Molecular dynamics and pharmacophore modelling studies of different subtype (ALK and EGFR (T790M)) inhibitors in NSCLC

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.

Structural Analysis of Interactions between Epidermal Growth Factor Receptor (EGFR) Mutants and Their Inhibitors

People’s lives and health are gravely threatened by non-small-cell lung cancer (NSCLC). Mutations in epidermal growth factor receptor (EGFR), a transmembrane receptor tyrosine kinase, are considered one of the causes of NSCLC. Tyrosine kinase inhibitors (TKIs) are typically used to treat patients with EGFR mutations. In this study, Gefitinib, a member of the first generation of TKIs, was used to treat an EGFR single-point mutation (single mutant, SM). Patients harboring additional T790M mutations in the kinase domain of the EGFR were resistant to Gefitinib. Then, the L858R/T790M double mutation (double mutant, DM) was treated with the second generation of TKIs, such as Afatinib. Here, we constructed four computational models to uncover the structural basis between EGFR mutants (SM and DM) and corresponding inhibitors (Gefitinib and Afatinib). The binding energy in the G-SM (representing Gefitinib in complex with SM) system was larger than that in the G-DM (Representing Gefitinib in complex with DM) system. Gefitinib’s affinity with L792 and M793 was drastically reduced by the longer side chain of M790 in the G-DM system, which pushed Gefitinib outside of the pocket. Additionally, the A-DM system’s binding energy was higher than the G-DM system’s. Afatinib, unlike Gefitinib, induced the P-loop region to move downwards to decrease the pocket entrance size to accommodate Afatinib properly and stably in the A-DM (Afatinib in complex with DM) system. These results uncover the details of interactions between EGFR and its inhibitors and shed light on the design of new tyrosine kinase inhibitors.

Structure based designing of thiazolidinone-pyrimidine derivatives as ERK2 inhibitors: Synthesis and in vitro evaluation

Breast cancer has been associated with an overexpression of various molecular targets; accordingly, various target-specific chemotherapeutic agents have been developed. Inhibition of ERK2, a member of MAPK pathway, is an important target involved in the treatment of both oestrogen receptor-positive and triple-negative breast cancer. Thus, in continuation of our previous work on the ERK2 target, we here report novel inhibitors of this kinase. Out of three lead molecules reported in our previous study, we selected the thiazolidinone-pyrimidine scaffold for further development of small molecule inhibitors of ERK2. Analogues of the lead molecule were docked in the target kinase, followed by molecular dynamic simulations and MM-GBSA calculations. Analogues maintaining key interactions with amino acid residues in the ATP-binding domain of ERK2 were selected and duly synthesized. In vitro biochemical evaluation of these molecules against ERK2 kinase disclosed that two molecules possess significant kinase inhibitory potential with IC₅₀ values ≤ 0.5 µM.

Ligand and structure-based computational designing of multi-target molecules directing FFAR-1, FFAR-4 and PPAR-G as modulators of insulin receptor activity

Multi-agent therapies are an important treatment modality in many diseases based on the assumption that combining agents may result in increased therapeutic benefit by overcoming the mechanism of resistance and providing superior efficiency. Extensively validated 3D pharmacophore models for free fatty acid receptor-1 (FFAR-1), free fatty acid receptor-4 (FFAR-4), and peroxisome proliferator-activated receptor-G (PPAR-G) was developed. The pharmacophore model for FFAR-1 (r² = 0.98, q² = 0.90) and PPAR-G (r² = 0.89, q² = 0.88) suggested that one hydrogen bond acceptor, one hydrogen bond donor, three aromatic rings, and two hydrophobic groups arranged in 3D space are essential for the binding affinity of FFAR-1 and PPAR-G inhibitors. Similarly, the pharmacophore model for FFAR-4 (r² = 0.92, q² = 0.87) suggested that the presence of a hydrogen bond acceptor, one negative atom, two aromatic rings, and three hydrophobic groups plays a vital role in the binding of an inhibitor of FFAR-4. These pharmacophore models allowed searches for novel FFAR-1, PPAR-G, and FFAR-4 triple inhibitors from multi-conformer 3D databases (Asinex). Finally, the twenty-five best hits were selected for molecular docking, to study the interaction of their complexes with all the proteins and final binding orientations of these molecules. After molecular docking, ten hits have been predicted to possess good binding affinity as per the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculation for FFAR-1, FFAR-4, and PPAR-G which can be further investigated for its experimental in-vitro/in-vivo anti-diabetic activities.Communicated by Ramaswamy H. Sarma.

Exploring RdRp-remdesivir interactions to screen RdRp inhibitors for the management of novel coronavirus 2019-nCoV

A novel coronavirus recently identified in Wuhan, China (2019-nCoV) has resulted in an increasing number of patients globally, and has become a highly lethal pathogenic member of the coronavirus family affecting humans. 2019-nCoV has established itself as one of the most threatening pandemics that human beings have faced, and therefore analysis and evaluation of all possible responses against infection is required. One such strategy includes utilizing the knowledge gained from the SARS and MERS outbreaks regarding existing antivirals. Indicating a potential for success, one of the drugs, remdesivir, under repurposing studies, has shown positive results in initial clinical studies. Therefore, in the current work, the authors have attempted to utilize the remdesivir-RdRp complex - RdRp (RNA-dependent RNA polymerase) being the putative target for remdesivir - to screen a library of the already reported RdRp inhibitor database. Further clustering on the basis of structural features and scoring refinement was performed to filter out false positive hits. Finally, molecular dynamics simulation was carried out to validate the identification of hits as RdRp inhibitors against novel coronavirus 2019-nCoV. The results yielded two putative hits which can inhibit RdRp with better potency than remdesivir, subject to further biological evaluation.

Revealing binding selectivity of inhibitors toward bromodomain-containing proteins 2 and 4 using multiple short molecular dynamics simulations and free energy analyses

Emerging evidences indicate bromodomain-containing proteins 2 and 4 (BRD2 and BRD4) play critical roles in cancers, inflammations, cardiovascular diseases and other pathologies. Multiple short molecular dynamics (MSMD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method were applied to investigate the binding selectivity of three inhibitors 87D, 88M and 89G towards BRD2 over BRD4. The root-mean-square fluctuation (RMSF) analysis indicates that the structural flexibility of BRD4 is stronger than that of BRD2. Moreover the calculated distances between the C_α atoms in the centres of the ZA_loop and BC_loop of BRD4 are also bigger than that of BRD2. The rank of binding free energies calculated using MM-GBSA method agrees well with that determined by experimental data. The results show that 87D can bind more favourably to BRD2 than BRD4, while 88M has better selectivity on BRD4 over BRD2. Residue-based free-energy decomposition method was utilized to estimate the inhibitor-residue interaction spectrum and the results not only identify the hot interaction spots of inhibitors with BRD2 and BRD4, but also demonstrate that several common residues, including (W370, W374), (P371, P375), (V376, V380) and (L381, L385) belonging to (BRD2, BRD4), generate significant binding difference of inhibitors to BRD2 and BRD4.

Molecular mechanism of inhibitor bindings to bromodomain-containing protein 9 explored based on molecular dynamics simulations and calculations of binding free energies

Recently, bromodomain-containing protein 9 (BRD9) has been a prospective therapeutic target for anticancer drug design. Molecular dynamics (MD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method were adopted to explore binding modes of three inhibitors (5SW, 5U2, and 5U6) to BRD9 and identify the hot spot of the inhibitor-BRD9 binding. The results indicate that the inhibitor 5SW has the strongest binding ability to BRD9 among the current three inhibitors. Furthermore, the rank of the binding free energies predicted by MM-GBSA approach agrees with that determined by the experimental values. In addition, inhibitor-residue interactions were computed by using residue-based free-energy decomposition method and the results suggest that residue His42 produces the CH-H interactions, residues Asn100, Ile53 and Val49 produce the CH-[Formula: see text] interactions with three inhibitors and Tyr106, Phe45 and Phe44 generate the π-π interactions with inhibitors. Notably, the residue Asn140 forms hydrogen bonding interactions with three inhibitors. This research is expected to provide useful molecular basis and dynamics information at atomic levels for the design of potent inhibitors inhibiting the activity of BRD9.

Drug metabolizing enzymes and their inhibitors' role in cancer resistance

Despite continuous research on chemotherapeutic agents, different mechanisms of resistance have become a major pitfall in cancer chemotherapy. Although, exhaustive efforts are being made by several researchers to target resistance against chemotherapeutic agents, there is another class of resistance mechanism which is almost carrying on unattended. This class of resistance includes pharmacokinetics resistance such as efflux by ABC transporters and drug metabolizing enzymes. ABC transporters are the membrane bound proteins which are responsible for the movement of substrates through the cell membrane. Drug metabolizing enzymes are an integral part of phase-II metabolism that helps in the detoxification of exogenous, endogenous and xenobiotics substrates. These include uridine diphospho-glucuronosyltransferases (UGTs), glutathione-S-transferases (GSTs), dihydropyrimidine dehydrogenases (DPDs) and thiopurine methyltransferases (TPMTs). These enzymes may affect the role of drugs in both positive as well negative manner, depending upon the type of tissue and cells present and when present in tumors, can result in drug resistance. However, the underlying mechanism of resistance by drug metabolizing enzymes is still not clear. Here, we have tried to cover various aspects of these enzymes in relation to anticancer drugs.