Accounting for large amplitude protein deformation during in silico macromolecular docking

Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes

RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods.

NOLB: Nonlinear Rigid Block Normal-Mode Analysis Method

We present a new conceptually simple and computationally efficient method for nonlinear normal-mode analysis called NOLB. It relies on the rotations-translations of blocks (RTB) theoretical basis developed by Y.-H. Sanejouand and colleagues [ Durand et al. Biopolymers 1994 , 34 , 759 - 771 . Tama et al. Proteins: Struct., Funct., Bioinf . 2000 , 41 , 1 - 7 ]. We demonstrate how to physically interpret the eigenvalues computed in the RTB basis in terms of angular and linear velocities applied to the rigid blocks and how to construct a nonlinear extrapolation of motion out of these velocities. The key observation of our method is that the angular velocity of a rigid block can be interpreted as the result of an implicit force, such that the motion of the rigid block can be considered as a pure rotation about a certain center. We demonstrate the motions produced with the NOLB method on three different molecular systems and show that some of the lowest frequency normal modes correspond to the biologically relevant motions. For example, NOLB detects the spiral sliding motion of the TALE protein, which is capable of rapid diffusion along its target DNA. Overall, our method produces better structures compared to the standard approach, especially at large deformation amplitudes, as we demonstrate by visual inspection, energy, and topology analyses and also by the MolProbity service validation. Finally, our method is scalable and can be applied to very large molecular systems, such as ribosomes. Standalone executables of the NOLB normal-mode analysis method are available at https://team.inria.fr/nano-d/software/nolb-normal-modes/ . A graphical user interface created for the SAMSON software platform will be made available at https://www.samson-connect.net .

Pushing the Backbone in Protein-Protein Docking

Conformational changes of proteins that occur upon binding typically confound computational docking algorithms. In this study, we test computational methods to capture protein backbone conformational change related to binding. To address how well existing algorithms can sample bound-like backbones, we query seven techniques including Monte Carlo-based sampling, molecular dynamics, and normal mode analysis. All methods tested rarely sample near-bound states from the unbound conformation. Nevertheless, the direction of the predicted motions overlap with the actual conformational change. We next forced the backbone from the unbound toward the bound conformation to create a family of docking energy landscapes. Seventy percent of docking targets succeed when the unbound backbones is pushed to within 0.6 Å of the bound. Current methods can capture an average of 22% of unbound-bound transitions through conformer selection methods and another 57% through induced-fit methodologies, delineating a stubborn gap (21%) in backbone motion not covered by any current approach.

Computational modeling of protein-RNA complex structures

Protein-RNA interactions play fundamental roles in many biological processes, such as regulation of gene expression, RNA splicing, and protein synthesis. The understanding of these processes improves as new structures of protein-RNA complexes are solved and the molecular details of interactions analyzed. However, experimental determination of protein-RNA complex structures by high-resolution methods is tedious and difficult. Therefore, studies on protein-RNA recognition and complex formation present major technical challenges for macromolecular structural biology. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental measurements, theoretical models of macromolecular structures can be sufficiently accurate to prompt functional hypotheses and guide e.g. identification of important amino acid or nucleotide residues. In this article we present an overview of strategies and methods for computational modeling of protein-RNA complexes, including software developed in our laboratory, and illustrate it with practical examples of structural predictions.

Balancing target flexibility and target denaturation in computational fragment-based inhibitor discovery

Accounting for target flexibility and selecting "hot spots" most likely to be able to bind an inhibitor continue to be challenges in the field of structure-based drug design, especially in the case of protein-protein interactions. Computational fragment-based approaches using molecular dynamics (MD) simulations are a promising emerging technology having the potential to address both of these challenges. However, the optimal MD conditions permitting sufficient target flexibility while also avoiding fragment-induced target denaturation remain ambiguous. Using one such technology (Site Identification by Ligand Competitive Saturation, SILCS), conditions were identified to either prevent denaturation or identify and exclude trajectories in which subtle but important denaturation was occurring. The target system used was the well-characterized protein cytokine IL-2, which is involved in a protein-protein interface and, in its unliganded crystallographic form, lacks surface pockets that can serve as small-molecule binding sites. Nonetheless, small-molecule inhibitors have previously been discovered that bind to two "cryptic" binding sites that emerge only in the presence of ligand binding, highlighting the important role of IL-2 flexibility. Using the above conditions, SILCS with hydrophobic fragments was able to identify both sites based on favorable fragment binding while avoiding IL-2 denaturation. An important additional finding was that acetonitrile, a water-miscible fragment, fails to identify either site yet can induce target denaturation, highlighting the importance of fragment choice.

Protein-protein HADDocking using exclusively pseudocontact shifts

In order to enhance the structure determination process of macromolecular assemblies by NMR, we have implemented long-range pseudocontact shift (PCS) restraints into the data-driven protein docking package HADDOCK. We demonstrate the efficiency of the method on a synthetic, yet realistic case based on the lanthanide-labeled N-terminal ε domain of the E. coli DNA polymerase III (ε186) in complex with the HOT domain. Docking from the bound form of the two partners is swiftly executed (interface RMSDs < 1 Å) even with addition of very large amount of noise, while the conformational changes of the free form still present some challenges (interface RMSDs in a 3.1-3.9 Å range for the ten lowest energy complexes). Finally, using exclusively PCS as experimental information, we determine the structure of ε186 in complex with the HOT-homologue θ subunit of the E. coli DNA polymerase III.