BACKGROUND: The concept of partial atomic charges was first applied in physical and organic chemistry and was later also adopted in computational chemistry, bioinformatics and chemoinformatics. The electronegativity equalization method (EEM) is the most frequently used approach for calculating partial atomic charges. EEM is fast and its accuracy is comparable to the quantum mechanical charge calculation method for which it was parameterized. Several EEM parameter sets for various types of molecules and QM charge calculation approaches have been published and new ones are still needed and produced. Methodologies for EEM parameterization have been described in a few articles, but a software tool for EEM parameterization and EEM parameter sets validation has not been available until now. RESULTS: We provide the software tool NEEMP (http://ncbr.muni.cz/NEEMP), which offers three main functionalities: EEM parameterization [via linear regression (LR) and differential evolution with local minimization (DE-MIN)]; EEM parameter set validation (i.e., validation of coverage and quality) and EEM charge calculation. NEEMP functionality is shown using a parameterization and a validation case study. The parameterization case study demonstrated that LR is an appropriate approach for smaller and homogeneous datasets and DE-MIN is a suitable solution for larger and heterogeneous datasets. The validation case study showed that EEM parameter set coverage and quality can still be problematic. Therefore, it makes sense to verify the coverage and quality of EEM parameter sets before their use, and NEEMP is an appropriate tool for such verification. Moreover, it seems from both case studies that new EEM parameterizations need to be performed and new EEM parameter sets obtained with high quality and coverage for key structural databases. CONCLUSION: We provide the software tool NEEMP, which is to the best of our knowledge the only available software package that enables EEM parameterization and EEM parameter set validation. Additionally, its DE-MIN parameterization method is an innovative approach, developed by ourselves and first published in this work. In addition, we also prepared four high-quality EEM parameter sets tailored to ligand molecules.Graphical abstract.

CEITEC Central European Institute of Technology Masaryk University Brno Kamenice 5 625 00 Brno Czech Republic

CEITEC Central European Institute of Technology Masaryk University Brno Kamenice 5 625 00 Brno Czech Republic ; Faculty of Informatics Masaryk University Brno Botanická 68a 602 00 Brno Czech Republic

CEITEC Central European Institute of Technology Masaryk University Brno Kamenice 5 625 00 Brno Czech Republic ; Institute of Computer Science Masaryk University Brno Botanická 68a 602 00 Brno Czech Republic

CEITEC Central European Institute of Technology Masaryk University Brno Kamenice 5 625 00 Brno Czech Republic ; National Centre for Biomolecular Research Faculty of Science Masaryk University Brno Kamenice 5 625 00 Brno Czech Republic

CEITEC Central European Institute of Technology Masaryk University Brno Kamenice 5 625 00 Brno Czech Republic ; National Centre for Biomolecular Research Faculty of Science Masaryk University Brno Kamenice 5 625 00 Brno Czech Republic ; Faculty of Informatics Masaryk University Brno Botanická 68a 602 00 Brno Czech Republic

Zobrazit více v PubMed

Park H, Lee J, Lee S. Critical assessment of the automated AutoDock as a new docking tool for virtual screening. Proteins. 2006;65(3):549–554. doi: 10.1002/prot.21183. PubMed DOI

Vainio MJ, Johnson MS. Generating conformer ensembles using a multiobjective genetic algorithm. J Chem Inf Model. 2007;47(6):2462–2474. doi: 10.1021/ci6005646. PubMed DOI

Rappe AK, Goddard WA. Charge equilibration for molecular dynamics simulations. J Phys Chem. 1991;95(8):3358–3363. doi: 10.1021/j100161a070. DOI

Chenoweth K, Van Duin AC, Goddard WA. Reaxff reactive force field for molecular dynamics simulations of hydrocarbon oxidation. J Phys Chem A. 2008;112(5):1040–1053. doi: 10.1021/jp709896w. PubMed DOI

Kearsley SK, Sallamack S, Fluder EM, Andose JD, Mosley RT, Sheridan RP. Chemical similarity using physiochemical property descriptors. J Chem Inf Model. 1996;36(1):118–127.

Holliday JD, Jelfs SP, Willett P, Gedeck P. Calculation of intersubstituent similarity using R-group descriptors. J Chem Inf Comput Sci. 2003;43(2):406–411. doi: 10.1021/ci025589v. PubMed DOI

Tervo AJ, Rönkkö T, Nyrönen TH, Poso A. BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. 1. Alignment and virtual screening applications. J Med Chem. 2005;48(12):4076–4086. doi: 10.1021/jm049123a. PubMed DOI

Lemmen C, Lengauer T, Klebe G. FLEXS: a method for fast flexible ligand superposition. J Med Chem. 1998;41(23):4502–4520. doi: 10.1021/jm981037l. PubMed DOI

Svobodová Vařeková R, Geidl S, Ionescu C-M, Skřehota O, Kudera M, Sehnal D, Bouchal T, Abagyan R, Huber HJ, Koča J. Predicting pKa values of substituted phenols from atomic charges: comparison of different quantum mechanical methods and charge distribution schemes. J Chem Inf Model. 2011;51(8):1795–1806. doi: 10.1021/ci200133w. PubMed DOI

Svobodová Vařeková R, Geidl S, Ionescu C-M, Skřehota O, Bouchal T, Sehnal D, Abagyan R, Koča J. Predicting pKa values from EEM atomic charges. J Cheminf. 2013;5(1):18. doi: 10.1186/1758-2946-5-18. PubMed DOI PMC

Geidl S, Svobodová Vařeková R, Bendová V, Petrusek L, Ionescu C-M, Jurka Z, Abagyan R, Koča J. How does the methodology of 3D structure preparation influence the quality of pKa prediction? J Chem Inf Model. 2015;55(6):1088–1097. doi: 10.1021/ci500758w. PubMed DOI PMC

Gross KC, Seybold PG, Hadad CM. Comparison of different atomic charge schemes for predicting pKa variations in substituted anilines and phenols. Int J Quantum Chem. 2002;90:445–458. doi: 10.1002/qua.10108. DOI

Galvez J, Garcia R, Salabert MT, Soler R. Charge indexes: new topological descriptors. J Chem Inf Model. 1994;34(3):520–525. doi: 10.1021/ci00019a008. DOI

Stalke D. Meaningful structural descriptors from charge density. Chemistry. 2011;17(34):9264–9278. doi: 10.1002/chem.201100615. PubMed DOI

MacDougall PJ, Henze CE. Fleshing-out pharmacophores with volume rendering of the laplacian of the charge density and hyperwall visualization technology. In: Matta CF, Boyd RJ, editors. The quantum theory of atoms in molecules: from solid state to DNA and drug design. Weinheim: Wiley; 2007. pp. 499–514.

Bissantz C, Folkers G, Rognan D. Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J Med Chem. 2000;43(25):4759–4767. doi: 10.1021/jm001044l. PubMed DOI

Mulliken RS. Electronic population analysis on LCAO-MO molecular wave functions. II. Overlap populations, bond orders, and covalent bond energies. J Chem Phys. 1955;23(10):1841. doi: 10.1063/1.1740589. DOI

Mulliken RS. Electronic population analysis on LCAO-MO molecular wave functions. I J Chem Phys. 1955;23(10):1833. doi: 10.1063/1.1740588. DOI

Reed AE, Weinhold F. Natural bond orbital analysis of near-Hartree-Fock water dimer. J Chem Phys. 1983;78(6):4066–4073. doi: 10.1063/1.445134. DOI

Reed AE, Weinstock RB, Weinhold F. Natural population analysis. J Chem Phys. 1985;83(2):735. doi: 10.1063/1.449486. DOI

Bader RFW. Atoms in molecules. Acc Chem Res. 1985;18(1):9–15. doi: 10.1021/ar00109a003. DOI

Bader RFW. A quantum theory of molecular structure and its applications. Chem Rev. 1991;91(5):893–928. doi: 10.1021/cr00005a013. DOI

Breneman CM, Wiberg KB. Determining atom-centered monopoles from molecular electrostatic potentials. The need for high sampling density in formamide conformational analysis. J Comput Chem. 1990;11(3):361–373. doi: 10.1002/jcc.540110311. DOI

Singh UC, Kollman PA. An approach to computing electrostatic charges for molecules. J Comput Chem. 1984;5(2):129–145. doi: 10.1002/jcc.540050204. DOI

Besler BH, Merz KM, Kollman PA. Atomic charges derived from semiempirical methods. J Comput Chem. 1990;11(4):431–439. doi: 10.1002/jcc.540110404. DOI

Gasteiger J, Marsili M. A new model for calculating atomic charges in molecules. Tetrahedron Lett. 1978;19(34):3181–3184. doi: 10.1016/S0040-4039(01)94977-9. DOI

Gasteiger J, Marsili M. Iterative partial equalization of orbital electronegativity–a rapid access to atomic charges. Tetrahedron. 1980;36(22):3219–3228. doi: 10.1016/0040-4020(80)80168-2. DOI

Cho K-H, Kang YK, No KT, Scheraga HA. A fast method for calculating geometry-dependent net atomic charges for polypeptides. J Phys Chem B. 2001;105(17):3624–3634. doi: 10.1021/jp0023213. DOI

Oliferenko AA, Pisarev SA, Palyulin VA, Zefirov NS. Atomic charges via electronegativity equalization: generalizations and perspectives. Adv Quantum Chem. 2006;51:139–156. doi: 10.1016/S0065-3276(06)51004-4. DOI

Shulga DA, Oliferenko AA, Pisarev SA, Palyulin VA, Zefirov NS. Fast tools for calculation of atomic charges well suited for drug design. SAR QSAR Environ Res. 2010;19(1–2):153–165. PubMed

Mortier WJ, Ghosh SK, Shankar S. Electronegativity equalization method for the calculation of atomic charges in molecules. J Am Chem Soc. 1986;108:4315–4320. doi: 10.1021/ja00275a013. DOI

Nistor RA, Polihronov JG, Müser MH, Mosey NJ. A generalization of the charge equilibration method for nonmetallic materials. J Chem Phys. 2006;125(9):094108. doi: 10.1063/1.2346671. PubMed DOI

Mathieu D. Split charge equilibration method with correct dissociation limits. J Chem Phys. 2007;127(22):224103. doi: 10.1063/1.2803060. PubMed DOI

Baekelandt BG, Mortier WJ, Lievens JL, Schoonheydt RA. Probing the reactivity of different sites within a molecule or solid by direct computation of molecular sensitivities via an extension of the electronegativity equalization method. J Am Chem Soc. 1991;113(18):6730–6734. doi: 10.1021/ja00018a003. DOI

Svobodová Vařeková R, Jiroušková Z, Vaněk J, Suchomel S, Koča J. Electronegativity equalization method: parameterization and validation for large sets of organic, organohalogene and organometal molecule. Int J Mol Sci. 2007;8:572–582. doi: 10.3390/i8070572. DOI

Jiroušková Z, Vařeková RS, Vaněk J, Koča J. Electronegativity equalization method: parameterization and validation for organic molecules using the Merz-Kollman-Singh charge distribution scheme. J Comput Chem. 2009;30(7):1174–1178. doi: 10.1002/jcc.21142. PubMed DOI

Bultinck P, Langenaeker W, Lahorte P, De Proft F, Geerlings P, Van Alsenoy C, Tollenaere JP. The electronegativity equalization method II: applicability of different atomic charge schemes. J Phys Chem A. 2002;106(34):7895–7901. doi: 10.1021/jp020547v. DOI

Ouyang Y, Ye F, Liang Y. A modified electronegativity equalization method for fast and accurate calculation of atomic charges in large biological molecules. Phys Chem Chem Phys. 2009;11(29):6082–6089. doi: 10.1039/b821696g. PubMed DOI

Bultinck P, Vanholme R, Popelier PLA, De Proft F, Geerlings P. High-speed calculation of AIM charges through the electronegativity equalization method. J Phys Chem A. 2004;108(46):10359–10366. doi: 10.1021/jp046928l. DOI

Geidl S, Bouchal T, Raček T, Svobodová Vařeková R, Hejret V, Křenek A, Abagyan R, Koča J. High-quality and universal empirical atomic charges for chemoinformatics applications. J Cheminf. 2015;7(1):59. doi: 10.1186/s13321-015-0107-1. PubMed DOI PMC

O’Boyle N, Banck M, James C, Morley C, Vandermeersch T, Hutchison G. Open babel: an open chemical toolbox. J Cheminf. 2011;3(1):33–47. doi: 10.1186/1758-2946-3-33. PubMed DOI PMC

Vainio MJ, Johnson MS. Generating conformer ensembles using a multiobjective genetic algorithm. J Chem Inf Model. 2007;47(6):2462–2474. doi: 10.1021/ci6005646. PubMed DOI

Svobodová Vařeková R, Koča J. Optimized and parallelized implementation of the electronegativity equalization method and the atom-bond electronegativity equalization method. J Comput Chem. 2006;3:396–405. PubMed

Feng Z, Chen L, Maddula H, Akcan O, Oughtred R, Berman HM, Westbrook J. Ligand depot: a data warehouse for ligands bound to macromolecules. Bioinformatics. 2004;20(13):2153–2155. doi: 10.1093/bioinformatics/bth214. PubMed DOI

Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M. Drugbank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36(suppl 1):901–906. PubMed PMC

Bolton EE, Wang Y, Thiessen PA, Bryant SH. Pubchem: integrated platform of small molecules and biological activities. Ann Rep Comput Chem. 2008;4:217–241. doi: 10.1016/S1574-1400(08)00012-1. DOI

MJD Powell (2006) The NEWUOA software for unconstrained optimization without derivatives. In: Large-scale nonlinear optimization, pp. 255–297. Springer, Oxford

Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J, Du Croz J, Greenbaum A, Hammarling S, McKenney A, Sorensen D. LAPACK users’ guide. 3. Philadelphia: Society for Industrial and Applied Mathematics; 1999.

Open NCI Database, Release 4. http://cactus.nci.nih.gov/download/nci/

Berman H, Henrick K, Nakamura H. Announcing the worldwide protein data bank. Nat Struct Mol Biol. 2003;10(12):980–980. doi: 10.1038/nsb1203-980. PubMed DOI

Sadowski J, Gasteiger J. From atoms and bonds to three-dimensional atomic coordinates: automatic model builders. Chem Rev. 1993;93:2567–2581. doi: 10.1021/cr00023a012. DOI

MJ Frisch, GW Trucks, HB Schlegel, GE Scuseria, MA Robb, JRCheeseman, JA Montgomery Jr, T Vreven, KN Kudin, JC Burant, JMMillam, SS Iyengar, J Tomasi, V Barone, B Mennucci, M Cossi, GScalmani, N Rega, GA Petersson, H Nakatsuji, M Hada, M Ehara, KToyota, R Fukuda, J Hasegawa, M Ishida, T Nakajima, Y Honda, OKitao, H Nakai, M Klene, X Li, JE Knox, HP Hratchian, JB Cross, VBakken, C Adamo, J Jaramillo, R Gomperts, RE Stratmann, O Yazyev,AJ Austin, R Cammi, C Pomelli, JW Ochterski, PY Ayala, K Morokuma,GA Voth, P Salvador, JJ Dannenberg, VG Zakrzewski, S Dapprich, ADDaniels, MC Strain, O Farkas, DK Malick, AD Rabuck, K Raghavachari,JB Foresman, JV Ortiz, Q Cui, AG Baboul, S Clifford, J Cioslowski,BB Stefanov, G Liu, A Liashenko, P Piskorz, I Komaromi, RL Martin,DJ Fox, T Keith, MA Al-Laham, CY Peng, A Nanayakkara, M Challacombe,PMW Gill, B Johnson, W Chen, MW Wong, C Gonzalez, JA Pople, Gaussian09, Revision E.01. http://www.gaussian.com

Jelfs S, Ertl P, Selzer P. Estimation of pka for druglike compounds using semiempirical and information-based descriptors. J Chem Inf Model. 2007;47(2):450–459. doi: 10.1021/ci600285n. PubMed DOI

Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA. Development and testing of a general amber force field. J Comput Chem. 2004;25(9):1157–1174. doi: 10.1002/jcc.20035. PubMed DOI

Bren U, Hodošček M, Koller J. Development and validation of empirical force field parameters for netropsin. J Chem Inf Model. 2005;45(6):1546–1552. doi: 10.1021/ci050151r. PubMed DOI

Udommaneethanakit T, Rungrotmongkol T, Bren U, Frecer V, Stanislav M. Dynamic behavior of Avian Influenza A Virus Neuraminidase Subtype H5N1 in Complex with Oseltamivir, Zanamivir, Peramivir, and Their Phosphonate Analogues. J Chem Inf Model. 2009;49(10):2323–2332. doi: 10.1021/ci900277r. PubMed DOI

Ison J, Rapacki K, Ménager H, Kalaš M, Rydza E, Chmura P, Anthon C, Beard N, Berka K, Bolser D, Booth T, Bretaudeau A, Brezovsky J, Casadio R, Cesareni G, Coppens F, Cornell M, Cuccuru G, Davidsen K, Vedova GD, Dogan T, Doppelt-Azeroual O, Emery L, Gasteiger E, Gatter T, Goldberg T, Grosjean M, Grüning B, Helmer-Citterich M, Ienasescu H, Ioannidis V, Jespersen MC, Jimenez R, Juty N, Juvan P, Koch M, Laibe C, Li J-W, Licata L, Mareuil F, Mičetić I, Friborg RM, Moretti S, Morris C, Möller S, Nenadic A, Peterson H, Profiti G, Rice P, Romano P, Roncaglia P, Saidi R, Schafferhans A, Schwämmle V, Smith C, Sperotto MM, Stockinger H, Vařeková RS, Tosatto SCE, de la Torre V, Uva P, Via A, Yachdav G, Zambelli F, Vriend G, Rost B, Parkinson H, Løngreen P, Brunak S. Tools and data services registry: a community effort to document bioinformatics resources. Nucleic Acids Res. 2016;44(D1):D38–D47. doi: 10.1093/nar/gkv1116. PubMed DOI PMC

Optimized SQE atomic charges for peptides accessible via a web application

Atomic Charge Calculator II: web-based tool for the calculation of partial atomic charges