3D QSAR (COMFA) of a series of potent and highly selective VLA-4 antagonists

Molecular Modeling Insights into the Structure and Behavior of Integrins: A Review

Integrins are heterodimeric glycoproteins crucial to the physiology and pathology of many biological functions. As adhesion molecules, they mediate immune cell trafficking, migration, and immunological synapse formation during inflammation and cancer. The recognition of the vital roles of integrins in various diseases revealed their therapeutic potential. Despite the great effort in the last thirty years, up to now, only seven integrin-based drugs have entered the market. Recent progress in deciphering integrin functions, signaling, and interactions with ligands, along with advancement in rational drug design strategies, provide an opportunity to exploit their therapeutic potential and discover novel agents. This review will discuss the molecular modeling methods used in determining integrins' dynamic properties and in providing information toward understanding their properties and function at the atomic level. Then, we will survey the relevant contributions and the current understanding of integrin structure, activation, the binding of essential ligands, and the role of molecular modeling methods in the rational design of antagonists. We will emphasize the role played by molecular modeling methods in progress in these areas and the designing of integrin antagonists.

An integrated QSAR modeling approach to explore the structure-property and selectivity relationships of N-benzoyl-L-biphenylalanines as integrin antagonists

Integrins [Formula: see text] and [Formula: see text] are important targets to treat different inflammatory diseases, such as multiple sclerosis, inflammatory bowel diseases, rheumatoid arthritis, atherosclerosis, and asthma. Despite being valuable targets, only a few work has been reported to date regarding molecular modeling studies on these integrins. Not only that, none of these reports addressed the selectivity issue between integrins [Formula: see text] and [Formula: see text]. Therefore, a major challenge regarding the design and discovery of selective integrin antagonists remains. In this study, a series of 142 N-benzoyl-L-biphenylalanines having both integrin [Formula: see text] and [Formula: see text] inhibitory activities were considered for a variety of QSAR approaches including regression and classification-based 2D-QSARs, Hologram QSARs, 3D-QSAR CoMFA and CoMSIA studies to identify the structural requirements of these integrin antagonists. All these QSAR models were statistically validated and subsequently correlated with each other to get a detailed understanding of the activity and selectivity profiles of these molecules.

5-aminomethyloxazolidine-2,4-dione hybrid α/β-dipeptide scaffolds as inductors of constrained conformations: Applications to the synthesis of integrin antagonists

Peptidomimetics represent an attractive starting point for drug discovery programs; in particular, peptidomimetics that result from the incorporation of a heterocycle may take advantage of increased enzymatic stability and higher ability to reproduce the bioactive conformations of the parent peptides, resulting in enhanced therapeutic potential. Herein, we present mimetics of the α4β1 integrin antagonist BIO1211 (MPUPA-Leu-Asp-Val-Pro-OH), containing a aminomethyloxazolidine-2,4-dione scaffold (Amo). Interestingly, the retro-sequences PhCOAsp(OH)-Amo-APUMP including either (S)- or (R)-configured Amo displayed significant ability to inhibit the adhesion of α4β1 integrin expressing cells, and remarkable stability in mouse serum. Possibly, the conformational bias exerted by the Amo scaffold determined the affinity for the receptors. These peptidomimetics could be of interest for the development of small-molecule agents effective against inflammatory processes and correlated autoimmune diseases.

Synthesis and assay of retro-α4β1 integrin-targeting motifs

In recent years, several research groups proposed new peptidomimetic antagonists of integrins αvβ3, α5β1, αIIbβ3, αvβ6, αvβ5, etc. based on retro sequences of the classic integrin-binding motif RGD. The retro strategy is still largely ignored for the non-RGD-binding α4β1 integrin. Herein we present the first examples of retro sequences for targeting this integrin, composed of Asp or isoAsp equipped with an aromatic cap at the N-terminus, (S)-pyrrolidine-3-carboxylic acid (β(2)-Pro) as a constrained core, and the amino variant (AMPUMP) of the well-known α4-targeting diphenylurea MPUPA. We discuss α4β1 receptor affinity (SPA), cell adhesion assays, stability in mouse serum, and conformational analysis. For their significant ability to inhibit cell adhesion and remarkable stability, the retro-peptide mimetics BnCO-Asp-β-Pro-AMPUMP (3) and BnCO-isoAsp-β-Pro-AMPUMP (4) represent promising candidates for designing small molecules as potential anti-inflammatory agents.

Modeling the molecular basis for α4β1 integrin antagonism

We report a 3D QSAR study of almost 300 structurally diverse small molecule antagonists of the integrin α4β1 whose biological activity spans six orders of magnitude. The alignment of the molecules was based on the conformation of a structurally related ligand bound to the αIIBβ3 and αvβ3 integrins in X-ray crystallographic studies. The molecular field method, CoMSIA, was used to generate the 3D QSAR models. The resulting models showed that the lipophilic properties were the most important, with hydrogen bond donor and steric properties less relevant. The models were highly significant (r(2)=0.89, q2(LOO)=0.67, r(2) (test set)=0.76), and could make robust predictions of the data (SEE=0.46, SEP=0.78, SEP (test set)=0.66). We predicted the antagonist activities of a further ten compounds with useful accuracy. The model appears capable of predicting α4β1 integrin antagonist activity to within a factor of five for compounds within its domain of applicability. The implications for design of improved integrin antagonists will be discussed.

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.

Insights into phenylalanine derivatives recognition of VLA-4 integrin: from a pharmacophoric study to 3D-QSAR and molecular docking analyses

The very late antigen-4 (VLA-4), also known as integrin alpha4beta1, is expressed on monocytes, T- and B-lympohocytes, basophils, and eosinophils and is involved in the massive recruitment of granulocytes in different pathological conditions such as multiple sclerosis and asthma. VLA-4 interacts with its endogenous ligand VCAM-1 during chronic inflammation, and blockade of VLA-4 /VCAM-1 interaction is a potential target for immunosuppression. Two classes of VLA-4 antagonists have so far been reported: beta-amino acid derivatives containing a diaryl urea moiety (BIO-1211) and phenylalanine derivatives (TR-14035). With the aim of clarifying the structural basis responsible for VLA-4 recognition by phenylalanine derivatives, we developed a combined computational study on a set of 128 antagonists available through the literature. Our computational approach is composed of three parts. (i) A VCAM-1 based pharmacophore was constructed with a restricted number of phenylalanine derivatives to identify the region of the protein that resembles synthetic antagonists. The pharmacophore was instrumental in constructing an alignment of a set of 128 compounds. This alignment was exploited to build a pseudoreceptor model with the RECEPTOR program. (ii) 3D-QSAR analysis was carried out on the computed electrostatic and steric interaction energies with the pseudoreceptor surface. The 3D-QSAR analysis yielded a predictive model able to explain much of the variance of the 128 antagonists. (iii) A homology modeling study of the headpiece of VLA-4 based on the crystal structure of alphavbeta3 was performed. Docking experiments of TR-14035 into the binding site of VLA-4 aided the interpretation of the 3D-QSAR model. The obtained results will be fruitful for the design of new potent and selective antagonists of VLA-4.

Exploration of a binding mode of indole amide analogues as potent histone deacetylase inhibitors and 3D-QSAR analyses

Docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were conducted on a series of indole amide analogues as potent histone deacetylase inhibitors. The studies include comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Selected ligands were docked into the active site of human HDAC1. Based on the docking results, a novel binding mode of indole amide analogues in the human HDAC1 catalytic core is presented, and enzyme/inhibitor interactions are discussed. The indole amide group is located in the open pocket, and anchored to the protein through a pair of hydrogen bonds with Asp99 O-atom and amide NH group on ligand. Based on the binding mode, predictive 3D-QSAR models were established, which had conventional r2 and cross-validated coefficient values (r(cv)2) up to 0.982 and 0.601 for CoMFA and 0.954 and 0.598 for CoMSIA, respectively. A comparison of the 3D-QSAR field contributions with the structural features of the binding site showed good correlation between the two analyses. The results of 3D-QSAR and docking studies validate each other and provided insight into the structural requirements for activity of this class of molecules as HDAC inhibitors. The CoMFA and CoMSIA PLS contour maps and MOLCAD-generated active site electrostatic, lipophilicity, and hydrogen-bonding potential surface maps, as well as the docking studies, provided good insights into inhibitor-HDAC interactions at the molecular level. Based on these results, novel molecules with improved activity can be designed.

Comparative molecular similarity indices analysis (CoMSIA) studies of 1,2-naphthoquinone derivatives as PTP1B inhibitors

Protein tyrosine phosphatase-1B (PTP1B) has been demonstrated to play a key role in the negative signalling pathway of insulin. Potent and orally active PTP1B inhibitors are considered to be promising pharmacological agents for the treatment of type-2 diabetes and resistance to weight gain. CoMSIA studies have been preformed on 1,2-naphthoquinone derivatives that are reported to be potential non-peptidic inhibitors of PTP1B. For the selection of dataset to develop the model, the reported molecules were subjected to property filters and segregated into training and test set. As the crystal structure of PTP1B-naphthoquinone derivative is not known, the most active molecule was subjected to simulated annealing dynamics method and the lowest energy conformer was reminimised and considered as the bioactive conformation. Database-inertial alignment was followed for aligning the molecules. Different CoMSIA models were built to get the best related field.