Towards Personalized Medicine: Computational Approaches to Support Drug Design and Clinical Decision Making

The future looks bright for a clinical practice that tailors the therapy with the best efficacy and highest safety to a patient. Substantial amounts of funding have resulted in technological advances regarding patient-centered data acquisition --- particularly genetic data. Yet, the challenge of translating this data into clinical practice remains open. To support drug target characterization, we developed a global maximum entropy-based method that predicts protein-protein complexes including the three-dimensional structure of their interface from sequence data. To further speed up the drug development process, we present methods to reposition drugs with established safety profiles to new indications leveraging paths in cellular interaction networks. We validated both methods on known data, demonstrating their ability to recapitulate known protein complexes and drug-indication pairs, respectively. After studying the extent and characteristics of genetic variation with a predicted impact on protein function across 60,607 individuals, we showed that most patients carry variants in drug-related genes. However, for the majority of variants, their impact on drug efficacy remains unknown. To inform personalized treatment decisions, it is thus crucial to first collate knowledge from open data sources about known variant effects and to then close the knowledge gaps for variants whose effect on drug binding is still not characterized. Here, we built an automated annotation pipeline for patient-specific variants whose value we illustrate for a set of patients with hepatocellular carcinoma. We further developed a molecular modeling protocol to predict changes in binding affinity in proteins with genetic variants which we evaluated for several clinically relevant protein kinases. Overall, we expect that each presented method has the potential to advance personalized medicine by closing knowledge gaps about protein interactions and genetic variation in drug-related genes. To reach clinical applicability, challenges with data availability need to be overcome and prediction performance should be validated experimentally.Therapien mit der besten Wirksamkeit und höchsten Sicherheit werden in Zukunft auf den Patienten zugeschnitten werden. Hier haben erhebliche finanzielle Mittel zu technologischen Fortschritten bei der patientenzentrierten Datenerfassung geführt, aber diese Daten in die klinische Praxis zu übertragen, bleibt aktuell noch eine Herausforderung. Um die Wirkstoffforschung in der Charakterisierung therapeutischer Zielproteine zu unterstützen, haben wir eine Maximum-Entropie-Methode entwickelt, die Protein-Interaktionen und ihre dreidimensionalen Struktur aus Sequenzdaten vorhersagt. Darüber hinaus, stellen wir Methoden zur Repositionierung von etablierten Arzneimitteln auf neue Indikationen vor, die Pfade in zellulären Interaktionsnetze nutzen. Diese Methoden haben wir anhand bekannter Daten validiert und ihre Fähigkeit demonstriert, bekannte Proteinkomplexe bzw. Wirkstoff-Indikations-Paare zu rekapitulieren. Unsere Analyse genetischer Variation mit einem Einfluss auf die Proteinfunktion in 60,607 Individuen konnte zeigen, dass nahezu jeder Patient funktionsverändernde Varianten in Medikamenten-assoziierten Genen trägt. Der direkte Einfluss der meisten beobachteten Varianten auf die Medikamenten-Wirksamkeit ist jedoch noch unbekannt. Um dennoch personalisierte Behandlungsentscheidungen treffen zu können, präsentieren wir eine Annotationspipeline für genetische Varianten, deren Wert wir für Patienten mit hepatozellulärem Karzinom illustrieren konnten. Darüber hinaus haben wir ein molekulares Modellierungsprotokoll entwickelt, um die Veränderungen in der Bindungsaffinität von Proteinen mit genetischen Varianten voraussagen. Insgesamt sind wir davon überzeugt, dass jede der vorgestellten Methoden das Potential hat, Wissenslücken über Proteininteraktionen und genetische Variationen in medikamentenbezogenen Genen zu schlie{\ss}en und somit das Feld der personalisierten Medizin voranzubringen. Um klinische Anwendbarkeit zu erreichen, gilt es in der Zukunft, verbleibende Herausforderungen bei der Datenverfügbarkeit zu bewältigen und unsere Vorhersagen experimentell zu validieren

Exploring protein flexibility during docking to investigate ligand-target recognition

Ligand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue. The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline. Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes

Non-covalent interactions in organotin(IV) derivatives of 5,7-ditertbutyl- and 5,7-diphenyl-1,2,4-triazolo[1,5-a]pyrimidine as recognition motifs in crystalline self- assembly and their in vitro antistaphylococcal activity

Non-covalent interactions are known to play a key role in biological compounds due to their stabilization of the tertiary and quaternary structure of proteins [1]. Ligands similar to purine rings, such as triazolo pyrimidine ones, are very versatile in their interactions with metals and can act as model systems for natural bio-inorganic compounds [2]. A considerable series (twelve novel compounds are reported) of 5,7-ditertbutyl-1,2,4-triazolo[1,5-a]pyrimidine (dbtp) and 5,7-diphenyl- 1,2,4-triazolo[1,5-a]pyrimidine (dptp) were synthesized and investigated by FT-IR and 119Sn M\uf6ssbauer in the solid state and by 1H and 13C NMR spectroscopy, in solution [3]. The X-ray crystal and molecular structures of Et2SnCl2(dbtp)2 and Ph2SnCl2(EtOH)2(dptp)2 were described, in this latter pyrimidine molecules are not directly bound to the metal center but strictly H-bonded, through N(3), to the -OH group of the ethanol moieties. The network of hydrogen bonding and aromatic interactions involving pyrimidine and phenyl rings in both complexes drives their self-assembly. Noncovalent interactions involving aromatic rings are key processes in both chemical and biological recognition, contributing to overall complex stability and forming recognition motifs. It is noteworthy that in Ph2SnCl2(EtOH)2(dptp)2 \u3c0\u2013\u3c0 stacking interactions between pairs of antiparallel triazolopyrimidine rings mimick basepair interactions physiologically occurring in DNA (Fig.1). M\uf6ssbauer spectra suggest for Et2SnCl2(dbtp)2 a distorted octahedral structure, with C-Sn-C bond angles lower than 180\ub0. The estimated angle for Et2SnCl2(dbtp)2 is virtually identical to that determined by X-ray diffraction. Ph2SnCl2(EtOH)2(dptp)2 is characterized by an essentially linear C-Sn-C fragment according to the X-ray all-trans structure. The compounds were screened for their in vitro antibacterial activity on a group of reference staphylococcal strains susceptible or resistant to methicillin and against two reference Gramnegative pathogens [4] . We tested the biological activity of all the specimen against a group of staphylococcal reference strains (S. aureus ATCC 25923, S. aureus ATCC 29213, methicillin resistant S. aureus 43866 and S. epidermidis RP62A) along with Gram-negative pathogens (P. aeruginosa ATCC9027 and E. coli ATCC25922). Ph2SnCl2(EtOH)2(dptp)2 showed good antibacterial activity with a MIC value of 5 \u3bcg mL-1 against S. aureus ATCC29213 and also resulted active against methicillin resistant S. epidermidis RP62A

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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