Free Energy Calculations by the Molecular Mechanics ... · After the simulations, structures for the conformational ensemble are extracted from the generated MD trajectories. Water

DOI: 10.1002/minf.201100135

Free Energy Calculations by the Molecular MechanicsPoisson�Boltzmann Surface Area MethodNadine Homeyer[a] and Holger Gohlke*[a]

114 � 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Mol. Inf. 2012, 31, 114 – 122

Methods Corner

1 Introduction

Free energy calculations have become part of the standardrepertoire of computational biochemical research due totheir widespread applicability, reaching from the predictionof binding affinities of small drug-candidate compounds tothe evaluation of relative stabilities of large biomacromo-lecular structures. The calculations can be performed bythe rigorous and computationally expensive free energyperturbation (FEP) and thermodynamic integration (TI)methods, which have recently been described in this”Methods Corner”.[1] The high computational costs of TIand FEP are caused, first, by the explicit treatment of sol-vent and, second, by determining the difference in free en-ergies of two states based on simulations that are carriedout at intermediate points along a transition path from onestate to another. The computationally intensive evaluationof individual contributions of a large number of solventmolecules can be avoided by regarding solvent effects onlyimplicitly, using continuum solvent methods. The computa-tional cost can be further reduced by considering only theend-point states in the free energy calculations. End-point,implicit solvent approaches thus have the advantage of arelatively low computational cost, while important solventeffects are still regarded, so that they provide a trade-offbetween efficiency and accuracy. As a consequence, withtoday’s computational resources, the binding of large com-pound data sets to biomacromolecules can be analyzedwithin a reasonable time span, and these approaches aremore accurate in binding pose and affinity predictions thansimple scoring functions.[2]

Probably the most well-known end-point, implicit solventfree energy method is the Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) approach, for the firsttime so denoted in 1998.[3] The approach has been used inmore than one hundred studies to determine the free ener-gies of molecular systems. Topics addressed in these stud-ies include evaluating docking poses, determining structur-al stability, and predicting binding affinities and hot-spots.This clearly demonstrates the wide applicability of the ap-proach. As a further advantage, MM-PBSA allows analyzing

individual energy contributions by means of a free energydecomposition, which gives additional insights into the en-ergetics of the investigated system. Furthermore, MM-PBSAcan be used to study very different molecular systems rang-ing from small chemical compounds binding to an RNAaptamer to large protein complexes with thousands ofatoms.[4,5]

In this review we provide an account of selected topicsthat have been addressed with the MM-PBSA approach,thereby focusing on more recent studies, and impartingknowledge to the reader about planning and evaluatingMM-PBSA analyses.

2 State-of-the-Art

2.1 Methodology and Practical Proceeding

In MM-PBSA the free energy of a molecule is estimated asthe sum of its gas-phase energy, the solvation free energy,and a contribution due to the configurational entropy ofthe solute (see Frontispiece[6]). The gas-phase energy is ap-proximated by the molecular mechanics energy of the mol-ecule, determined from a force field comprising terms forbond, angle, and torsion energies as well as van der Waalsand electrostatic interactions. In the calculation of the sol-vation free energy, two contributions are considered, apolar and a nonpolar one. For the polar contribution, thechange in the free energy upon transfer of a charged mole-cule from gas-phase (modeled as a homogeneous mediumwith dielectric constant e= 1) to solvent (modeled as a ho-mogeneous medium with e= 78 or 80) is estimated by animplicit solvent model.[7,8] In the initial MM-PBSA implemen-tation and still widely used, the polar contribution is calcu-

[a] N. Homeyer, H. GohlkeHeinrich-Heine-Universit�t, Institut f�r Pharmazeutische undMedizinische ChemieD�sseldorf, Germany*e-mail : [email protected]

Supporting Information for this article is available on the WWWunder http://dx.doi.org/10.1002/minf.201100135.

Abstract : Detailed knowledge of how molecules recognizeinteraction partners and of the conformational preferencesof biomacromolecules is pivotal for understanding bio-chemical processes. Such knowledge also provides thefoundation for the design of novel molecules, as undertak-en in pharmaceutical research. Computer-based freeenergy calculations enable a detailed investigation of theenergetic factors that are responsible for molecular stabilityor binding affinity. The Molecular Mechanics Poisson–Boltz-mann Surface Area (MM-PBSA) approach is an efficientmethod for the calculation of free energies of diverse mo-

lecular systems. Here we describe the concepts of this ap-proach and outline the practical proceeding. Furthermorewe give an overview of the wide spectrum of problemsthat have been addressed with this method and of success-ful analyses carried out, thereby focussing on ambitiousand recent studies. Limits of the approach in terms of accu-racy and applicability are discussed. Despite these limita-tions MM-PBSA is a method with great potential thatallows comparative free energy analyses for various molec-ular systems at low computational cost.

Keywords: MM-PBSA · Binding affinity · Implicit solvent · Molecular recognition · Drug design

Mol. Inf. 2012, 31, 114 – 122 � 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim www.molinf.com 115

Free Energy Calculations by the Molecular Mechanics Poisson�Boltzmann Surface Area Method

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lated via a finite-difference solution of the Poisson–Boltz-mann equation described in detail elsewhere.[9,10] Alterna-tively, an implicit solvent model based on Generalized Born(GB) theory can be used, which is a computationally moreefficient approximation to Poisson theory. This then leadsto the so-called MM-GBSA variant.[11,12] The nonpolar contri-bution to the solvation free energy primarily arises fromthe free energy required for cavity formation for the solutewithin the solvent. Most frequently, this contribution is esti-mated by a term proportional to the solvent accessible sur-face area of the solute. As a more appropriate alternative,the nonpolar contribution is computed as the sum of a dis-favorable energy resulting from cavity formation and a fa-vorable energy resulting from attractive interactions be-tween solute and solvent molecules.[12,13] Finally, the config-urational entropy of the solute is usually estimated using arigid-rotor harmonic oscillator approximation, applyingeither normal mode analysis[3] or quasi-harmonic analysis.[14]

From the so-computed free energies of interacting mole-cules and by applying a thermodynamic cycle a bindingfree energy can be calculated as the difference betweenthe free energy of a complex and the sum of the free ener-gies of its components.[15] According to Figure 1, this bind-ing free energy is equivalent to the sum of energy and con-figurational entropy contributions associated with complexformation in the gas-phase and the difference in solvationfree energies between the complex and the unbound mol-ecules. Changes in the configurational entropy are fre-quently neglected if only the relative binding free energy

of similar ligands shall be analyzed; this then results in aneffective binding energy.

Although MM-PBSA calculations can be performed basedon single structures, e.g. minimized structures[2,16,17] or crys-tal structures,[18] they are typically conducted based on aconformational ensemble generated by molecular dynam-ics (MD) simulations. In the latter case, each energy compo-nent is determined by averaging over the respectiveenergy contributions from all conformers in the ensemble.That way, effects of conformational changes taking place inthe molecular system are considered in the free energy cal-culation, and an estimate of the precision of the calcula-tions is obtained.

It is common to run MD simulations with explicit solventfor generating the conformational ensemble, because con-tinuum solvent simulations have been shown to yield lessaccurate results.[19] Usually, the molecular system is solvatedin a box of water, and counter ions are added to enforceneutrality. In binding free energy studies it is also possibleto just add a water sphere centered on the ligand andmove only those atoms located in the core of this sphereduring the simulation,[20] which decreases the computation-al costs. The simulations are typically conducted with thesame force field that is used for the MM-PBSA calculationsin order to avoid inconsistencies,[19] and long-range electro-static interactions are computed by the particle-meshEwald procedure.[3,21]

After the simulations, structures for the conformationalensemble are extracted from the generated MD trajectories.Water molecules and counter ions are removed, and thefree energy is calculated as described above.

In practice, depending on the information one wishes togain from the free energy calculations, three main types ofMM-PBSA calculations can be distinguished. If the stabilityof two conformations of a molecule shall be compared,simulations and free energy calculations are conductedseparately for both conformers, and the free energies aredirectly evaluated (Figure 2A). If a binding free energy shallbe computed the difference between the free energies ofthe complex and its components is calculated either from asingle trajectory of the complex (“single-trajectory ap-proach”, Figure 2B) or from separate trajectories of com-plex, receptor, and ligand (“three-trajectory approach”, Fig-ure 2C). Although the singe-trajectory approach neglectsthe conformational flexibility of the unbound structures, itis usually applied in all cases where no large structuralchanges upon binding are expected, because it gives lessnoisy results due to the cancellation of intramolecular con-tributions[20,22] and, therefore, allows performing MM-PBSAanalyses based on shorter simulations. Usual simulationtimes range from a few picoseconds to several nanosec-onds depending on system flexibility, size of the data set,and desired precision of the results, although longer simu-lation times are recommended for obtaining properly ther-malized conformational ensembles.

Nadine Homeyer is a postdoctoral re-search fellow in the Institute for Phar-maceutical and Medicinal Chemistry atthe Heinrich-Heine-University, D�ssel-dorf. She received her PhD from theUniversity Erlangen-Nuremberg (Ger-many) in 2008 for her work on the ef-fects of phosphorylation on proteinstructure, dynamics, and interaction.Her research interests lie in molecularrecognition, lead optimization, and thedynamics of biomacromolecules.

Holger Gohlke is a Professor of Pharma-ceutical and Medicinal Chemistry atthe Heinrich-Heine-University, D�ssel-dorf. His research aims at understand-ing and predicting receptor-ligand in-teractions and the modulation of bio-logical processes by pharmacologicallyrelevant molecules. His group devel-ops and applies methods at the inter-face of computational pharmaceuticaland biophysical chemistry and molecu-lar bioinformatics.

116 www.molinf.com � 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Mol. Inf. 2012, 31, 114 – 122

Methods Corner N. Homeyer, H. Gohlke

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2.2 Application in Drug Design

The lower computational costs, compared to TI and FEP, to-gether with a more sophisticated computation of the freeenergy components, compared to common scoring func-tions, makes MM-PBSA an attractive method for all stagesof drug design. In early stages, large compound data basesare screened for promising drug candidates using dockingprograms and scoring functions to assess the binding capa-bility of the molecules to a target receptor. However,simple scoring functions may neglect important energeticcontributions, such as the solvation free energy, and, thus,may have difficulties in correctly predicting the bindingpose and binding affinity of a molecule. A combination ofdocking, molecular mechanics calculations, and MM-PBSA

proved to be more successful in the identification of thecorrect ligand binding pose than pure docking approachesin several cases.[2,17,23,24]

As such, in an extensive performance study, Huo et al.[2]

analyzed the performance of MM-PBSA to discriminate cor-rect from false ligand binding poses in 98 complexes. Ini-tially, ligands were docked to receptors with the Auto-dock[25] program. For the 100 best-scored docking poses aswell as the experimentally determined binding pose ofeach complex the positions of all atoms within a sphere of9 � centered at the ligand were optimized by molecularmechanics minimization. The optimized structures were re-evaluated using implicit solvent free energy calculations. Al-though outperformed by another implicit solvent ap-

Figure 1. Thermodynamic cycle for calculating a binding free energy. In MM-PBSA, the binding free energy results as the sum of theenergy and configurational entropy associated with complex formation in the gas-phase and the difference in solvation free energies be-tween the complex and the unbound molecules.



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proach, MM-PBSA reached a success rate of 79.6 % in there-evaluation of the structures, if the top five scored poseswere regarded.

While, until recently, the use of MM-PBSA in virtualscreening has been limited to evaluating a few hundreddocking solutions,[26] it can nowadays be applied for scoringthousands of compounds due to the great increase in com-puter power. As the potential of MM-PBSA in discriminatingtrue binders from a much larger number of decoys hasbeen demonstrated in initial high-throughput virtualscreening studies,[27,28] we expect the method to be usedmore extensively in lead identification in the future. How-ever, the general benefit of MM-PBSA in this field still re-quires further validation on more protein systems andligand data sets.[29]

When leads have been identified they need to be furtherimproved in order to obtain drug-candidates with goodpharmacokinetic (absorption, distribution, metabolism, ex-cretion, and toxicity) features that, at the same time, showa high binding affinity and selectivity for the target. In thisstage of drug design, it is essential to be able to determinethe relative binding affinity of a small number of drug-can-didates, usually less than 50, with high accuracy. The mainchallenge for the applied method lies in the small range ofbinding affinities studied, which is typically below 10 kcalmol�1.

In initial studies, promising compound rankings were de-scribed based on MM-PBSA relative binding affinities forsingle receptor systems.[20,30,31] However, it has been recog-nized recently that a more extensive testing is needed tovalidate the general applicability of the approach for leadoptimization. With the required computational power nowat hand, extensive performance studies investigating the

accuracy of MM-PBSA for determining relative binding af-finities for several different protein-ligand systems can beconducted.

In one such study, Huo et al.[22] calculated the bindingfree energies of 59 ligands interacting with six differentproteins and found good correlations with r2>0.5 for threeout of the six protein systems studied. In addition, Yanget al.[32] recently described the results of MM-PBSA analysesin which they determined the binding affinities of a total of156 ligands to seven different protein families separatedinto six groups. Here, correlations of r2>0.5 were found forthree out of six groups. However, it needs to be consideredthat the correlations might have come out weaker due tothe sequence variation within the protein families in thiscase.

Overall the results of the two studies suggest that thegoodness of the agreement between experimentally deter-mined binding affinities and those computed with MM-PBSA does not only depend on the binding affinity rangeof the analyzed ligand data set, but is also determined bythe specific features of the protein-ligand interaction andthe degree of structural similarity between the studiedcompounds. While for sets of ligands with similar structuresusually satisfactory results were obtained, the performancevaried for those with diverse chemical structures.[22] Further-more, the number of polar or charged groups involved inligand binding seems to have a high influence on the cal-culated binding free energies. For systems with a largenumber of charged residues in the binding pocket and,hence, where a strong change in the polarization occursupon binding, using e>1 for the “gas-phase state” wasshown to yield better results.[22,32] Thus, carefully choosing e

according to the expected degree of polarization in the

Figure 2. Main types of MM-PBSA calculations: Comparison of the stability of two conformations (A) as well as binding free energy calcula-tions according to the single-trajectory approach (B) and the three-trajectory approach (C).



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binding pocket and/or based on test calculations with eval-uation data may prove beneficial.

Finally, applications of MM-PBSA in lead optimizationhave shown that for ligand data sets with a binding affinityrange of less than 3 kcal mol�1 only unsatisfactory correla-tions of experimentally determined and computed relativebinding affinities can be expected (Table S1). Although ex-ceptions to this observation have been described for indi-vidual data sets,[22] a statistical uncertainty of at least1.1 kcal mol�1 mean absolute deviation in the solvation freeenergy relative to a method-weighted average[18] and astandard deviation of 0.7–1.0 kcal mol�1 in the MM-PBSAbinding free energy, found for 200 snapshots from a 2 nssimulation,[19] make it difficult to obtain good relative bind-ing free energy predictions for ligand data sets with smallbinding affinity ranges. For data sets with wider binding af-finity ranges the quality of the prediction depends on thespecific features of the binding interaction and on the simi-larity of the ligands. The best results are obtained forligand series with high structural similarity and/or high hy-drophobicity as well as with a uniform total charge.

2.3 Application to Biomacromolecules

Free energy studies on biomacromolecules can give impor-tant insights into the structures and mechanisms underly-ing biological processes. As such, MM-PBSA has been ap-plied to study the stability of DNA,[3,33] RNA,[34] and pro-tein[35] conformers in order to identify the native foldamong a set of decoy structures or the fold that is moststable in a given environment. Recently, it has been dem-onstrated that MM-PBSA is also able to predict disulfideconnectivities in proteins.[36] When small cysteine-rich pro-teins of the knottin family with different possible patternsof disulfide bridges and similar overall structure were ana-lyzed with MM-PBSA, the connectivities proposed by previ-ous experimental studies were identified as being energeti-cally most favorable. This study showed that, in those caseswhere determining the correct cysteine pairing is difficultwith X-ray crystallography and nuclear magnetic resonancespectroscopy, MM-PBSA free energy calculations can helpto identify the most favorable pattern. However, it has tobe considered that the energetically most stable pattern ofdisulfide bridges does not need to be the connectivityfound in nature, where the oxidative folding pathwaymight be kinetically controlled.

Binding free energy calculations of biomacromoleculescan yield information about the stability of complexes andthe binding mechanisms. For example, additional knowl-edge about DNA binding of Cys2His2 zinc finger proteinscould be gained from an energetic analysis of the interac-tion between the Zif268 zinc finger protein and DNA.[37]

Binding affinity calculations for one-finger, two-finger, andthree-finger Zif268 protein/DNA complexes revealed thatthe binding affinity does not increase linearly with thenumber of zinc fingers; rather, it is mainly enhanced by the

addition of the third zinc finger domain. Molecular dynam-ics simulations and detailed energy decomposition analysesshowed that the non-linear cooperativity in Zif268/DNAbinding is primarily caused by a tighter interaction be-tween these components, enabled by an enlargement ofthe DNA major grove and an unwinding of the DNA helixupon attachment of the third zinc finger. The gain in polarinteraction energy caused by the tight DNA contact of thethree finger protein is much larger than the cost of theDNA conformational change, so that the binding affinity ishighly favorable for the protein with three zinc finger do-mains.

The specificity of protein-protein, protein-DNA, and pro-tein-RNA interactions can be studied by modifying residuesinvolved in complex formation and analyzing the differen-ces in binding free energies caused by these modifications.Such modifications can involve interconversions of DNA orRNA bases,[37,38] transformations of phospho-amino acid res-idues,[39] and amino acid mutations.[38] As for the latter, it isa popular approach to investigate protein-protein interac-tions by mutating residues in the contact interface consec-utively to alanine and, then, calculating the binding freeenergy for wildtype and mutants. Since alanine can be as-sumed to not significantly contribute to binding due to itssmall nonpolar side chain, the change in binding freeenergy upon an alanine mutation reveals interaction “hot-spots”, i.e. , amino acids that are essential for complex for-mation. Already in 1999, Massova and Kollman proposed afast alanine scanning procedure,[40] which has proven to besuccessful in hot-spot identification[5,41] and is still widelyapplied today[42–44] to reveal the specific “fingerprints” of in-teractions. The speed up compared to conventional MM-PBSA calculations, in which the binding free energies arecalculated based on separate trajectories for wildtype andmutants, is achieved by creating mutants directly from thestructures sampled during a simulation of the wild typecomplex. Despite neglecting conformational adjustmentsthat could occur upon a mutation, this approach has beenshown to be as good as[40] or even better[45] than the sepa-rate trajectory alternative for all residues that are not in-volved in highly polar interactions. In contrast, binding freeenergy contributions of buried residues involved in saltbridges have often been overestimated.[41,45] Thus, the influ-ence on binding of the latter type of residues should bedetermined by the conventional, separate trajectory ap-proach.[41]

Insights into the determinants of binding can be ob-tained with even less computational effort by applying adecomposition of the binding free energy in terms of sub-structural elements, such as residues, side chains, or back-bone parts, both within the MM-PBSA[46,47] and the MM-GBSA[11] frameworks. In the case of protein-protein interac-tions, these calculations have been shown to yield resultsin agreement with computational alanine scanning[48] andhave even been used in a prospective manner.[49] Thus,MM-PBSA based screens of binding interfaces of biomacro-



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molecular complexes have been demonstrated to be veryvaluable for revealing the specific features of an interac-tion.

3 Current Limitations

Although MM-PBSA proved its strengths in many drugdesign and biomacromolecular analyses, the studies alsorevealed weaknesses of the method. Sources of error in-clude the prediction of solute entropies, the estimation ofsolvation free energies for charged, buried groups, confor-mational sampling, and parameter selection. Although ab-solute free energies of binding have been computed thatare in good agreement with experimental data,[20,30,37,50]

MM-PBSA performs better in determining relative free ener-gies.[22] This is probably due to a cancellation of errors, es-pecially when the relative free energies of similar moleculesor conformers are analyzed.

The largest uncertainty in MM-PBSA calculations seemsto originate from the estimation of the solute entropy.[19]

While this contribution can be neglected if relative bindingfree energies of very similar molecules shall be computed,it needs to be considered for absolute free energies or ifsignificant conformational changes occur upon binding.[22,30]

Estimating changes in the vibrational entropy by normalmode analysis probably leads to systematic errors, becauseanharmonic contributions are neglected.[22,30] Variations inthe changes of the vibrational entropy upon complexationof ~5 kcal mol�1 have been observed for individually mini-mized structures from the same trajectory.[31] These signifi-cant fluctuations in the entropic contribution are potential-ly caused by a mismatch in the geometry between mini-mized structures of the complex and of the receptor orligand.[22,51] In addition, it has been speculated that the en-tropy change upon ligand binding is overestimated bynormal mode analysis. However, this overestimation seemsto be compensated by a likewise overestimation of the ef-fective binding energy of similar magnitude, so that the re-sulting binding free energies still agreed well with thosefrom experimental studies.[52] In contrast to normal modeanalysis, quasiharmonic analysis implicitly takes into ac-count some anharmonic effects.[30] However, with thismethod, a convergence of the entropy estimates canhardly be reached within currently common simulationtimes, even for small biomacromolecular structures.[12] Thus,although efforts to improve the accuracy of entropy calcu-lations have been undertaken,[53–55] determining entropiccontributions remains a challenging task.

Another contribution that may not be accurately cap-tured by MM-PBSA is the solvation free energy of polarcompounds and of charged groups, especially those locat-ed in the interior of biomacromolecules. As the uncertaintyin the estimation of the solvation free energy by the Pois-son–Boltzmann method is proportional to the size of thisenergy, the uncertainty increases with the polarity of the

studied compounds.[18] Furthermore, while the solvationfree energy of solvent-exposed residues of biomoleculescan be fairly predicted, computing the free energy of solva-tion of buried groups is a difficult task due to the inhomo-geneity of the interior of the molecules.[52] By using a singledielectric constant for the solute in this case, the inhomo-geneous screening of electrostatic interactions cannot beadequately accounted for. This explains to some extentwhy binding free energies of polar ligands and ion-ion in-teractions in biomacromolecular complexes are oftenwrongly estimated.[30,41] In some cases, this problem hasbeen overcome by selecting a dielectric constant for thesolute that properly describes the polarization effectswithin a specific binding pocket.[22,32]

Furthermore, contributions of structural water moleculesto the binding free energy are not accounted for by implic-it solvation models. Explicitly considering such water mole-cules in the free energy calculations may provide a possibil-ity to overcome this shortcoming of MM-PBSA.[2,56]

Representative conformational ensembles are a prerequi-site for accurate free energy estimates of a molecule. There-fore, an adequate sampling to cover as much of the confor-mational space of the solute as possible is essential for en-semble-based MM-PBSA calculations. Among others, thelength of the MD simulation required for representativesampling depends on the flexibility of the investigatedsystem. Generally, a stable trajectory, e.g. , as judged by theroot-mean-square deviation to the initial structure, andstable MM-PBSA energies should be used as minimal crite-ria for determining the necessary sampling time.[57] Forbinding free energy estimates of small compounds by thesingle-trajectory approach short simulation times of 400–500 ps have been reported to yield converged results forindividual systems.[32,58] In contrast, the three-trajectory ap-proach shows usually larger fluctuations in the binding freeenergy and, hence, requires more extensive sampling.[58]

Still, the higher computational costs of the latter approachcan be justified in those cases where larger conformationalchanges take place upon complex formation, as then theconformational strain energy needs to be explicitly takeninto account.[37]

Whether longer sampling times can improve the accura-cy of the free energy estimates also depends on the qualityof the parameters used for the description of the molecularsystem. If the force field cannot correctly describe the sys-tem’s features, longer simulations will not lead to better re-sults.[22] Therefore, a good force field is a prerequisite forgenerating representative conformational ensembles, too.In addition, intrinsic parameters of the method, such as theradii used for the calculation of the solvation free energyand the dielectric constant of the solute, also affect thequality of the results.[12,22,32] Consequently, a careful selec-tion of parameters, force field, and sampling conditions iscritical for the success of MM-PBSA free energy estimates.



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4 Outlook

Out of the wide spectrum of applications of MM-PBSA, twoareas are especially promising and will probably play amajor role in future research: Virtual screening for drug-candidate compounds and residue-wise energy analyses ofbinding sites to characterize molecular interactions. Theusefulness of MM-PBSA for the latter type of applicationhas been proven in numerous studies, and it is highly likelythat MM-PBSA will play an important role in the detailedenergetic investigation of complex formation in the future.However, as for the field of drug design, the focus of MM-PBSA applications will presumably change. As the currentaccuracy of MM-PBSA hampers the investigation of com-pounds with small differences in the binding affinities, themain focus will be on re-scoring large compound sets cov-ering a wider range of binding affinities during the leadidentification stage of drug design. For lead optimization,the large increase in computer power will soon allow fine-tuning of drug candidates with the help of more rigorousfree energy calculation approaches on a routine basis. Inorder to be able to compete with the less computationallydemanding scoring functions in terms of efficiency, fasthigh-throughput procedures for MM-PBSA analyses areneeded. First efforts in this direction have been undertak-en.[27,59] The developed procedures allow screening thou-sands of compounds within one day, and a special adapta-tion of MM-PBSA for enterprise grid-computing allowsusing otherwise idle computers for these calculations.[59,60]

Acknowledgements

We are grateful to Bayer Schering Pharma AG for financialsupport.

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Received: June 27, 2011Accepted: November 26, 2011

Published online: January 10, 2012



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