How Do Small Molecule Aggregates Inhibit Enzyme Activity? A Molecular Dynamics Study

Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter

The ability to conduct effective high throughput screening (HTS) campaigns in drug discovery is often hampered by the detection of false positives in these assays due to small colloidally aggregating molecules (SCAMs). SCAMs can produce artifactual hits in HTS by nonspecific inhibition of the protein target. In this work, we present a new computational prediction tool for detecting SCAMs based on their 2D chemical structure. The tool, called the boosted aggregation detection (BAD) molecule filter, employs decision tree ensemble methods, namely, the CatBoost classifier and the light gradient-boosting machine, to significantly improve the detection of SCAMs. In developing the filter, we explore models trained on individual data sets, a consensus approach using these models, and, third, a merged data set approach, each tailored for specific drug discovery needs. The individual data set method emerged as most effective, achieving 93% sensitivity and 90% specificity, outperforming existing state-of-the-art models by 20 and 5%, respectively. The consensus models offer broader chemical space coverage, exceeding 90% for all testing sets. This feature is an important aspect particularly for early stage medicinal chemistry projects, and provides information on applicability domain. Meanwhile, the merged data set models demonstrated robust performance, with a notable sensitivity of 79% in the comprehensive 10-fold cross-validation test set. A SHAP analysis of model features indicates the importance of hydrophobicity and molecular complexity as primary factors influencing the aggregation propensity. The BAD molecule filter is readily accessible for the public usage on https://molmodlab-aau.com/Tools.html. This filter provides a new, more robust tool for aggregate prediction in the early stages of drug discovery to optimize hit rates and reduce associated testing and validation overheads.

Development of Novel Phosphonodifluoromethyl-Containing Phosphotyrosine Mimetics and a First-In-Class, Potent, Selective, and Bioavailable Inhibitor of Human CDC14 Phosphatases

Together with protein tyrosine kinases, protein tyrosine phosphatases (PTPs) control protein tyrosine phosphorylation and regulate numerous cellular functions. Dysregulated PTP activity is associated with the onset of multiple human diseases. Nevertheless, understanding of the physiological function and disease biology of most PTPs remains limited, largely due to the lack of PTP-specific chemical probes. In this study, starting from a well-known nonhydrolyzable phosphotyrosine (pTyr) mimetic, phosphonodifluoromethyl phenylalanine (F2Pmp), we synthesized 7 novel phosphonodifluoromethyl-containing bicyclic/tricyclic aryl derivatives with improved cell permeability and potency toward various PTPs. Furthermore, with fragment- and structure-based design strategies, we advanced compound 9 to compound 15, a first-in-class, potent, selective, and bioavailable inhibitor of human CDC14A and B phosphatases. This study demonstrates the applicability of the fragment-based design strategy in creating potent, selective, and bioavailable PTP inhibitors and provides a valuable probe for interrogating the biological roles of hCDC14 phosphatases and assessing their potential for therapeutic interventions.

Investigating the Aryl Hydrocarbon Receptor Agonist/Antagonist Conformational Switch Using Well-Tempered Metadynamics Simulations

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that mediates biological signals to control various complicated cellular functions. It plays a crucial role in environmental sensing and xenobiotic metabolism. Dysregulation of AhR is associated with health concerns, including cancer and immune system disorders. Upon binding to AhR ligands, AhR, along with heat shock protein 90 and other partner proteins undergoes a transformation in the nucleus, heterodimerizes with the aryl hydrocarbon receptor nuclear translocator (ARNT), and mediates numerous biological functions by inducing the transcription of various AhR-responsive genes. In this manuscript, the 3-dimensional structure of the entire human AhR is obtained using an artificial intelligence tool, and molecular dynamics (MD) simulations are performed to study different structural conformations. These conformations provide insights into the protein's function and movement in response to ligand binding. Understanding the dynamic behavior of AhR will contribute to the development of targeted therapies for associated health conditions. Therefore, we employ well-tempered metadynamics (WTE-metaD) simulations to explore the conformational landscape of AhR and obtain a better understanding of its functional behavior. Our computational results are in excellent agreement with previous experimental findings, revealing the closed and open states of helix α1 in the basic helix-loop-helix (bHLH domain) in the cytoplasm at the atomic level. We also predict the inactive form of AhR and identify Arginine 42 as a key residue that regulates switching between closed and open conformations in existing AhR modulators.

Molecular dynamics simulations as a guide for modulating small molecule aggregation

Small colloidally aggregating molecules (SCAMs) can be problematic for biological assays in drug discovery campaigns. However, the self-associating properties of SCAMs have potential applications in drug delivery and analytical biochemistry. Consequently, the ability to predict the aggregation propensity of a small organic molecule is of considerable interest. Chemoinformatics-based filters such as ChemAGG and Aggregator Advisor offer rapid assessment but are limited by the assay quality and structural diversity of their training set data. Complementary to these tools, we explore here the ability of molecular dynamics (MD) simulations as a physics-based method capable of predicting the aggregation propensity of diverse chemical structures. For a set of 32 molecules, using simulations of 100 ns in explicit solvent, we find a success rate of 97% (one molecule misclassified) as opposed to 75% by Aggregator Advisor and 72% by ChemAGG. These short timescale MD simulations are representative of longer microsecond trajectories and yield an informative spectrum of aggregation propensities across the set of solutes, capturing the dynamic behaviour of weakly aggregating compounds. Implicit solvent simulations using the generalized Born model were less successful in predicting aggregation propensity. MD simulations were also performed to explore structure-aggregation relationships for selected molecules, identifying chemical modifications that reversed the predicted behaviour of a given aggregator/non-aggregator compound. While lower throughput than rapid cheminformatics-based SCAM filters, MD-based prediction of aggregation has potential to be deployed on the scale of focused subsets of moderate size, and, depending on the target application, provide guidance on removing or optimizing a compound's aggregation propensity.

Applications of Molecular Dynamics Simulations in Drug Discovery

In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.

Non-apoptotic cell death induction via sapogenin based supramolecular particles

The discovery of novel chemotherapeutics that act through different mechanisms is critical for dealing with tumor heterogeneity and therapeutic resistance. We previously reported a saponin analog (AG-08) that induces non-canonical necrotic cell death and is auspicious for cancer therapy. Here, we describe that the key element in triggering this unique cell death mechanism of AG-08 is its ability to form supramolecular particles. These self-assembled particles are internalized via a different endocytosis pathway than those previously described. Microarray analysis suggested that AG-08 supramolecular structures affect several cell signaling pathways, including unfolded protein response, immune response, and oxidative stress. Finally, through investigation of its 18 analogs, we further determined the structural features required for the formation of particulate structures and the stimulation of the unprecedented cell death mechanism of AG-08. The unique results of AG-08 indicated that supramolecular assemblies of small molecules are promising for the field of anticancer drug development, although they have widely been accepted as nuisance in drug discovery studies.

Thermostability mechanisms of β-agarase by analyzing its structure through molecular dynamics simulation

Agarase is a natural catalyst with a good prospect in the industry. However, most of the currently discovered β-agarases are unsuitable for relatively high-temperature and high-pressure conditions required by industrial production. In this study, molecular dynamics simulations were first used to investigate the dynamic changes of folding and unfolding of mesophile and thermophile β-agarases (i.e., 1URX and 3WZ1) to explore the thermostability mechanism at three high temperatures (300 K, 400 K, and 500 K). Results showed that the sequence identity of 3WZ1 and 1URX reaches 48.8%. 1URX has a higher thermal sensitivity and less thermostability than 3WZ1 as more thermostable regions and hydrogen bonds exist in 3WZ1 compared with 1URX. The structures of 1URX and 3WZ1 become unstable with increasing temperatures up to 500 K. The strategies to increase the thermostability of 1URX and 3WZ1 are discussed. This study could provide insights into the design and modification of β-agarases at a high temperature.

Inhibition of Class A β-Lactamase (TEM-1) by Narrow Fractions of Humic Substances

Antimicrobial resistance is a global threat. The use of biologically active natural products alone or in combination with the clinically proven antimicrobial agents might be a useful strategy to fight the resistance. The scientific hypotheses of this study were twofold: (1) the natural humic substances rich in dicarboxyl, phenolic, heteroaryl, and other fragments might possess inhibitory activity against β-lactamases, and (2) this inhibitory activity might be linked to the molecular composition of the humic ensemble. To test these hypotheses, we used humic substances (HS) from different sources (coal, peat, and soil) and of different fractional compositions (humic acids, hymatomelanic acids, and narrow fractions from solid-phase extraction) for inhibiting serine β-lactamase TEM-1. Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) was used to characterize the molecular composition of all humic materials used in this study. The kinetic assay with chromogenic substrate CENTA was used for assessment of inhibitory activity. The inhibition data have shown that among all humic materials tested, a distinct activity was observed within apolar fractions of hymatomelanic acid isolated from lignite. The decrease in the hydrolysis rate in the presence of most active fractions was 42% (with sulbactam-87%). Of particular importance is that these very fractions caused a synergistic effect (2-fold) for the combinations with sulbactam. Linking the observed inhibition effects to molecular composition revealed the preferential contribution of low-oxidized aromatic and acyclic components such as flavonoid-, lignin, and terpenoid-like molecules. The binding of single low-molecular-weight components to the cryptic allosteric site along with supramolecular interactions of humic aggregates with the protein surface could be considered as a major contributor to the observed inhibition. We believe that fine fractionation of hydrophobic humic materials along with molecular modeling studies on the interaction between humic molecules and β-lactamases might contribute to the development of novel β-lactamase inhibitors of humic nature.

Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.