Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

Proper evaluation of chemical cross-linking-based spatial restraints improves the precision of modeling homo-oligomeric protein complexes

Background

The function of oligomeric proteins is inherently linked to their quaternary structure. In the absence of high-resolution data, low-resolution information in the form of spatial restraints can significantly contribute to the precision and accuracy of structural models obtained using computational approaches. To obtain such restraints, chemical cross-linking coupled with mass spectrometry (XL-MS) is commonly used. However, the use of XL-MS in the modeling of protein complexes comprised of identical subunits (homo-oligomers) is often hindered by the inherent ambiguity of intra- and inter-subunit connection assignment.

Results

We present a comprehensive evaluation of (1) different methods for inter-residue distance calculations, and (2) different approaches for the scoring of spatial restraints. Our results show that using Solvent Accessible Surface distances (SASDs) instead of Euclidean distances (EUCs) greatly reduces the assignation ambiguity and delivers better modeling precision. Furthermore, ambiguous connections should be considered as inter-subunit only when the intra-subunit alternative exceeds the distance threshold. Modeling performance can also be improved if symmetry, characteristic for most homo-oligomers, is explicitly defined in the scoring function.

Conclusions

Our findings provide guidelines for proper evaluation of chemical cross-linking-based spatial restraints in modeling homo-oligomeric protein complexes, which could facilitate structural characterization of this important group of proteins.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3032-x) contains supplementary material, which is available to authorized users.

Exploring the binding mechanism between human profilin (PFN1) and polyproline-10 through binding mode screening

The large magnitude of protein-protein interaction (PPI) pairs within the human interactome necessitates the development of predictive models and screening tools to better understand this fundamental molecular communication. However, despite enormous efforts from various groups to develop predictive techniques in the last decade, PPI complex structures are in general still very challenging to predict due to the large number of degrees of freedom. In this study, we use the binding complex of human profilin (PFN1) and polyproline-10 (P10) as a model system to examine various approaches, with the aim of going beyond normal protein docking for PPI prediction and evaluation. The potential of mean force (PMF) was first obtained from the time-consuming umbrella sampling, which confirmed that the most stable binding structure identified by the maximal PMF difference is indeed the crystallographic binding structure. Moreover, crucial residues previously identified in experimental studies, W3, H133, and S137 of PFN1, were found to form favorable hydrogen bonds with P10, suggesting a zipping process during the binding between PFN1 and P10. We then explored both regular molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, seeking for better criteria of ranking the PPI prediction. Despite valuable information obtained from conventional MD simulations, neither the commonly used interaction energy between the two binding parties nor the long-term root mean square displacement correlates well with the PMF results. On the other hand, with a sizable collection of trajectories, we demonstrated that the average and minimal rupture works calculated from SMD simulations correlate fairly well with the PMFs (R ² = 0.67), making this a promising PPI screening method.

Cripto stabilizes GRP78 on the cell membrane

The ER resident chaperone molecule GRP78 has been shown to translocate to the cell surface where it associates with Cripto and signals cell growth, playing a still partially understood role in tumorigenesis. Consequently, a better understanding of GRP78 topology and structure at the surface of cancer cells represents an important step in the development of a new class of therapeutics. Here, we used a set of programs for creation of a complex containing GRP78 and Cripto proteins. We elucidated possible interactions of GRP78, Cripto, and their complex with the membrane. Using molecular dynamics simulations, we demonstrated that Cripto binding to GRP78 completely changes the dynamics of its behavior on the membrane, not allowing GRP78 to disconnect from it, thus enabling GRP78 tumorigenic functions.

Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks

BACKGROUND

Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved.

RESULTS

In this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway.

CONCLUSIONS

Protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques.

Modeling complexes of modeled proteins

Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such "double" modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å Cα RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5-6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470-478. © 2016 Wiley Periodicals, Inc.

G-actin guides p53 nuclear transport: potential contribution of monomeric actin in altered localization of mutant p53

p53 preserves genomic integrity by restricting anomaly at the gene level. Till date, limited information is available for cytosol to nuclear shuttling of p53; except microtubule-based trafficking route, which utilizes minus-end directed motor dynein. The present study suggests that monomeric actin (G-actin) guides p53 traffic towards the nucleus. Histidine-tag pull-down assay using purified p53(1–393)-His and G-actin confirms direct physical association between p53 and monomeric G-actin. Co-immunoprecipitation data supports the same. Confocal imaging explores intense perinuclear colocalization between p53 and G-actin. To address atomistic details of the complex, constraint-based docked model of p53:G-actin complex was generated based on crystal structures. MD simulation reveals that p53 DNA-binding domain arrests very well the G-actin protein. Docking benchmark studies have been carried out for a known crystal structure, 1YCS (complex between p53DBD and BP2), which validates the docking protocol we adopted. Co-immunoprecipitation study using “hot-spot” p53 mutants suggested reduced G-actin association with cancer-associated p53 conformational mutants (R175H and R249S). Considering these findings, we hypothesized that point mutation in p53 structure, which diminishes p53:G-actin complexation results in mutant p53 altered subcellular localization. Our model suggests p53Arg249 form polar-contact with Arg357 of G-actin, which upon mutation, destabilizes p53:G-actin interaction and results in cytoplasmic retention of p53R249S.