Structure-based drug design

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

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.

Characterization of monobody scaffold interactions with ligand via force spectroscopy and steered molecular dynamics

Monobodies are antibody alternatives derived from fibronectin that are thermodynamically stable, small in size, and can be produced in bacterial systems. Monobodies have been engineered to bind a wide variety of target proteins with high affinity and specificity. Using alanine-scanning mutagenesis simulations, we identified two scaffold residues that are critical to the binding interaction between the monobody YS1 and its ligand, maltose-binding protein (MBP). Steered molecular dynamics (SMD) simulations predicted that the E47A and R33A mutations in the YS1 scaffold substantially destabilize the YS1-MBP interface by reducing the bond rupture force and the lifetime of single hydrogen bonds. SMD simulations further indicated that the R33A mutation weakens the hydrogen binding between all scaffold residues and MBP and not just between R33 and MBP. We validated the simulation data and characterized the effects of mutations on YS1-MBP binding by using single-molecule force spectroscopy and surface plasmon resonance. We propose that interfacial stability resulting from R33 of YS1 stacking with R344 of MBP synergistically stabilizes both its own bond and the interacting scaffold residues of YS1. Our integrated approach improves our understanding of the monobody scaffold interactions with a target, thus providing guidance for the improved engineering of monobodies.

A brief history of macromolecular crystallography, illustrated by a family tree and its Nobel fruits

As a contribution to the celebration of the year 2014, declared by the United Nations to be 'The International Year of Crystallography', the FEBS Journal is dedicating this issue to papers showcasing the intimate union between macromolecular crystallography and structural biology, both in historical perspective and in current research. Instead of a formal editorial piece, by way of introduction, this review discusses the most important, often iconic, achievements of crystallographers that led to major advances in our understanding of the structure and function of biological macromolecules. We identified at least 42 scientists who received Nobel Prizes in Physics, Chemistry or Medicine for their contributions that included the use of X-rays or neutrons and crystallography, including 24 who made seminal discoveries in macromolecular sciences. Our spotlight is mostly, but not only, on the recipients of this most prestigious scientific honor, presented in approximately chronological order. As a summary of the review, we attempt to construct a genealogy tree of the principal lineages of protein crystallography, leading from the founding members to the present generation.

Structural ensemble in computational drug screening

IMPORTANCE OF THE FIELD

Structure-based in silico drug screening is now widely used in drug development projects. Structure-based in silico drug screening is generally performed using a protein-compound docking program and docking scoring function. Many docking programs have been developed over the last 2 decades, but their prediction accuracy remains insufficient.

AREAS COVERED IN THIS REVIEW

This review highlights the recent progress of the post-processing of protein-compound complexes after docking.

WHAT THE READER WILL GAIN

These methods utilize ensembles of docking poses of compounds to improve the prediction accuracy for the ligand-docking pose and screening results. While the individual docking poses are not reliable, the free energy surface or the most probable docking pose can be estimated from the ensemble of docking poses.

TAKE HOME MESSAGE

The protein-compound docking program provides an arbitral rather than a canonical ensemble of docking poses. When the ensemble of docking poses satisfies the canonical ensemble, we can discuss how these post-docking analysis methods work and fail. Thus, improvements to the docking software will be needed in order to generate well-defined ensembles of docking poses.

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.

Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening

We developed a new method to evaluate the distances and similarities between receptor pockets or chemical compounds based on a multi-receptor versus multi-ligand docking affinity matrix. The receptors were classified by a cluster analysis based on calculations of the distance between receptor pockets. A set of low homologous receptors that bind a similar compound could be classified into one cluster. Based on this line of reasoning, we proposed a new in silico screening method. According to this method, compounds in a database were docked to multiple targets. The new docking score was a slightly modified version of the multiple active site correction (MASC) score. Receptors that were at a set distance from the target receptor were not included in the analysis, and the modified MASC scores were calculated for the selected receptors. The choice of the receptors is important to achieve a good screening result, and our clustering of receptors is useful to this purpose. This method was applied to the analysis of a set of 132 receptors and 132 compounds, and the results demonstrated that this method achieves a high hit ratio, as compared to that of a uniform sampling, using a receptor-ligand docking program, Sievgene, which was newly developed with a good docking performance yielding 50.8% of the reconstructed complexes at a distance of less than 2 A RMSD.

In-silico drug screening method based on the protein–compound affinity matrix using the factor selection technique

We have developed a new in-silico drug screening method, a modified version of a docking score index (DSI) method, based on a protein-compound docking affinity matrix. By using this method, the docking scores are converted to the docking score indexes by the principal component analysis (PCA) method and each compound is projected into a PCA space. In this study, we propose a method to select a set of suitable principal component axes and evaluate the database enrichment for 12 target proteins. This method selects the new active compounds or hits, which are close to the known active compounds, thereby enhancing the database enrichment.

Finding ligands for G protein-coupled receptors based on the protein–compound affinity matrix

We developed a novel method of identifying new active ligands based on information related to known active compounds using protein-compound docking simulations, even when the tertiary structure of the actual target receptor protein is unknown. This method was used to find ligands of G protein-coupled receptors (GPCRs), i.e., agonists and antagonists of histamine, adrenaline, serotonin and dopamine receptors. The principal component analysis (PCA) method was applied to the protein-compound affinity matrix, which was given by thorough docking calculations between sets of many protein pockets and chemical compounds. The set of protein pockets did not necessary include the target protein. Each compound was depicted as a point in the PCA space. Compounds in a sphere, whose center was set to the known active compound in the multi-dimensional PCA space or to the average position of several known active compounds, were selected as candidate-hit compounds. Our method was found to be effective for finding the ligands of GPCRs based on known native ligands, even when only the soluble protein structures were used in the docking simulations.

Noise reduction method for molecular interaction energy: application to in silico drug screening and in silico target protein screening

We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.

An Efficient in Silico Screening Method Based on the Protein−Compound Affinity Matrix and Its Application to the Design of a Focused Library for Cytochrome P450 (CYP) Ligands

A new method has been developed to design a focused library based on available active compounds using protein-compound docking simulations. This method was applied to the design of a focused library for cytochrome P450 (CYP) ligands, not only to distinguish CYP ligands from other compounds but also to identify the putative ligands for a particular CYP. Principal component analysis (PCA) was applied to the protein-compound affinity matrix, which was obtained by thorough docking calculations between a large set of protein pockets and chemical compounds. Each compound was depicted as a point in the PCA space. Compounds that were close to the known active compounds were selected as candidate hit compounds. A machine-learning technique optimized the docking scores of the protein-compound affinity matrix to maximize the database enrichment of the known active compounds, providing an optimized focused library.

Multiple target screening method for robust and accurate in silico ligand screening

We developed a new in silico multiple target screening (MTS) method, based on a multi-receptor versus multi-ligand docking affinity matrixes, and examined its robustness against changes in the scoring system. According to this method, compounds in a database are docked to multiple proteins. The compounds among these proteins that are likely bind to the target protein are selected as the members of the candidate-hit compound group. Then, the compounds in the group are sorted into descending order using the docking score: the first (n-th) compound is expected to be the most (n-th) probable hit compound. This method was applied to the analysis of a set of 142 receptors and 142 compounds using a receptor-ligand docking program, Sievgene [Y. Fukunishi, Y. Mikami, H. Nakamura, Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening, J. Mol. Graphics Modelling, 24 (2005) 34-45], and the results demonstrated that this method achieves a high hit ratio compared to uniform sampling. We prepared two new scores: the DeltaG score, designed to reproduce the protein-ligand binding free energy, and the hit-optimized score, designed to maximize the hit ratio of in silico screening. Using the Sievgene docking score, DeltaG score and hit-optimized score, the MTS method is more robust than the multiple active-site correction scoring method [G.P.A. Vigers, J.P. Rizzi, Multiple active site corrections for docking and virtual screening, J. Med. Chem., 47 (2004) 80-89].

A virtual active compound produced from the negative image of a ligand-binding pocket, and its application to in-silico drug screening

We developed a new structure-based in-silico screening method using a negative image of a ligand-binding pocket and a multi-protein-compound interaction matrix. Based on the structure of the ligand pocket of the target protein, we designed a negative image, which consists of virtual atoms whose radii are close to those of carbon atoms. The virtual atoms fit the pocket ideally and achieve an optimal Coulomb interaction. A protein-compound docking program calculates the protein-compound interaction matrix for many proteins and many compounds including the negative image, which can be treated as a virtual compound. With specific attention to a vector of docking scores for a single compound with many proteins, we selected a compound whose score vector was similar to that of the negative image as a candidate hit compound. This method was applied to representative target proteins and showed high database enrichment with a relatively quick procedure.

Classification of Chemical Compounds by Protein−Compound Docking for Use in Designing a Focused Library

We developed a new method for the classification of chemical compounds and protein pockets and applied it to a random screening experiment for macrophage migration inhibitory factor (MIF). The principal component analysis (PCA) method was applied to the protein-compound interaction matrix, which was given by thorough docking calculations between a set of many protein pockets and chemical compounds. Each compound and protein pocket was depicted as a point in the PCA spaces of compounds and proteins, respectively. This method was applied to distinguish active compounds from negative compounds of MIF. A random screening experiment for MIF was performed, and our method revealed that the active compounds were localized in the PCA space of compounds, while the negative compounds showed a wide distribution. Furthermore, protein pockets, which bind similar compounds, were classified and were found to form a cluster in the PCA space.

Folded conformation of an immunostimulating tetrapeptide rigin: high temperature molecular dynamics simulation study

Employing high temperature quenched molecular dynamics (QMD) stimulations the conformational energy space of an immunostimulating tetrapeptide rigin: H-Gly341-Gln-Pro-Arg344-OH, is explored. Using distance dependent dielectric (epsilon =r(ij)) 31 different low energy starting structures with identical sequence were computed for their conformational preferences. According to the hypothesis of O'Connors et al. [J. Med. Chem. 35 (1992), 2870], 83 low-energy conformers resulted from unrestrained molecular dynamics (MD) simulations, could be classified into two energy minimized families: A and B, comprised of 64 (Pro C(gamma)-endo orientation) and 19 (Pro C(gamma)-exo orientation) structures, respectively. An examination of these families revealed the existence of a remarkably similar folded backbone conformation: torsion angles being phi(i+1) approximately -65 degrees, psi(i+1) approximately -65 degrees, phi(i+2) approximately -65 degrees, psi(i+2) approximately -60 degrees, characterizing a distorted type III beta-turn structure across the central Gln-Pro segment. The folded conformation of rigin is devoid of a classical 1 <-- 4 intra-molecular hydrogen bond nevertheless, the conformation is stabilized by an effective 'salt-bridge', i.e., Gly H(3)N(+)...C(alpha)OO(-) Arg interaction. Surprisingly, in both the families the unusual folded side-chain dispositions of the Gln residue favor the formation of a unique intra-residue 'main-chain to side-chain' H-bond, i.e., N(alpha)-H...N(epsilon) interaction, encompassing a seven-membered ring motif. The conformational attributes may be valuable in de novo construction of structure-based drug candidates having sufficient stimulating activity.

Mapping of stereoselective recognition sites on human serum transferrin by capillary electrophoresis and molecular modelling

Stereoselective recognition of chiral compounds can be used for mapping of surface interaction sites on proteins. Iron-free human serum transferrin is a suitable chiral selector in capillary electrophoresis used in native form in solution. Separation of optical isomers of tryptophan-methylester, tryptophan-ethylester and tryptophan-butylester and various drugs were studied in capillary zone electrophoresis applying a distinct transferrin zone prior to sample injection. Changes in the electrophoretic patterns (i.e., in the migration properties) of the molecules reflected the possible interactions with the protein. The tryptophan derivatives and eight drugs possessed stereoselective interactions, seven drugs showed interactions without appreciable chiral separation, and the others did not present any direct complexation with the protein molecules. Molecular modelling was performed to characterize the binding areas at the iron binding site of iron-free transferrin. The docking of tryptophan derivatives on transferrin showed that the R-enantiomers possess a stronger complexation with transferrin, whereas the S-enantiomers are bound by weaker interactions, which is in excellent agreement with the capillary electrophoresis results, where the R-enantiomers were always retarded stronger by transferrin. A ranking of drugs by the lipo score parameter of the docking shows an accordance with the stereoselective interactions by the protein.

Structure-based design and in-parallel synthesis of inhibitors of AmpC beta-lactamase

BACKGROUND

Group I beta-lactamases are a major cause of antibiotic resistance to beta-lactams such as penicillins and cephalosporins. These enzymes are only modestly affected by classic beta-lactam-based inhibitors, such as clavulanic acid. Conversely, small arylboronic acids inhibit these enzymes at sub-micromolar concentrations. Structural studies suggest these inhibitors bind to a well-defined cleft in the group I beta-lactamase AmpC; this cleft binds the ubiquitous R1 side chain of beta-lactams. Intriguingly, much of this cleft is left unoccupied by the small arylboronic acids.

RESULTS

To investigate if larger boronic acids might take advantage of this cleft, structure-guided in-parallel synthesis was used to explore new inhibitors of AmpC. Twenty-eight derivatives of the lead compound, 3-aminophenylboronic acid, led to an inhibitor with 80-fold better binding (2; K(i) 83 nM). Molecular docking suggested orientations for this compound in the R1 cleft. Based on the docking results, 12 derivatives of 2 were synthesized, leading to inhibitors with K(i) values of 60 nM and with improved solubility. Several of these inhibitors reversed the resistance of nosocomial Gram-positive bacteria, though they showed little activity against Gram-negative bacteria. The X-ray crystal structure of compound 2 in complex with AmpC was subsequently determined to 2.1 A resolution. The placement of the proximal two-thirds of the inhibitor in the experimental structure corresponds with the docked structure, but a bond rotation leads to a distinctly different placement of the distal part of the inhibitor. In the experimental structure, the inhibitor interacts with conserved residues in the R1 cleft whose role in recognition has not been previously explored.

CONCLUSIONS

Combining structure-based design with in-parallel synthesis allowed for the rapid exploration of inhibitor functionality in the R1 cleft of AmpC. The resulting inhibitors differ considerably from beta-lactams but nevertheless inhibit the enzyme well. The crystal structure of 2 (K(i) 83 nM) in complex with AmpC may guide exploration of a highly conserved, largely unexplored cleft, providing a template for further design against AmpC beta-lactamase.

Developing a dynamic pharmacophore model for HIV-1 integrase

We present the first receptor-based pharmacophore model for HIV-1 integrase. The development of "dynamic" pharmacophore models is a new method that accounts for the inherent flexibility of the active site and aims to reduce the entropic penalties associated with binding a ligand. Furthermore, this new drug discovery method overcomes the limitation of an incomplete crystal structure of the target protein. A molecular dynamics (MD) simulation describes the flexibility of the uncomplexed protein. Many conformational models of the protein are saved from the MD simulations and used in a series of multi-unit search for interacting conformers (MUSIC) simulations. MUSIC is a multiple-copy minimization method, available in the BOSS program; it is used to determine binding regions for probe molecules containing functional groups that complement the active site. All protein conformations from the MD are overlaid, and conserved binding regions for the probe molecules are identified. Those conserved binding regions define the dynamic pharmacophore model. Here, the dynamic model is compared to known inhibitors of the integrase as well as a three-point, ligand-based pharmacophore model from the literature. Also, a "static" pharmacophore model was determined in the standard fashion, using a single crystal structure. Inhibitors thought to bind in the active site of HIV-1 integrase fit the dynamic model but not the static model. Finally, we have identified a set of compounds from the Available Chemicals Directory that fit the dynamic pharmacophore model, and experimental testing of the compounds has confirmed several new inhibitors.

Macromolecular crystallography as a tool for investigating drug, enzyme and receptor interactions

1. Protein crystallography is an essential tool for the discovery and investigation of pharmacological interactions at the molecular level. It allows investigators to directly visualize the three-dimensional structures of proteins, including enzymes, receptors and hormones. 2. Increasingly, knowledge of these interactions is being used in the drug-discovery process. This is popularly called structure-based drug design. The desired drug could be an enzyme inhibitor or an agonist that mimics endogenous transmitters or hormones. 3. Once the 3-D structure of a pharmacologically relevant target is known, computational processes can be used to search databases of compounds to identify ones that may interact strongly with the target. Lead compounds can be improved using the 3-D structure of the complex of the lead compound and its biological target. 4. The present review describes the processes involved in the determination of a structure by means of protein crystallography and the use of structures in the drug-discovery process. A number of successful examples of structure-based drug design are described. The limitations of the techniques are discussed.

Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking

We report on a test of FLEXX, a fully automatic docking tool for flexible ligands, on a highly diverse data set of 200 protein-ligand complexes from the Protein Data Bank. In total 46.5% of the complexes of the data set can be reproduced by a FLEXX docking solution at rank 1 with an rms deviation (RMSD) from the observed structure of less than 2 A. This rate rises to 70% if one looks at the entire generated solution set. FLEXX produces reliable results for ligands with up to 15 components which can be docked in 80% of the cases with acceptable accuracy. Ligands with more than 15 components tend to generate wrong solutions more often. The average runtime of FLEXX on this test set is 93 seconds per complex on a SUN Ultra-30 workstation. In addition, we report on "cross-docking" experiments, in which several receptor structures of complexes with identical proteins have been used for docking all cocrystallized ligands of these complexes. In most cases, these experiments show that FLEXX can acceptably dock a ligand into a foreign receptor structure. Finally we report on screening runs of ligands out of a library with 556 entries against ten different proteins. In eight cases FLEXX is able to find the original inhibitor within the top 7% of the total library.

Crystal structure of the potent natural product inhibitor balanol in complex with the catalytic subunit of cAMP-dependent protein kinase

Endogenous protein kinase inhibitors are essential for a wide range of physiological functions. These endogenous inhibitors may mimic peptide substrates as in the case of the heat-stable protein kinase inhibitor (PKI), or they may mimic nucleotide triphosphates. Natural product inhibitors, endogenous to the unique organisms producing them, can be potent exogenous inhibitors against foreign protein kinases. Balanol is a natural product inhibitor exhibiting low nanomolar Ki values against serine and threonine specific kinases, while being ineffective against protein tyrosine kinases. To elucidate balanol's specific inhibitory effects and provide a basis for understanding inhibition-regulated biological processes, a 2.1 A resolution crystal structure of balanol in complex with cAMP-dependent protein kinase (cAPK) was determined. The structure reveals conserved binding regions and displays extensive complementary interactions between balanol and conserved cAPK residues. This report describes the structure of a protein kinase crystallized with a natural ATP mimetic in the absence of metal ions and peptide inhibitor.

Protein structure prediction and design

Proteins have a unique native conformation, which can be proven in many instances to be determined by the amino acid sequence alone. The folding problem, that is the understanding of how the amino acid sequence directs folding, is still unsolved, despite more than 30 years of effort. However, many new methods have appeared in the past few years. This chapter describes the different principles underlying them and tries to give an overview of their successes and pitfalls.

Mechanism-based screening: discovery of the next generation of anthelmintics depends upon more basic research

The therapeutic arsenal for the control of helminth infections contains only a few chemical classes. The development and spread of resistance has eroded the utility of most currently available anthelmintics, at least for some indications, and is a constant threat to further reduce the options for treatment. Discovery and development of novel anthelmintic templates is strategically necessary to preserve the economic and health advantages now gained through chemotherapy. As the costs of development escalate, the question of how best to discover new drugs becomes paramount. Although random screening in infected animals led to the discovery of all currently available anthelmintics, cost constraints and a perception of diminishing returns require new approaches. Taking a cue from drug discovery programmes for human illnesses, we suggest that mechanism-based screening will provide the next generation of anthelmintic molecules. Critical to success in this venture will be the exploitation of the Caenorhabditis elegans genome through bioinformatics and genetic technologies. The greatest obstacle to success in this endeavour is the paucity of information available about the molecular physiology of helminths, making the choice of a discovery target a risky proposition.

Molecular mechanics calculations of proteins. Comparison of different energy minimization strategies

A general strategy for performing energy minimization of proteins using the SYBYL molecular modelling program has been developed. The influence of several variables including energy minimization procedure, solvation, dielectric function and dielectric constant have been investigated in order to develop a general method, which is capable of producing high quality protein structures. Avian pancreatic polypeptide (APP) and bovine pancreatic phospholipase A2 (BP PLA2) were selected for the calculations, because high quality X-ray structures exist and because all classes of secondary structure are represented in the structures. The energy minimized structures were evaluated relative to the corresponding X-ray structures. The overall similarity was checked by calculating RMS distances for all atom positions. Backbone conformation was checked by Ramachandran plots and secondary structure elements evaluated by the length on hydrogen bonds. The dimensions of active site in BP PLA2 is very dependent on electrostatic interactions, due to the presence of the positively charged calcium ion. Thus, the distances between calcium and the calcium-coordinating groups were used as a quality index for this protein. Energy minimized structures of the trimeric PLA2 from Indian cobra (N.n.n. PLA2) were used for assessing the impact of protein-protein interactions. Based on the above mentioned criteria, it could be concluded that using the following conditions: Dielectric constant epsilon = 4 or 20; a distance dependent dielectric function and stepwise energy minimization, it is possible to reproduce X-ray structures very accurately without including explicit solvent molecules.

Multiple automatic base selection: protein-ligand docking based on incremental construction without manual intervention

A possible way of tackling the molecular docking problem arising in computer-aided drug design is the use of the incremental construction method. This method consists of three steps: the selection of a part of a molecule, a so-called base fragment, the placement of the base fragment into the active site of a protein, and the subsequent reconstruction of the complete drug molecule. Assuming that a part of a drug molecule is known, which is specific enough to be a good base fragment, the method is proven to be successful for a large set of docking examples. In addition, it leads to the fastest algorithms for flexible docking published so far. In most real-world applications of docking, large sets of ligands have to be tested for affinity to a given protein. Thus, manual selection of a base fragment is not practical. On the other hand, the selection of a base fragment is critical in that only few selections lead to a low-energy structure. We overcome this limitation by selecting a representative set of base fragments instead of a single one. In this paper, we present a set of rules and algorithms to automate this selection. In addition, we extend the incremental construction method to deal with multiple fragmentations of the drug molecule. Our results show that with multiple automated base selection, the quality of the docking predictions is almost as good as with one manually preselected base fragment. In addition, the set of solutions is more diverse and alternative binding modes with low scores are found. Although the run time of the overall algorithm increases, the method remains fast enough to search through large ligand data sets.

PRO_SELECT: combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology

This paper describes a novel methodology, PRO_SELECT, which combines elements of structure-based drug design and combinatorial chemistry to create a new paradigm for accelerated lead discovery. Starting with a synthetically accessible template positioned in the active site of the target of interest, PRO_SELECT employs database searching to generate lists of potential substituents for each substituent position on the template. These substituents are selected on the basis of their being able to couple to the template using known synthetic routes and their possession of the correct functionality to interact with specified residues in the active site. The lists of potential substituents are then screened computationally against the active site using rapid algorithms. An empirical scoring function, correlated to binding free energy, is used to rank the substituents at each position. The highest scoring substituents at each position can then be examined using a variety of techniques and a final selection is made. Combinatorial enumeration of the final lists generates a library of synthetically accessible molecules, which may then be prioritized for synthesis and assay. The results obtained using PRO_SELECT to design thrombin inhibitors are briefly discussed.

The statistical-thermodynamic basis for computation of binding affinities: a critical review

Although the statistical thermodynamics of noncovalent binding has been considered in a number of theoretical papers, few methods of computing binding affinities are derived explicitly from this underlying theory. This has contributed to uncertainty and controversy in certain areas. This article therefore reviews and extends the connections of some important computational methods with the underlying statistical thermodynamics. A derivation of the standard free energy of binding forms the basis of this review. This derivation should be useful in formulating novel computational methods for predicting binding affinities. It also permits several important points to be established. For example, it is found that the double-annihilation method of computing binding energy does not yield the standard free energy of binding, but can be modified to yield this quantity. The derivation also makes it possible to define clearly the changes in translational, rotational, configurational, and solvent entropy upon binding. It is argued that molecular mass has a negligible effect upon the standard free energy of binding for biomolecular systems, and that the cratic entropy defined by Gurney is not a useful concept. In addition, the use of continuum models of the solvent in binding calculations is reviewed, and a formalism is presented for incorporating a limited number of solvent molecules explicitly.

Enzyme flexibility, solvent and 'weak' interactions characterize thrombin-ligand interactions: implications for drug design

BACKGROUND

The explosive growth in the rate of X-ray determination of protein structures is fuelled largely by the expectation that structural information will be useful for pharmacological and biotechnological applications. For example, there have been intensive efforts to develop orally administrable antithrombotic drugs using information about the crystal structures of blood coagulation factors, including thrombin. Most of the low molecular weight thrombin inhibitors studied so far are based on arginine and benzamidine. We sought to expand the database of information on thrombin-inhibitor binding by studying new classes of inhibitors.

RESULTS

We report the structures of three new inhibitors complexed with thrombin, two based on 4-aminopyridine and one based on naphthamidine. We observe several geometry changes in the protein main chain and side chains which accompany inhibitor binding. The two inhibitors based on 4-aminopyridine bind in notably different ways: one forms a water-mediated hydrogen bond to the active site Ser195, the other induces a rotation of the Ser214-Trp215 peptide plane that is unprecedented in thrombin structures. These binding modes also differ in their 'weak' interactions, including CH-O hydrogen bonds and interactions between water molecules and aromatic pi-clouds. Induced-fit structural changes were also seen in the structure of the naphthamidine inhibitor complex.

CONCLUSIONS

Protein flexibility and variable water structures are essential elements in protein-ligand interactions. Ligand design strategies that fail to take this into account may overlook or underestimate the potential of lead structures. Further, the significance of 'weak' interactions must be considered both in crystallographic refinement and in analysis of binding mechanisms.

Computational methods for biomolecular docking

With the rapidly increasing amount of molecular biological data available, the computer-based analysis of molecular interactions becomes more and more feasible. Methods for computer-aided molecular docking have to include a reasonably accurate model of energy and must be able to deal with the combinatorial complexity incurred by the molecular flexibility of the docking partners. In both respects, recent years have seen substantial progress.

The role of structure-based ligand design and molecular modelling in drug discovery

Structure-based ligand design is a technique that is used in the initial stages of a drug development programme. The role of various computational methods in the characterization of the chemical properties and behaviour of molecular systems is discussed. The determination of the three-dimensional properties of small molecules and macromolecular receptor structures is a core activity in the efforts towards a better understanding of structure-activity relationships.

Prospects for rational approaches to anthelmintic discovery

Rational approaches to anthelmintic discovery include the design of screens for compounds directed at specific proteins in helminths that are pharmacologically distinguishable from their vertebrate homologues. The existence of several anthelmintics that selectively target the neuromusculature of helminths (e.g. levamisole, ivermectin, praziquantel, metrifonate), together with recent basic research in helminth physiology, have contributed to the recognition that neurobiology distinguishes these organisms from their vertebrate hosts. In this survey, we focus on mechanism-based screening and its application to anthelmintic discovery, with particular emphasis on targets in the neuromusculature of helminths. Few of these proteins have been exploited in chemotherapy. However, recent studies in comparative pharmacology and molecular biology, including the C. elegans genome project, have provided insights on potential new targets and, in some cases, molecular probes useful for their incorporation in mechanism-based screens.

SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions

The SURFNET program generates molecular surfaces and gaps between surfaces from 3D coordinates supplied in a PDB-format file. The gap regions can correspond to the voids between two or more molecules, or to the internal cavities and surface grooves within a single molecule. The program is particularly useful in clearly delineating the regions of the active site of a protein. It can also generate 3D contour surfaces of the density distributions of any set of 3D data points. All output surfaces can be viewed interactively, along with the molecules or data points in question, using some of the best-known molecular modeling packages. In addition, PostScript output is available, and the generated surfaces can be rendered using various other graphics packages.

PRO_LIGAND: an approach to de novo molecular design. 4. Application to the design of peptides

In some instances, peptides can play an important role in the discovery of lead compounds. This paper describes the peptide design facility of the de novo drug design package, PRO_LIGAND. The package provides a unified framework for the design of peptides that are similar or complementary to a specified target. The approach uses single amino acid residues, selected from preconstructed libraries of different residues and conformations, and places them on top of predefined target interaction sites. This approach is a well-tested methodology for the design of organics but has not been used for peptides before. Peptides represent a difficulty because of their great conformational flexibility and a study of the advantages and disadvantages of this simple approach is an important step in the development of design tools. After a description of our general approach, a more detailed discussion of its adaptation to peptides is given. The method is then applied to the design of peptide-based inhibitors to HIV-1 protease and the design of structural mimics of the surface region of lysozyme. The results are encouraging and point the way towards further development of interaction site-based approaches for peptide design.

Rational drug design: a multidisciplinary approach

The pharmaceutical industry developed spasmodically and, in its early years, was shaped by several notable events and scientists. Paul Ehrlich, whose studies initiated the field of chemotherapy, was one of the major contributors. From early experiments on the efficacy of the biological stain methylene blue as an anti-malarial, Ehrlich realized that small molecules could be used to treat infections. Further studies identified trypan dyes and arsenical compounds as treatments of trypanosomal infection, and the arsenical compounds were found to be effective against syphilis. Ehrlich's experience of the chemical industry, in particular the chemistry of dyes, led to the realization that synthetic molecules could be medically useful and provided a powerful stimulus to pharmaceutical research.