Young - Computational chemistry
.pdfBIBLIOGRAPHY 293
M. Zimmer, Chem. Rev. 95, 2629 (1995).
B. P. Hay, Coord. Chem. Rev. 126, 177 (1993).
Organometallic modeling is reviewed in
A. Streitwieser, K. Sorger, Encycl. Comput. Chem. 3, 2100 (1998).
Silylenes are reviewed in
L.NyulaÂszi, T. VeszpreÂmi, Encycl. Comput. Chem. 4, 2589 (1998).
Sulfur compounds are discussed in
D.C. Young, M. L. McKee, Computational Chemistry Reviews of Current Trends Volume 3 149, J. Leszczynski, Ed., World Scienti®c, Singapore (1999).
Zeolites are reviewed in
B. van de Graaf, S. C. Njo, K. S. Smirnov, Rev. Coput. Chem. 14, 137 (2000). J. Sauer, Encycl. Comput. Chem. 5, 3248 (1998).
Other review articles pertinent to main group inorganics are
M. S. Gordon, Modern Electronic Structure Theory Part 1 D. R. Yarkdony, Ed., 311, World Scienti®c, Singapore (1995).
Books relevant to transition metal modeling are
I.B. Bersuker, Electronic Structure and Properties of Transition Metal Compounds John Wiley & Sons, New York (1996).
P.Comba, T. W. Hambley, Molecular Modeling of Inorganic Compounds VCH, Weinheim (1995).
The Challenge of d and f Electrons D. R. Salahub, M. C. Zerner, Ed., American Chemical Society, Washington (1989).
V.K. Grigorovich, The Metallic Bond and the Structure of Metals Nova Science, Huntington, NY (1989).
Quantum Chemistry: The Challenge of transition Metals and Coordination Chemistry A. Veillard, Ed., D. Reidel, Dordrecht (1986).
J.K. burdett, Molecular Shapes: Theoretical Models of Inorganic Stereochemistry John Wiley & Sons, New York (1980).
K.Bernauer, M. S. Wrighton, A. Albini, H. Krisch, Theoretical Inorganic Chemistry II Springer-Verlag, Berlin (1976).
C.K. Jorgensen, H. Brunner, L. H. Pignolet, S. Veprek, Theoretical Inorganic Chemistry Springer-Verlag, Berlin (1975).
Review articles pertinent to transition metal modeling are
Chem. Rev. 100, number 2 (2000).
G. Frenking, T. Wagener, Encycl. Comput. Chem. 5, 3073 (1998).
C. W. Bauschlicker, Jr., Encycl. Comput. Chem. 5, 3084 (1998).
P. PyykkoÈ, Chem. Rev. 97, 597 (1997).
294 37 THE COMPUTATIONAL CHEMIST'S VIEW OF THE PERIODIC TABLE
A.Berces, T. Ziegler, Density Functional Theory III R. F. Nalewajski, Ed., 41, SpringerVerlag, Berlin (1996).
G.Frenking, I. Antes, M. Boehme, S. Dapprich, A. W. Ehlers, V. Jonas, A. Neuhaus, M. Otto, R. Stegmann, A. Veldkamp, S. F. Vyboisikov, Rev. Comput. Chem. 8, 63 (1996).
T.R. Cundari, M. T. Benson, M. L. Lutz, S. O. Sommerer, Rev. Comput. Chem. 8, 63 (1996).
I. Bytheway, M. B. Hall, Chem. Rev. 94, 639 (1994).
A. Veillard, Chem. Rev. 91, 743 (1991).
M.G. Cory, M. C. Zerner, Chem. Rev. 91, 813 (1991).
N.Koga, K. Morokuma, Chem. Rev. 91, 823 (1991).
D. E. Ellis, J. Guo, H.-P. Cheng, J. J. Low, Adv. Quantum Chem. 22, 125 (1991). S. R. Langho¨, C. W. Bauschlicker, Jr., Ann. Rev. Phys. Chem. 39, 181 (1988).
A.Dedieu, M.-M. Rohmer, A. Veillart, Adv. Quantum Chem. 16, 43 (1982).
G.D. BroackeÁre, Adv. Chem. Phys. 37, 203 (1978).
A.Veillard, J. Demuynck, Applications of Electronic Structure Theory H. F. Schaefer, III, Ed., 187, Plenum, New York (1977).
G.Berthier, Adv. Quantum Chem. 8, 183 (1974).
Molecular mechanics techniques for transition metals are reviewed in
A.K. RappeÂ, C. J. Casewit, Molecular Mechanics across Chemistry University Science Books, Sausalito (1997).
C.R. Landis, D. M. Root, T. Cleveland, Rev. Comput. Chem. 6, 73, (1995).
M. Zimmer, Chem. Rev. 95, 2629 (1995).
B. P. Hay, Coord. Chem. Rev. 126, 177 (1993).
P. Comba, Coord. Chem. Rev. 123, 1 (1993).
Semiempirical and ab initio methods for transition metals are compared in
A. J. Holder, Encycl. Comput. Chem. 4, 2578 (1998).
J. P. Dahl, C. J. Ballhausen, Adv. Quantum Chem. 4, 170 (1968).
Catalyst design is reviewed in
S. Nakamura, S. Sieber, Encycl. Comput. Chem. 1, 246 (1998).
Cluster calculations are reviewed in
V. Bonacic-Koutecky, P. Fantucci, J. Koutecky, Encycl. Comput. Chem. 2, 876 (1998).
Metal complex calculations are reviewed in
B.P. Hay, O. Clement, Encycl. Comput. Chem. 3, 1580 (1998).
Books discussing modeling of lanthanides and actinides are
P.Comba, T. W. Hambley, Molecular Modeling of Inorganic Compounds VCH, Weinheim (1995).
BIBLIOGRAPHY 295
The Challenge of d and f Electrons D. R. Salahub, M. C. Zerner, Eds., American Chemical Society, Washington (1989).
Review articles pertinent to lanthanides and actinides are
M. Dolg, Encycl. Comput. Chem. 2, 1478 (1998).
M. Dolg, H. Stoll, Handbook on the Physics and Chemistry of Rare Earths K. A. Gschneidner, Jr., L. Eyring, Eds., 22, 607, Elsevier, Amsterdam (1994).
M. Pepper, B. E. Bursten, Chem. Rev. 91, 719 (1991).
M. S. S. Brooks, ActinidesÐChemistry and Pysical Properties L. Manes, Ed., 261, Springer-Verlag, Berlin (1985).
Molecular mechanics methods for lanthanides and actinides are reviewed in
M. Zimmer, Chem. Rev. 95, 2629 (1995).
B. P. Hay, Coord. Chem. Rev. 126, 177 (1993).
Computational Chemistry: A Practical Guide for Applying Techniques to Real-World Problems. David C. Young Copyright ( 2001 John Wiley & Sons, Inc.
ISBNs: 0-471-33368-9 (Hardback); 0-471-22065-5 (Electronic)
38 Biomolecules
The process of designing a new drug and bringing it to market is very complex. According to a 1997 government report, it takes 12 years and 350 million dollars for the average new drug to go from the research laboratory to patient use. At several points in this process, computer-modeling techniques provide a signi®- cant cost savings. This makes biomolecule modeling a very important part of the ®eld. The same can be said of agrochemical research and many other applications. For the sake of convenience, this chapter discusses drug design, although most of the discussion is applicable to any biomolecular application.
Due to the incredible complexity of biological systems, molecular modeling is not at all an easy task. It can be divided into two general categories: speci®c and general interactions. The design of a drug or pesticide aims to elicit a very speci®c biological reaction by interaction of the compound with a very speci®c biomolecule (which may be unknown). At the opposite extreme is the need to predict general interactions, which are due to a variety of processes. Some of these general interactions are biodegradation and toxicity.
38.1METHODS FOR MODELING BIOMOLECULES
Due to the large size of most biologically relevant molecules, molecular mechanics is most often the method of choice for biochemical modeling. There are molecular mechanics force ®elds for both modeling speci®c classes of molecules and organic molecules in general. In some cases, even molecular mechanics is too time-consuming to model a very large system and mesoscale techniques can be used (Chapter 35).
At the other extreme is a trend toward the increasing use of orbital-based techniques, particularly QM/MM calculations (Chapter 23). These orbital-based techniques are needed to accurately model the actual process of chemical bond breaking and formation.
The ®rst step in designing a new compound is to ®nd compounds that have even a slight amount of usefulness for the intended purpose. These are called lead compounds. Once such compounds are identi®ed, the problem becomes one of re®nement. Computational techniques are a fairly minor part of ®nding lead compounds. The use of computer-based techniques for lead compound identi®cation is usually limited to searching databases for compounds similar to known lead compounds or known to treat diseases with similar causes or symptoms.
296
38.2 SITE-SPECIFIC INTERACTIONS |
297 |
Once a number of lead compounds have been found, computational and laboratory techniques are very successful in re®ning the molecular structures to yield greater drug activity and fewer side e¨ects. This is done both in the laboratory and computationally by examining the molecular structures to determine which aspects are responsible for both the drug activity and the side e¨ects. These are the QSAR techniques described in Chapter 30. Recently, 3D QSAR has become very popular for this type of application. These techniques have been very successful in the re®nement of lead compounds.
A more logical approach would be to model the binding site for a target molecule and then ®nd molecules that will dock in this site. Unfortunately, the binding site may not be known. It is fairly easy to determine the sequences of proteins and nucleotides. However, it is much more di½cult to obtain structural information by X-ray crystallography. Because of this disparity, there has been an immense amount of work on solving the protein folding problem, which is to determine the three dimensional structure of a protein from its sequence. Although computing the relative energies of conformations is one of the greatest successes of computational chemistry techniques, the incredibly huge number of possible conformers of a protein make this a daunting task. Two ingenious methods for simplifying this problem are distance geometry and homology modeling. Distance geometry is a means for imposing constraints on the problem, which are obtained from two-dimensional NMR studies. Homology modeling is used to ®nd the known structure with the most similar sequence, then using that geometry for those sections of the unknown. These techniques are discussed in more detail in Chapter 21. Once a binding site is known, a molecule to bind in that site can be determined with the techniques described in the next section.
38.2SITE-SPECIFIC INTERACTIONS
If it is known that a drug must bind to a particular spot on a particular protein or nucleotide, then a drug can be tailor-made to bind at that site. This is often modeled computationally using any of several di¨erent techniques. Traditionally, the primary way of determining what compounds would be tested computationally was provided by the researcher's understanding of molecular interactions. A second method is the brute force testing of large numbers of compounds from a database of available structures.
More recently, a set of techniques, called rational drug design or De Novo techniques, have been used. These techniques attempt to reproduce the researcher's understanding of how to choose likely compounds. Such an understanding is built into a software package that is capable of modeling a very large number of compounds in an automated way. Many di¨erent algorithms have been used for this type of testing, many of which were adapted from arti- ®cial intelligence applications. No clear standard has yet emerged in this area so it is not possible to say which is the best technique.
298 38 BIOMOLECULES
38.3GENERAL INTERACTIONS
Interestingly, QSAR is as useful for predicting general interactions as it is for the optimization of activity for very speci®c interactions. In this case, QSAR rather than 3D QSAR is most e¨ective. It has been used for predicting environmental toxicity, biodegradation, and other processes. This serves as a good screening technique to determine which compounds should be examined closer. These methods are never completely reliable and should not be considered a substitute for standard testing techniques. They are best used for categorizing compounds as having a high or low likelihood of acceptability.
It is possible to obtain the sequence of a DNA strand, but that does not give an understanding of the attribute of the organism described by a particular piece of genetic code. Homology modeling can be used to shed light on this type of information, as well as for determining structure. Homology modeling is the systematic comparison of DNA sequences to determine regions of similarities and di¨erences. This can yield information as broad as the di¨erences between reptiles and mammals or information as narrow as the di¨erences between individuals.
38.4RECOMMENDATIONS
The modeling of biomolecules is a very broad and sophisticated ®eld. The description given in this chapter is only meant to provide the connections between the topics in this book and this ®eld. Before embarking on a computational biochemical study, it is recommended that the researcher investigate the literature pertaining to this ®eld more closely. The references provided below should provide a good starting point for such a survey.
BIBLIOGRAPHY
Books presenting relevant computational techniques are
A.K. RappeÂ, C. J. Casewit, Molecular Mechanics across Chemistry University Science Books, Sausalito (1997).
A.R. Leach, Molecular Modelling Principles and Applications Longman, Essex (1996).
H.-D. Holtje, G. Folkers, T. Bierer, W. Sippl, D. Rognan, Molecular ModelingÐBasic Principles and Applications John Wiley & Sons, New York (1996).
G.H. Grant, W. G. Richards, Computational Chemistry Oxford, Oxford (1995).
Books speci®cally addressing biomolecules are
Practical Application of Computer-Aided Drug Design P. S. Charifson, Ed., Marcel Dekker, New York (1997).
BIBLIOGRAPHY 299
Guidebook on Molecular Modeling in Drug Design N. C. Cohen, Ed., Academic, San Diego (1996).
H.Van de Waterbeemd, Advanced Computer-Asissted Techiques in Drug Discovery John Wiley & Sons, New York (1995).
G. L. Patrick, An Introduction to Medicinal Chemistry Oxford, Oxford (1995).
Computer-Aided Molecular Design Applications in Agrochemicals, Materials and Pharmaceuticals C. H. Reynolds, M. K. Holloway, H. K. Cox, Eds., American Chemical Society, Washington (1995).
Molecular Modelling and Drug Design J. G. Vintner, M. Gardner, Eds., CRC, Boca Raton (1994).
A.Warshel, Computer Modeling of Chemical Reactions in Enzymes and Solutions John Wiley & Sons, New York (1991).
Computer-Aided Drug Design Methods and Applications T. J. Perun, C. L. Propst, Eds., Dekker, New York (1989).
J.A. McCammon, S. C. Harvey, Dynamics of Proteins and Nucleic Acids Cambridge, Cambridge (1987).
General review articles are
D.B. Boyd, Encycl. Comput. Chem. 1, 795 (1998).
G. W. A. Milne, Encycl. Comput. Chem. 3, 2046 (1998).
L. M. Balbes, S. W. Mascarella and D. B. Boyd, Rev. Comput. Chem. 5, 337 (1994).
P. Kollman, Chem. Rev. 93, 2395 (1993).
B. Pullman, Adv. Quantum Chem. 10, 251 (1977).
L. Balbes, Guide to Rational (Computer-aided) Drug Design is online at http:// www.ccl.net/cca/documents/drug.design.shtml
There are many links to online information on Soaring Bear's web page at
http://ellington.pharm.arizona.edu/%7Ebear/
Applications of arti®cial intelligence are reviewed in
D. P. Dolata, Encycl. Comput. Chem. 1, 44 (1998).
Predicting biodegredation is reviewed in
G. Klopman, M. Tu, Encycl. Comput. Chem. 1, 128 (1998).
Carcinogenicity is reviewed in
L. v. SzentpaÂly, R. Ghosh, Theoretical Organic Chemistry 447 C. PaÂrkaÂnyi, Ed., Elsevier, Amsterdam (1998).
Chemometrics are reviewed in
K.Varmuza, Encycl. Comput. Chem. 1, 347 (1998).
Conformation searching of biomolecules is reviewed in
M. VaÂsquez, G. NeÂmethy, H. A. Scheraga, Chem. Rev. 94, 2183 (1994).
300 38 BIOMOLECULES
Information about De Novo techniques is in
Rational Drug Design A. Parrill, M. R. Reddy, Eds., Oxford, Oxford (1999). A. P. Johnson, S. M. Green, Encycl. Comput. Chem. 1, 650 (1998).
H. J. BoÈhm, S. Fischer, Encycl. Comput. Chem. 1, 657 (1998).
D. E. Clark, C. W. Murray, J. Li, Rev. Comput. Chem. 11, 67 (1997). S. Borman, Chem. and Eng. News 70, 18 (1992).
Distance geometry techniques are reviewed in
T. F. Havel, Encycl. Comput. Chem. 1, 723 (1998).
M. P. Williamson, J. P. Walto, Chem. Soc. Rev. 21, 227 (1992).
Modeling DNA is reviewed in
J. Sponer, P. Hobza, Encycl. Comput. Chem. 1, 777 (1998).
R.Lavery, Encycl. Comput. Chem. 3, 1913 (1998).
S.Lemieux, S. Oldziej, F. Major, Encycl. Comput. Chem. 3, 1930 (1998).
D. L. Beveridge, Encycl. Comput. Chem. 3, 1620 (1998).
P. Au½nger, E. Westhof, Encycl. Comput. Chem. 3, 1628 (1998).
G.Ravishanker, P. Au½nger, P. R. Langley, B. Jayaram, M. A. Young, Rev. Comput. Chem. 11, 317 (1997).
Docking techniques are reviewed in
C. M. Oshiro, I. D. Kuntz, R. M. A. Knegtel, Encycl. Comput. Chem. 3, 1606 (1998). M. Vieth, J. D. Hirst, A. Kolinski, C. L. Brooks, III, J. Comput. Chem. 19, 1612 (1998). M. Vieth, J. D. Hirst, B. N. Dominy, H. Daigler, C. L. Brooks, III, J. Comput. Chem.
19, 1623 (1998).
Electron transfer is reviewed in
T. Hayashi, H. Ogoshi, Chem. Soc. Rev. 26, 355 (1997).
Ligand design is reviewed in
M. A. Murcko, Rev. Comput. Chem. 11, 1 (1997).
Modeling membranes is reviewed in
S. Yoneda, T. Yoneda, H. Umeyamn, Encycl. Comput. Chem. 1, 135 (1998). H. J. c. Berendsen, D. P. Tieleman, Encycl. Comput. Chem. 3, 1638 (1998). A. Pullman, Chem. Rev. 91, 793 (1991).
J. Houk, R. H. Guy, Chem. Rev. 88, 455 (1988).
Modeling micelles is reviewed in
P. L. Luisi, Adv. Chem. Phys. 92, 425 (1996).
Molecular dynamics of biomolecules is reviewed in
T. P. Lybrand, Rev. Comput. Chem. 1, 295 (1990).
BIBLIOGRAPHY 301
Neural network reviews are
J. A. Burns, G. M. Whitesides, Chem. Rev. 93, 2583 (1993).
Oligosaccharide modeling is reviewed in
R. J. Woods, Rev. Comput. Chem. 9, 129 (1996).
Pesticide modeling is reviewed in
E.L. Plumber, Rev. Comput. Chem. 1, 119 (1990).
Protein & peptide reviews are
J.Skolnick, A. Kolinski, Encycl. Comput. Chem. 3, 2200 (1998). B. Rost, Encycl. Comput. Chem. 3, 2243 (1998).
L. Pedersen, T. Darden, Encycl. Comput. Chem. 3, 1650 (1998). K. E. Laidig, V. Daggett, Encycl. Comput. Chem. 3, 2211 (1998). C. L. Brooks, III, D. A. Case, Chem. Rev. 93, 2487 (1993).
G. E. Marlow, J. S. Perkyns, B. M. Pettitt, Chem. Rev. 93, 2503 (1993).
Ê
J. Aqvist, A. Warshel, Chem. Rev. 93, 2523 (1993).
H.Scheraga, Rev. Comput. Chem. 3, 73 (1992).
J. M. Troyer, F. E. Cohen, Rev. Comput. Chem. 2, 57 (1991).
M. Karplus, Modelling of Molecular Structures and Properties J.-L. Rivail, Ed., 427, Elsevier, Amsterdam (1990).
J. Skolnick, A. Kolinski, Ann. Rev. Phys. Chem. 40, 207 (1989).
Adv. Chem. Phys. C. L. Brooks, III, M. Karplus, B. M. Pettitt, Eds., vol. 71 (1988).
J.A. McCammon, M. Karplus, Ann. Rev. Phys. Chem. 31, 29 (1980).
QSAR reviews are
H.Kubinyi, Encycl. Comput. Chem. 4, 2309 (1998). S. P. Gupta, Chem. Rev. 94, 1507 (1994).
H. H. Ja¨eÂ, Chem. Rev. 53, 191 (1953).
An introduction to structure-based techniques is
I. D. Kuntz, E. C. Meng, B. K. Shoichet, Acct. Chem. Res. 27, 117 (1994).
Toxicity prediction is reviewed in
D. F. Lewis, Rev. Comput. Chem. 3, 173 (1992).
Computational Chemistry: A Practical Guide for Applying Techniques to Real-World Problems. David C. Young Copyright ( 2001 John Wiley & Sons, Inc.
ISBNs: 0-471-33368-9 (Hardback); 0-471-22065-5 (Electronic)
38 Biomolecules
The process of designing a new drug and bringing it to market is very complex. According to a 1997 government report, it takes 12 years and 350 million dollars for the average new drug to go from the research laboratory to patient use. At several points in this process, computer-modeling techniques provide a signi®- cant cost savings. This makes biomolecule modeling a very important part of the ®eld. The same can be said of agrochemical research and many other applications. For the sake of convenience, this chapter discusses drug design, although most of the discussion is applicable to any biomolecular application.
Due to the incredible complexity of biological systems, molecular modeling is not at all an easy task. It can be divided into two general categories: speci®c and general interactions. The design of a drug or pesticide aims to elicit a very speci®c biological reaction by interaction of the compound with a very speci®c biomolecule (which may be unknown). At the opposite extreme is the need to predict general interactions, which are due to a variety of processes. Some of these general interactions are biodegradation and toxicity.
38.1METHODS FOR MODELING BIOMOLECULES
Due to the large size of most biologically relevant molecules, molecular mechanics is most often the method of choice for biochemical modeling. There are molecular mechanics force ®elds for both modeling speci®c classes of molecules and organic molecules in general. In some cases, even molecular mechanics is too time-consuming to model a very large system and mesoscale techniques can be used (Chapter 35).
At the other extreme is a trend toward the increasing use of orbital-based techniques, particularly QM/MM calculations (Chapter 23). These orbital-based techniques are needed to accurately model the actual process of chemical bond breaking and formation.
The ®rst step in designing a new compound is to ®nd compounds that have even a slight amount of usefulness for the intended purpose. These are called lead compounds. Once such compounds are identi®ed, the problem becomes one of re®nement. Computational techniques are a fairly minor part of ®nding lead compounds. The use of computer-based techniques for lead compound identi®cation is usually limited to searching databases for compounds similar to known lead compounds or known to treat diseases with similar causes or symptoms.
296