Structural feature-driven pattern analysis for multitarget modulator landscapes

Abstract

Motivation Multitargeting features of small-molecules have been of increasing interest in recent years. Polypharmacological drugs that address several therapeutic targets may provide greater therapeutic benefits for patients. Furthermore, multitarget compounds can be used to address proteins of the same (or similar) protein families for their exploration as potential pharmacological targets. In addition, the knowledge of multitargeting features is of major importance in the drug selection process; particularly in ultra-large virtual screening procedures to gain high-quality compound collections. However, large-scale multitarget modulator landscapes are almost non-existent. Results We implemented a specific feature-driven computer-aided pattern analysis (C@PA) to extract molecular-structural features of inhibitors of the model protein family of ATP-binding cassette (ABC) transporters. New molecular-structural features have been identified that successfully expanded the known multitarget modulator landscape of pan-ABC transporter inhibitors. The prediction capability was biologically confirmed by the successful discovery of pan-ABC transporter inhibitors with a distinct inhibitory activity profile. Availability and implementation The multitarget dataset is available under the http://www.panabc.info URL and its use is free of charge. Supplementary information Supplementary data is available at Bioinformatics online.

Strategies to gain novel Alzheimer’s disease diagnostics and therapeutics using modulators of ABCA transporters

“…In addition to these classical computational approaches, similarity search and pharmacophore modelling, a pattern analysis approach (‘C@PA’ = computer-aided pattern analysis’) has been reported recently. 18 , 19 , 495 Pattern analysis extracts both basic scaffolds and the statistical distribution of substructural elements amongst the template ligands. It works similarly to non-physicochemical properties-related fingerprints and conserves substructural features as they are present in the molecules.…”

“… 19 Adaption of the model (‘C@PA_1.2’) through additional non-statistical and exploratory measures increased the biological hit rate to 40%, 18 and an additional extension of the model enabled the discovery of the ‘outer multitarget modulator landscape’, which represented weak multitarget bioactivities (>10 μM) supporting the discovery of a larger number of multitarget agents. 495 The hit rates are impressive considering that this approach takes several targets with individual ‘ligand preferences’ into account. Furthermore, as several ABC transporters of distinct subfamilies were considered (ABCB1, ABCC1, ABCG2), the resultant multitarget agents open up the possibility to explore under-studied ABC transporters, 18 in particular ABCA transporters in terms of AD.…”

“… 493 Also, certain pattern analysis approaches have used a data set derived from a similarity search and pharmacophore modelling approach, and hence, can also be considered a combined computational approach. 18 , 495 …”

“…This has led to new lead molecules in the past. 18 , 19 , 493 , 495 Particularly the knowledge on ABCA1 and ABCA8 inhibitors and substrates is of interest, because these compounds inherit the molecular-structural information that is critical for direct binding to these transporters. Considering the newly developed pattern analysis methodology, C@PA, 18 , 19 , 495 the scaffolds and substructural composition of this set of molecules may reveal the critical necessities for direct interaction with ABCA transporters.…”

“…18 , 19 , 493 , 495 Particularly the knowledge on ABCA1 and ABCA8 inhibitors and substrates is of interest, because these compounds inherit the molecular-structural information that is critical for direct binding to these transporters. Considering the newly developed pattern analysis methodology, C@PA, 18 , 19 , 495 the scaffolds and substructural composition of this set of molecules may reveal the critical necessities for direct interaction with ABCA transporters. C@PA is therefore of high relevance because it was specifically developed to gain multitargeting pan-ABC transporter modulators 18 , 19 , 495 – molecules that particularly interact with different ABC transporters of different subfamilies.…”