Behind the screen: drug discovery using the big data of phenotypic analysis.

Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today's Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed "Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis" to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.

Web of venom: exploration of big data resources in animal toxin research.

Research on animal venoms and their components spans multiple disciplines, including biology, biochemistry, bioinformatics, pharmacology, medicine, and more. Manipulating and analyzing the diverse array of data required for venom research can be challenging, and relevant tools and resources are often dispersed across different online platforms, making them less accessible to nonexperts. In this article, we address the multifaceted needs of the scientific community involved in venom and toxin-related research by identifying and discussing web resources, databases, and tools commonly used in this field. We have compiled these resources into a comprehensive table available on the VenomZone website (https://venomzone.expasy.org/10897). Furthermore, we highlight the challenges currently faced by researchers in accessing and using these resources and emphasize the importance of community-driven interdisciplinary approaches. We conclude by underscoring the significance of enhancing standards, promoting interoperability, and encouraging data and method sharing within the venom research community.

Design, synthesis, and in vitro biological evaluation of meta-sulfonamidobenzamide-based antibacterial LpxH inhibitors.

New antibacterial compounds are urgently needed, especially for infections caused by the top-priority Gram-negative bacteria that are increasingly difficult to treat. Lipid A is a key component of the Gram-negative outer membrane and the LpxH enzyme plays an important role in its biosynthesis, making it a promising antibacterial target. Inspired by previously reported ortho-N-methyl-sulfonamidobenzamide-based LpxH inhibitors, novel benzamide substitutions were explored in this work to assess their in vitro activity. Our findings reveal that maintaining wild-type antibacterial activity necessitates removal of the N-methyl group when shifting the ortho-N-methyl-sulfonamide to the meta-position. This discovery led to the synthesis of meta-sulfonamidobenzamide analogs with potent antibacterial activity and enzyme inhibition. Moreover, we demonstrate that modifying the benzamide scaffold can alter blocking of the cardiac voltage-gated potassium ion channel hERG. Furthermore, two LpxH-bound X-ray structures show how the enzyme-ligand interactions of the meta-sulfonamidobenzamide analogs differ from those of the previously reported ortho analogs. Overall, our study has identified meta-sulfonamidobenzamide derivatives as promising LpxH inhibitors with the potential for optimization in future antibacterial hit-to-lead programs.

DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening.

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

Exploring the constitutive activation mechanism of the class A orphan GPR20.

GPR20, an orphan G protein-coupled receptor (GPCR), shows significant expression in intestinal tissue and represents a potential therapeutic target to treat gastrointestinal stromal tumors. GPR20 performs high constitutive activity when coupling with Gi. Despite the pharmacological importance of GPCR constitutive activation, determining the mechanism has long remained unclear. In this study, we explored the constitutive activation mechanism of GPR20 through large-scale unbiased molecular dynamics simulations. Our results unveil the allosteric nature of constitutively activated GPCR signal transduction involving extracellular and intracellular domains. Moreover, the constitutively active state of the GPR20 requires both the N-terminal cap and Gi protein. The N-terminal cap of GPR20 functions like an agonist and mediates long-range activated conformational shift. Together with the previous study, this study enhances our knowledge of the self-activation mechanism of the orphan receptor, facilitates the drug discovery efforts that target GPR20.

Recent advancement in understanding of Alzheimer's disease: Risk factors, subtypes, and drug targets and potential therapeutics.

Alzheimer's disease (AD) is a significant neocortical degenerative disorder characterized by the progressive loss of neurons and secondary alterations in white matter tracts. Understanding the risk factors and mechanisms underlying AD is crucial for developing effective treatments. The risk factors associated with AD encompass a wide range of variables, including gender differences, family history, and genetic predispositions. Additionally, environmental factors such as air pollution and lifestyle-related conditions like cardiovascular disease, gut pathogens, and liver pathology contribute substantially to the development and progression of AD and its subtypes. This review provides current update and deeper insights into the role of diverse risk factors, categorizing AD into its distinct subtypes and elucidating their specific pathophysiological mechanisms. Unlike previous studies that often focus on isolated aspects of AD, our review integrates these factors to offer a comprehensive understanding of the disease. Furthermore, the review explores a variety of drug targets linked to the neuropathology of different AD subtypes, highlighting the potential for targeted therapeutic interventions. We further discussed the novel therapeutic options and categorized them according to their targets. The roles of different drug targets were comprehensively studied, and the mechanism of action of their inhibitors was discussed in detail. By comprehensively covering the interplay of risk factors, subtype differentiation, and drug targets, this review provides a deeper understanding of AD and suggests directions for future research and therapeutic strategies.

An update on small molecule compounds targeting synthetic lethality for cancer therapy.

Targeting cancer-specific vulnerabilities through synthetic lethality (SL) is an emerging paradigm in precision oncology. A SL strategy based on PARP inhibitors has demonstrated clinical efficacy. Advances in DNA damage response (DDR) uncover novel SL gene pairs. Beyond BRCA-PARP, emerging SL targets like ATR, ATM, DNA-PK, CHK1, WEE1, CDK12, RAD51, and RAD52 show clinical promise. Selective and bioavailable small molecule inhibitors have been developed to induce SL, but optimization for potency, specificity, and drug-like properties remains challenging. This article illuminated recent progress in the field of medicinal chemistry centered on the rational design of agents capable of eliciting SL specifically in neoplastic cells. It is envisioned that innovative strategies harnessing SL for small molecule design may unlock novel prospects for targeted cancer therapeutics going forward.

Discovery of 1,3,4-oxadiazoles with slow-action activity against Plasmodium falciparum malaria parasites.

To achieve malaria eradication, new preventative agents that act differently to front-line treatment drugs are needed. To identify potential chemoprevention starting points we screened a sub-set of the CSIRO Australia Compound Collection for compounds with slow-action in vitro activity against Plasmodium falciparum. This work identified N,N-dialkyl-5-alkylsulfonyl-1,3,4-oxadiazol-2-amines as a new antiplasmodial chemotype (e.g., 1 96 h IC50 550 nM; 3 96 h IC50 160 nM) with a different action to delayed-death slow-action drugs. A series of analogues were synthesized from thiotetrazoles and carbomoyl derivatives using Huisgen 1,3,4-oxadiazole synthesis followed by oxidation of the resultant thioethers to target sulfones. Structure activity relationship analysis of analogues identified compounds with potent and selective in vitro activity against drug-sensitive and multi-drug resistant Plasmodium parasites (e.g., 31 and 32 96 h IC50 <40 nM; SI > 2500). Subsequent studies in mice with compound 1, which had the best microsomal stability of the compounds assessed (T1/2 >255 min), demonstrated rapid clearance and poor oral in vivo efficacy in a P. berghei murine malaria model. These data indicate that while N,N-dialkyl-5-alkylsulfonyl-1,3,4-oxadiazol-2-amines are a novel class of slow-acting antiplasmodial agents, the further development of this chemotype for malaria chemoprophylaxis will require pharmacokinetic profile improvements.

An escape from ESKAPE pathogens: A comprehensive review on current and emerging therapeutics against antibiotic resistance.

The rise of antimicrobial resistance has positioned ESKAPE pathogens as a serious global health threat, primarily due to the limitations and frequent failures of current treatment options. This growing risk has spurred the scientific community to seek innovative antibiotic therapies and improved oversight strategies. This review aims to provide a comprehensive overview of the origins and resistance mechanisms of ESKAPE pathogens, while also exploring next-generation treatment strategies for these infections. In addition, it will address both traditional and novel approaches to combating antibiotic resistance, offering insights into potential new therapeutic avenues. Emerging research underscores the urgency of developing new antimicrobial agents and strategies to overcome resistance, highlighting the need for novel drug classes and combination therapies. Advances in genomic technologies and a deeper understanding of microbial pathogenesis are crucial in identifying effective treatments. Integrating precision medicine and personalized approaches could enhance therapeutic efficacy. The review also emphasizes the importance of global collaboration in surveillance and stewardship, as well as policy reforms, enhanced diagnostic tools, and public awareness initiatives, to address resistance on a worldwide scale.

A brief review on the lessons learned from COVID-19 on drug discovery and research.

COVID-19 pandemic has taught many lessons regarding drug discovery and development. This review covers these aspects of drug discovery and research for COVID-19 which might be used as a tool for future. It summarizes the positives such as progresses in antiviral drug discovery, drug repurposing, adaptations of clinical trial and its regulations, as well as the negative points such as the need to develop more collaboration among stakeholders and future directions. It also discusses the benefits and limitations of finding new indications for existing drugs, and the lessons learned regarding rigorous and robust clinical trials, pharmacokinetic/pharmacodynamic modelling, as well as combination therapy. The pandemic has also revealed some gaps regarding global collaboration and coordination, data sharing and transparency and equitable distribution. Finally, the review enumerates the future directions and implications of drug discovery and research for COVID-19 and other infectious diseases such as preparedness and resilience, interdisciplinary and integrative approaches, diversity and inclusion, and personalized and precision medicine.

CHARMM-GUI PDB Reader and Manipulator: Covalent Ligand Modeling and Simulation.

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.

Blocking the WNT/ß-catenin pathway in cancer treatment:pharmacological targets and drug therapeutic potential.

The WNT/ß-catenin signaling pathway plays crucial roles in tumorigenesis and relapse, metastasis, drug resistance, and tumor stemness maintenance. In most tumors, the WNT/ß-catenin signaling pathway is often aberrantly activated. The therapeutic usefulness of inhibition of WNT/ß-catenin signaling has been reported to improve the efficiency of different cancer treatments and this inhibition of signaling has been carried out using different methods including pharmacological agents, short interfering RNA (siRNA), and antibodies. Here, we review the WNT-inhibitory effects of some FDA-approved drugs and natural products in cancer treatment and focus on recent progress of the WNT signaling inhibitors in improving the efficiency of chemotherapy, immunotherapy, gene therapy, and physical therapy. We also classified these FDA-approved drugs and natural products according to their structure and physicochemical properties, and introduced briefly their potential mechanisms of inhibiting the WNT signaling pathway. The review provides a comprehensive understanding of inhibitors of WNT/ß-catenin pathway in various cancer therapeutics. This will benefit novel WNT inhibitor development and optimal clinical use of WNT signaling-related drugs in synergistic cancer therapy.

A high content imaging assay for identification of specific inhibitors of native Plasmodium liver stage protein synthesis.

Plasmodium parasite resistance to antimalarial drugs is a serious threat to public health in malaria-endemic areas. Compounds that target core cellular processes like translation are highly desirable, as they should be capable of killing parasites in their liver and blood stage forms, regardless of molecular target or mechanism. Assays that can identify these compounds are thus needed. Recently, specific quantification of native Plasmodium berghei liver stage protein synthesis, as well as that of the hepatoma cells supporting parasite growth, was achieved via automated confocal feedback microscopy of the o-propargyl puromycin (OPP)-labeled nascent proteome, but this imaging modality is limited in throughput. Here, we developed and validated a miniaturized high content imaging (HCI) version of the OPP assay that increases throughput, before deploying this approach to screen the Pathogen Box. We identified only two hits; both of which are parasite-specific quinoline-4-carboxamides, and analogs of the clinical candidate and known inhibitor of blood and liver stage protein synthesis, DDD107498/cabamiquine. We further show that these compounds have strikingly distinct relationships between their antiplasmodial and translation inhibition efficacies. These results demonstrate the utility and reliability of the P. berghei liver stage OPP HCI assay for the specific, single-well quantification of Plasmodium and human protein synthesis in the native cellular context, allowing the identification of selective Plasmodium translation inhibitors with the highest potential for multistage activity.

Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks.

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

Exploring Holy Basil's Bioactive Compounds for T2DM Treatment: Docking and Molecular Dynamics Simulations with Human Omentin-1.

Type 2 Diabetes Mellitus (T2DM) presents a substantial health concern on a global scale, driving the search for innovative therapeutic strategies. Phytochemicals from medicinal plants, particularly Ocimum tenuiflorum (Holy Basil), have garnered attention for their potential in T2DM management. The increased focus on plant-based treatments stems from their perceived safety profile, lower risk of adverse effects, and the diverse range of bioactive molecules they offer, which can target multiple pathways involved in T2DM. Computational techniques explored the binding interactions between O. tenuiflorum phytochemicals and Human Omentin-1, a potential T2DM target. ADMET evaluation and targeted docking identified lead compounds: Luteolin (-4.84 kcal/mol), Madecassic acid (-4.12 kcal/mol), Ursolic acid (-5.91 kcal/mol), Stenocereol (-5.59 kcal/mol), and Apigenin (-4.64 kcal/mol), to have a better binding affinity to target protein compared to the control drug, Metformin (-2.01 kcal/mol). Subsequent molecular dynamics simulations evaluated the stability of Stenocereol, Luteolin, and Metformin complexes for 200 nanoseconds, analysing RMSD, RMSF, RG, SASA, PCA, FEL, and MM-PBSA parameters. Results indicated Stenocereol's strong binding affinity with Omentin-1, suggesting its potential as a potent therapeutic agent for T2DM management. These findings lay the groundwork for further experimental validation and drug discovery endeavours.

Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.

CDK2 plays a pivotal role in controlling the progression of the cell cycle and is a target for anticancer drugs. The last 30 years of structural studies focused on CDK2 provided the basis for understanding its inhibition and furnished the data to develop machine-learning models to study intermolecular interactions. This review addresses the application of computational models to estimate the inhibition of CDK2. It focuses on machine-learning models developed to predict binding affinity against CDK2 using the program SAnDReS. A search of previously published articles on PubMed showed machine-learning models built to evaluate CDK2 inhibition. BindingDB information for CDK2 furnished the data to generate updated machine-learning models to predict the inhibition of this enzyme. The application of SAnDReS to model CDK2-inhibitor interactions showed that this approach can build machine-learning models with superior predictive performance compared with classical and deep-learning scoring functions. Also, the innovative DOME analysis of the predictive performance of machine learning and universal scoring function indicates that this method is adequate to select computational models to address protein-ligand interactions. The available structural and functional data about CDK2 is a rich source of information to build machine-learning models to predict the inhibition of this protein target. SAnDReS can build superior models to predict pKi and outperform universal scoring functions, including one developed using deep learning.

Metal ions in biomedically relevant macromolecular structures.

Understanding the functions of metal ions in biological systems is crucial for many aspects of research, including deciphering their roles in diseases and potential therapeutic use. Structural information about the molecular or atomic details of these interactions, generated by methods like X-ray crystallography, cryo-electron microscopy, or nucleic magnetic resonance, frequently provides details that no other method can. As with any experimental method, they have inherent limitations that sometimes lead to an erroneous interpretation. This manuscript highlights different aspects of structural data available for metal-protein complexes. We examine the quality of modeling metal ion binding sites across different structure determination methods, where different kinds of errors stem from, and how they can impact correct interpretations and conclusions.

Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence.

Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.

Phenotypic-based Discovery and Exploration of a Resorufin Scaffold with Activity against Mycobacterium Tuberculosis.

Tuberculosis remains a leading cause of death by infectious disease. The long treatment regimen and the spread of drug-resistant strains of the causative agent Mycobacterium tuberculosis (Mtb) necessitates the development of new treatment options. In a phenotypic screen, a nitrofuran-resorufin conjugate 1 was identified as a potent sub-micromolar inhibitor of whole cell Mtb. Complete loss of activity was observed for this compound in Mtb mutants affected in enzyme cofactor F420 biosynthesis (fbiC), suggesting that 1 undergoes prodrug activation in a manner similar to anti-tuberculosis prodrug pretomanid. Exploration of the structure-activity relationship led to the discovery of novel resorufin analogues that do not rely on the deazaflavin-dependent nitroreductase (Ddn) bioactivation pathway for their antimycobacterial activity. These analogues are of interest as they work through an alternative, currently unknown mechanism that may expand our chemical arsenal towards the treatment of this devastating disease.