De novo design of a fluorescence-activating ß-barrel.

The regular arrangements of ß-strands around a central axis in ß-barrels and of α-helices in coiled coils contrast with the irregular tertiary structures of most globular proteins, and have fascinated structural biologists since they were first discovered. Simple parametric models have been used to design a wide range of α-helical coiled-coil structures, but to date there has been no success with ß-barrels. Here we show that accurate de novo design of ß-barrels requires considerable symmetry-breaking to achieve continuous hydrogen-bond connectivity and eliminate backbone strain. We then build ensembles of ß-barrel backbone models with cavity shapes that match the fluorogenic compound DFHBI, and use a hierarchical grid-based search method to simultaneously optimize the rigid-body placement of DFHBI in these cavities and the identities of the surrounding amino acids to achieve high shape and chemical complementarity. The designs have high structural accuracy and bind and fluorescently activate DFHBI in vitro and in Escherichia coli, yeast and mammalian cells. This de novo design of small-molecule binding activity, using backbones custom-built to bind the ligand, should enable the design of increasingly sophisticated ligand-binding proteins, sensors and catalysts that are not limited by the backbone geometries available in known protein structures.

The design, analysis and application of mouse clinical trials in oncology drug development.

BACKGROUND: Mouse clinical trials (MCTs) are becoming wildly used in pre-clinical oncology drug development, but a statistical framework is yet to be developed. In this study, we establish such as framework and provide general guidelines on the design, analysis and application of MCTs. METHODS: We systematically analyzed tumor growth data from a large collection of PDX, CDX and syngeneic mouse tumor models to evaluate multiple efficacy end points, and to introduce statistical methods for modeling MCTs. RESULTS: We established empirical quantitative relationships between mouse number and measurement accuracy for categorical and continuous efficacy endpoints, and showed that more mice are needed to achieve given accuracy for syngeneic models than for PDXs and CDXs. There is considerable disagreement between methods on calling drug responses as objective response. We then introduced linear mixed models (LMMs) to describe MCTs as clustered longitudinal studies, which explicitly model growth and drug response heterogeneities across mouse models and among mice within a mouse model. Case studies were used to demonstrate the advantages of LMMs in discovering biomarkers and exploring drug's mechanisms of action. We introduced additive frailty models to perform survival analysis on MCTs, which more accurately estimate hazard ratios by modeling the clustered mouse population. We performed computational simulations for LMMs and frailty models to generate statistical power curves, and showed that power is close for designs with similar total number of mice. Finally, we showed that MCTs can explain discrepant results in clinical trials. CONCLUSIONS: Methods proposed in this study can make the design and analysis of MCTs more rational, flexible and powerful, make MCTs a better tool in oncology research and drug development.

Guiding automated NMR structure determination using a global optimization metric, the NMR DP score.

ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases (15)N-(1)H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta.

Assessment of template-based protein structure predictions in CASP10.

Template-based modeling (TBM) is a major component of the critical assessment of protein structure prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X-ray crystal and NMR structures using a composite score that included both global alignment metrics and distance-matrix-based metrics. These included GDT-HA and GDC-all global alignment scores, and the superimposition-independent LDDT distance-matrix-based score. In addition, a superimposition-independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. To score well on all four of these metrics, models must feature accurate predictions of both backbone and side-chain conformations. Performance rankings were determined independently for server and the combined server plus human-curated predictor groups. Final rankings were made using paired head-to-head Student's t-test analysis of raw metric scores among the top 25 performing groups in each category.

Protein NMR structures refined with Rosetta have higher accuracy relative to corresponding X-ray crystal structures.

We have found that refinement of protein NMR structures using Rosetta with experimental NMR restraints yields more accurate protein NMR structures than those that have been deposited in the PDB using standard refinement protocols. Using 40 pairs of NMR and X-ray crystal structures determined by the Northeast Structural Genomics Consortium, for proteins ranging in size from 5-22 kDa, restrained Rosetta refined structures fit better to the raw experimental data, are in better agreement with their X-ray counterparts, and have better phasing power compared to conventionally determined NMR structures. For 37 proteins for which NMR ensembles were available and which had similar structures in solution and in the crystal, all of the restrained Rosetta refined NMR structures were sufficiently accurate to be used for solving the corresponding X-ray crystal structures by molecular replacement. The protocol for restrained refinement of protein NMR structures was also compared with restrained CS-Rosetta calculations. For proteins smaller than 10 kDa, restrained CS-Rosetta, starting from extended conformations, provides slightly more accurate structures, while for proteins in the size range of 10-25 kDa the less CPU intensive restrained Rosetta refinement protocols provided equally or more accurate structures. The restrained Rosetta protocols described here can improve the accuracy of protein NMR structures and should find broad and general for studies of protein structure and function.

Mathematical Modeling of Tumor Growth in Preclinical Mouse Models with Applications in Biomarker Discovery and Drug Mechanism Studies.

Oncology drug efficacy is evaluated in mouse models by continuously monitoring tumor volumes, which can be mathematically described by growth kinetic models. Although past studies have investigated various growth models, their reliance on small datasets raises concerns about whether their findings are truly representative of tumor growth in diverse mouse models under different vehicle or drug treatments. In this study, we systematically evaluated six parametric models (exponential, exponential quadratic, monomolecular, logistic, Gompertz, and von Bertalanffy) and the semiparametric generalized additive model (GAM) on fitting tumor volume data from more than 30,000 mice in 930 experiments conducted in patient-derived xenografts, cell line-derived xenografts, and syngeneic models. We found that the exponential quadratic model is the best parametric model and can adequately model 87% studies, higher than other models including von Bertalanffy (82%) and Gompertz (80%) models; the latter is often considered the standard growth model. At the mouse group level, 7.5% of growth data could not be fit by any parametric model and were fitted by GAM. We show that endpoint gain integrated in time, a GAM-derived efficacy metric, is equivalent to exponential growth rate, a metric we previously proposed and conveniently calculated by simple algebra. Using five studies on paclitaxel, anti-PD1 antibody, cetuximab, irinotecan, and sorafenib, we showed that exponential and exponential quadratic models achieve similar performance in uncovering drug mechanism and biomarkers. We also compared exponential growth rate-based association analysis and exponential modeling approach in biomarker discovery and found that they complement each other. Modeling methods herein are implemented in an open-source R package freely available at https://github.com/hjzhou988/TuGroMix. SIGNIFICANCE: We present a general strategy for mathematically modeling tumor growth in mouse models using data from 30,000 mice and show that modeling and nonmodeling approaches are complementary in biomarker discovery and drug mechanism studies.

An NGS-based assay for accurate detection and quantification of immune gene expression in mouse tumor models.

Tumor microenvironment (TME) is a complex dynamic system with many tumor-interacting components including tumor-infiltrating leukocytes (TILs), cancer associated fibroblasts, blood vessels, and other stromal constituents. It intrinsically affects tumor development and pharmacology of oncology therapeutics, particularly immune-oncology (IO) treatments. Accurate measurement of TME is therefore of great importance for understanding the tumor immunity, identifying IO treatment mechanisms, developing predictive biomarkers, and ultimately, improving the treatment of cancer. Here, we introduce a mouse-IO NGS-based (NGSmIO) assay for accurately detecting and quantifying the mRNA expression of 1080 TME related genes in mouse tumor models. The NGSmIO panel was shown to be superior to the commonly used microarray approach by hosting 300 more relevant genes to better characterize various lineage of immune cells, exhibits improved mRNA and protein expression correlation to flow cytometry, shows stronger correlation with mRNA expression than RNAseq with 10x higher sequencing depth, and demonstrates higher sensitivity in measuring low-expressed genes. We describe two studies; firstly, detecting the pharmacodynamic change of interferon-γ expression levels upon anti-PD-1: anti-CD4 combination treatment in MC38 and Hepa 1-6 tumors; and secondly, benchmarking baseline TILs in 14 syngeneic tumors using transcript level expression of lineage specific genes, which demonstrate effective and robust applications of the NGSmIO panel.

PDBStat: a universal restraint converter and restraint analysis software package for protein NMR.

The heterogeneous array of software tools used in the process of protein NMR structure determination presents organizational challenges in the structure determination and validation processes, and creates a learning curve that limits the broader use of protein NMR in biology. These challenges, including accurate use of data in different data formats required by software carrying out similar tasks, continue to confound the efforts of novices and experts alike. These important issues need to be addressed robustly in order to standardize protein NMR structure determination and validation. PDBStat is a C/C++ computer program originally developed as a universal coordinate and protein NMR restraint converter. Its primary function is to provide a user-friendly tool for interconverting between protein coordinate and protein NMR restraint data formats. It also provides an integrated set of computational methods for protein NMR restraint analysis and structure quality assessment, relabeling of prochiral atoms with correct IUPAC names, as well as multiple methods for analysis of the consistency of atomic positions indicated by their convergence across a protein NMR ensemble. In this paper we provide a detailed description of the PDBStat software, and highlight some of its valuable computational capabilities. As an example, we demonstrate the use of the PDBStat restraint converter for restrained CS-Rosetta structure generation calculations, and compare the resulting protein NMR structure models with those generated from the same NMR restraint data using more traditional structure determination methods. These results demonstrate the value of a universal restraint converter in allowing the use of multiple structure generation methods with the same restraint data for consensus analysis of protein NMR structures and the underlying restraint data.

Statistical Assessment of Drug Synergy from In Vivo Combination Studies Using Mouse Tumor Models.

Drug combination therapy is a promising strategy for treating cancer; however, its efficacy and synergy require rigorous evaluation in preclinical studies before going to clinical trials. Existing methods have limited power to detect synergy in animal studies. Here, we introduce a novel approach to assess in vivo drug synergy with high sensitivity and low false discovery rate. It can accurately estimate combination index and synergy score under the Bliss independence model and the highest single agent (HSA) model without any assumption on tumor growth kinetics, study duration, data completeness and balance for tumor volume measurement. We show that our method can effectively validate in vitro drug synergy discovered from cell line assays in in vivo xenograft experiments, and can help to elucidate the mechanism of action for immune checkpoint inhibitors in syngeneic mouse models by combining an anti-PD-1 antibody and several tumor-infiltrating leukocytes depletion treatments. We provide a unified view of in vitro and in vivo synergy by presenting a parallelism between the fixed-dose in vitro and the 4-group in vivo combination studies, so they can be better designed, analyzed, and compared. We emphasize that combination index, when defined here via relative survival of tumor cells, is both dose and time dependent, and give guidelines on designing informative in vivo combination studies. We explain how to interpret and apply Bliss and HSA synergies. Finally, we provide an open-source software package named invivoSyn that enables automated analysis of in vivo synergy using our method and several other existing methods. SIGNIFICANCE: This work presents a general solution to reliably determine in vivo drug synergy in single-dose 4-group animal combination studies.

Mouse Stromal Cells Confound Proteomic Characterization and Quantification of Xenograft Models.

Xenografts are essential models for studying cancer biology and developing oncology drugs, and are more informative with omics data. Most reported xenograft proteomics projects directly profiled tumors comprising human cancer cells and mouse stromal cells, followed by computational algorithms for assigning peptides to human and mouse proteins. We evaluated the performance of three main algorithms by carrying out benchmark studies on a series of human and mouse cell line mixtures and a set of liver patient-derived xenograft (PDX) models. Our study showed that approximately half of the characterized peptides are common between human and mouse proteins, and their allocations to human or mouse proteins cannot be satisfactorily achieved by any algorithm. As a result, many human proteins are erroneously labeled as differentially expressed proteins (DEP) between samples from the same human cell line mixed with different percentages of mouse cells, and the number of such false DEPs increases superquadratically with the mouse cell percentage. When mouse stromal cells are not removed from PDX tumors, about 30%-40% of DEPs from pairwise comparisons of PDX models are false positives, and about 20% of real DEPs cannot be identified irrespective of the threshold for calling differential expression. In conclusion, our study demonstrated that it is advisable to separate human and mouse cells in xenograft tumors before proteomic profiling to obtain more accurate measurement of species-specific protein expression. Significance: This study advocates the separate-then-run over the run-then-separate approach as a better strategy for more reliable proteomic profiling of xenografts.

HX009, a novel BsAb dual targeting PD1 x CD47, demonstrates potent anti-lymphoma activity in preclinical models.

Both PD1/PD-L1 and CD47 blockades have demonstrated limited activity in most subtypes of NHL save NK/T-cell lymphoma. The hemotoxicity with anti-CD47 agents in the clinic has been speculated to account for their limitations. Herein we describe a first-in-class and rationally designed bispecific antibody (BsAb), HX009, targeting PD1 and CD47 but with weakened CD47 binding, which selectively hones the BsAb for tumor microenvironment through PD1 interaction, potentially reducing toxicity. In vitro characterization confirmed: (1) Both receptor binding/ligand blockade, with lowered CD47 affinity; (2) functional PD1/CD47 blockades by reporter assays; (3) T-cell activation in Staphylococcal-enterotoxin-B-pretreated PBMC and mixed-lymphocyte-reaction. In vivo modeling demonstrated antitumor activity in Raji-B and Karpass-229-T xenograft lymphomas. In the humanized mouse syngeneic A20 B-lymphoma (huCD47-A20) HuGEMM model, which has quadruple knocked-in hPD1xhPD-L1xhCD47xhSIRPα genes and an intact autologous immune-system, a contribution of effect is demonstrated for each targeted biologic (HX008 targeting PD1 and SIRPα-Fc targeting CD47), which is clearly augmented by the dual targeting with HX009. Lastly, the expression of the immune-checkpoints PD-L1/L2 and CD47 seemed co-regulated among a panel of lymphoma-derived-xenografts, where HX009 maybe more effective in those with upregulated CD47. Our data warrants HX009's further clinical development for treating NHLs.

A living biobank of matched pairs of patient-derived xenografts and organoids for cancer pharmacology.

Patient-derived tumor xenograft (PDX)/organoid (PDO), driven by cancer stem cells (CSC), are considered the most predictive models for translational oncology. Large PDX collections reflective of patient populations have been created and used extensively to test various investigational therapies, including population-trials as surrogate subjects in vivo. PDOs are recognized as in vitro surrogates for patients amenable for high-throughput screening (HTS). We have built a biobank of carcinoma PDX-derived organoids (PDXOs) by converting an existing PDX library and confirmed high degree of similarities between PDXOs and parental PDXs in genomics, histopathology and pharmacology, suggesting "biological equivalence or interchangeability" between the two. Here we demonstrate the applications of PDXO biobank for HTS "matrix" screening for both lead compounds and indications, immune cell co-cultures for immune-therapies and engineering enables in vitro/in vivo imaging. This large biobank of >550 matched pairs of PDXs/PDXOs across different cancers could become powerful tools for the future cancer drug discovery.

Segmental isotope labeling of proteins for NMR structural study using a protein S tag for higher expression and solubility.

A common obstacle to NMR studies of proteins is sample preparation. In many cases, proteins targeted for NMR studies are poorly expressed and/or expressed in insoluble forms. Here, we describe a novel approach to overcome these problems. In the protein S tag-intein (PSTI) technology, two tandem 92-residue N-terminal domains of protein S (PrS(2)) from Myxococcus xanthus is fused at the N-terminal end of a protein to enhance its expression and solubility. Using intein technology, the isotope-labeled PrS(2)-tag is replaced with non-isotope labeled PrS(2)-tag, silencing the NMR signals from PrS(2)-tag in isotope-filtered (1)H-detected NMR experiments. This method was applied to the E. coli ribosome binding factor A (RbfA), which aggregates and precipitates in the absence of a solubilization tag unless the C-terminal 25-residue segment is deleted (RbfAΔ25). Using the PrS(2)-tag, full-length well-behaved RbfA samples could be successfully prepared for NMR studies. PrS(2) (non-labeled)-tagged RbfA (isotope-labeled) was produced with the use of the intein approach. The well-resolved TROSY-HSQC spectrum of full-length PrS(2)-tagged RbfA superimposes with the TROSY-HSQC spectrum of RbfAΔ25, indicating that PrS(2)-tag does not affect the structure of the protein to which it is fused. Using a smaller PrS-tag, consisting of a single N-terminal domain of protein S, triple resonance experiments were performed, and most of the backbone (1)H, (15)N and (13)C resonance assignments for full-length E. coli RbfA were determined. Analysis of these chemical shift data with the Chemical Shift Index and heteronuclear (1)H-(15)N NOE measurements reveal the dynamic nature of the C-terminal segment of the full-length RbfA protein, which could not be inferred using the truncated RbfAΔ25 construct. CS-Rosetta calculations also demonstrate that the core structure of full-length RbfA is similar to that of the RbfAΔ25 construct.

Distinct genomic landscape of Chinese pediatric acute myeloid leukemia impacts clinical risk classification.

Studies have revealed key genomic aberrations in pediatric acute myeloid leukemia (AML) based on Western populations. It is unknown to what extent the current genomic findings represent populations with different ethnic backgrounds. Here we present the genomic landscape of driver alterations of Chinese pediatric AML and discover previously undescribed genomic aberrations, including the XPO1-TNRC18 fusion. Comprehensively comparing between the Chinese and Western AML cohorts reveal a substantially distinct genomic alteration profile. For example, Chinese AML patients more commonly exhibit mutations in KIT and CSF3R, and less frequently mutated of genes in the RAS signaling pathway. These differences in mutation frequencies lead to the detection of previously uncharacterized co-occurring mutation pairs. Importantly, the distinct driver profile is clinical relevant. We propose a refined prognosis risk classification model which better reflected the adverse event risk for Chinese AML patients. These results emphasize the importance of genetic background in precision medicine.

Different syngeneic tumors show distinctive intrinsic tumor-immunity and mechanisms of actions (MOA) of anti-PD-1 treatment.

Cancers are immunologically heterogeneous. A range of immunotherapies target abnormal tumor immunity via different mechanisms of actions (MOAs), particularly various tumor-infiltrate leukocytes (TILs). We modeled loss of function (LOF) in four common anti-PD-1 antibody-responsive syngeneic tumors, MC38, Hepa1-6, CT-26 and EMT-6, by systematical depleting a series of TIL lineages to explore the mechanisms of tumor immunity and treatment. CD8+-T-cells, CD4+-T-cells, Treg, NK cells and macrophages were individually depleted through either direct administration of anti-marker antibodies/reagents or using DTR (diphtheria toxin receptor) knock-in mice, for some syngeneic tumors, where specific subsets were depleted following diphtheria toxin (DT) administration. These LOF experiments revealed distinctive intrinsic tumor immunity and thus different MOAs in their responses to anti-PD-1 antibody among different syngeneic tumors. Specifically, the intrinsic tumor immunity and the associated anti-PD-1 MOA were predominately driven by CD8+ cytotoxic TILs (CTL) in all syngeneic tumors, excluding Hepa1-6 where CD4+ Teff TILs played a key role. TIL-Treg also played a critical role in supporting tumor growth in all four syngeneic models as well as M2-macrophages. Pathway analysis using pharmacodynamic readouts of immuno-genomics and proteomics on MC38 and Hepa1-6 also revealed defined, but distinctive, immune pathways of activation and suppression between the two, closely associated with the efficacy and consistent with TIL-pharmacodynamic readouts. Understanding tumor immune-pathogenesis and treatment MOAs in the different syngeneic animal models, not only assists the selection of the right model for evaluating new immunotherapy of a given MOA, but also can potentially help to understand the potential disease mechanisms and strategize optimal immune-therapies in patients.

Adenosine-related small molecules show utility of recall antigen assay to screen compounds for off-target effects on memory T cells.

Extracellular adenosine suppresses T cell immunity in the tumor microenvironment and in vitro treatment of memory T cells with adenosine can suppress antigen-mediated memory T cell expansion. We describe utilizing the recall antigen assay platform to screen small molecule drug off-target effects on memory T cell expansion/function using a dosing regimen based on adenosine treatment. As a proof of principle, we show low dose GS-5734, a monophosphoramidate prodrug of an adenosine analog, does not alter memory T cell recall at lower doses whereas toxicity observed at high dose favors antigen-specific memory T cell survival/proliferation over non-specific CD8+ T cells. Conversely, parent nucleoside GS-441524 at high dosage does not result in cellular toxicity and reduces antigen-specific T cell recall in most donors. Despite similar chemical structure, these drugs displayed opposing effects on memory T cell expansion and viability highlighting the sensitivity of this assay setup in screening compounds for off-target effects.

Diastolic dysfunction and impaired cardiac output reserve in dysmetabolic nonhuman primate with proteinuria.

BACKGROUND: Cardiorenal complications are common in patients with dysmetabolism and diabetes. The present study aimed to examine if a nonhuman primate (NHP) model with spontaneously developed metabolic disorder and diabetes develops similar complications to humans, such as proteinuria and cardiac dysfunction at resting condition or diminished cardiac functional reserve following dobutamine stress echocardiography (DSE). METHODS AND RESULTS: A total of 66 dysmetabolic and diabetic cynomolgus (Macaca fascicularis) NHPs were enrolled to select 19 NHPs (MetS) with marked metabolic disorders and diabetes (fasting blood glucose: 178 ±â€¯18 vs. 61 ±â€¯3 mg/dL) accompanied by proteinuria (ACR: 134 ±â€¯34 vs. 1.5 ±â€¯0.4 mg/mmol) compared to 8 normal NHPs (CTRL). Under resting condition, MetS NHPs showed mild left ventricular (LV) diastolic dysfunction (E/A: 1 ±â€¯0.06 vs. 1.5 ±â€¯0.13), but with preserved ejection fraction (EF: 65 ±â€¯2 vs. 71 ±â€¯3%) compared to CTRL. DSE with an intravenous infusion of dobutamine at ascending doses (5, 10, 20, 30 and 40 µg/kg/min, 7 min for each dose) resulted in a dose-dependent increase in cardiac function, however, with a significantly diminished magnitude at the highest dose of dobutamine infusion (40 µg/kg/min) in both diastole (E/A: -12 ±â€¯3 vs. -38 ±â€¯5%) and systole (EF: 25 ±â€¯3 vs. 33 ±â€¯5%) as well as ~42% reduced cardiac output reserve (COR: 63 ±â€¯8 vs. 105 ±â€¯18%, p < 0.02) in the MetS compared to CTRL NHPs. CONCLUSION: These data demonstrate that MetS NHPs with cardiorenal complications: proteinuria, LV diastolic dysfunction and preserved LV systolic function under resting conditions displayed compromised cardiac functional reserve under dobutamine stress. Based on these phenotypes, this NHP model of diabetes with cardiorenal complications can be used as a highly translational model mimic human disease for pharmaceutical research.

Creating Matched In vivo/In vitro Patient-Derived Model Pairs of PDX and PDX-Derived Organoids for Cancer Pharmacology Research.

Patient-derived tumor xenografts (PDXs) are considered the most predictive preclinical models, largely believed to be driven by cancer stem cells (CSC) for conventional cancer drug evaluation. A large library of PDXs is reflective of the diversity of patient populations and thus enables population based preclinical trials ("Phase II-like mouse clinical trials"); however, PDX have practical limitations of low throughput, high costs and long duration. Tumor organoids, also being patient-derived CSC-driven models, can be considered as the in vitro equivalent of PDX, overcoming certain PDX limitations for dealing with large libraries of organoids or compounds. This study describes a method to create PDX-derived organoids (PDXO), thus resulting in paired models for in vitro and in vivo pharmacology research. Subcutaneously-transplanted PDX-CR2110 tumors were collected from tumor-bearing mice when the tumors reached 200-800 mm3, per an approved autopsy procedure, followed by removal of the adjacent non-tumor tissues and dissociation into small tumor fragments. The small tumor fragments were washed and passed through a 100 µm cell strainer to remove the debris. Cell clusters were collected and suspended in basement membrane extract (BME) solution and plated in a 6-well plate as a solid droplet with surrounding liquid media for growth in a CO2 incubator. Organoid growth was monitored twice weekly under light microscopy and recorded by photography, followed by liquid medium change 2 or 3 times a week. The grown organoids were further passaged (7 days later) at a 1:2 ratio by disrupting the BME embedded organoids using mechanical shearing, aided by addition of trypsin and the addition of 10 µM Y-27632. Organoids were cryopreserved in cryo-tubes for long-term storage, after release from BME by centrifugation, and also sampled (e.g., DNA, RNA and FFPE block) for further characterization.

Accurate automated protein NMR structure determination using unassigned NOESY data.

Conventional NMR structure determination requires nearly complete assignment of the cross peaks of a refined NOESY peak list. Depending on the size of the protein and quality of the spectral data, this can be a time-consuming manual process requiring several rounds of peak list refinement and structure determination. Programs such as Aria, CYANA, and AutoStructure can generate models using unassigned NOESY data but are very sensitive to the quality of the input peak lists and can converge to inaccurate structures if the signal-to-noise of the peak lists is low. Here, we show that models with high accuracy and reliability can be produced by combining the strengths of the high-resolution structure prediction program Rosetta with global measures of the agreement between structure models and experimental data. A first round of models generated using CS-Rosetta (Rosetta supplemented with backbone chemical shift information) are filtered on the basis of their goodness-of-fit with unassigned NOESY peak lists using the DP-score, and the best fitting models are subjected to high resolution refinement with the Rosetta rebuild-and-refine protocol. This hybrid approach uses both local backbone chemical shift and the unassigned NOESY data to direct Rosetta trajectories toward the native structure and produces more accurate models than AutoStructure/CYANA or CS-Rosetta alone, particularly when using raw unedited NOESY peak lists. We also show that when accurate manually refined NOESY peak lists are available, Rosetta refinement can consistently increase the accuracy of models generated using CYANA and AutoStructure.