Biomolecular Simulations in Structure-Based Drug Discovery

Francesco L. Gervasio, Vojtech Spiwok, Raimund Mannhold, Helmut Buschmann, Jörg Holenz

• Publisher: Wiley-VCH
• Year: 2019
• Print ISBN:
  9783527342655
• eBook ISBN:
  9783527806850
• Edition: 1
• Topic: Medicine
• Subtopic: Pharmacology

A guide to applying the power of modern simulation tools to better drug design

Biomolecular Simulations in Structure-based Drug Discovery offers an up-to-date and comprehensive review of modern simulation tools and their applications in real-life drug discovery, for better and quicker results in structure-based drug design. The authors describe common tools used in the biomolecular simulation of drugs and their targets and offer an analysis of the accuracy of the predictions. They also show how to integrate modeling with other experimental data.

Filled with numerous case studies from different therapeutic fields, the book helps professionals to quickly adopt these new methods for their current projects. Experts from the pharmaceutical industry and academic institutions present real-life examples for important target classes such as GPCRs, ion channels and amyloids as well as for common challenges in structure-based drug discovery. Biomolecular Simulations in Structure-based Drug Discovery is an important resource that:

-Contains a review of the current generation of biomolecular simulation tools that have the robustness and speed that allows them to be used as routine tools by non-specialists
-Includes information on the novel methods and strategies for the modeling of drug-target interactions within the framework of real-life drug discovery and development
-Offers numerous illustrative case studies from a wide-range of therapeutic fields
-Presents an application-oriented reference that is ideal for those working in the various fields

Written for medicinal chemists, professionals in the pharmaceutical industry, and pharmaceutical chemists, Biomolecular Simulations in Structure-based Drug Discovery is a comprehensive resource to modern simulation tools that complement and have the potential to complement or replace laboratory assays for better results in drug design.

Part I Principles

1
Predictive Power of Biomolecular Simulations

Vojtěch Spiwok

University of Chemistry and Technology, Prague, Department of Biochemistry and Microbiology, Technická 3, 166 28 Prague 6, Czech Republic

Biomolecular simulations are becoming routine in structure‐based drug design and related fields. This chapter briefly presents the history of molecular simulations, basic principles and approximations, and the most common designs of computational experiments. I also discuss statistical analysis of simulation results together with possible limits of accuracy.

The history of computational modeling of molecular structure and dynamics goes back to 1953, to the work of Rosenbluth and coworkers [1]. It introduced the Markov chain Monte Carlo as a method to study a simplified model of the fluid system. Atoms of the studied system were perfectly inelastic and the system was two‐dimensional (2D) instead of three‐dimensional (3D), so the analogy with real molecular systems was not perfect. The first molecular dynamics simulation (i.e. modeling of motions) on the same system was done by Alder and Wainwright in 1957 [2] using perfectly elastic collision between 2D particles. The first molecular simulation with specific atom types was done by Rahman in 1964 [3]. Rahman used a CDC 3600 computer to simulate dynamics of 864 argon atoms modeled using Lennard‐Jones potential. The first simulation of liquid water was published by Rahman and Stillinger in 1971 [4].

Another big milestone was the first biomolecular simulation. McCammon, Gelin, and 2013 Nobel Prize winner Karplus simulated 9.2 ps of the life of the bovine pancreatic trypsin inhibitor (BPTI, also known as aprotinin) in vacuum [5]. The simulation was performed during the CECAM (Centre Européen de Calcul Atomic et Moléculaire) workshop “Models of Protein Dynamics” in Orsay, France on CECAM computer facilities [6]. It was one of the first works showing proteins as a dynamic species with fluid‐like internal motions, even though in the native state.

Biomolecular simulations have undergone a huge progress in terms of accuracy, size of simulated systems, and simulated times since their pioneer times. However, the question arises whether this progress is enough for their practical application in drug discovery, protein engineering, and related applied fields. To address this issue, let me present here the concept of the hype cycle [7] developed by Gartner Inc. and depicted in Figure 1.1. According to this concept, every new invention starts by a Technology Trigger. Visibility of the invention grows until it reaches the Peak of Inflated Expectations. At this point, failures of the invention start to dominate over its benefits and the invention falls into the phase of Trough of Disillusionment. From this phase a new and slower progress starts in the phase of Slope of Enlightenment toward the Plateau of Productivity. Biomolecular simulation passed the Technology Trigger and Peak of Inflated Expectations as many expected that biomolecular simulation would become routine and an inexpensive alternative to experimental testing of compounds for biological activity. Now, in my opinion, biomolecular simulations are located on the Slope of Enlightenment with a slow but steady progress toward the Plateau of Productivity.

Figure 1.1 Gartner hype cycle of inventions.

1.1 Design of Biomolecular Simulations

Biomolecular simulations can follow different designs. I use the term design to describe the setup of the simulation procedure chosen in order to answer the research hypothesis. There are three major designs of molecular simulation. The first design starts from a predicted structure of the molecular system, which we want to evaluate, for example, a protein–ligand complex predicted by a simple protein–ligand docking. I refer to this as the evaluative design (Figure 1.2). The research hypothesis is: Does the predicted structure represent real structure? The basic assumption behind this design is that an accurately predicted structure of the system, for example, an accurately modeled structure of the complex, is lower in free energy than an inaccurately predicted one. The system therefore tends to be stable in a simulation starting from an accurately modeled structure and tends to be unstable in a simulation starting from an inaccurate structure. The evaluative design can be represented by the study of Cavalli et al. [8]. This study was published in 2004, and simulated times are therefore significantly shorter (typically 2.5 ns) than those available today. Nevertheless, the same length of simulations can be used today with much higher throughput in terms of the number of tested compounds or their binding poses; therefore, the study is still highly actual. Docking of propidium into human acetylcholine esterase (Alzheimer disease target) by the program Dock resulted in the prediction of 36 possible binding poses (clusters of docked binding poses). Six of them were then subjected to 2.5‐ns simulation. Evolution of these systems was analyzed in terms of root‐mean‐square deviation (RMSD). Binding poses with high stability in simulations were similar to experimentally determined binding poses for a homologous enzyme.

Figure 1.2 Schematic illustration of designs of biomolecular simulations. Horizontal dimensions correspond to coordinates of the system, and contours correspond to the free energy.

The second design is referred to as refinement design (Figure 1.2). It uses an assumption similar to the evaluative design, i.e. that molecular simulations tend to evolve from high‐free energy states to low‐free energy states. In the refinement design, it is hoped that the dynamics can drive the system from the predicted structure, even though incorrectly predicted, to global free energy minimum, the correct structure, or at least close to it. Naturally, shorter simulation times are necessary to demonstrate correctness or incorrectness of a model by the evaluative design. Longer simulation times are necessary to drive the system from the incorrect to the correct state by the refinement design. In the previous paragraph, I used the study of Cavalli et al. from 2004 [8] as an example of evaluative design. I can present the refinement design on the work published by the same author 11 years later [9]. They used unbiased simulation to predict the binding pose of picomolar inhibitor 4′‐deaza‐1′‐aza‐2′‐deoxy‐1′‐(9‐methylene)‐immucillin‐H in human purine nucleoside phosphorylase. They carried out 14 simulations (500 ns each) of the system containing the trimeric enzyme, 9 ligand molecules (to increase its concentration) placed outside the protein molecule, solvent, and ions. From these simulations, 11 evolved toward binding with a good agreement with the experimentally determined structure of the compl...

Table of contents

• Cover
• Table of Contents
• Foreword
• Part I Principles
• Part II Advanced Algorithms
• Part III Applications and Success Stories
• Index
• End User License Agreement