Docking-based Comparative Intermolecular Contacts Analysis Research Articles

Pharmacophore Mapping Combined with dbCICA Reveal New Structural Features for the Development of Novel Ligands Targeting α4β2 and α7 Nicotinic Acetylcholine Receptors

The neuronal nicotinic acetylcholine receptors (nAChRs) belong to the ligand-gated ion channel (GLIC) group, presenting a crucial role in several biological processes and neuronal disorders. The α4β2 and α7 nAChRs are the most abundant in the central nervous system (CNS), being involved in challenging diseases such as epilepsy, Alzheimer's disease, schizophrenia, and anxiety disorder, as well as alcohol and nicotine dependencies. In addition, in silico-based strategies may contribute to revealing new insights into drug design and virtual screening to find new drug candidates to treat CNS disorders. In this context, the pharmacophore maps were constructed and validated for the orthosteric sites of α4β2 and α7 nAChRs, through a docking-based Comparative Intermolecular Contacts Analysis (dbCICA). In this sense, bioactive ligands were retrieved from the literature for each receptor. A molecular docking protocol was developed for all ligands in both receptors by using GOLD software, considering GoldScore, ChemScore, ASP, and ChemPLP scoring functions. Output GOLD results were post-processed through dbCICA to identify critical contacts involved in protein-ligand interactions. Moreover, Crossminer software was used to construct a pharmacophoric map based on the most well-behaved ligands and negative contacts from the dbCICA model for each receptor. Both pharmacophore maps were validated by using a ROC curve. The results revealed important features for the ligands, such as the presence of hydrophobic regions, a planar ring, and hydrogen bond donor and acceptor atoms for α4β2. Parallelly, a non-planar ring region was identified for α7. These results can enable fragment-based drug design (FBDD) strategies, such as fragment growing, linking, and merging, allowing an increase in the activity of known fragments. Thus, our results can contribute to a further understanding of structural subunits presenting the potential for key ligand-receptor interactions, favoring the search in molecular databases and the design of novel ligands.

Structure-based discovery of new polo-like kinase 1 (PLK1) inhibitors as potential anticancer agents via docking-based comparative intermolecular contacts analysis (dbCICA)

Structure-based discovery of new polo-like kinase 1 (PLK1) inhibitors as potential anticancer agents via docking-based comparative intermolecular contacts analysis (dbCICA)

Pharmacophore modeling of JAK1: A target infested with activity-cliffs

Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs). ACs represent fundamental challenge to successful QSAR/pharmacophore modeling because QSAR modeling rely on the basic premise that activity is a smooth continuous function of structure. We propose that ACs exist because active ACs members exhibit subtle, albeit critical, enthalpic features absent from their inactive twins. In this context we compared the performances of two computational modeling workflows in extracting valid pharmacophores from 151 diverse JAK1 inhibitors that include ACs: QSAR-guided pharmacophore selection versus docking-based comparative intermolecular contacts analysis (db-CICA). The two methods were judged based on the receiver operating characteristic (ROC) curves of their corresponding pharmacophore models and their abilities to distinguish active members among established JAK1 ACs. db-CICA modeling significantly outperformed ligand-based pharmacophore modeling. The resulting optimal db-CICA pharmacophore was used as virtual search query to scan the National Cancer Institute (NCI) database for novel JAK1 inhibitory leads. The most active hit showed IC50 of 1.04 μM. This study proposes the use of db-CICA modeling as means to extract valid pharmacophores from SAR data infested with ACs.

Discovery of new JNK3 inhibitory chemotypes via QSAR-Guided selection of docking-based pharmacophores and comparison with other structure-based pharmacophore modeling methods

The kinase c-Jun N-terminal Kinase 3 (JNK3) plays an important role in neurodegenerative diseases. JNK3 inhibitors have shown promising results in treating Alzheimer's and Parkinson's diseases. This prompted us to model this interesting target via three established structure-based computational workflows; namely, docking-based Comparative Intermolecular Contacts Analysis (db-CICA), pharmacophore modeling via molecular-dynamics based Ligand-Receptor Contact Analysis (md-LRCA), and QSAR-guided selection of crystallographic pharmacophores. Moreover, we compared the performances of resulting pharmacophores against binding models generated via a newly introduced technique, namely, QSAR-guided selection of docking-based pharmacophores. The resulting pharmacophores were validated by receiver operating characteristic (ROC) curve analysis and used as virtual search queries to screen the National Cancer Institute (NCI) database for promising anti-JNK3 hits of novel chemotypes. Eleven nanomolar and low micromolar hits were identified, three of which were captured by QSAR-guided docking-based pharmacophores.

Discovery of novel Flt3 inhibitory chemotypes through extensive ligand-based and new structure-based pharmacophore modelling methods

Flt3 is an oncogenic kinase involved in different types of leukemia. It is most prominently associated with acute myeloid leukemia (AML). Flt3-specific inhibitors have shown promising results in interfering with AML prompting us to model this interesting target. We implemented ligand-based, QSAR-guided, pharmacophore exploration combined with novel structure-based computational workflow based on docking-based comparative intermolecular contacts analysis (db-CICA) combined with homology modelling to explore the pharmacophoric features of 93 diverse cyclic Flt3 inhibitors. The resulting pharmacophore models were used as virtual search queries to scan the National Cancer Institute (NCI) database for novel Flt3 inhibitory leads. Ten hits of novel scaffolds were captured showing anti-Flt3 IC50 values ranging from 1.2 to 14.7 μM. Interestingly, six of them illustrated low micromolar and submicromolar potencies against the mutated active form of Flt3 (Flt3D835Y) and the related vascular endothelial growth factor receptor 2 (VEGFR2).

Discovery of new Gyrase β inhibitors via structure based modeling

Gyrase B is an essential enzyme in the prokaryotes which became an attractive target for antibacterial agents. In our study, we implemented a wide range of docking configurations to dock 120 inhibitors into the in the ATP- binding pocket of Gyrase B enzyme (PDB code: 4GEE). LigandFit docking engines and six scoring functions were utilized in the study. Furthermore, the ligands were docked in their ionized and unionized forms into the hydrous and anhydrous binding pocket. We used docking-based Comparative Intermolecular Contacts Analysis (db-CICA) which is a novel methodology to validate and identify the optimal docking configurations. Three docking configurations were found to achieve self-consistent db-CICA models. The resulting db-CICA models were used to construct corresponding pharmacophoric models that were used to screen the National Cancer Institute (NCI) list of compounds. In-vitro study represents antibacterial activities for twelve hit molecules with the most active having IC50 of 20.9 μM.

Docking-based comparative intermolecular contacts analysis and in silico screening reveal new potent acetylcholinesterase inhibitors

The positive impact of acetylcholinesterase enzyme inhibitors on neurodegenerative diseases impelled continuous attempts to discover and optimize new acetylcholinesterase enzyme inhibitors. The combined recent interest inacetylcholinesterase enzyme inhibitors, together with known shortages of docking and docking validation methods prompted us to use our new 3D-QSAR method, namely, docking-based comparative intermolecular contacts analysis, to identify optimal docking conditions required to dock certain group of inhibitors into acetylcholinesterase enzyme binding site. Additionally, optimal docking-based comparative intermolecular contacts analysis models were converted into pharmacophore models, which were validated by receiver operating characteristic curve analysis. The pharmacophores were subsequently used as search queries to mine the national cancer institute list of compounds for new acetylcholinesterase enzyme inhibitors. Five low micromolar acetylcholinesterase enzyme inhibitors were identified. The most potent gave IC50 value of 2.55 μM.

Computer-aided discovery of new FGFR-1 inhibitors followed by in vitro validation.

FGFR-1 is an oncogenic kinase involved in several cancers. FGFR1-specific inhibitors have shown promising results against several human cancers prompting us to model this interesting target. Toward the end, we implemented elaborate ligand-based and structure-based computational workflows to explore the pharmacophoric requirements for potent FGFR-1 inhibitors. Results & methodology: Structure-based and ligand-based modeling applied on 59 diverse FGFR-1 inhibitors yielded novel pharmacophore and quantitative structure-activity relationship models that were used to scan the National Cancer Institute's structural database for novel leads. Four potent hits were captured, with the most active having IC50 of 426 nM. Identities and purities of active hits were established using nuclear magnetic resonance and mass spectroscopy. Elaborate ligand-based (pharmacophore/quantitaive structure-activity relationship) and structure-based (docking-based comparative intermolecular contacts analysis) modeling provided deep understanding of ligand binding within FGFR-1 as evidenced by the virtually captured new potent leads.

Combining docking-based comparative intermolecular contacts analysis and k-nearest neighbor correlation for the discovery of new check point kinase 1 inhibitors.

Check point kinase 1 (Chk1) is an important protein in G2 phase checkpoint arrest required by cancer cells to maintain cell cycle and to prevent cell death. Therefore, Chk1 inhibitors should have potential as anti-cancer therapeutics. Docking-based comparative intermolecular contacts analysis (dbCICA) is a new three-dimensional quantitative structure activity relationship method that depends on the quality and number of contact points between docked ligands and binding pocket amino acid residues. In this presented work we implemented a novel combination of k-nearest neighbor/genetic function algorithm modeling coupled with dbCICA to select critical ligand-Chk1 contacts capable of explaining anti-Chk1 bioactivity among a long list of inhibitors. The finest set of contacts were translated into two valid pharmacophore hypotheses that were used as 3D search queries to screen the National Cancer Institute's structural database for new Chk1 inhibitors. Three potent Chk1 inhibitors were discovered with IC50 values ranging from 2.4 to 69.7 µM.

The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activators

Glucokinase (GK) is involved in normal glucose homeostasis and therefore it is a valid target for drug design and discovery efforts. GK activators (GKAs) have excellent potential as treatments of hyperglycemia and diabetes. The combined recent interest in GKAs, together with docking limitations and shortages of docking validation methods prompted us to use our new 3D-QSAR analysis, namely, docking-based comparative intermolecular contacts analysis (dbCICA), to validate docking configurations performed on a group of GKAs within GK binding site. dbCICA assesses the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models were validated by receiver operating characteristic curve analysis and comparative molecular field analysis. dbCICA models were also converted into valid pharmacophores that were used as search queries to mine 3D structural databases for new GKAs. The search yielded several potent bioactivators that experimentally increased GK bioactivity up to 7.5-folds at 10 μM.

Application of docking-based comparative intermolecular contacts analysis to validate Hsp90α docking studies and subsequent in silico screening for inhibitors

Heat shock protein (Hsp90α) has been recently implicated in cancer, prompting several attempts to discover and optimize new Hsp90α inhibitors. Towards this end, we docked 83 diverse Hsp90α inhibitors into the ATP-binding site of this chaperone using several docking-scoring settings. Subsequently, we applied our newly developed computational tool--docking-based comparative intramolecular contacts analysis (dbCICA)--to assess the different docking conditions and select the best settings. dbCICA is based on the number and quality of contacts between docked ligands and amino acid residues within the binding pocket. It assesses a particular docking configuration based on its ability to align a set of ligands within a corresponding binding pocket in such a way that potent ligands come into contact with binding site spots distinct from those approached by low-affinity ligands, and vice versa. The optimal dbCICA models were translated into valid pharmacophore models that were used as 3D search queries to mine the National Cancer Institute's structural database for new inhibitors of Hsp90α that could potentially be used as anticancer agents. The process culminated in 15 micromolar Hsp90α ATPase inhibitors.

Docking-Based Comparative Intermolecular Contacts Analysis as New 3-D QSAR Concept for Validating Docking Studies and in Silico Screening: NMT and GP Inhibitors as Case Studies

The significant role played by docking algorithms in drug discovery combined with their serious pitfalls prompted us to envisage a novel concept for validating docking solutions, namely, docking-based comparative intermolecular contacts analysis (dbCICA). This novel approach is based on the number and quality of contacts between docked ligands and amino acid residues within the binding pocket. It assesses a particular docking configuration on the basis of its ability to align a set of ligands within a corresponding binding pocket in such a way that potent ligands come into contact with binding site spots distinct from those approached by low-affinity ligands and vice versa. In other words, dbCICA evaluates the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models can be translated into valid pharmacophore models that can be used as 3-D search queries to mine structural databases for new bioactive compounds. dbCICA was implemented to search for new inhibitors of candida N-myristoyl transferase as potential antifungal agents and glycogen phosphorylase (GP) inhibitors as potential antidiabetic agents. The process culminated in five selective micromolar antifungal leads and nine GP inhibitory leads.