CaverDock: Software tool for fast screening of un/binding of ligands in protein engineering

Protein tunnels, channels and gates are important for enzymatic catalysis and also represent attractive targets for rational protein design and drug design [1]. Drug molecules blocking the access of natural substrate or release of products are very efficient modulators of biological activity. Here we demonstrate the application of newly in-house developed software tool CaverDock [2,3] for the analysis of the transport of ligands through tunnels in biomolecular targets. Caverdock is a new addition to the Caver Suite [4-6]. We performed virtual screening of large databases of drugs against two pharmacologically relevant targets. We have used FDA-approved drugs for both targets. Oncological drugs (133 molecules), taken from the NIH website, and anti-inflammatory (56 molecules), taken from the Drugbank website, as the libraries of ligands for the two molecular targets: (i) cytochrome P450 17A1 and (ii) leukotriene A4 hydrolase/aminopeptidase. Moreover, we will also show the unbinding of the 2,3-dichloropropan-1-ol product from a buried active site of an haloalkane dehalogenase and its variant. With this study we identified hot-spots that may be used for directed evolution or site-directed mutagenesis to create new variants for faster 2,3-dichloropropan-1-ol release [7]. Finally, we will show the difference on ligand transportation when a protein is in an open and closed conformations [8]. We will show how CaverDock tackles the problem of protein flexibility. 1. Marques, S.M., et al., 2017: Enzyme Tunnels and Gates as Relevant Targets in Drug Design. Medicinal Research Reviews 37: 1095-1139. 2. Vavra, O., et al., 2019: CaverDock 1.0: A New Tool for Analysis of Ligand Binding and Unbinding Based on Molecular Docking. Bioinformatics (under review). 3. Filipovic, J., et al, 2019: A Novel Method for Analysis of Ligand Binding and Unbinding Based on Molecular Docking. Transactions on Computational Biology and Bioinformatics (under review) 4. Chovancova, E., et al., 2012: CAVER 3.0: A Tool for Analysis of Transport Pathways in Dynamic Protein Structures. PLOS Computational Biology 8: e1002708. 5. Jurcik, A., et al., 2018: CAVER Analyst 2.0: Analysis and Visualization of Channels and Tunnels in Protein Structures and Molecular Dynamics Trajectories. Bioinformatics 34: 3586-3588. 6. Stourac, J., et al., 2019: Caver Web 1.0: Identification of Tunnels and Channels in Proteins and Analysis of Ligand Transport. Nucleic Acids Research (under review). 7. Marques, S.M., et al., 2019: Computational Study of Protein-Ligand Unbinding for Enzyme Engineering. Frontiers in Chemistry 6: 650. 8. Kokkonen, P., et al., 2018: Molecular Gating of an Engineered Enzyme Captured in Real Time. Journal of the American Chemical Society 140: 17999–18008

Visual Analysis of Ligand Trajectories in Molecular Dynamics

In many cases, protein reactions with other small molecules (ligands) occur in a deeply buried active site. When studying these types of reactions, it is crucial for biochemists to examine trajectories of ligand motion. These trajectories are predicted with in-silico methods that produce large ensembles of possible trajectories. In this paper, we propose a novel approach to the interactive visual exploration and analysis of large sets of ligand trajectories, enabling the domain experts to understand protein function based on the trajectory properties. The proposed solution is composed of multiple linked 2D and 3D views, enabling the interactive exploration and filtering of trajectories in an informed way. In the workflow, we focus on the practical aspects of the interactive visual analysis specific to ligand trajectories. We adapt the small multiples principle to resolve an overly large number of trajectories into smaller chunks that are easier to analyze. We describe how drill-down techniques can be used to create and store selections of the trajectories with desired properties, enabling the comparison of multiple datasets. In appropriately designed 2D and 3D views, biochemists can either observe individual trajectories or choose to aggregate the information into a functional boxplot or density visualization. Our solution is based on a tight collaboration with the domain experts, aiming to address their needs as much as possible. The usefulness of our novel approach is demonstrated by two case studies, conducted by the collaborating protein engineers.acceptedVersio

CAVERDOCK: A new tool for analysis of ligand binding and unbinding based on molecular docking

Understanding the protein-ligand interactions is crucial for engineering improved catalysts. The interaction of a protein and a ligand molecule often takes place in enzymes active site. Such functional sites may be buried inside the protein core, and therefore a transport of a ligand from outside environment to the protein inside needs to be understood. Here we present the CaverDock [1], implementing a novel method for analysis of these important transport processes. Our method is based on a modified molecular docking algorithm. It iteratively places the ligand along the tunnel in such a way that the ligand movement is contiguous and its energy is minimized. The output of the calculation is ligand trajectory and energy profile of transport process. CaverDock uses a modified version of the program AutoDock Vina [2] for molecular docking and implements a parallel heuristic algorithm to search the space of possible trajectories. Our method lies in between of geometrical approaches and molecular dynamics simulations. Contrary to geometrical methods, it provides an evaluation of chemical forces. However, it is not as computationally demanding as the methods based on molecular dynamics. The typical input of CaverDock requires setup for molecular docking and tunnel geometry obtained from Caver [3]. Typical computational time is in dozens of minutes at a single node, allowing virtual screening of a large pool of molecules. We demonstrate CaverDock usability by comparison of a ligand trajectory in different tunnels of wild type and engineered proteins; and computation of energetic profiles for a large set of substrates and inhibitors. CaverDock is available from the web site http://www.caver.cz. 1. Vavra, O., Filipovic, J., Plhak, J., Bednar, D., Marques, S., Brezovsky, J., Matyska, L., Damborsky, J., CAVERDOCK: A New Tool for Analysis of Ligand Binding and Unbinding Based on Molecular Docking. PLOS Computational Biology (submitted). 2. Trott, O., Olson, A.J., AutoDock Vina: Improving the Speed and Accuracy of Docking with a New scoring function, efficient optimization and multithreading, Journal of Computational Chemistry 31: 455-461, 2010. 3. Chovancova, E., Pavelka, A., Benes, P., Strnad, O., Brezovsky, J., Kozlikova, B., Gora, A., Sustr, V., Klvana, M., Medek, P., Biedermannova, L., Sochor, J., Damborsky, J., 2012: CAVER 3.0: A Tool for Analysis of Transport Pathways in Dynamic Protein Structures. PLOS Computational Biology 8: e1002708

Strategies and software tools for engineering protein tunnels and dynamical gates

Improvements in the catalytic activity, substrate specificity or enantioselectivity of enzymes are traditionally achieved by modification of enzymes’ active sites. We have recently proposed that the enzyme engineering endeavors should target both the active sites and the access tunnels/channels [1,2]. Using the model enzymes haloalkane dehalogenases, we have demonstrated that engineering of access tunnels provides enzymes with significantly improved catalytic properties [3] and stability [4]. User-friendly software tools Caver [5], Caver Analyst [6], CaverDock [7] and Caver Web [8], have been developed for the computational design of protein tunnels/channels; FireProt [9] and HotSpot Wizard [10] for automated design of stabilizing mutations and smart libraries. Using these tools we were able to introduce a new tunnel to a protein structure and tweak its conformational dynamics. This engineering strategy has led to improved catalytic efficiency [2], enhanced promiscuity or even a functional switch (unpublished). Our concepts and software tools are widely applicable to various enzymes with known structures and buried active sites. 1. Damborsky, J., et al., 2009: Computational Tools for Designing and Engineering Biocatalysts. Current Opinion in Chemical Biology 13: 26-34. 2. Prokop, Z., et al., 2012: Engineering of Protein Tunnels: Keyhole-lock-key Model for Catalysis by the Enzymes with Buried Active Sites. Protein Engineering Handbook, Wiley-VCH, Weinheim, pp. 421-464. 3. Brezovsky, J., et al., 2016: Engineering a de Novo Transport Tunnel. ACS Catalysis 6: 7597-7610. 4. Koudelakova, T., et al., 2013: Engineering Enzyme Stability and Resistance to an Organic Cosolvent by Modification of Residues in the Access Tunnel. Angewandte Chemie 52: 1959-1963. 5. Chovancova, E., et al., 2012: CAVER 3.0: A Tool for Analysis of Transport Pathways in Dynamic Protein Structures. PLOS Computational Biology 8: e1002708. 6. Jurcik, A., et al., 2018: CAVER Analyst 2.0: Analysis and Visualization of Channels and Tunnels in Protein Structures and Molecular Dynamics Trajectories. Bioinformatics 34: 3586-3588. 7. Vavra, O., et al., 2019: CaverDock 1.0: A New Tool for Analysis of Ligand Binding and Unbinding Based on Molecular Docking. Bioinformatics (under review). 8. Stourac, J., et al. 2019: Caver Web 1.0: Identification of Tunnels and Channels in Proteins and Analysis of Ligand Transport. Nucleic Acids Research (under review). 9. Musil, M., et al., 2017: FireProt: Web Server for Automated Design of Thermostable Proteins. Nucleic Acids Research 45: W393-W399. 10. Sumbalova, L. et al., 2018: HotSpot Wizard 3.0: Automated Design of Site-Specific Mutations and Smart Libraries in Protein Engineering. Nucleic Acids Research 46: W356-W362

Planar S-(S/F)-S Josephson junctions induced by the inverse proximity effect

We present a utilization of the inverse proximity effect in superconductor-ferromagnet (S-F) bilayer to generate lateral Josephson junctions. The weak link is created by a Pd0.95Fe0.05 strip across a Nb bridge. Close to TC and in perpendicular magnetic field the junctions exhibit a modulation of the critical current IC(B) similar to a Fraunhofer interference pattern which proves the dc Josephson effect. The structure contains three weak links (two different areas) in series which result in the observation of two periods scalable with the areas penetrated by magnetic flux. Measurements of Shapiro steps prove the presence of the ac Josephson effect

Current-controllable plana S-(S/F)-S Josephson junction

We report on the experimental realization of a current-controllable lateral S-(S/F)-S Josephson junction based on the inverse proximity effect in the superconductor-ferromagnet bilayer (S/F). The dependence of the critical current on the magnetic field Ic(B) shows a Fresenel-like pattern, which could qualitatively be understood with the theory of Josephson junctions in a magnetic field gradient. The amplitude and the period of the Ic(B) pattern can be controlled by spin-polarized quasiparticles injection into the weak link. The period change suggests controllability of effective area of the Josephson junction. Furthermore, a temperature-induced transition from a weak-link behavior to a strong coupling between the superconducting banks is also observed in these lateral Josephson junctions

Screening of World Approved Drugs against Highly Dynamical Spike Glycoprotein SARS-CoV-2 using CaverDock and Machine Learning

The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes pathological pulmonary symptoms. Most efforts to develop vaccines and drugs against this virus target the spike glycoprotein, particularly its S1 subunit, which is recognised by angiotensin-converting enzyme 2. Here we use the in-house developed tool CaverDock to perform virtual screening against spike glycoprotein using a cryogenic electron microscopy structure (PDB-ID: 6VXX) and the representative structures of five most populated clusters from a previously published molecular dynamics simulations. The dataset of ligands was obtained from the ZINC database and consists of drugs approved for clinical use worldwide. Trajectories for the passage of individual drugs through the tunnel of the spike glycoprotein homotrimer, their binding energies within the tunnel, and the duration of their contacts with the trimer’s three subunits were computed for the full dataset. Multivariate statistical methods were then used to establish structure-activity relationships and select top candidate molecules. This new protocol for rapid screening of globally approved drugs (4359 ligands) in a multi-state protein structure (6 states) required a total of 26,148 calculations and showed high robustness. The protocol is universal and can be applied to any target protein with an experimental tertiary structure containing protein tunnels or channels. The protocol will be implemented in the next version of CaverWeb (https://loschmidt.chemi.muni.cz/caverweb/) to make it accessible to the wider scientific communit

Visual Analysis of Ligand Trajectories in Molecular Dynamics

In many cases, protein reactions with other small molecules (ligands) occur in a deeply buried active site. When studying these types of reactions, it is crucial for biochemists to examine trajectories of ligand motion. These trajectories are predicted with in-silico methods that produce large ensembles of possible trajectories. In this paper, we propose a novel approach to the interactive visual exploration and analysis of large sets of ligand trajectories, enabling the domain experts to understand protein function based on the trajectory properties. The proposed solution is composed of multiple linked 2D and 3D views, enabling the interactive exploration and filtering of trajectories in an informed way. In the workflow, we focus on the practical aspects of the interactive visual analysis specific to ligand trajectories. We adapt the small multiples principle to resolve an overly large number of trajectories into smaller chunks that are easier to analyze. We describe how drill-down techniques can be used to create and store selections of the trajectories with desired properties, enabling the comparison of multiple datasets. In appropriately designed 2D and 3D views, biochemists can either observe individual trajectories or choose to aggregate the information into a functional boxplot or density visualization. Our solution is based on a tight collaboration with the domain experts, aiming to address their needs as much as possible. The usefulness of our novel approach is demonstrated by two case studies, conducted by the collaborating protein engineers