sampl6-pKa-prediction.bib

% Encoding: UTF-8

@Article{Pickard2016,
  author  = {Pickard, Frank C. and K{\"o}nig, Gerhard and Tofoleanu, Florentina and Lee, Juyong and Simmonett, Andrew C. and Shao, Yihan and Ponder, Jay W. and Brooks, Bernard R.},
  title   = {Blind Prediction of Distribution in the {{SAMPL5}} Challenge with {{QM}} Based Protomer and {{pK}} a Corrections},
  journal = {J. Comput. Aided Mol. Des.},
  year    = {2016},
  volume  = {30},
  number  = {11},
  pages   = {1087-1100},
  month   = nov,
  issn    = {0920-654X, 1573-4951},
  doi     = {10.1007/s10822-016-9955-7},
}

@Article{VanKreel1985,
  author  = {Van Kreel, B. K.},
  title   = {The Estimation of the Apparent Standard Free Energy Change {{$\Delta$GopH}} of a Biochemical Reaction from the Standard Free Energy of Formation and Apparent Free Energy of Ionization of the Participating Molecules and Its Application to the Reactions of the Purine Metabolism},
  journal = {Biochem. Mol. Biol. Educ.},
  year    = {1985},
  volume  = {13},
  number  = {3},
  pages   = {125--130},
}

@Article{Krol2006,
  author  = {Kr{\'o}l, Marcin and Wrona, Marta and Page, Christopher S. and Bates, Paul A.},
  title   = {Macroscopic p {{{\emph{K}}}} {\textsubscript{a}} {{Calculations}} for {{Fluorescein}} and {{Its Derivatives}}},
  journal = {J. Chem. Theory Comput.},
  year    = {2006},
  volume  = {2},
  number  = {6},
  pages   = {1520-1529},
  month   = nov,
  issn    = {1549-9618, 1549-9626},
  doi     = {10.1021/ct600235y},
}

@Article{Aguilar2010,
  author  = {Aguilar, Boris and Anandakrishnan, Ramu and Ruscio, Jory Z. and Onufriev, Alexey V.},
  title   = {Statistics and {{Physical Origins}} of {{pK}} and {{Ionization State Changes}} upon {{Protein}}-{{Ligand Binding}}},
  journal = {Biophys. J.},
  year    = {2010},
  volume  = {98},
  number  = {5},
  pages   = {872-880},
  month   = mar,
  issn    = {00063495},
  doi     = {10.1016/j.bpj.2009.11.016},
}

@Article{Jordan2005,
  author  = {Jordan, I. King and Kondrashov, Fyodor A. and Adzhubei, Ivan A. and Wolf, Yuri I. and Koonin, Eugene V. and Kondrashov, Alexey S. and Sunyaev, Shamil},
  title   = {A Universal Trend of Amino Acid Gain and Loss in Protein Evolution},
  journal = {Nature},
  year    = {2005},
  volume  = {433},
  number  = {7026},
  pages   = {633-638},
  month   = feb,
  issn    = {0028-0836, 1476-4679},
  doi     = {10.1038/nature03306},
}

@Article{Bochevarov2013,
  author  = {Bochevarov, Art D. and Harder, Edward and Hughes, Thomas F. and Greenwood, Jeremy R. and Braden, Dale A. and Philipp, Dean M. and Rinaldo, David and Halls, Mathew D. and Zhang, Jing and Friesner, Richard A.},
  title   = {Jaguar: {{A}} High-Performance Quantum Chemistry Software Program with Strengths in Life and Materials Sciences},
  journal = {Int. J. Quantum Chem.},
  year    = {2013},
  volume  = {113},
  number  = {18},
  pages   = {2110-2142},
  month   = sep,
  issn    = {00207608},
  doi     = {10.1002/qua.24481},
}

@Article{tenBrink2010,
  author  = {{ten Brink}, Tim and Exner, Thomas E.},
  title   = {{{pKa}} Based Protonation States and Microspecies for Protein\textendash{}ligand Docking},
  journal = {J. Comput. Aided Mol. Des.},
  year    = {2010},
  volume  = {24},
  number  = {11},
  pages   = {935-942},
  month   = nov,
  issn    = {0920-654X, 1573-4951},
  doi     = {10.1007/s10822-010-9385-x},
  file    = {:tenBrink2010 - PKa Based Protonation States and Microspecies for Proteinligand Docking.pdf:PDF},
}

@Article{Manchester2010,
  author  = {Manchester, John and Walkup, Grant and Rivin, Olga and You, Zhiping},
  title   = {Evaluation of p {{{\emph{K}}}} {\textsubscript{a}} {{Estimation Methods}} on 211 {{Druglike Compounds}}},
  journal = {J. Chem. Inf. Model.},
  year    = {2010},
  volume  = {50},
  number  = {4},
  pages   = {565-571},
  month   = apr,
  issn    = {1549-9596, 1549-960X},
  doi     = {10.1021/ci100019p},
}

@Article{Shelley2007Epik,
  author  = {Shelley, John C. and Cholleti, Anuradha and Frye, Leah L. and Greenwood, Jeremy R. and Timlin, Mathew R. and Uchimaya, Makoto},
  title   = {Epik: A Software Program for {{pK}} a Prediction and Protonation State Generation for Drug-like Molecules},
  journal = {J. Comput. Aided Mol. Des.},
  year    = {2007},
  volume  = {21},
  number  = {12},
  pages   = {681-691},
  month   = dec,
  issn    = {0920-654X, 1573-4951},
  comment = {Epik},
  doi     = {10.1007/s10822-007-9133-z},
}

@Misc{SchroedingerLLC,
  author = {Schr\"{o}dinger{, }LLC},
  title  = {Epik 4.5},
}

@Book{Perrin1981HammettTaft,
  title     = {pKa prediction for organic acids and bases},
  publisher = {Springer},
  year      = {1981},
  author    = {Perrin, Douglas Dalzell and Dempsey, Boyd and Serjeant, Eldon P},
  volume    = {1},
  comment   = {HammettTaft},
}

@Misc{SMARTSDaylight,
  title   = {SMARTS, SMiles ARbitrary Target Specification, is a registered trademark of Daylight Chemical Information Systems},
  comment = {SMARTS Daylight},
}

@Article{Martin2009,
  author          = {Martin, Yvonne Connolly},
  title           = {Let's not forget tautomers.},
  journal         = {Journal of computer-aided molecular design},
  year            = {2009},
  volume          = {23},
  pages           = {693--704},
  month           = oct,
  issn            = {1573-4951},
  abstract        = {A compound exhibits tautomerism if it can be represented by two structures that are related by an intramolecular movement of hydrogen from one atom to another. The different tautomers of a molecule usually have different molecular fingerprints, hydrophobicities and pKa's as well as different 3D shape and electrostatic properties; additionally, proteins frequently preferentially bind a tautomer that is present in low abundance in water. As a result, the proper treatment of molecules that can tautomerize, approximately 25% of a database, is a challenge for every aspect of computer-aided molecular design. Library design that focuses on molecular similarity or diversity might inadvertently include similar molecules that happen to be encoded as different tautomers. Physical property measurements might not establish the properties of individual tautomers with the result that algorithms based on these measurements may be less accurate for molecules that can tautomerize-this problem influences the accuracy of filtering for library design and also traditional QSAR. Any 2D or 3D QSAR analysis must involve the decision of if or how to adjust the observed Ki or IC50 for the tautomerization equilibria. QSARs and recursive partitioning methods also involve the decision as to which tautomer(s) to use to calculate the molecular descriptors. Docking virtual screening must involve the decision as to which tautomers to include in the docking and how to account for tautomerization in the scoring. All of these decisions are more difficult because there is no extensive database of measured tautomeric ratios in both water and non-aqueous solvents and there is no consensus as to the best computational method to calculate tautomeric ratios in different environments.},
  citation-subset = {IM},
  completed       = {2010-02-12},
  country         = {Netherlands},
  doi             = {10.1007/s10822-009-9303-2},
  issn-linking    = {0920-654X},
  issue           = {10},
  keywords        = {Drug Design; Molecular Structure; Quantitative Structure-Activity Relationship},
  nlm-id          = {8710425},
  owner           = {NLM},
  pmc             = {PMC2776169},
  pmid            = {19842045},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2017-02-20},
}

@Article{Bax2017,
  author          = {Bax, Ben and Chung, Chun Wa and Edge, Colin},
  title           = {Getting the chemistry right: protonation, tautomers and the importance of H atoms in biological chemistry.},
  journal         = {Acta crystallographica. Section D, Structural biology},
  year            = {2017},
  volume          = {73},
  pages           = {131--140},
  month           = feb,
  issn            = {2059-7983},
  abstract        = {There are more H atoms than any other type of atom in an X-ray crystal structure of a protein-ligand complex, but as H atoms only have one electron they diffract X-rays weakly and are `hard to see'. The positions of many H atoms can be inferred by our chemical knowledge, and such H atoms can be added with confidence in `riding positions'. For some chemical groups, however, there is more ambiguity over the possible hydrogen placements, for example hydroxyls and groups that can exist in multiple protonation states or tautomeric forms. This ambiguity is far from rare, since about 25% of drugs have more than one tautomeric form. This paper focuses on the most common, `prototropic', tautomers, which are isomers that readily interconvert by the exchange of an H atom accompanied by the switch of a single and an adjacent double bond. Hydrogen-exchange rates and different protonation states of compounds (e.g. buffers) are also briefly discussed. The difference in heavy (non-H) atom positions between two tautomers can be small, and careful refinement of all possible tautomers may single out the likely bound ligand tautomer. Experimental methods to determine H-atom positions, such as neutron crystallography, are often technically challenging. Therefore, chemical knowledge and computational approaches are frequently used in conjugation with experimental data to deduce the bound tautomer state. Proton movement is a key feature of many enzymatic reactions, so understanding the orchestration of hydrogen/proton motion is of critical importance to biological chemistry. For example, structural studies have suggested that, just as a chemist may use heat, some enzymes use directional movement to protonate specific O atoms on phosphates to catalyse phosphotransferase reactions. To inhibit `wriggly' enzymes that use movement to effect catalysis, it may be advantageous to have inhibitors that can maintain favourable contacts by adopting different tautomers as the enzyme `wriggles'.},
  chemicals       = {Ligands, Proteins, Protons, Saccharomyces cerevisiae Proteins, Small Molecule Libraries, Hydrogen, DNA, DNA Topoisomerases, Type II},
  citation-subset = {IM},
  completed       = {2017-10-25},
  country         = {United States},
  doi             = {10.1107/S2059798316020283},
  issn-linking    = {2059-7983},
  issue           = {Pt 2},
  keywords        = {Crystallization, methods; Crystallography, X-Ray, methods; DNA, chemistry; DNA Topoisomerases, Type II, chemistry; Hydrogen, chemistry; Isomerism; Ligands; Models, Molecular; Protein Conformation; Proteins, chemistry; Protons; Saccharomyces cerevisiae, chemistry; Saccharomyces cerevisiae Proteins, chemistry; Small Molecule Libraries, chemistry; H atoms; chemistry; ligands; tautomers},
  nlm-id          = {101676043},
  owner           = {NLM},
  pii             = {S2059798316020283},
  pmc             = {PMC5297916},
  pmid            = {28177309},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2018-06-19},
}

@Article{Greenwood2010,
  author          = {Greenwood, Jeremy R and Calkins, David and Sullivan, Arron P and Shelley, John C},
  title           = {Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution.},
  journal         = {Journal of computer-aided molecular design},
  year            = {2010},
  volume          = {24},
  pages           = {591--604},
  month           = jun,
  issn            = {1573-4951},
  abstract        = {Generating the appropriate protonation states of drug-like molecules in solution is important for success in both ligand- and structure-based virtual screening. Screening collections of millions of compounds requires a method for determining tautomers and their energies that is sufficiently rapid, accurate, and comprehensive. To maximise enrichment, the lowest energy tautomers must be determined from heterogeneous input, without over-enumerating unfavourable states. While computationally expensive, the density functional theory (DFT) method M06-2X/aug-cc-pVTZ(-f) [PB-SCRF] provides accurate energies for enumerated model tautomeric systems. The empirical Hammett-Taft methodology can very rapidly extrapolate substituent effects from model systems to drug-like molecules via the relationship between pK(T) and pK(a). Combining the two complementary approaches transforms the tautomer problem from a scientific challenge to one of engineering scale-up, and avoids issues that arise due to the very limited number of measured pK(T) values, especially for the complicated heterocycles often favoured by medicinal chemists for their novelty and versatility. Several hundreds of pre-calculated tautomer energies and substituent pK(a) effects are tabulated in databases for use in structural adjustment by the program Epik, which treats tautomers as a subset of the larger problem of the protonation states in aqueous ensembles and their energy penalties. Accuracy and coverage is continually improved and expanded by parameterizing new systems of interest using DFT and experimental data. Recommendations are made for how to best incorporate tautomers in molecular design and virtual screening workflows.},
  chemicals       = {Heterocyclic Compounds, Pharmaceutical Preparations, Solutions, Water},
  citation-subset = {IM},
  completed       = {2010-09-27},
  country         = {Netherlands},
  doi             = {10.1007/s10822-010-9349-1},
  issn-linking    = {0920-654X},
  issue           = {6-7},
  keywords        = {Heterocyclic Compounds, chemistry; Isomerism; Models, Chemical; Pharmaceutical Preparations, chemistry; Quantum Theory; Solutions, chemistry; Water, chemistry},
  nlm-id          = {8710425},
  owner           = {NLM},
  pmid            = {20354892},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2017-10-21},
}

@Article{Lim1991protonsolvation,
  author    = {Lim, Carmay and Bashford, Don and Karplus, Martin},
  title     = {Absolute pKa calculations with continuum dielectric methods},
  journal   = {The Journal of Physical Chemistry},
  year      = {1991},
  volume    = {95},
  number    = {14},
  pages     = {5610--5620},
  comment   = {protonsolvation},
  publisher = {ACS Publications},
}

@Article{Philipp2018macropka,
  author        = {Philipp, Dean M and Watson, Mark A and Yu, Haoyu S and Steinbrecher, Thomas B and Bochevarov, Art D},
  title         = {Quantum chemical prediction for complex organic molecules},
  journal       = {International Journal of Quantum Chemistry},
  year          = {2018},
  volume        = {118},
  number        = {12},
  pages         = {e25561},
  __markedentry = {[Bas:]},
  comment       = {macropka
},
  publisher     = {Wiley Online Library},
}

@Article{Bochevarov2016multiconformation,
  author        = {Bochevarov, Art D and Watson, Mark A and Greenwood, Jeremy R and Philipp, Dean M},
  title         = {Multiconformation, Density Functional Theory-Based p K a Prediction in Application to Large, Flexible Organic Molecules with Diverse Functional Groups},
  journal       = {Journal of chemical theory and computation},
  year          = {2016},
  volume        = {12},
  number        = {12},
  pages         = {6001--6019},
  __markedentry = {[Bas:6]},
  comment       = {multiconformation},
  publisher     = {ACS Publications},
}

@Comment{jabref-meta: databaseType:bibtex;}