W. Graham Richards

Ultrafast shape recognition to search compound databases for similar molecular shapes

Finding a set of molecules, which closely resemble a given lead molecule, from a database containing potentially billions of chemical structures is an important but daunting problem. Similar molecular shapes are particularly important, given that in biology small organic molecules frequently act by binding into a defined and complex site on a macromolecule. Here, we present a new method for molecular shape comparison, named ultrafast shape recognition (USR), capable of screening billions of compounds for similar shapes using a single computer and without the need of aligning the molecules before testing for similarity. Despite its extremely fast comparison rate, USR will be shown to be highly accurate at describing, and hence comparing, molecular shapes.

Similarity of molecular shape.

SummaryThe similarity of one molecule to another has usually been defined in terms of electron densities or electrostatic potentials or fields. Here it is expressed as a function of the molecular shape. Formulations of similarity (S) reduce to very simple forms, thus rendering the computerised calculation straightforward and fast. ‘Elements of similarity’ are identified, in the same spirit as ‘elements of chirality’, except that the former are understood to be variable rather than present-or-absent. Methods are presented which bypass the time-consuming mathematical optimisation of the relative orientation of the molecules. Numerical results are presented and examined, with emphasis on the similarity of isomers. At the extreme, enantiomeric pairs are considered, where it is the dissimilarity (D=1−S) that is of consequence. We argue that chiral molecules can be graded by dissimilarity, and show that D is the shape-analog of the ‘chirality coefficient’, with the simple form of the former opening up numerical access to the latter.

Virtual screening using grid computing: the screensaver project

Discovering small molecules that interact with protein targets identified by structural genomics, proteomics and bioinformatics will be a key part of future drug discovery efforts. Computational screening of drug-like molecules is likely to be valuable in this respect; however, the vast number of such molecules makes the potential size of this task enormous. Here, I describe how massively distributed computing using screensavers has allowed databases of billions of compounds to be screened against protein targets in a matter of days.

Free energy calculations in molecular biophysics

The free energy perturbation method, and the related thermodynamic integration methods, have recently had a great influence on theoretical chemists interested in applying computational techniques to study problems of biological importance. The infinite order free energy perturbation equation was derived almost forty years ago, but only within the last five years has it been widely used in conjunction with Monte Carlo or molecular dynamics simulations to calculate free energy differences between similar macromolecular systems in solution. The purpose of this review is firstly to describe the formalism behind the methods but secondly and more importantly to describe the wide range of problems to which it has been applied. Here the focus is primarily on problems of chemical or biological interest rather than on problems studied for the sake of methodological development. The methods, of necessity, are generally used in conjunction with empirical force fields and the limitations arising from this and related ...

Ultrafast shape recognition for similarity search in molecular databases

Molecular databases are routinely screened for compounds that most closely resemble a molecule of known biological activity to provide novel drug leads. It is widely believed that three-dimensional molecular shape is the most discriminating pattern for biological activity as it is directly related to the steep repulsive part of the interaction potential between the drug-like molecule and its macromolecular target. However, efficient comparison of molecular shape is currently a challenge. Here, we show that a new approach based on moments of distance distributions is able to recognize molecular shape at least three orders of magnitude faster than current methodologies. Such an ultrafast method permits the identification of similarly shaped compounds within the largest molecular databases. In addition, the problematic requirement of aligning molecules for comparison is circumvented, as the proposed distributions are independent of molecular orientation. Our methodology could be also adapted to tackle similar hard problems in other fields, such as designing content-based Internet search engines for three-dimensional geometrical objects or performing fast similarity comparisons between proteins. From a broader perspective, we anticipate that ultrafast pattern recognition will soon become not only useful, but also essential to address the data explosion currently experienced in most scientific disciplines.

Acetyl-CoA enolization in citrate synthase: A quantum mechanical/molecular mechanical (QM/MM) study

Citrate synthase forms citrate by deprotonation of acetyl‐CoA followed by nucleophilic attack of this substrate on oxaloacetate, and subsequent hydrolysis. The rapid reaction rate is puzzling because of the instability of the postulated nucleophilic intermediate, the enolate of acetyl‐CoA. As alternatives, the enol of acetyl‐CoA, or an enolic intermediate sharing a proton with His‐274 in a “low‐barrier” hydrogen bond have been suggested. Similar problems of intermediate instability have been noted in other enzymic carbon acid deprotonation reactions. Quantum mechanical/molecular mechanical calculations of the pathway of acetyl‐CoA enolization within citrate synthase support the identification of Asp‐375 as the catalytic base. His‐274, the proposed general acid, is found to be neutral. The acetyl‐CoA enolate is more stable at the active site than the enol, and is stabilized by hydrogen bonds from His‐274 and a water molecule. The conditions for formation of a low‐barrier hydrogen bond do not appear to be met, and the calculated hydrogen bond stabilization in the reaction is less than the gas‐phase energy, due to interactions with Asp‐375 at the active site. The enolate character of the intermediate is apparently necessary for the condensation reaction to proceed efficiently. Proteins 27:9–25

Sequence Selective Binding to the DNA Major Groove: Tris(1,10-phenanthroline) Metal Complexes Binding to Poly(dG-dC) and Poly(dA-dT)

Molecular modelling and energy minimisation calculations that incorporate solvent effects have been used to investigate the complexation of delta and lambda-[Ru(1,10-phenanthroline]2+ to DNA. The most stable binding geometry for both enantiomers is one in which a phenanthroline chelate is positioned in the major groove. The chelate is partially inserted between neighbouring base pairs, but is not intercalated. For delta, though not for lambda, a geometry with two chelates in the major groove is only slightly less favourable. Minor groove binding is shown to be no more favourable than external electrostatic binding. The optimised geometries of the DNA/[Ru(1,10-phenanthroline]2+ complexes enable published linear dichroism spectra to be used to determine the percentage of each enantiomer in the two most favourable major groove sites. For delta 57 +/- 15% and for lambda 82 +/- 7% of bound molecules are in the partially inserted site.

The interleukin-2 and interleukin-4 receptors studied by molecular modelling

BACKGROUND Interleukin-2 (IL2) and interleukin-4 (IL4) are members of the four-helix bundle family of cytokines, whose receptors show similarity to each other and to the growth hormone receptor fold. These proteins help to control, among other things, the rate of clonal expansion of lymphocytes, and thus play an important role in the regulation of the immune system. They are therefore of interest as transmembrane signalling proteins, as well as potential pharmaceutical targets. RESULTS We have modelled structures of the extracellular components of the IL2 and IL4 receptors based on the structure of the complex of human growth hormone with its receptor, and incorporating the recently discovered shared gamma c chain. The models provide possible explanations for several experimental observations, including those from site-directed mutagenesis around the binding sites. Receptor residues that may be close to important side chains on IL2 and IL4 are identified and possible effects of their mutation are discussed. A comparison is made between the models and the growth hormone complex, and between the gamma c chain bound to IL2 and to IL4. CONCLUSIONS The models offer structural explanations for observed behaviour such as the effects of mutation of the A- and D-helices of the cytokines. In addition, they may be of use in the identification of residues which may interact in the ligand-receptor interfaces, and which would therefore be worthy of further investigation.

Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases

There is currently a shortage of chemical molecules that can be used as bioactive probes to study molecular targets and potentially as starting points for drug discovery. One inexpensive way to address this problem is to use computational methods to screen a comprehensive database of small molecules to discover novel structures that could lead to alternative and better bioactive probes. Despite that pleasing logic the results have been somewhat mixed. Here we describe a virtual screening technique based on ligand–receptor shape complementarity, Ultrafast Shape Recognition (USR). USR is specifically applied to identify novel inhibitors of arylamine N-acetyltransferases by computationally screening almost 700 million molecular conformers in a time- and resource-efficient manner. A small number of the predicted active compounds were purchased and tested obtaining a confirmed hit rate of 40 per cent which is an outstanding result for a prospective virtual screening.

Ultrafast shape recognition: evaluating a new ligand-based virtual screening technology.

Large scale database searching to identify molecules that share a common biological activity for a target of interest is widely used in drug discovery. Such an endeavour requires the availability of a method encoding molecular properties that are indicative of biological activity and at least one active molecule to be used as a template. Molecular shape has been shown to be an important indicator of biological activity; however, currently used methods are relatively slow, so faster and more reliable methods are highly desirable. Recently, a new non-superposition based method for molecular shape comparison, called Ultrafast Shape Recognition (USR), has been devised with computational performance at least three orders of magnitude faster than previously existing methods. In this study, we investigate the performance of USR in retrieving biologically active compounds through retrospective Virtual Screening experiments. Results show that USR performs better on average than a commercially available shape similarity method, while screening conformers at a rate that is more than 2500 times faster. This outstanding computational performance is particularly useful for searching much larger portions of chemical space than previously possible, which makes USR a very valuable new tool in the search for new lead molecules for drug discovery programs.