Discovery of geranylgeranyltransferase-I inhibitors with novel scaffolds by the means of quantitative structure-activity relationship modeling, virtual screening, and experimental validation

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Computational Approach to Drug Penetration across the Blood-Brain and Blood-Milk Barrier Using Chromatographic Descriptors

Drug penetration through biological barriers is an important aspect of pharmacokinetics. Although the structure of the blood-brain and blood-milk barriers is different, a connection can be found in the literature between drugs entering the central nervous system (CNS) and breast milk. This study was created to reveal such a relationship with the use of statistical modelling. The basic physicochemical properties of 37 active pharmaceutical compounds (APIs) and their chromatographic retention data (TLC and HPLC) were incorporated into calculations as molecular descriptors (MDs). Chromatography was performed in a thin layer format (TLC), where the plates were impregnated with bovine serum albumin to mimic plasma protein binding. Two columns were used in high performance liquid chromatography (HPLC): one with immobilized human serum albumin (HSA), and the other containing an immobilized artificial membrane (IAM). Statistical methods including multiple linear regression (MLR), cluster analysis (CA) and random forest regression (RF) were performed with satisfactory results: the MLR model explains 83% of the independent variable variability related to CNS bioavailability; while the RF model explains up to 87%. In both cases, the parameter related to breast milk penetration was included in the created models. A significant share of reversed-phase TLC retention values was also noticed in the RF model.

Past and Future Strategies to Inhibit Membrane Localization of the KRAS Oncogene

KRAS is one of the most studied oncogenes. It is well known that KRAS undergoes post-translational modifications at its C-terminal end. These modifications are essential for its membrane location and activity. Despite significant efforts made in the past three decades to target the mechanisms involved in its membrane localization, no therapies have been approved and taken into the clinic. However, many studies have recently reintroduced interest in the development of KRAS inhibitors, either by directly targeting KRAS or indirectly through the inhibition of critical steps involved in post-translational KRAS modifications. In this review, we summarize the approaches that have been applied over the years to inhibit the membrane localization of KRAS in cancer and propose a new anti-KRAS strategy that could be used in clinic.

Molecular and Pharmacological Characterization of the Interaction between Human Geranylgeranyltransferase Type I and Ras-Related Protein Rap1B

Geranylgeranyltransferase type-I (GGTase-I) represents an important drug target since it contributes to the function of many proteins that are involved in tumor development and metastasis. This led to the development of GGTase-I inhibitors as anti-cancer drugs blocking the protein function and membrane association of e.g., Rap subfamilies that are involved in cell differentiation and cell growth. In the present study, we developed a new NanoBiT assay to monitor the interaction of human GGTase-I and its substrate Rap1B. Different Rap1B prenylation-deficient mutants (C181G, C181S, and ΔCQLL) were designed and investigated for their interaction with GGTase-I. While the Rap1B mutants C181G and C181S still exhibited interaction with human GGTase-I, mutant ΔCQLL, lacking the entire CAAX motif (defined by a cysteine residue, two aliphatic residues, and the C-terminal residue), showed reduced interaction. Moreover, a specific, peptidomimetic and competitive CAAX inhibitor was able to block the interaction of Rap1B with GGTase-I. Furthermore, activation of both Gα_s-coupled human adenosine receptors, A_2A (A_2AAR) and A_2B (A_2BAR), increased the interaction between GGTase-I and Rap1B, probably representing a way to modulate prenylation and function of Rap1B. Thus, A_2AAR and A_2BAR antagonists might be promising candidates for therapeutic intervention for different types of cancer that overexpress Rap1B. Finally, the NanoBiT assay provides a tool to investigate the pharmacology of GGTase-I inhibitors.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

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.

Consensus QSPR modelling for the prediction of cellular response and fibrinogen adsorption to the surface of polymeric biomaterials

In the current study, we have developed predictive quantitative structure-activity relationship (QSAR) models for cellular response (foetal rate lung fibroblast proliferation) and protein adsorption (fibrinogen adsorption (FA)) on the surface of tyrosine-derived biodegradable polymers designed for tissue engineering purpose using a dataset of 66 and 40 biodegradable polymers, respectively, employing two-dimensional molecular descriptors. Best four individual models have been selected for each of the endpoints. These models are developed using partial least squares regression with a unique combination of six and four descriptors for cellular response and protein adsorption, respectively. The generated models were strictly validated using internal and external metrics to determine the predictive ability and robustness of proposed models. Subsequently, the validated individual models for each response endpoints were used for the generation of 'intelligent' consensus models ( http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ ) to improve the quality of predictions for the external data set. These models may help in prediction of virtual polymer libraries for rational design/optimization for properties relevant to biomedical applications prior to their synthesis.

Discovery of Novel Succinate Dehydrogenase Inhibitors by the Integration of in Silico Library Design and Pharmacophore Mapping

Succinate dehydrogenase (SDH) has been demonstrated as a promising target for fungicide discovery. Crystal structure data have indicated that the carboxyl "core" of current SDH inhibitors contributed largely to their binding affinity. Thus, identifying novel carboxyl "core" SDH inhibitors would remarkably improve the biological potency of current SDHI fungicides. Herein, we report the discovery and optimization of novel carboxyl scaffold SDH inhibitor via the integration of in silico library design and a highly specific amide feature-based pharmacophore model. To our delight, a promising SDH inhibitor, A16c (IC₅₀ = 1.07 μM), with a novel pyrazol-benzoic scaffold was identified, which displayed excellent activity against Rhizoctonia solani (EC₅₀ = 11.0 μM) and improved potency against Sclerotinia sclerotiorum (EC₅₀ = 5.5 μM) and Phyricularia grisea (EC₅₀ = 12.0 μM) in comparison with the positive control thifluzamide, with EC₅₀ values of 0.09, 33.2, and 33.4 μM, respectively. The results showed that our virtual screening strategy could serve as a powerful tool to accelerate the discovery of novel SDH inhibitors.

Discovery of Natural Product-Derived 5-HT1A Receptor Binders by Cheminfomatics Modeling of Known Binders, High Throughput Screening and Experimental Validation

The human 5-hydroxytryptamine receptor subtype 1A (5-HT1A) is highly expressed in the raphe nuclei region and limbic structures; for that reason 5-HT1A has served as a promising target for treating human mood disorders and neurodegenerative diseases. We have developed binary quantitative structure-activity relationship (QSAR) models for 5- HT1A binding using data retrieved from the WOMBAT database and the k-Nearest Neighbor (kNN) machine learning method. A rigorous QSAR modeling and screening workflow had been followed, with extensive internal and external validation processes. The models' classification accuracies to discriminate 5-HT1A binders from the non-binders are as high as 96% for the external validation. These models were employed further to mine two major natural products screening libraries, i.e. TimTec Natural Product Library (NPL) and Natural Derivatives Library (NDL). In the end five screening hits were tested by radioligand binding assays with a success rate of 40%, and two Library compounds were confirmed to be binders at the μM concentration against the human 5-HT1A receptor. The combined application of rigorous QSAR modeling and model-based virtual screening presents a powerful means for profiling natural products compounds with important biomedical activities.

Cheminfomatic-based Drug Discovery of Human Tyrosine Kinase Inhibitors

Receptor Tyrosine Kinases (RTKs) are essential components for regulating cell-cell signaling and communication events in cell growth, proliferation, differentiation, survival and metabolism. Deregulation of RTKs and their associated signaling pathways can lead to a wide variety of human diseases such as immunodeficiency, diabetes, arterosclerosis, psoriasis and cancer. Thus RTKs have become one of the most important drug targets families in recent decade. Pharmaceutical companies have dedicated their research efforts towards the discovery of small-molecule inhibitors of RTKs, many of which had been approved by the U.S. Food and Drug Administration (US FDA) or are currently in clinical trials. The great successes in the development of small-molecule inhibitors of RTKs are largely attributed to the use of modern cheminformatic approaches to identifying lead scaffolds. Those include the quantitative structure-activity relationship (QSAR) modeling, as well as the structure-, and ligand-based pharmacophore modeling techniques in this case. Herein we inspected the literature thoroughly in an effort to conduct a comparative analysis of major findings regarding the essential structure-activity relationships (SARs)/pharmacophore features of known active RTK inhibitors, most of which were collected from cheminformatic modeling approaches.

Combined rational design and a high throughput screening platform for identifying chemical inhibitors of a Ras-activating enzyme

The Ras family small GTPases regulate multiple cellular processes, including cell growth, survival, movement, and gene expression, and are intimately involved in cancer pathogenesis. Activation of these small GTPases is catalyzed by a special class of enzymes, termed guanine nucleotide exchange factors (GEFs). Herein, we developed a small molecule screening platform for identifying lead hits targeting a Ras GEF enzyme, SOS1. We employed an ensemble structure-based virtual screening approach in combination with a multiple tier high throughput experimental screen utilizing two complementary fluorescent guanine nucleotide exchange assays to identify small molecule inhibitors of GEF catalytic activity toward Ras. From a library of 350,000 compounds, we selected a set of 418 candidate compounds predicted to disrupt the GEF-Ras interaction, of which dual wavelength GDP dissociation and GTP-loading experimental screening identified two chemically distinct small molecule inhibitors. Subsequent biochemical validations indicate that they are capable of dose-dependently inhibiting GEF catalytic activity, binding to SOS1 with micromolar affinity, and disrupting GEF-Ras interaction. Mutagenesis studies in conjunction with structure-activity relationship studies mapped both compounds to different sites in the catalytic pocket, and both inhibited Ras signaling in cells. The unique screening platform established here for targeting Ras GEF enzymes could be broadly useful for identifying lead inhibitors for a variety of small GTPase-activating GEF reactions.

Statistical analysis, optimization, and prioritization of virtual screening parameters for zinc enzymes including the anthrax toxin lethal factor

The anthrax toxin lethal factor (LF) and matrix metalloproteinase-3 (MMP-3, stromelysin-1) are popular zinc metalloenzyme drug targets, with LF primarily responsible for anthrax-related toxicity and host death, while MMP-3 is involved in cancer- and rheumatic disease-related tissue remodeling. A number of in silico screening techniques, most notably docking and scoring, have proven useful for identifying new potential drug scaffolds targeting LF and MMP-3, as well as for optimizing lead compounds and investigating mechanisms of action. However, virtual screening outcomes can vary significantly depending on the specific docking parameters chosen, and systematic statistical significance analyses are needed to prioritize key parameters for screening small molecules against these zinc systems. In the current work, we present a series of chi-square statistical analyses of virtual screening outcomes for cocrystallized LF and MMP-3 inhibitors docked into their respective targets, evaluated by predicted enzyme-inhibitor dissociation constant and root-mean-square deviation (RMSD) between predicted and experimental bound configurations, and we present a series of preferred parameters for use with these systems in the industry-standard Surflex-Dock screening program, for use by researchers utilizing in silico techniques to discover and optimize new scaffolds.

Farnesyltransferase and geranylgeranyltransferase I: structures, mechanism, inhibitors and molecular modeling

Farnesyltransferase (FTase) and geranylgeranyltransferase type I (GGTase-I) have crucial roles in the post-translational modifications of Ras proteins and, therefore, they are promising therapeutic targets for the treatment of various Ras-induced cancers and several other kinds of diseases. In this review, we provide an overview of the structures and biological functions of FTase and GGTase-I. Then, we summarize the typical inhibitors of FTase and GGTase-I, and highlight the drug candidates in clinical trials. In addition, we survey some recent advances in computer-aided drug design (CADD) and molecular modeling studies of FTase and GGTase-I.

Targeting protein prenylation for cancer therapy

Protein farnesylation and geranylgeranylation, together referred to as prenylation, are lipid post-translational modifications that are required for the transforming activity of many oncogenic proteins, including some RAS family members. This observation prompted the development of inhibitors of farnesyltransferase (FT) and geranylgeranyl-transferase 1 (GGT1) as potential anticancer drugs. In this Review, we discuss the mechanisms by which FT and GGT1 inhibitors (FTIs and GGTIs, respectively) affect signal transduction pathways, cell cycle progression, proliferation and cell survival. In contrast to their preclinical efficacy, only a small subset of patients responds to FTIs. Identifying tumours that depend on farnesylation for survival remains a challenge, and strategies to overcome this are discussed. One GGTI has recently entered the clinic, and the safety and efficacy of GGTIs await results from clinical trials.

Identification and characterization of mechanism of action of P61-E7, a novel phosphine catalysis-based inhibitor of geranylgeranyltransferase-I

Small molecule inhibitors of protein geranylgeranyltransferase-I (GGTase-I) provide a promising type of anticancer drugs. Here, we first report the identification of a novel tetrahydropyridine scaffold compound, P61-E7, and define effects of this compound on pancreatic cancer cells. P61-E7 was identified from a library of allenoate-derived compounds made through phosphine-catalyzed annulation reactions. P61-E7 inhibits protein geranylgeranylation and blocks membrane association of geranylgeranylated proteins. P61-E7 is effective at inhibiting both cell proliferation and cell cycle progression, and it induces high p21(CIP1/WAF1) level in human cancer cells. P61-E7 also increases p27(Kip1) protein level and inhibits phosphorylation of p27(Kip1) on Thr187. We also report that P61-E7 treatment of Panc-1 cells causes cell rounding, disrupts actin cytoskeleton organization, abolishes focal adhesion assembly and inhibits anchorage independent growth. Because the cellular effects observed pointed to the involvement of RhoA, a geranylgeranylated small GTPase protein shown to influence a number of cellular processes including actin stress fiber organization, cell adhesion and cell proliferation, we have evaluated the significance of the inhibition of RhoA geranylgeranylation on the cellular effects of inhibitors of GGTase-I (GGTIs). Stable expression of farnesylated RhoA mutant (RhoA-F) results in partial resistance to the anti-proliferative effect of P61-E7 and prevents induction of p21(CIP1/WAF1) and p27(Kip1) by P61-E7 in Panc-1 cells. Moreover, stable expression of RhoA-F rescues Panc-1 cells from cell rounding and inhibition of focal adhesion formation caused by P61-E7. Taken together, these findings suggest that P61-E7 is a promising GGTI compound and that RhoA is an important target of P61-E7 in Panc-1 pancreatic cancer cells.

Software and resources for computational medicinal chemistry

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

The role of computational methods in the identification of bioactive compounds

Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design

Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.