Effective virtual screening strategy focusing on the identification of novel Bruton's tyrosine kinase inhibitors

Ligand and Structure-Based Virtual Screening in Combination, to Evaluate Small Organic Molecules as Inhibitors for the XIAP Anti-Apoptotic Protein: The Xanthohumol Hypothesis

Herein, we propose two chalcone molecules, (E)-1-(4-methoxyphenyl)-3-(p-tolyl) prop-2-en-1-one and (E)-3-(4-hydroxyphenyl)-1-(2,4,6-trihydroxyphenyl) prop-2-en-1-one, based on the anticancer bioactive molecule Xanthohumol, which are suitable for further in vitro and in vivo studies. Their ability to create stable complexes with the antiapoptotic X-linked IAP (XIAP) protein makes them promising anticancer agents. The calculations were based on ligand-based and structure-based virtual screening combined with the pharmacophore build. Additionally, the structures passed Lipinski’s rule for drug use, and their reactivity was confirmed using density functional theory studies. ADMET studies were also performed to reveal the pharmacokinetic potential of the compounds. The candidates were chosen from 10,639,400 compounds, and the docking protocols were evaluated using molecular dynamics simulations.

CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors

Global QSAR modelling was performed to predict the pIC₅₀ values of 233 diverse heterocyclic compounds as BTK inhibitors with the Monte Carlo algorithm of CORAL software using the DCW hybrid descriptors extracted from SMILES notations of molecules. The dataset of 233 BTK inhibitors was randomly split into training, invisible training, calibration and validation sets. The index of ideality of correlation was also applied to build and judge the predictability of the QSAR models. Eight global QSAR models based on the hybrid optimal descriptor using two target functions, i.e. TF₁ (W_IIC = 0) and TF₂ (W_IIC = 0.2) have been constructed. The statistical parameters of QSAR models computed by TF₂ are more reliable and robust and were used to predict the pIC₅₀ values. The model constructed for split 4 via TF₂ is regarded as the best model and the numerical values of r²_Train, r²_Valid, Q²_Train and Q²_Valid are equal to 0.7981, 0.7429, 0.7898 and 0.6784, respectively. By internal and external validation techniques, the predictability and reliability of the designed models have been assessed. The structural attributes responsible for the increase and decrease of pIC₅₀ of BTK inhibitors were also identified.

Identification of Novel Cathepsin B Inhibitors with Implications in Alzheimer's Disease: Computational Refining and Biochemical Evaluation

Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer’s disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer’s drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act as cathepsin B inhibitors. The LBVS model was generated by using hydrophobic (HY), hydrogen bond acceptor (HBA), and hydrogen bond donor (HBD) features, using a dataset of 24 known cathepsin B inhibitors of both natural and synthetic origins. A validated eight-feature pharmacophore hypothesis (Hypo III) was utilized to screen the Maybridge chemical database. The docking score, MM-PBSA, and MM-GBSA methodology was applied to prioritize the lead compounds as virtual screening hits. These compounds share a common amide scaffold, and showed important interactions with Gln23, Cys29, His110, His111, Glu122, His199, and Trp221. The identified inhibitors were further evaluated for cathepsin-B-inhibitory activity. Our study suggests that pyridine, acetamide, and benzohydrazide compounds could be used as a starting point for the development of novel therapeutics.

Site-directed mutations of anti-amantadine scFv antibody by molecular dynamics simulation: prediction and validation

A recombinant single-chain variable fragment (scFv) antibody was produced from a hybridoma cell strain secreting the monoclonal antibody for amantadine (AMD), and then its recognition mechanisms for AMD were studied using the molecular docking and molecular dynamics. Complex dockings revealed that three regions are involved in antibody recognition; framework 2 of the VL chain (LFR2) GLU40 and TYR42, complementarity-determining region of the VL chain (LCDR3) TYR116, and framework 2 of the VH chain (HFR2) HIS40 and TRP52 were the key amino acid residues. The results of molecular dynamics show that the most important amino acid residues in the interaction between AMD and scFv are HIS40 and TYR116. On the basis of the results of virtual mutation, the scFv antibody was evolved by directional mutagenesis of amino acid residue GLY107 to PHE. Indirect competitive ELISA (icELISA) results indicated that the scFv mutant had highly increased affinity for AMD with up to 3.9-fold improved sensitivity. Thus, the scFv antibody can be applied for mechanistic studies of intermolecular interactions, and our work offered affinity maturated antibodies by site mutations, which were beneficial for valuable anti-AMD antibody design and preparation in future.

Bayesian machine learning to discover Bruton's tyrosine kinase inhibitors

Bruton's tyrosine kinase (BTK) has a crucial role in multiple cell signaling pathways including B-cell antigen receptor (BCR) and Fc receptor (FcR) signaling cascades, which has attracted much attention to find BTK inhibitors to treat autoimmune diseases. In this work, we constructed a Bayesian classification model for virtually seeking novel BTK inhibitors, which showed good performance in terms of screening efficiency and accuracy. Through searching for several chemical libraries including Chembl_17 (1,317,484 compounds), Chembridge (103,473 compounds), and Chemdiv (700,000 compounds) using this model followed by molecular docking and activity prediction, 52 compounds with novel scaffolds were acknowledged as potential BTK inhibitors, which could be promising starting points for further exploration. This study also provided a guide to construct an efficient and effective protocol for virtual screening by integrating machine learning methods.

Turning to Computer-aided Drug Design in the Treatment of Diffuse Large B-cell Lymphoma: Has it been Helpful?

INTRODUCTION

Amidst the numerous effective therapeutic options available for the treatment of Diffuse Large B-cell Lymphoma (DLBCL), about 30-40% of patients treated with first-line chemoimmunotherapy still experience a relapse or refractory DLBCL. This has necessitated a continuous search for new therapeutic agents to augment the existing therapeutic arsenal.

METHODS

The dawn of Computer-Aided Drug Design (CADD) in the drug discovery process has accounted for persistency in the application of computational approaches either alone or in combinatorial strategies with experimental methods towards the identification of potential hit compounds with high therapeutic efficacy in abrogating DLBCL.

RESULTS

This review showcases the interventions of structure-based and ligand-based computational approaches which have led to the identification of numerous small molecule inhibitors against implicated targets in DLBCL therapy, even though many of these potential inhibitors are piled-up awaiting further experimental validation and exploration.

CONCLUSION

We conclude that a successful and a conscious amalgamation of CADD and experimental approaches could pave the way for the discovery of the next generation potential leads in DLBCL therapy with improved activities and minimal toxicities.

Discovery of natural product inhibitors of phosphodiesterase 10A as novel therapeutic drug for schizophrenia using a multistep virtual screening

The major complaint that most of the schizophrenic patients' face is the cognitive impairment which affects the patient's quality of life. The current antipsychotic drugs treat only the positive symptoms without alleviating the negative or cognitive symptoms of the disease. In addition, the existing therapies are known to produce extrapyramidal side effects that affect the patient adherence to the treatment. PDE10A inhibitor is the new therapeutic approach which has been proven to be effective in alleviating the negative and cognitive symptoms of the disease. A number of PDE10A inhibitors have been developed, but no inhibitor has made it beyond the clinical trials so far. Thus, the present study has been conducted to identify a PDE10A inhibitor from natural sources to be used as a lead compound for the designing of novel selective PDE10A inhibitors. Ligand and structure-based pharmacophore models for PDE10A inhibitors were generated and employed for virtual screening of universal natural products database. From the virtual screening results, 37 compounds were docked into the active site of the PDE10A. Out of 37 compounds, three inhibitors showed the highest affinity for PDE10A where UNPD216549 showed the lowest binding energy and has been chosen as starting point for designing of novel PDE10A inhibitors. The structure-activity-relationship studies assisted in designing of selective PDE10A inhibitors. The optimization of the substituents on the phenyl ring resulted in 26 derivatives with lower binding energy with PDE10A as compared to the lead compound. Among these, MA 8 and MA 98 exhibited the highest affinity for PDE10A with binding energy (-10.90 Kcal/mol).

The discovery of a novel compound with potent antitumor activity: virtual screening, synthesis, biological evaluation and preliminary mechanism study

Farnesyltransferase has been regarded as a promising drug target against cancer as it is critical for membrane association of several signal transduction proteins. In this study, a novel farnesyltransferase inhibitor (IMB-1406) was identified through virtual screening. It exhibits stronger potency (IC₅₀s: 6.92–8.99 μM) than Sunitinib against all of the tested cancer cell lines. Preliminary studies on mechanism reveal that IMB-1406 induces apoptosis in HepG2 cells by arresting the cell cycle at the S phase, altering anti- and pro-apoptotic proteins leading to mitochondrial dysfunction and activation of caspase-3. This anti-tumor effect is most probably related to the inhibition of farnesyltransferase as indicated by molecular docking. Overall, IMB-1406 is a novel lead compound with potent antitumor activity and deserves further structural modifications.

Computer-aided drug design of capuramycin analogues as anti-tuberculosis antibiotics by 3D-QSAR and molecular docking

Capuramycin and a few semisynthetic derivatives have shown potential as anti-tuberculosis antibiotics.To understand their mechanism of action and structureactivity relationships a 3D-QSAR and molecular docking studies were performed. A set of 52 capuramycin derivatives for the training set and 13 for the validation set was used. A highly predictive MFA model was obtained with crossvalidated q2 of 0.398, and non-cross validated partial least-squares (PLS) analysis showed a conventional r2 of 0.976 and r2pred of 0.839. The model has an excellent predictive ability. Combining the 3D-QSAR and molecular docking studies, a number of new capuramycin analogs with predicted improved activities were designed. Biological activity tests of one analog showed useful antibiotic activity against Mycobacterium smegmatis MC2 155 and Mycobacterium tuberculosis H37Rv. Computer-aided molecular docking and 3D-QSAR can improve the design of new capuramycin antimycobacterial antibiotics.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.