Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles.

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs.

Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for the de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.

Clinically-observed FOXA1 mutations upregulate SEMA3C through transcriptional derepression in prostate cancer.

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.

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.

Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.

A novel class of broad-spectrum active-site-directed 3C-like protease inhibitors with nanomolar antiviral activity against highly immune-evasive SARS-CoV-2 Omicron subvariants.

Antivirals with broad coronavirus activity are important for treating high-risk individuals exposed to the constantly evolving SARS-CoV-2 variants of concern (VOCs) as well as emerging drug-resistant variants. We developed and characterized a novel class of active-site-directed 3-chymotrypsin-like protease (3CLpro) inhibitors (C2-C5a). Our lead direct-acting antiviral (DAA), C5a, is a non-covalent, non-peptide with a dissociation constant of 170 nM against recombinant SARS-CoV-2 3CLpro. The compounds C2-C5a exhibit broad-spectrum activity against Omicron subvariants (BA.5, BQ.1.1, and XBB.1.5) and seasonal human coronavirus-229E infection in human cells. Notably, C5a has median effective concentrations of 30-50 nM against BQ.1.1 and XBB.1.5 in two different human cell lines. X-ray crystallography has confirmed the unique binding modes of C2-C5a to the 3CLpro, which can limit virus cross-resistance to emerging Paxlovid-resistant variants. We tested the effect of C5a with two of our newly discovered host-directed antivirals (HDAs): N-0385, a TMPRSS2 inhibitor, and bafilomycin D (BafD), a human vacuolar H+-ATPase [V-ATPase] inhibitor. We demonstrated a synergistic action of C5a in combination with N-0385 and BafD against Omicron BA.5 infection in human Calu-3 lung cells. Our findings underscore that a SARS-CoV-2 multi-targeted treatment for circulating Omicron subvariants based on DAAs (C5a) and HDAs (N-0385 or BafD) can lead to therapeutic benefits by enhancing treatment efficacy. Furthermore, the high-resolution structures of SARS-CoV-2 3CLpro in complex with C2-C5a will facilitate future rational optimization of our novel broad-spectrum active-site-directed 3C-like protease inhibitors.

Dependency of Tamoxifen Sensitive and Resistant ER+ Breast Cancer Cells on Semaphorin 3C (SEMA3C) for Growth.

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.

Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra-large virtual screening.

Androgen receptor (AR) inhibition remains the primary strategy to combat the progression of prostate cancer (PC). However, all clinically used AR inhibitors target the ligand-binding domain (LBD), which is highly susceptible to truncations through splicing or mutations that confer drug resistance. Thus, there exists an urgent need for AR inhibitors with novel modes of action. We thus launched a virtual screening of an ultra-large chemical library to find novel inhibitors of the AR DNA-binding domain (DBD) at two sites: protein-DNA interface (P-box) and dimerization site (D-box). The compounds selected through vigorous computational filtering were then experimentally validated. We identified several novel chemotypes that effectively suppress transcriptional activity of AR and its splice variant V7. The identified compounds represent previously unexplored chemical scaffolds with a mechanism of action that evades the conventional drug resistance manifested through LBD mutations. Additionally, we describe the binding features required to inhibit AR DBD at both P-box and D-box target sites.

PurificationDB: database of purification conditions for proteins.

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 URL https://purificationdatabase.herokuapp.com/.

Large-Scale Virtual Screening for the Discovery of SARS-CoV-2 Papain-like Protease (PLpro) Non-covalent Inhibitors.

The rapid global spread of the SARS-CoV-2 virus facilitated the development of novel direct-acting antiviral agents (DAAs). The papain-like protease (PLpro) has been proposed as one of the major SARS-CoV-2 targets for DAAs due to its dual role in processing viral proteins and facilitating the host's immune suppression. This dual role makes identifying small molecules that can effectively neutralize SARS-CoV-2 PLpro activity a high-priority task. However, PLpro drug discovery faces a significant challenge due to the high mobility and induced-fit effects in the protease's active site. Herein, we virtually screened the ZINC20 database with Deep Docking (DD) to identify prospective noncovalent PLpro binders and combined ultra-large consensus docking with two pharmacophore (ph4)-filtering strategies. The analysis of active compounds revealed their somewhat-limited diversity, likely attributed to the induced-fit nature of PLpro's active site in the crystal structures, and therefore, the use of rigid docking protocols poses inherited limitations. The top hits were assessed against recombinant viral proteins and live viruses, demonstrating desirable inhibitory activities. The best compound VPC-300195 (IC50: 15 µM) ranks among the top noncovalent PLpro inhibitors discovered through in silico methodologies. In the search for novel SARS-CoV-2 PLpro-specific chemotypes, the identified inhibitors could serve as diverse templates for the development of effective noncovalent PLpro inhibitors.

In silico discovery of small molecules for efficient stem cell differentiation into definitive endoderm.

Improving methods for human embryonic stem cell differentiation represents a challenge in modern regenerative medicine research. Using drug repurposing approaches, we discover small molecules that regulate the formation of definitive endoderm. Among them are inhibitors of known processes involved in endoderm differentiation (mTOR, PI3K, and JNK pathways) and a new compound, with an unknown mechanism of action, capable of inducing endoderm formation in the absence of growth factors in the media. Optimization of the classical protocol by inclusion of this compound achieves the same differentiation efficiency with a 90% cost reduction. The presented in silico procedure for candidate molecule selection has broad potential for improving stem cell differentiation protocols.

Dynamic phase separation of the androgen receptor and its coactivators key to regulate gene expression.

Numerous cancers, including prostate cancer (PCa), are addicted to transcription programs driven by specific genomic regions known as super-enhancers (SEs). The robust transcription of genes at such SEs is enabled by the formation of phase-separated condensates by transcription factors and coactivators with intrinsically disordered regions. The androgen receptor (AR), the main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the transcriptional coactivator mediator complex subunit 1 (MED1) to SEs in androgen-dependent PCa cells, thereby promoting oncogenic transcriptional programs. In this work, we reveal that full-length AR forms foci with liquid-like properties in different PCa models. We demonstrate that foci formation correlates with AR transcriptional activity, as this activity can be modulated by changing cellular foci content chemically or by silencing MED1. AR ability to phase separate was also validated in vitro by using recombinant full-length AR protein. We also demonstrate that AR antagonists, which suppress transcriptional activity by targeting key regions for homotypic or heterotypic interactions of this receptor, hinder foci formation in PCa cells and phase separation in vitro. Our results suggest that enhanced compartmentalization of AR and coactivators may play an important role in the activation of oncogenic transcription programs in androgen-dependent PCa.

Discovery of lead natural products for developing pan-SARS-CoV-2 therapeutics.

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains a global public health crisis. The reduced efficacy of therapeutic monoclonal antibodies against emerging SARS-CoV-2 variants of concern (VOCs), such as omicron BA.5 subvariants, has underlined the need to explore a novel spectrum of antivirals that are effective against existing and evolving SARS-CoV-2 VOCs. To address the need for novel therapeutic options, we applied cell-based high-content screening to a library of natural products (NPs) obtained from plants, fungi, bacteria, and marine sponges, which represent a considerable diversity of chemical scaffolds. The antiviral effect of 373 NPs was evaluated using the mNeonGreen (mNG) reporter SARS-CoV-2 virus in a lung epithelial cell line (Calu-3). The screening identified 26 NPs with half-maximal effective concentrations (EC50) below 50 µM against mNG-SARS-CoV-2; 16 of these had EC50 values below 10 µM and three NPs (holyrine A, alotaketal C, and bafilomycin D) had EC50 values in the nanomolar range. We demonstrated the pan-SARS-CoV-2 activity of these three lead antivirals against SARS-CoV-2 highly transmissible Omicron subvariants (BA.5, BA.2 and BA.1) and highly pathogenic Delta VOCs in human Calu-3 lung cells. Notably, holyrine A, alotaketal C, and bafilomycin D, are potent nanomolar inhibitors of SARS-CoV-2 Omicron subvariants BA.5 and BA.2. The pan-SARS-CoV-2 activity of alotaketal C [protein kinase C (PKC) activator] and bafilomycin D (V-ATPase inhibitor) suggest that these two NPs are acting as host-directed antivirals (HDAs). Future research should explore whether PKC regulation impacts human susceptibility to and the severity of SARS-CoV-2 infection, and it should confirm the important role of human V-ATPase in the VOC lifecycle. Interestingly, we observed a synergistic action of bafilomycin D and N-0385 (a highly potent inhibitor of human TMPRSS2 protease) against Omicron subvariant BA.2 in human Calu-3 lung cells, which suggests that these two highly potent HDAs are targeting two different mechanisms of SARS-CoV-2 entry. Overall, our study provides insight into the potential of NPs with highly diverse chemical structures as valuable inspirational starting points for developing pan-SARS-CoV-2 therapeutics and for unravelling potential host factors and pathways regulating SARS-CoV-2 VOC infection including emerging omicron BA.5 subvariants.

Discovery of Novel Lin28 Inhibitors to Suppress Cancer Cell Stemness.

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.

Lin28 Regulates Cancer Cell Stemness for Tumour Progression.

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.

Structure-Based Study to Overcome Cross-Reactivity of Novel Androgen Receptor Inhibitors.

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.

Targeting SARS-CoV-2 papain-like protease in the postvaccine era.

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.

X-ray crystallographic characterization of the SARS-CoV-2 main protease polyprotein cleavage sites essential for viral processing and maturation.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the pathogen that causes COVID-19, produces polyproteins 1a and 1ab that contain, respectively, 11 or 16 non-structural proteins (nsp). Nsp5 is the main protease (Mpro) responsible for cleavage at eleven positions along these polyproteins, including at its own N- and C-terminal boundaries, representing essential processing events for viral assembly and maturation. Using C-terminally substituted Mpro chimeras, we have determined X-ray crystallographic structures of Mpro in complex with 10 of its 11 viral cleavage sites, bound at full occupancy intermolecularly in trans, within the active site of either the native enzyme and/or a catalytic mutant (C145A). Capture of both acyl-enzyme intermediate and product-like complex forms of a P2(Leu) substrate in the native active site provides direct comparative characterization of these mechanistic steps as well as further informs the basis for enhanced product release of Mpro's own unique C-terminal P2(Phe) cleavage site to prevent autoinhibition. We characterize the underlying noncovalent interactions governing binding and specificity for this diverse set of substrates, showing remarkable plasticity for subsites beyond the anchoring P1(Gln)-P2(Leu/Val/Phe), representing together a near complete analysis of a multiprocessing viral protease. Collectively, these crystallographic snapshots provide valuable mechanistic and structural insights for antiviral therapeutic development.

Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks.

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.