Protein Preparation Automatic Protocol for High-Throughput Inverse Virtual Screening: Accelerating the Target Identification by Computational Methods.

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by “wet” approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.

Computational virtual screening (VS) plays a vital role in early-stage drug discovery by enabling the efficient selection of candidate compounds and reducing associated costs. However, the absence of experimentally determined three-dimensional protein structures often limits the applicability of structure-based VS. Advances in protein structure prediction, notably AlphaFold2, have begun to address this gap. Yet, studies indicate that direct use of AlphaFold2-predicted structures often leads to suboptimal VS performance-likely because these structures fail to capture ligand-induced conformational changes (apo-to-holo transitions). To overcome this, we propose an approach that explores and modifies the structural space of AlphaFold2 predictions to generate conformations more amenable to VS. Our method deliberately alters the multiple sequence alignment (MSA) by introducing alanine mutations at key residues in the ligand-binding site, thereby inducing significant conformational shifts. The exploration process is guided by iterative ligand docking simulations, with mutation strategies optimized either by a genetic algorithm or via random search. Our evaluation shows that when sufficient active compounds are available, the genetic algorithm significantly enhances VS accuracy. In contrast, with limited active compound data, a random search strategy proves more effective. Moreover, our approach is particularly promising for targets that yield poor screening results when using experimentally determined structures from the PDB. Overall, these findings underscore the practical utility of modified AlphaFold2-derived structures in VS and expand the potential of computationally predicted protein models in drug discovery.

Inverse Virtual Screening allows the discovery of the biological activity of natural compounds

High-throughput and virtual screening are important components of modern drug discovery research. Typically, these screening technologies are considered distinct approaches, as one is experimental and the other is theoretical in nature. However, given their similar tasks and goals, these approaches are much more complementary to each other than often thought. Various statistical, informatics and filtering methods have recently been introduced to foster the integration of experimental and in silico screening and maximize their output in drug discovery. Although many of these ideas and efforts have not yet proceeded much beyond the conceptual level, there are several success stories and good indications that early-stage drug discovery will benefit greatly from a more unified and knowledge-based approach to biological screening, despite the many technical advances towards even higher throughput that are made in the screening arena.

Discovery of new erbB4 inhibitors: Repositioning an orphan chemical library by inverse virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient leaddiscovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than thetraditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledgeof the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applicationsof Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initialstages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoringhits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, inducedfit and consensus docking are also discussed. The review highlights advances in the field within the framework ofseveral success studies that have led to nM inhibition directly from VS and provides recent trends in library design as wellas discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enablethe discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteinsare also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increaseinhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed toinhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for theRXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on bindingsite shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein targetthrough the SBVS process.

An alternative biomimetic tool – Dual hydrophilic/reversed-phase interaction mode

Cancer continues to remain a disease of scientific concern. Significant interest in targeting the Polo-Box-Domain (PBD) of Polo-like-kinase-1 (PLK-1) by novel ligands has arisen. The 'cleft' constituted by amino acid residues W414, H538, and K540 is the traditional target of PLK-1-PBD-inhibitors. However, this 'cleft' is merely a small part of the larger 'Y'-shaped cavity present therein. The objective of this study was to discover inhibitors of the PLK-1-PBD precisely directed against its trimodular 'Y'-pocket. High-throughput structure-based virtual screening (SBVS) of more than 5 million ligands against the aforementioned PLK-1 'Y'-pocket was performed. The SBVS hits were successively subjected to pass through various filters: VINA score ranking, toxicity checker, 'Special Criteria'-filtration, holistic tri-modular 'Y'-pocket interaction check, drug-likeness filters, and medicinal chemistry filters. Accordingly, we arrived at a single top ligand, 'SHAZ-i.' The top ligand, 3-{2-[(2-Methyl-2-propanyl)sulfonyl]phenyl}-5-phenyl-1,2-oxazole-4-carboxamide, displayed a robust interaction with the target crevice through 15 amino acid residues, an acceptable ΔG value of -7.8 kcal/mol, and a favorable pharmacokinetic profile with no adverse effects on humans. Hence, 3-{2-[(2-Methyl-2-propanyl)sulfonyl]phenyl}-5-phenyl-1,2-oxazole-4-carboxamide could emerge as a potent PLK-1-PBD inhibitor or might act as a 'seed' molecule for design of future inhibitors with a closely related backbone structure.

Panel docking of small-molecule libraries — Prospects to improve efficiency of lead compound discovery

Culinary herbs and spices are widely used as a traditional medicine in the treatment of diabetes and its complications, and there are several scientific studies in the literature supporting the use of these medicinal plants. However, there is often a lack of knowledge on the bioactive compounds of these herbs and spices and their mechanisms of action. The aim of this study was to use inverse virtual screening to provide insights into the bioactive compounds of common herbs and spices, and their potential molecular mechanisms of action in the treatment of diabetes. In this study, a library of over 2300 compounds derived from 30 common herbs and spices were screened in silico with the DIA-DB web server against 18 known diabetes drug targets. Over 900 compounds from the herbs and spices library were observed to have potential anti-diabetic activity and liquorice, hops, fennel, rosemary, and fenugreek were observed to be particularly enriched with potential anti-diabetic compounds. A large percentage of the compounds were observed to be potential polypharmacological agents regulating three or more anti-diabetic drug targets and included compounds such as achillin B from yarrow, asparasaponin I from fenugreek, bisdemethoxycurcumin from turmeric, carlinoside from lemongrass, cinnamtannin B1 from cinnamon, crocin from saffron and glabridin from liquorice. The major targets identified for the herbs and spices compounds were dipeptidyl peptidase-4 (DPP4), intestinal maltase-glucoamylase (MGAM), liver receptor homolog-1 (NR5A2), pancreatic alpha-amylase (AM2A), peroxisome proliferator-activated receptor alpha (PPARA), protein tyrosine phosphatase non-receptor type 9 (PTPN9), and retinol binding protein-4 (RBP4) with over 250 compounds observed to be potential inhibitors of these particular protein targets. Only bay leaves, liquorice and thyme were found to contain compounds that could potentially regulate all 18 protein targets followed by black pepper, cumin, dill, hops and marjoram with 17 protein targets. In most cases more than one compound within a given plant could potentially regulate a particular protein target. It was observed that through this multi-compound-multi target regulation of these specific protein targets that the major anti-diabetic effects of reduced hyperglycemia and hyperlipidemia of the herbs and spices could be explained. The results of this study, taken together with the known scientific literature, indicated that the anti-diabetic potential of common culinary herbs and spices was the result of the collective action of more than one bioactive compound regulating and restoring several dysregulated and interconnected diabetic biological processes.

Insect molting is regulated by 20-hydroxyecdsysone (20E), and the disruption of the molting process leads to death. To date, non-steroidal ecdysone agonists, diacylhydrazine (DAH)-type compounds, have been used as insecticides. We will review classical quantitative structure activity relationship (QSAR) studies for the in vivo and in vitro activity of DAHs and ecdysteroids. In the QSAR study for ecdysteroids, we applied three-dimensional QSAR, where hydrogen bond number was used as a parameter. The ligand-receptor complex was constructed by the homology modeling technique and the number of hydrogen bonds was counted. In recent drug design, computational approaches such as virtual (in silico) screening (VS) and high throughput screening (HTS) are often used. The VS procedure is classified as ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS); both are often used for the design of pharmaceuticals. We applied LBVS to find novel ecdysone agonists, in which ponasterone A (PoA), the most potent ecdysteroid, was used as a template molecule. Other computational techniques such as homology modeling and bioinformatics are also briefly reviewed.

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software.

Triterpenoids are well known metabolic syndrome (MetS) modulators. One of the suggested molecular mechanisms of action involves peroxisome proliferator-activated receptor gamma (PPARγ) activation. In this study we aimed to: (i) develop a virtual screening (VS) protocol for PPARγ weak partial agonists, (ii) predict potential metabolic transformations of naturally-derived triterpenoids, and (iii) perform VS of the triterpenoids and their metabolites. The NIH PubMed system was searched for publications about naturally-derived oleanane triterpenoids which are agonists or up-regulators of PPARγ. Structure- and ligand-based methods were combined in the development of the VS protocol. Metabolites were predicted using Meteor Nexus expert system (Lhasa Limited). Two in-house virtual libraries of PPARγ weak partial agonists and naturally-derived triterpenoids with their predicted metabolites were compiled. The pharmacophore-based docking protocol was applied for VS of the collected triterpenoids. Most of the docking poses reproduced the binding mode of caulophyllogenin (a weak partial agonist) in a complex with PPARγ (PDB ID 5F9B). Our results contribute to the mechanistic explanation of the effects of triterpenoids suggesting possible weak partial agonistic activity toward PPARγ. This research can direct further studies on triterpenoids’ role in MetS modulation. The developed protocol can be applied for VS of any PPARγ weak partial agonists.