3D-QSAR and receptor modeling of tyrosine kinase inhibitors with flexible atom receptor model (FLARM)

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Pharmacophore approaches in protein kinase inhibitors design

Protein kinases constitute a superfamily of therapeutic targets for a number of human and animal diseases that include more than 500 members accordingly to sequencing data of the human genome. The well characterized nature of protein kinases makes them excellent targets for drug development. Pharmacophore approaches have become one of the major tools in the area of drug discovery. Application of pharmacophore modeling approaches allows reducing of expensive overall cost associated with drug development project. Pharmacophore models are important functional groups of atoms in the proper spatial position for interaction with target protein. Various ligand-based and structure-based methods have been developed for pharmacophore model generation. Despite the successes in pharmacophore models generation these approaches have not reached their full capacity in application for drug discovery. In the following review, we summarize the published data on pharmacophore models for inhibitors of tyrosine protein kinases (EGFR, HER2, VEGFR, JAK2, JAK3, Syk, ZAP-70, Tie2) and inhibitors of serine/threonine kinases (Clk, Dyrk, Chk1, IKK2, CDK1, CDK2, PLK, JNK3, GSK3, mTOR, p38 MAPK, PKB). Here, we have described the achievements of pharmacophore modeling for protein kinase inhibitors, which provide key points for further application of generated pharmacophore hypotheses in virtual screening, de novo design and lead optimization.

Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors

AIM

To develop a novel 3D-QSAR approach for study of the epidermal growth factor receptor tyrosine kinase (EGFR TK) and its inhibitors.

METHODS

One hundred thirty nine EGFR TK inhibitors were classified into 3 clusters. Ensemble docking of these inhibitors with 19 EGFR TK crystal structures was performed. Three protein structures that showed the best recognition of each cluster were selected based on the docking results. Then, a novel QSAR (ensemble-QSAR) building method was developed based on the ligand conformations determined by the corresponding protein structures.

RESULTS

Compared with the 3D-QSAR model, in which the ligand conformations were determined by a single protein structure, ensemble-QSAR exhibited higher R2 (0.87) and Q2 (0.78) values and thus appeared to be a more reliable and better predictive model. Ensemble-QSAR was also able to more accurately describe the interactions between the target and the ligands.

CONCLUSION

The novel ensemble-QSAR model built in this study outperforms the traditional 3D-QSAR model in rationality, and provides a good example of selecting suitable protein structures for docking prediction and for building structure-based QSAR using available protein structures.

Computational studies of epidermal growth factor receptor: docking reliability, three-dimensional quantitative structure-activity relationship analysis, and virtual screening studies

An aberrant activity of the epidermal growth factor receptor (EGFR) has been shown to be related to many human cancers, such as breast and liver cancers, thus making EGFR an attractive target for antitumor drug discovery. In this study we evaluated the reliability of various kinds of docking software and procedures to predict the binding disposition of EGFR inhibitors. By application of the best procedure and use of more than 200 compounds, a receptor-based 3D-QSAR model for EGFR inhibition was developed. On the basis of the results obtained, the possibility of developing virtual screening studies was also evaluated. The VS procedure that proved to be the most reliable from a computational point of view was then used to filter the Maybridge database in order to identify new EGFR inhibitors. Enzymatic assays revealed that among the eight top-scoring compounds, seven proved to inhibit EGFR activity at a concentration of 100 microM, two of them exhibiting IC(50) values in the low micromolar range and one in the nanomolar range. These results demonstrate the validity of the methodologies followed. Furthermore, the two low micromolar compounds may be considered as very interesting leads for the development of new EGFR inhibitors.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

Improving the Quality of 3D-QSAR by Using Flexible-Ligand Receptor Models

To address the problems associated with molecular conformations and alignments in the 3D-QSAR studies, we have developed the Flexible Ligand - Atomic Receptor Model (FLARM) 2.0 method. The FLARM 2.0 method has three unique features as compared to other pseudoreceptor model methods: (1) the training ligands are flexibly optimized inside the receptors to achieve minimal docking energies; (2) the receptor atoms are spatially moveable in the process of genetic evolving in order to avoid improper initial receptor shapes; and (3) void receptor sites are specially favored in order to obtain open receptor models that allow large gaps. Advantages of an open model include less noise information, a smaller risk of overfitting, and ease of locating the key interaction sites. The latter two features, inherited from the previous FLARM 1.0 method, can improve the predictive ability of the 3D-QSAR models, while the first feature is newly implemented to relieve the uncertainty caused by improper conformation and alignment. Three FLARM 2.0 case studies were performed, and the results show that FLARM 2.0 models are highly predictive and robust. FLARM 2.0 pseudoreceptor models can correspond well with the pharmacophore models and/or the binding sites of the real protein receptors.

Novel Receptor Surface Approach for 3D-QSAR: The Weighted Probe Interaction Energy Method

A 3D-QSAR technique, called the WeP (weighted probe interaction energy) method, has been developed based on the notion that certain regions of the receptor surface contribute, to varying extents, to the differences in the activities of the ligands, while other regions do not. The probes, placed around the surface of a superimposed set of ligands, were associated with fractional weights, and then an optimal distribution of probe weights that accounts for the activity profile of the training ligands was determined using a genetic algorithm. It has been shown for the three test samples that the pseudoreceptors, which consist of the surviving probes with nonzero weight values, have good predictabilities. Especially, in the case of dihydrofolate reductase inhibitors, the pseudoreceptor resembles the real protein in that there is no surviving probe in the solvent-exposed region.

Elucidating inhibitory models of the inhibitors of epidermal growth factor receptor by docking and 3D-QSAR

Epidermal growth factor receptor (EGFR) protein tyrosine kinases (PTKs) are attractive targets for anti-tumor drug design. Although thousands of their ligands have been studied as potential inhibitors against PTKs, there is no QSAR study that covers different kinds of inhibitors with observable structural diversity. However, by using this approach, we could mine far more useful information. Hence in order to better understand the binding model and the relationship between the physicochemical properties and the inhibitory activities of different kind of various inhibitors, molecular docking and 3D-QSAR, viz. CoMFA and CoMSIA, were combined to study 124 reported inhibitors with different scaffolds. Based on the docked binding conformations, highly reliable and predictive 3D-QSAR models were derived, which reveal how steric, electrostatic, and hydrophobic interactions contribute to inhibitors' bioactivities. This result also demonstrates that it is possible to include different kinds of inhibitors with observable structural diversity into one 3D-QSAR study. Therefore, this study not only casts light on binding mechanism between EGFR and its inhibitors, but also provides new hints for de novo design of new EGFR inhibitors with observable structural diversity.