Development of a Knowledge-Based Potential for Crystals of Small Organic Molecules:  Calculation of Energy Surfaces for C=0···H−N Hydrogen Bonds

Grzybowski, Bartosz A.; Ishchenko, Alexey; DeWitte, Robert S.; Whitesides, and George M.; Shakhnovich, Eugene I.

doi:10.1021/jp000644t

Cited by 39 publications

(63 citation statements)

References 52 publications

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“…The inverting of frequency distributions to obtain potentials of mean force is justified for a set of systems frozen in very low energy states, where the total energy is the sum of many independent contributions that are functions of some parameter p; in such ensembles, the negative logarithm of the frequency of occurrence of a particular value of p is proportional to the interaction energy for that value of p (14). A set of protein crystal structures constitutes such an ensemble to a good approximation, and hence the frequencies f protein (p), p ϭ (␦ HA , , , X) with which hydrogen bond parameters ␦ HA , , , or X are observed in protein structures can be related to the hydrogen bond interaction energies according to the Boltzmann-like expression:…”

Section: Generation Of Dimerization Energy Landscapesmentioning

confidence: 99%

“…Numerous studies of experimentally available protein and small molecule structures have revealed the directional character of hydrogen bonds and in particular the nonlinear geometry at the acceptor atom (5)(6)(7)(8). Computational modeling of hydrogen bonding energy landscapes is a challenging problem; current approaches include quantum mechanical calculations on model systems (usually small molecules analogous to either a main-chain peptide unit or an amino acid side chain) (9,10), molecular mechanics (force field) approaches (11)(12)(13), and knowledge-based potentials derived from small molecule structure databases (14) or the Protein Data Bank (PDB) (15,16).…”

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Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations

Morozov

Kortemme

Tsemekhman

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

235

236

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Hydrogen bonding is a key contributor to the exquisite specificity of the interactions within and between biological macromolecules, and hence accurate modeling of such interactions requires an accurate description of hydrogen bonding energetics. Here we investigate the orientation and distance dependence of hydrogen bonding energetics by combining two quite disparate but complementary approaches: quantum mechanical electronic structure calculations and protein structural analysis. We find a remarkable agreement between the energy landscapes obtained from the electronic structure calculations and the distributions of hydrogen bond geometries observed in protein structures. In contrast, molecular mechanics force fields commonly used for biomolecular simulations do not consistently exhibit close correspondence to either quantum mechanical calculations or experimentally observed hydrogen bonding geometries. These results suggest a route to improved energy functions for biological macromolecules that combines the generality of quantum mechanical electronic structure calculations with the accurate context dependence implicit in protein structural analysis. Hydrogen bonds are partially covalent interactions between a hydrogen atom covalently bound to an electronegative atom and an electronegative acceptor atom (1). They play an important role in defining the structure and function of biological macromolecular systems and contribute to the specificity of molecular recognition (2), the formation of secondary structures (3), and the energetics of protein folding (4). Numerous studies of experimentally available protein and small molecule structures have revealed the directional character of hydrogen bonds and in particular the nonlinear geometry at the acceptor atom (5-8). Computational modeling of hydrogen bonding energy landscapes is a challenging problem; current approaches include quantum mechanical calculations on model systems (usually small molecules analogous to either a main-chain peptide unit or an amino acid side chain) (9, 10), molecular mechanics (force field) approaches (11-13), and knowledge-based potentials derived from small molecule structure databases (14) or the Protein Data Bank (PDB) (15,16).For application to macromolecular systems, different approaches have complementary strengths and weaknesses. Quantum mechanical electronic structure calculations are clearly the most fundamental and general but can be carried out at a rigorous level of theory only for systems much smaller than biological macromolecules, and the results are not necessarily transferable to the complex macromolecular environment. Empirical molecular mechanics force fields are constructed to apply generally to macromolecules and so are not subject to the size limitations of electronic structure calculations, but their accuracy may be limited by the (necessary) simplification of the quantum mechanical system and the large number of parameters that need to be obtained by fitting against experimental data. Inference of interaction ener...

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Section: Generation Of Dimerization Energy Landscapesmentioning

confidence: 99%

mentioning

confidence: 99%

Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations

Morozov

Kortemme

Tsemekhman

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

235

236

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“…(iii) Because the potential is based on the large representative set of protein-ligand complexes, it provides a statistically significant description of interactions between proteins and small molecules. Unlike most semiempirical force fields used in drug design, the potential used in CombiSMoG is solidly based on statistical mechanics and does not have any arbitrary adjustable parameters (15,16).Ligands are generated in the active site of the target protein from 100 common organic groups (e.g., hydroxyl, carbonyl, furan, phenyl, etc.) deposited in the program's virtual combinatorial library.…”

mentioning

confidence: 99%

“…In this potential, two atoms-one on the ligand and one on the protein-are said to be in contact if the distance between them is less than a specified cutoff value (usually 5 Å). The contacts are classified according to the constituent atom types, and the frequencies of their occurrences in the database are transformed into energies by means of a Boltzmann-like relation to give the scoring function used in CombiSMoG (15,16). This potential has three main advantages over the commonly used force fields (17)(18)(19)(20).…”

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confidence: 99%

“…Because the measured binding constant of the R-stereoisomer is Ϸ7 times smaller than that of the S isomer, we expect the CombiSMoG score of R to be lower than that of S. Indeed, for both predicted and x-ray structures, the scores for R are lower (Ϫ37.58 predicted; Ϫ30.5 x-ray) than for S (Ϫ23.42 predicted; Ϫ25.08 x-ray). Moreover, because the CombiSMoG scores have the meaning of free energies (13,16), we also expect they should have a linear relationship to the logarithms of the binding constants (K d ). This relationship is observed for 20 ligands that have had their binding affinities measured under the same conditions as our R and S isomers, and for which the ligand-HCA x-ray structures are available (Fig.…”

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Combinatorial computational method gives new picomolar ligands for a known enzyme

Grzybowski

Ishchenko

Kim

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

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Combinatorial small molecule growth algorithm was used to design inhibitors for human carbonic anhydrase II. Two enantiomeric candidate molecules were predicted to bind with high potency (with R isomer binding stronger than S), but in two distinct conformations. The experiments verified that computational predictions concerning the binding affinities and the binding modes were correct for both isomers. The designed R isomer is the best-known inhibitor (K d ϳ 30 pM) of human carbonic anhydrase II. T he development of new drugs often depends on the identification of molecules (''leads'') that have high affinities for specified macromolecular targets. Although tight binding is only one important characteristic of a drug (1), it is often used as a guide in initial stages of drug discovery. Two contrasting methods-combinatorial (2-4) and rational (5-8)-represent the extremes in strategies for discovery of high-affinity leads. Combinatorial methods make it possible to screen large numbers of potential candidates and do not require prior knowledge of the structure of the receptor molecule. Rational methods attempt to design high-affinity ligands based on knowledge of the atomlevel structure of the receptor and of molecular interactions. In practice, both combinatorial and rational methods can efficiently identify relatively low-affinity leads; both are inefficient and unreliable in identifying high-affinity (K d ϳ nM) ligands (9-12).Here, we describe a computational methodology that combines combinatorial and rational strategies in the form of a computational system that is rapid enough to generate biased libraries of leads and accurate enough to give useful predictions of energetics and geometry. In its first experimental test, this method yielded a new ligand for a human carbonic anhydrase II (HCA) that has the highest known affinity for this enzyme (K d ϳ 30 pM). To our knowledge, it is the first time that a computational method has created a ligand that has the highest known affinity for a protein target.Our method, called CombiSMoG for combinatorial small molecule growth, incorporates the philosophy of combinatorial synthesis into computational drug design and is based on two interrelated components: a knowledge-based potential and a Monte Carlo ligand growth algorithm. The knowledge-based potential (13) is derived from a set of 1,000 protein-ligand complexes, whose structures are deposited in the Protein Data Bank crystallographic database (14). In this potential, two atoms-one on the ligand and one on the protein-are said to be in contact if the distance between them is less than a specified cutoff value (usually 5 Å). The contacts are classified according to the constituent atom types, and the frequencies of their occurrences in the database are transformed into energies by means of a Boltzmann-like relation to give the scoring function used in CombiSMoG (15,16). This potential has three main advantages over the commonly used force fields (17)(18)(19)(20). (i) The binary definition of atom-atom interactions all...

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Stereoelectronic Requirements for Optimal Hydrogen‐Bond‐Catalyzed Enolization

Pápai

Hamza

Pihko

et al. 2011

Chemistry A European J

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Protein crystallographic analysis of the active sites of enolizing enzymes and structural analysis of hydrogen-bonded carbonyl compounds in small molecule crystal structures, complemented by quantum chemical calculations on related model enolization reactions, suggest a new stereoelectronic model that accounts for the observed out-of-plane orientation of hydrogen-bond donors (HBDs) in the oxyanion holes of enolizing enzymes. The computational results reveal that the lone-pair directionality of HBDs characteristic for hydrogen-bonded carbonyls is reduced upon enolization, and the enolate displays almost no directional preference for hydrogen bonding. Positioning the HBDs perpendicular to the carbonyl plane induces strain in the catalyst-substrate complex, which is released upon enolization, resulting in more favorable kinetics and thermodynamics than the in-plane arrangement of HBDs.

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Development of a Knowledge-Based Potential for Crystals of Small Organic Molecules: Calculation of Energy Surfaces for C=0···H−N Hydrogen Bonds

Cited by 39 publications

References 52 publications

Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations

Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations

Combinatorial computational method gives new picomolar ligands for a known enzyme

Stereoelectronic Requirements for Optimal Hydrogen‐Bond‐Catalyzed Enolization

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