Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation

Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery. Prior work focused on supervised learning methods using a large set of binding affinity data for small molecules, but it is hard to apply the same strategy to other drug classes like antibodies as labelled data is limited. In this paper, we explore unsupervised approaches and reformulate binding energy prediction as a generative modeling task. Specifically, we train an energy-based model on a set of unlabelled protein-ligand complexes using SE(3) denoising score matching and interpret its log-likelihood as binding affinity. Our key contribution is a new equivariant rotation prediction network called Neural Euler's Rotation Equations (NERE) for SE(3) score matching. It predicts a rotation by modeling the force and torque between protein and ligand atoms, where the force is defined as the gradient of an energy function with respect to atom coordinates. We evaluate NERE on protein-ligand and antibody-antigen binding affinity prediction benchmarks. Our model outperforms all unsupervised baselines (physics-based and statistical potentials) and matches supervised learning methods in the antibody case

Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

Dendritic cells (DCs) and monocytes play a central role in pathogen sensing, phagocytosis, and antigen presentation and consist of multiple specialized subtypes. However, their identities and interrelationships are not fully understood. Using unbiased single-cell RNA sequencing (RNA-seq) of ~2400 cells, we identified six human DCs and four monocyte subtypes in human blood. Our study reveals a new DC subset that shares properties with plasmacytoid DCs (pDCs) but potently activates T cells, thus redefining pDCs; a new subdivision within the CD1C+ subset of DCs; the relationship between blastic plasmacytoid DC neoplasia cells and healthy DCs; and circulating progenitor of conventional DCs (cDCs). Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease

A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Published in final edited form as: Nat Biotechnol. 2020 February ; 38(2): 199–209. doi:10.1038/s41587-019-0322-9.Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.P01 CA229092 - NCI NIH HHS; P50 CA101942 - NCI NIH HHS; T32 HG002295 - NHGRI NIH HHS; T32 CA009172 - NCI NIH HHS; U24 CA224331 - NCI NIH HHS; R21 CA216772 - NCI NIH HHS; R01 CA155010 - NCI NIH HHS; U01 CA214125 - NCI NIH HHS; T32 CA207021 - NCI NIH HHS; R01 HL103532 - NHLBI NIH HHS; U24 CA210986 - NCI NIH HHSAccepted manuscrip

Aryl Hydrocarbon Receptor Controls Monocyte Differentiation into Dendritic Cells versus Macrophages

International audienceAfter entering tissues, monocytes differentiate into cells that share functional features with either macrophages or dendritic cells (DCs). How monocyte fate is directed toward monocyte-derived macrophages (mo-Macs) or monocyte-derived DCs (mo-DCs) and which transcription factors control these differentiation pathways remains unknown. Using an in vitro culture model yielding human mo-DCs and mo-Macs closely resembling those found in vivo in ascites, we show that IRF4 and MAFB were critical regulators of monocyte differentiation into mo-DCs and mo-Macs, respectively. Activation of the aryl hydrocarbon receptor (AHR) promoted mo-DC differentiation through the induction of BLIMP-1, while impairing differentiation into mo-Macs. AhR deficiency also impaired the in vivo differentiation of mouse mo-DCs. Finally, AHR activation correlated with mo-DC infiltration in leprosy lesions. These results establish that mo-DCs and mo-Macs are controlled by distinct transcription factors and show that AHR acts as a molecular switch for monocyte fate specification in response to micro-environmental factors

Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq

© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc. Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here we developed Cumulus—a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies

The HLA-II immunopeptidome of SARS-CoV-2

Summary: Targeted synthetic vaccines have the potential to transform our response to viral outbreaks, yet the design of these vaccines requires a comprehensive knowledge of viral immunogens. Here, we report severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) peptides that are naturally processed and loaded onto human leukocyte antigen-II (HLA-II) complexes in infected cells. We identify over 500 unique viral peptides from canonical proteins as well as from overlapping internal open reading frames. Most HLA-II peptides colocalize with known CD4+ T cell epitopes in coronavirus disease 2019 patients, including 2 reported immunodominant regions in the SARS-CoV-2 membrane protein. Overall, our analyses show that HLA-I and HLA-II pathways target distinct viral proteins, with the structural proteins accounting for most of the HLA-II peptidome and nonstructural and noncanonical proteins accounting for the majority of the HLA-I peptidome. These findings highlight the need for a vaccine design that incorporates multiple viral elements harboring CD4+ and CD8+ T cell epitopes to maximize vaccine effectiveness

Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN) Harbors Frequent Splicesosome Mutations That Cause Aberrant RNA Splicing Affecting Genes Critical in pDC Differentiation and Function

Abstract Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is an aggressive malignancy thought to result from transformation of plasmacytoid dendritic cells (pDCs). Clinical outcomes are poor and pathogenesis is unclear. To better understand BPDCN genomics and disease mechanisms, we performed whole exome- (12 BPDCNs), targeted DNA- (additional 12 BPDCNs), bulk whole transcriptome RNA- (12 BPDCNs and 6 BPDCN patient-derived xenografts [PDXs]), and single cell RNA-sequencing (scRNA-seq) compared to normal DCs. We observed RNA splicing factor mutations in 16/24 cases (7 ZRSR2, 6 SRSF2, 1 each SF3B1, U2AF1, SF3A2, SF3B4). Additional recurrent alterations were in genes known to be mutated in other blood cancers: TET2, ASXL1, TP53, GNB1, NRAS, IDH2, ETV6, DNMT3A, and RUNX1. From exome sequencing we also discovered recurrent mutations in CRIPAK (6/12 cases), NEFH (4/12), HNF1A (2/12), PAX3 (2/12), and SSC5D (2/12) that may be unique to BPDCN. ZRSR2 is notable among the recurrently mutated splicing factors in hematologic malignancies in that all mutations are loss-of-function (e.g., nonsense, frameshift). Of note, BPDCN is very male predominant, ZRSR2 is located on chrX and all mutations are in males. ZRSR2 plays a critical role in "minor" or U12-type intron splicing (only 0.3% of all introns). Thus, we hypothesized that mis-splicing, possibly of U12 genes, contributes to BPDCN pathogenesis. Using RNA-seq, we measured aberrant splicing in BPDCN. Intron retention was the most frequent abnormality in ZRSR2 mutant BPDCNs and PDXs compared to non-mutant cases. ZRSR2 mutant intron retention predominantly affected U12 introns (patients: 29.4% of retained introns, P<0.0001; PDX: 94%, P<0.0001). To test if ZRSR2 loss directly causes U12 intron retention in otherwise isogenic cells, we performed ZRSR2 knockdown using doxycycline-inducible shRNAs in the BPDCN cell line, CAL1, which has no known splicing factor mutation. RNA-seq was performed 0, 2, and 7 days after addition of doxycycline in 3 independent clones each of control or ZRSR2 knockdown. Consistent with what we observed in primary BPDCN, intron retention events were higher in ZRSR2 compared to control shRNA cells after 7 days of doxycycline (mean 885.7 vs 122.7 events, P=0.041). Aberrant intron retention after ZRSR2 knockdown largely involved U12 introns (30/732 U12 vs 37/207,344 U2 introns, P<0.0001). SRSF2 and SF3B1 mutations in BPDCN were at hotspots seen in other cancers: SRSF2 P95H/L/R and SF3B1 K666N, mutants that induce specific types of aberrant splicing (Kim, Ca Cell 2015; Darman, Cell Rep 2015). Mutant BPDCNs demonstrated the same aberrations: SRSF2, exon inclusion/exclusion based on CCNG/GGNG exonic splicing enhancer motifs; SF3B1, aberrant 3' splice site recognition. We hypothesized that aberrant splicing may affect RNAs important for pDC development or function. To further define genes uniquely important in BPDCN, we performed scRNA-seq on 4 BPDCNs and on DCs from healthy donors. By principal component analysis, BPDCNs were more similar to pDCs than to conventional DCs (cDCs) or other HLA-DR+ cells. However, several critical genes for pDC function had markedly lower expression in BPDCN including the transcription factors IRF4 and IRF7. Next we determined which genes were commonly mis-spliced in splicing factor mutant BPDCNs. Strikingly, this list included genes already known to be important in driving DC biology or identified in our scRNA-seq as being differentially expressed between BPDCN and healthy pDCs, including IRF7, IRF8, IKZF1, FLT3, and DERL3. To determine if splicing factor mutations affect DC function, we stimulated ZRSR2 knockdown or control CAL1 cells with Toll-like receptor (TLR) 7, 8, and 9 agonists (R848 or CpG oligo). ZRSR2 knockdown inhibited upregulation of the CD80 costimulatory molecule and aggregation of CAL1 cells, suggesting impairment in activation. Using mouse conditional knock-in bone marrow in ex vivo multipotent progenitor assays, DC differentiation induced by FLT3 ligand was biased toward pDCs and away from cDCs in SRSF2 P95H mutant compared to wild-type cells. However, cDC and monocyte differentiation in the presence of GM-CSF was not affected. In conclusion, splicing factors are frequently mutated in BPDCN and lead to specific splicing defects. Splicing factor mutations may promote BPDCN by affecting pathways important in DC maturation or activation, which could contribute to transformation. Disclosures Seiler: H3 Biomedicine: Employment. Buonamici:H3 Biomedicine: Employment. Lane:Stemline Therapeutics: Research Funding; N-of-1: Consultancy