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.

The influence of variations of ligand protonation and tautomerism on protein-ligand recognition and binding energy landscape.

We investigate the influence of variations of ligand protonation and tautomeric states on the protein-ligand binding energy landscape by applying the concept of structural consensus. In docking simulations, allowing full flexibility of the ligand, we explore whether the native binding mode could be successfully recovered using a non-native ligand protonation state. Here, we consider three proteins, dihydrofolate reductase, transketolase, and alpha-trichosanthin, complexed with ligands having multiple tautomeric forms. We find that for the majority of protonation and tautomeric states the native binding mode can be recovered without a great loss of accuracy.

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.

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.

Combinatorial ligand design targeted at protein families.

We describe a method to create ligands specific for a given protein family. The method is applied to generate ligand candidates for the cyclin-dependent kinase (CDK) family. The CDK family of proteins is involved in regulating the cell cycle by alternately activating and deactivating the cell's progression through the cycle. CDKs are activated by association with cyclin and are inhibited by complexation with small molecules. X-ray crystal structures are available for three of the thirteen known CDK family members: CDK2, CDK5 and CDK 6. In this work, we use novel computational approaches to design ligand candidates that are potentially inhibitory across the three CDK family members as well as more specific molecules which can potentially inhibit one or any combination of two of the three CDK family members. We define a new scoring term, SpecScore, to quantify the potential inhibitory power of the generated structures. According to a search of the World Drug Alerts, the highest scoring SpecScore molecule that is specific for the three CDK family members shows very similar chemical characteristics and functional groups to numerous molecules known to deactivate several members of the CDK family.

The effect of tightly bound water molecules on the structural interpretation of ligand-derived pharmacophore models.

The importance of the consideration of water molecules in the structural interpretation of ligand-derived pharmacophore models is explored. We compare and combine results from recently introduced methods for bound-water molecule identification in protein binding sites and ligand-superposition-based pharmacophore derivation, for the interpretation of ligand-derived pharmacophore models. In the analysis of thymidine kinase (HSV-1) and poly (ADP-ribose) polymerase (PARP), the concurrent application of both methods leads to an agreement in the prediction of tightly bound water molecules as key pharmacophoric points in the binding site of these proteins. This agreement has implications for approaching binding site analysis and consensus drug design, as it highlights how pharmacophore-based models of binding sites can include interaction features not only with protein groups but also with bound water molecules.

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.

Assessment of multiple binding modes in ligand-protein docking.

Computational ligand-protein docking is routinely used for binding mode prediction. We have quantified the effect of considering multiple docking solutions on the success rate of obtaining the crystallographic binding mode. By selection of a small set of representatives, the experimentally observed binding mode can be predicted with a higher probability after a ligand-protein docking simulation. The proportion of correctly predicted complexes improved from 69% to 87% when five distinct binding modes were considered.

Receptor flexibility in the in silico screening of reagents in the S1' pocket of human collagenase.

A major difficulty in structure-based molecular design is the prediction of the structure of the protein-ligand complex because of the enormous number of degrees of freedom. Commonly, the target protein is kept rigid in a single low-energy conformation. However, this does not reflect the dynamic nature of protein structures. In this work, we investigate the influence of receptor flexibility in virtual screening of reagents on a common scaffold in the S1' pocket of human collagenase (matrix metalloproteinase-1). We compare screening using a single-crystal structure and multiple NMR structures, both apo and holo forms. We also investigate two computational methods of addressing receptor flexibility that can be used when NMR data are not available. The results from virtual screening using the experimental structures are compared to those obtained using the two computational methods. From the results, we draw conclusions about the impact of target flexibility on the identification of active and diverse reagents in a virtual screening protocol.

Ligand-protein docking using a quantum stochastic tunneling optimization method.

A novel hybrid optimization method called quantum stochastic tunneling has been recently introduced. Here, we report its implementation within a new docking program called EasyDock and a validation with the CCDC/Astex data set of ligand-protein complexes using the PLP score to represent the ligand-protein potential energy surface and ScreenScore to score the ligand-protein binding energies. When taking the top energy-ranked ligand binding mode pose, we were able to predict the correct crystallographic ligand binding mode in up to 75% of the cases. By using this novel optimization method run times for typical docking simulations are significantly shortened.

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.

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.