Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification

Repurposing designed mutants: a valuable strategy for computer-aided laccase engineering – the case of POXA1b

The broad specificity of laccases, a direct consequence of their shallow binding site, makes this class of enzymes a suitable template to build specificity toward putative substrates. In this work, a computational methodology that accumulates beneficial interactions between the enzyme and the substrate in productive conformations is applied to oxidize 2,4-diamino-benzenesulfonic acid with POXA1b laccase. Although the experimental validation of two designed variants yielded negative results, most likely due to the hard oxidizability of the target substrate, molecular simulations suggest that a novel polar binding scaffold was designed to anchor negatively charged groups. Consequently, the oxidation of three such molecules, selected as representative of different classes of substances with different industrial applications, significantly improved. According to molecular simulations, the reason behind such an improvement lies in the more productive enzyme–substrate binding achieved thanks to the designed polar scaffold. In the future, mutant repurposing toward other substrates could be first carried out computationally, as done here, testing molecules that share some similarity with the initial target. In this way, repurposing would not be a mere safety net (as it is in the laboratory and as it was here) but rather a powerful approach to transform laccases into more efficient multitasking enzymes.This work was funded by INDOX (KBBE-2013-7-613549) European project and CTQ2013-48287-R Spanish National Project. V. G. and E. M. acknowledge Università degli Studi di Napoli and Generalitat de Catalunya for their respective predoctoral fellowships.Peer ReviewedPostprint (author's final draft

Pyrone-based inhibitors of metalloproteinase types 2 and 3 may work as conformation-selective inhibitors.

Matrix metalloproteinases are zinc-containing enzymes capable of degrading all components of the extracellular matrix. Owing to their role in human disease, matrix metalloproteinase have been the subject of extensive study. A bioinorganic approach was recently used to identify novel inhibitors based on a maltol zinc-binding group, but accompanying molecular-docking studies failed to explain why one of these inhibitors, AM-6, had approximately 2500-fold selectivity for MMP-3 over MMP-2. A number of studies have suggested that the matrix-metalloproteinase active site is highly flexible, leading some to speculate that differences in active-site flexibility may explain inhibitor selectivity. To extend the bioinorganic approach in a way that accounts for MMP-2 and MMP-3 dynamics, we here investigate the predicted binding modes and energies of AM-6 docked into multiple structures extracted from matrix-metalloproteinase molecular dynamics simulations. Our findings suggest that accounting for protein dynamics is essential for the accurate prediction of binding affinity and selectivity. Additionally, AM-6 and other similar inhibitors likely select for and stabilize only a subpopulation of all matrix-metalloproteinase conformations sampled by the apo protein. Consequently, when attempting to predict ligand affinity and selectivity using an ensemble of protein structures, it may be wise to disregard protein conformations that cannot accommodate the ligand