Integrative structure modeling of macromolecular assemblies from proteomics data

Three-dimensional modeling of protein interactions and complexes is going 'omics

High-throughput interaction discovery initiatives have revealed the existence of hundreds of multiprotein complexes whose functions are regulated through thousands of protein-protein interactions (PPIs). However, the structural details of these interactions, often necessary to understand their function, are only available for a tiny fraction, and the experimental difficulties surrounding complex structure determination make computational modeling techniques paramount. In this manuscript, we critically review some of the most recent developments in the field of structural bioinformatics applied to the modeling of protein interactions and complexes, from large macromolecular machines to domain-domain and peptide-mediated interactions. In particular, we place a special emphasis on those methods that can be applied in a proteome-wide manner, and discuss how they will help in the ultimate objective of building 3D interactome networks.

Modeling of proteins and their assemblies with the integrative modeling platform

To understand the workings of the living cell, we need to characterize protein assemblies that constitute the cell (for example, the ribosome, 26S proteasome, and the nuclear pore complex). A reliable high-resolution structural characterization of these assemblies is frequently beyond the reach of current experimental methods, such as X-ray crystallography, NMR spectroscopy, electron microscopy, footprinting, chemical cross-linking, FRET spectroscopy, small-angle X-ray scattering, and proteomics. However, the information garnered from different methods can be combined and used to build computational models of the assembly structures that are consistent with all of the available datasets. Here, we describe a protocol for this integration, whereby the information is converted to a set of spatial restraints and a variety of optimization procedures can be used to generate models that satisfy the restraints as much as possible. These generated models can then potentially inform about the precision and accuracy of structure determination, the accuracy of the input datasets, and further data generation. We also demonstrate the Integrative Modeling Platform (IMP) software, which provides the necessary computational framework to implement this protocol, and several applications for specific-use cases.

Modeling pilus structures from sparse data

Bacterial Type II secretion systems (T2SS) and type IV pili (T4P) biogenesis machineries share the ability to assemble thin filaments from pilin protein subunits in the plasma membrane. Here we describe in detail the calculation strategy that served to determine a detailed atomic model of the T2SS pilus from Klebsiella oxytoca (Campos et al., PNAS 2010). The strategy is based on molecular modeling with generalized distance restraints and experimental validation (salt bridge charge inversion; double cysteine substitution and crosslinking). It does not require directly fitting structures into an envelope obtained from electron microscopy, but relies on lower resolution information, in particular the symmetry parameters of the helix forming the pilus. We validate the strategy with T4P where either a higher resolution structure is available (for the gonococcal (GC) pilus from Neisseria gonorrhoeae), or where we can compare our results to additional experimental data (for Vibrio cholerae TCP). The models are of sufficient precision to compare the architecture of the different pili in detail.

Statistical mechanics analysis of sparse data

Inferential structure determination uses Bayesian theory to combine experimental data with prior structural knowledge into a posterior probability distribution over protein conformational space. The posterior distribution encodes everything one can say objectively about the native structure in the light of the available data and additional prior assumptions and can be searched for structural representatives. Here an analogy is drawn between the posterior distribution and the canonical ensemble of statistical physics. A statistical mechanics analysis assesses the complexity of a structure calculation globally in terms of ensemble properties. Analogs of the free energy and density of states are introduced; partition functions evaluate the consistency of prior assumptions with data. Critical behavior is observed with dwindling restraint density, which impairs structure determination with too sparse data. However, prior distributions with improved realism ameliorate the situation by lowering the critical number of observations. An in-depth analysis of various experimentally accessible structural parameters and force field terms will facilitate a statistical approach to protein structure determination with sparse data that avoids bias as much as possible.

Toward an integrated structural model of the 26S proteasome

The 26S proteasome is the end point of the ubiquitin-proteasome pathway and degrades ubiquitylated substrates. It is composed of the 20S core particle (CP), where degradation occurs, and the 19S regulatory particle (RP), which ensures substrate specificity of degradation. Whereas the CP is resolved to atomic resolution, the architecture of the RP is largely unknown. We provide a comprehensive analysis of the current structural knowledge on the RP, including structures of the RP subunits, physical protein-protein interactions, and cryoelectron microscopy data. These data allowed us to compute an atomic model for the CP-AAA-ATPase subcomplex. In addition to this atomic model, further subunits can be mapped approximately, which lets us hypothesize on the substrate path during its degradation.

Computational tools for the interactive exploration of proteomic and structural data

Linking proteomics and structural data is critical to our understanding of cellular processes, and interactive exploration of these complementary data sets can be extremely valuable for developing or confirming hypotheses in silico. However, few computational tools facilitate linking these types of data interactively. In addition, the tools that do exist are neither well understood nor widely used by the proteomics or structural biology communities. We briefly describe several relevant tools, and then, using three scenarios, we present in depth two tools for the integrated exploration of proteomics and structural data.