From covalent transition states in chemistry to noncovalent in biology: from ß- to Φ-value analysis of protein folding.

Solving the mechanism of a chemical reaction requires determining the structures of all the ground states on the pathway and the elusive transition states linking them. 2024 is the centenary of Brønsted's landmark paper that introduced the ß-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium-free energies. Protein engineering was introduced for an analogous procedure, Φ-value analysis, to analyse the noncovalent interactions in proteins central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis. The mature procedure was then applied to study transition states in the pathway of protein folding - 'part (b) of the protein folding problem'. This review describes the development of Φ-value analysis of transition states and compares and contrasts the interpretation of ß- and Φ-values and their limitations. Φ-analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ-analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ-values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.

Sulfated Polysaccharides with Anticoagulant Potential: A Review Focusing on Structure-Activity Relationship and Action Mechanism.

Thromboembolism is the culprit of cardiovascular diseases, leading to the highest global mortality rate. Anticoagulation emerges as the primary approach for managing thrombotic conditions. Notably, sulfated polysaccharides exhibit favorable anticoagulant efficacy with reduced side effects. This review focuses on the structure-anticoagulant activity relationship of sulfated polysaccharides and the underlying action mechanisms. It is concluded that chlorosulfonicacid-pyridine method serves as the preferred technique to synthesize sulfated polysaccharides. The anticoagulant activity of sulfated polysaccharides is linked to the substitution site of sulfate groups, degree of substitution, molecular weight, main side chain structure, and glycosidic bond conformation. Moreover, sulfated polysaccharides exert anticoagulant activity via various pathways, including the inhibition of blood coagulation factors, activation of antithrombin III and heparin cofactor II, antiplatelet aggregation, and promotion of the fibrinolytic system.

Piperlongumine and its derivatives against cancer: A recent update and future prospective.

Piperlongumine, or piplartine (PL), is a bioactive alkaloid isolated from Piper longum L. and a potent phytoconstituent in Indian Ayurveda and traditional Chinese medicine with a lot of therapeutic benefits. Apart from all of its biological activities, it demonstrates multimodal anticancer activity by targeting various cancer-associated pathways and being less toxic to normal cells. According to their structure-activity relationship (SAR), the trimethylphenyl ring (cinnamoyl core) and 5,6-dihydropyridin-2-(1H)-one (piperdine core) are responsible for the potent anticancer activity. However, it has poor intrinsic properties (low aqueous solubility, poor bioavailability, etc.). As a result, pharmaceutical researchers have been trying to optimise or modify the structure of PL to improve the drug-likeness profiles. The present review selected 26 eligible research articles on PL derivatives published between 2012 and 2023, followed by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) format. We have thoroughly summarised the anticancer potency, mode of action, SAR and drug chemistry of the proposed PL-derivatives against different cancer cells. Overall, SAR analyses with respect to anticancer potency and drug-ability revealed that substitution of methoxy to hydroxyl, attachment of ligustrazine and 4-hydroxycoumarin heterocyclic rings in place of phenyl rings, and attachment of heterocyclic rings like indole at the C7-C8 olefin position in native PL can help to improve anticancer activity, aqueous solubility, cell permeability, and bioavailability, making them potential leads. Hopefully, the large-scale collection and critical drug-chemistry analyses will be helpful to pharmaceutical and academic researchers in developing potential, less-toxic and cost-effective PL-derivatives that can be used against different cancers.

Alleviating Neurodegenerative Diseases Associated with Mitochondrial Defects by Therapeutic Biomolecules.

Neurodegenerative diseases are emerging as a global health concern in the current sce-nario, and their association with mitochondrial defects has been a potential area of research. Mi-tochondria, one of the essential organelles of the cell, serve as the cell's powerhouse, producing energy and ensuring cellular health. Neurodegenerative diseases such as Alzheimer's, Parkinson's, Huntington's, amyotrophic lateral sclerosis, and Pelizaeus-Merzbacher disease have been found to be primarily triggered by mitochondrial malfunction. One of the key byproducts of mitochondrial respiration, reactive oxygen species, also contributes significantly to mitochondrial DNA muta-tions that eventually cause mitochondrial breakdown. This review paper comprehensively examines the potential of therapeutic biomolecules, specifi-cally mitochondria-specific antioxidants, in mitigating the impact of mitochondrial defects on neurodegenerative diseases. It provides a detailed analysis of the mechanisms involved in mito-chondrial dysfunction, the potential therapeutic targets of these biomolecules, and their structure-activity relationship information are also discussed in this review. Various research articles and publications were used extensively in compiling the data, and the structures of biomolecules were prepared using software such as ChemDraw and ChemSketch. Crucial elements triggering mitochondrial abnormalities were identified and a tabular compilation of bioactive antioxidant compounds along with their therapeutic targets, was presented. Mitochondria-specific antioxidant therapy is an innovative and promising strategy for the man-agement of neurodegenerative diseases associated with mitochondrial defects. This review pro-vides a thorough summary of the current state of research and promising avenues of research and development in this field, emphasizing the importance of further investigations and clinical trials to elucidate their therapeutic benefits.

A promising future of metal-N-heterocyclic carbene complexes in medicinal chemistry: The emerging bioorganometallic antitumor agents.

Metal complexes based on N-heterocyclic carbene (NHC) ligands have emerged as promising broad-spectrum antitumor agents in bioorganometallic medicinal chemistry. In recent decades, studies on cytotoxic metal-NHC complexes have yielded numerous compounds exhibiting superior cytotoxicity compared to cisplatin. Although the molecular mechanisms of these anticancer complexes are not fully understood, some potential targets and modes of action have been identified. However, a comprehensive review of their biological mechanisms is currently absent. In general, apoptosis caused by metal-NHCs is common in tumor cells. They can cause a series of changes after entering cells, such as mitochondrial membrane potential (MMP) variation, reactive oxygen species (ROS) generation, cytochrome c (cyt c) release, endoplasmic reticulum (ER) stress, lysosome damage, and caspase activation, ultimately leading to apoptosis. Therefore, a detailed understanding of the influence of metal-NHCs on cancer cell apoptosis is crucial. In this review, we provide a comprehensive summary of recent advances in metal-NHC complexes that trigger apoptotic cell death via different apoptosis-related targets or signaling pathways, including B-cell lymphoma 2 (Bcl-2 family), p53, cyt c, ER stress, lysosome damage, thioredoxin reductase (TrxR) inhibition, and so forth. We also discuss the challenges, limitations, and future directions of metal-NHC complexes to elucidate their emerging application in medicinal chemistry.

[Hologram quantitative structure-activity relationship on the gas chromatographic retention index of plant essential oil constituents].

The gas chromatography retention index (RI) is an important parameter for the identification of different types of compounds in the field of chromatographic analysis; however, the experimental collection of RI values is a extremely cumbersome process. Thus, there is an urgent need for the establishment of a simple, efficient, and accurate model for the prediction of the RI values of compounds. In this study, first, the experimental RI values for 60 plant essential oil constituents were obtained. Next, a model describing the hologram quantitative structure-activity relationship (HQSAR) between the structural properties of the essential oil constituents and their RI values was investigated and constructed. The optimal HQSAR model was established by setting the model parameters "fragment size", "fragment distinction", "hologram length" and "principal components" to "1-4", "C, Ch", "199", and "4", respectively. Finally, the predictive ability of the model was verified using external test set validation and leave-one-out cross-validation (LOO-CV). The experimental results were as follows, the root mean square error of prediction (RMSEP), predictive determination coefficient ([Formula: see text]), concordance correlation coefficient (CCC), and mean relative error (MRE) for external test set validation were 40.45, 0.984, 0.968, and 2.20%, respectively. Meanwhile, the root mean square error of cross validation (RMSECV) and MRE for LOO-CV were 72.56 and 4.17%, respectively. These findings demonstrate that the established HQSAR model has a good predictive ability and can accurately predict the RI values of plant essential oil constituents. In addition, the molecular contribution maps of the HQSAR model revealed that the RI values of aromatic compounds increase when hydroxyl groups are connected to their alkyl chains. Aliphatic compounds feature long chain alkyl groups, which can lead to an increase in RI values. The above phenomena highlight the promising application prospects of HQSAR for studying the RI values of plant essential oil constituents. Therefore, this study provides a reliable theoretical basis for predicting the RI values of other essential oil constituents.

Inferring molecular inhibition potency with AlphaFold predicted structures.

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Simple Scattering: Lipid nanoparticle structural data repository.

Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

Schiff Bases: A Captivating Scaffold with Potential Anticonvulsant Activity.

One of the most important organic compounds, also known as a Schiff base, imine, or azomethine, has been associated with several biological processes. The group is a component of both natural or synthetic chemicals and functions as both a precursor and an intermediary in the synthesis of therapeutically active substances. The review highlights the various non-metal Schiff bases' structure-activity relationship (SAR) studies, general model, docking, and design approach for anticonvulsant actions. Schiff bases serve as linkers in numerous synthetic compounds with a variety of activities, according to the findings of several investigations. As a result, the current review will give readers a thorough understanding of the key ideas put forth by different researchers regarding the anticonvulsant properties of Schiff bases. It will serve as a valuable information source for those planning to synthesize new anticonvulsant molecules that contain Schiff bases as pharmacophores or biologically active moieties.

The anticancer therapeutic potential of pyrimidine-sulfonamide hybrids.

Cancer as a devastating malignancy, seriously threatens human life and health, but most chemotherapeutics have long been criticized for unsatisfactory therapeutic efficacy due to drug resistance and severe off-target toxicity. Pyrimidines, including fused pyrimidines, are privileged scaffolds for various biological cancer targets and are the most important class of metalloenzyme carbonic anhydrase inhibitors. Pyrimidine-sulfonamide hybrids can act on different targets in cancer cells simultaneously and possess potent activity against various cancers, revealing that hybridization of pyrimidine with sulfonamide is a promising approach to generate novel effective anticancer candidates. This review aims to summarize the recent progress of pyrimidine-sulfonamide hybrids with anticancer potential, covering papers published from 2020 to present, to facilitate further rational design of more effective candidates.

A structure-functionality insight into the bioactivity of microbial polysaccharides toward biomedical applications: A review.

Microbial polysaccharides (MPs) are biopolymers secreted by microorganisms such as bacteria and fungi during their metabolic processes. Compared to polysaccharides derived from plants and animals, MPs have advantages such as wide sources, high production efficiency, and less susceptibility to natural environmental influences. The most attractive feature of MPs lies in their diverse biological activities, such as antioxidative, anti-tumor, antibacterial, and immunomodulatory activities, which have demonstrated immense potential for applications in functional foods, cosmetics, and biomedicine. These bioactivities are precisely regulated by their sophisticated molecular structure. However, the mechanisms underlying this precise regulation are not yet fully understood and continue to evolve. This article presents a comprehensive review of the most representative species of MPs, including their fermentation and purification processes and their biomedical applications in recent years. In particular, this work presents an in-depth analysis into the structure-activity relationships of MPs across multiple molecular levels. Additionally, this review discusses the challenges and prospects of investigating the structure-activity relationships, providing valuable insights into the broad and high-value utilization of MPs.

An Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum.

OBJECTIVE: This study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multidisciplinary science that aims to build software tools that mimic human intelligence. AI is revolutionizing pharmaceutical research and patient care. Hence, it is important to include AI in pharmacy education to prepare a competent workforce of pharmacists with skills in this area. METHODS: AI principles were introduced in a required medicinal chemistry course for first-year pharmacy students. An AI software, KNIME, was used to examine structure-activity relationships for 5 drugs. Students completed a data sheet that required comprehension of molecular structures and drug-protein interactions. These data were then used to make predictions for molecules with novel substituents using AI. The familiarity of students with AI was surveyed before and after this activity. RESULTS: There was an increase in the number of students indicating familiarity with use of AI in pharmacy (before vs after: 25.3% vs 74.5%). The introduction of AI stimulated interest in the course content (> 60% of students indicated increased interest in medicinal chemistry) without compromising the learning outcomes. Almost 70% of students agreed that more AI should be taught in the PharmD curriculum. CONCLUSION: This is a successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course. This approach is likely to stimulate student interest in AI applications in pharmacy.

Systematical mutational analysis of teriparatide on anti-osteoporosis activity by alanine scanning.

Osteoporosis is a progressive systemic skeletal disease that decreases bone density and bone quality, making them fragile and easy to break. In spite of effective anti-osteoporosis potency, teriparatide, the first anabolic medications approved for the treatment of osteoporosis, was proven to exhibit various side effects. And the relevant structure-activity relationship (SAR) of teriparatide was in need. In this work, we performed a systematical alanine scanning against teriparatide and synthesized 34 teriparatide derivatives. Their biological activities were evaluated and the importance of each residue for anti-osteoporosis activity was also revealed. A remarkable decrease in activity was observed for alanine replacement of the residue Gly12, His14, Ser17, Arg20 and Leu24, showcasing the important role of these residues in teriparatide on anti-osteoporosis activity. On contrary, when Gly13 and Gln30 were mutated to Ala, the peptide derivatives exhibited the significantly increased activities, demonstrating that these two residues could be readily replaced. Our research expanded the peptide library of teriparatide analogues and presented a potential opportunity for designing the more powerful anti-osteoporosis peptide agents.

A mini-review of the anti-SARS-CoV-2 potency of Amaryllidaceae alkaloids.

BACKGROUND: Nature has perennially served as an infinite reservoir of diverse chemicals with numerous applications benefiting humankind. In recent years, due to the emerging COVID-19 pandemic, there has been a surge in studies on repurposing natural products as anti-SARS-CoV-2 agents, including plant-derived substances. Among all types of natural products, alkaloids remain one of the most important groups with various known medicinal values. The current investigation focuses on Amaryllidaceae alkaloids (AAs) since AAs have drawn significant scientific attention as anti-SARS-CoV-2 agents over the past few years. PURPOSE AND STUDY DESIGN: This study serves as a mini-review, summarizing recent advances in studying the anti-SARS-CoV-2 potency of AAs, covering two aspects: structure-activity relationship and mechanism of action (MOA). METHODS: The study covers the period from 2019 to 2023. The information in this review were retrieved from common databases including Web of Science, ScienceDirect, PubMed and Google scholar. Reported anti-SARS-CoV-2 potency, cytotoxicity and possible biological targets of AAs were summarized and classified into different skeletal subclasses. Then, the structure-activity relationship (SAR) was explored, pinpointing the key pharmacophore-related structural moieties. To study the mechanism of action of anti-SARS-CoV-2 AAs, possible biological targets were discussed. RESULTS: In total, fourteen research articles about anti-SARS-CoV-2 was selected. From the SAR point of view, four skeletal subclasses of AAs (lycorine-, galanthamine-, crinine- and homolycorine-types) appear to be promising for further investigation as anti-SARS-CoV-2 agents despite experimental inconsistencies in determining in vitro half maximal inhibitory effective concentration (EC50). Narciclasine, haemanthamine- and montanine-type skeletons were cytotoxic and devoid of anti-SARS-CoV-2 activity. The lycorine-type scaffold was the most structurally diverse in this study and preliminary structure-activity relationships revealed the crucial role of ring C and substituents on rings A, C and D in its anti-SARS-CoV-2 activity. It also appears that two enantiomeric skeletons (haemanthamine- and crinine-types) displayed opposite activity/toxicity profiles regarding anti-SARS-CoV-2 activity. Pharmacophore-related moieties of the haemanthamine/crinine-type skeletons were the substituents on rings B, C and the dioxymethylene moiety. All galanthamine-type alkaloids in this study were devoid of cytotoxicity and it appears that varying substituents on rings C and D could enhance the anti-SARS-CoV-2 potency. Regarding MOAs, initial experimental results suggested Mpro and RdRp as possible viral targets. Dual functionality between anti-inflammatory activity on host cells and anti-SARS-CoV-2 activity on the SARS-CoV-2 virus of isoquinoline alkaloids, including AAs, were suggested as the possible MOAs to alleviate severe complications in COVID-19 patients. This dual functionality was proposed to be related to the p38 MAPK signaling pathway. CONCLUSION: Overall, Amaryllidaceae alkaloids appear to be promising for further investigation as anti-SARS-CoV-2 agents. The skeletal subclasses holding the premise for further investigation are lycorine-, crinine-, galanthamine- and homolycorine-types.

Advances in the discovery of activin receptor-like kinase 5 (ALK5) inhibitors.

Activin receptor­like kinase-5 (ALK5) is an outstanding member of the transforming growth factor-ß (TGF-ß) family. (TGF-ß) signaling pathway integrates pleiotropic proteins that regulate various cellular processes such as growth, proliferation, and differentiation. Dysregulation within the signaling pathway can cause variety of diseases, such as fibrosis, cardiovascular disease, and especially cancer, rendering ALK5 a potential drug target. Hence, various small molecules have been designed and synthesized as potent ALK5 inhibitors. In this review, we shed light on the current ATP-competitive inhibitors of ALK5 through diverse heterocyclic based scaffolds that are in clinical or pre-clinical phases of development. Moreover, we focused on the binding interactions of the compounds to the ATP binding site and the structure-activity relationship (SAR) of each scaffold, revealing new scopes for designing novel candidates with enhanced selectivity and metabolic profiles.

Organophosphate toxicity patterns: A new approach for assessing organophosphate neurotoxicity.

Organophosphorus compounds or organophosphates (OPs) are widely used as flame retardants, plasticizers, lubricants and pesticides. This contributes to their ubiquitous presence in the environment and to the risk of human exposure. The persistence of OPs and their bioaccumulative characteristics raise serious concerns regarding environmental and human health impacts. To address the need for safer OPs, this study uses a New Approach Method (NAM) to analyze the neurotoxicity pattern of 42 OPs. The NAM consists of a 4-step process that combines computational modeling with in vitro and in vivo experimental studies. Using spherical harmonic-based cluster analysis, the OPs were grouped into four main clusters. Experimental data and quantitative structure-activity relationships (QSARs) analysis were used in conjunction to provide information on the neurotoxicity profile of each group. Results showed that one of the identified clusters had a favorable safety profile, which may help identify safer OPs for industrial applications. In addition, the 3D-computational analysis of each cluster was used to identify meta-molecules with specific 3D features. Toxicity was found to correspond to the level of phosphate surface accessibility. Substances with conformations that minimize phosphate surface accessibility caused less neurotoxic effect. This multi-assay NAM could be used as a guide for the classification of OP toxicity, helping to minimize the health and environmental impacts of OPs, and providing rapid support to the chemical regulators, whilst reducing reliance on animal testing.

Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information.

Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-called new approach methodologies. In this light, we recently introduced the Bio-QSAR concept for multispecies aquatic toxicity regression tasks. These machine learning models, trained on both chemical and biological information, are capable of both cross-chemical and cross-species predictions. Here, we significantly extend these models' applicability. This was realized by increasing the quantity of training data by a factor of approximately 20, accomplished by considering both additional chemicals and aquatic organisms. Additionally, variable test durations and associated random effects were accommodated by employing a machine learning algorithm that combines tree-boosting with mixed-effects modeling (i.e., Gaussian Process Boosting). We also explored various biological descriptors including Dynamic Energy Budget model parameters, taxonomic distances, as well as genus-specific traits and investigated the inclusion of mode-of-action information. Through these efforts, we developed Bio-QSARs for fish and aquatic invertebrates with exceptional predictive power (R squared of up to 0.92 on independent test sets). Moreover, we made considerable strides to make models applicable for a range of use cases in environmental risk assessment as well as research and development of chemicals. Models were made fully explainable by implementing an algorithmic multicollinearity correction combined with SHapley Additive exPlanations. Furthermore, we devised novel approaches for applicability domain construction that take feature importance into account. We are hence confident these models, which are available via open access, will make a significant contribution towards the implementation of new approach methodologies and ultimately have the potential to support "Green Chemistry" and "Green Toxicology".

Discovery and optimization of narrow spectrum inhibitors of Tousled like kinase 2 (TLK2) using quantitative structure activity relationships.

The oxindole scaffold has been the center of several kinase drug discovery programs, some of which have led to approved medicines. A series of two oxindole matched pairs from the literature were identified where TLK2 was potently inhibited as an off-target kinase. The oxindole has long been considered a promiscuous kinase inhibitor template, but across these four specific literature oxindoles TLK2 activity was consistent, while the kinome profile was radically different ranging from narrow to broad spectrum kinome coverage. We synthesized a large series of analogues, utilizing quantitative structure-activity relationship (QSAR) analysis, water mapping of the kinase ATP binding sites, kinome profiling, and small-molecule x-ray structural analysis to optimize TLK2 inhibition and kinome selectivity. This resulted in the identification of several narrow spectrum, sub-family selective, chemical tool compounds including 128 (UNC-CA2-103) that could enable elucidation of TLK2 biology.

Recent advances in the synthesis of extensive libraries of heparan sulfate oligosaccharides for structure-activity relationship studies.

Heparan sulfate (HS) is a linear, sulfated and highly negatively-charged polysaccharide that plays important roles in many biological events. As a member of the glycosaminoglycan (GAG) family, HS is commonly found on mammalian cell surfaces and within the extracellular matrix. The structural complexities of natural HS polysaccharides have hampered the comprehension of their biological functions and structure-activity relationships (SARs). Although the sulfation patterns and backbone structures of HS can be major determinants of their biological activities, obtaining significant amounts of pure HS from natural sources for comprehensive SAR studies is challenging. Chemical and enzyme-based synthesis can aid in the production of structurally well-defined HS oligosaccharides. In this review, we discuss recent innovations enabling the syntheses of large libraries of HS and how these libraries can provide insights into the structural preferences of various HS binding proteins.

Structure-activity relationship studies of tetrahydroquinolone derivatives as GPR41 modulators.

GPR41, a G protein-coupled receptor, serves as a sensor for short-chain fatty acids and plays a crucial role in regulating multiple physiological processes such as the maintenance of metabolic and immune homeostasis. Therefore, the modulation of GPR41 has garnered attention as a potential strategy for the treatment of various disorders. We conducted a structure-activity relationship study on a lead tetrahydroquinolone derivative bearing an o-trifluoromethoxybenzene group that displayed antagonistic activity toward GPR41. Modification of the aryl group attached to the furan moiety revealed that derivatives containing di- or trifluorobenzene, instead of o-trifluoromethoxybenzene, exhibited agonistic activity toward GPR41, comparable with the reported agonistic modulator AR420626. These results suggest that the aryl group plays a pivotal role in regulating the activity of compounds toward GPR41, providing valuable insights for the design of GPR41 modulators.