Hot-spot analysis for drug discovery targeting protein-protein interactions

Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

PocketPicker: analysis of ligand binding-sites with shape descriptors

Background Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITEcs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITEcs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusions The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections

Novel pharmacological maps of protein lysine methyltransferases: key for target deorphanization

Epigenetic therapies are being investigated for the treatment of cancer, cognitive disorders, metabolic alterations and autoinmune diseases. Among the diferent epigenetic target families, protein lysine methyltransferases (PKMTs), are especially interesting because it is believed that their inhibition may be highly specifc at the functional level. Despite its relevance, there are currently known inhibitors against only 10 out of the 50 SET-domain containing members of the PKMT family. Accordingly, the identifcation of chemical probes for the validation of the therapeutic impact of epigenetic modulation is key. Moreover, little is known about the mechanisms that dictate their substrate specifcity and ligand selectivity. Consequently, it is desirable to explore novel methods to characterize the pharmacological similarity of PKMTs, going beyond classical phylogenetic relationships. Such characterization would enable the prediction of ligand of-target efects caused by lack of ligand selectivity and the repurposing of known compounds against alternative targets. This is particularly relevant in the case of orphan targets with unreported inhibitors. Here, we frst perform a systematic study of binding modes of cofactor and substrate bound ligands with all available SET domain-containing PKMTs. Protein ligand interaction fngerprints were applied to identify conserved hot spots and contact-specifc residues across subfamilies at each binding site; a relevant analysis for guiding the design of novel, selective compounds. Then, a recently described methodology (GPCR-CoINPocket) that incorporates ligand contact information into classical alignment-based comparisons was applied to the entire family of 50 SET-containing proteins to devise pharmacological similarities between them. The main advantage of this approach is that it is not restricted to proteins for which crystallographic data with bound ligands is available. The resulting family organization from the separate analysis of both sites (cofactor and substrate) was retrospectively and prospectively validated. Of note, three hits (inhibition>50% at 10 µM) were identifed for the orphan NSD1

Probing Local Atomic Environments to Model RNA Energetics and Structure

Ribonucleic acids (RNA) are critical components of living systems. Understanding RNA structure and its interaction with other molecules is an essential step in understanding RNA-driven processes within the cell. Experimental techniques like X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and chemical probing methods have provided insights into RNA structures on the atomic scale. To effectively exploit experimental data and characterize features of an RNA structure, quantitative descriptors of local atomic environments are required. Here, I investigated different ways to describe RNA local atomic environments. First, I investigated the solvent-accessible surface area (SASA) as a probe of RNA local atomic environment. SASA contains information on the level of exposure of an RNA atom to solvents and, in some cases, is highly correlated to reactivity profiles derived from chemical probing experiments. Using Bayesian/maximum entropy (BME), I was able to reweight RNA structure models based on the agreement between SASA and chemical reactivities. Next, I developed a numerical descriptor (the atomic fingerprint), that is capable of discriminating different atomic environments. Using atomic fingerprints as features enable the prediction of RNA structure and structure-related properties. Two detailed examples are discussed. Firstly, a classification model was developed to predict Mg$^{2+}$ ion binding sites. Results indicate that the model could predict Mg$^{2+}$ binding sites with reasonable accuracy, and it appears to outperform existing methods. Secondly, a set of models were developed to identify cavities in RNA that are likely to accommodate small-molecule ligands. The models were also used to identify bound-like conformations from an ensemble of RNA structures. The frameworks presented here provide paths to connect the local atomic environment to RNA structure, and I envision they will provide opportunities to develop novel RNA modeling tools.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163135/1/jingrux_1.pd

Computational approaches to predict protein functional families and functional sites.

Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features

Current state-of-the-art of the research conducted in mapping protein cavities – binding sites of bioactive compounds, peptides or other proteins

Ο σκοπός της διπλωματικής εργασίας είναι η διερεύνηση και αποτύπωση των ερευνητικών μελετών που αφορούν στον χαρακτηρισμό μιας πρωτεϊνικής κοιλότητας – κέντρου πρόσδεσης βιοδραστικών ενώσεων, πεπτιδίων ή άλλων πρωτεϊνών. Στην παρούσα εργασία χρησιμοποιήθηκε η μέθοδος της βιβλιογραφικής επισκόπησης. Παρουσιάζονται τα κυριότερα ευρήματα προηγούμενων ερευνών που σχετίζονται με τη διαδικασία σχεδιασμού φαρμάκων και τον εντοπισμό φαρμακοφόρων με βάση ένα σύνολο προσδετών. Στη συνέχεια συγκρίνονται διαδικασίες επεξεργασίας και ανάλυσης της πρωτεϊνικής κοιλότητας προγενέστερων ερευνών με τη προσέγγιση που προτάθηκε από τους Παπαθανασίου και Φωτόπουλου το 2015. Αναδεικνύονται βασικά πλεονεκτήματα της προσέγγισης αυτής, όπως η εφαρμογή του αλγορίθμου πολυδιάστατη k-means ομαδοποίηση (multidimensional k-means clustering). Η εύρεση βιβλιογραφίας βασίστηκε σε αναζήτηση επιστημονικών άρθρων σε ξενόγλωσσα επιστημονικά περιοδικά, σε κεφάλαια βιβλίων και σε διάφορα άρθρα σε ηλεκτρονικούς ιστότοπους σχετικά με τον σχεδιασμό φαρμάκων και τις κοιλότητες που απαντώνται στις πρωτεΐνες. Στην παρούσα εργασία παρουσιάζονται εν συντομία εργαλεία που εντοπίστηκαν χρησιμοποιώντας λέξεις κλειδιά όπως για παράδειγμα δυναμική πρωτεϊνικής κοιλότητας, καταλυτικό κέντρο ενός ενζύμου, πρόσδεση, πρωτεϊνική θήκη κλπ. Στη συνέχεια συγκροτήθηκε κατάλογος με τα εργαλεία βιοπληροφορικής ανάλυσης που βρέθηκαν και ακολούθησε εκτενής αναφορά επιλεκτικά σε κάποια από αυτά. Κριτήριο επιλογής αυτών των εργαλείων αποτέλεσε η ημερομηνία δημοσίευσής τους, οι αλγόριθμοι και η μεθοδολογία που χρησιμοποιούν. Τα εργαλεία αυτά κατηγοριοποιήθηκαν με βάση τις λέξεις κλειδιά που χρησιμοποιήθηκαν για την εξόρυξη των δεδομένων από την βιβλιογραφία. Τέλος πραγματοποιήθηκε συγκριτική μελέτη αυτών αναδεικνύοντας τα πλεονεκτήματα και εστιάζοντας στην περαιτέρω αξιοποίησή τους.The aim of this thesis was to report on the current state-of-the-art of the research conducted concerning mapping of protein cavities with a potential function role as binding sites of bioactive compounds, peptides or other proteins. A literature review was performed with emphasis on the relevant tools developed during the last decade. In addition, the main research findings regarding drug design and druggable targets based on binding sites are presented. Processes performed in protein cavity detection and analysis, of previous research articles, are compared with the approach described by Anaxagoras Fotopoulos and Athanasios Papathanasiou (2015). The results showed that a competitive advantage of their approach is the multidimensional k-means algorithm for clustering. For the bibliographic review the scientific knowledgebase has been used, which includes international articles and journals, book chapters, as well as online articles regarding drug design and protein cavity. Search keywords such as protein cavity dynamics, catalytic sites of enzymes, protein pocket etc. were used to identify bioinformatics tools with text mining. A catalogue of the most recently developed tools is presented followed by a brief description of selected tools. The selection criteria imposed for preparing the catalogue and the detailed description included the publication date, as well as the algorithms and the methods they use. The tools were then classified according to the search keywords. The findings of this research are discussed, and the algorithms and methods they use are compared, highlighting the advantages of protein cavity detection

CavBench: a benchmark for protein cavity detection methods

Extensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.info:eu-repo/semantics/publishedVersio

Technological developments in Virtual Screening for the discovery of small molecules with novel mechanisms of action

Programa de Doctorat en Recerca, Desenvolupament i Control de Medicaments[eng] Advances in structural and molecular biology have favoured the rational development of novel drugs thru structure-based drug design (SBDD). Particularly, computational tools have proven to be rapid and efficient tools for hit discovery and optimization. The main motivation of this thesis is to improve and develop new methods in the area of computer-based drug discovery in order to study challenging targets. Specifically, this thesis is focused on docking and Virtual Screening (VS) methodologies to be able to exploit non-standard sites, like protein-protein interfaces or allosteric sites, and discover bioactive molecules with novel mechanisms of action. First, I developed an automatic pipeline for binding mode prediction that applies knowledge- based restraints and validated the approach by participating in the CELPP Challenge, a blind pose prediction challenge. The aim of the first VS in this thesis is to find small molecules able to not only disrupt the RANK-RANKL interaction but also inhibit the constitutive activation of the receptor. With a combination of computational, biophysical, and cell-based assays we were able to identify the first small molecule binders for RANK that could be used as a treatment for Triple Negative Breast Cancer. When working with challenging targets, or with non-standard mechanisms of action, the relationship between binding and the biological response is unpredictable, because the biological response (if any) will depend on the biological function of the particular allosteric site, which is generally unknown. For this reason, we then tested the applicability of the combination of ultrahigh-throughput VS with low-throughput high content assay. This allowed us to characterize a novel allosteric pocket in PTEN and also describe the first allosteric modulators for this protein. Finally, as the accessible Chemical Space grows at a rapid pace, we developed an algorithm to efficiently explore ultra-large Chemical Collections using a Bottom-up approach. We prospectively validated the approach in BRD4 and identified novel BRD4 inhibitors with an affinity comparable to advanced drug candidates for this target.[spa] Els avenços en biologia estructural i molecular han afavorit el desenvolupament racional de nous fàrmacs a través del disseny de fàrmacs basat en l'estructura (SBDD). En particular, les eines computacionals han demostrat ser ràpides i eficients per al descobriment i l'optimització de fàrmacs. La principal motivació d'aquesta tesi és millorar i desenvolupar nous mètodes en l'àrea del descobriment de fàrmacs per ordinador per tal d'estudiar proteïnes complexes. Concretament, aquesta tesi se centra en les metodologies d'acoblament i de cribratge virtual (CV) per poder explotar llocs no estàndard, com interfícies proteïna-proteïna o llocs al·lostèrics, i descobrir molècules bioactives amb nous mecanismes d'acció. En primer lloc, vaig desenvolupar un protocol automàtic per a la predicció del mode d’unió aplicant restriccions basades en el coneixement i vaig validar l'enfocament participant en el repte CELPP, un repte de predicció del mode d’unió a cegues. L'objectiu del primer CV d'aquesta tesi és trobar petites molècules capaces no només d'interrompre la interacció RANK-RANKL sinó també d'inhibir l'activació constitutiva del receptor. Amb una combinació d'assajos computacionals, biofísics i basats en cèl·lules, vam poder identificar les primeres molècules petites per a RANK que es podrien utilitzar com a tractament per al càncer de mama triple negatiu. Quan es treballa amb proteïnes complexes, o amb mecanismes d'acció no estàndard, la relació entre la unió i la resposta biològica és impredictible, perquè la resposta biològica (si n'hi ha) dependrà de la funció biològica del lloc al·lostèric particular, que generalment és desconeguda. Per aquest motiu, després vam provar l'aplicabilitat de la combinació de CV d'alt rendiment amb assaig de contingut alt de baix rendiment. Això ens va permetre caracteritzar un nou lloc d’unió al·lostèric en PTEN i també descriure els primers moduladors al·lostèrics d'aquesta proteïna. Finalment, a mesura que l'espai químic accessible creix a un ritme ràpid, hem desenvolupat un algorisme per explorar de manera eficient col·leccions de productes químics molt grans mitjançant un enfocament de baix a dalt. Vam validar aquest enfocament amb BRD4 i vam identificar nous inhibidors de BRD4 amb una afinitat comparable als candidats a fàrmacs més avançats per a aquesta proteïna