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– Sensitivity analysis and optimization: Monte Carlo simulation allows for sensitiv-
ity analysis to assess the impact of individual input variables on the output re-
sults. By systematically varying the input values, the sensitivity of the model to
different parameters can be quantified. Additionally, optimization techniques can
be applied to identify the optimal values or settings of the input variables that
lead to the desired outcomes.
– Result interpretation and decision-making: The results of the Monte Carlo simula-
tion are interpreted to gain insights into the system’s behavior, make predictions,
and inform decision-making processes. The probabilistic nature of the simulation
outputs provides a range of possible outcomes, enabling risk assessment, uncer-
tainty analysis, and scenario planning.
– Monte Carlo simulation is a powerful tool for tackling complex problems with un-
certainty and stochasticity. It allows for the exploration of a wide range of input
possibilities, capturing the probabilistic nature of real-world systems. This makes
it useful in various applications including risk analysis, option pricing, reliability
assessment, project management, and experimental design.
It is worth emphasizing that the accuracy and dependability of Monte Carlo simulations
rely on the quality of input distributions, the appropriateness of mathematical models,
and the sample size utilized. To enhance the efficiency and convergence of Monte Carlo
simulations, advanced techniques such as Markov chain Monte Carlo methods or vari-
ance reduction methods can be employed. In conclusion, Monte Carlo simulation is a
computational approach that employs random sampling to model and analyze systems
characterized by uncertainty and randomness. Through the generation of numerous
simulations and the evaluation of output results, it provides a probabilistic framework
for decision-making, risk assessment, and the comprehension of complex systems.
10.6.5 Molecular mechanics Poisson–Boltzmann surface
area (MMPBSA)
MM Poisson–Boltzmann surface area (MMPBSA) is a computational method used to
calculate the binding free energies of biomolecular systems such as protein–ligand
complexes. It combines MM calculations to evaluate the molecular interactions and
Poisson–Boltzmann (PB) or GB models to account for solvation effects. In MMPBSA
calculations, the binding free energy (ΔG
bind
) is estimated as the difference between
the free energy of the bound complex (ΔG
complex
) and the sum of the free energies of
the individual components (ΔG
ligand
+ ΔG
protein
). The key steps involved in MMPBSA
are as follows:
– MM calculations: The first step in MMPBSA involves performing MM calculations
to optimize the structures of the ligand, protein, and complex. These calculations
utilize force fields that describe the interatomic interactions such as bond stretch-
234 Abhimannu Shome et al.
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ing, angle bending, dihedral rotation, and nonbonded interactions (van der Waals
and electrostatic forces).
– Solvent model selection: MMPBSA allows for the use of different solvation models,
primarily the PB model or the GB model, to estimate solvation effects. The PB
model considers th e electrostatic interactions between the solute and solvent,
while the GB model provides an approximation by considering only the average
electrostatic potential.
– Calculation of solvation free energies: In MMPBSA, the solvation free energies are
computed by evaluating the electrostatic and nonpolar contributions separately.
The electrostatic term involves solving the PB equation or applying the GB equa-
tion, while the nonpolar term is often estimated using empirical parameters or a
SASA model [81].
– Energy calculations: The energy contributions for the ligand, protein, and com-
plex are computed by summing the corresponding terms from the MM calcula-
tions. These terms include bonded energy terms (bond stretching, angle bending,
dihedral rotation), nonbonded energy terms (van der Waals and electrostatic in-
teractions), and the solvation energy terms (from the PB or GB model).
– Free energy calculation: The binding free energy (ΔG
bind
) is obtained by subtract-
ing the sum of the free energies of the individual components (ΔG
ligand
+ ΔG
protein
)
from the free energy of the bound complex (ΔG
complex
). The binding free energy
can also be decomposed into various energy terms to analyze the contributions of
different molecular interactions.
– MMPBSA provides a computationally efficient method for estimating binding free
energies in biomolecular systems. It takes into account both the molecular inter-
actions and solvation effects, which are crucial in understanding ligand binding
and protein stability. MMPBSA is widely used in drug discovery, protein engineer-
ing, and structure-based design to evaluate and rank ligand binding affinities, an-
alyze protein-ligand interactions, and guide lead optimization.
It is important to note that MMPBSA calculations have limitations such as the reliance
on force field parameters, the choice of solvation model, and the neglect of conforma-
tional changes and dynamics during the binding process. Advanced techniques like
MD simulations or combined QM/MM approaches can be employed to address these
limitations and improve the accuracy of MMPBSA calculations. In summary, MMPBSA
is a computational method that combines MM calculations with solvation models to
estimate the binding free energies of biomolecular systems. It provides valuable in-
sights into ligand binding, protein stability, and molecular interactions. MMPBSA is a
useful tool in drug discovery, lead optimization, and structure-based design, aiding in
the evaluation and optimization of ligand binding affinities [85].
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10.7 Conclusion
In recent years, significant progress has been made in the field of binding free en-
ergy calculations, leading to more precise and reliable predictions of ligand binding
affinity to target proteins. These advancements have been fueled by improvements
in computational methodologies, force fields, sampling techniques, and the avail-
ability of high-performance computing resources. The utilization of these advance-
ments has sig nificantly enhanced our understanding of molecular recognition and
has profound implications for the design and discovery of new drugs. One notable
advancement is the integration of enhance d sampling techniques, such as metady-
namics, replica exchange, and adaptive biasing force, with MD simulations. These
techniques enable a more thorough exploration of the ligand binding process and a
more accurate estimation of free energy landscapes. By effectively sampling a
broader range of conformational states and surmounting energy barriers, these
methods provide more reliable predictions of binding affinities and better capture
the thermodynamics of ligand–protein interactions. Another significant progress
lies in th e development of advanced scoring functions and force fields that better
characterize the intermol ecular interactions between ligands and proteins. These
scoring functions incorporate diverse physicochemical descriptors, including shape
complementarity, electrostatics, and hydrophobicity, resulting in more precise pre-
dictions of binding affinities. Moreover, force fields have been refined to more accu-
rately represent the dynamic behavior of biomolecules and the solv ation effects,
thereby enhancing the accuracy of free energy calculations. The integration of machine
learning and data-driven approaches has also made noteworthy contributions to binding
free energy calculations. By training on large datasets of experimentally measured bind-
ing affinities, machine learning models can predict binding free energies for new
ligand–protein systems with exceptional accuracy. These models have the potential to sig-
nificantly expedite the drug discovery process by efficiently screening large compound
libraries and prioritizing compounds for experimental testing. Furthermore, the incorpo-
ration of QM methods, such as QM/MM approaches, has enabled more precise treatment
of electronic and quantum effect in binding freeenergycalculations.Thesemethodsare
particularly valuable for studying enzyme reactions and protein-ligand interactions in-
volving metal ions or covalent bonds. By incorporating QM into the calculations, more
accurate estimates of binding free energies can be obtained, leading to improved under-
standing of reaction mechanisms and binding affinity predictions. In conclusion, recent
advancements in binding free energy calculations have substantially enhanced our ability
to predict and comprehend ligand–protein interactions. These breakthroughs have pro-
vided invaluable insights into the binding process, guided rational drug design, and facili-
tated the discovery of novel lead compounds. Although challenges persist, such as
accurately accounting for protein flexibility and estimating solvation effects, ongoing de-
velopments in computational methodologies and technologies continue to drive the field
forward. It is anticipated that these advancements will further refine the accuracy and
236 Abhimannu Shome et al.
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reliability of binding free energy calculations, ultimately revolutionizing the efficiency
and success of drug discovery endeavors.
References
[1] Zhou, T., Huang, D., & Caflisch, A. Quantum mechanical methods for drug design. Current Topics in
Medicinal Chemistry, 2010, 10(1), 33– 45.
[2] Geromichalos, G. D., Alifieris, C. E., Geromichalou, E. G., & Trafalis, D. T. Overview on the current
status of virtual high-throughput screening and combinatorial chemistry approaches in multi-target
anticancer drug discovery; Part I. J Buon, 2016, 21(4), 764–779.
[3] Totrov, M., & Abagyan, R. Flexible protein–ligand docking by global energy optimization in internal
coordinates. Proteins: Structure, Function, and Bioinformatics, 1997, 29(S1), 215–220.
[4] Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. E., Belew, R. K., et al. Automated
docking using a Lamarckian genetic algorithm and an empirical binding free energy function.
Journal of Computational Chemistry, 1998, 19(14), 1639–1662.
[5] Wang, E., Sun, H., Wang, J., Wang, Z., Liu, H., Zhang, J. Z., et al. End-point binding free energy
calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chemical
Reviews, 2019, 119(16), 9478–9508.
[6] Kim, M. O., & McCammon, J. A. Computation of pH‐dependent binding free energies. Biopolymers,
2016, 105(1), 43–49.
[7] de Azevedo, J., Walter, F., & Dias, R. Experimental approaches to evaluate the thermodynamics of
protein-drug interactions. Current Drug Targets, 2008, 9(12), 1071–1076.
[8] Hoffer, L., Muller, C., Roche, P., & Morelli, X. Chemistry‐driven hit‐to‐lead optimization guided by
structure‐based approaches. Molecular Informatics, 2018, 37(9–10), 1800059.
[9] Pickett, S. D., McLay, I. M., & Clark, D. E. Enhancing the hit-to-lead properties of lead optimization
libraries. Journal of Chemical Information and Computer Sciences, 2000, 40(2), 263 –272.
[10] Baumgartner, M. P., & Evans, D. A. Lessons learned in induced fit docking and metadynamics in the
drug design data resource grand challenge 2. Journal of Computer-Aided Molecular Design, 2018,
32(1), 45–58.
[11] Grimme, S., & Schreiner, P. R. Steric crowding can stabilize a labile molecule: Solving the
hexaphenylethane riddle. Angewandte Chemie International Edition, 2011, 50(52), 12639–12642.
[12] Fokin, A. A., Zhuk, T. S., Blomeyer, S., Perez, C., Chernish, L. V., Pashenko, A. E., et al. Intramolecular
London dispersion interaction effects on gas-phase and solid-state structures of diamondoid
dimers. Journal of the American Chemical Society, 2017, 139(46), 16696–16707.
[13] Ou-Yang, S. s., J. y., L., X. q., K., Z. j., L., Luo, C., & Jiang, H. Computational drug discovery. Acta
Pharmacologica Sinica, 2012, 33(9), 1131–1140.
[14] Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J. L., Dror, R. O., et al. Improved
side-chain torsion potentials for the Amber ff99SB protein force field. Proteins: Structure. Function,
and Bioinformatics, 2010, 78(8), 1950– 1958.
[15] Akamatsu, M. Current state and perspectives of 3D-QSAR. Current Topics in Medicinal Chemistry,
2002, 2(12), 1381–1394.
[16] Kubinyi, H. QSAR and 3D QSAR in drug design Part 1: Methodology. Drug Discovery Today, 1997,
2(11), 457–467.
[17] Hao, M. H., Haq, O., & Muegge, I. Torsion angle preference and energetics of small-molecule
ligands bound to proteins. Journal of Chemical Information and Modeling, 2007, 47(6), 2242–2252.
10 Recent advancement in binding free-energy calculation 237
https://t.me/med1917
[18] Li, H., Sze, K. H., Lu, G., & Ballester, P. J. Machine‐learning scoring functions for structure‐based drug
lead optimization. Wiley Interdisciplinary Reviews: Computational Molecular Science, 2020, 10(5),
e1465.
[19] Jhoti, H., & Leach, A. R. Structure-based Drug Discovery. 2007, Springer: United Kingdom.
[20] Yilmazer, N. D., & Korth, M. Comparison of molecular mechanics, semi-empirical quantum
mechanical, and density functional theory methods for scoring protein–ligand interactions. The
Journal of Physical Chemistry B, 2013, 117(27), 8075–8084.
[21] Chen, L. Q. Chemical potential and Gibbs free energy. Mrs Bulletin, 2019, 44(7), 520–523.
[22] Carson, E. M., & Watson, J. R. Undergraduate students’ understandings of entropy and Gibbs free
energy. University Chemistry Education, 2002, 6(1), 4–12.
[23] Alberty, R. A. Standard Gibbs free energy, enthalpy, and entropy changes as a function of pH and
pMg for several reactions involving adenosine phosphates. Journal of Biological Chemistry, 1969,
244(12), 3290–3302.
[24] Rietman, E. A., Platig, J., Tuszynski, J. A., & Lakka Klement, G. Thermodynamic measures of cancer:
Gibbs free energy and entropy of protein–protein interactions. Journal of Biological Physics, 2016,
42, 339–350.
[25] Hanel, R., & Thurner, S. Generalized Boltzmann factors and the maximum entropy principle:
Entropies for complex systems. Physica A: Statistical Mechanics and Its Applications, 2007, 380,
109–114.
[26] Bruylants, G., Wouters, J., & Michaux, C. Differential scanning calorimetry in life science:
Thermodynamics, stability, molecular recognition and application in drug design. Current Medicinal
Chemistry, 2005, 12(17), 2011–2020.
[27] Martin, Y. C. Quantitative Drug Design: A Critical Introduction. 2010, CRC Press: Boca Raton.
[28] de Ruiter, A., Boresch, S., & Oostenbrink, C. Comparison of thermodynamic integration and Bennett
acceptance ratio for calculating relative protein‐ligand binding free energies. Journal of
Computational Chemistry, 2013, 34(12), 1024–1034.
[29] Reddy, M. R., & Erion, M. D. Calculation of relative binding free energy differences for fructose 1, 6-
bisphosphatase inhibitors using the thermodynamic cycle perturbation approach. Journal of the
American Chemical Society, 2001, 123(26), 6246–6252.
[30] Rajoka, M., K-u-, R., Tabish, T., & Zia, M. Purification and characterization of caprine kidney uricase,
possessing novel kinetic and thermodynamic properties. World Journal of Microbiology and
Biotechnology, 2006, 22, 289–291.
[31] Chen, W. Y., Huang, H. M., Lin, C. C., Lin, F. Y., & Chan, Y. C. Effect of temperature on hydrophobic
interaction between proteins and hydrophobic adsorbents: Studies by isothermal titration
calorimetry and the van’t Hoff equation. Langmuir, 2003, 19(22), 9395–9403.
[32] Varani, K., Gessi, S., Merighi, S., & Borea, P. A. Van’t Hoff based thermodynamics. Thermodynamics
and Kinetics of Drug Binding, 2015, 15–35.
[33] Vanommeslaeghe, K., & Guvench, O. Molecular mechanics. Current Pharmaceutical Design, 2014,
20(20), 3281–3292.
[34] Allinger, N. L., Zhou, X., & Bergsma, J. Molecular mechanics parameters. Journal of Molecular
Structure: Theochem, 1994, 312(1), 69–83.
[35] Combes, J. M., Duclos, P., & Seiler, R. The Born-Oppenheimer approximation. Rigorous Atomic and
Molecular Physics, 1981, (74), 185–213.
[36] Woolley, R., & Sutcliffe, B. Molecular structure and the born – Oppenheimer approximation.
Chemical Physics Letters, 1977, 45(2), 393–398.
[37] Cisneros, G. A., Wikfeldt, K. T., Ojamäe, L., Lu, J., Xu, Y., Torabifard, H., et al. Modeling molecular
interactions in water: From pairwise to many-body potential energy functions. Chemical Reviews,
2016, 116(13), 7501–7528.
238 Abhimannu Shome et al.
https://t.me/med1917
[38] Berne, B. J., & Pechukas, P. Gaussian model potentials for molecular interactions. The Journal of
Chemical Physics, 1972, 56(8), 4213–4216.
[39] Leimkuhler, B., & Matthews, C. Molecular dynamics. Interdisciplinary Applied Mathematics, 2015,
39, 443.
[40] Hayik, S. A., Dunbrack, Jr R., & Merz, Jr K. M. Mixed quantum mechanics/molecular mechanics
scoring function to predict protein − ligand binding affinity. Journal of Chemical Theory and
Computation, 2010, 6(10), 3079–3091.
[41] Ponder, J. W., & Richards, F. M. An efficient newton‐like method for molecular mechanics energy
minimization of large molecules. Journal of Computational Chemistry, 1987, 8(7), 1016–1024.
[42] Kini, R. M., & Evans, H. J. Molecular modeling of proteins: A strategy for energy minimization by
molecular mechanics in the AMBER force field. Journal of Biomolecular Structure and Dynamics,
1991, 9(3), 475–488.
[43] Petrenko, T., & Neese, F. Efficient and automatic calculation of optical band shapes and resonance
Raman spectra for larger molecules within the independent mode displaced harmonic oscillator
model. The Journal of Chemical Physics, 2012, 137(23), 2341071– 23410719.
[44] Georgievskii, Y., Hsu, C. P., & Marcus, R. Linear response in theory of electron transfer reactions as
an alternative to the molecular harmonic oscillator model. The Journal of Chemical Physics, 1999,
110(11), 5307–5317.
[45] Urry, D. W. Molecular machines: How motion and other functions of living organisms can result
from reversible chemical changes. Angewandte Chemie International Edition in English, 1993, 32(6),
819–841.
[46] Engler, E. M., Andose, J. D., & Schleyer, P. V. Critical evaluation of molecular mechanics. Journal of
the American Chemical Society, 1973, 95(24), 8005–8025.
[47] Sears, A., & Batra, R. C. Macroscopic properties of carbon nanotubes from molecular-mechanics
simulations. Physical Review B, 2004, 69(23), 235406.
[48] Ponder, J. W., & Case, D. A. Force fields for protein simulations. Advances in Protein Chemistry, 2003,
66, 27–85.
[49] Halgren, T. A., & Damm, W. Polarizable force fields. Current Opinion in Structural Biology, 2001,
11(2), 236–242.
[50] Allinger, N. L., Yuh, Y. H., & Lii, J. H. Molecular mechanics. The MM3 force field for hydrocarbons. 1.
Journal of the American Chemical Society, 1989, 111(23), 8551–8566.
[51] Halgren, T. A. Merck molecular force field. I. Basis, form, scope, parameterization, and performance
of MMFF94. Journal of Computational Chemistry, 1996, 17(5–6), 490– 519.
[52] MacKerell, A. D., Jr, Banavali, N., & Foloppe, N. Development and current status of the CHARMM
force field for nucleic acids. Biopolymers: Original Research on Biomolecules, 2000, 56(4), 257–265.
[53] Pastor, R., & MacKerell, A., Jr. Development of the CHARMM force field for lipids. The Journal of
Physical Chemistry Letters, 2011, 2(13), 1526–1532.
[54] Zhu, X., Lopes, P. E., & MacKerell, A. D., Jr. Recent developments and applications of the CHARMM
force fields. Wiley Interdisciplinary Reviews: Computational Molecular Science, 2012, 2(1), 167–185.
[55] Lagant, P., Nolde, D., Stote, R., Vergoten, G., & Karplus, M. Increasing normal modes analysis
accuracy: The SPASIBA spectroscopic force field introduced into the CHARMM program. The Journal
of Physical Chemistry A, 2004, 108(18), 4019–4029.
[56] Xu, Y., Vanommeslaeghe, K., Aleksandrov, A., MacKerell, A. D., Jr., & Nilsson, L. Additive CHARMM
force field for naturally occurring modified ribonucleotides. Journal of Computational Chemistry,
2016, 37(10), 896–912.
[57] Halgren, T. A. The representation of van der Waals (vdW) interactions in molecular mechanics force
fields: Potential form, combination rules, and vdW parameters. Journal of the American Chemical
Society, 1992, 114(20), 7827–7843.
10 Recent advancement in binding free-energy calculation 239
https://t.me/med1917
[58] Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A., & Case, D. A. Development and testing of a
general amber force field. Journal of Computational Chemistry, 2004, 25(9), 1157–1174.
[59] Galindo-Murillo, R., Robertson, J. C., Zgarbova, M., Sponer, J., Otyepka, M., Jurecka, P., et al.
Assessing the current state of amber force field modifications for DNA. Journal of Chemical Theory
and Computation, 2016, 12(8), 4114 – 4127.
[60] Wang, J., Wang, W., Kollman, P. A., & Case, D. A. Automatic atom type and bond type perception in
molecular mechanical calculations. Journal of Molecular Graphics and Modelling, 2006, 25(2),
247–260.
[61] Doshi, U., & Hamelberg, D. Reoptimization of the AMBER force field parameters for peptide bond
(Omega) torsions using accelerated molecular dynamics. The Journal of Physical Chemistry B, 2009,
113(52), 16590–16595.
[62] Jorgensen, W. L., Maxwell, D. S., & Tirado-Rives, J. Development and testing of the OPLS all-atom
force field on conformational energetics and properties of organic liquids. Journal of the American
Chemical Society, 1996, 118(45), 11225– 11236.
[63] Jorgensen, W. L., & Tirado-Rives, J. The OPLS [optimized potentials for liquid simulations] potential
functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. Journal of
the American Chemical Society, 1988, 110(6), 1657–1666.
[64] Dahlgren, M. K., Schyman, P., Tirado-Rives, J., & Jorgensen, W. L. Characterization of biaryl torsional
energetics and its treatment in OPLS all-atom force fields. Journal of Chemical Information and
Modeling, 2013, 53(5), 1191–1199.
[65] Roth, C. M., Neal, B. L., & Lenhoff, A. M. Van der Waals interactions involving proteins. Biophysical
Journal, 1996, 70(2), 977–987.
[66] Tsutsumi, Y. Schrodinger Equation. 1987, (Vol. 30, pp. 115–125). Funkcialaj Ekvacioj.
[67] Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V., & Mee, R. P. Empirical scoring functions:
I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in
receptor complexes. Journal of Computer-aided Molecular Design, 1997, 11, 425–445.
[68] Krovat, E., Steindl, T., & Langer, T. Recent advances in docking and scoring. Current Computer-aided
Drug Design, 2005, 1(1), 93 – 102.
[69] Bikadi, Z., & Hazai, E. Application of the PM6 semi-empirical method to modeling proteins enhances
docking accuracy of AutoDock. Journal of Cheminformatics, 2009, 1, 1–16.
[70] Clark, R. D., Strizhev, A., Leonard, J. M., Blake, J. F., & Matthew, J. B. Consensus scoring for ligand/
protein interactions. Journal of Molecular Graphics and Modelling, 2002, 20(4), 281–295.
[71] Muegge, I., & Rarey, M. Small molecule docking and scoring. Reviews in Computational Chemistry,
2001, 17, 1–60.
[72] Charifson, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. Consensus scoring: A method for
obtaining improved hit rates from docking databases of three-dimensional structures into proteins.
Journal of Medicinal Chemistry, 1999, 42(25), 5100–5109.
[73] Blanes-Mira, C., Fernández-Aguado, P., de Andrés-López, J., Fernández-Carvajal, A., Ferrer-Montiel,
A., & Fernández-Ballester, G. Comprehensive survey of consensus docking for high-throughput
virtual screening. Molecules, 2022, 28(1), 175.
[74] Gohlke, H., Hendlich, M., & Klebe, G. Knowledge-based scoring function to predict protein-ligand
interactions. Journal of Molecular Biology, 2000, 295(2), 337–356.
[75] Huang, S. Y., & Zou, X. An iterative knowledge‐based scoring function for protein–protein
recognition. Proteins: Structure. Function, and Bioinformatics, 2008, 72(2), 557–579.
[76] de Ruiter, A., & Oostenbrink, C. Advances in the calculation of binding free energies. Current
Opinion in Structural Biology, 2020, 61, 207–212.
[77] Hansson, T., Oostenbrink, C., & Van Gunsteren, W. Molecular dynamics simulations. Current Opinion
in Structural Biology, 2002, 12(2), 190–196.
240 Abhimannu Shome et al.
https://t.me/med1917
[78] Gapsys, V., Seeliger, D., & de Groot, B. L. New soft-core potential function for molecular dynamics
based alchemical free energy calculations. Journal of Chemical Theory and Computation, 2012, 8(7),
2373–2382.
[79] Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., et al. Extra
precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein −
ligand complexes. Journal of Medicinal Chemistry, 2006, 49(21), 6177–6196.
[80] Pathak, A., Nahid, S., Manik, A. S., Zahra, K., Mehjabin, F. Z., Jisan, S. A., et al. Molecular docking and
ADME/T analysis for identification of novel potential COX inhibitors of some isolated compounds
from Clausena lansium for analgesic treatment. The Pharma Innovation Journal, 2017, 6(9), 152–154.
[81] Genheden, S., & Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding
affinities. Expert Opinion on Drug Discovery, 2015, 10(5), 449–461.
[82] Srivastava, H. K., & Sastry, G. N. Molecular dynamics investigation on a series of HIV protease
inhibitors: Assessing the performance of MM-PBSA and MM-GBSA approaches. Journal of Chemical
Information and Modeling, 2012, 52(11), 3088–3098.
[83] Nosé, S. A molecular dynamics method for simulations in the canonical ensemble. Molecular
Physics, 1984, 52(2), 255–268.
[84] Harrison, R. L. (eds). Introduction to Monte Carlo Simulation. AIP Conference Proceedings. 2010,
American Institute of Physics, College Park, Maryland.
[85] Kuhn, B., Gerber, P., Schulz-Gasch, T., & Stahl, M. Validation and use of the MM-PBSA approach for
drug discovery. Journal of Medicinal Chemistry, 2005, 48(12), 4040–4048.
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Anuradha Mehra, Vanktesh Kumar, Bhupinder Kapoor, Monica Gulati,
and Pankaj Wadhwa
✶
11 Role of structural genomics
in drug discovery
Abstract: Massive g enome sequencing projectsemergedinthe1990s,whichledto
structural genomics emerging as a global initiative. There were many protein struc-
tures identified through this approach, but many of their functions were unknown,
sparking debate at the time about the value of structural genomics, though the signifi-
cance of this endeavor went beyond the mere accumulation of structures. The re-
cently deciphered protein structures acted as a catalyst for the rise of extensive data
science and infrastructure projects, and in conjunction with advancements in deep
learning, they sparked a transformative revolution in computational molecular biol-
ogy. Various tools for structure prediction, analysis, and annotation were vital to the
success of structural genomics initiatives. These developments have not only enriched
our understanding of protein structures and functions but have also contributed sig-
nificantly to the advancement of computational approaches in molecular biology.
Keywords: Genomics, drug design, structure-based modification, interactions, drug re-
sistance, target identification
11.1 Introduction
Biomolecular structures, like those of nucleic acids and proteins, are determined and
analyzed in structural genomics, a subfield of genomics [1, 2]. This article delves into
the historical background and the evolution of structural genomics, highlighting key
milestones, challenges, and advancements that have shaped the field’s current state.
From early efforts to the establishment of large-scale initiatives and integrat ive ap-
proaches, the pursuit of understanding the 3D structures of biomolecules has played a
vital role in advancing our knowledge of biological systems and drug discovery [3, 4].
Structural genomics emerged as a response to the growing need for high-throughput
✶
Corresponding author: Pankaj Wadhwa, School of Pharmaceutical Sciences, Lovely Professional
University, Jalandhar-Delhi G. T. Road (NH-1), Phagwara 144411, Punjab, India,
e-mail: pankaj.23400@lpu.co.in
Anuradha Mehra, Vanktesh Kumar, Bhupinder Kapoor, School of Pharmaceutical Sciences, Lovely
Professional University, Jalandhar-Delhi G.T. Road (NH-1), Phagwara, 144411, Punjab, India
Monica Gulati, School of Pharmaceutical Sciences, Lovely Professional University, Jalandhar-Delhi
G.T. Road (NH-1), Phagwara, 144411, Punjab, India; Faculty of Health, Australian Research Centre in
Complementary and Integrative Medicine, University of Technology Sydney, NSW 2007, Australia
https://doi.org/10.1515/9783111207117-011
https://t.me/med1917
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