NMR spectroscopy has played a pivotal role in fragment-based drug discovery by coupling detection of weak ligand-target binding with structural mapping of the binding site. Fragment-based screening by NMR has been successfully applied to many soluble protein targets, but only to a limited number of membrane proteins, despite the fact that many drug targets are membrane proteins. This is partly because of difficulties preparing membrane proteins for NMR-especially human membrane proteins-and because of the inherent complexity associated with solution NMR spectroscopy on membrane protein samples, which require the inclusion of membrane-mimetic agents such as micelles, nanodiscs, or bicelles. Here, we developed a generalizable protocol for fragment-based screening of membrane proteins using NMR. We employed two human membrane protein targets, both in fully protonated detergent micelles: the single-pass C-terminal domain of the amyloid precursor protein, C99, and the tetraspan peripheral myelin protein 22 (PMP22). For both we determined the optimal NMR acquisition parameters, protein concentration, protein-to-micelle ratio, and upper limit to the concentration of D6-DMSO in screening samples. Furthermore, we conducted preliminary screens of a plate-format molecular fragment mixture library using our optimized conditions and were able to identify hit compounds that selectively bound to the respective target proteins. It is hoped that the approaches presented here will be useful in complementing existing methods for discovering lead compounds that target membrane proteins.

The indoor environment is a typical source for organophosphorus flame retardants and plasticizers (OPFRs), yet the source characteristics of OPFRs in different microenvironments remain less clear. This study collected 109 indoor air samples and 34 paired indoor dust samples from 4 typical microenvironments within a university in Tianjin, China, including the dormitory, office, library, and information center. 29 target OPFRs were analyzed, and novel organophosphorus compounds (NOPs) were identified by fragment-based nontarget analysis. Target OPFRs exhibited the highest air and dust concentrations of 46.2-234 ng/m3 and 20.4-76.0 μg/g, respectively, in the information center, where chlorinated OPFRs were dominant. Triphenyl phosphate (TPHP) was the primary OPFR in office air, while tris(2-chloroethyl) phosphate dominated in the dust. TPHP was predominant in the library. Triethyl phosphate (TEP) was ubiquitous in the dormitory, and tris(2-butoxyethyl) phosphate was particularly high in the dust. 9 of 25 NOPs were identified for the first time, mainly from the information center and office, such as bis(chloropropyl) 2,3-dichloropropyl phosphate. Diphenyl phosphinic acid, two hydroxylated and methylated metabolites of tris(2,4-ditert-butylphenyl) phosphite (AO168), and a dimer phosphate were newly reported in the indoor environment. NOPs were widely associated with target OPFRs, and their human exposure risk and environmental behaviors warrant further study.

MALDI-TOF mass spectrometry enables high-throughput screening of covalent fragment libraries and SAR compound progressions of selective KRAS G12C inhibitors. Using the MALDI-TOF platform instead of the more traditional ESI-MS TOF/orbitrap instrumentation can radically shorten sample acquisition time, allowing up to 384 samples to be screened in 30 min. The typical throughput for a covalent library screen is 1152 samples per 8 h, including processing, calculation, and reporting steps. The throughput can be doubled without any significant assay modification.

BACKGROUND: Ligand-observed 19F NMR detection is an efficient method for screening libraries of fluorinated molecules in fragment-based drug design campaigns. Screening fluorinated molecules in large mixtures makes 19F NMR a high-throughput method. Typically, these mixtures are generated from pools of well-characterized fragments. By predicting 19F NMR chemical shift, mixtures could be generated for arbitrary fluorinated molecules facilitating for example focused screens.
METHODS: In a previous publication, we introduced a method to predict 19F NMR chemical shift using rooted fluorine fingerprints and machine learning (ML) methods. Having observed that the quality of the prediction depends on similarity to the training set, we here propose to assist the prediction with quantum mechanics (QM) based methods in cases where compounds are not well covered by a training set.
RESULTS: Beyond similarity, the performance of ML methods could be associated with individual features in compounds. A combination of both could be used as a procedure to split input data sets into those that could be predicted by ML and those that required QM processing. We could show on a proprietary fluorinated fragment library, known as LEF (Local Environment of Fluorine), and a public Enamine data set of 19F NMR chemical shifts that ML and QM methods could synergize to outperform either method individually. Models built on Enamine data, as well as model building and QM workflow tools, can be found at https://github.com/PatrickPenner/lefshift and https://github.com/PatrickPenner/lefqm .

Oil refinery activity can be an emission source of perfluoroalkyl and polyfluoroalkyl substances (PFAS) to the environment, while the contamination profiles in soils remain unknown. This study investigated 44 target PFAS in soil samples collected from an oil refinery in Southeastern China, identified novel PFAS, and characterized their behaviors by assessing their changes before and after employing advanced oxidation using a combination of nontarget analysis and a total oxidizable precursor (TOP) assay. Thirty-four target PFAS were detected in soil samples. Trifluoroacetic acid (TFA) and hexafluoropropylene oxide dimer acid (HFPO-DA) were the dominant PFAS. Twenty-three novel PFAS of 14 classes were identified, including 8 precursors, 11 products, and 4 stable PFAS characterized by the TOP assay. Particularly, three per-/polyfluorinated alcohols were identified for the first time, and hexafluoroisopropanol (HFIP) quantified up to 657 ng/g dw is a novel precursor for TFA. Bistriflimide (NTf2) potentially associated with an oil refinery was also reported for the first time in the soil samples. This study highlighted the advantage of embedding the TOP assay in nontarget analysis to reveal not only the presence of unknown PFAS but also their roles in environmental processes. Overall, this approach provides an efficient way to uncover contamination profiles of PFAS especially in source-impacted areas.

Crystallography-based fragment screening is a highly effective technique employed in structure-based drug discovery to expand the range of lead development opportunities. It allows screening and sorting of weakly binding, low molecular mass fragments, which can be developed into larger high-affinity lead compounds. Technical improvements at synchrotron beamlines, design of innovative libraries mapping chemical space efficiently, effective soaking methods and enhanced data analysis have enabled the implementation of high-throughput fragment screening pipelines at multiple synchrotron facilities. This widened access to CBFS beyond the pharma industry has allowed academic users to rapidly screen large quantities of fragment-soaked protein crystals. The positive outcome of a CBFS campaign is a set of structures that present the three-dimensional arrangement of fragment-protein complexes in detail, thereby providing information on the location and the mode of interaction of bound fragments. Through this review, we provide users with a comprehensive guide that sets clear expectations before embarking on a crystallography-based fragment screening campaign. We present a list of essential pre-requirements that must be assessed, including the suitability of your current crystal system for a fragment screening campaign. Furthermore, we extensively discuss the available methodological options, addressing their limitations and providing strategies to overcome them. Additionally, we provide a brief perspective on how to proceed once hits are obtained. Notably, we emphasize the solutions we have implemented for instrumentation and software development within our Fast Fragment and Compound Screening pipeline. We also highlight third-party software options that can be utilized for rapid refinement and hit assessment.

We report a two-step validation approach to evaluate the suitability of metal-binding groups for targeting DNA damage-repair metalloenzymes using model enzyme SNM1A. A fragment-based screening approach was first used to identify metal-binding fragments suitable for targeting the enzyme. Effective fragments were then incorporated into oligonucleotides using the copper-catalysed azide-alkyne cycloaddition reaction. These modified oligonucleotides were recognised by SNM1A at >1000-fold lower concentrations than their fragment counterparts. The exonuclease SNM1A is a key enzyme involved in the repair of interstrand crosslinks, a highly cytotoxic form of DNA damage. However, SNM1A and other enzymes of this class are poorly understood, as there is a lack of tools available to facilitate their study. Our novel approach of incorporating functional fragments into oligonucleotides is broadly applicable to generating modified oligonucleotide structures with high affinity for DNA damage-repair enzymes.

Interest in exploiting allosteric sites for the development of new therapeutics has grown considerably over the last two decades. The chief driving force behind the interest in allostery for drug discovery stems from the fact that in comparison to orthosteric sites, allosteric sites are less conserved across a protein family, thereby offering greater opportunity for selectivity and ultimately tolerability. While there is significant overlap between structure-based drug design for orthosteric and allosteric sites, allosteric sites offer additional challenges mostly involving the need to better understand protein flexibility and its relationship to protein function. Here we examine the extent to which structure-based drug design is impacting allosteric drug design by highlighting several targets across a variety of target classes.

Natural chemical compounds have been widely investigated for their programmed necrosis causing characteristics. One of the conventional methods for screening such compounds is the use of concentrated plant extracts without isolation of active moieties for understanding pharmacological activity. For the last two decades, modern medicine has relied mainly on the isolation and purification of one or two complicated active and isomeric compounds. The idea of multi-target drugs has advanced rapidly and impressively from an innovative model when first proposed in the early 2000s to one of the popular trends for drug development in 2021. Alternatively, fragment-based drug discovery is also explored in identifying target-based drug discovery for potent natural anticancer agents which is based on well-defined fragments opposite to use of naturally occurring mixtures. This review summarizes the current key advancements in natural anticancer compounds; computer-assisted/fragment-based structural elucidation and a multi-target approach for the exploration of natural compounds.

The variety and complexity of drug targets are expanding rapidly. At the same time, there is significant interest in exploring a larger chemical space to identify new candidates. Fragment-based screening (FBS) has emerged as a popular alternative to traditional high-throughput screening campaigns to identify such drug candidates. FBS identifies hit fragments that exhibit weak interactions with the target of interest, thereby enabling the rational design of small-molecule compounds from the identified hit fragments, which serve as building blocks. This strategy reduces the number of molecules to screen while also allowing the exploration of a greater chemical space.Here we use temperature-related intensity change (TRIC) technology to perform FBS against the target MAPK/ERK kinase-1 (Mek1). TRIC describes the change in fluorescence intensity of a fluorescently labeled molecule upon a change in temperature. This intensity variation is dependent on the physicochemical environment in the vicinity of the dye and strongly affected by binding events. Thus, the detection of binding events is independent of mass, making TRIC an ideal tool for FBS.Using only 150 pmol of labeled Mek1, the authors screened 193 fragments from a prescreened library in less than 1 h of measurement time, leading to 66 hits. Among those hits, they identified more than 80% of the published top hits found using orthogonal techniques. Furthermore, TRIC allowed the identification of fragments that were of poor solubility but could be mistaken as false-positive hits in other methods.