In silico fragment-based discovery of DPP-IV S1 pocket binders

Novel peptide inhibitor of dipeptidyl peptidase IV (Tyr-Pro-D-Ala-NH2) with anti-inflammatory activity in the mouse models of colitis

Protease inhibition has become a new possible approach in the inflammatory bowel disease (IBD) therapy. A serine exopeptidase, dipeptidyl peptidase IV (DPP IV) is responsible for inactivation of incretin hormone, glucagon-like peptide 2 (GLP-2), a potent stimulator of intestinal epithelium regeneration and growth. Recently we showed that the novel peptide analog of endomorphin-2, EMDB-1 (Tyr-Pro-D-ClPhe-Phe-NH₂) is a potent blocker of DPP IV and exhibits an anti-inflammatory activity in vivo. The aim of this study was to design, synthesize and characterize the therapeutic activity and mechanism of action of a series of novel EMDB-1 analogs. The inhibitory potential of all peptides was evaluated using the fluorometric screening assay employing Gly-Pro-Aminomethylcoumarin (AMC) to measure DPP IV activity. Consequently, one compound, namely DI-1 was selected and its therapeutic activity evaluated using mouse models of experimental colitis (induced by TNBS and DSS). Macro- and microscopic score, ulcer score, colonic wall thickness as well as myeloperoxidase activity were measured. We showed that DI-1 blocks DPP IV in vitro (IC₅₀ = 0.76 ± 0.04 nM) and attenuates acute, semichronic and relapsing TNBS- as well as DSS-induced colitis in mice after topical administration. Its anti-inflammatory action is associated with the increase of colonic GLP-2 but not GLP2 receptor or DPP IV expression. Our results validate DPP IV as a pharmacological target for the anti-IBD drugs and its inhibitors, such as DI-1, have the potential to become valuable anti-inflammatory therapeutics.

In silico fragment-based drug design with SEED

We report on two fragment-based drug design protocols, SEED2XR and ALTA, which start by high-throughput docking. SEED2XR is a two-stage protocol for fragment-based drug design. The first stage is in silico and consists of the automatic docking of 10³-10⁴ fragments using SEED, which requires about 1 s per fragment. SEED is a docking software developed specifically for fragment docking and binding energy evaluation by a force field with implicit solvent. In the second stage of SEED2XR, the 10-10² fragments with the most favorable predicted binding energies are validated by protein X-ray crystallography. The recent applications of SEED2XR to bromodomains demonstrate that the whole SEED2XR protocol can be carried out in about a week of working time, with hit rates ranging from 10% to 40%. Information on fragment-target interactions generated by the SEED2XR protocol or directly from SEED docking has been used for the discovery of hundreds of hits. ALTA is a computational protocol for screening which identifies candidate ligands that preserve the interactions between the optimal SEED fragments and the protein target. Medicinal chemistry optimization of ligands predicted by ALTA has resulted in pre-clinical candidates for protein kinases and bromodomains. The high-throughput, very low cost, sustainability, and high hit rate of the SEED-based protocols, unreachable by purely experimental techniques, make them perfectly suitable for both academic and industrial drug discovery research.

Activity and selectivity cliffs for DPP-IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening

The inhibition of dipeptidyl peptidase-IV (DPP-IV) has emerged over the last decade as one of the most effective treatments for type 2 diabetes mellitus, and consequently (a) 11 DPP-IV inhibitors have been on the market since 2006 (three in 2015), and (b) 74 noncovalent complexes involving human DPP-IV and drug-like inhibitors are available at the Protein Data Bank (PDB). The present review aims to (a) explain the most important activity cliffs for DPP-IV noncovalent inhibition according to the binding site structure of DPP-IV, (b) explain the most important selectivity cliffs for DPP-IV noncovalent inhibition in comparison with other related enzymes (i.e., DPP8 and DPP9), and (c) use the information deriving from this activity/selectivity cliff analysis to suggest how virtual screening protocols might be improved to favor the early identification of potent and selective DPP-IV inhibitors in molecular databases (because they have not succeeded in identifying selective DPP-IV inhibitors with IC₅₀ ≤ 100 nM). All these goals are achieved with the help of available homology models for DPP8 and DPP9 and an analysis of the structure-activity studies used to develop the noncovalent inhibitors that form part of some of the complexes with human DPP-IV available at the PDB.

Ephedrine as a lead compound for the development of new DPP-IV inhibitors

AIM

Extracts from Ephedra species have been reported to be effective as antidiabetics. A previous in silico study predicted that ephedrine and five ephedrine derivatives could contribute to the described antidiabetic effect of Ephedra extracts by inhibiting dipeptidyl peptidase IV (DPP-IV). Finding selective DPP-IV inhibitors is a current therapeutic strategy for Type 2 diabetes mellitus management. Therefore, the main aim of this work is to experimentally determine whether these alkaloids are DPP-IV inhibitors. Materials & methods: The DPP-IV inhibition of Ephedra's alkaloids was determined via a competitive-binding assay. Then, computational analyses were used in order to find out the protein-ligand interactions and to perform a lead optimization.

RESULTS

Our results show that all six molecules are DPP-IV inhibitors, with IC₅₀ ranging from 124 μM for ephedrine to 28 mM for N-methylpseudoephedrine.

CONCLUSION

Further computational analysis shows how Ephedra's alkaloids could be used as promising lead molecules for designing more potent and selective DPP-IV inhibitors.

Discovery of novel 1,3,5-triazine-thiazolidine-2,4-diones as dipeptidyl peptidase-4 inhibitors with antibacterial activity targeting the S1 pocket for the treatment of type 2 diabetes

A novel series of 1,3,5-triazine-thiazolidine-2,4-diones was synthesized and characterized by a number of analytical and spectroscopic techniques.

Structure based virtual screening of MDPI database: discovery of structurally diverse and novel DPP-IV inhibitors

Inhibition of dipeptidyl peptidase IV (DPP-IV) has been emerged as a promising approach for the treatment of type 2 diabetes (T2D). Structure based virtual screening (SBVS) of Molecular Diversity Preservation International (MDPI) database was performed using Glide and Gold against DPP-IV enzyme. Six promising hits were identified and tested for DPP-IV inhibition. Three compounds were found to be active at low micromolar concentration. The 3-(1-hydrazinyl-1-(phenylamino)ethyl)-4-hydroxy-1-methylquinolin-2(1H)-one (compound A) was found to be the most potent hit with an IC50 of 0.73 μM. These three compounds (A, B and D) were then assessed for their glucose lowering effects in glucose fed hyperglycemic female Wistar rats. The glucose lowering effects of compounds also confirms their potential as anti-diabetic agents. The present study demonstrates a successful utilization of in silico SBVS tools in identification of novel and potential DPP-IV inhibitor.

Insilico docking study of compounds elucidated from helicteres isora fruits with ampkinase- insulin receptor

Insulin receptor (IR) proteins were essential intracellular signaling peptides in the insulin action cascade. Insulin receptor substrate proteins (IRS-1and IRS-2) serve and regulate the insulin level in the normal insulin action. The broad role of IRS-1 and IRS-2 in cell growth and survival reveals a common regulatory pathway linking development, somatic growth, fertility, neuronal proliferation, and aging to the core mechanisms used by vertebrates for nutrient sensing. Such type of proteins were cyclic adenosine monophosphate-activated protein kinase, this proteins play a key role in the insulin response and regulation. Type -2 Diabetes mellitus occurs during prolonged periods of peripheral insulin resistance due to inactivation of IRS proteins. The compounds isolated from the medicinal plants were safer than synthetic drugs and possess high bio activity. In the present study, four compounds were elucidated from fruits of Helicteres isora. The elucidated compounds were evaluated for the antidiabetic activity using in silico docking study. The receptor was analyzed for the active site and pocket finder tools. The aminoacids such as Phenylalanine, Lysine, Glutamic acid and Asparigine were predicted as active site binding residues. Docking studies were done through Autodock 4 software. All the compounds from fruits of Helicteres isora showed good docking profiles with AMP Kinase, except compound-3 (1,2,3,4-tetrahydro-1,5,6,8-tetramethyl-7-(2-methylprop-1-enylnaphthalene-4-ylpivalate). Finally the result from the study demonstrates that the HS-1, HS-2 and HS-4 posses potent anti diabetic activity against type-2 diabetes mellitus through drug action on AMP kinase cascade system.

New leads for DPP IV inhibition: structure-based pharmacophore mapping and virtual screening study

Dipeptidyl peptidase IV (DPP IV) is an attractive target for the development of new antidiabetic drugs. DPP IV inhibitors improve glycemic control by preventing the rapid inactivation of the incretin hormones; glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide. In the current study, virtual screening, using 2D and 3D filters implemented in a hierarchical cascade, was employed to identify new DPP IV inhibitors. Co-crystallized ligands, with potent DPP IV-inhibitory activities, were utilized to generate structure-based pharmacophore models using DS Visualizer software. The derived pharmacophore maps were validated using in-house built database containing active and inactive DPP IV inhibitors. Subsequently, the optimum validated pharmacophore model was used as a search query against two 3D-databases (NCI and in-house built drug databases). Further hit filtration was carried out employing 2D virtual filters based on Lipinski's rule of 5; number of rotatable bonds and other physicochemical filters. 3D filter using high-throughput molecular docking was also applied. As a result, 5 novel DPP IV inhibitors were discovered as potential lead compounds and later confirmed via in vitro bioassay.

Fragment-based approaches and computer-aided drug discovery

Fragment-based design has significantly modified drug discovery strategies and paradigms in the last decade. Besides technological advances and novel therapeutic avenues, one of the most significant changes brought by this new discipline has occurred in the minds of drug designers. Fragment-based approaches have markedly impacted rational computer-aided design both in method development and in applications. The present review illustrates the importance of molecular fragments in many aspects of rational ligand design, and discusses how thinking in "fragment space" has boosted computational biology and chemistry.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

Lead Discovery Using Virtual Screening

The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.

Dehydrogenation of the indoline-containing drug 4-chloro-N-(2-methyl-1-indolinyl)-3-sulfamoylbenzamide (indapamide) by CYP3A4: correlation with in silico predictions

4-Chloro-N-(2-methyl-1-indolinyl)-3-sulfamoylbenzamide (indapamide), an indoline-containing diuretic drug, has recently been evaluated in a large Phase III clinical trial (ADVANCE) with a fixed-dose combination of an angiotensin-converting enzyme inhibitor, perindopril, and shown to significantly reduce the risks of major vascular toxicities in people with type 2 diabetes. The original metabolic studies of indapamide reported that the indoline functional group was aromatized to indole through a dehydrogenation pathway by cytochromes P450. However, the enzymatic efficiency of indapamide dehydrogenation was not elucidated. A consequence of indoline aromatization is that the product indoles might have dramatically different therapeutic potencies. Thus, studies that characterize dehydrogenation of the functional indoline of indapamide were needed. Here we identified several indapamide metabolic pathways in vitro with human liver microsomes and recombinant CYP3A4 that include the dehydrogenation of indapamide to its corresponding indole form, and also hydroxylation and epoxidation metabolites, as characterized by liquid chromatography/mass spectrometry. Indapamide dehydrogenation efficiency (V(max)/K(m)=204 min/mM) by CYP3A4 was approximately 10-fold greater than that of indoline dehydrogenation. In silico molecular docking of indapamide into two CYP3A4 crystal structures, to evaluate the active site parameters that control dehydrogenation, produced conflicting results about the interactions of Arg212 with indapamide in the active site. These conflicting theories were addressed by functional studies with a CYP3A4R212A mutant enzyme, which showed that Arg212 does not seem to facilitate positioning of indapamide for dehydrogenation. However, the metabolites of indapamide were precisely consistent with in silico predictions of binding orientations using three diverse computer methods to predict drug metabolism pathways.

Virtual screening for novel COX-2 inhibitors using the ZINC database

Cyclooxygenase-2 (COX-2) enzyme binds to arachidonic acid and releases metabolites that are used to induce pain and inflammation. COX-2 selective inhibitors such as celecoxib, rofecoxib and valdecoxib are currently used to reduce inflammatory response. However, they lack anti-thrombotic activity and hence lead to cardiovascular and renal liabilities apart from gastrointestinal irritation. Therefore, there is still a need to develop more potent COX-2 inhibitors. In this paper, we report the screening of various compounds from the ZINC database (contains 4.6 million small molecule compounds) using the eHiTS (electronic High Throughput Screening) software tool against the COX-2 protein. The strategy employed can be conveniently split into two categories, viz. screening and docking, respectively. Screening was performed using molecular constraints tool to filter compounds with physico-chemical properties similar to the 6COX bound ligand SC-558. The analysis resulted in 1042 Lipinski compliant hits which are docked and scored to identify structurally novel ligands that make similar interactions to those of known ligands or may have different interactions with other parts of the binding site. Our screening approach identified two molecules ZINC00663976 (eHITS score of -7.135 kcal/mol) and ZINC02062094 (eHITS score of -7.242 kcal/mol) from the ZINC database. Their energy scores are better than the 6COX bound co-crystallized ligand SC-558 with an eHiTS score of -6.559 kcal/mol. Both the ligands were docked within the binding pocket forming interactions with Leu352, Phe518, Met522, Val523, Ala527 and Ser353. Visual inspection suggested similar orientation and binding mode for ZINC02062094 with SC-558 ligand. The NH group of the ligand formed hydrogen bond interactions with the backbone NH of Ala527.

Towards improving compound selection in structure-based virtual screening

Structure-based virtual screening is now an established technology for supporting hit finding and lead optimisation in drug discovery. Recent validation studies have highlighted the poor performance of currently used scoring functions in estimating binding affinity and hence in ranking large datasets of docked ligands. Progress in the analysis of large datasets can be made through the use of appropriate data mining techniques and the derivation of a broader range of descriptors relevant to receptor-ligand binding. In addition, simple scoring functions can be supplemented by simulation-based scoring protocols. Developments in workflow design allow the automation of repetitive tasks, and also encourage the routine use of simulation-based methods and the rapid prototyping of novel modelling and analysis procedures.

A three-dimensional pharmacophore model for dipeptidyl peptidase IV inhibitors

Dipeptidyl peptidase IV (DPP-IV) is a valid drug target for type-2 diabetes and DPP-IV inhibitors have been proven to efficiently improve glucose tolerance. In our study, 3D pharmacophore models were generated using a training set of 22 DPP-IV inhibitors. The best model consisted of important chemical features and mapped well into the active site of DPP-IV. The model gave high correlation coefficients of 0.97 and 0.84 for the training set and the test set, respectively, showing its good predictive ability for biological activity. Furthermore, the pharmacophore model demonstrated the capability to retrieve inhibitors from database with a high enrichment factor of 42.58. All results suggest that the model provides a useful tool for designing novel DPP-IV inhibitors.

In-silico fragment-based identification of novel angiogenesis inhibitors

Inhibition of vascular endothelial growth factor receptor-2 (VEGFR2) kinase blocks angiogenesis, the process of generating new capillary blood vessels that are important in tumor growth. To identify small molecule inhibitors of the VEGFR2 kinase, we undertook a computer assisted fragment-based screening that used 3-D structural models of the VEGFR2 kinase, and hinge region as a fragment anchor point. Seven novel non-cytotoxic compounds were identified which limited the induction of capillary networks by human umbilical vein endothelial cells in the low micromolar range.

Homology models of dipeptidyl peptidases 8 and 9 with a focus on loop predictions near the active site

Dipeptidyl peptidase 4 (DP4) inhibitors are currently under intensive investigation in late-stage clinical trials as a treatment for type II diabetes. Lack of selectivity toward the related enzymes DP8 and DP9 has recently emerged as a possible source of drug-induced toxicity. Unlike DP4, X-ray structures of DP8 and DP9 are not yet available. As an aid to understanding the structural basis for selectivity, the authors have constructed homology models of DP8 and DP9 based on the X-ray coordinates of DP4. Accurate sequence alignment reveals common structural features indicative for a well-preserved overall fold comprising two domains, namely, a hydrolase domain and a so-called beta-propeller, which together form the active site deeply buried within the protein. The conformation of two loops inside this deep cavity is particularly relevant for the active sites. The authors used a published protocol for loop prediction based on conformational sampling and energy analysis to generate plausible solutions for these two loops. The predictive power of the approach was successfully evaluated for the template protein DP4 and two additional known structures from the same protein family, namely, FAP and DPX. The authors also show that inclusion of the covalent ligand NVP-728 greatly enhances the refinement. Based on the established evaluation protocol, the corresponding loops of DP8 and DP9 were predicted and the resulting active sites were compared with DP4. In particular, the authors conclude that differences in the P2-pocket are relevant for the design of selective DP4 inhibitors. The loss of key interactions in DP8 and DP9 as predicted from their models is consistent with the selectivity profile of the DP4 clinical candidate MK-431.

Fragment-based lead discovery: a chemical update

Fragment-based lead discovery constructs drug leads from small molecular fragments. In theory, this is a highly efficient method for drug discovery, and the technique has become enormously popular in the past few years. In this review, I describe how a variety of approaches in fragment-based lead discovery--including NMR, X-ray crystallography, mass spectrometry, functional screening, and in silico screening--have produced drug leads. Although the examples show that the technique can reliably generate potent molecules, there is still much work to be done to maintain the efficiency of molecules' binding affinities as fragments are linked, expanded, and otherwise improved.