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More than 70% of human NRAS melanomas are resistant to MEK inhibitors highlighting the crucial need for efficient therapeutic strategies for these tumors. CD147, a membrane receptor, is overexpressed in most cancers including melanoma and is associated with poor prognosis. We show here that CD147i, a specific inhibitor of CD147/VEGFR-2 interaction represents a potential therapeutic strategy for NRAS melanoma cells. It significantly inhibited the malignant properties of NRAS melanomas ex vivo and in vivo. Importantly, NRAS patient’s-derived xenografts, which were resistant to MEKi, became sensitive when combined with CD147i leading to decreased proliferation ex vivo and tumor regression in vivo. Mechanistic studies revealed that CD147i effects were mediated through STAT3 pathway. These data bring a proof of concept on the impact of the inhibition of CD147/VEGFR-2 interaction on melanoma progression and represents a new therapeutic opportunity for NRAS melanoma when combined with MEKi., This work was supported by La Ligue Nationale contre le Cancer, the Institut National de la Santé et de la Recherche Médicale (INSERM) and La Société Française de Dermatologie. AL was supported by a PhD funding from La Ligue Nationale contre le Cancer and the Spanish Ministry of Science and Innovation (PID2019-110167RB-I00). We thank technological platform of the Institut Recherche Saint-Louis (IRSL) for confocal microscopy analyses and Dr Benoit Souquet for technical support.

The study of the 3D structural details of protein interactions is essential to understand biomolecular functions at the molecular level. In this context, the limited availability of experimental structures of protein–protein complexes at atomic resolution is propelling the development of computational docking methods that aim to complement the current structural coverage of protein interactions. One of these docking approaches is pyDock, which uses van der Waals, electrostatics, and desolvation energy to score docking poses generated by a variety of sampling methods, typically FTDock or ZDOCK. The method has shown a consistently good prediction performance in community-wide assessment experiments like CAPRI or CASP, and has provided biological insights and insightful interpretation of experiments by modeling many biomolecular interactions of biomedical and biotechnological interest. Here, we describe in detail how to perform structural modeling of protein assemblies with pyDock, and the application of its modules to different biomolecular recognition phenomena, such as modeling of binding mode, interface, and hot-spot prediction, use of restraints based on experimental data, inclusion of low-resolution structural data, binding affinity estimation, or modeling of homo- and hetero-oligomeric assemblies., This work was supported by the Spanish Ministry of Science (grant BIO2016-79930-R).

Background Protein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces. Results Here we show that this correlation can be efficiently used for the scoring of rigid-body docking poses. When integrated into the pyDock energy-based docking method, the new combined scoring function significantly improved the results of the individual components as shown on a standard docking benchmark. This improvement was particularly remarkable for specific protein complexes, depending on the shape, size, type, or flexibility of the proteins involved. Conclusions The network-based representation of protein structures can be used to identify protein-protein binding regions and to efficiently score docking poses, complementing energy-based approaches.

Background Urokinase, its receptor and the integrins are functionally associated and involved in regulation of cell signaling, migration, adhesion and proliferation. No structural information is available on this potential multimolecular complex. However, the tri-dimensional structure of urokinase, urokinase receptor and integrins is known. Results We have modeled the interaction of urokinase on two integrins, αIIbβ3 in the open configuration and αvβ3 in the closed configuration. We have found that multiple lowest energy solutions point to an interaction of the kringle domain of uPA at the boundary between α and β chains on the surface of the integrins. This region is not far away from peptides that have been previously shown to have a biological role in urokinase receptor/integrins dependent signaling. Conclusions We demonstrated that in silico docking experiments can be successfully carried out to identify the binding mode of the kringle domain of urokinase on the scaffold of integrins in the open and closed conformation. Importantly we found that the binding mode was the same on different integrins and in both configurations. To get a molecular view of the system is a prerequisite to unravel the complex protein-protein interactions underlying urokinase/urokinase receptor/integrin mediated cell motility, adhesion and proliferation and to design rational in vitro experiments.

Background Protein-protein interactions are fundamental for the majority of cellular processes and their study is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational approaches to the protein-protein docking problem have been reported, with encouraging results. Most of the currently available protein-protein docking algorithms are composed of two clearly defined parts: the sampling of the rotational and translational space of the interacting molecules, and the scoring and clustering of the resulting orientations. Although this kind of strategy has shown some of the most successful results in the CAPRI blind test http://www.ebi.ac.uk/msd-srv/capri, more efforts need to be applied. Thus, the sampling protocol should generate a pool of conformations that include a sufficient number of near-native ones, while the scoring function should discriminate between near-native and non-near-native proposed conformations. On the other hand, protocols to efficiently include full flexibility on the protein structures are increasingly needed. Results In these work we present new computational tools for protein-protein docking. We describe here the RotBUS (Rotation-Based Uniform Sampling) method to generate uniformly distributed sets of rigid-body docking poses, with a new fast calculation of the optimal contacting distance between molecules. We have tested the method on a standard benchmark of unbound structures and we can find near-native solutions in 100% of the cases. After applying a new fast filtering scheme based on residue-based desolvation, in combination with FTDock plus pyDock scoring, near-native solutions are found with rank ≤ 50 in 39% of the cases. Knowledge-based experimental restraints can be easily included to reduce computational times during sampling and improve success rates, and the method can be extended in the future to include flexibility of the side-chains. Conclusions This new sampling algorithm has the advantage of its high speed achieved by fast computing of the intermolecular distance based on a coarse representation of the interacting surfaces. In addition, a fast desolvation scoring permits the screening of millions of conformations at low computational cost, without compromising accuracy. The protocol presented here can be used as a framework to include restraints, flexibility and ensemble docking approaches.

Background Modelling proteins with multiple domains is one of the central challenges in Structural Biology. Although homology modelling has successfully been applied for prediction of protein structures, very often domain-domain interactions cannot be inferred from the structures of homologues and their prediction requires ab initio methods. Here we present a new structural prediction approach for modelling two-domain proteins based on rigid-body domain-domain docking. Results Here we focus on interacting domain pairs that are part of the same peptide chain and thus have an inter-domain peptide region (so called linker). We have developed a method called pyDockTET (tet hered-docking), which uses rigid-body docking to generate domain-domain poses that are further scored by binding energy and a pseudo-energy term based on restraints derived from linker end-to-end distances. The method has been benchmarked on a set of 77 non-redundant pairs of domains with available X-ray structure. We have evaluated the docking method ZDOCK, which is able to generate acceptable domain-domain orientations in 51 out of the 77 cases. Among them, our method pyDockTET finds the correct assembly within the top 10 solutions in over 60% of the cases. As a further test, on a subset of 20 pairs where domains were built by homology modelling, ZDOCK generates acceptable orientations in 13 out of the 20 cases, among which the correct assembly is ranked lower than 10 in around 70% of the cases by our pyDockTET method. Conclusion Our results show that rigid-body docking approach plus energy scoring and linker-based restraints are useful for modelling domain-domain interactions. These positive results will encourage development of new methods for structural prediction of macromolecules with multiple (more than two) domains.