FragLites-Minimal, Halogenated Fragments Displaying Pharmacophore Doublets. An Efficient Approach to Druggability Assessment and Hit Generation

F2X-Universal and F2X-Entry: Structurally Diverse Compound Libraries for Crystallographic Fragment Screening

Crystallographic fragment screening (CFS) provides excellent starting points for projects concerned with drug discovery or biochemical tool compound development. One of the fundamental prerequisites for effective CFS is the availability of a versatile fragment library. Here, we report on the assembly of the 1,103-compound F2X-Universal Library and its 96-compound sub-selection, the F2X-Entry Screen. Both represent the available fragment chemistry and are highly diverse in terms of their 3D-pharmacophore variations. Validation of the F2X-Entry Screen in CFS campaigns using endothiapepsin and the Aar2/RNaseH complex yielded hit rates of 30% and 21%, respectively, and revealed versatile binding sites. Dry presentation of the libraries allows CFS campaigns to be carried out with or without the co-solvent DMSO present. Most of the hits in our validation campaigns could be reproduced also in the absence of DMSO. Consequently, CFS can be carried out more efficiently and for a wider range of conditions and targets.

Fragments: where are we now?

Fragment-based drug discovery (FBDD) has become a mainstream technology for the identification of chemical hit matter in drug discovery programs. To date, the food and drug administration has approved four drugs, and over forty compounds are in clinical studies that can trace their origins to a fragment-based screen. The challenges associated with implementing an FBDD approach are many and diverse, ranging from the library design to developing methods for identifying weak affinity compounds. In this article, we give an overview of current progress in fragment library design, fragment to lead optimisation and on the advancement in techniques used for screening. Finally, we will comment on the future opportunities and challenges in this field.

AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters

Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.

Cosolvent Analysis Toolkit (CAT): a robust hotspot identification platform for cosolvent simulations of proteins to expand the druggable proteome

Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. In this work, we present CAT (Cosolvent Analysis Toolkit): an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with commonly used molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranks the dynamic interactions between the macromolecular target and cosolvent molecules. To benchmark, CAT was used for three validated protein targets with allosteric and orthosteric binding sites, using five chemically distinct cosolvent molecules. For all systems, CAT has accurately identified all known sites. CAT can thus assist in computational studies aiming at identification of protein “hotspots” in a wide range of systems. As an easy-to-use computational tool, we expect that CAT will contribute to an increase in the size of the potentially ‘druggable’ human proteome.

If You Cannot Win Them, Join Them: Understanding New Ways to Target STAT3 by Small Molecules

Signal transducer activator of transcription 3 (STAT3) is among the most investigated oncogenic transcription factors, as it is highly associated with cancer initiation, progression, metastasis, chemoresistance, and immune evasion. Evidences from both preclinical and clinical studies have demonstrated that STAT3 plays a critical role in several malignancies associated with poor prognosis such as glioblastoma and triple-negative breast cancer, and STAT3 inhibitors have shown efficacy in inhibiting cancer growth and metastasis. Constitutive activation of STAT3 by mutations occurs frequently in tumor cells and directly contributes to many malignant phenotypes. Unfortunately, detailed structural biology studies on STAT3 as well as target-based drug discovery efforts have been hampered by difficulties in the expression and purification of the full-length STAT3 and a lack of ligand-bound crystal structures. Considering these, molecular modeling and simulations offer an attractive strategy for the assessment of the "druggability" of STAT3 dimers and allow investigations of reported activating and inhibiting STAT3 mutants at the atomistic level of detail. In the present study, we focused on the effects exerted by reported STAT3 mutations on the protein structure, dynamics, DNA-binding, and dimerization, thus linking structure, dynamics, energetics, and the biological function. By employing atomistic molecular dynamics and umbrella-sampling simulations to a series of human STAT3 dimers, which comprised wild-type protein and four mutations, we explained the modulation of STAT3 activity by these mutations. Counter-intuitively, our results show that the D570K inhibitory mutation exerts its effect by enhancing rather than weakening STAT3-DNA interactions, which interfere with the DNA release by the protein dimer and thus inhibit STAT3 function as a transcription factor. We mapped the binding site and characterized the binding mode of a clinical candidate napabucasin/BBI-608 at STAT3, which resembles the effect of a D570K mutation. Our results contribute to understanding the activation/inhibition mechanism of STAT3, to explain the molecular mechanism of STAT3 inhibition by BBI-608. Alongside the characterization of the BBI-608 binding mode, we also discovered a novel binding site amenable to bind small-molecule ligands, which may pave the way to design novel STAT3 inhibitors and to suggest new strategies for pharmacological interventions to combat cancers associated with poor prognosis.