Structural Effects of Some Relevant Missense Mutations on the MECP2-DNA Binding: A MD Study Analyzed by Rescore+, a Versatile Rescoring Tool of the VEGA ZZ Program

Approaching Pharmacological Space: Events and Components

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

Novel D-modified heterocyclic androstane derivatives as potential anticancer agents: Synthesis, characterization, in vitro and in silico studies

Cancer remains a major health concern worldwide. The most frequently diagnosed types of cancer are caused by abnormal production or action of steroid hormones. In the present study, the synthesis and structural characterization of new heterocyclic androstane derivatives with D-homo lactone, 17α-(pyridine-2''-ylmethyl) or 17(E)-(pyridine-2''-ylmethylidene) moiety are presented. All compounds were evaluated for their anti-proliferative activity against HeLa cervical cancer cell line and non-cancerous kidney MDCK cells, where A-homo lactam compound 9A showed the greatest selectivity. Based on in vitro binding assays, N-formyl lactam compound 18 appeared to be the strong and isoform-selective ligand for ERα, while compound 9A displayed binding affinity for the GR-LBD, but also inhibited aldo-keto reductase 1C4 enzyme. Out of four selected compounds, methylpyrazolo derivative 13 showed potential for aromatase binding, while in silico studies provided insight into experimentally confirmed protein-ligand interactions.

Chemoenzymatic approach towards the synthesis of the antitumor and antileishmanial marine metabolite (+)-Harzialactone A via the stereoselective, biocatalyzed reduction of a prochiral ketone

As a rich source of biological active compounds, marine natural products have been increasingly screened as candidates for developing new drugs. Among the several marine products and metabolites, (+)-Harzialactone A has drawn considerable attention for its antitumor and antileishmanial activity. In this work a chemoenzymatic approach has been implemented for the preparation of the marine metabolite (+)-Harzialactone A. The synthesis involved a stereoselective, biocatalyzed reduction of the prochiral ketone 4-oxo-5-phenylpentanoic acid or the corresponding esters, all generated by chemical reactions. A collection of different promiscuous oxidoreductases (both wild-type and engineered) and diverse microorganism strains were investigated to mediate the bioconversions. After co-solvent and co-substrate investigation in order to enhance the bioreduction performance, T. molischiana in presence of NADES (choline hydrochloride-glucose) and ADH442 were identified as the most promising biocatalysts, allowing the obtainment of the (S)-enantiomer with excellent ee (97% to > 99% respectively) and good to excellent conversion (88% to 80% respectively). The successful attempt in this study provides a new chemoenzymatic approach for the synthesis of (+)-Harzialactone A.

Ensemble of structure and ligand-based classification models for hERG liability profiling

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

MeCP2 heterochromatin organization is modulated by arginine methylation and serine phosphorylation

Rett syndrome is a human intellectual disability disorder that is associated with mutations in the X-linked MECP2 gene. The epigenetic reader MeCP2 binds to methylated cytosines on the DNA and regulates chromatin organization. We have shown previously that MECP2 Rett syndrome missense mutations are impaired in chromatin binding and heterochromatin reorganization. Here, we performed a proteomics analysis of post-translational modifications of MeCP2 isolated from adult mouse brain. We show that MeCP2 carries various post-translational modifications, among them phosphorylation on S80 and S421, which lead to minor changes in either heterochromatin binding kinetics or clustering. We found that MeCP2 is (di)methylated on several arginines and that this modification alters heterochromatin organization. Interestingly, we identified the Rett syndrome mutation site R106 as a dimethylation site. In addition, co-expression of protein arginine methyltransferases (PRMT)1 and PRMT6 lead to a decrease of heterochromatin clustering. Altogether, we identified and validated novel modifications of MeCP2 in the brain and show that these can modulate its ability to bind as well as reorganize heterochromatin, which may play a role in the pathology of Rett syndrome.

The VEGA suite of programs: an versatile platform for cheminformatics and drug design projects

The purpose of the article is to offer an overview of the latest release of the VEGA suite of programs. This software has been constantly developed and freely released during the last 20 years and has now reached a significant diffusion and technology level as confirmed by the about 22 500 registered users. While being primarily developed for drug design studies, the VEGA package includes cheminformatics and modeling features, which can be fruitfully utilized in various contexts of the computational chemistry. To offer a glimpse of the remarkable potentials of the software, some examples of the implemented features in the cheminformatics field and for structure-based studies are discussed. Finally, the flexible architecture of the VEGA program which can be expanded and customized by plug-in technology or scripting languages will be described focusing attention on the HyperDrive library including highly optimized functions.

AVAILABILITY AND IMPLEMENTATION

The VEGA suite of programs and the source code of the VEGA command-line version are available free of charge for non-profit organizations at http://www.vegazz.net.

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available.

Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays

Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM₂ muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM₂. Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM₂ ligands. Overall, the study enhances the understanding of the factors governing the hM₂ allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties.

Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on The hTRPM8 Channel

BACKGROUND

There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 μs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen™ provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets.

Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns

The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average.

DNA hypermethylation of SFRP2 influences the pathology of rheumatoid arthritis through the canonical Wnt signaling in model rats

In this work, the expression of secreted frizzled related protein 2 (SFRP2) in rheumatoid arthritis (RA) model rats and the mechanisms of SFRP2 on the RA pathogenesis were investigated. Data suggested that SFRP2 was significantly down-regulated in RA model rats compared with normal control, and overexpression of SFRP2 suppressed the RA pathogenesis and the canonical Wnt signaling in fibroblast-like synovial cells (FLS) from RA model rats, whereas knockdown of SFRP2 got an opposite observation. Interestingly, 5-azadC treatment up-regulated the SFRP2 expression, inhibited the FLS proliferation, suppressed the expression of IL-6 and IL-8 and the fibronectin production, suggesting that the decreased SFRP2 in RA model rats was due to the DNA methylation. Furthermore, DNMT1 knockdown up-regulated the SFRP2 expression, DNMT1 overexpression inhibited the SFRP2, and the quantitative methylation-specific PCR (qMSP) confirmed that the DNMT1 has direct methylation roles for the SFRP2 promoter, leading to a regulation of FLS proliferation and fibronectin expression in RA model rats. In addition, up-regulated MeCP2 was involved in the SFRP2 regulation and the pathogenesis of RA model rats, and MeCP2 and DNMT1 have synergistic inhibition roles in the SFRP2 expression. Combination of DNMT1 and DNA methylation may be a promising treatment strategy for individuals with RA in which SFRP2 is down-regulated.

Binding Space Concept: A New Approach To Enhance the Reliability of Docking Scores and Its Application to Predicting Butyrylcholinesterase Hydrolytic Activity

Docking simulations are very popular approaches able to assess the capacity of a given ligand to interact with a target. Docking simulations are usually focused on a single best complex even though many studies showed that ligands retain a significant mobility within a binding pocket by assuming different binding modes all of which may contribute to the monitored ligand affinity. The present study describes an innovative concept, the binding space, which allows an exploration of the ligand mobility within the binding pocket by simultaneously considering several ligand poses as generated by docking simulations. The multiple poses and the relative docking scores can then be analyzed by taking advantage of the same concepts already used in the property space analysis; hence the binding space can be parametrized by (a) mean scores, (b) score ranges, and (c) score sensitivity values. The first parameter represents a very simple procedure to account for the contribution of the often neglected alternative binding modes, while the last two descriptors encode the degree of mobility which a given ligand retains within the binding cavity (score range) as well as the ease with which a ligand explores such a mobility (score sensitivity). Here, the binding space concept is applied to the prediction of the hydrolytic activity of BChE by synergistically considering multiple poses and multiple protein structures. The obtained results shed light on the remarkable potential of the binding space concept, whose parameters allow a significant increase of the predictive power of the docking results as revealed by extended correlative analyses. Mean scores are the parameters affording the largest statistical improvement, and all the here proposed docking-based descriptors show enhancing effects in developing predictive models. Finally, the study describes a new score function (Contacts score) simply based on the number of surrounding residues which appears to be particularly productive in the framework of the binding space.

Data from docking simulations to develop an efficient strategy able to evaluate the interactions between RAGE and MDA-induced albumin adducts

This data article contains the results of docking simulations performed in order to develop a suitable in silico strategy able to assess the stability of the putative complexes between RAGE and MDA induced adducts on human albumin as experimentally determined doi: 10.1016/j.redox.2016.12.017, (Degani et al., 2017) [1]. The docking simulations involved different approaches to give a simplified yet realistic representation of the protein adducts and their environment. With increasing complexity, simulations involved the corresponding albumin tripeptides and pentapeptides with the modified residue in the central position as well as pseudo-structures which were generated by collecting the albumin residues around the adducted residue within a sphere of 7.5 Å and 5 Å radius. The reliability of the tested approaches was assessed by monitoring the score differences between adducted and unmodified residues. The obtained results revealed the greater predictive power of the spherical pseudo-structures compared to the simple tri- or pentapeptidic sequences thus suggesting that RAGE recognition involves residues which are spatially close to the modified residue even though not necessarily adjacent in the primary sequence.

Peptides Derived from Soy and Lupin Protein as Dipeptidyl-Peptidase IV Inhibitors: In Vitro Biochemical Screening and in Silico Molecular Modeling Study

Dipeptidyl peptidase IV (DPP-IV) is a new molecular target correlated with the development of type 2 diabetes. Literature describes the identification of some inhibitory peptides from the hydrolysis of different food proteins. This article reports a study on six peptides from soybean and lupin proteins, i.e., Soy 1 (IAVPTGVA), Soy 2 (YVVNPDNDEN), Soy 3 (YVVNPDNNEN), Lup 1 (LTFPGSAED), Lup 2 (LILPKHSDAD), and Lup 3 (GQEQSHQDEGVIVR), which were screened for their capacity to inhibit the activity of DPP-IV, using an in vitro bioassay against human recombinant DPP-IV. Two peptides Soy 1 and Lup 1 resulted to be efficient inhibitors with IC50 values equal to 106 and 228 μM, respectively. A molecular docking analysis predicted the key molecular interactions, stabilizing the active peptides within DPP-IV enzyme. Soy and lupin proteins may be sources of DPP-IV inhibitory peptides potentially useful for the prevention of type 2 diabetes.