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Quantitativestructure–activity relationship modeling is one of the majorcomputational tools employed in medicinal chemistry. However, throughoutits entire history it has drawn both praise and criticism concerningits reliability, limitations, successes, and failures. In this paper,we discuss (i) the development and evolution of QSAR; (ii) the currenttrends, unsolved problems, and pressing challenges; and (iii) severalnovel and emerging applications of QSAR modeling. Throughout thisdiscussion, we provide guidelines for QSAR development, validation,and application, which are summarized in best practices for buildingrigorously validated and externally predictive QSAR models. We hopethat this Perspective will help communications between computationaland experimental chemists toward collaborative development and useof QSAR models. We also believe that the guidelines presented herewill help journal editors and reviewers apply more stringent scientificstandards to manuscripts reporting new QSAR studies, as well as encouragethe use of high quality, validated QSARs for regulatory decision making. [ABSTRACT FROM AUTHOR]

Abstract: Plasma sex hormone-binding globulin (SHBG) regulates the access of androgens and estrogens to their target tissues and cell types. An SHBG homologue, known as the androgen-binding protein, is expressed in Sertoli cells of many mammalians, but testicular expression of human SHBG is restricted to germ cells. The primary structure of SHBG comprises tandem laminin G-like (LG) domains. The amino-terminal LG-domain includes the steroid-binding site and dimerization interface, and its tertiary structure, resolved in complex with natural and synthetic sex steroids, has revealed unanticipated mechanisms of steroid binding at the atomic level. This LG-domain interacts with fibulin-1D and fibulin-2 in a ligand-specific manner, and this is attributed to the unique way estrogens reside within the steroid-binding site, and the ordering of an otherwise flexible loop structure covering the entrance of the steroid-binding pocket. This mechanism enables estradiol to enhance the sequestration of plasma SHBG by the stroma of some tissues through binding to these extra-cellular matrix-associated proteins. The human SHBG amino-terminal LG-domain also contains several cation-binding sites, and occupancy of a zinc-binding site influences its affinity for estradiol. The complete quaternary structure of SHBG remains unresolved but structural predictions suggest that the carboxy-terminal LG-domains extend laterally from the dimerized amino-terminal LG-domains. The carboxy-terminal LG-domain contains two N-glycosylation sites, but their biological significance remains obscure. Knowledge of the SHBG tertiary structure has helped develop computational techniques based on the use of a “bench-mark data set” of steroid ligands, and created novel drug discovery and toxicology risk assessment platforms. [Copyright &y& Elsevier]

Background: Insertions and deletions (indels) represent a common type of sequence variations, which are less studied and pose many important biological questions. Recent research has shown that the presence of sizable indels in protein sequences may be indicative of protein essentiality and their role in protein interaction networks. Examples of utilization of indels for structure-based drug design have also been recently demonstrated. Nonetheless many structural and functional characteristics of indels remain less researched or unknown. Description: We have created a web-based resource, Indel PDB, representing a structural database of insertions/deletions identified from the sequence alignments of highly similar proteins found in the Protein Data Bank (PDB). Indel PDB utilized large amounts of available structural information to characterize 1-, 2- and 3-dimensional features of indel sites. Indel PDB contains 117,266 non-redundant indel sites extracted from 11,294 indel-containing proteins. Unlike loop databases, Indel PDB features more indel sequences with secondary structures including alpha-helices and beta-sheets in addition to loops. The insertion fragments have been characterized by their sequences, lengths, locations, secondary structure composition, solvent accessibility, protein domain association and three dimensional structures. Conclusion: By utilizing the data available in Indel PDB, we have studied and presented here several sequence and structural features of indels. We anticipate that Indel PDB will not only enable future functional studies of indels, but will also assist protein modeling efforts and identification of indel-directed drug binding sites. [ABSTRACT FROM AUTHOR]

Abstract: Elongation factor-1α plays an essential role in eukaryotic protein biosynthesis. Recently, we have shown by protein structure modeling the presence of a hairpin-loop of 12 amino acids in mammalian EF-1α that is absent in the leishmania homologue [D. Nandan, A. Cherkasov, R. Sabouti, T. Yi, N.E. Reiner, Molecular cloning, biochemical and structural analysis of elongation factor-1 alpha from Leishmania donovani: comparison with the mammalian homologue, Biochem. Biophys. Res. Commun. 302 (2003) 646–652]. As a consequence of this deletion, an exposed region is available on the main body of leishmania EF-1α. Here we report the generation of an anti-EF-1α antibody (DN-3) which bound selectively to the exposed region of leishmania EF-1α, with no reactivity with human EF-1α. In a leishmania cell-free protein translation system, DN-3 substantially inhibited protein translation. A similar inhibitory effect was observed when a specific peptide based on the exposed region was used in the cell-free protein translation assay. The application of structure-based in silico methods to identify potential ligands to target the exposed region identified a small molecule that selectively attenuated in vitro translation using leishmania extracts. Moreover, this small molecule showed selective suppressive effect on multiplication of leishmania in culture. Taken together, these findings identify a novel, exposed region in leishmania EF-1α that may be involved in protein synthesis and a potential site for drug targeting. [Copyright &y& Elsevier]

Background: We establish that the occurrence of protein folds among genomes can be accurately described with a Weibull function. Systems which exhibit Weibull character can be interpreted with reliability theory commonly used in engineering analysis. For instance, Weibull distributions are widely used in reliability, maintainability and safety work to model time-to-failure of mechanical devices, mechanisms, building constructions and equipment. Results: We have found that the Weibull function describes protein fold distribution within and among genomes more accurately than conventional power functions which have been used in a number of structural genomic studies reported to date. It has also been found that the Weibull reliability parameter β for protein fold distributions varies between genomes and may reflect differences in rates of gene duplication in evolutionary history of organisms. Conclusions: The results of this work demonstrate that reliability analysis can provide useful insights and testable predictions in the fields of comparative and structural genomics. [ABSTRACT FROM AUTHOR]

Background: A new sequence independent bioinformatics approach allowing genome-wide search for proteins with similar three dimensional structures has been developed. By utilizing the numerical output of the sequence threading it establishes putative non-obvious structural similarities between proteins. When applied to the testing set of proteins with known three dimensional structures the developed approach was able to recognize structurally similar proteins with high accuracy. Results: The method has been developed to identify pathogenic proteins with low sequence identity and high structural similarity to host analogues. Such protein structure relationships would be hypothesized to arise through convergent evolution or through ancient horizontal gene transfer events, now undetectable using current sequence alignment techniques. The pathogen proteins, which could mimic or interfere with host activities, would represent candidate virulence factors. The developed approach utilizes the numerical outputs from the sequence-structure threading. It identifies the potential structural similarity between a pair of proteins by correlating the threading scores of the corresponding two primary sequences against the library of the standard folds. This approach allowed up to 64% sensitivity and 99.9% specificity in distinguishing protein pairs with high structural similarity. Conclusion: Preliminary results obtained by comparison of the genomes of Homo sapiens and several strains of Chlamydia trachomatis have demonstrated the potential usefulness of the method in the identification of bacterial proteins with known or potential roles in virulence. [ABSTRACT FROM AUTHOR]

Background: Using sequence-structure threading we have conducted structural characterization of complete proteomes of 37 archaeal, bacterial and eukaryotic organisms (including worm, fly, mouse and human) totaling 167,888 genes. Results: The reported data represent first rather general evaluation of performance of full sequence-structure threading on multiple genomes providing opportunity to evaluate its general applicability for large scale studies. According to the estimated results the sequence-structure threading has assigned protein folds to more then 60% of eukaryotic, 68% of archaeal and 70% of bacterial proteomes. The repertoires of protein classes, architectures, topologies and homologous superfamilies (according to the CATH 2.4 classification) have been established for distant organisms and superkingdoms. It has been found that the average abundance of CATH classes decreases from "alpha and beta" to "mainly beta", followed by "mainly alpha" and "few secondary structures". 3-Layer (aba) Sandwich has been characterized as the most abundant protein architecture and Rossman fold as the most common topology. Conclusion: The analysis of genomic occurrences of CATH 2.4 protein homologous superfamilies and topologies has revealed the power-law character of their distributions. The corresponding double logarithmic "frequency -- genomic occurrence" dependences characteristic of scale-free systems have been established for individual organisms and for three superkingdoms. Supplementary materials to this works are available at [1]. [ABSTRACT FROM AUTHOR]

The Src-homology 2 domain containing protein tyrosine phosphatase-1 (SHP-1) is involved in the pathogenesis of infection with Leishmania. Recently, we identified elongation factor-1α (EF-1α) from Leishmania donovani as a SHP-1 binding and activating protein [J. Biol. Chem. 277 (2002) 50190]. To characterize this apparent Leishmania virulence factor further, the cDNA encoding L. donovani EF-1α was cloned and sequenced. Whereas nearly complete sequence conservation was observed amongst EF-1α proteins from trypanosomatids, the deduced amino acid sequence of EF-1α of L. donovani when compared to mammalian EF-1α sequences showed a number of significant changes. Protein structure modeling-based upon the known crystal structure of EF-1α for Saccharomyces cerevisiae-identified a hairpin loop present in mammalian EF-1α and absent from the Leishmania protein which corresponded to a 12 amino acid deletion. Consistent with these structural differences, the sub-cellular distributions of L. donovani EF-1α and host EF-1α were strikingly different. Interestingly, infection of macrophages with L. donovani caused redistribution of host as well as pathogen EF-1α. Since EF-1α is essential for survival, the distinct biochemical and structural properties of Leishmania EF-1α may provide a novel target for drug development. [Copyright &y& Elsevier]

Acid-base properties and reactivity of series of alpha-aminophosphonates has been investigated. The reaction of addition of the aminophosphonates to phenylisocyanate was used as a model reaction. The influence of steric and electronic effects of substituents at alpha-carbon or nitrogen in amino group on the mechanism inversion from AdN to AdE is discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer stem cells (CSCs) are a small population of tumour cells bearing stemness characteristics. They have been deemed as a root of cancer development and a promising drug target for anticancer therapy. Herein, we discuss the roles of an RNA-binding protein, Lin28, in regulating cancer cell stemness to drive tumour progression. Lin28 acts on various types of RNAs in cancer cells and regulates these RNA functions to control the expression of oncogenes. In these ways, Lin28 promotes cancer cell survival, growth, and invasion. We also discuss recent efforts in developing Lin28 inhibitors targeting CSCs in tumours. Tumours develop therapy resistance through complex mechanisms, one of which is that cancer stem cell (CSC) populations within the tumours present self-renewable capability and phenotypical plasticity to endure therapy-induced stress conditions and allow tumour progression to the therapy-resistant state. Developing therapeutic strategies to cope with CSCs requires a thorough understanding of the critical drivers and molecular mechanisms underlying the aforementioned processes. One such hub regulator of stemness is Lin28, an RNA-binding protein. Lin28 blocks the synthesis of let-7, a tumour-suppressor microRNA, and acts as a global regulator of cell differentiation and proliferation. Lin28also targets messenger RNAs and regulates protein translation. In this review, we explain the role of the Lin28/let-7 axis in establishing stemness, epithelial-to-mesenchymal transition, and glucose metabolism reprogramming. We also highlight the role of Lin28 in therapy-resistant prostate cancer progression and discuss the emergence of Lin28-targeted therapeutics and screening methods. [ABSTRACT FROM AUTHOR]

FOXA1 is a pioneer transcription factor that is frequently mutated in prostate, breast, bladder, and salivary gland malignancies. Indeed, metastatic castration-resistant prostate cancer (mCRPC) commonly harbour FOXA1 mutations with a prevalence of 35%. However, despite the frequent recurrence of FOXA1 mutations in prostate cancer, the mechanisms by which FOXA1 variants drive its oncogenic effects are still unclear. Semaphorin 3C (SEMA3C) is a secreted autocrine growth factor that drives growth and treatment resistance of prostate and other cancers and is known to be regulated by both AR and FOXA1. In the present study, we characterize FOXA1 alterations with respect to its regulation of SEMA3C. Our findings reveal that FOXA1 alterations lead to elevated levels of SEMA3C both in prostate cancer specimens and in vitro. We further show that FOXA1 negatively regulates SEMA3C via intronic cis elements, and that mutations in FOXA1 forkhead domain attenuate its inhibitory function in reporter assays, presumably by disrupting DNA binding of FOXA1. Our findings underscore the key role of FOXA1 in prostate cancer progression and treatment resistance by regulating SEMA3C expression and suggest that SEMA3C may be a driver of growth and tumor vulnerability of mCRPC harboring FOXA1 alterations. [ABSTRACT FROM AUTHOR]

Estrogen receptor positive (ER+) breast cancer (BCa) accounts for the highest proportion of breast cancer-related deaths. While endocrine therapy is highly effective for this subpopulation, endocrine resistance remains a major challenge and the identification of novel targets is urgently needed. Previously, we have shown that Semaphorin 3C (SEMA3C) is an autocrine growth factor that drives the growth and treatment resistance of various cancers, but its role in breast cancer progression and endocrine resistance is poorly understood. Here, we report that SEMA3C plays a role in maintaining the growth of ER+ BCa cells and is a novel, tractable therapeutic target for the treatment of ER+ BCa patients. Analyses of publicly available clinical datasets indicate that ER+ BCa patients express significantly higher levels of SEMA3C mRNA than other subtypes. Furthermore, SEMA3C mRNA expression was positively correlated with ESR1 mRNA expression. ER+ BCa cell lines (MCF7 and T47D) expressed higher levels of SEMA3C mRNA and protein than a normal mammary epithelial MCF10A cell line. ER siRNA knockdown was suppressed, while dose-dependent beta-estradiol treatment induced SEMA3C expression in both MCF7 and T47D cells, suggesting that SEMA3C is an ER-regulated gene. The stimulation of ER+ BCa cells with recombinant SEMA3C activated MAPK and AKT signaling in a dose-dependent manner. Conversely, SEMA3C silencing inhibited Estrogen Receptor (ER) expression, MAPK and AKT signaling pathways while simultaneously inducing apoptosis, as monitored by flow cytometry and Western blot analyses. SEMA3C silencing significantly inhibited the growth of ER+ BCa cells, implicating a growth dependency of ER+ BCa cells on SEMA3C. Moreover, the analysis of tamoxifen resistant (TamR) cell models (TamC3 and TamR3) showed that SEMA3C levels remain high despite treatment with tamoxifen. Tamoxifen-resistant cells remained dependent on SEMA3C for growth and survival. Treatment with B1SP Fc fusion protein, a SEMA3C pathway inhibitor, attenuated SEMA3C-induced signaling and growth across a panel of tamoxifen sensitive and resistant ER+ breast cancer cells. Furthermore, SEMA3C silencing and B1SP treatment were associated with decreased EGFR signaling in TamR cells. Here, our study implicates SEMA3C in a functional role in ER+ breast cancer signaling and growth that suggests ER+ BCa patients may benefit from SEMA3C-targeted therapy. [ABSTRACT FROM AUTHOR]

Discusses the effect of three-dimensional correlation analysis on reaction series concerning organoelement and organophosphorus reactivity. Equation used in the analysis of free energy-related quantitative parameters.

Elaborates on new approaches for the effective analysis of organic and organoelement reactivity and the reaction mechanisms on the basis of previously suggested models of steric and inductive substituents effects. Characteristics of the models; How the correctness of the models was confirmed; Role of elaborated approaches in understanding organophosphorus reactivity.

The isolation of proteins of interest from cell lysates is an integral step to study protein structure and function. Liquid chromatography is a technique commonly used for protein purification, where the separation is performed by exploiting the differences in physical and chemical characteristics of proteins. The complex nature of proteins requires researchers to carefully choose buffers that maintain stability and activity of the protein while also allowing for appropriate interaction with chromatography columns. To choose the proper buffer, biochemists often search for reports of successful purification in the literature; however, they often encounter roadblocks such as lack of accessibility to journals, non-exhaustive specification of components and unfamiliar naming conventions. To overcome such issues, we present PurificationDB (https://purificationdatabase.herokuapp.com/), an open-access and user-friendly knowledge base that contains 4732 curated and standardized entries of protein purification conditions. Buffer specifications were derived from the literature using named-entity recognition techniques developed using common nomenclature provided by protein biochemists. PurificationDB also incorporates information associated with well-known protein databases: Protein Data Bank and UniProt. PurificationDB facilitates easy access to data on protein purification techniques and contributes to the growing effort of creating open resources that organize experimental conditions and data for improved access and analysis. Database URLhttps://purificationdatabase.herokuapp.com/ [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer cells gaining stem cell gene signatures has been deemed as the root of cancers. Blocking the driver genes that control cancer stemness will lead to effective tumor suppression. Lin28 is a stemness driver gene that exerts its tumor-promoting activity through its RNA binding capability. Here, we analyzed the protein structure of Lin28 and discovered several chemicals that can block Lin28 RNA-binding activity. The identified Lin28 inhibitors strongly block the cancer cell stemness features, indicating that these chemicals may be further developed into clinically used drugs for cancer patients. Lin28 is a pluripotency factor that regulates cancer cell stem-like phenotypes to promote cancer development and therapy-resistant tumor progression. It acts through its cold shock domain and zinc knuckle domain (ZKD) to interact with the Let-7 pre-microRNA and block Let-7 biosynthesis. Chemical inhibition of Lin28 from interacting with Let-7 presents a therapeutic strategy for cancer therapy. Herein, we present the computer-aided development of small molecules by in silico screening 18 million compounds from the ZINC20 library, followed by the biological validation of 163 predicted compounds to confirm 15 new Lin28 inhibitors. We report three lead compounds, Ln7, Ln15, and Ln115, that target the ZKD of both Lin28A and Lin28B isoforms and block Lin28 from binding Let-7. They restore Let-7 expression and suppress tumor oncogenes such as SOX2 in cancer cells and show strong inhibitory effects on cancer cell stem-like phenotypes. However, minimal impacts of these compounds were observed on Lin28-negative cells, confirming the on-target effects of these compounds. We conclude from this study the discovery of several new Lin28 inhibitors as promising candidate compounds that warrant further drug development into potential anticancer therapies. [ABSTRACT FROM AUTHOR]

While vaccines remain at the forefront of global healthcare responses, pioneering therapeutics against SARS-CoV-2 are expected to fill the gaps for waning immunity. Rapid development and approval of orally available direct-acting antivirals targeting crucial SARS-CoV-2 proteins marked the beginning of the era of small-molecule drugs for COVID-19. In that regard, the papain-like protease (PLpro) can be considered a major SARS-CoV-2 therapeutic target due to its dual biological role in suppressing host innate immune responses and in ensuring viral replication. Here, we summarize the challenges of targeting PLpro and innovative early-stage PLpro-specific small molecules. We propose that state-of-the-art computer-aided drug design (CADD) methodologies will play a critical role in the discovery of PLpro compounds as a novel class of COVID-19 drugs. New emergency-approved drugs against COVID-19 represent the beginning of powerful oral anti-COVID-19 inhibitors in the postvaccine era. Vaccines remain at the forefront of global healthcare response, but oral drugs can fill the gaps when immunity starts waning. The papain-like protease (PLpro) ensures viral replication by processing viral polyproteins and host post-translational modifications. However, a lack of diverse high-quality scaffolds against the protease hinders drug development. Promising compounds are derived from an older noncovalent compound previously identified for SARS-CoV-1. There is a methodological transition focusing on computational screening complimented by experimental validation of inhibitory activity and on-target effects. Deploying machine-learning-enhanced in silico methods should increase the success of identifying diverse and new scaffolds against PLpro. [ABSTRACT FROM AUTHOR]

The mutation-driven transformation of clinical anti-androgen drugs into agonists of the human androgen receptor (AR) represents a major challenge for the treatment of prostate cancer patients. To address this challenge, we have developed a novel class of inhibitors targeting the DNA-binding domain (DBD) of the receptor, which is distanced from the androgen binding site (ABS) targeted by all conventional anti-AR drugs and prone to resistant mutations. While many members of the developed 4-(4-phenylthiazol-2-yl)morpholine series of AR-DBD inhibitors demonstrated the effective suppression of wild-type AR, a few represented by 4-(4-(3-fluoro-2-methoxyphenyl)thiazol-2-yl)morpholine (VPC14368) exhibited a partial agonistic effect toward the mutated T878A form of the receptor, implying their cross-interaction with the AR ABS. To study the molecular basis of the observed cross-reactivity, we co-crystallized the T878A mutated form of the AR ligand binding domain (LBD) with a bound VPC14368 molecule. Computational modelling revealed that helix 12 of AR undergoes a characteristic shift upon VPC14368 binding causing the agonistic behaviour. Based on the obtained structural data we then designed derivatives of VPC14368 to successfully eliminate the cross-reactivity towards the AR ABS, while maintaining significant anti-AR DBD potency. [ABSTRACT FROM AUTHOR]

Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein–ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)—the hallmark target of SARS-CoV-2 coronavirus. [ABSTRACT FROM AUTHOR]

Recurrent, metastatic prostate cancer continues to be a leading cause of cancer-death in men. The androgen receptor (AR) is a modular, ligand-inducible transcription factor that regulates the expression of genes that can drive the progression of this disease, and as a consequence, this receptor is a key therapeutic target for controlling prostate cancer. The current drugs designed to directly inhibit the AR are called anti-androgens, and all act by competing with androgens for binding to the androgen/ligand binding site. Unfortunately, with the inevitable progression of the cancer to castration resistance, many of these drugs become ineffective. However, there are numerous other regulatory sites on this protein that have not been exploited therapeutically. The regulation of AR activity involves a cascade of complex interactions with numerous chaperones, co-factors and co-regulatory proteins, leading ultimately to direct binding of AR dimers to specific DNA androgen response elements within the promoter and enhancers of androgen-regulated genes. As part of the family of nuclear receptors, the AR is organized into modular structural and functional domains with specialized roles in facilitating their inter-molecular interactions. These regions of the AR present attractive, yet largely unexploited, drug target sites for reducing or eliminating androgen signaling in prostate cancers. The design of small molecule inhibitors targeting these specific AR domains is only now being realized and is the culmination of decades of work, including crystallographic and biochemistry approaches to map the shape and accessibility of the AR surfaces and cavities. Here, we review the structure of the AR protein and describe recent advancements in inhibiting its activity with small molecules specifically designed to target areas distinct from the receptor's androgen binding site. It is anticipated that these new classes of anti-AR drugs will provide an additional arsenal to treat castration-resistant prostate cancer. [ABSTRACT FROM AUTHOR]

Abstract: A novel series of hydrazones were synthesized and evaluated as inhibitors of methicillin-resistant Staphylococcus aureus (MRSA) pyruvate kinase (PK). PK has been identified as one of the most highly connected ‘hub proteins’ in MRSA. PK has been shown to be critical for bacterial survival which makes it a potential target for development of novel antibiotics and the high degree of connectivity implies it should be very sensitive to mutations and thus less able to develop resistance. PK is not unique to bacteria and thus a critical requirement for such a PK inhibitor would be that it does not inhibit the homologous human enzyme(s) at therapeutic concentrations. Several MRSA PK inhibitors (including 8d) were identified using in silico screening combined with enzyme assays and were found to be selective for bacterial enzyme compared to four human PK isoforms (M1, M2, R and L). However these lead compounds did not show significant inhibitory activity for MRSA growth presumably due to poor bacterial cell penetration. Structure–activity relationship (SAR) studies were carried out on 8d and led us to discover more potent compounds with enzyme inhibiting activities in the low nanomolar range and some were found to effectively inhibit bacteria growth in culture with minimum inhibitory concentrations (MIC) as low as 1μg/mL. These inhibitors bind in two elongated flat clefts found at the minor interfaces in the homo-tetrameric enzyme complex and the observed SAR is in keeping with the size and electronic constraints of these binding sites. Access to the corresponding sites in the human enzyme is blocked. [Copyright &y& Elsevier]

Background: Protein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) [1,2]. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets. Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions. Results: A hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy. A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species. Conclusion: The successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets -- even in those organisms that currently lack large-scale protein interaction data. [ABSTRACT FROM AUTHOR]

The eukaryotic pathogen Leishmania donovani possesses a housekeeping protein Elongation-Factor-1α (EF-1α) which has been found to be unexpectedly involved in the pathogen's virulence. Because it is associated with virulence and essential for cell survival, this protein is an attractive choice for drug targeting; however, its sequence is highly similar (> 80% sequence identity) to that of its human homolog, rendering it a risky choice for a drug target. The chief difference between these two proteins has been found to be a 12 amino acid sequence present in human EF-1α but absent from leishmania EF-1α. Furthermore, it has been shown that this 12 amino acid insert in the human sequence corresponds to a hairpin loop on the surface of the protein. In this study, we searched for those spatial features in leishmania EF-1α that are impacted or obscured by the extra hairpin loop in the human counterpart. We have also conducted a large-scale in silico screening for small molecules that could plausibly bind to these protein features. While experimental evidence is required to verify our results, our findings thus far appear to support this approach as a new strategy for the development of antagonists against pathogenic targets having close human homologs. [ABSTRACT FROM AUTHOR]

Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach—DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of "hybrid" chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug–gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity. [ABSTRACT FROM AUTHOR]

• There is a prevailing view that DNA-binding protein surfaces are not druggable. • We argue that protein-DNA surfaces are druggable and provide examples. • CADD helps to overcome challenges of protein-DNA interactions targeting. • The development of protein-DNA inhibitors provides new strategies to combat cancer. Transcription factors (TFs) act as major oncodrivers in many cancers and are frequently regarded as high-value therapeutic targets. The functionality of TFs relies on direct protein–DNA interactions, which are notoriously difficult to target with small molecules. However, this prior view of the 'undruggability' of protein–DNA interfaces has shifted substantially in recent years, in part because of significant advances in computer-aided drug discovery (CADD). In this review, we highlight recent examples of successful CADD campaigns resulting in drug candidates that directly interfere with protein–DNA interactions of several key cancer TFs, including androgen receptor (AR), ETS-related gene (ERG), MYC, thymocyte selection-associated high mobility group box protein (TOX), topoisomerase II (TOP2), and signal transducer and activator of transcription 3 (STAT3). Importantly, these findings open novel and compelling avenues for therapeutic targeting of over 1600 human TFs implicated in many conditions including and beyond cancer. [ABSTRACT FROM AUTHOR]

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19. [ABSTRACT FROM AUTHOR]

Summary Deep learning (DL) can significantly accelerate virtual screening of ultra-large chemical libraries, enabling the evaluation of billions of compounds at a fraction of the computational cost and time required by conventional docking. Here, we introduce DD-GUI, the graphical user interface for such DL approach we have previously developed, termed Deep Docking (DD). The DD-GUI allows for quick setups of large-scale virtual screens in an intuitive way, and provides convenient tools to track the progress and analyze the outcomes of a drug discovery project. Availability and implementation DD-GUI is freely available with an MIT license on GitHub at https://github.com/jamesgleave/DeepDockingGUI. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Accelerating growth and global expansion of antimicrobial resistance has deepened the need for discovery of novel antimicrobial agents. Antimicrobial peptides have clear advantages over conventional antibiotics which include slower emergence of resistance, broad-spectrum antibiofilm activity, and the ability to favourably modulate the host immune response. Broad bacterial susceptibility to antimicrobial peptides offers an additional tool to expand knowledge about the evolution of antimicrobial resistance. Structural and functional limitations, combined with a stricter regulatory environment, have hampered the clinical translation of antimicrobial peptides as potential therapeutic agents. Existing computational and experimental tools attempt to ease the preclinical and clinical development of antimicrobial peptides as novel therapeutics. This Review identifies the benefits, challenges, and opportunities of using antimicrobial peptides against multidrug-resistant pathogens, highlights advances in the deployment of novel promising antimicrobial peptides, and underlines the needs and priorities in designing focused development strategies taking into account the most advanced tools available. [ABSTRACT FROM AUTHOR]

Methicillin-resistant Staphylococcus aureus (MRSA) infections cause significant mortality and morbidity globally. MRSA resistance to β -lactam antibiotics is mediated by two divergons that control levels of a β -lactamase, PC1, and a penicillin-binding protein poorly acylated by β -lactam antibiotics, PBP2a. Expression of genes encoding these proteins is controlled by two integral membrane proteins, BlaR1 and MecR1, which both have an extracellular β -lactam-binding sensor domain. Here, we solved the X-ray crystallographic structures of the BlaR1 and MecR1 sensor domains in complex with avibactam, a diazabicy-clooctane β -lactamase inhibitor at 1.6-2.0 Á resolution. Additionally, we show that S. aureus SF8300, a clinically relevant strain from the USA300 clone of MRSA, responds to avibactam by up-regulating the expression of the blaZ and pbp2a antibiotic-resistance genes, encoding PC1 and PBP2a, respectively. The BlaRl-avibactam structure of the carbamoyl-enzyme intermediate revealed that avibactam is bound to the active-site serine in two orientations --180° to each other. Although a physiological role of the observed alternative pose remains to be validated, our structural results hint at the presence of a secondary sulfate-binding pocket that could be exploited in the design of future inhibitors of BlaRl/MecRl sensor domains or the structurally similar class D β -lactamases. The MecRl-avibactam structure adopted a singular avibactam orientation similar to one of the two states observed in the BlaRl-avibactam structure. Given avibactam up-regulates expression of blaZ and pbp2a antibiotic resistance genes, we suggest further consideration and research is needed to explore what effects administering β -lactam-avibactam combinations have on treating MRSA infections. [ABSTRACT FROM AUTHOR]

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure–activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge. [ABSTRACT FROM AUTHOR]

HSP27 is highly expressed in, and supports oncogene addiction of, many cancers. HSP27 phosphorylation is a limiting step for activation of this protein and a target for inhibition, but its highly disordered structure challenges rational structure-guided drug discovery. We performed multistep biochemical, structural, and computational experiments to define a spherical 24-monomer complex composed of 12 HSP27 dimers with a phosphorylation pocket flanked by serine residues between their N-terminal domains. Ivermectin directly binds this pocket to inhibit MAPKAP2-mediated HSP27 phosphorylation and depolymerization, thereby blocking HSP27-regulated survival signaling and client-oncoprotein interactions. Ivermectin potentiated activity of anti-androgen receptor and anti-EGFR drugs in prostate and EGFR/HER2-driven tumor models, respectively, identifying a repurposing approach for cotargeting stress-adaptive responses to overcome resistance to inhibitors of oncogenic pathway signaling. [ABSTRACT FROM AUTHOR]

Motivation Recent advances in the areas of bioinformatics and chemogenomics are poised to accelerate the discovery of small molecule regulators of cell development. Combining large genomics and molecular data sources with powerful deep learning techniques has the potential to revolutionize predictive biology. In this study, we present Deep gene COmpound Profiler (DeepCOP), a deep learning based model that can predict gene regulating effects of low-molecular weight compounds. This model can be used for direct identification of a drug candidate causing a desired gene expression response, without utilizing any information on its interactions with protein target(s). Results In this study, we successfully combined molecular fingerprint descriptors and gene descriptors (derived from gene ontology terms) to train deep neural networks that predict differential gene regulation endpoints collected in LINCS database. We achieved 10-fold cross-validation RAUC scores of and above 0.80, as well as enrichment factors of >5. We validated our models using an external RNA-Seq dataset generated in-house that described the effect of three potent antiandrogens (with different modes of action) on gene expression in LNCaP prostate cancer cell line. The results of this pilot study demonstrate that deep learning models can effectively synergize molecular and genomic descriptors and can be used to screen for novel drug candidates with the desired effect on gene expression. We anticipate that such models can find a broad use in developing novel cancer therapeutics and can facilitate precision oncology efforts. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The reactions of abamectin and ivermectin aglycones with equimolar quantities of acid chlorides, succinic anhydride (pyridine, DMAP), or aryl isocyanates (DCM, Et 3 N) at ambient temperature resulted in absolutely selective monoacylation of the OH group in position 5 (5-OH) of the macrocyclic skeleton and afforded the monoesters in 20–87 % yields. The reaction of the aglycones or their 5-monoesters with acid chlorides in a 1:2 M ratio (pyridine, DMAP, rt) afforded the diesters bearing identical or different acyloxy fragments in positions 5 and 13. Theoretical studies suggested that the higher reactivity of 5-OH compared to 13-OH is related to both thermodynamic and kinetic preference of the corresponding tetrahedral intermediate and transition state structures stabilized via an intramolecular hydrogen bond. 2009 Elsevier Ltd. All rights reserved. [Display omitted] [ABSTRACT FROM AUTHOR]

Myc (avian myelocytomatosis viral oncogene homolog) represents one of the most sought after drug targets in cancer. Myc transcription factor is an essential regulator of cell growth, but in most cancers it is overexpressed and associated with treatment-resistance and lethal outcomes. Over 40 years of research and drug development efforts did not yield a clinically useful Myc inhibitor. Drugging the "undruggable" is problematic, as Myc inactivation may negatively impact its physiological functions. Moreover, Myc is a disordered protein that lacks effective binding pockets on its surface. It is well established that the Myc function is dependent on dimerization with its obligate partner, Max (Myc associated factor X), which together form a functional DNA-binding domain to activate genomic targets. Herein, we provide an overview of the knowledge accumulated to date on Myc regulation and function, its critical role in cancer, and summarize various strategies that are employed to tackle Myc-driven malignant transformation. We focus on important structure-function relationships of Myc with its interactome, elaborating structural determinants of Myc-Max dimer formation and DNA recognition exploited for therapeutic inhibition. Chronological development of small-molecule Myc-Max prototype inhibitors and corresponding binding sites are comprehensively reviewed and particular emphasis is placed on modern computational drug design methods. On the outlook, technological advancements may soon provide the so long-awaited Myc-Max clinical candidate. [ABSTRACT FROM AUTHOR]

Inhibition of ubiquitin ligases with small molecule remains a very challenging task, given the lack of catalytic activity of the target and the requirement of disruption of its interactions with other proteins. Siah1/2, which are E3 ubiquitin ligases, are implicated in melanoma and prostate cancer and represent high-value drug targets. We utilized three independent screening approaches in our efforts to identify small-molecule Siah1/2 inhibitors: Affinity Selection-Mass Spectrometry, a protein thermal shift-based assay and an in silico based screen. Inhibitors were assessed for their effect on viability of melanoma and prostate cancer cultures, colony formation, prolyl-hydroxylase-HIF1α signaling, expression of selected Siah2-related transcripts, and Siah2 ubiquitin ligase activity. Several analogs were further characterized, demonstrating improved efficacy. Combination of the top hits identified in the different assays demonstrated an additive effect, pointing to complementing mechanisms that underlie each of these Siah1/2 inhibitors. [ABSTRACT FROM AUTHOR]

Abstract While Myc is an essential regulator of growth in normal cells, it is also frequently associated with cancer progression, therapy-resistance and lethal outcomes in most human cancers. In prostate cancer (PCa), Myc transcription factors are implicated in the pathogenesis and progression of the full spectrum of PCa, from adenocarcinoma to advanced castration-resistant and neuroendocrine phenotypes. Though a high-value therapeutic target, clinically approved anti-Myc drugs have yet to be discovered. To elicit its oncogenic effects, Myc must form a heterodimer with its partner Max, which together bind DNA and activate transcription of a spectrum of target genes that promote cell growth, proliferation, metabolism, and apoptosis while blocking differentiation. In this study, we identified a binding site on the DNA-binding domain of the structurally ordered Myc-Max complex and employed a computer-aided rational drug discovery approach to identify small molecules that effectively inhibit Myc-Max functionality. A large-scale virtual screening protocol implementing structure-based methodologies was utilized to select a set of top-ranked compounds that were subsequently evaluated experimentally and characterized mechanistically for their ability to inhibit Myc-Max transcriptional activity and subsequent downstream functions, to reduce viability in PCa cell lines, disrupt protein-DNA interactions and to induce apoptosis as their mechanism of action. Among compounds identified that effectively inhibit Myc-Max activity with low to mid-micromolar range potency and no or minimal generic cytotoxicity, VPC-70067, a close analog of the previously identified Myc inhibitor 10058-F4, served as proof-of-concept that our in silico drug discovery strategy performed as expected. Compound VPC-70063, of a chemically different scaffold, was the best performer in a panel of in vitro assays, and the forerunner for future hit-to-lead optimization efforts. These findings lay a foundation for developing more potent, specific and clinically optimized Myc-Max inhibitors that may serve as promising therapeutics, alone or in combination with current anti-cancer treatments, for treatment of specific phenotypes or heterogeneous tumors. Graphical abstract Image 1 Highlights • In silico identification of a druggable site on the DNA-binding domain of Myc-Max. • Computer-aided discovery of inhibitors of Myc-Max transcriptional activity. • Myc-Max inhibitors reduce viability of prostate cancer cell lines. • Best compound 70063 induces apoptosis and blocks Myc-Max interactions with DNA. • 70063 is the forerunner for hit-to-lead optimization efforts. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is a leading cause of death for men in North America. The androgen receptor (AR) - a hormone inducible transcription factor - drives expression of tumor promoting genes and represents an important therapeutic target in PCa. The AR is activated by steroid recruitment to its ligand binding domain (LBD), followed by receptor nuclear translocation and dimerization via the DNA binding domain (DBD). Clinically used small molecules interfere with steroid recruitment and prevent AR-driven tumor growth, but are rendered ineffective by emergence of LBD mutations or expression of constitutively active variants, such as ARV7, that lack the LBD. Both drug-resistance mechanisms confound treatment of this 'castration resistant' stage of PCa (CRPC), characterized by return of AR signalling. Here, we employ computer-aided drug-design to develop small molecules that block the AR-DBD dimerization interface, an attractive target given its role in AR activation and independence from the LBD. Virtual screening on the AR-DBD structure led to development of prototypical compounds that block AR dimerization, inhibiting AR-transcriptional activity through a LBD-independent mechanism. Such inhibitors may potentially circumvent AR-dependent resistance mechanisms and directly target CRPC tumor growth. [ABSTRACT FROM AUTHOR]

Context Although a number of studies have demonstrated the importance of constitutively active androgen receptor variants (AR-Vs) in prostate cancer, questions still remain about the precise role of AR-Vs in the progression of castration-resistant prostate cancer (CRPC). Objective Key stakeholders and opinion leaders in prostate cancer convened on May 11, 2017 in Boston to establish the current state of the field of AR-Vs. Evidence acquisition The meeting “Mission Androgen Receptor Variants” was the second of its kind sponsored by the Prostate Cancer Foundation (PCF). This invitation-only event was attended by international leaders in the field and representatives from sponsoring organizations (PCF and industry sponsors). Eighteen faculty members gave short presentations, which were followed by in-depth discussions. Discussions focused on three thematic topics: (1) potential of AR-Vs as biomarkers of therapeutic resistance; (2) role of AR-Vs as functionally active CRPC progression drivers; and (3) utility of AR-Vs as therapeutic targets in CRPC. Evidence synthesis The three meeting organizers synthesized this meeting report, which is intended to summarize major data discussed at the meeting and identify key questions as well as strategies for addressing these questions. There was a critical consensus that further study of the AR-Vs is an important research focus in CRPC. Contrasting views and emphasis, each supported by data, were presented at the meeting, discussed among the participants, and synthesized in this report. Conclusions This article highlights the state of knowledge and outlines the most pressing questions that need to be addressed to advance the AR-V field. Patient summary Although further investigation is needed to delineate the role of androgen receptor (AR) variants in metastatic castration-resistant prostate cancer, advances in measurement science have enabled development of blood-based tests for treatment selection. Detection of AR variants (eg, AR-V7) identified a patient population with poor outcomes to existing AR-targeting therapies, highlighting the need for novel therapeutic agents currently under development. [ABSTRACT FROM AUTHOR]

The Notch pathway is a cell‐cell communication system where membrane‐bound ligands interact with the extracellular region of Notch receptors to induce intracellular, downstream effects on gene expression. Aberrant Notch signaling promotes tumorigenesis, and the Notch pathway has tremendous potential for novel targeting strategies in cancer treatment. While γ‐secretase inhibitors as Notch‐inhibiting agents are already promising in clinical trials, they are highly non‐specific with adverse side‐effects. One of the underlying challenges is that two of the four known human Notch paralogs, NOTCH1 and 2, share very high structural similarity but play opposing roles in some tumorigenesis pathways. This perspective explores the feasibility of developing Notch‐specific small molecule inhibitors targeting the anti‐NOTCH2 antibody‐binding epitopes or the “S2‐Leu‐plug‐binding site” using a computer‐aided drug discovery approach. [ABSTRACT FROM AUTHOR]

Estrogen receptor-α positive (ERα+) breast cancers represent 75% of all invasive breast cancer cases, while de novo or acquired resistance to ER-directed therapy is also on the rise. Numerous factors contribute to this phenomenon including the recently-reported ESR1 gene mutations such as Y537S, which amplifies co-activator interactions with ERα and promotes constitutive activation of ERα function. Herein, we propose that direct targeting of the activation function-2 (AF2) site on ERα represents a promising alternative therapeutic strategy to overcome mutation-driven resistance in breast cancer. A systematic computer-guided drug discovery approach was employed to develop a potent ERα inhibitor that was extensively evaluated by a series of experiments to confirm its AF2-specific activity. We demonstrate that the developed small-molecule inhibitor effectively prevents ERα-coactivator interactions and exhibits a strong anti-proliferative effect against tamoxifen-resistant cells, as well as downregulates ERα-dependent genes and effectively diminishes the receptor binding to chromatin. Notably, the identified lead compound successfully inhibits known constitutively-active, resistance-associated mutant forms of ERα observed in clinical settings. Overall, this study reports the development of a novel class of ERα AF2 inhibitors, which have the potential to effectively inhibit ERα activity by a unique mechanism and to circumvent the issue of mutation-driven resistance in breast cancer. [ABSTRACT FROM AUTHOR]

Biofilms represent a multicellular growth state of bacteria that are intrinsically resistant to conventional antibiotics. It was recently shown that a synthetic immunomodulatory cationic peptide, 1018 (VRLIVAVRIWRR-NH2), exhibits broad-spectrum antibiofilm activity but the sequence determinants of antibiofilm peptides have not been systematically studied. In the present work, a peptide library consisting of 96 single amino acid substituted variants of 1018 was SPOT-synthesized on cellulose arrays and evaluated against methicillin resistant Staphylococcus aureus (MRSA) biofilms. This dataset was used to establish quantitative structure-activity relationship (QSAR) models relating the antibiofilm activity of these peptides to hundreds of molecular descriptors derived from their sequences. The developed 3D QSAR models then predicted the probability that a peptide would possess antibiofilm activity from a library of 100,000 virtual peptide sequences in silico. A subset of these variants were SPOT-synthesized and their activity assessed, revealing that the QSAR models resulted in ~85% prediction accuracy. Notably, peptide 3002 (ILVRWIRWRIQW-NH2) was identified that exhibited an 8-fold increased antibiofilm potency in vitro compared to 1018 and proved effective in vivo, significantly reducing abscess size in a chronic MRSA mouse infection model. This study demonstrates that QSAR modeling can successfully be used to identify antibiofilm specific peptides with therapeutic potential. [ABSTRACT FROM AUTHOR]

Darolutamide (ODM-201) is a novel androgen receptor (AR) antagonist with a chemical structure distinctly different from currently approved AR antagonists that targets both wild-type and mutated ligand binding domain variants to inhibit AR nuclear translocation. Here, we evaluate the activity of darolutamide in enzalutamide-resistant castration resistant prostate cancer (CRPC) as well as in AR mutants detected in patients after treatment with enzalutamide, abiraterone, or bicalutamide. Darolutamide significantly inhibited cell growth and AR transcriptional activity in enzalutamide-resistant MR49F cells in vitro, and led to decreased tumor volume and serum prostate-specific antigen levels in vivo, prolonging survival in mice bearing enzalutamide-resistant MR49F xenografts. Moreover, darolutamide inhibited the transcriptional activity of AR mutants identified in the plasma of CRPC patients progressing on traditional therapies. In particular, darolutamide significantly inhibited the transcriptional activity of the F877L, H875Y/T878A, F877L/T878A, and the previously unreported T878G AR mutants, that transform enzalutamide into a partial agonist. In silico cheminformatics computer modeling provided atomic level insights confirming darolutamide antagonist effect in F877L and T878G AR mutants. In conclusion, our results provide a rationale for further clinical evaluation of darolutamide in enzalutamide-resistant CRPC, in particular in combination with circulating tumor DNA assays that detect AR mutants sensitive to darolutamide, in a precision oncology setting. Patient summary In this study we evaluated the novel drug darolutamide in preclinical models of prostate cancer. We found that darolutamide delays growth of enzalutamide-resistant prostate cancer, in particular in cells with mutated forms of the androgen receptor after previous treatment. Our data supports further evaluation of darolutamide in clinical trials. [ABSTRACT FROM AUTHOR]

Computational prediction of the interaction between drugs and targets is a standing challenge in the field of drug discovery. A number of rather accurate predictions were reported for various binary drug-target benchmark datasets. However, a notable drawback of a binary representation of interaction data is that missing endpoints for non-interacting drug-target pairs are not differentiated from inactive cases, and that predicted levels of activity depend on pre-defined binarization thresholds. In this paper, we present a method called SimBoost that predicts continuous (non-binary) values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions. Additionally, we propose a version of the method called SimBoostQuant which computes a prediction interval in order to assess the confidence of the predicted affinity, thus defining the Applicability Domain metrics explicitly. We evaluate SimBoost and SimBoostQuant on two established drug-target interaction benchmark datasets and one new dataset that we propose to use as a benchmark for read-across cheminformatics applications. We demonstrate that our methods outperform the previously reported models across the studied datasets. [ABSTRACT FROM AUTHOR]

Summary There has been a resurgence of interest in the development of androgen receptor (AR) inhibitors with alternative modes of action to overcome the development of resistance to current therapies. We demonstrated previously that one promising strategy for combatting mutation-driven drug resistance is to target the Binding Function 3 (BF3) pocket of the receptor. Here we report the development of a potent BF3 inhibitor, 3-(2,3-dihydro-1 H -indol-2-yl)-1 H -indole, which demonstrates excellent antiandrogen potency and anti-PSA activity and abrogates the androgen-induced proliferation of androgen-sensitive (LNCaP) and enzalutamide-resistant (MR49F) PCa cell lines. Moreover, this compound effectively reduces the expression of AR-dependent genes in PCa cells and effectively inhibits tumor growth in vivo in both LNCaP and MR49F xenograft models. These findings provide evidence that targeting the AR BF3 pocket represents a viable therapeutic approach to treat patients with advanced and/or resistant prostate cancer. [ABSTRACT FROM AUTHOR]

The androgen receptor (AR) is a transcription factor that has a pivotal role in the occurrence and progression of prostate cancer. The AR is activated by androgens that bind to its ligand-binding domain (LBD), causing the transcription factor to enter the nucleus and interact with genes via its conserved DNA-binding domain (DBD). Treatment for prostate cancer involves reducing androgen production or using anti-androgen drugs to block the interaction of hormones with the AR-LBD. Eventually the disease changes into a castration-resistant form of PCa where LBD mutations render anti-androgens ineffective or where constitutively active AR splice variants, lacking the LBD, become overexpressed. Recently, we identified a surfaced exposed pocket on the AR-DBD as an alternative drug-target site for AR inhibition. Here, we demonstrate that small molecules designed to selectively bind the pocket effectively block transcriptional activity of full-length and splice variant AR forms at low to sub-micromolar concentrations. The inhibition is lost when residues involved in drug interactions are mutated. Furthermore, the compounds did not impede nuclear localization of the AR and blocked interactions with chromatin, indicating the interference of DNA binding with the nuclear form of the transcription factor. Finally, we demonstrate the inhibition of gene expression and tumor volume in mouse xenografts. Our results indicate that the AR-DBD has a surface site that can be targeted to inhibit all forms of the AR, including enzalutamide-resistant and constitutively active splice variants and thus may serve as a potential avenue for the treatment of recurrent and metastatic prostate cancer. [ABSTRACT FROM AUTHOR]

Thehuman androgen receptor (AR) is considered as a master regulatorin the development and progression of prostate cancer (PCa). As resistanceto clinically used anti-AR drugs remains a major challenge for thetreatment of advanced PCa, there is a pressing need for new anti-ARtherapeutic avenues. In this study, we identified a binding site onthe DNA binding domain (DBD) of the receptor and utilized virtualscreening to discover a set of micromolar hits for the target. Throughfurther exploration of the most potent hit (1), a structuralanalogue (6) was identified demonstrating 10-fold improvedanti-AR potency. Further optimization resulted in a more potent syntheticanalogue (25) with anti-AR potency comparable to a newlyFDA-approved drug Enzalutamide. Site-directed mutagenesis demonstratedthat the developed inhibitors do interact with the intended targetsite. Importantly, the AR DBD inhibitors could effectively inhibitthe growth of Enzalutamide-resistant cells as well as block the transcriptionalactivity of constitutively active AR splice variants, such as V7. [ABSTRACT FROM AUTHOR]

To overcome resistance to conventionalanti-androgens of humanandrogen receptor (AR), the allosteric site of the AR binding function3 (BF3) was investigated as an alternative target for small moleculetherapeutics. A library of 1H-indole-2-carboxamideswere discovered as BF3 inhibitors and exhibited strong antiproliferativeactivity against LNCaP and enzalutamide-resistant prostate cancercell lines. Several of the lead compounds may prove of particularbenefit as a novel alternative treatment for castration-resistantprostate cancers. [ABSTRACT FROM AUTHOR]

The Myc family of transcription factors are involved in the development and progression of numerous cancers, including prostate cancer (PCa). Under the pressure of androgen receptor (AR)-directed therapies resistance can occur, leading to the lethal form of PCa known as neuroendocrine prostate cancer (NEPC), characterized among other features by N-Myc overexpression. There are no clinically approved treatments for NEPC, translating into poor patient prognosis and survival. Therefore, there is a pressing need to develop novel therapeutic avenues to treat NEPC patients. In this study, we investigate the N-Myc-Max DNA binding domain (DBD) as a potential target for small molecule inhibitors and utilize computer-aided drug design (CADD) approaches to discover prospective hits. Through further exploration and optimization, a compound, VPC-70619, was identified with notable anti-N-Myc potency and strong antiproliferative activity against numerous N-Myc expressing cell lines, including those representing NEPC. [ABSTRACT FROM AUTHOR]

Background and Objectives: Although numerous studies have implicated TLR5, or its ligands, bacterial flagellins, in the pathogenesis of Crohn's disease (CD), genome-wide association studies (GWAS) have not reported associations with the TLR5 gene. We aimed to examine potential CD-associated TLR5 variants and assess whether they modified inflammatory responses to bacterial flagellins. Methods and Principal Results: A two-stage study was carried out. In stage 1, we genotyped tagging single-nucleotide polymorphisms (tag-SNPs) in the TLR5 gene in a sample of CD cases (<20 years of age, N = 566) and controls (N = 536). Single SNP and haplotype analysis was carried out. In Stage 2, we assessed the functional significance of potential CD-associated variant(s) vis-à-vis effects on the inflammatory response to bacterial flagellin using HEK293T cells. We observed marginal association between a non-synonymous coding SNP rs5744174 (p = 0.05) and CD. Associations between SNP rs851139 that is in high linkage disequilibrium (LD) with SNP rs5744174 were also suggested (p = 0.07). Haplotype analysis revealed that a 3 marker haplotype was significantly associated with CD (p = 0.01). Functional studies showed that the risk allele (616F) (corresponding to the C allele of SNP rs5744174) conferred significantly greater production of CCL20 in response to a range of flagellin doses than the comparator allele (616L). Conclusions: Our findings suggest that a non-synonymous coding variation in the TLR5 gene may confer modest susceptibility for CD. [ABSTRACT FROM AUTHOR]