QUASI: a novel method for simultaneous superposition of multiple flexible ligands and virtual screening using partial similarity.

The structure of many receptors is unknown, and only information about diverse ligands binding to them is available. A new method is presented for the superposition of such ligands, derivation of putative receptor site models and utilization of the models for screening of compound databases. In order to generate a receptor model, the similarity of all ligands is optimized simultaneously taking into account conformational flexibility and also the possibility that the ligands can bind to different regions of the site and only partially overlap. Ligand similarity is defined with respect to a receptor site model serving as a common reference frame. The receptor model is dynamic and coevolves with the ligand alignment until an optimal self-consistent superposition is achieved. When ligand conformational flexibility is permitted, different superposition models are possible and consistent with the data. Clustering of the superposition solutions is used to obtain diverse models. When the models are used to screen a database of compounds, high enrichments are obtained, comparable to those obtained in docking studies.

Exhaustive de novo design of low-molecular-weight fragments against the ATP-binding site of DNA-gyrase.

We present a de novo design approach to generating small fragments in the DNA-gyrase ATP-binding site using the computational drug design platform SkelGen. We have generated an exhaustive number of structural possibilities, which were subsequently filtered for site complementarity and synthetic tractability. A number of known active fragments are found, but most of the species created are potentially novel and could be valuable for further elaboration and development into lead-like structures.

SkelGen: a general tool for structure-based de novo ligand design.

The recent lapse in productivity in the pharmaceutical industry has facilitated the emergence of experimental and in silico structure-based design methodologies, based on identification of biologically active low molecular weight fragments that can be exploited to produce potential drug candidates with diverse chemistries. SkelGen, an in silico example of this methodology, is reviewed. The ability of this algorithm to identify chemically diverse low molecular weight fragments that would potentially bind to DNA gyrase is recounted, as is the first purely de novo structure-based design of five compounds that show at least micromolar activity against the estrogen receptor. The ability of the algorithm to incorporate partial protein flexibility during its design of compounds to the estrogen receptor is discussed, and an opinion as to the near and long-term futures for de novo design algorithms is expressed.

Computer-aided design of small molecules for chemical genomics.

De novo design provides an in silico toolkit for the design of novel molecular structures to a set of specified structural constraints, and is thus ideally suited for creating molecules for chemical genomics. The design process involves manipulation of the input, modification of structural constraints, and further processing of the de novo-generated molecules using various modular toolkits. The development of a theoretical framework for each of these stages will provide novel practical solutions to the problem of creating compounds with maximal chemical diversity. This chapter describes the fundamental problems encountered in the application of novel chemical design technologies to chemical genomics by means of a formal representation. Formal representations help to outline and clarify ideas and hypotheses that can then be explored using mathematical algorithms. It is only by developing this rigorous foundation, that in silico design can progress in a rational way.

Receptor flexibility in de novo ligand design and docking.

One of the major problems in computational drug design is incorporation of the intrinsic flexibility of protein binding sites. This is particularly crucial in ligand binding events, when induced fit can lead to protein structure rearrangements. As a consequence of the huge conformational space available to protein structures, receptor flexibility is rarely considered in ligand design procedures. In this work, we present an algorithm for integrating protein binding-site flexibility into de novo ligand design and docking processes. The approach allows dynamic rearrangement of amino acid side chains during the docking and design simulations. The impact of protein conformational flexibility is investigated in the docking of highly active inhibitors in the binding sites of acetylcholinesterase and human collagenase (matrix metalloproteinase-1) and in the design of ligands in the S1' pocket of MMP-1. The results of corresponding simulations for both rigid and flexible binding sites are compared in order to gauge the influence of receptor flexibility in drug discovery protocols.

Backbone-backbone geometry of tertiary contacts between alpha-helices.

Knowledge about how secondary-structure elements combine together to form the tertiary structure is crucial for understanding protein folding. We have examined packing solutions for alpha-helices by performing a crystal survey of the underlying backbone-backbone inter-geometry of tertiary contacts. The information content is different from, and complementary to, the results of side-chain to side-chain crystal surveys and studies of helix-helix-crossing angles. Six geometry descriptors were recorded from each tertiary contact in nonredundant data sets of globular and transmembrane proteins. The descriptors included distances, angles, and dihedral angles that together describe completely the underlying geometry of each contact. From the results it is possible to identify differences in the geometry requirements of tertiary contacts between alpha-helices in general and transmembrane alpha-helices. The differences become more apparent when the correlation between the different geometry descriptors is analyzed. The results are compared with those of other types of secondary structure. Finally, an investigation of how the geometry of tertiary contacts changes with the amino-acid types involved in the contact is performed using multivariate techniques. The results of this study provide a well-defined overview of the underlying structural framework of tertiary contacts between alpha-helices, in both globular and TM environments, that will have valuable implications for predicting protein tertiary structure.

De novo drug design: integration of structure-based and ligand-based methods.

Structure-based and ligand-based methods are used to derive predictive models in de novo drug design. Structure-based methods rely exclusively on prior knowledge of a protein structure to derive novel ligands, while ligand-based methods are traditionally used when no protein structure is available. Where there is sufficient information, these methods can be used in conjunction to increase the accuracy of simulation and enhance the drug design process. This review presents developments in the integration of these methods for de novo drug design, and recent results from both systems are highlighted.

Scaffold hopping in de novo design. Ligand generation in the absence of receptor information.

We report here the de novo generation of chemotypes and scaffolds for the estrogen receptor, without use of the receptor structure in the assembly phase. Through use of ligand superpositions or a single bound conformation of a known active, a pseudoreceptor can be generated as a design envelope, within which novel structures are readily assembled. Many of these structures have high similarity to known chemotypes. Scaffold hopping is readily achieved within this pseudoreceptor, indicating the advantages of such an approach in discovery research.

WaterScore: a novel method for distinguishing between bound and displaceable water molecules in the crystal structure of the binding site of protein-ligand complexes.

We have performed a multivariate logistic regression analysis to establish a statistical correlation between the structural properties of water molecules in the binding site of a free protein crystal structure, with the probability of observing the water molecules in the same location in the crystal structure of the ligand-complexed form. The temperature B-factor, the solvent-contact surface area, the total hydrogen bond energy and the number of protein-water contacts were found to discriminate between bound and displaceable water molecules in the best regression functions obtained. These functions may be used to identify those bound water molecules that should be included in structure-based drug design and ligand docking algorithms. FIGURE The binding site ( thin sticks) of penicillopepsin (3app) with its crystallographically determined water molecules ( spheres) and superimposed ligand (in thick sticks, from complexed structure 1ppk). Water molecules sterically displaced by the ligand upon complexation are shown in cyan. Bound water molecules are shown in blue. Displaced water molecules are shown in yellow. Water molecules removed from the analysis due to a lack of hydrogen bonds to the protein are shown in white. WaterScore correctly predicted waters in blue as Probability=1 to remain bound and waters in yellow as Probability<1x10(-20) to remain bound.

Efficient conformational sampling of local side-chain flexibility.

Side-chain flexibility of ligand-binding sites needs to be considered in the rational design of novel inhibitors. We have developed a method to generate conformational ensembles that efficiently sample local side-chain flexibility from a single crystal structure. The rotamer-based approach is tested here for the S1' pocket of human collagenase-1 (MMP-1), which is known to undergo conformational changes in multiple side-chains upon binding of certain inhibitors. First, a raw ensemble consisting of a large number of conformers of the S1' pocket was generated using an exhaustive search of rotamer combinations on a template crystal structure. A combination of principal component analysis and fuzzy clustering was then employed to successfully identify a core ensemble consisting of a low number of representatives from the raw ensemble. The core ensemble contained geometrically diverse conformers of stable nature, as indicated in several cases by a relative energy lower than that of the minimised template crystal structure. Through comparisons with X-ray crystallography and NMR structural data we show that the core ensemble occupied a conformational space similar to that observed under experimental conditions. The synthetic inhibitor RS-104966 is known to induce a conformational change in the side-chains of the S1' pocket of MMP-1 and could not be docked in the template crystal structure. However, the experimental binding mode was reproduced successfully using members of the core ensemble as the docking target, establishing the usefulness of the method in drug design.

Probes for chemical genomics by design.

Chemical genomics represents a convergence of biology and chemistry in the era of global approaches to target identification and intervention. The success of genomics has led to a bottleneck in target validation that could be overcome by using small diverse organic compounds to interfere with biological processes. Because of the limitations of existing compound collections, this diversity can only fully be exploited using in silico design techniques to guide the selection of molecules with optimal binding properties. Structure-based design is used to create structures de novo that can be synthesized for use as chemical probes and drug leads.

The use of chemical design tools to transform proteomics data into drug candidates.

The Human Genome Project has fueled the massive information-driven growth of genomics and proteomics and promises to deliver new insights into biology and medicine. Since proteins represent the majority of drug targets, these molecules are the focus of activity in pharmaceutical and biotechnology organizations. In this article, we describe the processes by which computational drug design may be used to exploit protein structural information to create virtual small molecules that may become novel medicines. Experimental protein structure determination, site exploration, and virtual screening provide a foundation for small molecule generation in silico, thus creating the bridge between proteomics and drug discovery.

A validation study on the practical use of automated de novo design.

The de novo design program Skelgen has been used to design inhibitor structures for four targets of pharmaceutical interest. The designed structures are compared to modeled binding modes of known inhibitors (i) visually and (ii) by means of a novel similarity measure considering the size and spatial proximity of the maximum common substructure of two small molecules. It is shown that the Skelgen algorithm generates representatives of many inhibitor classes within a very short time and that the new similarity measure is useful for comparing and clustering designed structures. The results demonstrate the necessity of properly defining search constraints in practical applications of de novo design.