Multi-scale modelling of macromolecular conformational changes

Modelling protein flexibility and plasticity is computationally challenging but important for understanding the function of biological systems. Furthermore, it has great implications for the prediction of (macro) molecular complex formation. Recently, coarse-grained normal mode approaches have emerged as efficient alternatives for investigating large-scale conformational changes for which more accurate methods like MD simulation are limited due to their computational burden. We have developed a Normal Mode based Simulation (NMSim) approach for efficient conformation generation of macromolecules. Combinations of low energy normal modes are used to guide a simulation pathway, whereas an efficient constraints correction approach is applied to generate stereochemically allowed conformations. Non-covalent bonds like hydrogen bonds and hydrophobic tethers and phi-psi favourable regions are also modelled as constraints. Conformations from our approach were compared with a 10 ns MD trajectory of lysozyme. A 2-D RMSD plot shows a good overlap of conformational space, and rms fluctuations of residues show a correlation coefficient of 0.78 between the two sets of conformations. Furthermore, a comparison of NMSim simulations starting from apo structures of different proteins show that ligand-bound conformations can be sampled for those cases where conformational changes are mainly correlated, e.g., domain-like motion in adenylate kinase. Efforts are currently being made to also model localized but functionally important motions for protein binding pockets and protein-protein interfaces using relevant normal mode selection criteria and implicit rotamer basin creation

The architecture of the 10-23 DNAzyme and its implications for DNA-mediated catalysis

Funding Information: The authors acknowledge access to the Jülich‐Düsseldorf Biomolecular NMR Center. HG is grateful for computational support and infrastructure provided by the ‘Zentrum für Informations‐ und Medientechnologie’ (ZIM) at the Heinrich Heine University Düsseldorf and the John von Neumann Institute for Computing (NIC) (user ID: HKF7, VSK33). We thank Hannah Rosenbach for providing activity data. This work was supported by the German Research Foundation (DFG) (103/4‐1, ET 103/4‐3, and the Heisenberg grant ET 103/5‐1) to ME, the Volkswagen Foundation to ME and HG (project no. 9B798) and the European Union‘s Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant agreement no. 660258 to AV. Open Access funding enabled and organized by Projekt DEAL. Publisher Copyright: © 2022 The Authors. The FEBS Journal published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.Understanding the molecular features of catalytically active DNA sequences, so-called DNAzymes, is essential not only for our understanding of the fundamental properties of catalytic nucleic acids in general, but may well be the key to unravelling their full potential via tailored modifications. Our recent findings contributed to the endeavour to assemble a mechanistic picture of DNA-mediated catalysis by providing high-resolution structural insights into the 10-23 DNAzyme (Dz) and exposing a complex interplay between the Dz's unique molecular architecture, conformational plasticity, and dynamic modulation by metal ions as central elements of the DNA catalyst. Here, we discuss key features of our findings and compare them to other studies on similar systems.publishersversionpublishe

Can constraint network analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase

Cellulases are industrially important enzymes, e.g., in the production of bioethanol, in pulp and paper industry, feedstock, and textile. Thermostability is often a prerequisite for high process stability and improving thermostability without affecting specific activities at lower temperatures is challenging and often time-consuming. Protein engineering strategies that combine experimental and computational are emerging in order to reduce experimental screening efforts and speed up enzyme engineering campaigns. Constraint Network Analysis (CNA) is a promising computational method that identifies beneficial positions in enzymes to improve thermostability. In this study, we compare CNA and directed evolution in the identification of beneficial positions in order to evaluate the potential of CNA in protein engineering campaigns (e.g., in the identification phase of KnowVolution). We engineered the industrially relevant endoglucanase EGLII from Penicillium verruculosum towards increased thermostability. From the CNA approach, six variants were obtained with an up to 2-fold improvement in thermostability. The overall experimental burden was reduced to 40% utilizing the CNA method in comparison to directed evolution. On a variant level, the success rate was similar for both strategies, with 0.27% and 0.18% improved variants in the epPCR and CNA-guided library, respectively. In essence, CNA is an effective method for identification of positions that improve thermostability

DrugScorePPI webserver: fast and accurate in silico alanine scanning for scoring protein–protein interactions

Protein–protein complexes play key roles in all cellular signal transduction processes. We have developed a fast and accurate computational approach to predict changes in the binding free energy upon alanine mutations in protein–protein interfaces. The approach is based on a knowledge-based scoring function, DrugScorePPI, for which pair potentials were derived from 851 complex structures and adapted against 309 experimental alanine scanning results. Based on this approach, we developed the DrugScorePPI webserver. The input consists of a protein–protein complex structure; the output is a summary table and bar plot of binding free energy differences for wild-type residue-to-Ala mutations. The results of the analysis are mapped on the protein–protein complex structure and visualized using J mol. A single interface can be analyzed within a few minutes. Our approach has been successfully validated by application to an external test set of 22 alanine mutations in the interface of Ras/RalGDS. The DrugScorePPI webserver is primarily intended for identifying hotspot residues in protein–protein interfaces, which provides valuable information for guiding biological experiments and in the development of protein–protein interaction modulators. The DrugScorePPI Webserver, accessible at http://cpclab.uni-duesseldorf.de/dsppi, is free and open to all users with no login requirement