Empirical force‐field calculations on biological molecules represent an effective method to obtain atomic detail information on the relationship of their structure to their function. Results from those calculations depend on the quality of the force field. In this manuscript, optimization of the CHARMM27 all‐atom empirical force field for nucleic acids is presented together with the resulting parameters. The optimization procedure is based on the reproduction of small molecule target data from both experimental and quantum mechanical studies and condensed phase structural properties of DNA and RNA. Via an iterative approach, the parameters were primarily optimized to reproduce macromolecular target data while maximizing agreement with small molecule target data. This approach is expected to ensure that the different contributions from the individual moieties in the nucleic acids are properly balanced to yield condensed phase properties of DNA and RNA, which are consistent with experiment. The quality of the presented force field in reproducing both crystal and solution properties are detailed in the present and an accompanying manuscript (MacKerell and Banavali, J Comput Chem, this issue). The resultant parameters represent the latest step in the continued development of the CHARMM all‐atom biomolecular force field for proteins, lipids, and nucleic acids. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 86–104, 2000

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The B-form of DNA can populate two different backbone conformations: BI and BII, defined by the difference between the torsion angles ε and ζ (BI = ε-ζ < 0 and BII = ε-ζ > 0). BI is the most populated state, but the population of the BII state, which is sequence dependent, is significant and accumulating evidence shows that BII affects the overall structure of DNA, and thus influences protein-DNA recognition. This work presents a reparametrization of the CHARMM27 additive nucleic acid force field to increase the sampling of the BII form in MD simulations of DNA. In addition, minor modifications of sugar puckering were introduced to facilitate sampling of the A form of DNA under the appropriate environmental conditions. Parameter optimization was guided by quantum mechanical data on model compounds, followed by calculations on several DNA duplexes in the condensed phase. The selected optimized parameters were then validated against a number of DNA duplexes, with the most extensive tests performed on the EcoRI dodecamer, including comparative calculations using the Amber Parm99bsc0 force field. The new CHARMM model better reproduces experimentally observed sampling of the BII conformation, including sampling as a function of sequence. In addition, the model reproduces the A form of the 1ZF1 duplex in 75 % ethanol, and yields a stable Z-DNA conformation of duplex (GTACGTAC) in its crystal environment. The resulting model, in combination with a recent reoptimization of the CHARMM27 force field for RNA, will be referred to as CHARMM36.

Identification of chemical compounds with specific biological activities is an important step in both chemical biology and drug discovery. When the structure of the intended target is available, one approach is to use molecular docking programs to assess the chemical complementarity of small molecules with the target; such calculations provide a qualitative measure of affinity that can be used in virtual screening (VS) to rank order a list of compounds according to their potential to be active. rDock is a molecular docking program developed at Vernalis for high-throughput VS (HTVS) applications. Evolved from RiboDock, the program can be used against proteins and nucleic acids, is designed to be computationally very efficient and allows the user to incorporate additional constraints and information as a bias to guide docking. This article provides an overview of the program structure and features and compares rDock to two reference programs, AutoDock Vina (open source) and Schrödinger's Glide (commercial). In terms of computational speed for VS, rDock is faster than Vina and comparable to Glide. For binding mode prediction, rDock and Vina are superior to Glide. The VS performance of rDock is significantly better than Vina, but inferior to Glide for most systems unless pharmacophore constraints are used; in that case rDock and Glide are of equal performance. The program is released under the Lesser General Public License and is freely available for download, together with the manuals, example files and the complete test sets, at http://rdock.sourceforge.net/

The design and synthesis of novel adenosine-derived inhibitors of HSP70, guided by modeling and X-ray crystallographic structures of these compounds in complex with HSC70/BAG-1, is described. Examples exhibited submicromolar affinity for HSP70, were highly selective over HSP90, and some displayed potency against HCT116 cells. Exposure of compound 12 to HCT116 cells caused significant reduction in cellular levels of Raf-1 and Her2 at concentrations similar to that which caused cell growth arrest.

The backbone states of B-DNA influence its helical parameters, groove dimensions, and overall curvature. Therefore, detection and fine characterization of these conformational states are desirable. Using routine NMR experiments on a nonlabeled B-DNA oligomer and analyzing high-resolution X-ray structures, we investigated the relationship between interproton distances and backbone conformational states. The three H2'i-H6/8i+1, H2' 'i-H6/8i+1, and H6/8i-H6/8i+1 sequential distances were found cross-correlated and linearly coupled to epsilon-zeta values in X-ray structures and 31P chemical shifts (deltaP) in NMR that reflect the interconversion between the backbone BI (epsilon-zeta < 0 degrees ) and BII (epsilon-zeta > 0 degrees) states. These relationships provide a detailed check of the NMR data consistency and the possibility to extend the set of restraints for structural refinement through various extrapolations. Furthermore, they allow translation of deltaP in terms of BI/BII ratios. Also, comparison of many published deltaP in solution to crystal data shows that the impact of sequence on the BI/BII propensities is similar in both environments and is therefore an intrinsic and general property of B-DNA. This quantification of the populations of BI and BII is of general interest because these sequence-dependent backbone states act on DNA overall structure, a key feature for DNA-protein-specific recognition.

Many cellular functions involve cysteine chemistry via thiol-disulfide exchange pathways. The nucleophilic cysteines of the enzymes involved are activated as thiolate. A thiolate is much more reactive than a neutral thiol. Therefore, determining and understanding the pK(a)s of functional cysteines are important aspects of biochemistry and molecular biology with direct implications for redox signaling. Here, we describe the experimental and theoretical methods to determine cysteine pK(a) values, and we examine the factors that control these pK(a)s. Drawing largely on experience gained with the thioredoxin superfamily, we examine the roles of solvation, charge-charge, helix macrodipole, and hydrogen bonding interactions as pK(a)-modulating factors. The contributions of these factors in influencing cysteine pK(a)s and the associated chemistry, including the relevance for the reaction kinetics and thermodynamics, are discussed. This analysis highlights the critical role of direct hydrogen bonding to the cysteine sulfur as a key factor modulating the equilibrium between thiol S-H and thiolate S(-). This role is easily understood intuitively and provides a framework for biochemical functional insights.

Computational conformational sampling is integral to small molecule pharmaceutical research, for detailed conformational analysis and high-throughput 3D library enumeration. These two regimes were tested in details for the general-purpose modeling program MOE, using its three conformational sampling methods, i.e. systematic search, stochastic search, and Conformation Import. The tests include i) identification of the global energy minimum, ii) reproduction of the bioactive conformation, iii) measures of conformational coverage with 3D descriptors, and iv) compute times. The bioactive conformers are from a new set of 256 diverse, druglike, protein-bound ligands compiled and analyzed with particular care. The MOE results are compared to those obtained from the established program Catalyst. Key parameters controlling the conformational coverage were varied systematically. Coverage and diversity of the conformational space were characterized with unique pharmacophore triplets or quadruplets. Overall, the protocols in both MOE and Catalyst performed well for their intended tasks. MOE performed at least as well as Catalyst for high-throughput library generation and detailed conformational modeling. This work provides a guide and specific recommendations regarding the usage of conformational sampling tools in MOE.