Native Mass Spectrometry-Guided Screening Identifies Hit Fragments for HOP-HSP90 PPI Inhibition

Contemporary medicinal chemistry considers fragment-based drug discovery (FBDD) and inhibition of protein-protein interactions (PPI) as important means of expanding the volume of druggable chemical space. However, the ability to robustly identify valid fragments and PPI inhibitors is an enormous challenge, requiring the application of sensitive biophysical methodology. Accordingly, in this study, we exploited the speed and sensitivity of nanoelectrospray (nano-ESI) native mass spectrometry to identify a small collection of fragments which bind to the TPR2AB domain of HOP. Follow-up biophysical assessment of a small selection of binding fragments confirmed binding to the single TPR2A domain, and that this binding translated into PPI inhibitory activity between TPR2A and the HSP90 C-terminal domain. An in-silico assessment of binding fragments at the PPI interfacial region, provided valuable structural insight for future fragment elaboration strategies, including the identification of losartan as a weak, albeit dose-dependent inhibitor of the target PPI.

Fragment-Based Drug Discovery against Mycobacteria: The Success and Challenges

The emergence of drug-resistant mycobacteria, including Mycobacterium tuberculosis (Mtb) and non-tuberculous mycobacteria (NTM), poses an increasing global threat that urgently demands the development of new potent anti-mycobacterial drugs. One of the approaches toward the identification of new drugs is fragment-based drug discovery (FBDD), which is the most ingenious among other drug discovery models, such as structure-based drug design (SBDD) and high-throughput screening. Specialized techniques, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and many others, are part of the drug discovery approach to combat the Mtb and NTM global menaces. Moreover, the primary drawbacks of traditional methods, such as the limited measurement of biomolecular toxicity and uncertain bioavailability evaluation, are successfully overcome by the FBDD approach. The current review focuses on the recognition of fragment-based drug discovery as a popular approach using virtual, computational, and biophysical methods to identify potent fragment molecules. FBDD focuses on designing optimal inhibitors against potential therapeutic targets of NTM and Mtb (PurC, ArgB, MmpL3, and TrmD). Additionally, we have elaborated on the challenges associated with the FBDD approach in the identification and development of novel compounds. Insights into the applications and overcoming the challenges of FBDD approaches will aid in the identification of potential therapeutic compounds to treat drug-sensitive and drug-resistant NTMs and Mtb infections.

1H Nuclear Magnetic Resonance: A Future Approach to the Metabolic Profiling of Psychedelics in Human Biofluids?

While psychedelics may have therapeutic potential for treating mental health disorders such as depression, further research is needed to better understand their biological effects and mechanisms of action when considering the development of future novel therapy approaches. Psychedelic research could potentially benefit from the integration of metabonomics by proton nuclear magnetic resonance (1H NMR) spectroscopy which is an analytical chemistry-based approach that can measure the breakdown of drugs into their metabolites and their metabolic consequences from various biofluids. We have performed a systematic review with the primary aim of exploring published literature where 1H NMR analysed psychedelic substances including psilocin, lysergic acid diethylamide (LSD), LSD derivatives, N,N-dimethyltryptamine (DMT), 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) and bufotenin. The second aim was to assess the benefits and limitations of 1H NMR spectroscopy-based metabolomics as a tool in psychedelic research and the final aim was to explore potential future directions. We found that the most current use of 1H NMR in psychedelic research has been for the structural elucidation and analytical characterisation of psychedelic molecules and that no papers used 1H NMR in the metabolic profiling of biofluids, thus exposing a current research gap and the underuse of 1H NMR. The efficacy of 1H NMR spectroscopy was also compared to mass spectrometry, where both metabonomics techniques have previously shown to be appropriate for biofluid analysis in other applications. Additionally, potential future directions for psychedelic research were identified as real-time NMR, in vivo 1H nuclear magnetic resonance spectroscopy (MRS) and 1H NMR studies of the gut microbiome. Further psychedelic studies need to be conducted that incorporate the use of 1H NMR spectroscopy in the analysis of metabolites both in the peripheral biofluids and in vivo to determine whether it will be an effective future approach for clinical and naturalistic research.

MCSS-Based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

Computational fragment-based approaches are widely used in drug design and discovery. One of their limitations is the lack of performance of docking methods, mainly the scoring functions. With the emergence of fragment-based approaches for single-stranded RNA ligands, we analyze the performance in docking and screening powers of an MCSS-based approach. The performance is evaluated on a benchmark of protein-nucleotide complexes where the four RNA residues are used as fragments. The screening power can be considered the major limiting factor for the fragment-based modeling or design of sequence-selective oligonucleotides. We show that the MCSS sampling is efficient even for such large and flexible fragments. Hybrid solvent models based on some partial explicit representations improve both the docking and screening powers. Clustering of the n best-ranked poses can also contribute to a lesser extent to better performance. A detailed analysis of molecular features suggests various ways to optimize the performance further.

Search for relevant subsets of binary predictors in high dimensional regression for discovering the lead molecule

One of the main problems that the drug discovery research field confronts is to identify small molecules, modulators of protein function, which are likely to be therapeutically useful. Common practices rely on the screening of vast libraries of small molecules (often 1-2 million molecules) in order to identify a molecule, known as a lead molecule, which specifically inhibits or activates the protein function. To search for the lead molecule, we investigate the molecular structure, which generally consists of an extremely large number of fragments. Presence or absence of particular fragments, or groups of fragments, can strongly affect molecular properties. We study the relationship between molecular properties and its fragment composition by building a regression model, in which predictors, represented by binary variables indicating the presence or absence of fragments, are grouped in subsets and a bi-level penalization term is introduced for the high dimensionality of the problem. We evaluate the performance of this model in two simulation studies, comparing different penalization terms and different clustering techniques to derive the best predictor subsets structure. Both studies are characterized by small sets of data relative to the number of predictors under consideration. From the results of these simulation studies, we show that our approach can generate models able to identify key features and provide accurate predictions. The good performance of these models is then exhibited with real data about the MMP-12 enzyme.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Design, synthesis and biological evaluation of anticholinesterase peptides: Fragment-based vs. template-based peptide design

The prevalence of Alzheimer's disease (AD) has become a substantial global concern. Approved AChE inhibitors have been used for symptomatic treatment of AD. Binding of amyloid β (Aβ) to the peripheral anionic site of AChE facilitates the formation of Aβ plaques. Blocking this proposed protein-protein interaction by inhibition of the peripheral anionic site of AChE, in addition to increasing the level of ACh, reduces the Aβ aggregation and might qualify to slow down the progression of disease besides the palliative treatment. Targeting protein-protein interactions consider as one of the most challenging issues in the realm of drug design in which peptides have potentials to excel in. In the present study, we applied two virtual fragment-based and template-based approaches to design peptidic inhibitors of the PAS of AChE. Based on the in silico studies, high scored peptides p2 (WTWYGYWVW) and p10 (NHRMLTRRY) obtained from fragment-based and template-based design respectively. Regarding in vitro results, p2 (IC₅₀ = 16 ± 3.2 μM) and p10 (IC₅₀ = 23.6 ± 4.9 μM) showed significant AChE inhibitory effects. The molecular mechanism of inhibition studied by Lineweaver-Burk plots was mixed inhibition for both peptides. The in vitro results conformed to the in silico results and showed that both peptides occupied the CAS and PAS of AChE. The comparison of two peptide-design approaches revealed that the fragment-based design had more chemical diversity and showed priority to the template-based design. According to the obtained results, peptidic inhibitors of AChE designed by the proposed fragment-based approach might be more efficient in comparison to traditional approaches.

Motifs and interface amino acid-mediated regulation of amyloid biogenesis in microbes to humans: potential targets for intervention

Amyloids are linked to many debilitating diseases in mammals. Some organisms produce amyloids that have a functional role in the maintenance of their biological processes. Microbes utilize functional bacterial amyloids (FuBA) for pathogenicity and infections. Amyloid biogenesis is regulated differentially in various systems to avoid its toxic accumulation. A familiar feature in the process of amyloid biogenesis from humans to microbes is its regulation by protein-protein interactions (PPI). The spatial arrangement of amino acid residues in proteins generates topologies like flat interface and linear motif, which participate in protein interactions. Motifs and interface residue-mediated interactions have a direct or an indirect impact on amyloid secretion and assembly. Some motifs undergo post-translational modifications (PTM), which effects interactions and dynamics of the amyloid biogenesis cascade. Interaction-induced local changes stimulate global conformational transitions in the PPI complex, which indirectly affects amyloid formation. Perturbation of such motifs and interface residues results in amyloid abolishment. Interface residues, motifs and their respective interactive protein partners could serve as potential targets for intervention to inhibit amyloid biogenesis.

A Practical Perspective on the Roles of Solution NMR Spectroscopy in Drug Discovery

Solution nuclear magnetic resonance (NMR) spectroscopy is a powerful tool to study structures and dynamics of biomolecules under physiological conditions. As there are numerous NMR-derived methods applicable to probe protein–ligand interactions, NMR has been widely utilized in drug discovery, especially in such steps as hit identification and lead optimization. NMR is frequently used to locate ligand-binding sites on a target protein and to determine ligand binding modes. NMR spectroscopy is also a unique tool in fragment-based drug design (FBDD), as it is able to investigate target-ligand interactions with diverse binding affinities. NMR spectroscopy is able to identify fragments that bind weakly to a target, making it valuable for identifying hits targeting undruggable sites. In this review, we summarize the roles of solution NMR spectroscopy in drug discovery. We describe some methods that are used in identifying fragments, understanding the mechanism of action for a ligand, and monitoring the conformational changes of a target induced by ligand binding. A number of studies have proven that ¹⁹F-NMR is very powerful in screening fragments and detecting protein conformational changes. In-cell NMR will also play important roles in drug discovery by elucidating protein-ligand interactions in living cells.

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.

Protein-protein interaction modulators: advances, successes and remaining challenges

Modulating disease-relevant protein-protein interactions (PPIs) using small-molecule inhibitors is a quite indispensable diagnostic and therapeutic strategy in averting pathophysiological cues and disease progression. Over the years, targeting intracellular PPIs as drug design targets has been a challenging task owing to their highly dynamic and expansive interfacial areas (flat, featureless and relatively large). However, advances in PPI-focused drug discovery technology have been reported and a few drugs are already on the market, with some potential drug-like candidates already in clinical trials. In this article, we review the advances, successes and remaining challenges in the application of small molecules as valuable PPI modulators in disease diagnosis and therapeutics.