Comparative Binding Energy Analysis Research Articles

Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal

The main protease (Mpro) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure–activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized Mpro-inhibitors and validated on available literature data. By means of deep optimization both models’ goodness and robustness reached final statistical values of r2/q2 values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEPPRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure–activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral Mpro-inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design.Graphical abstract

Prediction of the Drug–Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (koff) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of koff values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.

Prediction of Drug-Target Binding Kinetics by Comparative Binding Energy Analysis.

A growing consensus is emerging that optimizing the drug–target affinity alone under equilibrium conditions does not necessarily translate into higher potency in vivo and that instead binding kinetic parameters should be optimized to ensure better efficacy. Therefore, in silico methods are needed to predict the kinetic parameters and the mechanistic determinants of drug–protein binding. Here we demonstrate the application of COMparative BINding Energy (COMBINE) analysis to derive quantitative structure–kinetics relationships (QSKRs) for the dissociation rate constants (koff) of inhibitors of heat shock protein 90 (HSP90) and HIV-1 protease. We derived protein-specific scoring functions by correlating koff rate constants with a subset of weighted interaction energy components determined from the energy-minimized structures of drug–protein complexes. As the QSKRs derived for these sets of chemically diverse compounds have good predictive ability and provide insights into important drug–protein interactions for optimizing koff, COMBINE analysis offers a promising approach for binding kinetics-guided lead optimization.

Prospective virtual screening for novel p53–MDM2 inhibitors using ultrafast shape recognition

The p53 protein, known as the guardian of genome, is mutated or deleted in approximately 50% of human tumors. In the rest of the cancers, p53 is expressed in its wild-type form, but its function is inhibited by direct binding with the murine double minute 2 (MDM2) protein. Therefore, inhibition of the p53-MDM2 interaction, leading to the activation of tumor suppressor p53 protein presents a fundamentally novel therapeutic strategy against several types of cancers. The present study utilized ultrafast shape recognition (USR), a virtual screening technique based on ligand-receptor 3D shape complementarity, to screen DrugBank database for novel p53-MDM2 inhibitors. Specifically, using 3D shape of one of the most potent crystal ligands of MDM2, MI-63, as the query molecule, six compounds were identified as potential p53-MDM2 inhibitors. These six USR hits were then subjected to molecular modeling investigations through flexible receptor docking followed by comparative binding energy analysis. These studies suggested a potential role of the USR-selected molecules as p53-MDM2 inhibitors. This was further supported by experimental tests showing that the treatment of human colon tumor cells with the top USR hit, telmisartan, led to a dose-dependent cell growth inhibition in a p53-dependent manner. It is noteworthy that telmisartan has a long history of safe human use as an approved anti-hypertension drug and thus may present an immediate clinical potential as a cancer therapeutic. Furthermore, it could also serve as a structurally-novel lead molecule for the development of more potent, small-molecule p53-MDM2 inhibitors against variety of cancers. Importantly, the present study demonstrates that the adopted USR-based virtual screening protocol is a useful tool for hit identification in the domain of small molecule p53-MDM2 inhibitors.

Comparative Binding Energy (COMBINE) Analysis for Understanding the Binding Determinants of Type II Dehydroquinase Inhibitors

Herein we report comparative binding energy (COMBINE) analyses to derive quantitative structure-activity relationship (QSAR) models that help rationalize the determinants of binding affinity for inhibitors of type II dehydroquinase (DHQ2), the third enzyme of the shikimic acid pathway. Independent COMBINE models were derived for Helicobacter pylori and Mycobacterium tuberculosis DHQ2, which is an essential enzyme in both these pathogenic bacteria that has no counterpart in human cells. These studies quantify the importance of the hydrogen bonding interactions between the ligands and the water molecule involved in the DHQ2 reaction mechanism. They also highlight important differences in the ligand interactions with the interface pocket close to the active site that could provide guides for future inhibitor design.

A structure-based design of new C2- and C13-substituted taxanes: tubulin binding affinities and extended quantitative structure–activity relationships using comparative binding energy (COMBINE) analysis

Ten novel taxanes bearing modifications at the C2 and C13 positions of the baccatin core have been synthesized and their binding affinities for mammalian tubulin have been experimentally measured. The design strategy was guided by (i) calculation of interaction energy maps with carbon, nitrogen and oxygen probes within the taxane-binding site of β-tubulin, and (ii) the prospective use of a structure-based QSAR (COMBINE) model derived from an earlier series comprising 47 congeneric taxanes. The tubulin-binding affinity displayed by one of the new compounds (CTX63) proved to be higher than that of docetaxel, and an updated COMBINE model provided a good correlation between the experimental binding free energies and a set of weighted residue-based ligand-receptor interaction energies for 54 out of the 57 compounds studied. The remaining three outliers from the original training series have in common a large unfavourable entropic contribution to the binding free energy that we attribute to taxane preorganization in aqueous solution in a conformation different from that compatible with tubulin binding. Support for this proposal was obtained from solution NMR experiments and molecular dynamics simulations in explicit water. Our results shed additional light on the determinants of tubulin-binding affinity for this important class of antitumour agents and pave the way for further rational structural modifications.

Lead Hopping for PfDHODH Inhibitors as Antimalarials Based on Pharmacophore Mapping, Molecular Docking and Comparative Binding Energy Analysis (COMBINE): A Three-Layered Virtual Screening Approach.

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Exploring the binding of BACE-1 inhibitors using comparative binding energy analysis (COMBINE).

BackgroundThe inhibition of the activity of β-secretase (BACE-1) is a potentially important approach for the treatment of Alzheimer disease. To explore the mechanism of inhibition, we describe the use of 46 X-ray crystallographic BACE-1/inhibitor complexes to derive quantitative structure-activity relationship (QSAR) models. The inhibitors were aligned by superimposing 46 X-ray crystallographic BACE-1/inhibitor complexes, and gCOMBINE software was used to perform COMparative BINding Energy (COMBINE) analysis on these 46 minimized BACE-1/inhibitor complexes. The major advantage of the COMBINE analysis is that it can quantitatively extract key residues involved in binding the ligand and identify the nature of the interactions between the ligand and receptor.ResultsBy considering the contributions of the protein residues to the electrostatic and van der Waals intermolecular interaction energies, two predictive and robust COMBINE models were developed: (i) the 3-PC distance-dependent dielectric constant model (built from a single X-ray crystal structure) with a q2 value of 0.74 and an SDEC value of 0.521; and (ii) the 5-PC sigmoidal electrostatic model (built from the actual complexes present in the Brookhaven Protein Data Bank) with a q2 value of 0.79 and an SDEC value of 0.41.ConclusionsThese QSAR models and the information describing the inhibition provide useful insights into the design of novel inhibitors via the optimization of the interactions between ligands and those key residues of BACE-1.

Histone Deacetylase Inhibitors: Structure-Based Modeling and Isoform-Selectivity Prediction

An enhanced version of comparative binding energy (COMBINE) analysis, named COMBINEr, based on both ligand-based and structure-based alignments has been used to build several 3-D QSAR models for the eleven human zinc-based histone deacetylases (HDACs). When faced with an abundance of data from diverse structure-activity sources, choosing the best paradigm for an integrative analysis is difficult. A common example from studies on enzyme-inhibitors is the abundance of crystal structures characterized by diverse ligands complexed with different enzyme isoforms. A novel comprehensive tool for data mining on such inhomogeneous set of structure-activity data was developed based on the original approach of Ortiz, Gago, and Wade, and applied to predict HDAC inhibitors' isoform selectivity. The COMBINEr approach (apart from the AMBER programs) has been developed to use only software freely available to academics.

Comparative Binding Energy (COMBINE) Analysis Supports a Proposal for the Binding Mode of Epothilones to β‐Tubulin

The conformational preferences of epothilone A (EPA) and a 12,13-cyclopropyl C12-epimerized analogue were explored in aqueous solution using molecular dynamics simulations. The simulated conformers that provided an optimal fit in the paclitaxel binding site of mammalian β-tubulin were then selected. The resulting modeled complexes were simulated before and after refinement of the M-loop to improve the fitting and assess ligand stability within the binding pocket. The tubulin-bound conformation of EPA was found to be unlike a previously reported solution obtained through mixed crystallographic/NMR/modeling studies. However, our conformation was in agreement with an NMR-based proposal although the exact binding pose within the site was different. Molecular models were built for the complexes of 14 epothilone derivatives with β-tubulin. A projection to latent structures regression method succeeded in providing a good prediction of the experimentally measured binding enthalpies for the whole set of ligands by assigning weights to a selection of interaction energy terms. These receptor-based, quantitative structure-activity relationships support the proposed binding mode, help confirm and interpret previously acquired experimental data, shed additional light on the effect of several β-tubulin mutations on ligand binding, and can potentially direct further experimental studies.

Quantum Mechanical Pairwise Decomposition Analysis of Protein Kinase B Inhibitors: Validating a New Tool for Guiding Drug Design

Quantum mechanical semiempirical comparative binding energy analysis calculations have been carried out for a series of protein kinase B (PKB) inhibitors derived from fragment- and structure-based drug design. These protein−ligand complexes were selected because they represent a consistent set of experimental data that includes both crystal structures and affinities. Seven scoring functions were evaluated based on both the PM3 and the AM1 Hamiltonians. The optimal models obtained by partial least-squares analysis of the aligned poses are predictive as measured by a number of standard statistical criteria and by validation with an external data set. An algorithm has been developed that provides residue-based contributions to the overall binding affinity. These residue-based binding contributions can be plotted in heat maps so as to highlight the most important residues for ligand binding. In the case of these PKB inhibitors, the maps show that Met166, Thr97, Gly43, Glu114, Ala116, and Val50, among other residues, play an important role in determining binding affinity. The interaction energy map makes it easy to identify the residues that have the largest absolute effect on ligand binding. The structure−activity relationship (SAR) map highlights residues that are most critical to discriminating between more and less potent ligands. Taken together the interaction energy and the SAR maps provide useful insights into drug design that would be difficult to garner in any other way.

GCOMBINE: A graphical user interface to perform structure‐based comparative binding energy (COMBINE) analysis on a set of ligand‐receptor complexes

We present gCOMBINE, a Java-written graphical user interface (GUI) for performing comparative binding energy (COMBINE) analysis (Ortiz et al. J Med Chem 1995; 38:2681-2691) on a set of ligand-receptor complexeswith the aim of deriving highly informative quantitative structure-activity relationships. The essence of the method is to decompose the ligand-receptor interaction energies into a series of terms, explore the origins of the variance within the set using Principal Component Analysis, and then assign weights to selected ligandresidue interactions using partial least squares analysis to correlate with the experimental activities or binding affinities. The GUI allows plenty of interactivity and provides multiple plots representing the energy descriptors entering the analysis, scores, loadings, experimental versus predicted regression lines, and the evolution of parameterssuch as r(2) (correlation coefficient), q(2) (cross-validated r(2)), and prediction errors as the number of extracted latent variables increases. Other representative features include the implementation of a sigmoidal dielectric function for electrostatic energy calculations, alternative cross-validation procedures (leave-N-out and random groups), drawing of confidence ellipses, and the possibility to carry out several additional tasks such as optional truncation of positive interaction energy values and generation of ready-to-use PDB files containing information related to the importance for activity of individual protein residues. This information can be displayed and color-coded using a standard molecular graphics program such as PyMOL. It is expected that this user-friendly tool will expand the applicability of the COMBINE analysis method and encourage more groups to use it in their drug design research programs.

Comparative binding energy analysis for binding affinity and target selectivity prediction

A major challenge in drug design is to obtain compounds that bind selectively to their target receptors and do not cause side-effects by binding to other similar receptors. Here, we investigate strategies for applying COMBINE (COMparative BINding Energy) analysis, in conjunction with PIPSA (Protein Interaction Property Similarity Analysis) and ligand docking methods, to address this problem. We evaluate these approaches by application to diverse sets of inhibitors of three structurally related serine proteases of medical relevance: thrombin, trypsin, and urokinase-type plasminogen activator (uPA). We generated target-specific scoring functions (COMBINE models) for the three targets using training sets of ligands with known inhibition constants and structures of their receptor-ligand complexes. These COMBINE models were compared with the PIPSA results and experimental data on receptor selectivity. These scoring functions highlight the ligand-receptor interactions that are particularly important for binding specificity for the different targets. To predict target selectivity in virtual screening, compounds were docked into the three protein binding sites using the program GOLD and the docking solutions were re-ranked with the target-specific scoring functions and computed electrostatic binding free energies. Limits in the accuracy of some of the docking solutions and difficulties in scoring them adversely affected the predictive ability of the target specific scoring functions. Nevertheless, the target-specific scoring functions enabled the selectivity of ligands to thrombin versus trypsin and uPA to be predicted.

Whither Combine? New Opportunities for Receptor-Based QSAR

Receptor based QSAR methods represent a computational marriage of structure activity relationship analysis and receptor structure based design that is providing valuable pharmacological insight to a wide range of therapeutic targets. One implementation, called Comparative Binding Energy (COMBINE) analysis, is particularly powerful by virtue of its explicit consideration of interatomic interactions between the ligand and receptor as the QSAR variable space. This review outlines the methodological basis for the COMBINE model, contrasts it relative to other 3D QSAR techniques, and discusses sample applications that illustrate recent key innovations. One major development discussed is the rigorous integration of multiple receptors into unified COMBINE models for probing bioactivity trends as a function of amino acid variation across a series of homologous protein receptors, and as a function of conformational variation within one single protein. Other important examples include a recent extension of the method to account for covalent effects arising from ligand binding, as well as successful application of a COMBINE model to high throughput virtual screening. This review concludes with discussions about possible future methodological refinements and their applications, including potential extensions to four-dimensional QSAR, and a potential role of quantum chemistry in addressing covalent bonding effects and parametric adaptivity.

COMBINE analysis of the specificity of binding of Ras proteins to their effectors

The small guanosine triphosphate (GTP)-binding proteins of the Ras family are involved in many cellular pathways leading to cell growth, differentiation, and apoptosis. Understanding the interaction of Ras with other proteins is of importance not only for studying signalling mechanisms but also, because of their medical relevance as targets, for anticancer therapy. To study their selectivity and specificity, which are essential to their signal transfer function, we performed COMparative BINding Energy (COMBINE) analysis for 122 different wild-type and mutant complexes between the Ras proteins, Ras and Rap, and their effectors, Raf and RalGDS. The COMBINE models highlighted the amino acid residues responsible for subtle differences in binding of the same effector to the two different Ras proteins, as well as more significant differences in the binding of the two different effectors (RalGDS and Raf) to Ras. The study revealed that E37, D38, and D57 in Ras are nonspecific hot spots at its effector interface, important for stabilization of both the RalGDS-Ras and Raf-Ras complexes. The electrostatic interaction between a GTP analogue and the effector, either Raf or RalGDS, also stabilizes these complexes. The Raf-Ras complexes are specifically stabilized by S39, Y40, and D54, and RalGDS-Ras complexes by E31 and D33. Binding of a small molecule in the vicinity of one of these groups of amino acid residues could increase discrimination between the Raf-Ras and RalGDS-Ras complexes. Despite the different size of the RalGDS-Ras and Raf-Ras complexes, we succeeded in building COMBINE models for one type of complex that were also predictive for the other type of protein complex. Further, using system-specific models trained with only five complexes selected according to the results of principal component analysis, we were able to predict binding affinities for the other mutants of the particular Ras-effector complex. As the COMBINE analysis method is able to explicitly reveal the amino acid residues that have most influence on binding affinity, it is a valuable aid for protein design.

Binding affinity prediction of non-peptide inhibitors of HIV-1 protease using COMBINE model introduced from peptide inhibitors

Comparative binding energy (COMBINE) analysis method is one of the QSAR techniques for the prediction of biological activities of inhibitors based on interaction energies between ligands and proteins decomposed into each amino acid residue. We supposed that the predictive ability of the COMBINE method does not depend essentially on the molecular frameworks of ligands. To verify this idea, we performed the COMBINE analysis of non-peptide inhibitors of HIV-1 protease (HIVp), where the prediction model was constructed using inhibitors with a peptide scaffold as a training set. The predictive performance of the AMBER and CHARMm force fields was very high and at the same level ( q 2 = 0.75, 0.67, SDEP cv = 0.76, 0.89, and SDEP ex = 0.92, 0.66, respectively). The high predictive ability of the COMBINE method for the distinct scaffold compounds is due to the informative description of the interaction energies for compounds that are located at the binding site. This result suggests that COMBINE analysis may be applied not only to the lead optimization stage but also to the lead evolution stages.

Comparative Binding Energy Analysis Considering Multiple Receptors: A Step toward 3D-QSAR Models for Multiple Targets

Comparative binding energy analysis, a technique to derive receptor-based three-dimensional quantitative structure-activity relationships (3D-QSAR), is herein extended to consider both affinity and selectivity in the derivation of the QSAR model. The extension is based on allowing multiple structurally related receptors to enter the X-matrix employed in the derivation of the structure-activity model. As a result, a single model common to all of them is obtained that considers both intra- and inter-receptor affinity differences for a given congeneric series. We applied the technique to a series of 88 3-amidinophenylalanines, binding to thrombin, trypsin, and factor Xa (fXa). A single predictive regression model for the three receptors involving 202 complexes, with a leave-one out (LOO) cross-validated Q(2) of 0.689, was obtained, and selectivity requirements were investigated. We find that total or partial occupancy of any of the three main pockets in the binding site (D-site, P-site, and the rim of the S1-site) leads to higher affinity across the family. However, the fact that thrombin can make stronger interactions in the P-site, as a result of its exclusive 60-loop, makes of this site a specificity pocket for this thrombin. Occupancy of the D-site leads to more active inhibitors toward fXa for the same reason, but the model does not highlight strongly the D-box because inhibitors are too short to fully occupy it. Negative charge density in the neighborhood of position 88 (a Lys insertion in thrombin) is found to be a determinant for thrombin recognition. These results were consistent with previous studies on selectivity in the thrombin/trypsin/fXa system.

Semiempirical Comparative Binding Energy Analysis (SE-COMBINE) of a Series of Trypsin Inhibitors

A scheme to decompose the intermolecular interaction energy of a series of complexes at the semiempirical (SE) level has been developed and validated. The comparative binding energy analysis (COMBINE) (Ortiz, A. R.; Pisabarro, M. T.; Gago, F.; Wade, R. C. J. Med. Chem. 1995, 38, 2681-2691) and the semiempirical quantum mechanical method pairwise energy decomposition (PWD) (Raha, K.; van der Vaart, A. J.; Riley, K. E.; Peters, M. B.; Westerhoff, L. M. Kim, H.; Merz, K. M., Jr. J. Am. Chem. Soc. 2005, 127, 6583-6594) were coupled together to form SE-COMBINE. This approach calculates the residue pairwise electrostatic interaction energies, and QSAR models were built with the energies as descriptors using partial least squares (PLS). The application of SE-COMBINE was used as an investigation of the intermolecular interactions between 88 benzamidine inhibitors and trypsin and to test the ability of this new method to predict binding free energies. The predictive capability of SE-COMBINE is shown to be comparable to those of other QSAR methods, and using graphical intermolecular interaction maps (IMMs) enhances the interpretability of receptor-based QSARs.

Quantitative Analysis of Substrate Specificity of Haloalkane Dehalogenase LinB from Sphingomonas paucimobilis UT26

Haloalkane dehalogenases are microbial enzymes that cleave a carbon-halogen bond in halogenated compounds. The haloalkane dehalogenase LinB, isolated from Sphingomonas paucimobilis UT26, is a broad-specificity enzyme. Fifty-five halogenated aliphatic and cyclic hydrocarbons were tested for dehalogenation with the LinB enzyme. The compounds for testing were systematically selected using a statistical experimental design. Steady-state kinetic constants K(m) and k(cat) were determined for 25 substrates that showed detectable cleavage by the enzyme and low abiotic hydrolysis. Classical quantitative structure-activity relationships (QSARs) were used to correlate the kinetic constants with molecular descriptors and resulted in a model that explained 94% of the experimental data variability. The binding affinity of the tested substrates for this haloalkane dehalogenase correlated with hydrophobicity, molecular surface, dipole moment, and volume:surface ratio. Binding of the substrate molecules in the active site pocket of LinB depends nonlinearly on the size of the molecules. Binding affinity increases with increasing substrate size up to a chain length of six carbon atoms and then decreases. Comparative binding energy (COMBINE) analysis was then used to identify amino acid residues in LinB that modulate its substrate specificity. A model with three statistically significant principal components explained 95% of the experimental data variability. van der Waals interactions between substrate molecules and the enzyme dominated the COMBINE model, in agreement with the importance of substrate size in the classical QSAR model. Only a limited number of protein residues (6-8%) contribute significantly to the explanation of variability in binding affinities. The amino acid residues important for explaining variability in binding affinities are as follows: (i) first-shell residues Asn38, Asp108, Trp109, Glu132, Ile134, Phe143, Phe151, Phe169, Val173, Trp207, Pro208, Ile211, Leu248, and His272, (ii) tunnel residues Pro144, Asp147, Leu177, and Ala247, and (iii) second-shell residues Pro39 and Phe273. The tunnel and the second-shell residues represent the best targets for modulating specificity since their replacement does not lead to loss of functionality by disruption of the active site architecture. The mechanism of molecular adaptation toward a different specificity is discussed on the basis of quantitative comparison of models derived for two protein family members.

Comparative Structural and Energetic Analysis of WW Domain–Peptide Interactions

WW domains are small globular protein interaction modules found in a wide spectrum of proteins. They recognize their target proteins by binding specifically to short linear peptide motifs that are often proline-rich. To infer the determinants of the ligand binding propensities of WW domains, we analyzed 42 WW domains. We built models of the 3D structures of the WW domains and their peptide complexes by comparative modeling supplemented with experimental data from peptide library screens. The models provide new insights into the orientation and position of the peptide in structures of WW domain-peptide complexes that have not yet been determined experimentally. From a protein interaction property similarity analysis (PIPSA) of the WW domain structures, we show that electrostatic potential is a distinguishing feature of WW domains and we propose a structure-based classification of WW domains that expands the existent ligand-based classification scheme. Application of the comparative molecular field analysis (CoMFA), GRID/GOLPE and comparative binding energy (COMBINE) analysis methods permitted the derivation of quantitative structure-activity relationships (QSARs) that aid in identifying the specificity-determining residues within WW domains and their ligand-recognition motifs. Using these QSARs, a new group-specific sequence feature of WW domains that target arginine-containing peptides was identified. Finally, the QSAR models were applied to the design of a peptide to bind with greater affinity than the known binding peptide sequences of the yRSP5-1 WW domain. The prediction was verified experimentally, providing validation of the QSAR models and demonstrating the possibility of rationally improving peptide affinity for WW domains. The QSAR models may also be applied to the prediction of the specificity of WW domains with uncharacterized ligand-binding properties.