Structure-based design of SARS-CoV-2 papain-like protease inhibitors.

The COVID-19 pandemic is caused by SARS-CoV-2, an RNA virus with high transmissibility and mutation rate. Given the paucity of orally bioavailable antiviral drugs to combat SARS-CoV-2 infection, there is a critical need for additional antivirals with alternative mechanisms of action. Papain-like protease (PLpro) is one of the two SARS-CoV-2 encoded viral cysteine proteases essential for viral replication. PLpro cleaves at three sites of the viral polyproteins. In addition, PLpro antagonizes the host immune response upon viral infection by cleaving ISG15 and ubiquitin from host proteins. Therefore, PLpro is a validated antiviral drug target. In this study, we report the X-ray crystal structures of papain-like protease (PLpro) with two potent inhibitors, Jun9722 and Jun9843. Subsequently, we designed and synthesized several series of analogs to explore the structure-activity relationship, which led to the discovery of PLpro inhibitors with potent enzymatic inhibitory activity and antiviral activity against SARS-CoV-2. Together, the lead compounds are promising drug candidates for further development.

Dual domain recognition determines SARS-CoV-2 PLpro selectivity for human ISG15 and K48-linked di-ubiquitin.

The Papain-like protease (PLpro) is a domain of a multi-functional, non-structural protein 3 of coronaviruses. PLpro cleaves viral polyproteins and posttranslational conjugates with poly-ubiquitin and protective ISG15, composed of two ubiquitin-like (UBL) domains. Across coronaviruses, PLpro showed divergent selectivity for recognition and cleavage of posttranslational conjugates despite sequence conservation. We show that SARS-CoV-2 PLpro binds human ISG15 and K48-linked di-ubiquitin (K48-Ub2) with nanomolar affinity and detect alternate weaker-binding modes. Crystal structures of untethered PLpro complexes with ISG15 and K48-Ub2 combined with solution NMR and cross-linking mass spectrometry revealed how the two domains of ISG15 or K48-Ub2 are differently utilized in interactions with PLpro. Analysis of protein interface energetics predicted differential binding stabilities of the two UBL/Ub domains that were validated experimentally. We emphasize how substrate recognition can be tuned to cleave specifically ISG15 or K48-Ub2 modifications while retaining capacity to cleave mono-Ub conjugates. These results highlight alternative druggable surfaces that would inhibit PLpro function.

Dual domain recognition determines SARS-CoV-2 PLpro selectivity for human ISG15 and K48-linked di-ubiquitin.

The Papain-like protease (PLpro) is a domain of a multi-functional, non-structural protein 3 of coronaviruses. PLpro cleaves viral polyproteins and posttranslational conjugates with poly-ubiquitin and protective ISG15, composed of two ubiquitin-like (UBL) domains. Across coronaviruses, PLpro showed divergent selectivity for recognition and cleavage of posttranslational conjugates despite sequence conservation. We show that SARS-CoV-2 PLpro binds human ISG15 and K48-linked di-ubiquitin (K48-Ub 2 ) with nanomolar affinity and detect alternate weaker-binding modes. Crystal structures of untethered PLpro complexes with ISG15 and K48-Ub 2 combined with solution NMR and cross-linking mass spectrometry revealed how the two domains of ISG15 or K48-Ub 2 are differently utilized in interactions with PLpro. Analysis of protein interface energetics predicted differential binding stabilities of the two UBL/Ub domains that were validated experimentally. We emphasize how substrate recognition can be tuned to cleave specifically ISG15 or K48-Ub 2 modifications while retaining capacity to cleave mono-Ub conjugates. These results highlight alternative druggable surfaces that would inhibit PLpro function.

Structure of papain-like protease from SARS-CoV-2 and its complexes with non-covalent inhibitors.

The pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to expand. Papain-like protease (PLpro) is one of two SARS-CoV-2 proteases potentially targetable with antivirals. PLpro is an attractive target because it plays an essential role in cleavage and maturation of viral polyproteins, assembly of the replicase-transcriptase complex, and disruption of host responses. We report a substantive body of structural, biochemical, and virus replication studies that identify several inhibitors of the SARS-CoV-2 enzyme. We determined the high resolution structure of wild-type PLpro, the active site C111S mutant, and their complexes with inhibitors. This collection of structures details inhibitors recognition and interactions providing fundamental molecular and mechanistic insight into PLpro. All compounds inhibit the peptidase activity of PLpro in vitro, some block SARS-CoV-2 replication in cell culture assays. These findings will accelerate structure-based drug design efforts targeting PLpro to identify high-affinity inhibitors of clinical value.

Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs.

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69(± 0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51(± 0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60(± 0.04), and an F1 score of 0.58(± 0.04) corresponding to a PPV of 0.67(± 0.05) and TPR of 0.51(± 0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs.

The crystal structure of nsp10-nsp16 heterodimer from SARS-CoV-2 in complex with S-adenosylmethionine.

SARS-CoV-2 is a member of the coronaviridae family and is the etiological agent of the respiratory Coronavirus Disease 2019. The virus has spread rapidly around the world resulting in over two million cases and nearly 150,000 deaths as of April 17, 2020. Since no treatments or vaccines are available to treat COVID-19 and SARS-CoV-2, respiratory complications derived from the infections have overwhelmed healthcare systems around the world. This virus is related to SARS-CoV-1, the virus that caused the 2002-2004 outbreak of Severe Acute Respiratory Syndrome. In January 2020, the Center for Structural Genomics of Infectious Diseases implemented a structural genomics pipeline to solve the structures of proteins essential for coronavirus replication-transcription. Here we show the first structure of the SARS-CoV-2 nsp10-nsp16 2'-O-methyltransferase complex with S-adenosylmethionine at a resolution of 1.80 Å. This heterodimer complex is essential for capping viral mRNA transcripts for efficient translation and to evade immune surveillance.

Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting.

IMPORTANCE: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial training and is subject to significant interobserver variation. OBJECTIVE: To determine whether crowd innovation could be used to rapidly produce artificial intelligence (AI) solutions that replicate the accuracy of an expert radiation oncologist in segmenting lung tumors for RT targeting. DESIGN, SETTING, AND PARTICIPANTS: We conducted a 10-week, prize-based, online, 3-phase challenge (prizes totaled $55 000). A well-curated data set, including computed tomographic (CT) scans and lung tumor segmentations generated by an expert for clinical care, was used for the contest (CT scans from 461 patients; median 157 images per scan; 77 942 images in total; 8144 images with tumor present). Contestants were provided a training set of 229 CT scans with accompanying expert contours to develop their algorithms and given feedback on their performance throughout the contest, including from the expert clinician. MAIN OUTCOMES AND MEASURES: The AI algorithms generated by contestants were automatically scored on an independent data set that was withheld from contestants, and performance ranked using quantitative metrics that evaluated overlap of each algorithm's automated segmentations with the expert's segmentations. Performance was further benchmarked against human expert interobserver and intraobserver variation. RESULTS: A total of 564 contestants from 62 countries registered for this challenge, and 34 (6%) submitted algorithms. The automated segmentations produced by the top 5 AI algorithms, when combined using an ensemble model, had an accuracy (Dice coefficient = 0.79) that was within the benchmark of mean interobserver variation measured between 6 human experts. For phase 1, the top 7 algorithms had average custom segmentation scores (S scores) on the holdout data set ranging from 0.15 to 0.38, and suboptimal performance using relative measures of error. The average S scores for phase 2 increased to 0.53 to 0.57, with a similar improvement in other performance metrics. In phase 3, performance of the top algorithm increased by an additional 9%. Combining the top 5 algorithms from phase 2 and phase 3 using an ensemble model, yielded an additional 9% to 12% improvement in performance with a final S score reaching 0.68. CONCLUSIONS AND RELEVANCE: A combined crowd innovation and AI approach rapidly produced automated algorithms that replicated the skills of a highly trained physician for a critical task in radiation therapy. These AI algorithms could improve cancer care globally by transferring the skills of expert clinicians to under-resourced health care settings.

New aminopeptidase from "microbial dark matter" archaeon.

Marine sediments host a large population of diverse, heterotrophic, uncultured microorganisms with unknown physiologies that control carbon flow through organic matter decomposition. Recently, single-cell genomics uncovered new key players in these processes, such as the miscellaneous crenarchaeotal group. These widespread archaea encode putative intra- and extracellular proteases for the degradation of detrital proteins present in sediments. Here, we show that one of these enzymes is a self-compartmentalizing tetrameric aminopeptidase with a preference for cysteine and hydrophobic residues at the N terminus of the hydrolyzed peptide. The ability to perform detailed characterizations of enzymes from native subsurface microorganisms, without requiring that those organisms first be grown in pure culture, holds great promise for understanding key carbon transformations in the environment as well as identifying new enzymes for biomedical and biotechnological applications.

Structural characterization of AtmS13, a putative sugar aminotransferase involved in indolocarbazole AT2433 aminopentose biosynthesis.

AT2433 from Actinomadura melliaura is an indolocarbazole antitumor antibiotic structurally distinguished by its unique aminodideoxypentose-containing disaccharide moiety. The corresponding sugar nucleotide-based biosynthetic pathway for this unusual sugar derives from comparative genomics where AtmS13 has been suggested as the contributing sugar aminotransferase (SAT). Determination of the AtmS13 X-ray structure at 1.50-Å resolution reveals it as a member of the aspartate aminotransferase fold type I (AAT-I). Structural comparisons of AtmS13 with homologous SATs that act upon similar substrates implicate potential active site residues that contribute to distinctions in sugar C5 (hexose vs. pentose) and/or sugar C2 (deoxy vs. hydroxyl) substrate specificity.

A novel human cancer vaccine elicits cellular responses to the tumor-associated antigen, human chorionic gonadotropin beta.

PURPOSE: The oncofetal antigen, human chorionic gonadotropin beta subunit (hCGbeta), is expressed by a number of carcinomas and is a prognostic indicator in renal, colorectal, bladder, and pancreatic cancers. We describe the development of a novel antibody-based dendritic cell (DC)-targeted cancer vaccine capable of eliciting cellular immune responses directed against hCGbeta. EXPERIMENTAL DESIGN: The tumor-associated antigen hCGbeta was coupled genetically to a human anti-DC antibody (B11). The resulting fusion protein (B11-hCGbeta) was evaluated for its ability to promote tumor antigen-specific cellular immune responses in a human in vitro model. Monocyte-derived human DCs from normal donors were exposed to purified B11-hCGbeta, activated with CD40 ligand, mixed with autologous lymphocytes, and tested for their ability to promote hCGbeta-specific proliferative and cytotoxic T-lymphocyte responses. RESULTS: B11-hCGbeta was found to be a soluble, well-defined, and readily purified product that specifically recognized the human mannose receptor via the B11 antibody portion of the fusion protein. B11-hCGbeta functionally promoted the uptake and processing of tumor antigen by DCs, which led to the generation of tumor-specific HLA class I and class II-restricted T-cell responses, including CTLs capable of killing human cancer cell lines expressing hCGbeta. CONCLUSIONS: Although other hCG vaccines have been shown to be capable of eliciting antibody responses to hCGbeta, this is the first time that cellular immune responses to hCGbeta have been induced by a vaccine in a human system. This DC-targeted hCGbeta vaccine holds promise for the management of a number of cancers and merits additional clinical development.

Mannose receptor targeting of tumor antigen pmel17 to human dendritic cells directs anti-melanoma T cell responses via multiple HLA molecules.

Targeting recycling endocytic receptors with specific Abs provides a means for introducing a variety of tumor-associated Ags into human dendritic cells (DCs), culminating in their efficient presentation to T cells. We have generated a human mAb (B11) against the mannose receptor that is rapidly internalized by DCs through receptor-mediated endocytosis. By genetically linking the melanoma Ag, pmel17, to Ab B11, we obtained the fully human fusion protein, B11-pmel17. Treatment of DCs with B11-pmel17 resulted in the presentation of pmel17 in the context of HLA class I and class II molecules. Thus, potent pmel17-specific T cells were cytotoxic toward gp100(+) HLA-matched melanoma targets, but not HLA-mismatched melanoma or gp100(-) nonmelanoma tumor lines. Importantly, competitive inhibition of lysis of an otherwise susceptible melanoma cell line by cold targets pulsed with known gp100 CD8 T cell epitopes as well as a dose-dependent proliferative response to Th epitopes demonstrates that DCs can process targeted Ag for activation of cytotoxic as well as helper arms of the immune response. Thus, the specific targeting of soluble exogenous tumor Ag to the DC mannose receptor directly contributes to the generation of multiple HLA-restricted Ag-specific T cell responses.