Approach for the Simulation and Modeling of Flexible Rings:  Application to the α-d-Arabinofuranoside Ring, a Key Constituent of Polysaccharides from Mycobacterium tuberculosis

Abstract

A number of lower organisms (bacteria, fungi, and parasites) produce glycoconjugates that contain furanose rings. Of particular interest to our group are cell wall polysaccharides from mycobacteria, including the human pathogen, Mycobacterium tuberculosis, which contain a large number of arabinofuranose resides. As part of a larger project on the conformational analysis of these molecules, we report here molecular dynamics simulations on methyl α-D-arabinofuranoside (1) using the AMBER force field and the GLYCAM carbohydrate parameter set. We initially studied the ability of this method to predict rotamer populations about the hydroxymethyl group (C4-C5) bond. Importantly, we show that simulation times of up to 200 ns are required in order to obtain convergence of the rotamer populations for this ring system. We also propose a new charge derivation approach that accounts for the flexibility of the furanoside ring by taking an average of the charges from a large number of conformers across the psuedorotational itinerary. The approach yields rotamer populations that are in good agreement with available NMR data and, in addition, provides insight into the nature of the puckering angle and amplitude in 1.

Insights into furanose solution conformations: beyond the two-state model

“…Their conformational properties affect the structure and recognition of the polymers in which they are found. In marked contrast to six-membered sugar rings (pyranoses), the five-membered forms (furanoses) exhibit a high level of internal ring flexing (Bartenev et al 1987; Levitt and Warshel 1978; Seo et al 2008). As a result, furanoses can interconvert between multiple ring conformations (Houseknecht et al 2003a), whereas pyranoses are usually found in single, energy-favorable chair conformation (Angyal 1984).…”

“…1). However, the validity of a two-state model has been questioned when applied to other furanoses, particularly in the case of arabinofuranose (Hendrickx et al 2010; Seo et al 2008; Taha et al 2010; Taha et al 2011). An alternative approach that has shown promise, is to perform MD simulations of the furanose (Hatcher et al 2009), and then back-calculate the 3 J -values directly from the MD data (Hendrickx et al 2010; Raman et al 2010; Taha et al 2010).…”

Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

“…Although several alternative methods of charge derivation have been proposed, 47,53,60,62,63 restraining the charges of buried atoms to prevent the optimization from converging toward unreasonable values and/or to reduce conformational dependence of the charges became the most popular in force field development. [64][65][66][67][68][69][70][71][72][73][74][75][76][77] In most of these methods, besides a constraint on the total charge of the molecule, an additional restraining function is added to the LS sum (eq 1) to keep the buried atom charges close to some predefined values, despite its possible negative effect on the dipole moment values and the overall quality of MEP. 53,78 Considering the challenges presented by the relatively straightforward single-objective point charge fitting against the MEP, simultaneous optimization of point charges along with other force field parameters against a diverse training set could be expected to present even more pitfalls.…”

Development of a Karplus equation for 3JCOCH in ester-functionalized glucopyranoses and methylglucuronate.

“…The production runs were performed over 500 ns to sufficiently sample the available conformational space. 26,27 The MD simulation input parameters are also supplied in the Supplemental Material. All MD runs were monitored for successful equilibration prior to and stability during the production run based on energy and pressure data extracted using the existing process_mdout.perl and process_minout.perl scripts.…”

“…After this, the Amber14 software package and the carbohydrate-specific GLYCAM06 force field employing the TIP3P water model was used to run a 500 ns molecular dynamics simulation to sufficiently sample the conformational space of both α-1 and ß-1. 26,27 The molecular dynamics treatment of the model compound was employed due to its known capability to characterize the internal molecular motion and flexible nature of carbohydrates. 1,28 The MD simulations were analyzed with respect to the population maxima of  (C4-C5-C6-O6),  (C5-C6-O6-C1') and ' (C6-O6-C1'-C2') and 9 major conformational regions could be distinguished for both α-1 and ß-1 (sampling window ± 37°).…”