Structure-based drug design

An optimized purification protocol for enzymatically synthesized S-adenosyl-L-methionine (SAM) for applications in solution state infrared spectroscopic studies

S-adenosyl-L-methionine (SAM) is an abundant biomolecule used by methyltransferases to regulate a wide range of essential cellular processes such as gene expression, cell signaling, protein functions, and metabolism. Despite considerable effort, there remain many specificity challenges associated with designing small molecule inhibitors for methyltransferases, most of which exhibit off-target effects. Interestingly, NMR evidence suggests that SAM undergoes conformeric exchange between several states when free in solution. Infrared spectroscopy can detect different conformers of molecules if present in appreciable populations. When SAM is noncovalently bound within enzyme active sites, the nature and the number of different conformations of the molecule are likely to be altered from when it is free in solution. If there are unique structures or different numbers of conformers between different methyltransferase active sites, solution-state information may provide promising structural leads to increase inhibitor specificity for a particular methyltransferase. Toward this goal, frequencies measured in SAM's infrared spectra must be assigned to the motions of specific atoms via isotope incorporation at discrete positions. The incorporation of isotopes into SAM's structure can be accomplished via an established enzymatic synthesis using isotopically labeled precursors. However, published protocols produced an intense and highly variable IR signal which overlapped with many of the signals from SAM rendering comparison between isotopes challenging. We observed this intense absorption to be from co-purifying salts and the SAM counterion, producing a strong, broad signal at 1100 cm-1. Here, we report a revised SAM purification protocol that mitigates the contaminating salts and present the first IR spectra of isotopically labeled CD3-SAM. These results provide a foundation for isotopic labeling experiments of SAM that will define which atoms participate in individual molecular vibrations, as a means to detect specific molecular conformations.

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Fundamental considerations in drug design

The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.

Identifying Ortholog Selective Fragment Molecules for Bacterial Glutaredoxins by NMR and Affinity Enhancement by Modification with an Acrylamide Warhead

Illustrated here is the development of a new class of antibiotic lead molecules targeted at Pseudomonas aeruginosa glutaredoxin (PaGRX). This lead was produced to (a) circumvent efflux-mediated resistance mechanisms via covalent inhibition while (b) taking advantage of species selectivity to target a fundamental metabolic pathway. This work involved four components: a novel workflow for generating protein specific fragment hits via independent nuclear magnetic resonance (NMR) measurements, NMR-based modeling of the target protein structure, NMR guided docking of hits, and synthetic modification of the fragment hit with a vinyl cysteine trap moiety, i.e., acrylamide warhead, to generate the chimeric lead. Reactivity of the top warhead-fragment lead suggests that the ortholog selectivity observed for a fragment hit can translate into a substantial kinetic advantage in the mature warhead lead, which bodes well for future work to identify potent, species specific drug molecules targeted against proteins heretofore deemed undruggable.

Current advances in screening for bioactive components from medicinal plants by affinity ultrafiltration mass spectrometry

INTRODUCTION

Medicinal plants have played an important role in maintaining human health for thousands of years. However, the interactions between the active components in medicinal plants and some certain biological targets during a disease are still unclear in most cases.

OBJECTIVE

To conduct the high-throughput screening for small active molecules that can interact with biological targets, which is of great theoretical significance and practical value.

METHODOLOGY

The ultrafiltration mass spectrometry (UF-LC/MS) is a powerful bio-analytical method by combining affinity ultrafiltration and liquid chromatography-mass spectrometry (LC/MS), which could rapidly screen and identify small active molecules that bind to biological targets of interest at the same time. Compared with other analytical methods, affinity UF-LC/MS has the characteristics of fast, sensitive and high throughput, and is especially suitable for the complicated extracts of medicinal plants.

RESULTS

In this review, the basic principle, characteristics and some most recent challenges in UF-LC/MS have been demonstrated. Meanwhile, the progress and applications of affinity UF-LC/MS in the discovery of the active components from natural medicinal plants and the interactions between small molecules and biological target proteins are also briefly summarised. In addition, the future directions for UF-LC/MS are also prospected.

CONCLUSION

Affinity UF-LC/MS is a powerful tool in studies on the interactions between small active molecules and biological protein targets, especially in the high-throughput screening of active components from the natural medicinal plants.

A machine learning approach towards the prediction of protein–ligand binding affinity based on fundamental molecular properties

There is an exigency of transformation of the enormous amount of biological data available in various forms into some significant knowledge. We have tried to implement Machine Learning (ML) algorithm models on the protein–ligand binding affinity data already available to predict the binding affinity of the unknown. ML methods are appreciably faster and cheaper as compared to traditional experimental methods or computational scoring approaches. The prerequisites of this prediction are sufficient and unbiased features of training data and a prediction model which can fit the data well. In our study, we have applied Random forest and Gaussian process regression algorithms from the Weka package on protein–ligand binding affinity, which encompasses protein and ligand binding information from PdbBind database. The models are trained on the basis of selective fundamental information of both proteins and ligand, which can be effortlessly fetched from online databases or can be calculated with the availability of structure. The assessment of the models was made on the basis of correlation coefficient (R²) and root mean square error (RMSE). The Random forest model gave R² and RMSE of 0.76 and 1.31 respectively. We have also used our features and prediction models on the dataset used by others and found that our model with our features outperformed the existing ones.

There is an exigency of transformation of the enormous amount of biological data available in various forms into some significant knowledge.

Structure-Based Reverse Vaccinology Failed in the Case of HIV Because it Disregarded Accepted Immunological Theory

Two types of reverse vaccinology (RV) should be distinguished: genome-based RV for bacterial vaccines and structure-based RV for viral vaccines. Structure-based RV consists in trying to generate a vaccine by first determining the crystallographic structure of a complex between a viral epitope and a neutralizing monoclonal antibody (nMab) and then reconstructing the epitope by reverse molecular engineering outside the context of the native viral protein. It is based on the unwarranted assumption that the epitope designed to fit the nMab will have acquired the immunogenic capacity to elicit a polyclonal antibody response with the same protective capacity as the nMab. After more than a decade of intensive research using this type of RV, this approach has failed to deliver an effective, preventive HIV-1 vaccine. The structure and dynamics of different types of HIV-1 epitopes and of paratopes are described. The rational design of an anti-HIV-1 vaccine is shown to be a misnomer since investigators who claim that they design a vaccine are actually only improving the antigenic binding capacity of one epitope with respect to only one paratope and not the immunogenic capacity of an epitope to elicit neutralizing antibodies. Because of the degeneracy of the immune system and the polyspecificity of antibodies, each epitope studied by the structure-based RV procedure is only one of the many epitopes that the particular nMab is able to recognize and there is no reason to assume that this nMab must have been elicited by this one epitope of known structure. Recent evidence is presented that the trimeric Env spikes of the virus possess such an enormous plasticity and intrinsic structural flexibility that it is it extremely difficult to determine which Env regions are the best candidate vaccine immunogens most likely to elicit protective antibodies.

REdiii: a pipeline for automated structure solution

High-throughput crystallographic approaches require integrated software solutions to minimize the need for manual effort. REdiii is a system that allows fully automated crystallographic structure solution by integrating existing crystallographic software into an adaptive and partly autonomous workflow engine. The program can be initiated after collecting the first frame of diffraction data and is able to perform processing, molecular-replacement phasing, chain tracing, ligand fitting and refinement without further user intervention. Preset values for each software component allow efficient progress with high-quality data and known parameters. The adaptive workflow engine can determine whether some parameters require modifications and choose alternative software strategies in case the preconfigured solution is inadequate. This integrated pipeline is targeted at providing a comprehensive and efficient approach to screening for ligand-bound co-crystal structures while minimizing repetitiveness and allowing a high-throughput scientific discovery process.

Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase

The therapeutic challenges in the treatment of tuberculosis demand multidisciplinary approaches for the identification of potential drug targets as well as fast and accurate techniques to screen huge chemical libraries. Mycobacterial cyclopropane synthase (CmaA1) has been shown to be essential for the survival of the bacteria due to its critical role in the synthesis of mycolic acids. The present study proposes pharmacophore models based on the structure of CmaA1 taking into account its various states in the cyclopropanation process, and their dynamic nature as assessed using molecular dynamics (MD) simulations. The qualities of these pharmacophore models were validated by mapping 23 molecules that have been previously reported to exhibit inhibitory activities on CmaA1. Additionally, 1398 compounds that have been shown to be inactive for tuberculosis were collected from the ChEMBL database and were screened against the models for validation. The models were further validated by comparing the results from pharmacophore mapping with the results obtained from docking these molecules with the respective protein structures. The best models are suggested by validating all the models based on their screening abilities and by comparing with docking results. The models generated from the MD trajectories were found to perform better than the one generated based on the crystal structure demonstrating the importance of incorporating receptor flexibility in drug design.

Alpha shape and Delaunay triangulation in studies of protein-related interactions

In recent years, more 3D protein structures have become available, which has made the analysis of large molecular structures much easier. There is a strong demand for geometric models for the study of protein-related interactions. Alpha shape and Delaunay triangulation are powerful tools to represent protein structures and have advantages in characterizing the surface curvature and atom contacts. This review presents state-of-the-art applications of alpha shape and Delaunay triangulation in the studies on protein-DNA, protein-protein, protein-ligand interactions and protein structure analysis.

Solvent structure improves docking prediction in lectin-carbohydrate complexes

Recognition and complex formation between proteins and carbohydrates is a key issue in many important biological processes. Determination of the three-dimensional structure of such complexes is thus most relevant, but particularly challenging because of their usually low binding affinity. In silico docking methods have a long-standing tradition in predicting protein-ligand complexes, and allow a potentially fast exploration of a number of possible protein-carbohydrate complex structures. However, determining which of these predicted complexes represents the correct structure is not always straightforward. In this work, we present a modification of the scoring function provided by AutoDock4, a widely used docking software, on the basis of analysis of the solvent structure adjacent to the protein surface, as derived from molecular dynamics simulations, that allows the definition and characterization of regions with higher water occupancy than the bulk solvent, called water sites. They mimic the interaction held between the carbohydrate -OH groups and the protein. We used this information for an improved docking method in relation to its capacity to correctly predict the protein-carbohydrate complexes for a number of tested proteins, whose ligands range in size from mono- to tetrasaccharide. Our results show that the presented method significantly improves the docking predictions. The resulting solvent-structure-biased docking protocol, therefore, appears as a powerful tool for the design and optimization of development of glycomimetic drugs, while providing new insights into protein-carbohydrate interactions. Moreover, the achieved improvement also underscores the relevance of the solvent structure to the protein carbohydrate recognition process.

Basic research in HIV vaccinology is hampered by reductionist thinking

This review describes the structure-based reverse vaccinology approach aimed at developing vaccine immunogens capable of inducing antibodies that broadly neutralize HIV-1. Some basic principles of protein immunochemistry are reviewed and the implications of the extensive polyspecificity of antibodies for vaccine development are underlined. Although it is natural for investigators to want to know the cause of an effective immunological intervention, the classic notion of causality is shown to have little explanatory value for a system as complex as the immune system, where any observed effect always results from many interactions between a large number of components. Causal explanations are reductive because a single factor is singled out for attention and given undue explanatory weight on its own. Other examples of the negative impact of reductionist thinking on HIV vaccine development are discussed. These include (1) the failure to distinguish between the chemical nature of antigenicity and the biological nature of immunogenicity, (2) the belief that when an HIV-1 epitope is reconstructed by rational design to better fit a neutralizing monoclonal antibody (nMab), this will produce an immunogen able to elicit Abs with the same neutralizing capacity as the Ab used as template for designing the antigen, and (3) the belief that protection against infection can be analyzed at the level of individual molecular interactions although it has meaning only at the level of an entire organism. The numerous unsuccessful strategies that have been used to design HIV-1 vaccine immunogens are described and it is suggested that the convergence of so many negative experimental results justifies the conclusion that reverse vaccinology is unlikely to lead to the development of a preventive HIV-1 vaccine. Immune correlates of protection in vaccines have not yet been identified because this will become feasible only retrospectively once an effective vaccine exists. The finding that extensive antibody affinity maturation is needed to obtain mature anti-HIV-1 Abs endowed with a broad neutralizing capacity explains why antigens designed to fit matured Mabs are not effective vaccine immunogens since these are administered to naive recipients who possess only B-cell receptors corresponding to the germline version of the matured Abs.

Comparative assessment of scoring functions on a diverse test set

Scoring functions are widely applied to the evaluation of protein-ligand binding in structure-based drug design. We have conducted a comparative assessment of 16 popular scoring functions implemented in main-stream commercial software or released by academic research groups. A set of 195 diverse protein-ligand complexes with high-resolution crystal structures and reliable binding constants were selected through a systematic nonredundant sampling of the PDBbind database and used as the primary test set in our study. All scoring functions were evaluated in three aspects, that is, "docking power", "ranking power", and "scoring power", and all evaluations were independent from the context of molecular docking or virtual screening. As for "docking power", six scoring functions, including GOLD::ASP, DS::PLP1, DrugScore(PDB), GlideScore-SP, DS::LigScore, and GOLD::ChemScore, achieved success rates over 70% when the acceptance cutoff was root-mean-square deviation < 2.0 A. Combining these scoring functions into consensus scoring schemes improved the success rates to 80% or even higher. As for "ranking power" and "scoring power", the top four scoring functions on the primary test set were X-Score, DrugScore(CSD), DS::PLP, and SYBYL::ChemScore. They were able to correctly rank the protein-ligand complexes containing the same type of protein with success rates around 50%. Correlation coefficients between the experimental binding constants and the binding scores computed by these scoring functions ranged from 0.545 to 0.644. Besides the primary test set, each scoring function was also tested on four additional test sets, each consisting of a certain number of protein-ligand complexes containing one particular type of protein. Our study serves as an updated benchmark for evaluating the general performance of today's scoring functions. Our results indicate that no single scoring function consistently outperforms others in all three aspects. Thus, it is important in practice to choose the appropriate scoring functions for different purposes.

Insight into ligand selectivity in HCV NS5B polymerase: molecular dynamics simulations, free energy decomposition and docking

Modeling studies were performed on HCV NS5B polymerase in an effort to design new inhibitors. The binding models of five different scaffold inhibitors were investigated and compared by using molecular dynamics simulations, free energy calculation and decomposition. Our results show Tyr448 plays the most critical role in the binding of most inhibitors. In addition, favorable contributions of residues Pro197, Arg200, Cys366, Met414 and Tyr448 in a deep hydrophobic pocket prove to be important for the selectivity of inhibitors. Furthermore, an optimized docking protocol was presented based on cross-docking the five inhibitors in the palm binding site of this enzyme using the Autodock program. This protocol was used later to virtually screen NCI and Maybridge diversity set libraries. The binding site was profiled via the statistics and analysis of the hydrogen bond networks formed between the receptor and the top-ranked diversity set compounds. Based on our detailed binding site analysis two useful rules were proposed to guide the selection of promising hits.

Apurinic/apyrimidinic endonuclease/redox effector factor-1(APE/Ref-1): a unique target for the prevention and treatment of human melanoma

Management of melanoma is a growing and challenging public health issue requiring novel and multidisciplinary approaches to achieve more efficient prevention and therapeutic benefits. The aim of this article is to show the critical role of APE/Ref-1 on melanomagenesis and progression. APE/Ref-1 serves as a redox-sensitive node of convergence of various signals as well as a DNA-repair enzyme, and its activation protects melanocytes and melanoma cells from chronic oxidative stress and promotes cell survival via mediation of downstream pathways. APE/Ref-1 is a strong candidate as a potential drug-treatable target for the prevention and treatment of human melanoma. Lead compounds exhibiting inhibitory effects on APE/Ref-1 are also reviewed. We anticipate potential clinical benefit in the future through inhibition of APE/Ref-1 and/or Ref-1-mediated signaling.

Determination of protein-ligand binding modes using complexation-induced changes in (1)h NMR chemical shift

A new method for determining three-dimensional solution structures of protein-ligand complexes using experimentally determined complexation-induced changes in (1)H NMR chemical shift (CIS) is introduced. The method has been validated using the complex formed between the protein antitumor antibiotic neocarzinostatin (NCS) and a synthetic chromophore analogue. The X-ray crystal structure of the unbound protein and the backbone amide proton CIS were the input data used in the determination of the three-dimensional structure of the complex. The experimental CIS values were used in a continuous direct structure refinement process based on genetic algorithms to sample conformational space. The calculated structure of the complex agrees well with the NMR solution structure, indicating the potential of this approach for structure determination.

Structure-based development of target-specific compound libraries

The success or failure of a small-molecule drug discovery project ultimately lies in the choice of the scaffolds to be screened -- chosen from among the many millions of available compounds. Therefore, the methods used to design compound screening libraries are key for the development of new drugs that target a wide range of diseases. Currently, there is a trend towards the construction of receptor-structure-based focused libraries. Recent advances in high-throughput computational docking, NMR and crystallography have facilitated the development of these libraries. A structure-based target-specific library can save time and money by reducing the number of compounds to be experimentally tested, also improving the drug discovery success rate by identifying more-potent and specific binders.

Representing receptor flexibility in ligand docking through relevant normal modes

Inspired by the current representation of the ligand-receptor binding process, a normal-mode-based methodology is presented to incorporate receptor flexibility in ligand docking and virtual screening. However, the systematic representation of the deformation space grows geometrically with the number of modes, and furthermore, midscale loop rearrangements like those found in protein kinase binding pockets cannot be accounted for with the first lowest-frequency modes. We thus introduced a measure of relevance of normal modes on a given region of interest and showed that only very few modes in the low-frequency range are necessary and sufficient to describe loop flexibility in cAMP-dependent protein kinase. We used this approach to generate an ensemble of representative receptor backbone conformations by perturbing the structure along a combination of relevant modes. Each ensemble conformation is complexed with known non-native binders to optimize the position of the binding-pocket side chains through a full flexible docking procedure. The multiple receptor conformations thus obtained are used in a small-scale virtual screening using receptor ensemble docking. We evaluated this algorithm on holo and apo structures of cAMP-dependent protein kinase that exhibit backbone rearrangements on two independent loop regions close to the binding pocket. Docking accuracy is improved, since the ligands considered in the virtual screening docked within 1.5 A to at least one of the structures. The discrimination between binders and nonbinders is also enhanced, as shown by the improvement of the enrichment factor. This constitutes a new step toward the systematic integration of flexible ligand-flexible receptor docking tools in structure-based drug discovery.

Thermodynamic calculations in biological systems

The ability to compute intra- and inter-molecular interactions provides the opportunity to gain a deeper understanding of previously intractable problems in biochemistry and biophysics. This review presents three examples in which molecular dynamics calculations were used to gain insight into the atomic detail underlying important experimental observations. The three examples are the following: (1) Entropic contribution to rate acceleration that results from conformational constraints imposed on the reactants; (2) Mechanism of force unfolding of a small protein molecule by the application of a force that separates its N- and C-terminals; and (3) Loss of translational entropy experienced by small molecules when they bind to proteins.

Robotic sequential analysis of a library of metalloporphyrins as electrocatalysts for voltammetric nitric oxide sensors

A library of 83 metalloporphyrins with varying substitution pattern at the meso-position of the porphyrins and different central metal ions in the core region has been synthesized in small quantities using a parallel synthesis strategy. By means of a specially designed electrochemical robotic device integrating a 96-well microtitre plate and an easily movable assembly of working, counter and reference electrodes, the different porphyrins were automatically applied in sequence to an in-well electrochemical preparation and testing of NO sensors. Screening the entire compound collection suggested initial considerations concerning the influence of varied functionalities of the metalloporphyrins on their electrocatalytic properties for the oxidation of NO and helped to identify the quality of the investigated catalyst candidates. As compared to manually performed quality tests, the proposed strategy of automation has advantages in convenience, rapidity and especially reproducibility avoiding any inaccuracies introduced by manually performing all steps of the complex sensor formation and testing sequence.

Amidine derived 1,3-diazabuta-1,3-dienes as potential antibacterial and antifungal agents

Several 1-aryl-2-phenyl-4-piperidino-4-thioalkyl-1,3-diazabuta-1,3-dienes were prepared by the treatment of N-arylimino isothiocyanate with piperidine followed by S-alkylation with alkyl iodides in the presence of dry acetone and potassium hydroxide. The constitution of the products was supported by IR, PMR and mass spectral study. The compounds synthesized were tested in in vitro against E. coli, S. aureus, P. aeruginosa, B. cereus and B. subtilis and fungal stains, Candida albicans and Aspergillus niger. Standard drugs were also tested under identical conditions for comparing the results.

Protein flexibility in ligand docking and virtual screening to protein kinases

The main complicating factor in structure-based drug design is receptor rearrangement upon ligand binding (induced fit). It is the induced fit that complicates cross-docking of ligands from different ligand-receptor complexes. Previous studies have shown the necessity to include protein flexibility in ligand docking and virtual screening. Very few docking methods have been developed to predict the induced fit reliably and, at the same time, to improve on discriminating between binders and non-binders in the virtual screening process. We present an algorithm called the ICM-flexible receptor docking algorithm (IFREDA) to account for protein flexibility in virtual screening. By docking flexible ligands to a flexible receptor, IFREDA generates a discrete set of receptor conformations, which are then used to perform flexible ligand-rigid receptor docking and scoring. This is followed by a merging and shrinking step, where the results of the multiple virtual screenings are condensed to improve the enrichment factor. In the IFREDA approach, both side-chain rearrangements and essential backbone movements are taken into consideration, thus sampling adequately the conformational space of the receptor, even in cases of large loop movements. As a preliminary step, to show the importance of incorporating protein flexibility in ligand docking and virtual screening, and to validate the merging and shrinking procedure, we compiled an extensive small-scale virtual screening benchmark of 33 crystal structures of four different protein kinases sub-families (cAPK, CDK-2, P38 and LCK), where we obtained an enrichment factor fold-increase of 1.85+/-0.65 using two or three multiple experimental conformations. IFREDA was used in eight protein kinase complexes and was able to find the correct ligand conformation and discriminate the correct conformations from the "misdocked" conformations solely on the basis of energy calculation. Five of the generated structures were used in the small-scale virtual screening stage and, by merging and shrinking the results with those of the original structure, we show an enrichment factor fold increase of 1.89+/-0.60, comparable to that obtained using multiple experimental conformations. Our cross-docking tests on the protein kinase benchmark underscore the necessity of incorporating protein flexibility in both ligand docking and virtual screening. The methodology presented here will be extremely useful in cases where few or no experimental structures of complexes are available, while some binders are known.

HierVLS hierarchical docking protocol for virtual ligand screening of large-molecule databases

To provide practical means for rapidly scanning the extensive experimental combinatorial chemistry libraries now available for high-throughput screening (HTS), it is essential to establish computational virtual ligand screening (VLS) techniques to rapidly identify out of a large library all active compounds against a particular protein target. Toward this goal we developed HierVLS, a fast hierarchical docking approach that starts with a coarse grain conformational search over a large number of configurations filtered with a fast but crude energy function, followed by a succession of finer grain levels, using successively more accurate but more expensive descriptions of the ligand-protein-solvent interactions to filter successively fewer cases. The final step of this procedure optimizes one configuration of the ligand in the protein site using our most accurate energy expression and description of the solvent, which would be impractical for all conformations and sites sampled in the coarse level. HierVLS is based on the HierDock approach, but rather than allowing an hour or more to determine the best binding site and energy for each ligands (as in HierDock), we have adapted our procedure so that it can lead to reliable results while using only 4 min (866 MHz Pentium III processor) per ligand. To validate the accuracy for HierVLS to predict the experimentally observed binding conformation, we considered 37 cocrystal structures comprising 11 target proteins. We find that HierVLS identifies the correct binding mode for all 37 cocrystals. In addition, the calculated binding energies correlate well with available experimental binding constants. To validate how well HierVLS can identify the correct ligand in an extensive library of decoys, we considered a library of over 10 000 molecules. HierVLS identifies 26 out of the 37 cases in the top 2% ranked by binding affinity among the 10 037 molecules. The failures result from either metal-containing sites on the protein or water-mediated ligand-protein interactions, which we anticipate can be solved within the constraints of practical VLS. We then applied HierVLS to screen a 55000-compound virtual library against the target protein-tyrosine phosphatase 1B (ptp1b). The top 250 compounds by binding affinity included all six ptp1b cocrystal ligands added to the library plus three other experimentally confirmed binders. The best (top 1) binder is an experimentally confirmed positive. We conclude that HierVLS is useful for selecting leads for a particular target out of large combinatorial databases.

The most potent organophosphorus inhibitors of leucine aminopeptidase. Structure-based design, chemistry, and activity

A new class of very potent inhibitors of cytosol leucine aminopeptidase (LAP), a member of the metalloprotease family, is described. The X-ray structure of bovine lens leucine aminopeptidase complexed with the phosphonic acid analogue of leucine (LeuP) was used for structure-based design of novel LAP inhibitors and for the analysis of their interactions with the enzyme binding site. The inhibitors were designed by modification of phosphonic group in the LeuP structure toward finding the substituents bound at the S' side of the enzyme. This resulted in two classes of compounds, the phosphonamidate and phosphinate dipeptide analogues, which were synthesized and evaluated as inhibitors of the enzyme. The in vitro kinetic studies for the phosphinate dipeptide analogues revealed that these compounds belong to the group of the most effective LAP inhibitors found so far. Their further modification at the P1 position resulted in more active inhibitors, hPheP[CH(2)]Phe and hPheP[CH(2)]Tyr (K(i) values 66 nM and 67 nM, respectively, for the mixture of four diastereomers). The binding affinities of these inhibitors toward the enzyme are the highest, if considering all compounds containing a phosphorus atom that mimic the transition state of the reaction catalyzed by LAP. To evaluate selectivity of the designed LAP inhibitors, additional tests toward aminopeptidase N (APN) were performed. The key feature, which determines their selectivity, is structure at the P1' position. Aromatic and aliphatic substituents placed at this position strongly interact with the LAP S1' binding pocket, while a significant increase in binding affinity toward APN was observed for compounds containing aromatic versus leucine side chains at the P1' position. The most selective inhibitor, hPheP[CH(2)]Leu, binds to LAP with 15 times higher affinity than to APN. One of the studied compounds, hPheP[CH(2)]Tyr, appeared to be very potent inhibitor of APN (K(i) = 36 nM for the mixture of four diastereomers). The most promising LAP inhibitors designed by computer-aided approach, the phosphonamidate dipeptide analogues, were unstable at pH below 12, because of the P-N bond decomposition, which excluded the possibility of determination of their binding affinities toward LAP.

Predictive crystallization of ribonuclease A via rapid screening of osmotic second virial coefficients

Important progress has been made in recent years toward developing a molecular-level understanding of protein phase behavior in terms of the osmotic second virial coefficient, a thermodynamic parameter that characterizes pairwise protein interactions. Yet there has been little practical application of this knowledge to the field of protein crystallization, largely because of the difficult and time-consuming nature of traditional techniques for characterizing protein interactions. Self-interaction chromatography has recently been proposed as a highly efficient method for measuring the osmotic second virial coefficient. The utility of the technique is examined in this work by characterizing virial coefficients for ribonuclease A under 59 solution conditions using several crystallization additives, including PEG, sodium chloride, ammonium sulfate, and propanol. The virial coefficient measurements show some counterintuitive trends and shed light on the previous difficulties in crystallizing ribonuclease A. Crystallization experiments at the corresponding solution conditions were conducted by using ultracentrifugal crystallization. Using this methodology, ribonuclease A crystals were obtained under conditions for which the virial coefficients fell within the "crystallization slot." Crystallographic characterization showed that the crystals diffract to high resolution. Metastable crystals were also obtained for conditions outside, but near, the "crystallization slot," and they could also be frozen and used to collect structural information.

Expression of a unique drug-resistant Hsp90 ortholog by the nematode Caenorhabditis elegans

In all species studied to date, the function of heat shock protein 90 (Hsp90), a ubiquitous and evolutionarily conserved molecular chaperone, is inhibited selectively by the natural product drugs geldanamycin (GA) and radicicol. Crystal structures of the N-terminal region of yeast and human Hsp90 have revealed that these compounds interact with the chaperone in a Bergerat-type adenine nucleotide-binding fold shared throughout the gyrase, Hsp90, histidine kinase mutL (GHKL) superfamily of adenosine triphosphatases. To better understand the consequences of disrupting Hsp90 function in a genetically tractable multicellular organism, we exposed the soil-dwelling nematode Caenorhabditis elegans to GA under a variety of conditions designed to optimize drug uptake. Mutations in the gene encoding C elegans Hsp90 affect larval viability, dauer development, fertility, and life span. However, exposure of worms to GA produced no discernable phenotypes, although the amino acid sequence of worm Hsp90 is 85% homologous to that of human Hsp90. Consistent with this observation, we found that solid phase-immobilized GA failed to bind worm Hsp90 from worm protein extracts or when translated in a rabbit reticulocyte lysate system. Further, affinity precipitation studies using chimeric worm-vertebrate fusion proteins or worm C-terminal truncations expressed in reticulocyte lysate revealed that the conserved nucleotide-binding fold of worm Hsp90 exhibits the novel ability to bind adenosine triphosphate but not GA. Despite its unusual GA resistance, worm Hsp90 appeared fully functional when expressed in a vertebrate background. It heterodimerized with its vertebrate counterpart and showed no evidence of compromising its essential cellular functions. Heterologous expression of worm Hsp90 in tumor cells, however, did not render them GA resistant. These findings provide new insights into the nature of unusual N-terminal nucleotide-binding fold of Hsp90 and suggest that target-related drug resistance is unlikely to emerge in patients receiving GA-like chemotherapeutic agents.

Reductionism and the search for structure-function relationships in antibody molecules

One of the claims of reductionism is that it can explain all the features of living systems by an analysis of their physico-chemical constituents. Such a claim disregards the existence in biological systems of emergent properties that do not exist in their isolated components but which allow autonomous organisms to be directively organized in a self-regulated and integrated manner. It is not possible to describe biological systems adequately without using functional language that is meaningless in the physical sciences. The description of biological functions is also an essential part of immunology and functional explanations are more useful than causal explanations also in this discipline. Since causality is not a relation between a material object and an event, the structure of an antibody cannot be the cause of its binding activity. When structure-function relationships are analysed, the search should be for correlations rather than for causal relations. Methods used to find correlations between the atomic structure of antibody binding sites and their binding activity are mostly based on mutagenesis studies. Since the effect of any mutation depends on the molecular context, it is usually very difficult to predict the effects of multiple mutations on antibody function. Our knowledge of the molecular basis of antigen-antibody recognition has led to the expectation that it may be possible to develop new vaccines using molecular design principles. Such unwarranted hopes arise because of a confusion between antigenicity and immunogenicity. Although knowledge of antibody structure is of little use in vaccine design, it may help to develop therapeutic inhibitors and antibodies effective in the passive immunotherapy of viral infection.

Predicting the structure of protein cavities created by mutation

To assist in the efficient design of protein cavities, we have developed a minimization strategy that can predict with accuracy the fate of cavities created by mutation. We first modelled, under different conditions, the structures of six T4 lysozyme and cytochrome c peroxidase mutants of known crystal structure (where long, hydrophobic, buried side chains have been replaced by shorter ones) by minimizing the virtual structures derived from the corresponding wild-type co-ordinates. An unconstrained pathway together with an all-atom atom representation and a steepest descent minimization yielded modelled structures with lower root mean square deviations (r.m.s.d) from the crystal structures than other conditions. To test whether the method developed was generally applicable to other mutations of the kind, we have then modelled eighteen additional T4 lysozyme, barnase and cytochrome c peroxidase mutants of known crystal structure. The models of both cavity expanding and cavity collapsing mutants closely fit their crystal structures (average r.m.s.d. 0.33 +/- 0.25 A, with only one poorer prediction: L121A). The structure of protein cavities generated by mutation can thus be confidently simulated by energy minimization regardless of the tendency of the cavity to collapse or to expand. We think this is favoured by the fact that the typical response observed in these proteins to cavity-creating mutations is to experience only a limited rearrangement.

Pitfalls of reductionism in the design of peptide-based vaccines

It is widely believed that all biological phenomena can be reduced to chemistry and physics. Such a reductionist view disregards the fact that complex biological systems have relational (also called emergent) properties that their constituents lack and that cannot be deduced or predicted from the properties of the isolated components. When the individual components of the immune system are studied in isolation, many interconnections are lost and it is not possible to understand how the system functions at the level of the organism as a whole. Our increasing knowledge of the antigenic structure of viral proteins has also been of little help for improving the immunogenicity of individual viral epitopes and for enhancing their capacity to elicit a protective immune response against viral infection. When molecular design principles are used to optimize the binding properties of a synthetic peptide epitope with respect to one neutralizing monoclonal antibody, this does not ensure that the peptide, when used as immunogen, will be able to induce neutralizing antibodies that protect against disease. A reductionist approach does not provide the information required for designing peptide immunogens that will elicit neutralizing rather than non-neutralizing antibody responses.

Design, docking, and evaluation of multiple libraries against multiple targets

We present a general approach to the design, docking, and virtual screening of multiple combinatorial libraries against a family of proteins. The method consists of three main stages: docking the scaffold, selecting the best substituents at each site of diversity, and comparing the resultant molecules within and between the libraries. The core "divide-and-conquer" algorithm for side-chain selection, developed from an earlier version (Sun et al., J Comp Aided Mol Design 1998;12:597-604), provides a way to explore large lists of substituents with linear rather than combinatorial time dependence. We have applied our method to three combinatorial libraries and three serine proteases: trypsin, chymotrypsin, and elastase. We show that the scaffold docking procedure, in conjunction with a novel vector-based orientation filter, reproduces crystallographic binding modes. In addition, the free-energy-based scoring procedure (Zou et al., J Am Chem Soc 1999;121:8033-8043) is able to reproduce experimental binding data for P1 mutants of macromolecular protease inhibitors. Finally, we show that our method discriminates between a peptide library and virtual libraries built on benzodiazepine and tetrahydroisoquinolinone scaffolds. Implications of the docking results for library design are explored.

Design of inhibitors of Ras--Raf interaction using a computational combinatorial algorithm

Drugs that inhibit important protein-protein interactions are hard to find either by screening or rational design, at least so far. Most drugs on the market that target proteins today are therefore aimed at well-defined binding pockets in proteins. While computer-aided design is widely used to facilitate the drug discovery process for binding pockets, its application to the design of inhibitors that target the protein surface initially seems to be limited because of the increased complexity of the task. Previously, we had started to develop a computational combinatorial design approach based on the well-known 'multiple copy simultaneous search' (MCSS) procedure to tackle this problem. In order to identify sequence patterns of potential inhibitor peptides, a three-step procedure is employed: first, using MCSS, the locations of specific functional groups on the protein surface are identified; second, after constructing the peptide main chain based on the location of favorite locations of N-methylacetamide groups, functional groups corresponding to amino acid side chains are selected and connected to the main chain C(alpha) atoms; finally, the peptides generated in the second step are aligned and probabilities of amino acids at each position are calculated from the alignment scheme. Sequence patterns of potential inhibitors are determined based on the propensities of amino acids at each C(alpha) position. Here we report the optimization of inhibitor peptides using the sequence patterns determined by our method. Several short peptides derived from our prediction inhibit the Ras--Raf association in vitro in ELISA competition assays, radioassays and biosensor-based assays, demonstrating the feasibility of our approach. Consequently, our method provides an important step towards the development of novel anti-Ras agents and the structure-based design of inhibitors of protein--protein interactions.

The structure of inosine 5'-monophosphate dehydrogenase and the design of novel inhibitors

The enzyme IMPDH is a homotetramer of approximately 55 kDa subunits and consists of a (beta/alpha)(8) barrel core domain and a smaller subdomain. The active site has binding pockets for the two substrates IMP and NAD. The enzymatic reaction of oxidation of IMP to XMP proceeds through a covalent mechanism involving an active site cysteine residue. This enzyme is a target for immunosuppressive agents because it catalyzes a key step in purine nucleotide biosynthesis which is important for the proliferation of lymphocytes. Several X-ray structures of inhibitors bound to IMPDH have been published. The uncompetitive IMPDH inhibitor MPA is the active metabolite of the immunosuppressive agent mycophenolate mofetil (CellCept(R)) which is approved for the prevention of acute rejection after kidney and heart transplantation. The bicyclic ring system of MPA packs underneath the hypoxanthine ring of XMP*, thereby trapping this covalent intermediate of the enzymatic reaction. Ribavirin monophosphate, the active metabolite of the antiviral agent ribavirin, is a substrate mimic of IMP. The structure of the two inhibitors 6-Cl-IMP and SAD binding in the IMP and NAD pockets of IMPDH, respectively, gives information for the binding mode of the di-nucleotide cofactor to the enzyme. At Vertex Pharmaceuticals a structure-based drug design program for the design of IMPDH inhibitors was initiated. Several new lead compound classes unrelated to other IMPDH inhibitors were found. Integrating structural information into an iterative drug-design process led to the design of VX-497. VX-497 is a potent uncompetitive enzyme inhibitor of IMPDH. The phenyl-oxazole moiety of the molecule packs underneath XMP*, analogous to MPA. VX-497 also makes several new interactions that are not observed in the binding of MPA. VX-497 is a potent immunosuppressive agent in vitro and in vivo. A Phase I clinical trial has been successfully concluded and the compound is currently in Phase II trials in psoriasis and hepatitis C. The rapid progress from initiation of the drug design program to a compound entering clinical trials illustrates the power of structure-based drug design to accelerate the drug discovery process. The structural information on IMPDH has also significantly increased our knowledge about the mechanistic details of this fascinating enzyme.

Structure-based inhibitor design

Time and costs associated with the discovery of new drugs have been significantly reduced by enzyme structure-based approaches to the discovery of new chemotherapeutic agents. However, fundamental components of the overall approach continue to rely on technologies which, by their nature, involve relatively random processes (i.e., combinatorial chemistry and high-throughput screening). Thus, the efficiency of the drug discovery process potentially could be further improved through better use of structural information. In this regard, three-dimensional structures of enzymes are now being solved at high resolution and/or in conformations that provide data that should be more useful for inhibitor design or discovery. Scientists are beginning to appreciate the importance of water as a possible competitor of inhibitors for binding to target enzymes. New computational algorithms are improving the efficiency of identifying flexible inhibitors from among the large numbers of compounds in chemical databases. Also, tools of molecular genetics together with structures of target enzymes are likely to be used more frequently in dealing with the development of resistance to novel chemotherapeutic agents. Instead of detailing success stories in structure-based drug discovery, the following article considers how future efforts to discover or design new drugs may increasingly rely on information about molecular targets and less on data acquired via approaches involving random methodologies.

Structure of acetylcholinesterase complexed with (-)-galanthamine at 2.3 A resolution

(-)-Galanthamine (GAL), an alkaloid from the flower, the common snowdrop (Galanthus nivalis), shows anticholinesterase activity. This property has made GAL the target of research as to its effectiveness in the treatment of Alzheimer's disease. We have solved the X-ray crystal structure of GAL bound in the active site of Torpedo californica acetylcholinesterase (TcAChE) to 2.3 A resolution. The inhibitor binds at the base of the active site gorge of TcAChE, interacting with both the choline-binding site (Trp-84) and the acyl-binding pocket (Phe-288, Phe-290). The tertiary amine group of GAL does not interact closely with Trp-84; rather, the double bond of its cyclohexene ring stacks against the indole ring. The tertiary amine appears to make a non-conventional hydrogen bond, via its N-methyl group, to Asp-72, near the top of the gorge. The hydroxyl group of the inhibitor makes a strong hydrogen bond (2.7 A) with Glu-199. The relatively tight binding of GAL to TcAChE appears to arise from a number of moderate to weak interactions with the protein, coupled to a low entropy cost for binding due to the rigid nature of the inhibitor.

Molecular design versus empirical discovery in peptide-based vaccines. Coming to terms with fuzzy recognition sites and ill-defined structure-function relationships in immunology

In view of our increased understanding of the molecular basis of immunological recognition, it is commonly believed that it should be possible to apply molecular design strategies to the development of peptide-based vaccines. The stated aim is to transform the development of a vaccine from a trial and error empirical operation into a so-called rational, structure-based process. In the present review, it is argued that it is misleading to oppose rational and empirical approaches in vaccine research since both are needed in the practice of experimental science. Many reasons are given for the view that the molecular design of synthetic vaccines is not a realistic scientific enterprise. The capacity of a peptide to induce a protective immune response depends on many extrinsic factors and regulatory mechanisms of the recipient host which are not amenable to molecular design of the peptide immunogen. It seems safe to predict that the development of peptide-based vaccines will continue to be driven by empirical discovery rather than by so-called rational design.

A rapid method to identify exo-protease inhibitors

A new approach to the evaluation of exo-protease inhibitor candidates is presented. The application of new water-soluble substrates that release organic-soluble fluorescent groups upon proteolytic cleavage allows amplification of the assay signal via concentration of the cleavage product. A combinatorial library of disubstituted xanthenes designed to resemble a known inhibitor was screened and a new HLE inhibitor (Ki = 79 microM) was identified.

A method for computational combinatorial peptide design of inhibitors of Ras protein

A computational combinatorial approach is proposed for the design of a peptide inhibitor of Ras protein. The procedure involves three steps. First, a 'Multiple Copy Simultaneous Search' identifies the location of specific functional groups on the Ras surface. This search method allowed us to identify an important binding surface consisting of two beta strands (residues 5-8 and 52-56), in addition to the well known Ras effector loop and switch II region. The two beta strands had not previously been reported to be involved in Ras-Raf interaction. Second, after constructing the peptide inhibitor chain based on the location of N-methylacetamide (NMA) minima, functional groups are selected and connected to the main chain Calpha atom. This step generates a number of possible peptides with different sequences on the Ras surface. Third, potential inhibitors are designed based on a sequence alignment of the peptides generated in the second step. This computational approach reproduces the conserved pattern of hydrophobic, hydrophilic and charged amino acids identified from the Ras effectors. The advantages and limitations of this approach are discussed.