Docking proteins and peptides under evolutionary constraints in Critical Assessment of PRediction of Interactions rounds 38 to 45

Distinct RPA domains promote recruitment and the helicase-nuclease activities of Dna2

The Dna2 helicase-nuclease functions in concert with the replication protein A (RPA) in DNA double-strand break repair. Using ensemble and single-molecule biochemistry, coupled with structure modeling, we demonstrate that the stimulation of S. cerevisiae Dna2 by RPA is not a simple consequence of Dna2 recruitment to single-stranded DNA. The large RPA subunit Rfa1 alone can promote the Dna2 nuclease activity, and we identified mutations in a helix embedded in the N-terminal domain of Rfa1 that specifically disrupt this capacity. The same RPA mutant is instead fully functional to recruit Dna2 and promote its helicase activity. Furthermore, we found residues located on the outside of the central DNA-binding OB-fold domain Rfa1-A, which are required to promote the Dna2 motor activity. Our experiments thus unexpectedly demonstrate that different domains of Rfa1 regulate Dna2 recruitment, and its nuclease and helicase activities. Consequently, the identified separation-of-function RPA variants are compromised to stimulate Dna2 in the processing of DNA breaks. The results explain phenotypes of replication-proficient but radiation-sensitive RPA mutants and illustrate the unprecedented functional interplay of RPA and Dna2.

An enzymatic ensemble including Dna2 functions in DNA end resection; the function of the single-stranded DNA binding protein RPA in this complex has been underappreciated. Here the authors employ molecular modeling, biochemistry, and single molecule biophysics to reveal RPA directly promotes Dna2 recruitment, nuclease and helicase activities.

InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps

The InterEvDock3 protein docking server exploits the constraints of evolution by multiple means to generate structural models of protein assemblies. The server takes as input either several sequences or 3D structures of proteins known to interact. It returns a set of 10 consensus candidate complexes, together with interface predictions to guide further experimental validation interactively. Three key novelties were implemented in InterEvDock3 to help obtain more reliable models: users can (i) generate template-based structural models of assemblies using close and remote homologs of known 3D structure, detected through an automated search protocol, (ii) select the assembly models most consistent with contact maps from external methods that implement covariation-based contact prediction with or without deep learning and (iii) exploit a novel coevolution-based scoring scheme at atomic level, which leads to significantly higher free docking success rates. The performance of the server was validated on two large free docking benchmark databases, containing respectively 230 unbound targets (Weng dataset) and 812 models of unbound targets (PPI4DOCK dataset). Its effectiveness has also been proven on a number of challenging examples. The InterEvDock3 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock3/.

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.

How HIV-1 Integrase Associates with Human Mitochondrial Lysyl-tRNA Synthetase

Replication of human immunodeficiency virus type 1 (HIV-1) requires the packaging of tRNA^Lys,3 from the host cell into the new viral particles. The GagPol viral polyprotein precursor associates with mitochondrial lysyl-tRNA synthetase (mLysRS) in a complex with tRNA^Lys, an essential step to initiate reverse transcription in the virions. The C-terminal integrase moiety of GagPol is essential for its association with mLysRS. We show that integrases from HIV-1 and HIV-2 bind mLysRS with the same efficiency. In this work, we have undertaken to probe the three-dimensional (3D) architecture of the complex of integrase with mLysRS. We first established that the C-terminal domain (CTD) of integrase is the major interacting domain with mLysRS. Using the pBpa-photo crosslinking approach, inter-protein cross-links were observed involving amino acid residues located at the surface of the catalytic domain of mLysRS and of the CTD of integrase. In parallel, using molecular docking simulation, a single structural model of complex was found to outscore other alternative conformations. Consistent with crosslinking experiments, this structural model was further probed experimentally. Five compensatory mutations in the two partners were successfully designed which supports the validity of the model. The complex highlights that binding of integrase could stabilize the tRNA^Lys:mLysRS interaction.

Docking approaches for modeling multi-molecular assemblies

Computational docking approaches aim to overcome the limited availability of experimental structural data on protein-protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of binary complexes to more integrative approaches using template-based, data-driven modeling of multi-molecular assemblies. We will review here the predictive capabilities of current docking methods in blind conditions, based on the results from the most recent community-wide blind experiments. Integration of template-based and ab initio docking approaches is emerging as the optimal strategy for modeling protein complexes and multimolecular assemblies. We will also review the new methodological advances on ab initio docking and integrative modeling.