Recent advances in in silico target fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies

Structural Cheminformatics for Kinase-Centric Drug Design

Drug development is a long, expensive, and iterative process with a high failure rate, while patients wait impatiently for treatment. Kinases are one of the main drug targets studied for the last decades to combat cancer, the second leading cause of death worldwide. These efforts resulted in a plethora of structural, chemical, and pharmacological kinase data, which are collected in the KLIFS database. In this thesis, we apply ideas from structural cheminformatics to the rich KLIFS dataset, aiming to provide computational tools that speed up the complex drug discovery process. We focus on methods for target prediction and fragment-based drug design that study characteristics of kinase binding sites (also called pockets). First, we introduce the concept of computational target prediction, which is vital in the early stages of drug discovery. This approach identifies biological entities such as proteins that may (i) modulate a disease of interest (targets or on-targets) or (ii) cause unwanted side effects due to their similarity to on-targets (off-targets). We focus on the research field of binding site comparison, which lacked a freely available and efficient tool to determine similarities between the highly conserved kinase pockets. We fill this gap with the novel method KiSSim, which encodes and compares spatial and physicochemical pocket properties for all kinases (kinome) that are structurally resolved. We study kinase similarities in the form of kinome-wide phylogenetic trees and detect expected and unexpected off-targets. To allow multiple perspectives on kinase similarity, we propose an automated and production-ready pipeline; user-defined kinases can be inspected complementarily based on their pocket sequence and structure (KiSSim), pocket-ligand interactions, and ligand profiles. Second, we introduce the concept of fragment-based drug design, which is useful to identify and optimize active and promising molecules (hits and leads). This approach identifies low-molecular-weight molecules (fragments) that bind weakly to a target and are then grown into larger high-affinity drug-like molecules. With the novel method KinFragLib, we provide a fragment dataset for kinases (fragment library) by viewing kinase inhibitors as combinations of fragments. Kinases have a highly conserved pocket with well-defined regions (subpockets); based on the subpockets that they occupy, we fragment kinase inhibitors in experimentally resolved protein-ligand complexes. The resulting dataset is used to generate novel kinase-focused molecules that are recombinations of the previously fragmented kinase inhibitors while considering their subpockets. The KinFragLib and KiSSim methods are published as freely available Python tools. Third, we advocate for open and reproducible research that applies FAIR principles ---data and software shall be findable, accessible, interoperable, and reusable--- and software best practices. In this context, we present the TeachOpenCADD platform that contains pipelines for computer-aided drug design. We use open source software and data to demonstrate ligand-based applications from cheminformatics and structure-based applications from structural bioinformatics. To emphasize the importance of FAIR data, we dedicate several topics to accessing life science databases such as ChEMBL, PubChem, PDB, and KLIFS. These pipelines are not only useful to novices in the field to gain domain-specific skills but can also serve as a starting point to study research questions. Furthermore, we show an example of how to build a stand-alone tool that formalizes reoccurring project-overarching tasks: OpenCADD-KLIFS offers a clean and user-friendly Python API to interact with the KLIFS database and fetch different kinase data types. This tool has been used in this thesis and beyond to support kinase-focused projects. We believe that the FAIR-based methods, tools, and pipelines presented in this thesis (i) are valuable additions to the toolbox for kinase research, (ii) provide relevant material for scientists who seek to learn, teach, or answer questions in the realm of computer-aided drug design, and (iii) contribute to making drug discovery more efficient, reproducible, and reusable

Application of the SwissDrugDesign Online Resources in Virtual Screening.

SwissDrugDesign is an important initiative led by the Molecular Modeling Group of the SIB Swiss Institute of Bioinformatics. This project provides a collection of freely available online tools for computer-aided drug design. Some of these web-based methods, i.e., SwissSimilarity and SwissTargetPrediction, were especially developed to perform virtual screening, while others such as SwissADME, SwissDock, SwissParam and SwissBioisostere can find applications in related activities. The present review aims at providing a short description of these methods together with examples of their application in virtual screening, where SwissDrugDesign tools successfully supported the discovery of bioactive small molecules

Discovery and effects of pharmacological inhibition of the E3 ligase Skp2 by small molecule protein-protein interaction disruptors

Skp2 (S-phase kinase-associated protein 2), one component of the SCF E3 ubiquitin ligase complex, directly interacts with Skp1 and indirectly associates with Cullin1 and Rbx1 to bridge the E2 conjugating enzyme with its protein substrate to execute its E3 ligase activity. Skp2 is an Fbox protein (due to it containing an Fbox domain) and it is the rate-limiting component of the SCF complex. Skp2 targets several cell-cycle regulatory proteins for ubiquitination and degradation; most notable and significant for cancer are the cyclin-dependent kinase inhibitor, p27. Skp2 is an oncogene and studies have shown that over-expression of Skp2 leads to increased degradation of p27 and increased proliferation in several tumor types. Additionally, Skp2 is over-expressed in multiple human cancers. Clearly, Skp2 represents an attractive target for attenuating p27 ubiquitination and subsequent cell cycle progression. However, Skp2 does not have an easily identifiable and druggable “pocket” on which small molecules can bind; it interacts with Skp1 through the Fbox domain and binds to an accessory protein called Cks1 to bind to p27. Despite this hurdle, in this study, two selective small molecule inhibitors of the Skp2 SCF complex were discovered via an in silico screen that disrupt two places: the Skp1/Skp2 interaction site and the p27 binding site via targeting hot-spot residues. The Skp1/Skp2 inhibitor disruption resulted in restoring p27 levels in the nucleus and blocks cancer progression and cancer stem cell traits. Additionally, the inhibitors phenocopy the effects of genetic Skp2 deficiency. Two specific residues on Skp2 were predicted to bind to this Skp1/Skp2 inhibitor: Trp97 and Asp98. When these residues were mutated to alanine, the inhibitor lost its ability to bind to Skp2. To investigate the flexibility and understand the conformational change upon inhibitor binding and dynamics of the SCF complex, molecular dynamics simulations, homology models, and structural analysis was carried out on the complex with and without the inhibitors. These simulations showed that the contributions of the N-terminal tail region of Skp2 does not contribute directly to the binding of these inhibitors; but its conformation is important in the context of the other members of the SCF complex. Further dynamics analysis validated the mutagenesis results, showing that the two Skp2 mutants (Trp97Ala, Asp98Ala) that retained Skp1 binding but blocked inhibitor binding were stable, whereas the mutant that was unable to retain Skp1 binding (Trp127Ala) showed destabilization in the Fbox domain. Finally, active recruitment events after post-translational modifications are shown to be possible by the interaction of phosphorylated Ser256 on Skp2 with Lys104 loop region on Cul1 The model shows that this is due to the significant flexibility in the F-box domain of Skp2, making this interaction very likely. These results show that Skp2 is a promising target on which protein-protein interaction disruptors can be designed, and consideration of the dynamics of protein complexes is required to understand ligand binding

COMPUTATIONAL AND EXPERIMENTAL APPROACHES TO OVERCOME THE G PROTEIN-COUPLED RECEPTOR STRUCTURAL KNOWLEDGE GAP

COMPUTATIONAL AND EXPERIMENTAL APPROACHES TO OVERCOME THE G PROTEIN-COUPLED RECEPTOR STRUCTURAL KNOWLEDGE GA