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Quantum mechanical/molecular mechanical approaches to transition state structure: mechanism of sialidase action J. A. Barnes and I. H. Williams* School of Chemistry, University of Bath, Bath BA2 7AY, U.K.
Introduction Glycoside hydrolyses are very important biochemical processes [ 11 whose mechanisms pose a number of questions. Are they stepwise or concerted? Do they occur with retention or inversion of configuration at the anomeric centre? T h e s e issues demand a knowledge of transition state (TS) structure [2] which, from an experimental point of view, is best probed by means of kinetic isotope effects (KIEs). Primary KIEs indicate which atoms are directly involved in bond making or breaking in the rate-determining TS. Abbreviations used: TS, transition state; KIE, kinetic isotope effect; MM, molecular mechanics; QM, quantum mechanical; MO, molecular orbital; DANA, 2-deoxy-2,3-dehydro-N-acetylneuraminicacid; RMS; root mean square; FR, influenza with retention simulation; SR, S. typhimurzum with retention simulation; SI, S. typhzmurium with inversion simulation; MD, molecular dynamics. *To whom correspondence should be addressed.
Secondary KIEs, involving isotopic substitution at positions not directly involved in bond making or breaking, are often used to provide a measure of the location of the TS along the reaction coordinate between reactants and products. T h e most convincing examples of KIEs as probes of TS structure are those in which the effects of multiple isotopic substitutions are examined. From the viewpoint of theoretical modelling, questions of reactivity and TS structure require the use of a method capable of describing the idiosyncratic behaviour of electrons in making and breaking bonds. Molecular mechanics (MM) is inherently unsuited for this purpose, being an interpolation scheme for known properties of stable species. Quantum mechanical (QM) approaches are best able to provide the unknown information regarding TSs by means of molecular orbital (MO) theory. For large systems hybrid QM/MM methods may be applied, combining the merits of a QM treatment of bond making/break-
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ing with a MM description of the environment within which the chemical events occur, be it solvent or protein. In this paper we report use of the CHARMM program [31 to perform hybrid QM/MM calculations [4] upon enzyme-catalysed glycoside hydrolyses, combining an AM1 semiempirical MO theoretical description of the reacting system with a CHARMM22 empirical force field description of the surrounding protein and solvent molecules. We have successfully used a similar approach to study the (non-enzymic) acid-catalysed hydrolysis of a model for AMP embedded within a sphere of approx. 500 water molecules [5]; the reacting system was treated by AM1 and the solvent by the TIP3P empirical potential. The calculated energy surface for reaction suggested the possibility of concurrent stepwise and concerted mechanisms. KIEs may be computed by consideration of the shifts in vibrational frequencies resulting from isotopic substitution at various atoms of the reacting system, and comparison with experimental values provides a stern test of any TS structure predicted by theoretical modelling. We have modified CHARMM to compute second derivatives of the energy with respect to small displacements of the co-ordinates of the QM atoms within their MM environment, from which vibrational frequencies and KIEs may be obtained [6]. Four separate KIEs calculated for the stepwise mechanism on our QM/MM energy surface agreed within reported error of the experimentally measured values [7]. Computational modelling thus offers a tool, complementary with experiment, capable of providing valuable insight into complex reaction mechanisms. Sialidases (or neuraminidases, EC 3.2.1.18) are a family of glycohydrolases, found in many organisms, which catalyse the cleavage of terminal sialic acids (N-acetylneuraminic acid, NANA) which are linked a-ketosidically to glycoconjugates [8]. Only the a-anomers of sialic acid derivatives are substrates for these enzymes, and all known sialidases (with the possible exception of that from Salmonella typhimurium [9]) act with retention of configuration at the anomeric carbon. Many biological roles have been attributed to sialidases, including cell-cell recognition processes and the pathogenicity of some infections by some sialidase-bearing microorganisms [lo]. A sialidase is considered important for transporting the influenza virus through mucin [ 113 and for the elution of new virus from
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infected cells [ 121. Sialidase inhibitors are therefore attractive targets for potential anti-influenza drugs [13]. Detailed knowledge of the mechanisms of sialidase action in enzymes from different organisms would be highly desirable. The three-dimensional structures of sialidases from human influenza B virus [14] and S. typhimurium [15] show considerable similarity in the general folding and spatial arrangement of catalytic residues, which are highly conserved, despite a sequence homology of only 15%. The main features of the mechanism proposed [16,17] for influenza sialidase are binding of the substrate in the (normally unfavourable) B2,5boat conformation, protonation of the aglycone by an unknown general acid followed by departure of the leaving group, stabilization of a sialosyl zwitterionic intermediate in the active site and general base catalysis of attack by water on the intermediate, yielding the sialic acid product. However, the sialidase from S. typhimurium is suggested to act by a single displacement mechanism involving a TS derived from the 'CS chair conformation [ 181.
MM modelling of sialidases T o X-ray crystallographic co-ordinates for sialidases from influenza B virus at 1.8 A resolution [ 141 and from S. typhimurium at 2.2 A resolution [15], each as bound inhibitor complexes with 2-deoxy-2,3-dehydro-N-acetylneuraminic acid (DANA), hydrogen atoms were added using the standard ionization state of each residue in each amino acid sequence. Each structure was subjected to 100 steps of steepest-descent MM energy minimization to remove bad contacts, followed by geometry optimization with the Powell algorithm to root mean square (RMS) gradient
