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

Rapid Identification of Inhibitors and Prediction of Ligand Selectivity for Multiple Proteins: Application to Protein Kinases

Rapid identification of inhibitors for a family of proteins and prediction of ligand specificity are highly desirable for structure-based drug design. However, sequentially docking ligands into each protein target with conventional single-target docking methods is too computationally expensive to achieve these two goals, especially when the number of the targets is large. In this work, we use an efficient ensemble docking algorithm for simultaneous docking of ligands against multiple protein targets. We use protein kinases, a family of proteins that are highly important for many cellular processes and for rational drug design, as an example to demonstrate the feasibility of investigating ligand selectivity with this algorithm. Specifically, 14 human protein kinases were selected. First, native docking calculations were performed to test the ability of our energy scoring function to reproduce the experimentally determined structures of the ligand-protein kinase complexes. Next, cross-docking calculations were conducted using our ensemble docking algorithm to study ligand selectivity, based on the assumption that the native target of an inhibitor should have a more negative (i.e., favorable) energy score than the non-native targets. Staurosporine and Gleevec were studied as examples of nonselective and selective binding, respectively. Virtual ligand screening was also performed against five protein kinases that have at least seven known inhibitors. Our quantitative analysis of the results showed that the ensemble algorithm can be effective on screening for inhibitors and investigating their selectivities for multiple target proteins.

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.

Reverse docking: a powerful tool for drug repositioning and drug rescue

Reverse or inverse docking is proving to be a powerful tool for drug repositioning and drug rescue. It involves docking a small-molecule drug/ligand in the potential binding cavities of a set of clinically relevant macromolecular targets. Detailed analyses of the binding characteristics lead to ranking of the targets according to the tightness of binding. This process can potentially identify novel molecular targets for the drug/ligand which may be relevant for its mechanism of action and/or side effect profile. Another potential application of reverse docking is during the lead discovery and optimization stages of the drug-discovery cycle. This review summarizes the state-of-the-art and future prospects of the reverse docking with particular emphasis on computational molecular design.

Phenylboronic acid-functionalized glycopolymeric nanoparticles for biomacromolecules delivery across nasal respiratory

The aim of this study was to explore the potential of the mucoadhesive and enzyme-inhibitory phenylboronic acid-functionalized glycopolymeric nanoparticles as carriers for the nasal delivery of biomacromolecules. The glycopolymers were prepared by the random copolymerization of 3-acrylamidophenylboronic acid and N-acetyl glucosamine. Insulin, as a model, was encapsulated within self-assembled glypolymeric nanoparticles. Nanoparticle size, insulin loading, and insulin release were characterized. In vitro cytotoxicity experiment showed the glycopolymers were cytocompatible (≥ 80% cell viability). Adhesiveness was determined from the absorption amount of mucin, reaching up to 1180 μg/mL. Moreover, the results obtained from in vivo administration of insulin-loaded p(AAPBA-r-MAGA) nanoparticles to rats evidenced that the nanoparticles enhanced insulin absorption across the nasal mucosal barrier and did not induce irritation of nasal mucosa. Thus, insulin-loaded nanoparticles were able to significantly decrease plasma glucose levels (more than 35% reduction). These results suggest that p(AAPBA-r-MAGA) nanoparticles have potential application for the nasal delivery of biomacromolecules.

Inverse virtual screening of antitumor targets: pilot study on a small database of natural bioactive compounds

An inverse virtual screening in silico approach has been applied to natural bioactive molecules to screen their efficacy against proteins involved in cancer processes, with the aim of directing future experimental assays. Docking studies were performed on a panel of 126 protein targets extracted from the Protein Data Bank, to analyze their possible interactions with a small library of 43 bioactive compounds. Analysis of the molecular docking results was performed through the use of tables containing energy data organized in a matrix. The application of this approach may facilitate the prediction of the activity of unknown ligands for known targets involved in the development of cancer and could be applied to other models based on different libraries of ligands and different panels of targets.

Structure-Based Approaches to Target Fishing and Ligand Profiling

Chemogenomics is an emerging interdisciplinary field aiming at identifying all possible ligands of all possible targets. If one groups targets in columns and ligands in rows, chemogenomic approaches to drug discovery just fill the interaction matrix. Since experimental data do not suffice, several computational methods are currently actively developed to supplement time-consuming and costly experiments. They are either designed to fill rows and thus profile a ligand towards a heterogeneous set of targets (target profiling) or to fill columns and thus identify novel ligands for an existing target (standard virtual screening). At the interface of both strategies are now true chemogenomic computational methods filling well defined areas in the matrix. The present review will focus on (protein) structure-based approaches and illustrates major advances in this novel exciting field which is supposed to massively impact rational drug design in the next decade.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Novel 1,4-benzodiazepine derivatives with antiproliferative properties on tumor cell lines

Novel 1,4-benzodiazepine compounds were synthesized and evaluated for their ability to inhibit the proliferation of tumor cells. Some compounds revealed activities in the micromolar range and were more efficient than reference compound Ro 5-4864. Preliminary SAR helped to identify critical motifs for antiproliferative activity and led to the discovery of a compound selective for a melanoma cell line, known for its resistance to chemotherapy.

Prediction of functional sites based on the fuzzy oil drop model

A description of many biological processes requires knowledge of the 3-D structure of proteins and, in particular, the defined active site responsible for biological function. Many proteins, the genes of which have been identified as the result of human genome sequencing, and which were synthesized experimentally, await identification of their biological activity. Currently used methods do not always yield satisfactory results, and new algorithms need to be developed to recognize the localization of active sites in proteins. This paper describes a computational model that can be used to identify potential areas that are able to interact with other molecules (ligands, substrates, inhibitors, etc.). The model for active site recognition is based on the analysis of hydrophobicity distribution in protein molecules. It is shown, based on the analyses of proteins with known biological activity and of proteins of unknown function, that the region of significantly irregular hydrophobicity distribution in proteins appears to be function related.

Localization of ligand binding site in proteins identified in silico

Knowledge-based models for protein folding assume that the early-stage structural form of a polypeptide is determined by the backbone conformation, followed by hydrophobic collapse. Side chain-side chain interactions, mostly of hydrophobic character, lead to the formation of the hydrophobic core, which seems to stabilize the structure of the protein in its natural environment. The fuzzy-oil-drop model is employed to represent the idealized hydrophobicity distribution in the protein molecule. Comparing it with the one empirically observed in the protein molecule reveals that they are not in agreement. It is shown in this study that the irregularity of hydrophobic distributions is aim-oriented. The character and strength of these irregularities in the organization of the hydrophobic core point to the specificity of a particular protein's structure/function. When the location of these irregularities is determined versus the idealized fuzzy-oil-drop, function-related areas in the protein molecule can be identified. The presented model can also be used to identify ways in which protein-protein complexes can possibly be created. Active sites can be predicted for any protein structure according to the presented model with the free prediction server at http://www.bioinformatics.cm-uj.krakow.pl/activesite. The implication based on the model presented in this work suggests the necessity of active presence of ligand during the protein folding process simulation.

An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function

We have developed an iterative knowledge-based scoring function (ITScore) to describe protein-ligand interactions. Here, we assess ITScore through extensive tests on native structure identification, binding affinity prediction, and virtual database screening. Specifically, ITScore was first applied to a test set of 100 protein-ligand complexes constructed by Wang et al. (J Med Chem 2003, 46, 2287), and compared with 14 other scoring functions. The results show that ITScore yielded a high success rate of 82% on identifying native-like binding modes under the criterion of rmsd < or = 2 A for each top-ranked ligand conformation. The success rate increased to 98% if the top five conformations were considered for each ligand. In the case of binding affinity prediction, ITScore also obtained a good correlation for this test set (R = 0.65). Next, ITScore was used to predict binding affinities of a second diverse test set of 77 protein-ligand complexes prepared by Muegge and Martin (J Med Chem 1999, 42, 791), and compared with four other widely used knowledge-based scoring functions. ITScore yielded a high correlation of R2 = 0.65 (or R = 0.81) in the affinity prediction. Finally, enrichment tests were performed with ITScore against four target proteins using the compound databases constructed by Jacobsson et al. (J Med Chem 2003, 46, 5781). The results were compared with those of eight other scoring functions. ITScore yielded high enrichments in all four database screening tests. ITScore can be easily combined with the existing docking programs for the use of structure-based drug design.

Efficient molecular docking of NMR structures: application to HIV-1 protease

Docking ligands into an ensemble of NMR conformers is essential to structure-based drug discovery if only NMR structures are available for the target. However, sequentially docking ligands into each NMR conformer through standard single-receptor-structure docking, referred to as sequential docking, is computationally expensive for large-scale database screening because of the large number of NMR conformers involved. Recently, we developed an efficient ensemble docking algorithm to consider protein structural variations in ligand binding. The algorithm simultaneously docks ligands into an ensemble of protein structures and achieves comparable performance to sequential docking without significant increase in computational time over single-structure docking. Here, we applied this algorithm to docking with NMR structures. The HIV-1 protease was used for validation in terms of docking accuracy and virtual screening. Ensemble docking of the NMR structures identified 91% of the known inhibitors under the criterion of RMSD < 2.0 A for the best-scored conformation, higher than the average success rate of single docking of individual crystal structures (66%). In the virtual screening test, on average, ensemble docking of the NMR structures obtained higher enrichments than single-structure docking of the crystal structures. In contrast, docking of either the NMR minimized average structure or a single NMR conformer performed less satisfactorily on both binding mode prediction and virtual screening, indicating that a single NMR structure may not be suitable for docking calculations. The success of ensemble docking of the NMR structures suggests an efficient alternative method for standard single docking of crystal structures and for considering protein flexibility.

Free energy calculations show that acidic P1 variants undergo large pKa shifts upon binding to trypsin

Serine proteinases and their protein inhibitors belong to one of the most comprehensively studied models of protein-protein interactions. It is well established that the narrow trypsin specificity is caused by the presence of a negatively charged aspartate at the specificity pocket. X-ray crystallography as well as association measurements revealed, surprisingly, that BPTI with glutamatic acid as the primary binding (P1) residue was able to bind to trypsin. Previous free energy calculations showed that there was a substantially unfavorable binding free energy associated with accommodation of ionized P1 Glu at the S1-site of trypsin. In this study, the binding of P1 Glu to trypsin has been systematically investigated in terms of the protonation states of P1 Glu and Asp189, the orientation of Gln192, as well as the possible presence of counterions using the linear interaction energy (LIE) approach and the free energy perturbation (FEP) method. Twenty-four conceivable binding arrangements were evaluated and quantitative agreement with experiments is obtained when the P1 Glu binds in its protonated from. The results suggest that P1 Glu is one of the variants of BPTI that inhibit trypsin strongest at low pH, contrary to the specificity profile of trypsin, suggesting a new regulation mechanism of trypsin-like enzymes.

Representativity of target families in the Protein Data Bank: impact for family-directed structure-based drug discovery

Analysis of the population of enzyme structures in the Protein Data Bank across all levels of the functional classification based on enzyme commission (EC) numbers reveals that, in spite of the almost exponential growth in the number of structures deposited, progress in achieving complete occupancy at all EC levels is relatively slow. Moreover, inspection of the distribution of the population among the members of the different enzyme families uncovers a strong bias towards enzymes widely recognized as therapeutically relevant targets. The low representativity levels identified in some target families warn on the current scope and applicability of structure-based approaches to family-directed strategies in drug discovery.

Receptor systems: will mining the receptorome yield novel targets for pharmacotherapy?

We have recently defined the receptorome as 'that part of the proteome encoding receptors'. In this article, I provide a general overview of the members of the receptorome as well as methods used to screen the receptorome-both in silico and physically. Case histories of receptorome-based discovery efforts are then highlighted and the relevance of this approach to the discovery and validation of molecular targets for drug abuse treatment is emphasized.

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

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

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

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

Calculation of ligand-nucleic acid binding free energies with the generalized-born model in DOCK

The calculation of ligand-nucleic acid binding free energies is investigated by including solvation effects computed with the generalized-Born model. Modifications of the solvation module in DOCK, including introduction of all-atom parameters and revision of coefficients in front of different terms, are shown to improve calculations involving nucleic acids. This computing scheme is capable of calculating binding energies, with reasonable accuracy, for a wide variety of DNA-ligand complexes, RNA-ligand complexes, and even for the formation of double-stranded DNA. This implementation of GB/SA is also shown to be capable of discriminating strong ligands from poor ligands for a series of RNA aptamers without sacrificing the high efficiency of the previous implementation. These results validate this approach to screening large databases against nucleic acid targets.

Molecular recognition and docking algorithms

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

Computational design strategies for combinatorial libraries

Medicinal chemistry principles are being increasingly applied to the design of smaller, high purity, information-rich libraries. Recent computational advances in statistical methodology, the design of libraries to reduce ADMET problems, targeting protein families and revisiting natural products as sources of inspiration for scaffolds and reagents are all areas of progressive research.

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

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

Lead discovery using molecular docking

As the structures of more and more proteins and nucleic acids become available, molecular docking is increasingly considered for lead discovery. Recent studies consider the hit-rate enhancement of docking screens and the accuracy of docking structure predictions. As more structures are determined experimentally, docking against homology-modeled targets also becomes possible for more proteins. With more docking studies being undertaken, the 'drug-likeness' and specificity of docking hits is also being examined.

Protein flexibility and drug design: how to hit a moving target

The most advanced methods for computer-aided drug design and database mining incorporate protein flexibility. Such techniques are not only needed to obtain proper results; they are also critical for dealing with the growing body of information from structural genomics.

Structure-based combinatorial library design: methodologies and applications

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

Bio- and chemo-informatics beyond data management: crucial challenges and future opportunities

Bio- and chemo-informatics are now thought to be crucial to the success and integration of biotechnology and drug discovery. Research in this area has expanded to go beyond data- and information-management. Here, we review exemplary areas, such as target identification and validation, virtual screening, and prediction of downstream characteristics of leads, where further research will play a key role in progressing the field.

Subcellular pharmacokinetics and its potential for library focusing

Subcellular pharmacokinetics (SP) optimizes biology-related factors in the design of libraries for high throughput screening by defining comparatively narrow ranges of properties (lipophilicity, amphiphilicity, acidity, reactivity, 3D-structural features) of the included compounds. The focusing ensures appropriate absorption, distribution, metabolism, excretion, and toxicity (ADMET) in those test biosystems, which are more complex than isolated receptors, and in humans. The SP deploys conceptual models that include transport and accumulation in a series of membranes, protein binding, hydrolysis, and other reactions with cell constituents. The kinetics of drug disposition is described as a non-linear disposition function of drug structure and properties. The SP capabilities are illustrated here using a model-based quantitative structure-activity relationship of toxicity of phenolic compounds against Tetrahymena pyriformis as dependent on lipophilicity and acidity. The resulting SP models clearly outperform empirical models in predictive ability outside the parameter space, as revealed by the leave-extremes-out cross-validation technique with omission of compounds beyond pre-defined lipophilicity and acidity ranges. The SP models do not change substantially if the parameters space is shrunk within some limits. In contrast, the shapes of empirical models vary widely depending upon the fraction of the data set used for their optimization. Once calibrated for a given biosystem, the SP models provide a detailed recipe for tailoring the drug properties to ensure optimum ADMET. The focusing is more accurate than with traditional empirical QSAR studies, assessment of drug-likeness, or the rules for identification of compounds with permeability problems.

Principles of docking: An overview of search algorithms and a guide to scoring functions

The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the "correct" native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non-native solutions. It is universally recognized that docking of drugs is immensely important. However, protein-protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein-protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non-uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here.

Protein-protein docking with multiple residue conformations and residue substitutions

The protein docking problem has two major aspects: sampling conformations and orientations, and scoring them for fit. To investigate the extent to which the protein docking problem may be attributed to the sampling of ligand side-chain conformations, multiple conformations of multiple residues were calculated for the uncomplexed (unbound) structures of protein ligands. These ligand conformations were docked into both the complexed (bound) and unbound conformations of the cognate receptors, and their energies were evaluated using an atomistic potential function. The following questions were considered: (1) does the ensemble of precalculated ligand conformations contain a structure similar to the bound form of the ligand? (2) Can the large number of conformations that are calculated be efficiently docked into the receptors? (3) Can near-native complexes be distinguished from non-native complexes? Results from seven test systems suggest that the precalculated ensembles do include side-chain conformations similar to those adopted in the experimental complexes. By assuming additivity among the side chains, the ensemble can be docked in less than 12 h on a desktop computer. These multiconformer dockings produce near-native complexes and also non-native complexes. When docked against the bound conformations of the receptors, the near-native complexes of the unbound ligand were always distinguishable from the non-native complexes. When docked against the unbound conformations of the receptors, the near-native dockings could usually, but not always, be distinguished from the non-native complexes. In every case, docking the unbound ligands with flexible side chains led to better energies and a better distinction between near-native and non-native fits. An extension of this algorithm allowed for docking multiple residue substitutions (mutants) in addition to multiple conformations. The rankings of the docked mutant proteins correlated with experimental binding affinities. These results suggest that sampling multiple residue conformations and residue substitutions of the unbound ligand contributes to, but does not fully provide, a solution to the protein docking problem. Conformational sampling allows a classical atomistic scoring function to be used; such a function may contribute to better selectivity between near-native and non-native complexes. Allowing for receptor flexibility may further extend these results.

Complexity and simplicity of ligand-macromolecule interactions: the energy landscape perspective

The energy landscape approach has contributed to recent progress in understanding the complexity and simplicity of ligand-macromolecule interactions. Significant advances in computational structure prediction of ligand-protein complexes have been made using approaches that include the effects of protein flexibility and incorporate a hierarchy of energy functions. The results suggest that the complexity of structure prediction in molecular recognition may be determined by low-resolution properties of the underlying binding energy landscapes and by the nature of the energy funnels near the native structures of the complexes.

Definition of the extended substrate specificity determinants for beta-tryptases I and II

Tryptases betaI and betaII were heterologously expressed and purified in yeast to functionally characterize the substrate specificity of each enzyme. Three positional scanning combinatorial tetrapeptide substrate libraries were used to determine the primary and extended substrate specificity of the proteases. Both enzymes have a strict primary preference for cleavage after the basic amino acids, lysine and arginine, with only a slight preference for lysine over arginine. betaI and betaII tryptase share similar extended substrate specificity, with preference for proline at P4, preference for arginine or lysine at P3, and P2 showing a slight preference for asparagine. Measurement of kinetic constants with multiple substrates designed for beta-tryptases reveal that selectivity is highly dependent on ground state substrate binding. Coupled with the functional determinants, structural determinants of tryptase substrate specificity were identified. Molecular docking of the preferred substrate sequence to the three-dimensional tetrameric tryptase structure reveals a novel extended substrate binding mode that involves interactions from two adjacent protomers, including P4 Thr-96', P3 Asp-60B' and Glu-217, and P1 Asp-189. Based on the determined substrate information, a mechanism-based tetrapeptide-chloromethylketone inhibitor was designed and shown to be a potent tryptase inhibitor. Finally, the cleavage sites of several physiologically relevant substrates of beta-tryptases show consistency with the specificity data presented here.

Computational analysis of binding of P1 variants to trypsin

The binding of P1 variants of bovine pancreatic trypsin inhibitor (BPTI) to trypsin has been investigated by means of molecular dynamics simulations. The specific interaction formed between the amino acid at the primary binding (P1) position of the binding loop of BPTI and the specificity pocket of trypsin was estimated by use of the linear interaction energy (LIE) method. Calculations for 13 of the naturally occurring amino acids at the P1 position were carried out, and the results obtained were found to correlate well with the experimental binding free energies. The LIE calculations rank the majority of the 13 variants correctly according to the experimental association energies and the mean error between calculated and experimental binding free energies is only 0.38 kcal/mole, excluding the Glu and Asp variants, which are associated with some uncertainties regarding protonation and the possible presence of counter-ions. The three-dimensional structures of the complex with three of the P1 variants (Asn, Tyr, and Ser) included in this study have not at present been solved by any experimental techniques and, therefore, were modeled on the basis of experimental data from P1 variants of similar size. Average structures were calculated from the MD simulations, from which specific interactions explaining the broad variation in association energies were identified. The present study also shows that explicit treatment of the complex water-mediated hydrogen bonding network at the protein-protein interface is of crucial importance for obtaining reliable binding free energies. The successful reproduction of relative binding energies shows that this type of methodology can be very useful as an aid in rational design and redesign of biologically active macromolecules.

High-throughput docking for lead generation

Recent improvements in flexible docking technology may lead to a bigger role for computational methods in lead discovery. Although fast and accurate computational prediction of binding affinities for an arbitrary molecule is still beyond the limits of current methods, the docking and screening procedures can select small sets of likely lead candidates from large libraries of either commercially or synthetically available compounds.