Structural insights into the origin of phosphoinositide 3-kinase inhibition

A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling

Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.

Synthesis of Novel 2,9-Disubstituted-6-morpholino Purine Derivatives Assisted by Virtual Screening and Modelling of Class I PI3K Isoforms

The phosphatidylinositol-3 kinase (PI3K) pathway is one of the most frequently activated pathogenic signalling cascades in a wide variety of cancers. In the last 15 years, there has been an increase in the search for selective inhibitors of the four class I isoforms of PI3K, as they demonstrate better specificity and reduced toxicity in comparison to existing inhibitors. A ligand-based and target-based rational drug design strategy was employed to build a virtual library of 105 new compounds. Through this strategy, the four isoforms were compared regarding their activity pocket availability, amino acid sequences, and prone interactions. Additionally, a known active scaffold was used as a molecular base to design new derivatives. The virtual screening of the resultant library toward the four isoforms points to the obtention of 19 selective inhibitors for the PI3Kα and PI3Kγ targets. Three selective ligands, one for α-isoform and two for γ-isoform, present a ∆ (∆Gbinding) equal or greater than 1.5 Kcal/mol and were identified as the most promising candidates. A principal component analysis was used to establish correlations between the affinity data and some of the physicochemical and structural properties of the ligands. The binding modes and interactions established by the selective ligands in the active centre of the α and γ isoforms of PI3K were also investigated. After modelling studies, a synthetic approach to generate selective ligands was developed and applied in synthesising a set of derivatives that were obtained in good to excellent yield.

Critical features identification for chemical chronic toxicity based on mechanistic forecast models

Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors - random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.

Critical target identification and human health risk ranking of metal ions based on mechanism-driven modeling

Huge amounts of metals have been released into environment due to various anthropogenic activities, such as smelting and processing of metals and subsequent application in construction, automobiles, batteries, optoelectronic devices, and so on, resulting in widespread detection in environmental media. However, some metal ions are considered as "Environmental health hazards", leading to serious human health concerns through affecting critical targets. Hence, it is necessary to quickly and effectively recognize the key target of metal ions in living organisms. Fortunately, the development of high-throughput analysis and in silico approaches offer a promising tool for target identification. In this study, the key oncogenic target (tumor suppressor protein, p53) was screened by network analysis based on the comparative toxicogenomics database (CTD). Some metal ions could bind to p53 core domain, impair its function and induce the development of cancer risk, but its mechanisms were still unclear. Therefore, a quantitative structure-activity relationship (QSAR) model was constructed to characterize the binding constants (K_a) between DNA binding domain of p53 (p53 DBD) and nine metal ions (Mg²⁺, Ca²⁺, Cu²⁺, Zn²⁺, Co²⁺, Ni²⁺, Mn²⁺, Fe³⁺ and Ba²⁺). It had good robustness and predictive ability, which could be used to predict the K_a values of other six metal ions (Li⁺, Ag⁺, Cs⁺, Cd²⁺, Hg²⁺ and Pb²⁺) within application domain. The results showed strong binding affinity between Cd²⁺/Hg²⁺/Pb²⁺ and p53 DBD. Subsequent mechanism analyses revealed that first hydrolysis constant (|logK_OH|) and polarization force (Z²/r) were key metal ion-characteristic parameters. The metal ions with weak hydrolysis constants and strong polarization forces could readily interact with N-containing histidine and S-containing cysteine of p53 DBD, which resulted in high K_a values. This study identified p53 as potential target for metal ions, revealed the key characteristics affecting the actions and provide a basic understanding of metal ions-p53 DBD interaction.