Absolute pKa values of the amino acid side chains of arginine, aspartate, cysteine, histidine, and tyrosine; the C- and N-terminal group of tyrosine; and the tryptophan radical cation are calculated using a revised density functional based molecular dynamics simulation technique introduced previously [ Cheng , J. ; Sulpizi , M. ; Sprik , M. J. Chem. Phys. 2009 , 131 , 154504 ]. In the revised scheme, acid deprotonation is considered as a dissociation rather than a proton transfer reaction, and a correction term for treating the proton as a hydronium ion is suggested. The acidity constants of the amino acids are obtained from the vertical energy gaps for removal or insertion of the acidic proton and the computed solvation free energy of the proton. The unsigned mean error relative to experimental results is 2.1 pKa units with a maximum error of 4.0 pKa units. The estimated mean statistical uncertainty due to the finite length of the trajectories is ±1.1 pKa units. The solvation structures of the protonated and deprotonated amino acids are analyzed in terms of radial distribution functions, which can serve as reference data for future force field developments.

One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into lead compounds, which in turn may ultimately lead to a marketed drug. A commonly used screening protocol used for this task is high-throughput screening (HTS). However, the performance of HTS against antibacterial targets has generally been unsatisfactory, with high costs and low rates of hit identification. Here, we present a novel computational methodology that is able to identify a high proportion of structurally diverse inhibitors by searching unusually large molecular databases in a time-, cost- and resource-efficient manner. This virtual screening methodology was tested prospectively on two versions of an antibacterial target (type II dehydroquinase from Mycobacterium tuberculosis and Streptomyces coelicolor), for which HTS has not provided satisfactory results and consequently practically all known inhibitors are derivatives of the same core scaffold. Overall, our protocols identified 100 new inhibitors, with calculated Ki ranging from 4 to 250 μM (confirmed hit rates are 60% and 62% against each version of the target). Most importantly, over 50 new active molecular scaffolds were discovered that underscore the benefits that a wide application of prospectively validated in silico screening tools is likely to bring to antibacterial hit identification.

Feature-based pharmacophore modeling is a well-established concept to support early stage drug discovery, where large virtual databases are filtered for potential drug candidates. The concept is implemented in popular molecular modeling software, including Catalyst, Phase, and MOE. With these software tools we performed a comparative virtual screening campaign on HSP90 and FXIa, taken from the 'maximum unbiased validation' data set. Despite the straightforward concept that pharmacophores are based on, we observed an unexpectedly high degree of variation among the hit lists obtained. By harmonizing the pharmacophore feature definitions of the investigated approaches, the exclusion volume sphere settings, and the screening parameters, we have derived a rationale for the observed differences, providing insight on the strengths and weaknesses of these algorithms. Application of more than one of these software tools in parallel will result in a widened coverage of chemical space. This is not only rooted in the dissimilarity of feature definitions but also in different algorithmic search strategies.

The ability of the BACE-1 catalytic dyad to adopt multiple protonation states and the conformational flexibility of the active site have hampered the reliability of computational screening campaigns carried out on this drug target for Alzheimer's disease. Here, we propose a protocol that, for the first time, combining quantum mechanical calculations, molecular dynamics, and conformational ensemble virtual ligand screening addresses these issues simultaneously. The encouraging results prefigure this approach as a valuable tool for future drug discovery campaigns.

The reaction mechanism of a type II dehydroquinase (DHQase) from Streptomyces coelicolor was investigated using molecular dynamics simulation and density functional theory (DFT) calculations. DHQase catalyzes the elimination of a water molecule from dehydroquinate (DHQ), a key step in the biosynthesis of aromatic amino acids in bacteria, fungi, and plants. In the DFT calculations, 10 models, containing up to 230 atoms, were used to investigate different proposals for the reaction mechanism, suggested on the basis of crystal structures and kinetic data. Probing the flexibility of the active site, molecular dynamics simulation reveals that deprotonated Tyr28 can act as the base that catalyzes the first reaction step, the proton abstraction of the pro-S proton at C2 of DHQ, and formation of the enolate intermediate. The computed barrier for the first transition state (TS1), 13-15 kcal/mol, is only slightly affected by the active site model used and is in good agreement with the corresponding experimental barrier of 13.4 kcal/mol for the rate-determining step. The previously proposed enol form of the intermediate is found to be significantly higher in energy than the enolate form and is thus thermodynamically not competitive. In the second and final reaction step, protonation of the hydroxyl group at C1 by His106 followed by water elimination, there is a substantial buildup of dipole moment due to the net transfer of a proton from His106 to Tyr28. A barrier for the second transition state (TS2) that fits well with the corresponding experimental barrier could only be found if the buildup of dipole moment is at least partly compensated during the second reaction step. We speculate that this could be facilitated by regeneration of the Tyr28 anion or by proton transfer to the vicinity of His106 before TS2 is reached. A revised mechanism for type II DHQase is discussed in light of the results of the present calculations.