, molecules can become trapped in local energy minima representing nonrelevant conformations. For example, the X-ray structure corresponding to energy minimum (a) must visit the conformational state corresponding to energy minimum (b) before reaching the conformation given by the cryo-EM density, which corresponds to energy minimum (c). In MD simulations, a molecule may spend a long time sampling intermediate energy minima like (b) before overcoming the barriers to arrive at relevant conformations. The sampling can be performed more efficiently by employing enhanced sampling methods, such as temperature-accelerated MDFF (138), that facilitate the crossing of energy barriers.

Figure 4

Complexity and structural distinctiveness of biomolecular systems. (a) The ribosome with SecY translocon lipoprotein complex, (b) bacterial chemosensory array, and (c) lamellar chromatophore patch represent examples of complex systems composed of multiple classes of biomolecular components, including various proteins, RNA, and lipid membranes. Contrarily, the immature capsid of Rous sarcoma virus (d ) is made of multiple copies of a single protein. Although the structure of the bacterial chemosensory array is characterized by a regular, repeating arrangement, the ribosome is inherently asymmetric, the chromatophore is based on a heterogenous composition of subcomponents, and the virus capsid exhibits an irregular spherical shell lattice.

Figure 5

Construction and refinement of an atomistic model of the bacterial chemosensory array. X-ray structures of the kinase CheA (17), adaptor protein CheW (78), and chemoreceptor (102) from the thermophilic bacterium Thermotoga maritima were used to model key array substructures, which were arranged according to cryo-ET densities to produce models of the extended array architecture, namely the CheA2-trimer and CheA2-hexamer assemblies. Subsequently, molecular dynamics flexible fitting (MDFF) simulations with symmetry restraints (28) were carried out to refine the structures of the component models to their array-bound conformations (27).

Figure 6

Construction of an atomic-level model of the lamellar chromatophore patch in Rhodospirillum photometricum. The construction combined various experimental data of 2-Å (X-ray crystallography) to 75-Å (AFM) resolution, namely, X-ray structures of the light-harvesting protein LH2 (100), and models of the LH1 proteins derived through in silico structure prediction along with MDFF fitting to cryo-EM density maps. The stated structures were assembled according to AFM images within a realistic lipid membrane environment and refined through MD simulations (29). The AFM image shown was adapted from Reference 29 with permission.