Bonetta, R., Ebejer, J.-P., Seychell, B., Vella, M., Hunter, T., and Hunter, G. J. (2016).Xjenza Online, 4:126–130.

Xjenza Online - Journal of The Malta Chamber of Scientistswww.xjenza.orgDOI: 10.7423/XJENZA.2016.2.03

Review Article

Role of Protein Structure in Drug Discovery

Rosalin Bonetta1, Jean-Paul Ebejer1, Brandon Seychell2, Marita Vella2, Therese Hunter2 andGary J. Hunter∗21Centre of Molecular Medicine and Biobanking, University of Malta, Msida, MSD 2080, Malta2Department of Physiology and Biochemistry, University of Malta, Msida, MSD 2080, Malta

Abstract. Many pharmaceuticals currently availablewere discovered either during the screening of naturalof synthetic product libraries or by serendipitous ob-servation. Such a “random” approach entails testingnumerous compounds and developing countless high-throughput screening assays. On the other hand, a “ra-tional” approach involves the structure-based route todrug discovery, where the structure of a target protein isdetermined. Hypothetical ligands may be predicted bymolecular modelling, while movement of a molecule maybe predicted by Molecular Dynamics Simulations priorto synthetic chemical synthesis of a particular molecule.Here, we will be discussing protein structure-based ap-proaches to drug discovery.

Keywords: Protein Structure, X-ray crystallography,Molecular Dynamics Simulations, Drug design

1 Introduction

Proteins are complex molecules composed of long stringsof twenty different types of amino acid. The length ofthe string and the order of amino acids are vitally im-portant for the protein to function properly in its biolo-gical role. This part of the process of protein function,the gene encoding the protein determines these factors.A single mistake (mutation) in the gene may cause thewrong amino acid to be incorporated into the sequenceor a nonsense mutation may cause the protein to betruncated. However, protein function is more directlydetermined by the protein’s three dimensional shape,the protein structure, and the availability of non-proteincofactors.

2 Protein Structure - why is it import-ant?

As can be seen in Fig. 1, a complex protein such asxanthine oxidoreductase (XOR) forms a highly convo-luted structure, but one which accommodates cofactorsand substrates perfectly. Protein structure is typicallydetermined by one of three methods today; X-Ray crys-tallography, Nuclear Magnetic Resonance spectroscopy(NMR) or cryoelectron microscopy. The former is theoldest and most commonly used technique, while the lat-ter is only just becoming available for the analysis of pro-teins at atomic resolution. X-Ray crystallography relieson the ability of the protein to form a regular moleculararray and crystallise; a completely biologically unnat-ural condition for any protein. Even so, it is possible andthere are now over one hundred thousand entries in thebiological structures databank, RCSB (Deshpande etal., 2005). The advantage of NMR over X-Ray crystallo-graphy is that it can be performed in solution (no crys-tals required) but the major problem is size; NMR can-not be used to determine the structure of large proteins.Electron microscopy will soon be capable of providingstructural information about protein as good as X-Raycrystallography, and is performed in solution. Todayit can yield protein structures to 2.2 A (X-Rays typic-ally give resolutions as high as 0.6 to 1.3 A). In thelaboratory of Biochemistry and Protein Science, at theUniversity of Malta, we use X-Ray crystallography todetermine protein structure, with crystallisation condi-tions determined in our laboratory applied and subjec-ted to X-Ray diffraction at the University of Leeds, UKin collaboration with Dr Chi Trinh. We have determ-ined the structures of several superoxide dismutase en-zymes and mutants (to a minimum of 1.7 A) and arecurrently working to solve the structures of others, in-

*Correspondence to: Gary J. Hunter (gary.hunter@um.edu.mt)

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Figure 1: Bovine Xanthine Oxidoreductase. Only one of two identical protein subunits is shown, in cartoon representation withblue beta sheets (arrows) and cream alpha helices (spirals). Cofactors and substrates are shown surrounding the protein in ball-and-stick representation and their corresponding positions of binding within the protein as atomic spheres. NAD is in orange, FAD is inyellow, iron-sulphur clusters are brown, the molybdopterin is purple and xanthine (substrate) in green. Together, the protein forms ascaffold for the cofactors which form an electron transport chain from one side of the protein (substrate) to the other (FAD/NAD).The figure was created using the PyMOL molecular Graphics System (Schrodinger LLC, 2010).

cluding human XOR. Structures such as these help us tounderstand how the protein functions, and will help todesign chemicals to be used pharmaceutically as modifi-ers of enzyme activity. X-Ray structures usually provideus with a quite static picture of the protein, and it isbest combined with other techniques in order to obtaina detailed idea of how the protein functions.

3 Molecular Dynamics Simulations ofBiomolecules

Molecular Dynamics Simulations are applied in the in-vestigation of numerous dynamic properties and pro-cesses by scientists in a variety of fields that in-clude structural biochemistry, enzymology, biophys-ics, molecular biology, biotechnology and pharmaceut-ical chemistry. Molecular Dynamics Simulations allowthe researcher to study the thermodynamic and time-dependent (kinetic) properties of biomolecules such asproteins. This provides an understanding of numerous

dynamic aspects of biomolecular structure, recognition,and function (Adcock & McCammon, 2006) The tech-niques involving Molecular Dynamics Simulations in-volve Langevin’s or Newton’s equations of motion, aswell as a particular molecular bond structure, paramet-rized force fields, and an initial conformation of atomicpositions, together with the velocities that are neces-sary to generate the atomic dynamics in a molecularsystem. Molecular Dynamics Simulations have a lim-ited function when used in isolation. The trajectory ofMolecular Dynamics (i.e., the progress of a simulatedstructure correlated to time) usually generates data re-lated only to the level of atomic positions, velocitiesand single-point energies. Researchers are usually inter-ested in obtaining macroscopic properties. The latterrequires the application of statistical mechanics, whichcombines microscopic simulations together with macro-scopic observables. Statistical mechanics provide themathematical expressions associating the distributions

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128 Role of Protein Structure in Drug Discovery

and motions of atoms to macroscopic observables in-cluding free energy, pressure and heat capacity (Callen,1985; McQuarrie, Salvaterra, De Blas, Routes & Mahler,1976). Molecular Dynamics Simulation programs in-clude AMBER, CHARMM, NAMD and POLY-MD.

Kinetic rate constants of ligand-receptor interactionsare essential in enzymology (Bar-Even et al., 2011) anddrug discovery (Copeland, Pompliano & Meek, 2006), asthey provide a good indication of drug efficacy (Cope-land et al., 2006). Thus, the prediction and optimisa-tion of these parameters is an important challenge inmedicinal chemistry (Copeland, 2016). Even thoughthese values may be measured experimentally, an accur-ate computational prediction would result in a usefulalternative in cases where the experiment is either ex-pensive or difficult to perform. Additionally, advancesin computational power, have allowed simulations to becarried out in significantly less time. This provides agreat potential for methods that require vast amountsof computational power.

Predicting the interaction between an enzyme andits substrate and other ligands via Molecular DynamicsSimulations is essential to fully understand the mech-anism of the enzyme. Predicting hydrogen bonding inan enzyme is crucial for analysing the structure andfunction of this type of biological molecule, especially interms of enzyme catalysis. Molecular Dynamics Simula-tions provide information on the molecule that is not ob-servable in the data obtained via X-ray crystallographyexperiments alone. With this knowledge it is then pos-sible to design new chemicals based upon the bindingrequirements discovered to inhibit or enhance the bio-logical activity of the protein. In many cases it maybe adventitious to modify the structure of an existing,known effector molecule (enhancer or inhibitor) to in-crease or decrease its activity. With computer aidedrational design, a new or modified pharmaceutical maybe created with better effectiveness and reduced side ef-fects. Molecular simulations give us the power to suggestor reject such modifications prior to chemical synthesisof the compound. This saves time, effort and money.

4 Protein Structure, Molecular Dynam-ics, Drug Discovery - tying the knotwith computational approaches

Structure-based virtual screening is a computationalmethod employed to find small, bioactive moleculeswhich sterically fit and interact with a protein. A lib-rary of small molecules (ligands) is “docked” to the pro-tein’s binding site in a typical “lock-and-key” fashion(Meng, Zhang, Mezei & Cui, 2011). Three things are re-quired for this computational approach. Firstly, a pro-tein structure is either determined experimentally (asdescribed earlier) or modelled computationally, typic-

ally using homology modelling. In homology modelling,we use one or more known protein structures with closesequence similarity as a template to model our proteinof interest. The binding site on the protein needs to beidentified. Secondly, a library of small molecules mustbe prepared and provided to the docking algorithm.This preparation may imply many steps such as sanit-isation, setting the appropriate ionization state, remov-ing salts, etc. Thousands to millions of molecules formpart of the digital library, only a fraction of which couldpossibly be tested physically in a laboratory. Thirdly,a docking protocol is required which defines the para-meters used in the docking experiment. This includes,but is not limited to, ligand flexibility, protein side-chainflexibility, role of water molecule in the binding site, andwhich scoring function to use. The scoring function isof critical importance as it assesses the goodness of thefit, producing a quantitative score which can be usedto rank each individual ligand. Many aspects are takeninto consideration when evaluating the interaction of theprotein with each ligand including steric fit, electrostat-ics, polar interactions and hydrogen bonding. The prob-lem is compounded by the many possible conformationsthe ligand (or protein) takes on. The scoring functionmust evaluate each of these binding poses. Some of themajor critiques of docking are the inability to calculatethe free binding energy correctly (possibly because ofthe additive nature of most scoring functions), proteinmain-chain flexibility, the correct prediction of water inbinding and the intensive computational resources re-quired. In order to alleviate some of these issues, dock-ing is sometimes used as a filtering first step before amore rigorous and computationally intensive moleculardynamics simulation. The top hits of the docking exper-iment are then rescored using MD. In a typical workflow,large virtual screening databases are first filtered usingfast and inexpensive docking protocols. This rescoring isbased on more physically realistic techniques for bindingfree energy estimations such as thermodynamic integra-tion, free energy perturbation, linear interaction energyand molecular mechanics/Poisson-Boltzmann and sur-face area (MM/PB-SA). Overall, this provides a moreaccurate prediction of the binding affinity between theprotein and the ligand (compared to the scoring func-tion in docking tools). Computer-aided drug design isan active field of research, which has gained a lot ofmomentum in recent years - mostly driven by the de-creasing productivity of the pharmaceutical industry tofind new drugs.

5 Limitations of MD-based methods

The main force fields that are currently being employedfor biomolecular simulations include AMBER (Asensio& Jimenez-Barbero, 1995), CHARMM (MacKerell et

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Role of Protein Structure in Drug Discovery 129

Figure 2: Components of a computational protein-ligand docking experiment. The goodness-of-fit of different small-molecules in a protein’s pocket is assessed by means of a scoring function. The top-ranked results may serve as input to morecomputationally exhaustive techniques, such as Molecular Dynamics.

al., 1998), and OPLS (Jorgensen & Tirado-Rives, 1988).Although, extended parametrisation for amino acids,nucleic acids, lipids, carbohydrates, and several ionicspecies has been included in the parent force fields inrecent years, the variability of small molecules (i.e., lig-ands) still poses a challenge to condensed-phase forcefields. Thus, the user must carry out specific paramet-risation. The latter is a time-consuming and an error-prone procedure, and has lead to the development of

some general force field sets such as GAFF57 for AM-BER, and CGenFF58 for CHARMM, together with spe-cific parametrisation toolkits. Several challenges mustbe overcome to further increase the importance of MD-based methods on drug design. The molecular mechan-ics force fields that are presently available partially orfully neglect charge transfer and polarisation effects, aswell as many electronic-based interactions. The currentlimits of force field and MD-based methods allow certain

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130 Role of Protein Structure in Drug Discovery

target families, such as metalloproteins, to be studiedwith limited accuracy (De Vivo, Masetti, Bottegoni &Cavalli, 2016).

6 Conclusion

It is the combination of computational approaches thatencompass techniques such as molecular dynamics sim-ulations and docking, together with the interpretationof related experimental structural data, which is essen-tial to provide a comprehensive understanding of themotions in proteins and their assemblies. Informationon the latter is crucial when synthesising improved bio-molecules and designing new drugs.

Acknowledgements

We would like to thank Dr Rebeca Garcia Fandinhofrom University of Santiago de Compostela, Spain, whois collaborating with us on Molecular Dynamics Sim-ulations of different proteins. This collaboration wasmade possible due to our participation in COST ActionCM1306 “Understanding Movement and Mechanism inMolecular Machines”. We would also like to thank ourcollaborators Prof. Dr Arwen Pearson (University ofHamburg, Germany) and Dr Chi Trinh (University ofLeeds, UK) for our ongoing collaborations in the fieldsof X-ray crystallography.

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