Overview of the bit-mask construction in EleKit. (1) a near-or-inside mask of R P is created, (2) a near-but-not-inside mask of L P is created, (3) a near-but-not-inside mask of L SM is created, (4) the logical conjunction of the three masks is used to select points to correlate from the electrostatic potentials of L P and L SM . 

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... advent of the ‘omics’ era, it has become clear that most proteins do not act in solitude but depend on Protein-Protein Interactions (PPIs) to exert their biological function. It has been estimated that the number of PPIs in humans ranges from , 130,000 [1] to , 650,000 [2] and these PPIs are crucial for the regulation of many biological processes. PPIs are often involved in processes associated with diseases, therefore targeting PPIs with small molecule PPI inhibitors (SMPPIIs) opens a pipeline for the development of novel drug classes against a variety of diseases. While many small molecule drugs targeting enzymes, nuclear receptors, ion channels and G-protein coupled receptors have been developed, the number of reported successes in the discovery of SMPPIIs remains fairly low. As a matter of fact, PPIs were once thought to be high hanging fruits for drug discovery [3]. PPIs were even considered to be undruggable, mostly because of their relative flat but extensive interfaces [4]. Though initially thought to be undruggable, an increasing number of SMPPIIs have been reported in recent years [5]. However, the number of deposited 3D SMPPII receptor complex structures remain far more limited than the number of reported successful cases. This hinders the understanding of their mechanism of action and chemical space properties [6]. Commonly used methods for screening are computational docking [7] and pharmacophore-based screening [8]. It was observed that the crucial interactions between a protein ligand and its protein receptor are often similar to those between the SMPPII and the protein receptor [9,10]. Thus, the PPI interface can be used to create a pharmacophore query to screen for small molecule ligands [11,12]. Another approach is to exploit the principle of electrostatic complementarity in molecular recognition. Next to steric complementarity, electrostatics are one of the main driving forces involved in molecular recognition [13]. Despite the complex biophysical nature of the electrostatic potential, calculations for macromolecular systems are nowadays tractable [14,15]. Electrostatics are known to play a key role in protein-DNA [16], protein-protein [17] and protein-substrate [13] recognitions. Given the importance of electrostatics for the molecular recognition event, electrostatics have been used to study protein similarity [18–20] and the nature of protein-protein interactions [17,21–24]. More specifically, the electrostatic complementarity between protein-protein interfaces has long been a subject of investigation [22,23]. Using the correlation of electrostatic potentials as a quantitative measure, the electrostatic complementarity between PPI interfaces has been demonstrated [17,24]. Other studies focused on the conservation of the electrostatic potentials through evolution [25] and its role in molecular association kinetics [26]. It is generally accepted that there is a high degree of complementarity in shape and electrostatics between a ligand and its receptor. This implies that molecules with similar shape and electrostatic properties may bind to the same receptor. This principle has been used to identify small molecule inhibitors similar to natural substrates or known inhibitors by screening for compounds with similar shape, volume and electrostatics [27–30]. An SMPPII cannot occupy the same shape and volume as its much bigger protein-ligand counterpart. However, it can still be assumed that there is some local electrostatic potential similarity between an SMPPII and a ligand protein, since they recognize the same binding site on the receptor. A recent example of the usefulness of taking electrostatic potential similarity into account while designing an SMPPII can be found in the work of Cavalluzo et al. [31], where an SMPPII was designed de novo by including electrostatic similarity. This success has motivated our effort to systematically investigate the complementarity in electrostatic potential between small molecules and protein ligands binding to the same protein receptor, and its potential use to assist in the rational design of SMPPIIs. For this purpose, a tool named EleKit was developed. To compute the partial charges and electrostatic potentials, EleKit builds upon PDB2PQR [32] and APBS [15]. EleKit requires two sets of complex structures in order to calculate the electrostatic similarity between a protein ligand and a small molecule ligand: (i) the PPI complex of the protein-ligand (L P ) with the protein-receptor (R P ) and (ii) a small molecule ligand (L SM ) in its predicted or experimentally determined conformation on the protein-receptor (R P ). The EleKit method is shown schematically in figure 1. First, the electrostatic potentials around and are computed using APBS (parameters listed in table 1) and stored in 3D grids. Since only the area where and intersect is most likely to be relevant for molecular recognition, a bit mask is created on the electrostatic potential grids (figure 2). The goal of this mask is to take into account only those points in space that are not only in the solvent region around and but also near the interface atoms of R P . To create this mask, a distance cutoff is needed. This distance is used when dilating (a morphological mathematical operation) the molecular surface. Based on the hydrogen bond length ( , 2.5A ̊ ) and the facts that enough points are needed for correlation and that the local similarity is our focus, a cutoff value ranging from 1.4 A ̊ to 3.5 A ̊ seems reasonable. All experiments reported in this study were performed with an intermediate cutoff value of 2.0 A ̊ . Using 3.0 A ̊ or 4.0 A ̊ would have very little impact on the results (data not shown). Finally, the similarity between electrostatic potentials of and is assessed by correlating values at the grid points within the mask using the Spearman rank-order correlation coefficient ( r ). Additional similarity scores (Carbo index [33], Hodgkin index [34], Pearson’s r and a Tanimoto score) are also calculated. EleKit is written in OCaml [35] () and computations are parallelized by the Parmap library [36]. Experiments were run on Linux computing nodes with 2.4GHz Intel Xeon processors. EleKit takes between ten seconds to two minutes per ligand molecule and can parallelize the computation of several ligands when run on a multi-core machine. EleKit is released as open source and available from the authors’ website The EleKit method was applied to analyze previously reported cases of SMPPIIs, for which accurate structures of the PPI as well as the SMPPII receptor complex are available in the PDB (table 2). Additionally, the SMPPIIs are required to bind in the PPI interface, allowing for a substantial overlap between the protein ligand and the SMPPII and thus excluding allosteric inhibition mechanisms. The approach used in EleKit to perform comparison of electrostatic potentials resembles what has been done previously on proteins [18–21]. Analysis of Electrostatic Similarities of Proteins (AESOP) [20], the method of Dlugosz et al. [19] and Protein Interaction Property Similarity Analysis (PIPSA) [18,37] also use APBS as their electrostatic computation engine. PIPSA can also use University of Houston Brownian Dynamics [38] (UHBD). While EleKit relies on the Spearman rank-order correlation coefficient (as McCoy et al. [17]), PIPSA uses the Hodgkin index [34] to numerically assess the similarity of electrostatic potentials. AESOP uses the Average Normalized Difference [39]. The method of Dlugosz et al. [19] approximates the electrostatic potential with spherical harmonics and uses a similarity index specifically designed to compare the obtained rotation-invariant descriptors. EleKit, similarly to several other methods [18,19,37], uses boolean masks to select a region over which electrostatic potentials are compared. All methods vary in the way masks are constructed. Electrostatic similarity analysis for these different SMPPII- related structures indicate that several exhibit correlation. In general, correlation between electrostatic potentials of SMPPIIs and electrostatic potentials of the respective ligand proteins are observed (table 2). This is especially true for the SMPPIIs targeting the HDM2:p53, HIV-1 Integrase:LEDGF/p75, Integrin:Fibrinogen, IL2:IL2R and XIAP:smac interactions. The highest similarity between a protein ligand and a small molecule ligand can be observed in the HIV-1 Integrase:LEDGF/p75 and the Integrin:Fibrinogen interactions and their respective inhibitors. In these cases, r is on average , 0.52 and , 0.73 respectively (table 2). The origin of these classes of SMPPIIs can be traced back to pharmacophore based discovery of lead compounds designed to mimic the interactions observed at the PPI interface [40,41]. For the inhibitors of the HDM2:p53 interaction, the majority of the inhibitors exhibit electrostatic potential similarity. However, a few show low correlations ( r v 0 : 2 ) and in one case even some anti- correlation ( r & { 0 : 15 ). Interestingly, the Tanimoto score shows similarity in all HDM2:p53 cases. The electrostatic potentials between inhibitors and protein ligands in ZipA:FtsZ and VHL:HIF1 still correlate although less strongly than in other cases. These inhibitors are observed to be less active when tested. For inhibitors targeting the XIAP:smac interaction, which originated from peptidomimetic design, some compounds exhibit lower similarity than expected. This can be explained by the divergence of conformations of the receptor protein, since the XIAP:smac complex was solved by NMR while the structures of XIAP bound to inhibitors were solved by X-ray crystallography. The PPI complex solved by NMR spectroscopy are more difficult to superpose onto the crystal structure conformation obtained for the SMPPII complex. The inhibitors of the IL2:IL2R interaction are well known for binding to the IL2R interface by causing a rotameric change of a ...