In answering research questions, computational chemists have a vast selection of methodologies at their disposal. MM can be used to study very large molecules, because other QM methods, such as semiempirical calculations, ab initio, and DFT are relatively slow and would exhaust computational resources.
However, MM methods are unable to address interactions between the ligand and the receptor in metal-containing systems. QM-MM is thus the crucial component in computational drug discovery. Five key facets are imperative in planning a QM-MM calculation on an enzyme: choice of the QM method, choice of MM force field, segregation of the system into QM and MM regions, simulation type eg, MD simulation or calculation of potential energy profiles , and whether advanced conformational sampling will be performed.
The choice of QM method is crucial. A plethora of different QM methods exists, ranging from fast, semiempirical methods eg, AM1, PM3, SCC-DFTB; low accuracy and maximum of 2, atoms to more accurate but more computationally expensive Hartree—Fock and density-functional eg, B3LYP; medium accuracy and maximum of atoms , and molecular orbital ab initio eg, MP2, coupled cluster; very high accuracy and maximum of 20 atoms methods. Not all methods are applicable to all systems, for reasons of accuracy, practicality, or lack of parameters eg, for semiempirical methods Table 2.
Generally, but not always, improved accuracy comes at the price of increased calculation expenses. The use of supercomputing to calculate QM has been attributed to expensive calculations for small systems. However, the use of Hadoop 80 could make QM faster and more scalable and efficient. Hadoop could allow for better cluster utilization as well to accommodate larger jobs, which will help QM, as it needs more computational resources to run calculations for larger systems.
Tangible advances in the use of QM to solve relevant pharmaceutical problems have been seen in the last decade, eg, the use of the hybrid QM-MM approach to determine the free-energy landscape of the enzymatic reaction mechanism. However, there are concerns regarding the accuracy of these methods, particularly in the area of docking and scoring 90 — 93 and QSAR. The in silico approach is fast and environmentally friendly, but it does not replace experimentation. Regardless, failures encountered in the pharmaceutical industry at the drug-discovery stage can be attributed to a number of factors that are not limited to wrong force-field parameters, especially for metals, 96 disregard for protein flexibility 97 , 98 or domain of applicability, 99 or nonrigorous validation of the QSAR model.
QM, a method used to replicate an experimental work accurately, proffers a potential solution to the failures mentioned. The reaction mechanisms of these enzymes have been studied using the QM-MM approach. The application of QM is limited to relatively small systems Table 2. On the other hand, MM methods can treat millions of atoms or more. In addition, a recent article implicitly explained that the need for suitable computational approaches or tools could enhance success rates in the drug-discovery process.
The process of docking involves the correct prediction of ligand conformation and orientation within a targeted binding site, while scoring predicts the binding free energy of a complex formation. Each docking program is ideal for precise docking problems; however, combining different computational methods can improve the reliability and accuracy of results.
As such, implementation of QM docking would systematically improve the accuracy of description of enzyme—ligand interactions, as well as binding affinity. Limitations in scoring functions are being increasingly exposed, particularly as more challenging and electronically complex pockets are being probed, eg, systems with metals.
Calculations indicated generally improved poses after docking. The ability of QM-MM docking has been further evaluated in other studies to predict the poses of metalloproteins. Therefore, there appears to be evidence that QM-based models provide scoring functions that can improve the quality of predicted docking poses for challenging receptors. Virtual screening has become a powerful tool in the drug-discovery process to search for novel compounds with desired properties.
This is followed by a scoring function. QSAR is a mathematical representation that attempts to correlate a set of compounds with dependent variables activity values, eg, K i , EC 50 , ED 50 , IC 50 and a set of independent variables called descriptors. As the ADMET properties of a drug determine its activity, the development of a new drug with reasonable ADMET makes drug discovery a more difficult and challenging process in the pharmaceutical industry. The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run.
The profits that flow in with the discovery of new drugs have always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. In recent years, the use of CADD to simulate drug—receptor interactions has made rational DD feasible and cost-effective. However, more attention should be paid to the way pharmaceutical companies use in silico tools. While docking, virtual screening, QSAR, and MM manage computational resources and allow rapid scans of large libraries, the accuracy of the results is in question when it comes to experimental data correlation.
Therefore, using a combination of QM to parameterize the molecules and MM to describe and solvate the protein, a more accurate understanding of binding affinity and protein—molecule interaction could be gained Figure 3. Furthermore, using QSAR to predict the activity of an existent molecule may lead to remarkable savings with respect to development time and cost Table 3. Probably, most pharmaceutical companies today follow common technology processes for discovering drugs.
These include cloning and expression of human receptors and enzymes using high-throughput, automated screening and the application of combinatorial chemistry. We believe that pharmaceutical companies rarely use accurate DD tools, eg, QM, in their DD, owing to the fast pace of their work.
The use of QM is not limited to creating a computational model of a drug, but can also be applied to proteins, DNA, carbohydrates, and lipids, as well as solvent molecules that are involved in drug transportation, binding, and signaling. QM has featured in some medicinally relevant chemistry calculations in providing informative descriptors for QSAR and 3-D conformation for ligands. QM methods offer the ability to provide an accurate representation of ligands and proteins where MM parameterization struggles. QM approaches hold promise in addressing pharmacological problems on the time scale demanded by drug-discovery research.
After ups and downs in the perception of CADD and perhaps some overhyping of its promises in drug development, it could be said that CADD is becoming a routinely used component of drug discovery. Currently, sophisticated CADD tools are typically applied by modeling experts, but are increasingly spreading to the desktops of medicinal chemists as well.
Ligand poses predicted from docking to receptors, such as metalloproteins, have been shown to resemble experiments more closely when partial charges are derived from QM or QM-MM calculations. However, the QM-MM approach appears to be of most benefit for low-resolution X-ray structures, where an incorrectly assigned ligand structure due to its MM force field is more likely. Studies demonstrate that the use of accurate charges, in many cases, leads to improvement in docking accuracy in a wide range of Protein Data Bank complexes.
The principal uncertainty at this point is whether this improved performance in docking can be noticed in other in silico methods. In this review, we have discussed how the implementation of QM-based methods could help the drug-discovery and DD process in the pharmaceutical industry.
This method could have strong impact in future drug development, because of the endless demand for new drugs and the short time frame pharmaceutical companies have in developing them. Pharmaceutical companies have to reach a compromise between accuracy and productivity by applying QM in their research. Most importantly, before embarking on CADD, it is appropriate to evaluate the diversity and demand of accuracy of molecules to be designed in the project, which in turn dictates the most appropriate approach to select.
It is also possible to reparameterize approximate methods in order to improve the accuracy of results in specific reactions that require numerous energy evaluations. A number of studies have sought to incorporate QM and QM-MM into their approaches for calculating ligand—receptor binding affinities. These approaches show promising results, but require further development to be broadly applicable. Finally, QM methods have proved valuable in quantitative analysis of the energetics of ligand deformation on binding.
Although computation of binding energies remain a challenging and evolving area, current QM approaches could offer detailed information on the nature and relative strengths of complex active-site interaction, which is valuable in molecular design. It is likely that QM will become a more prominent tool in the repertoire of the computational medicinal chemist. Therefore, modern QM approaches will play a more direct role in informing and streamlining the drug-discovery process. This could provide better understanding of the in silico tools in drug design and development with improved ADMET, pharmacokinetics and the timely assessment of property profiles.
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Dr Marc Van der Kamp - People in the School of Chemistry publications
MIMIC — a molecular-field matching program: exploiting applicability of molecular similarity approaches. J Comput Chem. Rational drug design. Eur J Pharmacol. Structure-based methods for predicting target mutation-induced drug resistance and rational drug design to overcome the problem. Selected approaches for rational drug design and high throughput screening to identify anti-cancer molecules. Anticancer Agents Med Chem. Szymkowski DE. Creating the next generation of protein therapeutics through rational drug design.
Lessons learned from the development of an Abl tyrosine kinase inhibitor for chronic myelogenous leukemia. J Clin Invest. Cancer Cell. Interferons and uterine receptivity. Semin Reprod Med. Synthesis and cytotoxic activity of benzopyran-based platinum II complexes. Bioorg Med Chem Lett. Mandal S, Davie JR. An integrated analysis of genes and pathways exhibiting metabolic differences between estrogen receptor positive breast cancer cells. BMC Cancer. Receptor tyrosine kinase inhibitors as potent weapons in war against cancers. Curr Pharm Des.
Structure of the catalytic domain of human protein kinase C beta II complexed with a bisindolylmaleimide inhibitor. Patwardhan B, Bodeker G. Ayurvedic genomics: establishing a genetic basis for mind-body typologies. J Altern Complement Med. Atkins P, de Paula J. New York: Macmillan; Rogers DW.
Computational Chemistry Using the PC. Hoboken NJ : Wiley; Lewars, EG. Alphen aan den Rijn, Netherlands; Kluwer; Tully JC. Theor Chem Acc. Hotokka M. Calculation of vibrational frequencies by molecular mechanics. J Mulholland, J. N Harvey. Cadmium Sulfate Nanocrystals. Alex G. Taranto, Paulo Carvalho, Mitchell A.
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Drug Discovery Today , 12 , On the convergence improvement in the metadynamics simulations: A Wang-Landau recursion approach. The Journal of Chemical Physics , 19 , Transition state determination of enzyme reaction on free energy surface: Application to chorismate mutase.
Hongzhi Li, Wei Yang. Sampling enhancement for the quantum mechanical potential based molecular dynamics simulations: A general algorithm and its extension for free energy calculation on rugged energy surface. The Journal of Chemical Physics , 11 , Hai Lin, Donald G. Theoretical Chemistry Accounts , 2 , ChemMedChem , 2 1 , Comparative semiempirical and ab initio study of the structural and chemical properties of uric acid and its anions.
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The Journal of Chemical Physics , 7 , Nadra, Laura L. Modeling heme proteins using atomistic simulations. Perissinotti, Dami? Scherlis, Dar? Physical Chemistry Chemical Physics , 8 48 , David A. Case, Thomas E. The Amber biomolecular simulation programs. Journal of Computational Chemistry , 26 16 , Jie Li, Jason B. Cross, Thom Vreven, Samy O. Meroueh, Shahriar Mobashery, H. Proteins: Structure, Function, and Bioinformatics , 61 2 , ChemPhysChem , 6 9 , Representation of Zn II complexes in polarizable molecular mechanics. Further refinements of the electrostatic and short-range contributions.
Comparisons with parallelab initio computations. Journal of Computational Chemistry , 26 11 , Long-range electrostatic interactions in hybrid quantum and molecular mechanical dynamics using a lattice summation approach. The Journal of Chemical Physics , 23 , Jorgensen, J. Potential energy functions for atomic-level simulations of water and organic and biomolecular systems.
Proceedings of the National Academy of Sciences , 19 , Journal of Biomolecular NMR , 31 2 , Journal of Computational Chemistry , ,, Sonja Braun-Sand, Mats H. Olsson, Arieh Warshel. Computer modeling of enzyme catalysis and its relationship to concepts in physical organic chemistry.
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Walter Thiel. Semiempirical quantum-chemical methods in computational chemistry. Hrant P. Hratchian, H. Finding minima, transition states, and following reaction pathways on ab initio potential energy surfaces. Theoretical study of structure of catalytic copper site in nitrite reductase. International Journal of Quantum Chemistry , 5 , Homology modeling and SN2 displacement reaction of fluoroacetate dehalogenase from Burkholderia sp.
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Theoretical studies of hyperthermal O 3P collisions with hydrocarbon self-assembled monolayers. The Journal of Chemical Physics , 16 , Journal of Theoretical and Computational Chemistry , 03 01 , Chen, D. Zhang, J. Fractionation of peptide with disulfide bond for quantum mechanical calculation of interaction energy with molecules.
The Journal of Chemical Physics , 2 , Theoretical studies of hyperthermal O [sup 3]P collisions with hydrocarbon self-assembled monolayers. Jeffrey Evanseck, Orlando Acevedo. Transition States and Transition Structures. Jean-Louis Rivail. Zhang, X. Chen, J. Molecular caps for full quantum mechanical computation of peptide-water interaction energy. Journal of Computational Chemistry , 24 15 , Exploring potential energy surfaces for chemical reactions: An overview of some practical methods.
Journal of Computational Chemistry , 24 12 , Molecular fractionation with conjugate caps for full quantum mechanical calculation of protein—molecule interaction energy. Molecular Physics , 15 , The amide bond: pitfalls and drawbacks of the link atom scheme. Energy decomposition in molecular complexes: Implications for the treatment of polarization in molecular simulations.
Journal of Computational Chemistry , 24 10 , Arieh Warshel. Annual Review of Biophysics and Biomolecular Structure , 32 1 , Journal of Biological Chemistry , 7 , Lucia Banci. Molecular dynamics simulations of metalloproteins. Current Opinion in Chemical Biology , 7 1 , Ulf Ryde. Combined quantum and molecular mechanics calculations on metalloproteins. Jeehiun Katherine Lee, Dean J. Shurki, A. Javier Luque, C. Curutchet, J. Bidon-Chanal, I.
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Transition state docking: A probe for noncovalent catalysis in biological systems. Application to antibody-catalyzed ester hydrolysis. Journal of Computational Chemistry , 23 1 , Martin J. Simulating enzyme reactions: Challenges and perspectives. Ravi Rajagopalan, Stephen J. Preorganization and protein dynamics in enzyme catalysis.
The Chemical Record , 2 1 , On the modulation of the substrate activity for the racemization catalyzed by mandelate racemase enzyme. Mechanism of proton transfer in ice. Hydration, modes, and transport.
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The Journal of Chemical Physics , 10 , Santamaria, J. Building wave functions for large molecules from their fragments. Description of peptide and protein secondary structures employing semiempirical methods. Journal of Computational Chemistry , 22 5 , V Gogonea. New developments in applying quantum mechanics to proteins.
Current Opinion in Structural Biology , 11 2 , Generalized solvent boundary potential for computer simulations. Ralph A. Quinones and quinoidal radicals in photosynthesis. Physical Review A , ,, Troy Wymore, Hugh B. Nicholas, John Hempel.
Molecular dynamics simulation of class 3 aldehyde dehydrogenase. Chemico-Biological Interactions , , Joachim Sauer, Marek Sierka. Combining quantum mechanics and interatomic potential functions inab initio studies of extended systems. Journal of Computational Chemistry , 21 16 , Mechanism of fast proton transfer in ice: Potential energy surface and reaction coordinate analyses. The Journal of Chemical Physics , 20 , Valentin Gogonea, Lance M. Westerhoff, Kenneth M. Thomas C Bruice, Kalju Kahn. Computational enzymology. Current Opinion in Chemical Biology , 4 5 , Pedersen, David W.
Parallelab initio and molecular mechanics investigation of polycoordinated Zn II complexes with model hard and soft ligands: Variations of binding energy and of its components with number and charges of ligands. However, computing time and resources required for the electronic structure calculations increase rapidly with the size of the molecule. In this hybrid approach, the target molecule is divided into QM and MM regions. The electronic structure calculations are performed within the QM region to describe electronic structures and chemical reactions in the ground and excited states.
The rest of the molecule is treated by the classical mechanics such as MM method. The interactions between the QM and MM regions are described by using the MM force field electrostatic, through-bond, and van der Waals interactions. A QM region is composed of the sum of the QM sub-regions that are small enough to apply practical electronic structure calculations. Several benchmark examinations were carried out to figure out availabilities and limitations.