Structures in Solutions from Joint Experimental-Computational Analysis: Applications to Cyclic Molecules and Studies of Noncovalent Interactions

Conformational Searching with Quantum Mechanics

Estimating the range of three-dimensional structures (conformations) that are available to a molecule is a key component of computer-aided drug design. Quantum mechanical simulation offers improved accuracy over forcefield methods, but at a high computational cost. The question is whether this increased cost can be justified in a context in which high-throughput analysis of large numbers of molecules is often key. This chapter discusses the application of quantum mechanics to conformational searching, with a focus on three key challenges: (1) the generation of ensembles that include a good approximation to a molecule's bioactive conformation at as prominent a ranking as possible; (2) rational analysis and modification of a pre-established bioactive conformation in terms of its energetics; and (3) approximation of real solution-phase conformational ensembles in tandem with NMR data. The impact of QM on the high-throughput application (1) is debatable, meaning that for the moment its primary application is still lower-throughput applications such as (2) and (3). The optimal choice of QM method is also discussed. Rigorous benchmarking suggests that DFT methods are only acceptable when used with large basis sets, but a trickle of papers continue to obtain useful results with relatively low-cost methods, leading to a dilemma that the literature has yet to fully resolve.

The structure of tagetitoxin

Based on detailed analysis of newly acquired NMR data, we show that the previously revised structure of tagetitoxin is incorrect. A new structure of tagetitoxin is proposed which is consistent with the NMR and MS data.

Bayesian inference of conformational state populations from computational models and sparse experimental observables

We present a Bayesian inference approach to estimating conformational state populations from a combination of molecular modeling and sparse experimental data. Unlike alternative approaches, our method is designed for use with small molecules and emphasizes high-resolution structural models, using inferential structure determination with reference potentials, and Markov Chain Monte Carlo to sample the posterior distribution of conformational states. As an application of the method, we determine solution-state conformational populations of the 14-membered macrocycle cineromycin B, using a combination of previously published sparse Nuclear Magnetic Resonance (NMR) observables and replica-exchange molecular dynamic/Quantum Mechanical (QM)-refined conformational ensembles. Our results agree better with experimental data compared to previous modeling efforts. Bayes factors are calculated to quantify the consistency of computational modeling with experiment, and the relative importance of reference potentials and other model parameters.

Cyclisation reactions of N-cinnamoyl-9-aminoanthracenes

N-Cinnamoyl-9-aminoanthracenes cyclise with PPA or triflic acid to form novel 2-azahexacyclo[10.6.6.0(1,5).0(6,11).0(13,18).0(19,24)]tetracosa-6(11),7,9,13,15,17,19(24),20,22-nonaen-3-ones. In contrast, both N-cinnamoyl-N-methyl-9-(2-aminomethyl)anthracene and N-cinnamoyl-9-(2-aminoethyl)anthracene undergo an intramolecular Diels-Alder cycloaddition.

Motional timescale predictions by molecular dynamics simulations: case study using proline and hydroxyproline sidechain dynamics

We propose a new approach for force field optimizations which aims at reproducing dynamics characteristics using biomolecular MD simulations, in addition to improved prediction of motionally averaged structural properties available from experiment. As the source of experimental data for dynamics fittings, we use ¹³C NMR spin-lattice relaxation times T₁ of backbone and sidechain carbons, which allow to determine correlation times of both overall molecular and intramolecular motions. For structural fittings, we use motionally averaged experimental values of NMR J couplings. The proline residue and its derivative 4-hydroxyproline with relatively simple cyclic structure and sidechain dynamics were chosen for the assessment of the new approach in this work. Initially, grid search and simplexed MD simulations identified large number of parameter sets which fit equally well experimental J couplings. Using the Arrhenius-type relationship between the force constant and the correlation time, the available MD data for a series of parameter sets were analyzed to predict the value of the force constant that best reproduces experimental timescale of the sidechain dynamics. Verification of the new force-field (termed as AMBER99SB-ILDNP) against NMR J couplings and correlation times showed consistent and significant improvements compared to the original force field in reproducing both structural and dynamics properties. The results suggest that matching experimental timescales of motions together with motionally averaged characteristics is the valid approach for force field parameter optimization. Such a comprehensive approach is not restricted to cyclic residues and can be extended to other amino acid residues, as well as to the backbone. Proteins 2014; 82:195–215. © 2013 Wiley Periodicals, Inc.