Experimental-Like Affinity Constants and Enantioselectivity Estimates from Flexible Docking

Pathfinder-Driven Chemical Space Exploration and Multiparameter Optimization in Tandem with Glide/IFD and QSAR-Based Active Learning Approach to Prioritize Design Ideas for FEP+ Calculations of SARS-CoV-2 PLpro Inhibitors

A global pandemic caused by the SARS-CoV-2 virus that started in 2020 and has wreaked havoc on humanity still ravages up until now. As a result, the negative impact of travel restrictions and lockdowns has underscored the importance of our preparedness for future pandemics. The main thrust of this work was based on addressing this need by traversing chemical space to design inhibitors that target the SARS-CoV-2 papain-like protease (PLpro). Pathfinder-based retrosynthesis analysis was used to generate analogs of GRL-0617 using commercially available building blocks by replacing the naphthalene moiety. A total of 10 models were built using active learning QSAR, which achieved good statistical results such as an R2 > 0.70, Q2 > 0.64, STD Dev < 0.30, and RMSE < 0.31, on average for all models. A total of 35 ideas were further prioritized for FEP+ calculations. The FEP+ results revealed that compound 45 was the most active compound in this series with a ΔG of −7.28 ± 0.96 kcal/mol. Compound 5 exhibited a ΔG of −6.78 ± 1.30 kcal/mol. The inactive compounds in this series were compound 91 and compound 23 with a ΔG of −5.74 ± 1.06 and −3.11 ± 1.45 kcal/mol. The combined strategy employed here is envisaged to be of great utility in multiparameter lead optimization efforts, to traverse chemical space, maintaining and/or improving the potency as well as the property space of synthetically aware design ideas.

Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning

MOTIVATION

In recent years, cyclic peptide drugs have been receiving increasing attention because they can target proteins that are difficult to be tackled by conventional small-molecule drugs or antibody drugs. Plasma protein binding rate (%PPB) is a significant pharmacokinetic property of a compound in drug discovery and design. However, due to structural differences, previous computational prediction methods developed for small-molecule compounds cannot be successfully applied to cyclic peptides, and methods for predicting the PPB rate of cyclic peptides with high accuracy are not yet available.

RESULTS

Cyclic peptides are larger than small molecules, and their local structures have a considerable impact on PPB; thus, molecular descriptors expressing residue-level local features of cyclic peptides, instead of those expressing the entire molecule, as well as the circularity of the cyclic peptides should be considered. Therefore, we developed a prediction method named CycPeptPPB using deep learning that considers both factors. First, the macrocycle ring of cyclic peptides was decomposed residue by residue. The residue-based descriptors were arranged according to the sequence information of the cyclic peptide. Furthermore, the circular data augmentation method was used, and the circular convolution method CyclicConv was devised to express the cyclic structure. CycPeptPPB exhibited excellent performance, with mean absolute error (MAE) of 4.79% and correlation coefficient (R) of 0.92 for the public drug dataset, compared to the prediction performance of the existing PPB rate prediction software (MAE=15.08%, R=0.63).

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the online supplementary material. The source code of CycPeptPPB is available at https://github.com/akiyamalab/cycpeptppb.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Prospective computational design and in vitro bio-analytical tests of new chemical entities as potential selective CYP17A1 lyase inhibitors

The development and advancement of prostate cancer (PCa) into stage 4, where it metastasize, is a major problem mostly in elder males. The growth of PCa cells is stirred up by androgens and androgen receptor (AR). Therefore, therapeutic strategies such as blocking androgens synthesis and inhibiting AR binding have been explored in recent years. However, recently approved drugs (or in clinical phase) failed in improving the expected survival rates for this metastatic-castration resistant prostate cancer (mCRPC) patients. The selective CYP17A1 inhibition of 17,20-lyase route has emerged as a novel strategy. Such inhibition blocks the production of androgens everywhere they are found in the body. In this work, a three dimensional-quantitative structure activity relationship (3D-QSAR) pharmacophore model is developed on a diverse set of non-steroidal inhibitors of CYP17A1 enzyme. Highly active compounds are selected to define a six-point pharmacophore hypothesis with a unique geometrical arrangement fitting the following description: two hydrogen bond acceptors (A), two hydrogen bond donors (D) and two aromatic rings (R). The QSAR model showed adequate predictive statistics. The 3D-QSAR model is further used for database virtual screening of potential inhibitory hit structures. Density functional theory (DFT) optimization provides the electronic properties explaining the reactivity of the hits. Docking simulations discovers hydrogen bonding and hydrophobic interactions as responsible for the binding affinities of hits to the CYP17A1 Protein Data Bank structure. 13 hits from the database search (including five derivatives) are then synthesized in the laboratory as different scaffolds. Ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) in vitro experiments reveals three new chemical entities (NCEs) with half maximal inhibitory concentration (IC₅₀) values against the lyase route at mid-micromolar range with favorable selectivity to the lyase over the hydroxylase route (one of them with null hydroxylase inhibition). Thus, prospective computational design has enabled the design of potential lead lyase-selective inhibitors for further studies.

Computational simulations determining disulfonic stilbene derivative bioavailability within human serum albumin

Disulfonic stilbene (DS) derivatives are a member of the large family of compounds widely employed in medicine and biology as modulators for membrane transporters or inhibitors of a protein involved in DNA repair. They constitute interesting compounds that have not yet been investigated within the bioavailability framework. No crystallographic structures exist involving such compounds embedded in the most common drug carrier, human serum albumin (HSA). The present work studies, for the first time, the physico-chemical features driving the inclusion of three DS derivatives (amino, nitro and acetamido, named DADS, DNDS and DATDS, respectively) within the four common HSA binding sites using combined molecular docking and molecular dynamics simulations. A careful analysis of each ligand within each of the studied binding sites is carried out, highlighting specific interactions and key residues playing a role in stabilizing the ligand within each pocket. The comparison between DADS, DNDS and DATDS reveals that depending on the binding site, the conclusions are rather different. For instance, the IB binding site shows a specificity to DADS compounds while IIIA is the most favorable site for DNDS and DATDS.

In vitro, in silico and integrated strategies for the estimation of plasma protein binding. A review

Plasma protein binding (PPB) strongly affects drug distribution and pharmacokinetic behavior with consequences in overall pharmacological action. Extended plasma protein binding may be associated with drug safety issues and several adverse effects, like low clearance, low brain penetration, drug-drug interactions, loss of efficacy, while influencing the fate of enantiomers and diastereoisomers by stereoselective binding within the body. Therefore in holistic drug design approaches, where ADME(T) properties are considered in parallel with target affinity, considerable efforts are focused in early estimation of PPB mainly in regard to human serum albumin (HSA), which is the most abundant and most important plasma protein. The second critical serum protein α1-acid glycoprotein (AGP), although often underscored, plays also an important and complicated role in clinical therapy and thus the last years it has been studied thoroughly too. In the present review, after an overview of the principles of HSA and AGP binding as well as the structure topology of the proteins, the current trends and perspectives in the field of PPB predictions are presented and discussed considering both HSA and AGP binding. Since however for the latter protein systematic studies have started only the last years, the review focuses mainly to HSA. One part of the review highlights the challenge to develop rapid techniques for HSA and AGP binding simulation and their performance in assessment of PPB. The second part focuses on in silico approaches to predict HSA and AGP binding, analyzing and evaluating structure-based and ligand-based methods, as well as combination of both methods in the aim to exploit the different information and overcome the limitations of each individual approach. Ligand-based methods use the Quantitative Structure-Activity Relationships (QSAR) methodology to establish quantitate models for the prediction of binding constants from molecular descriptors, while they provide only indirect information on binding mechanism. Efforts for the establishment of global models, automated workflows and web-based platforms for PPB predictions are presented and discussed. Structure-based methods relying on the crystal structures of drug-protein complexes provide detailed information on the underlying mechanism but are usually restricted to specific compounds. They are useful to identify the specific binding site while they may be important in investigating drug-drug interactions, related to PPB. Moreover, chemometrics or structure-based modeling may be supported by experimental data a promising integrated alternative strategy for ADME(T) properties optimization. In the case of PPB the use of molecular modeling combined with bioanalytical techniques is frequently used for the investigation of AGP binding.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Study on the interaction of catalase with pesticides by flow injection chemiluminescence and molecular docking

The interaction mechanisms of catalase (CAT) with pesticides (including organophosphates: disulfoton, isofenphos-methyl, malathion, isocarbophos, dimethoate, dipterex, methamidophos and acephate; carbamates: carbaryl and methomyl; pyrethroids: fenvalerate and deltamethrin) were first investigated by flow injection (FI) chemiluminescence (CL) analysis and molecular docking. By homemade FI-CL model of lg[(I0-I)/I]=lgK+nlg[D], it was found that the binding processes of pesticides to CAT were spontaneous with the apparent binding constants K of 10(3)-10(5) L mol(-1) and the numbers of binding sites about 1.0. The binding abilities of pesticides to CAT followed the order: fenvalerate>deltamethrin>disulfoton>isofenphos-methyl>carbaryl>malathion>isocarbophos>dimethoate>dipterex>acephate>methomyl>methamidophos, which was generally similar to the order of determination sensitivity of pesticides. The thermodynamic parameters revealed that CAT bound with hydrophobic pesticides by hydrophobic interaction force, and with hydrophilic pesticides by hydrogen bond and van der Waals force. The pesticides to CAT molecular docking study showed that pesticides could enter into the cavity locating among the four subdomains of CAT, giving the specific amino acid residues and hydrogen bonds involved in CAT-pesticides interaction. It was also found that the lgK values of pesticides to CAT increased regularly with increasing lgP, Mr, MR and MV, suggesting that the hydrophobicity and steric property of pesticide played essential roles in its binding to CAT.

Pharmacophore-based virtual screening and experimental validation of novel inhibitors against cyanobacterial fructose-1,6-/sedoheptulose-1,7-bisphosphatase

Cyanobacterial fructose-1,6-/sedoheptulose-1,7-bisphoshatase (cy-FBP/SBPase) is a potential enzymatic target for screening of novel inhibitors that can combat harmful algal blooms. In the present study, we targeted the substrate binding pocket of cy-FBP/SBPase. A series of novel hit compounds from the SPECs database were selected by using a pharmacophore-based virtual screening strategy. Most of the compounds tested exhibited moderate inhibitory activities (IC50 = 20.7-176.9 μM) against cy-FBP/SBPase. Compound 2 and its analogues 10 and 11 exhibited strong inhibitory activities, with IC50 values of 20.7, 13.4, and 19.0 μM against cy-FBP/SBPase in vitro and EC50 values of 12.3, 10.9, and 2.9 ppm against cyanobacteria Synechocystis PCC6803 in vivo, respectively. The compound 10 was selected in order to perform a refined docking study to investigate the rational binding mode of inhibitors with cy-FBP/SBPase. Furthermore, possible interactions of the residues with inhibitors were examined by site-directed mutagenesis, enzymatic assays, and fluorescence spectral analyses. The results provide insight into the binding mode between the inhibitors and the substrate binding pocket. The observed theoretical and experimental results are in concert, indicating that the modeling strategies and screening methods employed are appropriate to search for novel lead compounds having both structural diversity and high inhibitory activity against cy-FBP/SBPase.