Fragment databases from screened ligands for drug discovery (FDSL-DD).

Fragment-based drug design (FBDD) is one major drug discovery method employed in computer-aided drug discovery. Due to its inherent limitations, this process experiences long processing times and limited success rates. Here we present a new Fragment Databases from Screened Ligands Drug Design method (FDSL-DD) that intelligently incorporates information about fragment characteristics into a fragment-based design approach to the drug development process. The initial step of the FDSL-DD is the creation of a fragment database from a library of docked, drug-like ligands for a specific target, which deviates from the traditional in silico FBDD strategy, incorporating structure-based design screening techniques to combine the advantages of both approaches. Three different protein targets have been tested in this study to demonstrate the potential of the created fragment library and FDSL-DD. Utilizing the FDSL-DD led to an increase in binding affinity for each protein target. The most substantial increase was exhibited by the ligand designed for TIPE2, with a 3.6 kcalmol-1 difference between the top ligand from the FDSL-DD and top ligand from the high throughput virtual screening (HTVS). Using drug-like ligands in the initial HTVS allows for a greater search of chemical space, with higher efficiency in fragments selection, less grid boxes, and potentially identifying more interactions.

Recent trends in fragment-based anticancer drug design strategies against different targets: A mini-review.

Cancer is a rapidly growing disease in modern society. Chemotherapy is the first choice for cancer treatment. Design and development of new chemotherapeutic drugs by targeting specific proteins are put down by a high attrition rate at different stages. Fragment-based drug design (FBDD) is one of the successful structure-based drug design processes to avoid attrition-related problems. This review highlighted the computational and experimental FBDD techniques used to design molecules with anticancer properties. This study describes FBBD strategies for different targets like aurora kinase, phosphoinositide-dependent protein kinase-1 (PDK1), signal transducer and activator of transcription 3 (STAT3), myeloid cell leukemia-1 (Mcl-1), tankyrase (TNKS), choline kinase, protein kinase, tyrosine kinase and lysine-specific demethylase 1 (LSD1) which are vital targets for cancer treatments. This review will enrich the scientific community to understand the fragment-based design strategies for finding suitable leads over high throughput screening (HTS) in the future.

NMR fragment screening reveals a novel small molecule binding site near the catalytic surface of the disulfide-dithiol oxidoreductase enzyme DsbA from Burkholderia pseudomallei.

The presence of suitable cavities or pockets on protein structures is a general criterion for a therapeutic target protein to be classified as 'druggable'. Many disease-related proteins that function solely through protein-protein interactions lack such pockets, making development of inhibitors by traditional small-molecule structure-based design methods much more challenging. The 22 kDa bacterial thiol oxidoreductase enzyme, DsbA, from the gram-negative bacterium Burkholderia pseudomallei (BpsDsbA) is an example of one such target. The crystal structure of oxidized BpsDsbA lacks well-defined surface pockets. BpsDsbA is required for the correct folding of numerous virulence factors in B. pseudomallei, and genetic deletion of dsbA significantly attenuates B. pseudomallei virulence in murine infection models. Therefore, BpsDsbA is potentially an attractive drug target. Herein we report the identification of a small molecule binding site adjacent to the catalytic site of oxidized BpsDsbA. 1HN CPMG relaxation dispersion NMR measurements suggest that the binding site is formed transiently through protein dynamics. Using fragment-based screening, we identified a small molecule that binds at this site with an estimated affinity of KD ~ 500 µM. This fragment inhibits BpsDsbA enzymatic activity in vitro. The binding mode of this molecule has been characterized by NMR data-driven docking using HADDOCK. These data provide a starting point towards the design of more potent small molecule inhibitors of BpsDsbA.

Efficient affinity ranking of fluorinated ligands by 19F NMR: CSAR and FastCSAR.

Fluorine NMR has recently gained high popularity in drug discovery as it allows efficient and sensitive screening of large numbers of ligands. However, the positive hits found in screening must subsequently be ranked according to their affinity in order to prioritize them for follow-up chemistry. Unfortunately, the primary read-out from the screening experiments, namely the increased relaxation rate upon binding, is not proportional to the affinity of the ligand, as it is polluted by effects such as exchange broadening. Here we present the method CSAR (Chemical Shift-anisotropy-based Affinity Ranking) for reliable ranking of fluorinated ligands by NMR, without the need of isotope labeled protein, titrations or setting up a reporter format. Our strategy is to produce relaxation data that is directly proportional to the binding affinity. This is achieved by removing all other contributions to relaxation as follows: (i) exchange effects are efficiently suppressed by using high power spin lock pulses, (ii) dipolar relaxation effects are approximately subtracted by measuring at two different magnetic fields and (iii) differences in chemical shift anisotropy are normalized using calculated values. A similar ranking can be obtained with the simplified approach FastCSAR that relies on a measurement of a single relaxation experiment at high field (preferably > 600 MHz). An affinity ranking obtained in this simple way will enable prioritizing ligands and thus improve the efficiency of fragment-based drug design.

Binding mode information improves fragment docking.

Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.