AI FOR DRUG DISCOVERY & OMICS

AI applied to human metabolism is a well-establish research topic in our laboratory. Since 2000, we worked to develop computational tools for human metabolism prediction by cytochromes P450, and with time other enzymes were included in the panel including Flavine monooxygenase 3 and aldehyde oxidase. Our published results are now the pillars in the MetaSite Software. In the last couple of years we are working to add the prediction of metabolism by phase II enzymes and the manuscript is in preparation. Stay tuned!

Cruciani G, Milani N, Benedetti P, Lepri S, Cesarini L, Baroni M, Spyrakis F, Tortorella S, Mosconi E, Goracci L. From Experiments to a Fast Easy-to-Use Computational Methodology to Predict Human Aldehyde Oxidase Selectivity and Metabolic Reactions. J Med Chem, 61(1), 360–371.

Lepri S, Ceccarelli M, Milani N, ortorella S, Cucco A, Valeri A, Goracci L, Brink A, Cruciani G. Structure-metabolism relationships in human-AOX: Chemical insights from a large database of aza-aromatic and amide compounds. PNAS 2017, 114(16), E3178-E3187.

Cruciani G, Valeri A, Goracci L, Pellegrino RM, Buonerba F, Baroni M. Flavin monooxygenase metabolism: why medicinal chemists should matter. J Med Chem, 2014, 57 (4), 6183-6196.

Cruciani G, Carosati E, De Boeck B, Ethirajulu K, Mackie C, Howe T, Vianello R. MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J Med Chem, 2005, 48(22), 6970-9.

We also contributed to develop Lipostar, a software for LC-MS based lipidomics, which has been recently updated to Lipostar2. Several functions were improved to support untargeted lipidomics, and additional effort was made in the analysis of the epilipidome, which is the term used to define modified lipids formed by enzymatic or non-enzymatic chemical reactions. We also added the trend analysis module, specifically designed to seek for biomarkers either with a hypothesis driven approach or a totally unbiased approach. Several collaborations in Europe and USA are in place to apply the software in exciting research fields.

Ni Z, Wölk M, Jukes G, Mendivelso Espinosa K, Ahrends R, Aimo L, Alvarez-Jarreta J, Andrews S, Andrews R, Bridge A, Clair GC, Conroy MJ, Fahy E, Gaud C, Goracci L, Hartler J, Hoffmann N, Kopczyinki D, Korf A, Lopez-Clavijo AF, Malik A, Ackerman JM, Molenaar MR, O'Donovan C, Pluskal T, Shevchenko A, Slenter D, Siuzdak G, Kutmon M, Tsugawa H, Willighagen EL, Xia J, O'Donnell VB, Fedorova M. Guiding the choice of informatics software and tools for lipidomics research applications. Nature Methods. 2022, 20, 193–204.

Criscuolo A, Nepachalovich P, Garcia-Del Rio DF, Lange M, Ni Z, Baroni M, Cruciani G, Goracci L, Blüher M, Fedorova M. Analytical and computational workflow for in-depth analysis of oxidized complex lipids in blood plasma. Nature Communications. 2022, Nov 1;13(1):6547.

Damiani T, Bonciarelli S, Thallinger GG, Koehler N, Krettler CA, Salihoglu AK, Korf A, Pauling JK, Pluskal, T, Ni Z, Goracci L. Software and Computational Tools for LC-MS-Based Epilipidomics: Challenges and Solutions. Analytical Chemistry, 2023, 95, 287-303.

Goracci L, Valeri A, Sciabola S, Aleo MD, Moritz W, Lichtenberg J, Cruciani G. A Novel Lipidomics-Based Approach to Evaluating the Risk of Clinical Hepatotoxicity Potential of Drugs in 3D Human Microtissues. Chemical Research in Toxicology, 2020, 33, 258-270.

Goracci L, Tortorella S, Tiberi P, Pellegrino RM, Di Veroli A, Valeri A, Cruciani G. Lipostar, a Comprehensive Platform-Neutral Cheminformatics Tool for Lipidomics. Analytical Chemistry 2017, 89(11), 6258-6265.

In this context, we developed BioGPS and more recently, Chromatic and Eliot. BioGPS was developed reasoning that identifying structurally similar sites can be useful for techniques such as drug repurposing, and also in a polypharmacological approach to deliberately affect multiple targets in a disease pathway, or to explain unwanted off-target effects. BioGPS is based on the software FLAP which combines GRID Molecular Interactions Fields (MIFs) and pharmacophoric fingerprints to enables pocket-pocket virtual screening. BioGPS comprises the automatic preparation of protein structure data, identification of binding sites, and subsequent comparison by aligning the sites and directly comparing the MIFs. Concerning Chromatic (CROss-relationship MAp of CaviTIes from Coronaviruses), this is a collection of all protein cavities on SARS-CoV-2, SARS-CoV, and MERS-CoV X-ray structures, which were computed to generate computed similarity map by using molecular interaction fields (MIFs). We also recently contributed to the development of the ELIOT platform to navigate the E3 pocketome and aid the design of new PROTACs. ELIOT (E3 LIgase pocketOme navigaTor) is an accurate and complete platform containing the E3 ligase pocketome to enable navigation and selection of new E3 ligases and new ligands for the design of new PROTACs. All E3 ligase pockets were described with innovative 3D descriptors including their PROTAC-ability score, and similarity analyses between E3 pockets are presented. Tissue specificity and their degree of involvement in patients with specific cancer types are also annotated for each E3 ligase, enabling appropriate selection for the design of a PROTAC with improved specificity.

Lo Piparo E, Siragusa L, Raymond F, Passeri GI, Cruciani G, Schilter B. Bisphenol A binding promiscuity: A virtual journey through the universe of proteins. ALTEX, 2020, 37(1), 85-94.
Duran-Frigola M, Siragusa L, Ruppin E, Barril X, Cruciani G, Aloy P. Detecting similar binding pockets to enable systems polypharmacology. PLoS Comput Biol, 2017, 13(6), e1005522.
Siragusa L, Luciani R, Borsari C, Ferrari S, Costi MP, Cruciani G, Spyrakis F. Comparing drugs image and repurposing drugs with BioGPS and FLAP dock: the thymidylate synthase case. ChemMedChem, 2016, 11(15), 1653-66.
Mangiatordi GF, Alberga D, Siragusa L, Goracci L, Lattanzi G, Nicolotti O. Challenging AQP4 druggability for NMO-IgG antibody binding using molecular dynamics and molecular interaction fields. BBA Biomembranes, 2015, 1848(7), 1462-71.
Siragusa L, Cross S, Baroni M, Goracci L, Cruciani G. BioGPS: Navigating biological space to predict polypharmacology, off-targeting, and selectivity. Bioinformatics, 2015, 83(3), 517-32.
Ferrario V, Siragusa L, Ebert C, Baroni M, Foscato M, Cruciani G, Gardossi L. BioGPS descriptors for rational engineering of enzyme promiscuity and structure based bioinformatic analysis. PLoS ONE, 2014, 9(10), e109354.

Siragusa L, Menna G, Buratta F, Baroni M, Desantis J, Cruciani G, Goracci L. CROMATIC: Cross-Relationship Map of Cavities from Coronaviruses. J Chem Inf Model. 2022, 62(12), 2901-08.

Palomba T, Baroni M, Cross S, Cruciani G, Siragusa L. ELIOT: A platform to navigate the E3 pocketome and aid the design of new PROTACs. Chem Biol Drug Des. 2023,101, 69-86.

Palomba T, Tassone G, Vacca C, Bartalucci M, Valeri A, Pozzi C, Cross S, Siragusa L, Desantis J. Exploiting ELIOT for scaffold-repurposing opportunities: TRIM33 a possible novel E3 ligase to expand the toolbox for PROTAC design. Int J Mol Sci, 2022, 23, 14218.