Detecting similar binding pockets to enable systems polypharmacology

<div><p>In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.</p></div

A selective target combination.

<p>(A) Structures of GPI (PDB ID: 1iri, chain C), DLD (PDB ID: 1zmc, chain C), PGD (PDB ID: 2jkv, chain D), SORD (PDB ID: 1pl8, chain C) and RPE (PDB ID: 3ovp, chain C) are displayed, together with cavity residues. Please note that these cavities are representative, as several structures exist for each of the proteins. For a deeper exploration, please refer to Supporting <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005522#pcbi.1005522.s001" target="_blank">S1 Data</a>. (B) Best similarity between cavities in GPI, DLD, PGD, SORD and RPE. Highest similarities represent, in principle, easier cases of polypharmacology design. In the upper triangle, the main metabolic chemotypes related to the enzymes are also displayed. (C) Reduction of biomass production upon the simultaneous inhibition in cancer (red) and normal (blue) cell lines. The effect of individual inhibitions in also showed. (D) Influence of each inhibition on metabolic cancer hallmarks. O₂ stands for oxygen consumption, Lac for lactate secretion, Glu for glucose uptake, and ROS for reactive oxygen species production. The assignment of ‘strong' and ‘mild’ reversals was based on visual inspection of the maximal flux of the corresponding reactions. When the maximal flux approached (>50% of the difference) the healthy cell line, it was classified as ‘strong reversal' ('strong worsening' if it otherwise diverged); `mild' effects were assigned to effects of less than 50%; ‘no effect' was assigned when one could observe essentially no change.</p

Distribution of multi-target opportunities in the binary interactome.

<p>(A) Network-based influence between proteins with similar cavities (red) and ligands (blue), compared to the background influence of random proteins in the interactome (gray). The relative influence between targets in drug combinations is plotted in purple. The distributions include groups with up to five comparisons, and the maximum influence among all of the pairs in the group was taken as the representative; in other words, closest pairs were picked in those drug combinations that involved more than one target per drug, and in cavity and ligand comparisons that brought together more than two proteins. To correct for the group size, we defined a Z-score by sampling 10,000 random groups in the size range. The orange shape spans the area between the quartiles of drug combinations and the unbiased sampling of nodes. (B) Characteristics of protein pairs that could be targeted simultaneously (polypharmacology opportunities) or that are used in successful drug combinations. In the upper plot, proportion of pairs of protein belonging to the same topological module in the network (as defined by the overlapping cluster generator [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005522#pcbi.1005522.ref042" target="_blank">42</a>]); in the middle plot, pairs of proteins with the same biological process (BP) broad (‘slim’) term [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005522#pcbi.1005522.ref070" target="_blank">70</a>]; similarly, in the bottom plot, pairs of proteins with the same molecular function (MF). For simplicity, in the case of drug combinations, only those with one target per drug are included here.</p

BioGPS Descriptors for Rational Engineering of Enzyme Promiscuity and Structure Based Bioinformatic Analysis

<div><p>A new bioinformatic methodology was developed founded on the Unsupervised Pattern Cognition Analysis of GRID-based BioGPS descriptors (Global Positioning System in Biological Space). The procedure relies entirely on three-dimensional structure analysis of enzymes and does not stem from sequence or structure alignment. The BioGPS descriptors account for chemical, geometrical and physical-chemical features of enzymes and are able to describe comprehensively the active site of enzymes in terms of “pre-organized environment” able to stabilize the transition state of a given reaction. The efficiency of this new bioinformatic strategy was demonstrated by the consistent clustering of four different Ser hydrolases classes, which are characterized by the same active site organization but able to catalyze different reactions. The method was validated by considering, as a case study, the engineering of amidase activity into the scaffold of a lipase. The BioGPS tool predicted correctly the properties of lipase variants, as demonstrated by the projection of mutants inside the BioGPS “roadmap”.</p></div

Schematic illustration of the generation of BioGPS molecular descriptors.

<p>(a) Starting from the GRID mapping of enzyme active site the BioGPS algorithm identifies points used for generating quadruplets and a Common Reference Framework. (b) In order to compare two cavities (active sites), the algorithm searches for similar quadruplets and then overlaps the corresponding 3D structures (all against all approach). At the end a series of probe scores is generated.</p

Comparison of 1GVK (protease) and 2W22 (lipase) active site H-bond acceptor pseudo-MIFs.

<p>1GVK and 2W22 are represented as green and magenta cartoon respectively. 1GVK pseudo-MIFs are represented as green surfaces. 2W22 pseudo-MIFs are represented as magenta surfaces.</p

Unsupervised Pattern Cognition Analysis (UPCA) and clustering of Ser hydrolases on the basis of BioGPS descriptors (global score).

<p>The enzymes are labelled according to their PDB code. Lipases are indicated in blue, esterases in green, amidases in red and proteases in cyan.</p

Comparison of 1GVK (protease) and 2W22 (lipase) active site shape.

<p>1GVK and 2W22 are represented as green and magenta cartoon respectively. Active site shapes are represented as wireframes: 1GVK active site shape in green while the active site shape of 2W22 is in magenta.</p

Unsupervised Pattern Cognition Analysis (UPCA) of BioGPS descriptors generated by DRY probe (hydrophobicity).

<p>The analyzed enzymes are labelled according to their PDB code and colored as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109354#pone-0109354-g003" target="_blank">figure 3</a>. Improved mutants are highlighted in black triangles and poor mutants are in pink triangles.</p

Unsupervised Pattern Cognition Analysis (UPCA) of BioGPS descriptors generated by O probe (H-bond donor capacity).

<p>The analyzed enzymes are labelled according to their PDB code and colored as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109354#pone-0109354-g003" target="_blank">figure 3</a>. Improved mutants are highlighted in black triangles whereas poor mutants are in pink triangles.</p