Structure-based design and combinatorial chemistry yield low nanomolar inhibitors of cathepsin D

A small step towards an important goal: fragment screen of the c-di-AMP-synthesizing enzyme CdaA

CdaA is the most widespread diadenylate cyclase in many bacterial species, including several multidrug-resistant human pathogens. The enzymatic product of CdaA, cyclic di-AMP, is a secondary messenger that is essential for the viability of many bacteria. Its absence in humans makes CdaA a very promising and attractive target for the development of new antibiotics. Here, the structural results are presented of a crystallographic fragment screen against CdaA from Listeria monocytogenes, a saprophytic Gram-positive bacterium and an opportunistic food-borne pathogen that can cause listeriosis in humans and animals. Two of the eight fragment molecules reported here were localized in the highly conserved ATP-binding site. These fragments could serve as potential starting points for the development of antibiotics against several CdaA-dependent bacterial species.

Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.

Biomimetic Macrocyclic Inhibitors of Human Cathepsin D: Structure-Activity Relationship and Binding Mode Analysis

Human cathepsin D (CatD), a pepsin-family aspartic protease, plays an important role in tumor progression and metastasis. Here, we report the development of biomimetic inhibitors of CatD as novel tools for regulation of this therapeutic target. We designed a macrocyclic scaffold to mimic the spatial conformation of the minimal pseudo-dipeptide binding motif of pepstatin A, a microbial oligopeptide inhibitor, in the CatD active site. A library of more than 30 macrocyclic peptidomimetic inhibitors was employed for scaffold optimization, mapping of subsite interactions, and profiling of inhibitor selectivity. Furthermore, we solved high-resolution crystal structures of three macrocyclic inhibitors with low nanomolar or subnanomolar potency in complex with CatD and determined their binding mode using quantum chemical calculations. The study provides a new structural template and functional profile that can be exploited for design of potential chemotherapeutics that specifically inhibit CatD and related aspartic proteases.

Hybrid 2D/3D-quantitative structure-activity relationship and modeling studies perspectives of pepstatin A analogs as cathepsin D inhibitors

AIM

Cathepsin D, one of the attractive targets in the treatment of breast cancer, has been implicated in HIV neuropathogenesis with potential proteolytic effects on chemokines. Methodology/result: Diverse modeling tools were used to reveal the key structural features affecting the inhibitory activities of 78 pepstatin A analogs. Analyses were performed to investigate the stability, rationality and fluctuation of the analogs. Results showed a clear correlation between the experimental and predicted activities of the analogs as well as the variation in their activities relative to structural modifications.

CONCLUSION

The insight gained from this study offers theoretical references for understanding the mechanism of action of cathepsin D and will aid in the design of more potent and clinically-relevant drugs. Graphical abstract [Formula: see text].

Fragment oriented molecular shapes

Molecular shape is an important concept in drug design and virtual screening. Shape similarity typically uses either alignment methods, which dynamically optimize molecular poses with respect to the query molecular shape, or feature vector methods, which are computationally less demanding but less accurate. The computational cost of alignment can be reduced by pre-aligning shapes, as is done with the Volumetric-Aligned Molecular Shapes (VAMS) method. Here, we introduce and evaluate fragment oriented molecular shapes (FOMS), where shapes are aligned based on molecular fragments. FOMS enables the use of shape constraints, a novel method for precisely specifying molecular shape queries that provides the ability to perform partial shape matching and supports search algorithms that function on an interactive time scale. When evaluated using the challenging Maximum Unbiased Validation dataset, shape constraints were able to extract significantly enriched subsets of compounds for the majority of targets, and FOMS matched or exceeded the performance of both VAMS and an optimizing alignment method of shape similarity search.

A strategy for screening of high-quality enzyme inhibitors from herbal medicines based on ultrafiltration LC-MS and in silico molecular docking

A novel strategy of ultrafiltration LC-MS and in silico molecular docking was proposed to discover high-quality enzyme inhibitors from herbal medicines. Using this strategy, two compounds were predicted and finally demonstrated as potent xanthine oxidase inhibitors, whose in vitro IC50 values were lower than that of a positive control allopurinol.

LoopIng: a template-based tool for predicting the structure of protein loops

Motivation: Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function.

Results: We describe a method, LoopIng, based on the Random Forest automated learning technique, which, given a target loop, selects a structural template for it from a database of loop candidates. Compared to the most recently available methods, LoopIng is able to achieve similar accuracy for short loops (4–10 residues) and significant enhancements for long loops (11–20 residues). The quality of the predictions is robust to errors that unavoidably affect the stem regions when these are modeled. The method returns a confidence score for the predicted template loops and has the advantage of being very fast (on average: 1 min/loop).

Availability and implementation:www.biocomputing.it/looping

Contact:anna.tramontano@uniroma1.it

Supplementary information:Supplementary data are available at Bioinformatics online.

New cathepsin D inhibitor library utilizing hydroxyethyl isosteres with cyclic tertiary amines

The design and synthesis of hydroxyethylamine isosteres as inhibitors of cathepsin D based on SAR data have been accomplished. A library of 96 of these hydroxyethylamine isosteres are described and many have proven to be very potent inhibitors of human cathepsin D activity as measured using a fluorometric assay technique, via peptide substrate Ac-Glu-Glu(Edans)-Lys-Pro-Ile-Cys-Phe-Phe-Arg-Leu-Gly-Lys(Methyl Red)-Glu-NH(2). Compounds showing strongest inhibition of cathepsin D activity were those that contain a hydroxyethyl-N'-2- or N'-(4-chlorophenyl)piperazine moiety (IC(50) values range from 0.55 to 8.5 nM), with N'-(2-pyrimidyl)piperizine (IC(50) values range from 0.5 to 21.6 nM), with N-N'- L-piperazinocolinamide (IC(50) values range from 0.001 - 0.25 nM), or N-N'-L-piperazinocolin-N-methylamide (IC(50) values range from 0.015 - 7.3 nM).

Validation of Polo-like kinase 1 as a therapeutic target in pancreatic cancer cells

Polo-like kinase 1 (PLK1) is a serine/threonine protein kinase and plays a critical role in mitosis. PLK1 has also been regarded as a valuable target for cancer treatment, and several PLK1 inhibitors are currently undergoing clinical investigations. In this study, our data show that the expression level of PLK1 is upregulated in human pancreatic cancer cells. Molecular modeling studies indicate that DMTC inhibits PLK1 activity through competitive displacement of ATP from its binding pocket. Our data further show that DMTC suppresses the proliferation of pancreatic cancer cells and induces the formation of multinucleated cells, ultimately resulting in apoptosis. In addition, combination index analysis demonstrates that DMTC acts synergistically with the chemotherapeutic drug gemcitabine in inhibiting the proliferation of pancreatic cancer cells. These results thus suggest a potential of using PLK1 inhibitors for the treatment of pancreatic cancer.

Dimethylenastron suppresses human pancreatic cancer cell migration and invasion in vitro via allosteric inhibition of mitotic kinesin Eg5

AIM

The mitotic kinesin Eg5 plays a critical role in bipolar spindle assembly, and its inhibitors have shown impressive anticancer activity in preclinical studies. This study was undertaken to investigate the effect of dimethylenastron, a specific inhibitor of Eg5, on the migration and invasion of pancreatic cancer cells.

METHODS

Human pancreatic cancer cell lines PANC1, EPP85, BxPC3, CFPAC1, and AsPAC1 were used. Eg5 expression was examined using immunofluorescence microscopy. Cell migration and invasion were analyzed with wound healing and transwell assays. Cell proliferation was examined using sulforhodamine B and MTT assays. The binding of dimethylenastron to Eg5 was analyzed with a molecular modeling study, and the ADP release rate was examined with the MANT-ADP reagent.

RESULTS

Eg5 expression was 9-16-fold up-regulated in the 5 pancreatic cancer cell lines. Treatment of PANC1 pancreatic cancer cells with dimethylenastron (3 and 10 μmol/L) for 24 h suppressed the migratory ability of the cancer cells in a concentration-dependent manner. The invasion ability of the cancer cells was also reduced by the treatment. However, treatment of PANC1 cells with dimethylenastron (3 and 10 μmol/L) for 24 h had no detectable effect on their proliferation, which was inhibited when the cancer cells were treated with the drug for 72 h. Molecular modeling study showed that dimethylenastron could allosterically inhibit the motor domain ATPase of Eg5 by decreasing the rate of ADP release.

CONCLUSION

Dimethylenastron inhibits the migration and invasion of PANC1 pancreatic cancer cells, independent of suppressing the cell proliferation. The findings provide a novel insight into the mechanisms of targeting Eg5 for pancreatic cancer chemotherapy.

Jeffamine derivatized TentaGel beads and poly(dimethylsiloxane) microbead cassettes for ultrahigh-throughput in situ releasable solution-phase cell-based screening of one-bead-one-compound combinatorial small molecule libraries

A method to efficiently immobilize and partition large quantities of microbeads in an array format in microfabricated poly(dimethylsiloxane) (PDMS) cassette for ultrahigh-throughput in situ releasable solution-phase cell-based screening of one-bead-one-compound (OBOC) combinatorial libraries is described. Commercially available Jeffamine triamine T-403 (∼440 Da) was derivatized such that two of its amino groups were protected by Fmoc and the remaining amino group capped with succinic anhydride to generate a carboxyl group. This resulting trifunctional hydrophilic polymer was then sequentially coupled two times to the outer layer of topologically segregated bilayer TentaGel (TG) beads with solid phase peptide synthesis chemistry resulting in beads with increased loading capacity, hydrophilicity, and porosity at the outer layer. We have found that such bead configuration can facilitate ultrahigh-throughput in situ releasable solution-phase screening of OBOC libraries. An encoded releasable OBOC small molecule library was constructed on Jeffamine derivatized TG beads with library compounds tethered to the outer layer via a disulfide linker and coding tags in the interior of the beads. Compound-beads could be efficiently loaded (5-10 min) into a 5 cm diameter Petri dish containing a 10,000-well PDMS microbead cassette, such that over 90% of the microwells were each filled with only one compound-bead. Jurkat T-lymphoid cancer cells suspended in Matrigel were then layered over the microbead cassette to immobilize the compound-beads. After 24 h of incubation at 37 °C, dithiothreitol was added to trigger the release of library compounds. Forty-eight hours later, MTT reporter assay was used to identify regions of reduced cell viability surrounding each positive bead. From a total of about 20,000 beads screened, 3 positive beads were detected and physically isolated for decoding. A strong consensus motif was identified for these three positive compounds. These compounds were resynthesized and found to be cytotoxic (IC(50) 50-150 μM) against two T-lymphoma cell lines and less so against the MDA-MB 231 breast cancer cell line. This novel ultrahigh-throughput OBOC releasable method can potentially be adapted to many existing 96- or 384-well solution-phase cell-based or biochemical assays.

Modeling effects of human single nucleotide polymorphisms on protein-protein interactions

A large set of three-dimensional structures of 264 protein-protein complexes with known nonsynonymous single nucleotide polymorphisms (nsSNPs) at the interface was built using homology-based methods. The nsSNPs were mapped on the proteins' structures and their effect on the binding energy was investigated with CHARMM force field and continuum electrostatic calculations. Two sets of nsSNPs were studied: disease annotated Online Mendelian Inheritance in Man (OMIM) and nonannotated (non-OMIM). It was demonstrated that OMIM nsSNPs tend to destabilize the electrostatic component of the binding energy, in contrast with the effect of non-OMIM nsSNPs. In addition, it was shown that the change of the binding energy upon amino acid substitutions is not related to the conservation of the net charge, hydrophobicity, or hydrogen bond network at the interface. The results indicate that, generally, the effect of nsSNPs on protein-protein interactions cannot be predicted from amino acids' physico-chemical properties alone, since in many cases a substitution of a particular residue with another amino acid having completely different polarity or hydrophobicity had little effect on the binding energy. Analysis of sequence conservation showed that nsSNP at highly conserved positions resulted in a large variance of the binding energy changes. In contrast, amino acid substitutions corresponding to nsSNPs at nonconserved positions, on average, were not found to have a large effect on binding affinity. pKa calculations were performed and showed that amino acid substitutions could change the wild-type proton uptake/release and thus resulting in different pH-dependence of the binding energy.

Loopholes and missing links in protein modeling

This paper provides an unbiased comparison of four commercially available programs for loop sampling, Prime, Modeler, ICM, and Sybyl, each of which uses a different modeling protocol. The study assesses the quality of results and examines the relative strengths and weaknesses of each method. The set of loops to be modeled varied in length from 4-12 amino acids. The approaches used for loop modeling can be classified into two methodologies: ab initio loop generation (Modeler and Prime) and database searches (Sybyl and ICM). Comparison of the modeled loops to the native structures was used to determine the accuracy of each method. All of the protocols returned similar results for short loop lengths (four to six residues), but as loop length increased, the quality of the results varied among the programs. Prime generated loops with RMSDs <2.5 A for loops up to 10 residues, while the other three methods met the 2.5 A criteria at seven-residue loops. Additionally, the ability of the software to utilize disulfide bonds and X-ray crystal packing influenced the quality of the results. In the final analysis, the top-ranking loop from each program was rarely the loop with the lowest RMSD with respect to the native template, revealing a weakness in all programs to correctly rank the modeled loops.

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these "random" methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

Strategies for targeting protein-protein interactions with synthetic agents

The development of small-molecule modulators of protein-protein interactions is a formidable goal, albeit one that possesses significant potential for the discovery of novel therapeutics. Despite the daunting challenges, a variety of examples exists for the inhibition of two large protein partners with low-molecular-weight ligands. This review discusses the strategies for targeting protein-protein interactions and the state of the art in the rational design of molecules that mimic the structures and functions of their natural targets.

Synthesis of gallic acid based naphthophenone fatty acid amides as cathepsin D inhibitors

Gallic acid, one of the most abundant plant phenolic acids, has been modified to cathepsin D protease inhibitors. The strategy of modification was proposed basing on some previously reported structure and activity relationship (SAR) studies. The synthesized naphthophenone fatty acid amide derivatives have been evaluated for in vitro cathepsin D inhibition activity. Two of them have shown significant inhibition activity with IC(50) values of 0.06 and 0.14 microM, respectively, as compared against pepstatin (0.0023 microM), the most potent inhibitor known so far. The study revealed that such attempts on gallic acid based pharmacophores might result in potent inhibitors of cathepsin D.

Combining docking and molecular dynamic simulations in drug design

A rational approach is needed to maximize the chances of finding new drugs, and to exploit the opportunities of potential new drug targets emerging from genomic and proteomic initiatives, and from the large libraries of small compounds now readily available through combinatorial chemistry. Despite a shaky early history, computer-aided drug design techniques can now be effective in reducing costs and speeding up drug discovery. This happy outcome results from development of more accurate and reliable algorithms, use of more thoughtfully planned strategies to apply them, and greatly increased computer power to allow studies with the necessary reliability to be performed. Our review focuses on applications and protocols, with the main emphasis on critical analysis of recent studies where docking calculations and molecular dynamics (MD) simulations were combined to dock small molecules into protein receptors. We highlight successes to demonstrate what is possible now, but also point out drawbacks and future directions. The review is structured to lead the reader from the simpler to more compute-intensive methods. Thus, while inexpensive and fast docking algorithms can be used to scan large compound libraries and reduce their size, more accurate but expensive MD simulations can be applied when a few selected ligand candidates remain. MD simulations can be used: during the preparation of the protein receptor before docking, to optimize its structure and account for protein flexibility; for the refinement of docked complexes, to include solvent effects and account for induced fit; to calculate binding free energies, to provide an accurate ranking of the potential ligands; and in the latest developments, during the docking process itself to find the binding site and correctly dock the ligand a priori.

Surrogate docking: structure-based virtual screening at high throughput speed

Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing approximately 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size - not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of approximately 13 and approximately 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself.

Virtual screening of chemical libraries

Virtual screening uses computer-based methods to discover new ligands on the basis of biological structures. Although widely heralded in the 1970s and 1980s, the technique has since struggled to meet its initial promise, and drug discovery remains dominated by empirical screening. Recent successes in predicting new ligands and their receptor-bound structures, and better rates of ligand discovery compared to empirical screening, have re-ignited interest in virtual screening, which is now widely used in drug discovery, albeit on a more limited scale than empirical screening.

Discovery of Ligands by a Combination of Computational and NMR-Based Screening: RNA as an Example Target

NMR for screening of knowledge-based focused libraries of compounds provides an efficient, cost-effective method to develop promising drug leads that target functionally important RNA structures. A knowledge-based focused library may be constructed from virtual (i.e., computational) screening of commercial or proprietary databases of available compounds for binding to the three-dimensional structure of a selected RNA target. Alternatively, the library may be constructed from compounds with properties deemed desirable, e.g., molecular moiety commonly found in drugs or known to bind RNA. The library ideally should be composed of small water-soluble, nonpeptide, nonnucleotide organic compounds. Various simple, robust NMR experiments are described that enable experimental screening of such a library for binding to a selected RNA structure. Some of the NMR experiments enable rapid mapping of the interaction site on the RNA to verify that the targeted structure is hit rather than the double helical region or a commonly occurring tetraloop. Other experiments enable elucidation of the ligand's binding moiety. Of course, any compounds thus identified should represent promising scaffolds suitable for easy chemical modification to enhance their pharmaceutical properties for subsequent drug development.

A very large diversity space of synthetically accessible compounds for use with drug design programs

We have constructed a very large virtual diversity space containing more than 10(13) chemical compounds. The diversity space is built from about 400 combinatorial libraries, which have been expanded by choosing sizeable collections of suitable R-groups that can be attached to each link point of their scaffolds. These R-group collections have been created by selecting reagents that have drug-like properties from catalogs of available chemicals. As members of known combinatorial libraries, the compounds in the diversity space are in general synthetically accessible and useful as potential drug leads. Hence, the diversity space can be used as a vast source of compounds by a de novo drug design program. For example, we have used such a program to generate inhibitors of HIV integrase enzyme that exhibited activity in the micromolar range.

Docking and scoring in virtual screening for drug discovery: methods and applications

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.

New approaches in identifying drugs to inactivate oncogene products

With an information explosion on the molecular mechanism of oncogenesis, the completion of the human genome sequence project, and the advances in genomic and proteomic methods, many therapeutic targets for various cancers have been identified. It is timely that a number of new drug development techniques have been developed in this last decade. Candidate drug targets can now be efficiently validated with RNA interference and transgenic animals studies. Combinatorial chemistry provides large numbers of chemical compounds for drug lead discovery and optimization. High throughput assays and high content cell-based assays, in conjunction with sophisticated robotics, are now available for screening large numbers of compounds. Based on X-ray crystallographic structure data, drug leads can be discovered through in silico screening of virtual libraries. By applying these various drug discovery techniques, it is anticipated that more potent and specific anti-cancer agents will be discovered within the next decade.

Assessment of the role of computations in structural biology

Computations are now an integrated part of structural biology and are used in data gathering, data processing, and data storage as well as in a full spectrum of theoretical pursuits. In this review, we focus on areas of great promise and call attention to important issues of internal consistency and error analysis.

Molecular recognition and docking algorithms

Molecular docking is an invaluable tool in modern drug discovery. This review focuses on methodological developments relevant to the field of molecular docking. The forces important in molecular recognition are reviewed and followed by a discussion of how different scoring functions account for these forces. More recent applications of computational chemistry tools involve library design and database screening. Last, we summarize several critical methodological issues that must be addressed in future developments.

Virtual screening of virtual libraries

Virtual screening of virtual libraries (VSVL) is a rapidly changing area of research. Great efforts are being made to produce better algorithms, selection methods and infrastructure. Yet, the number of successful examples in the literature is not impressive, although the quality of work certainly is high. Why is this? One reason is that these methods tend to be applied at the lead generation stage and therefore there is a large lead-time before successful examples appear in the literature. However, any computational chemist would confirm that these methods are successful and there exists a glut of start-up companies specialising in virtual screening. Moreover, the scientific community would not be focussing so much attention on this area if it were not yielding results. Even so, the paucity of literature data is certainly a hindrance to the development of better methods. The VSVL process is unique within the discovery process, in that it is the only method that can screen the > 10(30) genuinely novel molecules out there. Already, some VSVL methods are evaluating 10(13) compounds, a capacity that high throughput screening can only dream of. There is a huge potential advantage for the company that develops efficient and effective methods, for lead generation, lead hopping and optimization of both potency and ADME properties. To do this, it requires more than the software, it requires confidence to exploit the methodology, to commit synthesis on the basis of it, and to build this approach into the medicinal chemistry strategy. It is a fact that these tools remain quite daunting for the majority of scientists working at the bench. The routine use of these methods is not simply a matter of education and training. Integration of these methods into accessible and robust end user software, without dilution of the science, must be a priority. We have reached a coincidence, where several technologies have the required level of maturity predictive computational chemistry methods, algorithms that manage the combinatorial explosion, high throughput crystallography and ADME measurements and the massive increase in computational horsepower from distributed computing. The author is confident that the synergy of these technologies will bring great benefit to the industry, with more efficient production of higher quality clinical candidates. The future is bright. The future is virtual!

Combinatorial peptide library methods for immunobiology research

The field of combinatorial peptide chemistry has emerged as a powerful tool in the study of many biological systems. This review focuses on combinatorial peptide library methodology, which includes biological library methods, spatially addressable parallel library methods, library methods requiring deconvolution, the "one-bead one-compound" library method, and affinity chromatography selection method. These peptide libraries have successfully been employed to study a vast array of cell surface receptors, as well as have been useful in identifying protein kinase substrates and inhibitors. In recent immunobiological applications, peptide libraries have proven monumental in the definition of MHC anchor residues, in lymphocyte epitope mapping, and in the development of peptide vaccines. Peptides identified from such libraries, when presented in a chemical microarray format, may prove useful in immunodiagnostics. Combinatorial peptide libraries offer a high-throughput approach to study limitless biological targets. Peptides discovered from such studies may be therapeutically and diagnostically useful agents.

A structure-based design approach for the identification of novel inhibitors: application to an alanine racemase

We report a new structure-based strategy for the identification of novel inhibitors. This approach has been applied to Bacillus stearothermophilus alanine racemase (AlaR), an enzyme implicated in the biosynthesis of the bacterial cell wall. The enzyme catalyzes the racemization of L- and D-alanine using pyridoxal 5'-phosphate (PLP) as a cofactor. The restriction of AlaR to bacteria and some fungi and the absolute requirement for D-alanine in peptidoglycan biosynthesis make alanine racemase a suitable target for drug design. Unfortunately, known inhibitors of alanine racemase are not specific and inhibit the activity of other PLP-dependent enzymes, leading to neurological and other side effects. This article describes the development of a receptor-based pharmacophore model for AllaR, taking into account receptor flexibility (i.e. a 'dynamic' pharmacophore model). In order to accomplish this, molecular dynamics (MD) simulations were performed on the full AlaR dimer from Bacillus stearothermophilus (PDB entry, 1 sft) with a D-alanine molecule in one active site and the non-covalent inhibitor, propionate, in the second active site of this homodimer. The basic strategy followed in this study was to utilize conformations of the protein obtained during MD simulations to generate a dynamic pharmacophore model using the property mapping capability of the LigBuilder program. Compounds from the Available Chemicals Directory that fit the pharmacophore model were identified and have been submitted for experimental testing. The approach described here can be used as a valuable tool for the design of novel inhibitors of other biomolecular targets.

Small molecule affinity fingerprinting. A tool for enzyme family subclassification, target identification, and inhibitor design

Classifying proteins into functionally distinct families based only on primary sequence information remains a difficult task. We describe here a method to generate a large data set of small molecule affinity fingerprints for a group of closely related enzymes, the papain family of cysteine proteases. Binding data was generated for a library of inhibitors based on the ability of each compound to block active-site labeling of the target proteases by a covalent activity based probe (ABP). Clustering algorithms were used to automatically classify a reference group of proteases into subfamilies based on their small molecule affinity fingerprints. This approach was also used to identify cysteine protease targets modified by the ABP in complex proteomes by direct comparison of target affinity fingerprints with those of the reference library of proteases. Finally, experimental data were used to guide the development of a computational method that predicts small molecule inhibitors based on reported crystal structures. This method could ultimately be used with large enzyme families to aid in the design of selective inhibitors of targets based on limited structural/function information.

Combinatorial chemistry and high-throughput screening in drug discovery: different strategies and formats

Different strategies for the discovery of novel leads interacting with therapeutically relevant targets are thoroughly presented and discussed, using also three recent examples. Emphasis is given to approaches which do not require extensive resources and budgets, but rather prove how cleverness and creativity can provide active compounds in 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.

Structure-based combinatorial library design: methodologies and applications

Rational design of small focused libraries that are biased toward specific therapeutic targets is currently at the forefront of combinatorial library design. Various structure-based design strategies can be implemented in focused library design when the 3D structure of the target is available through X-ray or NMR determination. This review discusses the major methods and programs specifically developed for the purpose of designing combinatorial libraries under the constraint of the binding site of a biological target, with emphasis on their advantages and disadvantages. Examples of the successful application of these methodologies are highlighted, demonstrating their performances within the practical drug discovery process.