Utilising structural knowledge in drug design strategies: applications using Relibase

Water molecules at protein-drug interfaces: computational prediction and analysis methods

The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

The role of hydration effects in 5-fluorouridine binding to SOD1: insight from a new 3D-RISM-KH based protocol for including structural water in docking simulations

Misfolded Cu/Zn superoxide dismutase enzyme (SOD1) shows prion-like propagation in neuronal cells leading to neurotoxic aggregates that are implicated in amyotrophic lateral sclerosis (ALS). Tryptophan-32 (W32) in SOD1 is part of a potential site for templated conversion of wild type SOD1. This W32 binding site is located on a convex, solvent exposed surface of the SOD1 suggesting that hydration effects can play an important role in ligand recognition and binding. A recent X-ray crystal structure has revealed that 5-Fluorouridine (5-FUrd) binds at the W32 binding site and can act as a pharmacophore scaffold for the development of anti-ALS drugs. In this study, a new protocol is developed to account for structural (non-displaceable) water molecules in docking simulations and successfully applied to predict the correct docked conformation binding modes of 5-FUrd at the W32 binding site. The docked configuration is within 0.58 Å (RMSD) of the observed configuration. The docking protocol involved calculating a hydration structure around SOD1 using molecular theory of solvation (3D-RISM-KH, 3D-Reference Interaction Site Model-Kovalenko-Hirata) whereby, non-displaceable water molecules are identified for docking simulations. This protocol was also used to analyze the hydrated structure of the W32 binding site and to explain the role of solvation in ligand recognition and binding to SOD1. Structural water molecules mediate hydrogen bonds between 5-FUrd and the receptor, and create an environment favoring optimal placement of 5-FUrd in the W32 binding site.

Protocols for the Design of Kinase-focused Compound Libraries

Protocols for the design of kinase-focused compound libraries are presented. Kinase-focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure-based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators.

Estimation of kinetic and thermodynamic ligand-binding parameters using computational strategies

Kinetic and thermodynamic ligand–protein binding parameters are gaining growing importance as key information to consider in drug discovery. The determination of the molecular structures, using particularly x-ray and NMR techniques, is crucial for understanding how a ligand recognizes its target in the final binding complex. However, for a better understanding of the recognition processes, experimental studies of ligand–protein interactions are needed. Even though several techniques can be used to investigate both thermodynamic and kinetic profiles for a ligand–protein complex, these procedures are very often laborious, time consuming and expensive. In the last 10 years, computational approaches have enormous potential in providing insights into each of the above effects and in parsing their contributions to the changes in both kinetic and thermodynamic binding parameters. The main purpose of this review is to summarize the state of the art of computational strategies for estimating the kinetic and thermodynamic parameters of a ligand–protein binding.

Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design

Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.

Large-scale mining for similar protein binding pockets: with RAPMAD retrieval on the fly becomes real

Determination of structural similarities between protein binding pockets is an important challenge in in silico drug design. It can help to understand selectivity considerations, predict unexpected ligand cross-reactivity, and support the putative annotation of function to orphan proteins. To this end, Cavbase was developed as a tool for the automated detection, storage, and classification of putative protein binding sites. In this context, binding sites are characterized as sets of pseudocenters, which denote surface-exposed physicochemical properties, and can be used to enable mutual binding site comparisons. However, these comparisons tend to be computationally very demanding and often lead to very slow computations of the similarity measures. In this study, we propose RAPMAD (RApid Pocket MAtching using Distances), a new evaluation formalism for Cavbase entries that allows for ultrafast similarity comparisons. Protein binding sites are represented by sets of distance histograms that are both generated and compared with linear complexity. Attaining a speed of more than 20 000 comparisons per second, screenings across large data sets and even entire databases become easily feasible. We demonstrate the discriminative power and the short runtime by performing several classification and retrieval experiments. RAPMAD attains better success rates than the comparison formalism originally implemented into Cavbase or several alternative approaches developed in recent time, while requiring only a fraction of their runtime. The pratical use of our method is finally proven by a successful prospective virtual screening study that aims for the identification of novel inhibitors of the NMDA receptor.

Extended Graph-Based Models for Enhanced Similarity Search in Cavbase

To calculate similarities between molecular structures, measures based on the maximum common subgraph are frequently applied. For the comparison of protein binding sites, these measures are not fully appropriate since graphs representing binding sites on a detailed atomic level tend to get very large. In combination with an NP-hard problem, a large graph leads to a computationally demanding task. Therefore, for the comparison of binding sites, a less detailed coarse graph model is used building upon so-called pseudocenters. Consistently, a loss of structural data is caused since many atoms are discarded and no information about the shape of the binding site is considered. This is usually resolved by performing subsequent calculations based on additional information. These steps are usually quite expensive, making the whole approach very slow. The main drawback of a graph-based model solely based on pseudocenters, however, is the loss of information about the shape of the protein surface. In this study, we propose a novel and efficient modeling formalism that does not increase the size of the graph model compared to the original approach, but leads to graphs containing considerably more information assigned to the nodes. More specifically, additional descriptors considering surface characteristics are extracted from the local surface and attributed to the pseudocenters stored in Cavbase. These properties are evaluated as additional node labels, which lead to a gain of information and allow for much faster but still very accurate comparisons between different structures.

Compound activity prediction using models of binding pockets or ligand properties in 3D

Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocketbased and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges.

PeptiSite: a structural database of peptide binding sites in 4D

We developed PeptiSite, a comprehensive and reliable database of biologically and structurally characterized peptide-binding sites, in which each site is represented by an ensemble of its complexes with protein, peptide and small molecule partners. The unique features of the database include: (1) the ensemble site representation that provides a fourth dimension to the otherwise three dimensional data, (2) comprehensive characterization of the binding site architecture that may consist of a multimeric protein assembly with cofactors and metal ions and (3) analysis of consensus interaction motifs within the ensembles and identification of conserved determinants of these interactions. Currently the database contains 585 proteins with 650 peptide-binding sites. http://peptisite.ucsd.edu/ link allows searching for the sites of interest and interactive visualization of the ensembles using the ActiveICM web-browser plugin. This structural database for protein-peptide interactions enables understanding of structural principles of these interactions and may assist the development of an efficient peptide docking benchmark.

Protein-ligand interactions: fundamentals

Here are described the basic mechanisms governing the interactions between proteins and their natural or manmade ligands, together with the principles underlying their analysis. The consequences of these principles are detailed for the simplest case of one-to-one binding. The general features of experimental measurements of biomolecular interactions arise from properties of the molecules involved and, thus, are common to many methods of detection. Consequently, an understanding of these principles greatly simplifies adoption and comparison of experimental methods and provides the rationale underlying many common protocols. In seeking to understand and interpret the results of experiments or identify possible sources of error these fundamental ideas are a constant guide.

Impact of ligand and protein desolvation on ligand binding to the S1 pocket of thrombin

In the present study, we investigate the impact of a tightly bound water molecule on ligand binding in the S1 pocket of thrombin. The S1 pocket contains a deeply buried deprotonated aspartate residue (Asp189) that is, due to its charged state, well hydrated in the uncomplexed state. We systematically studied the importance of this water molecule by evaluating a series of ligands that contains pyridine-type P1 side chains that could potentially alter the binding properties of this water molecule. All of the pyridine derivatives retain the original hydration state albeit sometimes with a slight perturbance. In order to prevent a direct H-bond formation with Asp189, and to create a permanent positive charge on the P1 side chain that is positioned adjacent to the Asp189 carboxylate anion, we methylated the pyridine nitrogen. This methylation resulted in displacement of water but was accompanied by a loss in binding affinity. Quantum chemical calculations of the ligand solvation free energy showed that the positively charged methylpyridinium derivatives suffer a large penalty of desolvation upon binding. Consequently, they have a substantially less favorable enthalpy of binding. In addition to the ligand desolvation penalty, the hydration shell around Asp189 has to be overcome, which is achieved in nearly all pyridinium derivatives. Only for the ortho derivative is a partial population of a water next to Asp189 found. Possibly, the gain of electrostatic interactions between the charged P1 side chain and Asp189 helps to compensate for the desolvation penalty. In all uncharged pyridine derivatives, the solvation shell remains next to Asp189, partly mediating interactions between ligand and protein. In the case of the para-pyridine derivative, a strongly disordered cluster of water sites is observed between ligand and Asp189.

Pocketome: an encyclopedia of small-molecule binding sites in 4D

The importance of binding site plasticity in protein-ligand interactions is well-recognized, and so are the difficulties in predicting the nature and the degree of this plasticity by computational means. To assist in understanding the flexible protein-ligand interactions, we constructed the Pocketome, an encyclopedia of about one thousand experimentally solved conformational ensembles of druggable binding sites in proteins, grouped by location and consistent chain/cofactor composition. The multiplicity of pockets within the ensembles adds an extra, fourth dimension to the Pocketome entry data. Within each ensemble, the pockets were carefully classified by the degree of their pairwise similarity and compatibility with different ligands. The core of the Pocketome is derived regularly and automatically from the current releases of the Protein Data Bank and the Uniprot Knowledgebase; this core is complemented by entries built from manually provided seed ligand locations. The Pocketome website (www.pocketome.org) allows searching for the sites of interest, analysis of conformational clusters, important residues, binding compatibility matrices and interactive visualization of the ensembles using the ActiveICM web browser plugin. The Pocketome collection can be used to build multi-conformational docking and 3D activity models as well as to design cross-docking and virtual ligand screening benchmarks.

Molecular recognition at the active site of factor Xa: cation-π interactions, stacking on planar peptide surfaces, and replacement of structural water

Factor Xa, a serine protease from the blood coagulation cascade, is an ideal enzyme for molecular recognition studies, as its active site is highly shape-persistent and features distinct, concave sub-pockets. We developed a family of non-peptidic, small-molecule inhibitors with a central tricyclic core orienting a neutral heterocyclic substituent into the S1 pocket and a quaternary ammonium ion into the aromatic box in the S4 pocket. The substituents were systematically varied to investigate cation-π interactions in the S4 pocket, optimal heterocyclic stacking on the flat peptide walls lining the S1 pocket, and potential water replacements in both the S1 and the S4 pockets. Structure-activity relationships were established to reveal and quantify contributions to the binding free enthalpy, resulting from single-atom replacements or positional changes in the ligands. A series of high-affinity ligands with inhibitory constants down to K(i)=2 nM were obtained and their proposed binding geometries confirmed by X-ray co-crystal structures of protein-ligand complexes.

Water Diffusion In And Out Of The β-Barrel Of GFP and The Fast Maturing Fluorescent Protein, TurboGFP

The chromophore of fluorescent proteins is formed by an internal cyclization of the tripeptide 65SYG67 fragment and a subsequent oxidation. The oxidation is slow - the kinetics of this step is presumably improved in fast maturing GFPs. Water molecules can aid in the chromophore formation. We have used 50ns molecular dynamics simulations of the mature and immature forms of avGFP and TurboGFP to examine the diffusion of water molecules in-and-out of the protein β-barrel. Most crystal structures of GFPs have well-structured waters within hydrogen-bonding distance of Glu222 and Arg96. It has been proposed that they have an important role in chromophore formation. Stable waters are found in similar positions in all simulations conducted. The simulations confirm the existence of a pore that leads to the chromophore in the rapidly maturing TurboGFP; decreased water diffusion upon chromophore formation; and increased water diffusion due to the pore formation.

Residue preference mapping of ligand fragments in the Protein Data Bank

The interaction between small molecules and proteins is one of the major concerns for structure-based drug design because the principles of protein-ligand interactions and molecular recognition are not thoroughly understood. Fortunately, the analysis of protein-ligand complexes in the Protein Data Bank (PDB) enables unprecedented possibilities for new insights. Herein, we applied molecule-fragmentation algorithms to split the ligands extracted from PDB crystal structures into small fragments. Subsequently, we have developed a ligand fragment and residue preference mapping (LigFrag-RPM) algorithm to map the profiles of the interactions between these fragments and the 20 proteinogenic amino acid residues. A total of 4032 fragments were generated from 71 798 PDB ligands by a ring cleavage (RC) algorithm. Among these ligand fragments, 315 unique fragments were characterized with the corresponding fragment-residue interaction profiles by counting residues close to these fragments. The interaction profiles revealed that these fragments have specific preferences for certain types of residues. The applications of these interaction profiles were also explored and evaluated in case studies, showing great potential for the study of protein-ligand interactions and drug design. Our studies demonstrated that the fragment-residue interaction profiles generated from the PDB ligand fragments can be used to detect whether these fragments are in their favorable or unfavorable environments. The algorithm for a ligand fragment and residue preference mapping (LigFrag-RPM) developed here also has the potential to guide lead chemistry modifications as well as binding residues predictions.

Function and structure of GFP-like proteins in the protein data bank

The RCSB protein databank contains 266 crystal structures of green fluorescent proteins (GFP) and GFP-like proteins. This is the first systematic analysis of all the GFP-like structures in the pdb. We have used the pdb to examine the function of fluorescent proteins (FP) in nature, aspects of excited state proton transfer (ESPT) in FPs, deformation from planarity of the chromophore and chromophore maturation. The conclusions reached in this review are that (1) The lid residues are highly conserved, particularly those on the "top" of the β-barrel. They are important to the function of GFP-like proteins, perhaps in protecting the chromophore or in β-barrel formation. (2) The primary/ancestral function of GFP-like proteins may well be to aid in light induced electron transfer. (3) The structural prerequisites for light activated proton pumps exist in many structures and it's possible that like bioluminescence, proton pumps are secondary functions of GFP-like proteins. (4) In most GFP-like proteins the protein matrix exerts a significant strain on planar chromophores forcing most GFP-like proteins to adopt non-planar chromophores. These chromophoric deviations from planarity play an important role in determining the fluorescence quantum yield. (5) The chemospatial characteristics of the chromophore cavity determine the isomerization state of the chromophore. The cavities of highlighter proteins that can undergo cis/trans isomerization have chemospatial properties that are common to both cis and trans GFP-like proteins.

On the origin of fluorescence in bacteriophytochrome infrared fluorescent proteins

Tsien et al. (Science, 2009, 324, 804-807) recently reported the creation of the first infrared fluorescent protein (IFP). It was engineered from bacterial phytochrome by removing the PHY and histidine kinase-related domains, by optimizing the protein to prevent dimerization, and by limiting the biliverdins conformational freedom, especially around its D ring. We have used database analyses and molecular dynamics simulations with freely rotating chromophoric dihedrals in order to model the dihedral freedom available to the biliverdin D ring in the excited state and to show that the tetrapyrrole ligands in phytochromes are flexible and can adopt many conformations; however, their conformational space is limited/defined by the chemospatial characteristics of the protein cavity. Our simulations confirm that the reduced accessibility to conformations geared to an excited state proton transfer may be responsible for the fluorescence in IFP, just as has been suggested by Kennis et al. (Proc. Natl. Acad. Sci. U.S.A., 2010, 107, 9170-9175) for fluorescent bacteriophytochrome from Rhodopseudomonas palustris.

Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors

Structure- and ligand-based virtual-screening methods (docking, 2D- and 3D-similarity searching) were analysed for their effectiveness in virtual screening against four different targets: angiotensin-converting enzyme (ACE), cyclooxygenase 2 (COX-2), thrombin and human immunodeficiency virus 1 (HIV-1) protease. The relative performance of the tools was compared by examining their ability to recognise known active compounds from a set of actives and nonactives. Furthermore, we investigated whether the application of different virtual-screening methods in parallel provides complementary or redundant hit lists. Docking was performed with GOLD, Glide, FlexX and Surflex. The obtained docking poses were rescored by using nine different scoring functions in addition to the scoring functions implemented as objective functions in the docking algorithms. Ligand-based virtual screening was done with ROCS (3D-similarity searching), Feature Trees and Scitegic Functional Fingerprints (2D-similarity searching). The results show that structure- and ligand-based virtual-screening methods provide comparable enrichments in detecting active compounds. Interestingly, the hit lists that are obtained from different virtual-screening methods are generally highly complementary. These results suggest that a parallel application of different structure- and ligand-based virtual-screening methods increases the chance of identifying more (and more diverse) active compounds from a virtual-screening campaign.

How to replace the residual solvation shell of polar active site residues to achieve nanomolar inhibition of tRNA-guanine transglycosylase

In a computational and structural study, we investigated a series of 4-substituted lin-benzoguanines that are potent inhibitors of tRNA-guanine transglycosylase (TGT), a putative target for the treatment of shigellosis. At first glance, it appears self-evident that the placement of a positively charged ligand functional group between the carboxylate groups of two adjacent aspartate residues in the glycosylase catalytic center leads to enhanced ligand binding. The concomitant displacement of water molecules that partially solvate the aspartates prior to ligand binding appears to result as a consequence of this. However, the case study presented herein shows that this premise is much too superficial. Placement of a likely positively charged amino group at such a pivotal position, interfering with the residual water solvation shell, is at best cost-neutral compared with the unsubstituted parent ligand not conflicting with the residual water shell. A ligand that orients a hydroxy group in this position shows even decreased binding. Based on the cost-neutral placement of the amino functionality, hydrophobic side chains can now be further attached to fill, with increasing potency, a small hydrophobic pocket remote to the aspartates. Any attempts to cross the pivotal position between both aspartates with nonpolar scaffolds reveals only decreased binding, even though the waters of the residual solvation shell are successfully repelled. This surprising observation fostered a detailed analysis of the role of water molecules involved in the residual solvation of polar active site residues. Their geometry and putative replacement in the binding pocket of TGT has been studied by a comparative database analysis, computational active site mapping, and a series of crystal structure analyses. Furthermore, conformational preferences of attached hydrophobic moieties explain their contribution to a gradual increase in binding affinity.

Visualization of macromolecular structures

Structural biology is rapidly accumulating a wealth of detailed information about protein function, binding sites, RNA, large assemblies and molecular motions. These data are increasingly of interest to a broader community of life scientists, not just structural experts. Visualization is a primary means for accessing and using these data, yet visualization is also a stumbling block that prevents many life scientists from benefiting from three-dimensional structural data. In this review, we focus on key biological questions where visualizing three-dimensional structures can provide insight and describe available methods and tools.

Drug efficiency indices for improvement of molecular docking scoring functions

A dataset of protein-drug complexes with experimental binding energy and crystal structure were analyzed and the performance of different docking engines and scoring functions (as well as components of these) for predicting the free energy of binding and several ligand efficiency indices were compared. The aim was not to evaluate the best docking method, but to determine the effect of different efficiency indices on the experimental and predicted free energy. Some ligand efficiency indices, such as DeltaG/W (Wiener index), DeltaG/NoC (number of carbons), and DeltaG/P (partition coefficient), improve the correlation between experimental and calculated values. This effect was shown to be valid across the different scoring functions and docking programs. It also removes the common bias of scoring functions in favor of larger ligands. For all scoring functions, the efficiency indices effectively normalize the free energy derived indices, to give values closer to experiment. Compound collection filtering can be done prior or after docking, using pharmacokinetic as well as pharmacodynamic profiles. Achieving these better correlations with experiment can improve the ability of docking scoring functions to predict active molecules in virtual screening.

Virtual screening of compound libraries

During the last decade, Virtual Screening (VS) has definitively established itself as an important part of the drug discovery and development process. VS involves the selection of likely drug candidates from large libraries of chemical structures by using computational methodologies, but the generic definition of VS encompasses many different methodologies. This chapter provides an introduction to the field by reviewing a variety of important aspects, including the different types of virtual screening methods, and the several steps required for a successful virtual screening campaign within a state-of-the-art approach, from target selection to postfilter application. This analysis is further complemented with a small collection important VS success stories.

Deciphering ligand dependent degree of binding site closure and its implication in inhibitor design: A modeling study on human adenosine kinase

Protein flexibility plays a significant role in drug research due to its effect on accurate prediction of ligand binding mode and activity. Adenosine kinase (AK) represents a highly flexible binding site and is known to exhibit large conformational changes as a result of substrate or inhibitor binding. Here we propose a semi-open conformation for ligand binding in human AK, in addition to the known closed and open forms. The modeling study illustrates the necessity of thorough understanding of the conformational states of protein for docking and binding mode prediction. It has been shown that predicting activity in the context of correct binding mode can improve the insight into conserved interactions and mechanism of action for inhibition of AK. Integrating the knowledge about the binding modes of ligands in different conformational states of the protein, separate pharmacophore models were generated and used for virtual screening to explore potential novel hits. In addition, 2D descriptor based clustering was done to differentiate the ligands, binding to closed, semi-open and open conformations of human AK. The results indicated that binding of all AK inhibitors cannot be described by same rules, instead, they represent a rule based preference for inhibition. This inference about tubercidins binding to semi-open conformation of human AK may facilitate in finding much extensive space for AK inhibitors.

Structure-based chemogenomics: analysis of protein family landscapes

Analysis of the three-dimensional structures of protein ligand complexes provides valuable insight into both the common interaction patterns within a target family and the discriminating features between the different members of a target family. Knowledge of the common interaction patterns helps to design target family focused chemical libraries for hit finding, while the discriminating features can be exploited to optimize the selectivity profile of a lead compound against particular member of a target family. Herein, we review the computational tools which have been developed to analyze crystal structures of members of a target family.

Functional classification of protein kinase binding sites using Cavbase

Increasingly, drug-discovery processes focus on complete gene families. Tools for analyzing similarities and differences across protein families are important for the understanding of key functional features of proteins. Herein we present a method for classifying protein families on the basis of the properties of their active sites. We have developed Cavbase, a method for describing and comparing protein binding pockets, and show its application to the functional classification of the binding pockets of the protein family of protein kinases. A diverse set of kinase cavities is mutually compared and analyzed in terms of recurring functional recognition patterns in the active sites. We are able to propose a relevant classification based on the binding motifs in the active sites. The obtained classification provides a novel perspective on functional properties across protein space. The classification of the MAP and the c-Abl kinases is analyzed in detail, showing a clear separation of the respective kinase subfamilies. Remarkable cross-relations among protein kinases are detected, in contrast to sequence-based classifications, which are not able to detect these relations. Furthermore, our classification is able to highlight features important in the optimization of protein kinase inhibitors. Using small-molecule inhibition data we could rationalize cross-reactivities between unrelated kinases which become apparent in the structural comparison of their binding sites. This procedure helps in the identification of other possible kinase targets that behave similarly in "binding pocket space" to the kinase under consideration.

Potent non-nucleoside inhibitors of the measles virus RNA-dependent RNA polymerase complex

Measles virus (MV) is one of the most infectious pathogens known. In spite of the existence of a vaccine, approximately 350000 deaths/year result from MV or associated complications. Antimeasles compounds could conceivably diminish these statistics and provide a therapy that complements vaccine treatment. We recently described a high-throughput screening hit compound 1 (16677) against MV-infected cells with the capacity to eliminate viral reproduction at 250 nM by inhibiting the action of the virus's RNA-dependent RNA polymerase complex (RdRp). The compound, 1-methyl-3-(trifluoromethyl)- N-[4-sulfonylphenyl]-1 H-pyrazole-5-carboxamide, 1 carries a critical CF 3 moiety on the 1,2-pyrazole ring. Elaborating on the preliminary structure-activity (SAR) study, the present work presents the synthesis and SAR of a much broader range of low nanomolar nonpeptidic MV inhibitors and speculates on the role of the CF 3 functionality.