Computational Tools for Chemical Biology

Sonsoles Martín-Santamaría

• Publisher: Royal Society of Chemistry
• Year: 2017
• Print ISBN:
  9781782627005
• eBook ISBN:
  9781788012560
• Edition: 1
• Topic: Physical Sciences
• Subtopic: Biochemistry

The rapid development of efficient computational tools has allowed researchers to tackle biological problems and to predict, analyse and monitor, at an atomic level, molecular recognition processes. This book offers a fresh perspective on how computational tools can aid the chemical biology research community and drive new research.
Chapters from internationally renowned leaders in the field introduce concepts and discuss the impact of technological advances in computer hardware and software in explaining and predicting phenomena involving biomolecules, from small molecules to macromolecular systems. Important topics from the understanding of biomolecules to the modification of their functions are addressed, as well as examples of the application of tools in drug discovery, glycobiology, protein design and molecular recognition. Not only are the cutting-the-edge methods addressed, but also their limitations and possible future development.
For anyone wishing to learn how computational chemistry and molecular modelling can provide information not easily accessible through other experimental methods, this book will be a valuable resource. It will be of interest to postgraduates and researchers in the biological and chemical sciences, medicinal and pharmaceutical chemistry, and theoretical chemistry.

CHAPTER 1

Computational Chemistry and Molecular Modelling Basics

SAMUEL GENHEDEN^a, ANNA REYMER^a, PATRICIA SAENZ-MÉNDEZ^a,b AND LEIF A. ERIKSSON^{* a}

^a Department of Chemistry and Molecular Biology, University of Gothenburg, 405 30 Göteborg, Sweden

^b Computational Chemistry and Biology Group, Facultad de Química, UdelaR, 11800 Montevideo, Uruguay

^* Email: [email protected]

1.1 Introduction

The use of computers for predicting the structures and properties of biomolecules has closely paralleled computer development since the 1950s, and has been one of the core areas of theoretical or computational chemistry for the past 30 years. Initially, the focus was on force-field based methodologies for studying the structures, dynamics and interactions of biomolecules as such, and the development of accurate models for the main biological solvent, water. With the emergence of accurate quantum chemical techniques suitable for studying (from a quantum chemistry perspective) large systems, density functional theory entered the stage in the 1990s as the key approach for investigating enzymatic mechanisms or properties and reactions of small, but biologically relevant, molecules. The combined use of these tools, so-called QM/MM and lately QM/MM-MD techniques enables precise descriptions of biological phenomena and reactions.

With the exponential increase in data to be analysed, obtained through the introduction of automated whole genome and protein sequencing techniques, the field of bioinformatics rapidly emerged in the early 2000s from the pioneering laborious mapping and comparison of protein and gene sequences in molecular biology, via an intense phase, which to a large extent can be viewed as ‘database mining’ and the development of efficient computer based algorithms, into a science of its own, which today has reached a high level of maturity and sophistication. Tools in bioinformatics are nowadays used with great success in structural biology, computational chemistry, genetics, molecular biology, the pharmaceutical industry, pharmacology and more. The aspects of bioinformatics included herein focus on protein structure determination (often referred to as homology modelling), and the tools of database screening and prediction used in drug design.

In this chapter, a brief outline of simulation techniques are given, focusing on the interface between biology and medicinal chemistry; that is molecular mechanics/molecular dynamics to explore the evolution of a system, homology modelling to determine protein structures, and the use of bioinformatics tools such as molecular docking and pharmacophores in drug design. The aim is to provide a brief introduction to a vast and rapidly growing field. In subsequent chapters, more specialised applications are presented, that build upon the foundations given herein. The chapter is in no way intended to be an exhaustive coverage of the entire area of biomolecular simulations, and we have deliberately avoided the inclusion of quantum chemical methods.

The interested reader wishing to dig deeper into the basics of computational modelling is referred to any of the many excellent textbooks available.^1–11

1.2 Techniques in Biomolecular Simulations

1.2.1 Molecular Mechanics and Force Fields

The palette of computational chemistry methods has become increasingly versatile. Starting from quantum chemistry, where molecular orbitals and electrons occupying these are described, allows us to calculate any physical or chemical property that directly depends on the electron distribution; reaching all the way to coarse-grained molecular dynamics simulations, where groups of atoms described as beads interacting by laws of Newtonian mechanics, providing valuable insights into the complexity of biological processes on a bigger, cellular level scale. For comparison, a feasible size of a system treated by quantum chemistry calculations, even today, does not exceed a few hundred atoms, whereas the empirical methods, e.g. molecular mechanics (MM), can easily handle several hundred thousand atoms, and in case of a coarse-grained approach—several million atoms. Thus, the latter class of methods has become popular among researchers dealing with bio-macromolecular systems, which exist and function in aqueous solutions or lipid environments. The surrounding environment could take up to 90% of all atoms in a model system, and its presence is crucial for the correct representation of living matter. The giant leap in system size is possible due to reasonable simplicity of the MM potential energy functional. The potential energy is calculated by adding up the energy terms that describe interactions between bonded atoms (bonds, angles and torsions) and terms that describe the non-bonded interactions, such as van der Waals and electrostatic interactions (eqn (1.1)).

(1.1)

The bonded terms represent the stretching of bonds (l), bending of valence angles (θ) and rotation of torsional angles (ω); cf.Figure 1.1. Three force constants: k_l, k_θ and V_n characterise the energetic cost relative to the equilibrium value, needed to increase the value of a bond length (l₀), angle (θ₀) or rotation around a torsion angle. The torsion term represents a periodic rotation of a dihedral angle with periodicity n and phase γ. The non-bonded energy is the sum of repulsion, attraction and electrostatics between non-bonded atoms. The parameter ε_ij is related to the well-depth of Lennard-Jones (LJ) potential, r_0ij is the distance at which the LJ potential has its minimum. q_i is the partial atomic charge, ε₀ is the vacuum permittivity, and r_ij is the distance between atom i and atom j. The LJ and Coulomb potentials describe the short-range non-bonded interactions. The evaluations of the long-range electrostatic interactions can be difficult and was often ignored beyond a specific cut-off distance resulting in approximations in a calculation. With the introduction of Ewald summation and particle mesh Ewald (PME) method long-range electrostatic calculations have become significantly more accurate.^12,13

Figure 1.1 In molecular mechanics, molecular systems are treated by means of classical physics: atoms are represented as charged spheres, which have bonded (bond stretch, angle bend and torsional angle rotation) and non-bonded interactions (van der Waals and electrostatics).

The simplicity of the potential energy functional form means, on the one hand, fast and easy calculations, and on the other hand that the accuracy of the empirical methods is highly dependent on the set of empirically derived parameters describing atoms and their interactions. These parameters are either derived from ab initio or semi-empirical quantum chemistry calculations on small model systems or by fitting to experimental data, e.g. X-ray and electron diffraction, NMR and IR spectroscopy. The potential energy functional form and the empirically derived parameters can be both referred to as a force field.

There are a number of empirical force fields families available, having different degrees of complexity, and oriented to treat different kinds of systems. The most popular ones designed for biological macromolecules are AMBER,^14,15 CHARMM,¹⁶ and GROMOS.¹⁷ Other force fields, such as OPLS¹⁸ and COMPASS¹⁹ were originally developed to simulate condensed matter; GAFF^20...

Table of contents

• Cover
• Title
• Copyright
• Contents
• Chapter 1 Computational Chemistry and Molecular Modelling Basics
• Chapter 2 Molecular Dynamics Computer Simulations of Biological Systems
• Chapter 3 Designing Chemical Tools with Computational Chemistry
• Chapter 4 Computational Design of Protein Function
• Chapter 5 Computational Enzymology: Modelling Biological Catalysts
• Chapter 6 Computational Chemistry Tools in Glycobiology: Modelling of Carbohydrate–Protein Interactions
• Chapter 7 Molecular Modelling of Nucleic Acids
• Chapter 8 Uncovering GPCR and G Protein Function by Protein Structure Network Analysis
• Chapter 9 Current Challenges in the Computational Modelling of Molecular Recognition Processes
• Chapter 10 Novel Insights into Membrane Transport from Computational Methodologies
• Chapter 11 Application of Molecular Modelling to Speed-up the Lead Discovery Process
• Chapter 12 Molecular Modelling and Simulations Applied to Challenging Drug Discovery Targets
• Chapter 13 The Polypharmacology Gap Between Chemical Biology and Drug Discovery
• Subject Index