Clinical practice guidelines provide recommendations for the management of diseases. In orphan conditions such as uveal melanoma (UM), guideline developers are challenged to provide practical and useful guidance even in the absence of high-quality evidence. Here, we assessed the methodological quality and identified deficiencies of international guidelines on UM as a base for future guideline development.A systematic search was carried out in guideline databases, Medline and Embase until 27th May 2019 for guidelines on UM published between 2004 and 2019. Five independent reviewers assessed the methodological quality of the identified guidelines using the instruments “Appraisal of Guidelines for Research and Evaluation II” (AGREE II) and AGREE-REX (Recommendation EXcellence). Descriptive analysis was performed and subgroup differences were explored with the Kruskal–Wallis (H) test. The relationship between the individual domains and items of the instruments were examined using Spearman’s correlation.Five guidelines published from 2014 to 2018 by consortia of the United States of America, Canada and the United Kingdom (UK) were included. The highest scores were obtained by the UK guideline fulfilling 48–86% of criteria in AGREE II and 30–60% for AGREE-REX. All guidelines showed deficiencies in the domains “editorial independence”, “applicability”, and “recommendation”. Subgroup differences were identified only for the domain “editorial independence”.The UK guideline achieved the highest scores with both instruments and may serve as a basis for future guideline development in UM. The domains “editorial independence”, “recommendation”, and “applicability” were identified as methodological weaknesses and require particular attention and improvement in future guidelines.The online version of this article (10.1007/s00432-020-03141-w) contains supplementary material, which is available to authorized users. Uveal melanoma (UM) represents one of the most common ocular malignancies and accounts for about 5% of all melanomas. Primary tumors originate from the pigment cells of the choroid layer, the ciliary body or iris of the eye . The key search terms included “uveal melanoma”, “ocular melanoma”, “iris melanoma”, “choroidal melanoma”, “ciliary body melanoma” and the German translation “Aderhautmelanom”. Additionally, Medline and Embase (both via Ovid) were searched until 27 May 2019. The detailed search strategy is presented in Supplementary Table S2. After the elimination of double hits, two authors  independently screened the titles and abstracts of the records that were identified in the databases for eligibility. For records that were considered potentially relevant, the full-text guidelines were obtained, and the inclusion and exclusion criteria were applied. Whenever discrepancies arose, resolution was achieved by discussion with a third independent author (CB).Information on each included guideline regarding title, national authority/author, country of origin, publication date, methodological approach and scope were collected and summarized by two authors independently .https://www.agreetrust.org/).AGREE II was used by five independent reviewers to assess the methodological quality of each guideline identified in the search on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). Six domains with 23 items were assessed, including: scope and purpose (domain 1), stakeholder involvement (domain 2), rigor of development (domain 3), clarity of presentation (domain 4), applicability (domain 5), and editorial independence (domain 6). Based on the scores of the 6 domains, overall assessment was obtained to assess the quality of the guidelines. The different domains were followed by a general judgement of the guideline’s overall quality considering the evaluated criteria on a 7-point scale from “lowest possible quality” to “highest possible quality”. Furthermore, the evaluator was asked for an answer on the statement “I would recommend this guideline for use” . The evaluations were performed independently and blinded towards the other evaluators’ assessments using the platform provided by AGREE , values and preferences (domain 2) and implementability (domain 3) with 9 items that must be considered to ensure that guideline recommendations are of high quality. This instrument was used by the same five independent authors who rated the quality of the credibility and implementability on a 7-point scale ranging from 1  to 7 . Furthermore, the evaluator was asked for an answer on the recommendation of this guideline in the appropriate context or in the reviewers’ context. The evaluations were performed independently and blinded towards the other evaluators’ assessments using an internally piloted data extraction spreadsheet using Microsoft Excel 2010.Domain scores were calculated as suggested by the AGREE II and AGREE-REX instructions as a sum of the scores of all evaluators’ assessments of the individual items in the domain for both instruments. The total scores for each domain were then expressed as a percentage of the maximum possible score for that domain. Hence, the range of possible evaluations was 0–100%, with 0% and 100% representing the worst and best possible rating for each domain, respectively.Statistical analyses were conducted using SPSS . Descriptive analyses included mean  or median and interquartile ranges (IQR). Subgroup differences were explored with the Kruskal–Wallis (H) test. The relationship between the individual domains were examined using Spearman’s correlation. A significance level of 0.05 was considered statistically significant. The interrater agreement of the five reviewers was determined using Fleiss’ Kappa (Landis and Koch https://www.ncbi.nlm.nih.gov/books/NBK66047/). Another record was a comment on a guideline and was, therefore, excluded , Canada (n = 1), and United Kingdom (UK) (n = 1)  Table . All inc1) Table .Fig. 1FlThis domain assesses whether the main objectives and questions of the guidelines and whether the population to whom the guideline is meant to apply were specifically described. It achieved an average score of 4.73 (± 1.55) (Supplementary Table S3). The guideline from the UK scored highest and fulfilled 82% of criteria whereas the guideline from the National Comprehensive Cancer Network (NCCN) from the US achieved only 35% of criteria  and the fulfilled values ranged from 71% for the UK guideline to intermediate values for the remaining guidelines (range 41–53%) Fig. .This domain is about the methodological approaches of the guidelines and evaluates whether the identification of the evidence for the guideline was performed using systematic and transparent methods. Besides, this domain also assessed whether there was an explicit link between the recommendations and the supporting evidence, whether the guidelines had been externally reviewed and if a procedure for updating the guideline was available. It achieved an average rating of 3.92 (± 1.38). Besides, the UK guideline was evaluated by the reviewers to be of best methodological quality (77% of items fulfilled) in contrast to the remaining guidelines varying in terms of items fulfilled from 28 to 45%.This domain included the presentation and format of guidelines, i.e. whether the recommendations were specific and ambiguous, if key recommendations were easily identifiable and whether different options for the management of the condition were clearly presented. In general, all guidelines achieved high levels of fulfillment (5.01 ± 1.62) with the UK guideline being evaluated best with 86% of fulfilled criteria.This domain covers the processes related to guideline implementation, for instance facilitators, barriers, additional material provided and whether monitoring and/or auditing criteria were presented. This domain showed a mean score of 2.85 (± 1.43). The UK guideline achieved the highest score in comparison to the remaining guidelines (48%). The guidelines by Weis et al. and Simpson et al. achieved lowest possible scores with 17% and 18%, respectively.p = 0.038). No further subgroup differences were identified.This domain focuses on funders and competing interests of experts involved in guideline development, i.e. whether competing interests of the development group were recorded and addressed. This domain achieved the lowest score with a mean of 2.66 (± 1.38). However, the UK guideline still achieved 73% of fulfilled criteria. The five identified guidelines significantly differed from each other . The individual fulfilled criteria ranged from 40% (NCI guideline) to 70% (UK guideline). Hence, according to the assessment, only the UK guideline would be recommended whereas the remaining 4 guidelines are recommended with modifications only.This domain evaluates whether the guideline is evidence-based  as well as the degree to which the recommendations are applicable to the guideline’s target users’ practice context and patients. The guideline from the UK Melanoma group achieved the highest percentage value with 59%, and the guideline by Weis et al. the lowest with 36% Fig. .This domain comprises four different items and refers to the relative importance that target users, patients, policy/decision-makers as well as guideline developers place on the outcomes of interest. Their values and preferences are important in guideline development as they influence whether recommendations are acceptable and adopted into practice. Therefore, this domain assesses if their views and its impact had been explored and considered in the formulation of the recommendations. The mean score was 3.28 ± 0.97 for this domain. All guidelines achieved a value ranging from 32 to 35%, except for the UK guideline which achieved 55% in this domain.The implementability domain includes the items “purpose” and “local application and adoption”. This domain assesses the suitability of the guideline recommendations for patients/populations, and/or the health care systems in which they are being implemented and if the degree of change from current practice was addressed. Furthermore, the guideline should articulate relevant factors important to its successful dissemination. Besides, the purpose item evaluates weather guideline recommendations aligned with the implementation goals of the guidelines. Again, the UK guideline achieved highest results with 60%, followed by the guideline of the American Brachytherapy Society (47%). The mean score was 3.66 ± 1.42.This domain assessed whether the raters would recommend this guideline either in the appropriate context as well as in the rater’s context. Overall, none of the guidelines achieved sufficient values as they ranged from 17 to 30% and as the mean score was 2.50 ± 1.30.r = 0.81), the domain “scope and purpose” (r = 0.76), “clarity of presentation” (r = 0.48) and “rigor of development” (r = 0.48). Besides this, “clarity of presentation” was positively associated with “rigor of development” (r = 0.43) and “applicability” (r = 0.57). The AGREE-REX domains were all positively and statistically significantly correlated with each other, except for the domain “values and preferences” and the additional recommendation item.The majority of AGREE II domains significantly correlated with each other Table . The domUM is an orphan cancer condition of high unmet clinical need. Although UM differs from cutaneous melanoma both clinically and biologically, treatment options for advanced stages have largely been adopted from cutaneous melanoma, yet with much lower response rates and at the cost of high treatment-related toxicity  to the domains “overall recommendation”, “clinical applicability”, “values and preference”, and “implementability”, warranting improvement and special attention in future guideline efforts. The lowest values (≤ 25%) were observed for “applicability”, “editorial independence”, and “recommendation”. Applicability refers to how facilitators and barriers of the guideline application were discussed and if tools for monitoring or auditing the recommendations were provided. These parameters were not sufficiently addressed in any of the guidelines, possibly explaining the low ratings of this domain. The domain editorial independence aims at ensuring that the guideline is editorially independent from the funding body and that all conflicts of interest of guideline development members are correctly disclosed. The ratings for this domain were conspicuously low among the evaluated guidelines. On the one hand, it is possible that the editorial independency was present, but simply not indicated correctly. On the other hand, we cannot rule out the possibility that funding parties may have exerted an influence on the content of the guidelines or that the members of the guidelines group may have had conflicts of interest. Developing methodologically sound guidelines is costly and requires major financial, organizational and human resources. Especially in the case of orphan diseases such as UM, public and independent funding may not be available due to a low public awareness. Finally, consistently low values were achieved for “recommendation”, i.e. the usability of the recommendations made for a specific health-care context. Apparently, there was a major mismatch between the recommendations of the 5 guidelines with the specific care context of the assessor. Thus, the medical needs both of treating physicians and patients must be explored and aligned with the guideline recommendations, as has been performed for other orphan conditions  in 4 out of 5 guidelines. This domain considers Altogether, this analysis demonstrates that guidelines for UM as prime example of an orphan cancer have room for methodological improvement. The published UK guideline showed the best ratings in this study, suggesting that it may serve as good adaption basis for future guideline projects. In particular, the domains “applicability”, “editorial independence”, and “recommendation” should be improved to ensure that the guidelines give independent and context-specific guidance for clinicians.Supplementary file1 (DOCX 20 kb)Below is the link to the electronic supplementary material.


In this study, we found that Tm-22 self-associates upon recognition of MP. The CC domain of Tm-22 is the signaling domain and its function requires PM localization and self-association. The nucleotide-binding (NB-ARC) domain is important for Tm-22 self-interaction and regulates activation of the CC domain through its nucleotide-binding and self-association. (d)ATP binding may alter the NB-ARC conformation to release its suppression of Tm-22 CC domain-mediated cell death. Our findings provide the first example of signaling domain for PM-localized NLR and insight into PM-localized NLR activation.The nucleotide-binding, leucine-rich repeat-containing (NLR) class of immune receptors of plants and animals recognize pathogen-encoded proteins and trigger host defenses. Although animal NLRs form oligomers upon pathogen recognition to activate downstream signaling, the mechanisms of plant NLR activation remain largely elusive. Tm-2 2. Further, we report that nucleotide-binding (NB-ARC) domain is important for Tm-22 self-association and regulates activation of CC domain through its nucleotide-binding and self-association. Our findings provide the first example of signaling domain for PM-localized NLR and insight into PM-localized NLR activation.Nucleotide-binding, leucine-rich repeat proteins (NLR) can function as immune receptors of plants and animals and confer resistance against pathogens. However, despite their importance in immunity, the activation mechanism of PM-localized NLRs remains largely elusive. In this study, we demonstrate that CC domain is the signaling domain for inducing cell death for PM-localized NLR Tm-2 Plants use multi-layered sensing systems to defend against pathogens . One sucMost plant R proteins are immune receptors that possess nucleotide-binding (NB) domain and leucine-rich repeat-containing (LRR) domains . They arActivated animal NLRs usually oligomerize and form high molecular weight complexes known as inflammasomes and recruit caspases to stimulate immune signaling . Recogniin vivo and in vitro [In addition, N-terminal domains of several plant NLRs can self-associate and this self-association is essential for their function, supporting the notion that oligomerization is an important step in NLR-mediated immune signaling , 38, 39.in vitro . Consistin vitro , 15. How2 confers extreme resistance against Tobamoviruses such as Tobacco mosaic virus (TMV) and Tomato mosaic virus (ToMV) [2 functions at the plasma membrane (PM) and recognizes viral MP independent of its PD localization [2, several Arabidopsis CC-NLR (RPS5 and RPM1) and rice CC-NLR (Pit) are reported to function at the PM [The tomato CC-NLR Tm-2s (ToMV) , 45 by rs (ToMV) , 47. Tm-lization . Besidest the PM , 49, 50.t the PM  althought the PM . Howevert the PM , 33. Thet the PM . In addit the PM . Althoug2 as a model to investigate how PM-localized NLRs are regulated during activation. We found that Tm-22 self-associates post effector recognition. Although a sole CC domain was able to induce cell death once localized to the PM, CC self-association was essential for its function. Moreover, we found that the wild-type Tm-22 NB-ARC domain inhibited CC-mediated cell death, while an auto-active MHD motif mutation in the NB-ARC domain enhanced CC domain function by helping its self-association. These results suggest that the plant immune receptor Tm-22 requires NB-ARC domain-mediated oligomerization of the CC domain at the PM for its signaling.In this study, we used Tm-22 NLR exhibited effector-dependent self-association. For this, we transiently co-expressed Myc- and HA-tagged Tm-22 in the absence or presence of YFP-tagged TMV MP in Nicotiana benthamiana and performed co-immunoprecipitation (co-IP) assays. Tm-22-HA was co-expressed with MP-YFP as a negative control. The infiltrated leaves were collected when slight cell death began to occur at 23 hours post infiltration (hpi). In the absence of MP, Tm-22-HA barely co-immunoprecipitated with Tm-22-Myc, while in the presence of MP, Tm-22-HA co-immunoprecipitated with Tm-22-Myc  contains a mutation in the MHD motif and triggers cell death in the absence of viral MP perhaps due to the conformational switch to an active status [2 can self-associate, we co-expressed the Tm-22 (D481V) mutant with Myc and HA tags in N. benthamiana and performed co-IP assays. Samples were collected when slight cell death became visible. Indeed, we detected the self-association of the Tm-22 auto-active mutant  failed to co-immunoprecipitate with Myc-tagged Tm-22 (K191R), whereas the effector induced self-association of wild-type Tm-22  in the conserved P-loop motif inhibits the function of Tm-22 . NLR P-l2 , 53. We pe Tm-22 . Similarll death . These r2 self-association, we tested whether the isolated domains of Tm-22 can self-associate. For this, we expressed the Tm-22 CC (aa 1–141), NB-ARC (aa 141–492) and LRR (aa 493–861) domains, fused to C-terminal YFP, Myc or HA tags in N. benthamiana for subsequent co-IP assays. The CC domain of several CC-NLR proteins has been shown to self-associate, and self-association is essential for their function [2-CC-HA did not associate with Tm-22-CC-Myc, although we could detect self-association of the RPM1 CC domain as described previously [2 CC self-association when CC-YFP was used in co-IP assays with CC-Myc  that we have used previously to target proteins to the PM [N. benthamiana and examined their subcellular localization and their ability to induce cell death. Cellular fractionation assays showed that Rop-tag worked efficiently to tether all fusion proteins to the membrane fraction  induces self-association and a mutant form  fails to induce self-association [N. benthamiana to test the effect of DP on Tm-22 CC self-association and cell death. Interestingly, CC fused to DP, but not mDP, enhanced CC-mediated cell death  mutation. Surprisingly, CC-NB-ARC (D481V)-YFP-Rop exhibited a stronger cell death than CC-YFP-Rop . Our finSince the MHD mutation D481V enabled CC-NB-ARC to induce cell death, we investigated how this mutation regulated CC domain function. Considering that stronger self-association helps CC function , we hypo2 -Myc-Rop and Tm-22. The structure of Tm-22 NB-ARC domain was predicted using I-TASSER (https://zhanglab.ccmb.med.umich.edu/I-TASSER/) based on the structure of ZAR1. The helix-loop-helix located at the center of the inter-subunit-interface is required for NB-ARC oligomerization in ZAR1 and CED-4 complexes [2 NB-ARC through structural modeling  substitutions at residues L233, L242, and L246 in NB-ARC (D481V), and found that these three mutations weakened the self-association of NB-ARC (D481V) (N. benthamiana leaves. The L233A and L242A mutations delayed cell death mediated by CC-NB-ARC (D481V)-Myc-Rop by about 10 h, although these two mutants did eventually induce macroscopic HR, and the L246A mutation strongly blocked cell death. Co-IP assays demonstrated that the three mutations indeed disturbed the self-association of CC-NB-ARC (D481V)-Rop  . We also (D481V)  after ex81V)-Rop . We furtf TMV MP , althougof Tm-22 . These r2 exhibits effector-dependent self-association and that the CC domain is the signaling domain. Further, the Tm-22 CC domain requires PM localization and self-association mediated by the NB-ARC domain for its function. To our knowledge, our findings described here constitute the first report on the signaling domain of a PM-localized plant NLR immune receptor and its mechanism of action.In this study, we found that Tm-2Animal NLR proteins usually exhibit elicitor-induced oligomerization . Based o2 can self-associate after activation. Tm-22 self-association is triggered either by effector recognition or incorporation of an auto-activating mutation into the conserved MHD motif (2 self-association (2 self-association requires its binding to (d)ATP, and is necessary for triggering the downstream signaling. Considering the pentamer structure of ZAR1 and the induced self-association model, Tm-22 may also form oligomers upon activation.In this study, we found that Tm-2HD motif . Furtherociation , which i2  have the ability to self-associate, suggesting that the induced oligomerization of Tm-22 may involve multiple self-association surfaces, similar to RPP1.Previously reported plant NLR oligomerization events operate via different domains: N depends on its TIR domain, RPP1 needs multiple interaction interfaces, and ZAR1 relies on its CC and NB-ARC domains , 25, 32.2 CC domain alone fails to target the protein to the PM and does not induce cell death [2 CC triggers cell death in N. benthamiana once it is targeted to the PM via a heterologous S-acylation motif. These findings are consistent with the PM localization of full-length Tm-22 [The CC domains of several CC-NLRs have been reported to induce cell death, but these CC domains localize to the nucleus or cytosol , 20. Somll death . In thisth Tm-22 . Differe2 CC exhibited very weak self-association, and induced only a weak cell death phenotype. The self-association ability was correlated with the strength of cell death. GFP/YFP has weak dimerization ability, and fusion with GFP/YFP can force the fusion protein into a dimer [2 CC induced cell death compared to fusions with monomeric mYFP  and N-terminus exposure [2 CC oligomers at the PM might also perturb PM integrity and/or ion homeostasis, or interact with other PM-localized signaling components to trigger cell death and defense.Tm-2exposure  for funcexposure . Once acexposure . Similar2 CC and the necessity of self-association for its function suggest that other domains could contribute to CC oligomerization. Interestingly, NB-ARC inhibited CC-mediated cell death ATP-bound state, while K191R and R291A destroy nucleotide binding and compromise cell death induction. (d)ATP binding to NB-ARC may trigger conformational changes in both the CC and NB-ARC domains, releasing NB-ARC-mediated repression on CC. Once the removal of this repression, (d)ATP-bound NB-ARC domains self-associate, thus help CC self-associate for downstream signaling. Through analyses of the predicted structure by mutagenesis, we found that three site mutations  can affect the self-association of active NB-ARC. Furthermore, these mutations disturbed the self-association and cell death mediated by both the CC-NB-ARC (D481V) and full-length Tm-22 proteins -mediated cell death may come mainly from switching the protein into the (d)ATP-bound state, in which structural changes relieve the NB-ARC repression of CC, and drive stronger self-association to trigger cell death and defense.Different from the wild-type NB-ARC domain, NB-ARC (D481V) enhanced the CC-mediated cell death, indicating NB-ARC domain can act as a switch to regulate CC domain-mediated cell death. It will be interesting to investigate how D481V mutation positively regulates the function of CC-NB-ARC. We hypothesize that the D481V mutation may trigger the conformational changes to release NB-ARC inhibition of CC domain and strengthen the self-association of CC-NB-ARC for more effective CC function. This is partially supported from our observation that the D481V mutation can strengthen the self-association and cell death induction of CC-NB-ARC and CC Figs  and 6A. proteins . These m2, the LRR is required for recognition of effectors and recognition specificity [2 CC-NB-ARC fragment strongly self-associates ATP, leading to important conformational changes. (d)ATP binding releases suppression of CC domain and promotes stronger self-association of the NB-ARC domain. Meanwhile, self-association of NB-ARC enhances the dimerization or oligomerization of CC domain. Homo-dimerized/oligomerized CC domain at the PM activates downstream defense signaling  method [ApaI and treated with T4 DNA polymerase in the presence of dTTP. Tm-22 mutants were generated by overlapping PCR. All PCR products treated with T4 DNA polymerase in the presence of dATP were ligated into the treated LIC basic vectors as described [) method . pLIC-My) method . In thesescribed . PrimersAgrobacterium tumefaciens-mediated transient protein expression in N. benthamiana, Agrobacterium GV3101 strains containing the relevant expression vectors were cultivated overnight, pelleted, resuspended in infiltration buffer , and kept at room temperature for 2–6 h before infiltration into plant leaves. Usually, an optical density of OD600 for inoculation is adjusted to 1 for each single strain, while for the GV3101 strain expressing MP-YFP, OD600 = 0.5.For N. benthamiana leaves and then extracted with a ratio of 1 g leaf tissues:2 mL Laemmli buffer [2 and MP, samples were usually collected at 22–24 hpi. For expression of CC-NB-ARC (D481V)-Rop, samples were usually collected at 24–26 hpi.For protein analysis, target proteins were expressed for 22–48 h in i buffer . For co-Co-IP assays were performed as described previously  with folAnti-Myc beads (Abmart) and GFP-trap beads (ChromoTek) were used for co-IP assays. All protein samples were analyzed by SDS-PAGE, immunoblotted with anti-Myc (Abmart), anti-GFP (ChromoTek), and anti-HA  antibodies. Protein levels were detected using a SuperSignal West Pico PLUS Chemiluminescent Substrate or SuperSignal West Femto Maximum Sensitivity Substrate (Thermo).The leaves expressing related proteins were collected at 24–36 hpi. Confocal imaging with an inverted Zeiss LSM 710 laser scanning microscope  was used for observing target protein localization from the lower leaf surface.+-ATPase (Agrisera) was used for membrane protein indication.Plasma membrane protein isolation kit for Plants  was used for membrane fractionation. After homogenized by buffer A, plant tissues pass through a specialized filter cartridge with a zigzag path. After discard of the cartridge, the remaining homogenates were re-suspended, and then centrifuged at 700 X g for discarding the pellet containing nuclei, chloroplasts and larger debris. Total protein for immunoblotting was sampled from the supernatant. After new round of centrifuge at 16,000 X g, the original supernatant  can be divided into two parts: new supernatant (soluble fraction) and pellet . Anti-HN. benthamiana leaves were boiled for 5 min in a 2:1 mixture of ethanol and staining stock solution . The leaves were then faded with 2.5 g/mL chloral hydrate solution.N. benthamiana leaves. For every technical replicate, 6 leaf discs were collected using a puncher (disc area = 6 mm*6 mm) and floated on 4 mL of distilled water for 30 min. The distilled water was replaced by 4 mL fresh distilled water containing 0.001% Tween-20. The mixture of leaf disks and water was gently blended before every measurement. The electrolyte leakage was measured at room temperature for every 2 hours with FiveEasy PlusTM FE38-Standard conductivity meter (METTLER TOLEDO). Means and error bars were calculated from three replicates per time point and construct.Electrolyte leakage assays were performed as described previously  with folS1 Table(PDF)Click here for additional data file.S1 Fig2 (R291A) failed to induce cell death compared to WT in the presence of TMV MP. Myc-tagged Tm-22 and Tm-22 (R291A) were expressed with MP-YFP for cell death assays. The left picture was taken 2 dpi, and the right one represents the trypan blue staining of the same leaf. (B) Tm-22 (R291A) disrupted the self-association of activated Tm-22. Indicated protein samples were subjected to IP with anti-Myc bead. The markers of protein molecular weight (kDa) are indicated on the right. (C) Ion leakage caused by different Tm-22 mutants was measured over time after infiltration. The error bar indicates the standard deviation from 3 technical repetitions. The experiment was performed at least three times with similar results.(A) Tm-2(TIF)Click here for additional data file.S2 Fig2 CC-HA was not immunoprecipitated by Tm-22 CC-Myc, but RPM1 CC-HA interacted with RPM1 CC-Myc in the same condition. (B) Tm-22 NB-ARC-HA was immunoprecipitated by Tm-22 NB-ARC-Myc. (C) Tm-22 LRR-HA was immunoprecipitated by Tm-22 LRR-Myc relative to empty control.Fusion proteins with C-terminally HA or Myc tag were used to perform co-IP assays. All samples were subjected to IP with anti-Myc beads. (A) Tm-2(TIF)Click here for additional data file.S3 Fig+-ATPase is the PM marker detected by IB, and RbcL is the soluble protein marker detected by Ponceau S.Cell lysates were separated to the soluble and microsomal membrane fractionations. T, total protein; S, soluble fraction; M, microsomal membrane fraction. H(TIF)Click here for additional data file.S4 Fig(A) Fusion proteins were probed with anti-Myc antibody. (B) Fusion proteins were probed with anti-GFP antibody.(TIF)Click here for additional data file.S5 Fig(A) Confocal images showed YFP-Rop successfully tethered fusion proteins to the PM. (B) Protein levels were detected by anti-GFP antibody.(TIF)Click here for additional data file.S6 Fig(A) R291A mutation inhibited cell death mediated by CC-NB-ARC (D481V)-Myc-Rop. Pictures were taken at 3 dpi. (B) R291A disrupted the self-association of CC-NB-ARC (D481V)-Rop. All samples were subjected to IP with anti-GFP beads.(TIF)Click here for additional data file.S7 FigCo-IP assay showed that both K191R and R291A disrupted the self-association of NB-ARC (D481V). All samples were subjected to IP with anti-Myc beads.(TIF)Click here for additional data file.S8 Fig(A) Trypan blue staining showed that L233A, L242A and L246A disturbed the cell death phenotype induced by CC-NB-ARC (D481V)-Myc-Rop. (B) The expression of CC-NB-ARC-Myc-Rop and its mutants were detected by anti-Myc antibody. RbcL was stained by Ponceau S as loading control. (C) Co-IP assay showed that L233A, L242A and L246A disturbed the self-association of CC-NB-ARC (D481V)-Rop. All samples were subjected to IP with anti-GFP beads.(TIF)Click here for additional data file.


Deep neural networks have evolved significantly in the past decades and are now able to achieve better progression of sensor data. Nonetheless, most of the deep models verify the ruling maxim in deep learning—bigger is better—so they have very complex structures. As the models become more complex, the computational complexity and resource consumption of these deep models are increasing significantly, making them difficult to perform on resource-limited platforms, such as sensor platforms. In this paper, we observe that different layers often have different pruning requirements, and propose a differential evolutionary layer-wise weight pruning method. Firstly, the pruning sensitivity of each layer is analyzed, and then the network is compressed by iterating the weight pruning process. Unlike some other methods that deal with pruning ratio by greedy ways or statistical analysis, we establish an optimization model to find the optimal pruning sensitivity set for each layer. Differential evolution is an effective method based on population optimization which can be used to address this task. Furthermore, we adopt a strategy to recovery some of the removed connections to increase the capacity of the pruned model during the fine-tuning phase. The effectiveness of our method has been demonstrated in experimental studies. Our method compresses the number of weight parameters in LeNet-300-100, LeNet-5, AlexNet and VGG16 by  Deep neural networks have significant performance in computer vision, speech recognition, natural language processing, etc. For sensor data processing, deep neural networks can also achieve good results. However, we must face up to some problems. Deep neural networks often require huge computing storage resources, while the resources of sensor devices are usually limited. Several investigate researches on deep neural networks, such as , provideNeural network pruning is a traditional task that has received increasing interest with the development of neural networks. Especially, as deep neural network plays an increasing important role in practical applications, neural network pruning has become a highlight area of research, as it helps to achieve good working of deep neural networks on mobile or embedded devices. Moreover, neural network pruning is also a technique for designing or searching neural network structures. Back in 1990, LeCun et al. proposed Optimal Brain Damage (OBD) , which rIn model pruning, we not only want a sufficiently sparse network, but also want the network to retain as much of its original performance as possible. Most existing neural network weight pruning methods remove some unimportant weights or neurons by preset pruning ratio of each layer or global pruning ratio for the whole model. A simple experiment was conducted to verify the influence of pruning ratio per layer on pruning performance. In the experiment, we pruned the LeNet-300-100 that is a fully connected network with three layers, and the total pruning rate in the different pruning schemes is always 80%, but the pruning rate of each layer was different. The experimental results are shown in To address the above problems, we first propose a differential evolutionary neural network compression framework (DENNC). In DENNC, we use a differential evolutionary algorithm to solve the problem of layer sensitivity to pruning. The mathematical representation of model weight pruning is an optimization problem with two terms, two of which correspond to sparsity and network accuracy. The decision variable for the above problem is the pruning mask, which can be determined by the sensitivity of each layer to pruning. After pruning the model with differential evolution, we will fine-tune the pruned model. We also loop the above two steps using an iterative pruning framework to obtain better solutions. It is worth noting that at the beginning of the model fine-tuning, we randomly recover a few of the removed connections to maintain the capacity of the model.The remainder of this paper is organized as follows. In Differential evolution (DE) ,21,22 isDE/rand/1:DE/best/1:DE/rand-to-best/1:DE/best/2:DE/rand/2:where i-th individual g-th generation. n], where n is the size of population. And g. F is a positive scaling factor to scale the difference vector.For mutation operations, there are five popular mutation strategies  in DE alCrossover operation is performed on each population individual Before selection operations, all of the above generated trial individuals are evaluated on the fitness function corresponding to optimization problem. Let Equation .There is significant redundancy in deep neural networks which is mainly caused by over parameterization . These oNeural network pruning is a classic technique in the filed of model compression, and it can be traced back to the 90s in the 20th century ,7. In reParameter quantization is another efficient method for compressing models. Recently, parameter quantization has two main research directions, one is weights sharing, the basic idea of which is that multiple network connections share a weight, and the other is weights reduction, i.e., weight represent with low bit. Han et al.  proposedKnowledge distillation can be regarded as a transfer learning method where knowledge from a trained large model (teacher model) is extracted and transferred to a tiny model (student model). Hinton et al. introduced the concept of knowledge in  by learnIn this section, we introduce the proposed differential evolutionary neural network compression method in detail. The proposed method adopts an iterative pruning and fine-tuning framework, and the differential evolution is used to achieve neural network weight pruning. Moreover, we adjust the fine-tuning strategy in order to promote the model capacity, and a simple computational complexity analysis is given at the end of this section.i-th layer. i-th layer, respectively. If the dimension of K means the kernel size, L denotes the number of layers. We define l-th layer.We formally introduce the symbols and annotations for neural network weight pruning firstly. The deep neural network can be parameterized by We follow the iteration pruning framework  which diThe detailed framework of DENNC is summarized in For hierarchical neural networks, different layers can usually extract different features, and thus the weight distribution of each layer is different. Taking a fully connected neural network as example, it is impossible for us to remove 90% connections of each layer without losing accuracy. On the one hand, if we prune too many connections, the compressed neural network will not perform as well as before. On the other hand, if we remove too few connections, we will not get a sufficiently streamlined network. Therefore, it is not reasonable to use a uniform pruning threshold for all layers. To address this problem, we use a differential evolutionary algorithm to autonomously optimize the pruning sensitivity of each layer. We design the objective function of differential evolution to directly respond to the pruning effect, and set the pruning sensitivity of each layer as the decision variable of the objective function.W denotes the current weight of the model, Before establishing objective function to optimize pruning sensitivity, we first denote the error ratio of the neural network Equation . In ordequations  and 13)E(·) by(C denotes a constant. In this fitness function, we are interested in the pruning effect and the accuracy of the model after pruning, rather than the change in accuracy, which increases the probability of getting a more accurate model. The pseudocode of DEPS is shown in Algorithm 1. After running DEPS, we can obtain the final pruning sensitivity Z and the pruning mask Algorithm 1: Pseudocode of DEPSThe differential evolutionary algorithms have significant advantages in solving the NP-hard problem. The problem  is clearEquation , we simpEquation . Next, tEquation .AlgoritThrough the above pruning operation, we can obtain a sparse neural network, as shown in the second network in In the model fine-tuning stage, we not only update the remaining weights, but also recover a few pruned connections, which is known as the recovery connections fine-tuning strategy. Updating the remaining weights helps to improve the accuracy of the model, while restoring pruned connections can promote the capacity og the model. During fine-tuning, we randomly recover some previously pruned connections and set random weights to them firstly, and then train the pruned neural network for a few epochs as usual. The detailed fine-tuning parameters are the same as those of the model training, except that the epochs are few.P, the computational cost of fitness evaluation is N individuals, the computational cost is G. Assuming the computational cost of neural network training for each epoch is e denotes the number of epoch in fine-tuning. Thence, the computational cost of DENNC in each cycle is K stands for total number of iteration, the computational complexity of our method is The computational complexity of DENNC model consists of two parts, the computational cost of DEPS for model pruning and the computational cost of fine-tuning pruned model. In DEPS, the main complexity comes from fitness evaluation. Assuming the number of weight parameters is In this section, we demonstrate the performance of DENNC with experimental studies. Firstly, we simply introduce datasets and experimental settings. Secondly, experimental results on LeNet, AlexNet and VGG16 are presented in detail. Lastly, we analyze some parameters and restoring connections fine-tuning strategies in the ablation studies.In experimental studies, we choose four different neural networks on two datasets, LeNet-300-100 and LeNet-5  on MNISTLeNet-300-100 is a fully connected neural network which has two hidden layers with 300 and 100 neurons, respectively.LeNet-5 is a simple convolutional neural network with two convolutional layers and three fully connected layers.AlexNet is a deep convolutional neural network, which includes five convolutional layers and three fully connected layers.VGG16 is a more deeper convolutional neural network with sixteen layers in total, in which thirteen convolutional layers and three fully connected layers are included.Detailed network information, such as model type, accuracy and structure of each layer, is shown in the following P is the number of weight parameters. Iterative pruning method mentioned in [K, the computational cost of iterative pruning is e and T mean the epoch number in fine-tuning and the cost of fine-tuning for each epoch. The MONNP establishes a multi-objective neural network pruning model and uses multi-objective particle swarm optimization to handle the problem. Compared with proposed DENNC, the computational complexity is similar.The comparison methods consist of naive cut , iteratiioned in  also pruNote that all model used in experiment are trained by ourselves because we fail to obtain original model weights. Therefore there are some different results compared with that of reference papers.Firstly, overall experimental results of proposed DENNC and comparison methods are shown in Secondly, we analyze the performance of the proposed DENNC on LeNet-5 pruning task. Our DENNC belongs iterative pruning methods, and in each iteration, we pruning weights by the differential evolutionary layer-wise weight pruning method. So the fitness curves of the differential evolutionary layer-wise weight pruning under 100 generations are shown in Next, we will analyze pruning results of each model in detail as follow.We firstly prune LeNet-300-100 and LeNet5 on MNIST. The detailed pruning results of each layer of these two neural networks are shown in For LeNet-300-100, we present pruning results of each layer in For LeNet-5 model, detailed pruning results of each layer are shown in AlexNet is a deep neural network which has five convolutional layers and three fully connected layers. We present detailed pruning results of each layer in VGG16 model is a very classical deep model with very a large amount of weight parameter. In this part, we prune VGG16 model on CIFAR10 and the results of each layer are shown in In order to analyze the sensitivity of balance parameter and the effectiveness of used strategy, we make ablation studies as follow. The ablation studies involve two experiments, parametric sensitivity analysis and fine-tuning strategy analysis.According to definition above, the fitness function includes two terms, and these two terms are contradictory usually. Thence, the balance parameter In Equation , the larIn proposed DENNC, we adjust the fine-tuning strategy to obtain better model capacity. In previous iterative pruning methods, fine-tuning is used to update remained weights. However, we use fine-tuning to update not only remained weights but also some removed weights. In this part, we will analyze the effectiveness of recovery connections fine-tuning strategy.Firstly, we analyze the influence of recovery connections fine-tuning strategy for pruning fitness. As shown in Secondly, in order to explore the impact of fine-tuning strategy more intuitively, especially for model accuracy, we show two accuracy curves of the pruned models in Finally, we plot weight distribution of pruned LeNet-300-100 with and without recovery connection fine-tuning strategy in In this paper, we proposed a differential evolutionary weight pruning method to compress neural networks. It is based on iterative pruning framework, consists of two phases, model pruning and model fine-tuning, respectively. In model pruning phase, we analyzes the pruning sensitivity of each layer by differential evolutionary approach, and then the sensitivity is used to guide the pruning process. In model fine-tuning phase, the connections that have been removed are also considered for proper recovery, which is able to improve model capacity. Experimental results demonstrate that our method can efficiently compress model, there is least 10× compressing ratio for each model, especially 29× compressing ratio in AlexNet. Compared with similar threshold pruning methods, out method performs better than these comparison methods overall. Moreover, we also do ablation studies for parametric sensitivity analysis and fine-tuning strategy analysis. Experiments show parameter There are still some unresolved issues in this paper, for example, the time cost of the proposed method is expensive and with obtained sparse neural network it is also hard to accelerate inference. Therefore, in future work, we want to speed up the proposed method with parallel computing. Furthermore, we will also focus on neural network structural pruning with heuristic optimization.


Users can navigate both ontology terms and annotated domains through improved ontology hierarchy browsing. A newly added facility enables domain-based ontology enrichment analysis.Protein structural domains have been less studied than full-length proteins in terms of ontology annotations. The dcGO database has filled this gap by providing mappings from protein domains to ontologies. The dcGO update in 2023 extends annotations for protein domains of multiple definitions  with commonly used ontologies that are categorised into functions, phenotypes, diseases, drugs, pathways, regulators, and hallmarks. This update adds new dimensions to the utility of both ontology and protein domain resources. A newly designed website at   A growing number of ontologies have been created to annotate full-length proteins; however, there is a significant need for using ontologies to annotate protein domains. Domain-centric ontology annotation resources are essential since protein domains often act as the functional units of proteins and haven been shown to be useful in protein function prediction7 and more recently in hypothesis-free phenotype prediction.8Computational prediction of protein structures has become feasible,9 but also for domains from Pfam10 and InterPro.11 In parallel with the growth in ontology knowledge-bases, these domain-centric annotations are available across various knowledge contexts, ranging from functions and pathways to phenotypes and diseases, and even drugs. Systematic mappings from protein domains to ontology terms, via dcGO, maximise the utility of both ontology and domain resources.Over time, dcGO has evolved to support domain-centric annotations not only for protein domains taken from the structural classification of protein (SCOP) at both the superfamily and family levels,13 we have continued to expand ontologies and domains, and considerably, we have redesigned a new website 6 that records the observed number of proteins (i.e. matrix entries) with structural domains (in columns) and ontology terms (in rows).Prepare a correspondence matrix(ii)15 and the annotation strength is quantified by a hypergeometric distribution-based score (or ‘annotation score’) rescaled into the 1–100 range.Deduce associations/annotations between domains and terms from the corresponding matrix using Fisher’s exact test. The annotation significance is measured by false discovery rate (FDR) with Benjamini-Hochberg corrections for multiple hypothesis testing,(iii)16 This rule ensures that a protein domain annotated to a term must also be annotated by its top-level parent terms in paths towards the ontology root.5Propagate domain-centric annotations to all ancestor terms  according to the ‘True Path Rule’, which respects the directed acyclic graph of an ontology (e.g. GO).The building method has evolved over time and can be simplified into the following steps:5 In this 2023 update, the method has been applied to almost all commonly used ontologies for protein domains of different definitions, which are described in greater detail below.In summary, the dcGO building method takes as inputs ontology terms attached to proteins and the domain composition of proteins, and then statistically infers mappings from protein domains to ontology terms within a probabilistic framework. For further details, users are referred to our previous publications on the method.9 Pfam,10 and InterPro11 17 (accessed in October 2022), which includes GO Biological Process (GOBP), GO Molecular Function (GOMF), and GO Cellular Component (GOCC).Functions: GO(ii)18 (June 2022 release), Mammalian Phenotype Ontology (MPO)19 (accessed in July 2022), and other phenotype and anatomy ontologies for model organisms such as WormBase20 (WS284 release), FlyBase21 (6.48 release), ZFIN22 (accessed in July 2022), and TAIR23 (accessed in July 2022).Phenotypes: This category includes Human Phenotype Ontology (HPO)(iii)24 (v2023-01-04 release), and Experimental Factor Ontology (EFO) used to annotate genome-wide association study (GWAS) disease traits25 (3.44.0 release).Diseases: This category includes Mondo Disease Ontology (MONDO) that harmonises disease definitions across the world(iv)26 (2022-Feb release) and target tractability buckets (Bucket) from Open Targets27 (22.06 release).Drugs: That is, druggable categories from DGIdb(v)28 (103.0 release), REACTOME29 (version 81 release), PANTHER30 (17.0 release), WikiPathways31 (July 2022 release), and MitoPathways from MitoCarta32 (MitoCarta3.0 version).Pathways: This category primarily includes sources from KEGG(vi)33 (accessed in July 2022) and TRRUST34 (2018.04.16 release).Regulators: That is, ENRICHR Consensus TFs(vii)35 (v7.5.1 release).Hallmarks: Molecular signature hallmarks from MSigDBThe dcGO update in 2023 now conveniently organises ontologies into seven broad categories :(i)Funchttps://mojolicious.org) and Bootstrap (https://getbootstrap.com) to support a mobile-first and responsive web experience for all major browsers and devices. To enable faceted search, the website uses the typeahead JavaScript library (https://twitter.github.io/typeahead.js), which includes a suggestion engine for queries (ontology terms or protein domains) and a user interface view for rendering suggestions and handling hyperlinks from search results. Enrichment results from domain-based enrichment analysis are rendered using the bookdown R package (https://bookdown.org), which generates self-contained dynamic HTML files in the enrichment results page. The source code for the dcGO website is made available at GitHub (https://github.com/hfang-bristol/dcGO).The website has been revamped using the Mojolicious Perl real-time web framework . The user-request interface (Click 4 of ‘Dotplot of enriched ontology terms’ tab, which shows the top five terms with their respective Z-scores and FDR. All enrichment results are embedded into a self-contained dynamic HTML file, which can be downloaded and explored interactively in a new browser window, making it easy for users to explore the results further.The dcGO resource provides a unique reference knowledgebase for domain-centric ontology annotations, and a new facility has been developed to perform enrichment analysis for user-input protein domains. This facility enables the identification of enriched ontology terms, a feature not available in other web-based enrichment analysis tools (for example, DAVID web server for enrichment analysis focusing on genes/proteins38 SUPERFAMILY,39 and Priority index.42 Looking to the future, we are also excited to explore the potential of large language models43 in generating domain-centric ontologies, following their success in generating functional protein sequences.44In this updated version of the dcGO resource, our continued focus is on providing systematic mappings from protein domains to ontologies. We are excited to introduce a new website with enhanced data analyses and a unique facility for identifying ontology knowledge enrichments from the perspective of domain-centric annotations. Our commitment to updating the resource twice a year includes integrating information from our previously established resources such as XGR,


As a non-antibody scaffold, monobodies based on the fibronectin type III (FN3) domain overcome antibody size and complexity while maintaining analogous binding loops. However, antibodies and their derivatives remain the gold standard for the design of new therapeutics. In response, clinical-stage therapeutic proteins based on the FN3 domain are beginning to use native fibronectin function as a point of differentiation. The small and simple structure of monomeric monobodies confers increased tissue distribution and reduced half-life, whilst the absence of disulphide bonds improves stability in cytosolic environments. Where multi-specificity is challenging with an antibody format that is prone to mis-pairing between chains, multiple FN3 domains in the fibronectin assembly already interact with a large number of molecules. As such, multiple monobodies engineered for interaction with therapeutic targets are being combined in a similar beads-on-a-string assembly which improves both efficacy and pharmacokinetics. Furthermore, full length fibronectin is able to fold into multiple conformations as part of its natural function and a greater understanding of how mechanical forces allow for the transition between states will lead to advanced applications that truly differentiate the FN3 domain as a therapeutic scaffold. The Fibronectin Type 3 domain (FN3) is an established scaffold for developing non-antibody binding domains ,2,3,4. CA challenge for the diverse range of non-antibody protein scaffolds is to exploit native characteristics and produce tailored applications . The advFull-length fibronectin includes multiple repeats of the Type 3 Fibronectin (FN3) domain, combined with type 1 and 2 fibronectin domains and others, with each FN3 domain naturally associating with multiple targets involved in forming the extracellular matrix. This association was originally seen to occur via a conserved ‘RGD’ amino-acid motif in the FG loop of some FN3 domains A; as sucIn contrast to the rigidity in antibody beta-sheets imposed by internal disulphides, the mode of interaction between fibronectin and its targets involves a combination of loop flexibility and beta-sheet dynamics that allows FN3 domains to adopt multiple optimal binding conformations ,19. AddiThe Type 3 Fibronectin scaffold has been developed into a range of monobody derivatives C, similaAn initial alternative approach was to make use of similar FN3 domains from Tenascin instead of Fibronectin, which was then stabilised largely with the introduction of a cysteine bond between the C and E beta-strands . This TeThe current field of monobody development also benefits from mature technologies that can deliver binding reagents on demand  through The Adnectin-anti-VEGFR2 ‘CT-322′ was the first of the monobody family to reach clinical trials ,47. HoweIn spite of this clinical failure, the Adnectin-anti-VEGFR2 has become a model protein for development of technologies supporting the monobody scaffold, with recent studies aiming to enhance the Adenctin’s pharmacokinetic properties through PASylation  or improThe small size (~10 kDa molecular weight) of individual FN3 domains confers increased tissue distribution and reduced half-life, whilst the absence of disulphide bonds improves stability in cytosolic environments and facilitates the introduction of non-native cysteines. The applications of monobodies in fundamental science discovery have been covered elsewhere , alterinEarly Antibody Drug Conjugates (ADCs) were limited by properties of the antibody scaffold. The high molecular weight of intact antibodies prohibited distribution throughout a solid tumour and instead these antibodies and their toxic conjugates remained in blood circulation due to recycling processes that target the Fc region and glycosylation . This waMonobody domains have been developed as part of the latest wave of ADCs, as their smaller physical size results in improved diffusion and extravasation that enables faster tumour penetration . Small nThe ADC applications of monobodies and other small non-antibody scaffolds, however, are disadvantaged by the absence of certain features present in antibodies. Notably, cysteine conjugation in an antibody results in an average DAR of 4:1, where a single introduced cysteine on a monobody will result in a DAR of 1:1 A. This mGiven these challenges, design has shifted focus instead to biosensor applications, such as combining radioisotopes with tumour cell targeting binders. In one application, Adnectins were generated for non-invasive imaging of PD-L1 with radioisotopes B 65]. P. P65]. P18F loaded Adnectin, where imaging could be completed on the same day as injection and antibody-based reagents may take several days of radiation burden to clear non-specific binding . T. T29]. TAlteration of signalling pathways was also applied through steric inhibition on target interactions, as the monobody domain can be used intracellularly to understand and modify protein interaction networks. Although complete mutational knockouts are important for understanding the importance of a protein to its network, a single protein may interact with an average of four partners in multiple pathways which are all disrupted in a protein knockout . To overFurther to blocking activity in endogenous intracellular proteins, the monobody domain has been combined with ubiquitin-tagging domains to selectively induce degradation of those intracellular proteins. By fusing to domains that confer degradation, such as the Von Hippel–Lindau oncoprotein suppressor (VHL) or E3 ubiquitin ligases D, monoboThis mix-and-match approach of combining monobodies mimics the natural assembly of FN3 domains in fibronectin as a beads-on-a-chain pattern with each FN3 domain interacting with a unique set of targets in the extracellular matrix . This reThe small size and short half-life of the monobody scaffold has been exploited in imaging and delivery applications. However, these properties are limitations to developing the scaffold as a next-generation therapeutic platform. Whereas antibodies have extended circulation lifetime due to natural functions of recycling via their Fc domains and glycosylation, monobodies must be engineered to match these critical clinical features. To date, most non-antibody biologics have made use of polyethylene glycol (PEG) to increase their size, reduce renal filtration and lengthen circulating half-life. This polymer is produced as a mixture of molecular weights with high polydispersity, is not amenable to further chemical functionalization and is known to produce formaldehyde upon degradation. Alternatively, a straightforward application of fibronectin-like modular design is to increase circulating half-life and thus improve pharmacokinetic properties via fusion with non-monobody domains .Staphylococcus aureus infections [In order to maintain the structural simplicity of monobody constructs, multiple non-Fc fusions have been attempted. By conjugating to various mutants of the Albumin Binding Domain (ABD) from Protein G that have a gradient of affinity to Serum Albumin E, the hafections .The small and simple structure of a monobody is attractive given the importance of reducing manufacturing costs for biologics, if only to lessen this burden in the early stages of research translation. To this end, an Adnectin monobody was raised to mimic the effect of approved antibodies Evolocumab and Alirocumab on the Coronary Heart Disease target PCSK9 (Proprotein convertase subtilisin kexin 9). These antibodies showed a reduction of LDL Cholesterol over 60% by blocking PCSK9-led degradation of the LDL-C Receptor . HoweverConjugation of PEG to an Adnectin monobody that inhibited PCSK9 activity resulted in a molecule that showed a marked cholesterol decrease in pre-clinical models while extending circulation time . UnfortuThe final application of this modular assembly aims to combine useful antibody characteristics such as Fc recycling or bivalency, and build on them to generate improved therapeutics. For example, monobodies have been used in place of scFv domains A, which In a further extension of this design, Tn3 monobodies were fused to replace either the entire scFv region or individual variable domains A,B. The Further to replacing scFv domains in antibodies, significant challenges in generating bispecific antibodies  can be oMonobodies have also been designed to replace scFvs in other contexts including next-generation CAR-T therapies. Where antibodies and their resulting scFvs can be difficult to generate and fine-tune for affinity, monobody evolution systems readily generate binders which can be integrated into CAR-T systems . AdnectiTo further improve on this fusion approach, Centyrin monobodies were integrated into a CARTyrin expression system which combines selection markers and a safety self-killing switch into a simple mRNA and plasmid DNA transcription system . CARTyriA key limitation in developing antibodies, as discussed earlier, is uncontrolled mispairing between heavy and light chains. This is exacerbated in bispecific antibodies, where only one of many possible interdomain pairings will yield a viable multivalent molecule  with theMonobodies have previously been co-administered as a potential combination therapy . HoweverAn early example of this development is an Adnectin monobody which was evolved to target CD4, a target receptor for HIV entrance into cells, and thus sterically block HIV internalisation via CD4. The major advantage was that, in contrast to an antibody, the monobody could block the HIV interaction epitope without blocking the natural function of the CD4 receptor . In ordeA key improvement in moving away from the antibody scaffold is avoiding immunoreactive properties intrinsic to natural antibody function. For example, the CD40 receptor is involved in the activation of immune cells that produce autoantibodies and is a key route of pathology in autoimmune disease . BiologiWhile multi-specificity is an important design aim in therapeutics, antibody multivalency is a feature that the monobody domain can improve upon. Medimmune was able to take this approach of multivalent Tn3 monobody fusions further with a range of mono- to octa-valent constructs C to geneWhere multi-specificity is challenging with an antibody format that is prone to domain swapping, FN3 domains in the modular fibronectin assembly are already multivalent and multispecific. By utilising this native assembly of FN3 domains in fibronectins, multiple approaches can be designed that overcome head-to-head limitations against antibody half-life and circulation. More critically, modular combinations can easily match antibody multispecificity or multivalency to differentiate the monobody scaffold in a way that surpasses the limitations present in immunoglobulin domains. In the field, this has developed in fusions like the tandem bi-valent Tn3 monobody fused to a Serum Albumin domain  or tandeFull length fibronectin is able to fold into multiple conformations as part of its natural function. The fibronectin assembly is involved in the formation of the extracellular matrix (ECM), and thus has an internal mechanism for the transition from soluble monomeric conformations to larger oligomers and finally insoluble ECM . This reDomain swapping is usually removed through stabilisation of the monomeric fold, since any tendency for oligomerisation complicates developability , for exaEnhancing the thermostability of FN3 domains may reduce or even eliminate strand exchange function, however in practice mobility of domain-swapping beta-strands has been observed in structural studies of thermostable Centyrins ,121, molHowever, developing a greater understanding of the conformational flexibility of fibronectin and how mechanical forces allow for the transition between active and inactive states provides a route to context sensitive applications. Further, where inter-domain exchange may alter the presentation of a primary interface for binding, it may also create a unique secondary interface that is also active and context-specific . Applicath FN3 domain has been shown to be responsive to changes in pH [A well-studied feature of the fibronectin aggregation mechanism is conformational change in reaction to environmental conditions. One environmental change of interest is that the mechanical stability of the 10es in pH . This ines in pH , as welles in pH .pH responsiveness has previously been applied in other therapeutic domains to optimise release from various parts of the low-pH endosomal degradation pathway. As high-affinity binding often results in antibodies following their targets into endosomes, anti-PCSK9 antibodies have been evolved to almost completely lose their affinity in low-pH to promote recycling back into circulation . InsteadEscherichia coli expression systems [In a similar application to the anti-PCSK9 antibodies, monobody CDRs were evolved for reduction in affinity at low pH. Although this design was able to yield monobodies with a 10-fold difference in affinity between pH 5.5 and 7.4, most of the mutants suffered a trade-off in stability to achieve this function and did not survive the shift from complex mammalian expression used in directed evolution to the more crude  systems .The inclusion of a signalling moiety into proteins to create binding sensors remains a substantial challenge. Previously, changes in conformation have been exploited for FRET signalling , howeverOne of the best established examples of incorporating fibronectin function into monobodies is the FREX biosensor design, which exploits the tendency of wildtype fibronectin to kinetically exchange fragments between multiple FN3 domains . A beta-A major challenge for the developing field of antibody-agnostic binding proteins is to meaningfully differentiate from the current antibody gold-standard. However, it is important to understand the historical background to antibody development. Antibodies were originally developed in part because natural immune systems could be harnessed to non-rationally develop highly specific, high affinity binders of therapeutic targets. Now that the process of directed evolution has been largely abstracted into laboratory processes ,133,134,


The cytochrome P450 monooxygenase P450 BM3 (BM3) is a biotechnologically important and versatile enzyme capable of producing important compounds such as the medical drugs pravastatin and artemether, and the steroid hormone testosterone. BM3 is a natural fusion enzyme comprising two major domains: a cytochrome P450 (heme-binding) catalytic domain and a NADPH-cytochrome P450 reductase (CPR) domain containing FAD and FMN cofactors in distinct domains of the CPR. A crystal structure of full-length BM3 enzyme is not available in its monomeric or catalytically active dimeric state. In this study, we provide detailed insights into the protein-protein interactions that occur between domains in the BM3 enzyme and characterize molecular interactions within the BM3 dimer by using several hybrid mass spectrometry (MS) techniques, namely native ion mobility MS (IM-MS), collision-induced unfolding (CIU), and hydrogen-deuterium exchange MS (HDX-MS). These methods enable us to probe the structure, stoichiometry, and domain interactions in the ∼240 kDa BM3 dimeric complex. We obtained high-sequence coverage (88–99%) in the HDX-MS experiments for full-length BM3 and its component domains in both the ligand-free and ligand-bound states. We identified important protein interaction sites, in addition to sites corresponding to heme-CPR domain interactions at the dimeric interface. These findings bring us closer to understanding the structure and catalytic mechanism of P450 BM3. Bacillus megaterium and was found to hydroxylate a range of different saturated fatty acids with ∼12–18 carbon chain lengths at the ω-1 to ω-3 positions  intermediate which catalyzes oxygen insertion into the substrate (−1 with arachidonic acid substrate) (Flavocytochrome P450 BM3 (BM3)1–6–ositions . The CPRositions . The conubstrate . The P45bstrate) . Other hbstrate) .Although several crystal structures have been determined for WT and mutant forms of the BM3 heme domain, no structures have been solved for the intact CPR domain, or for the full-length (monomeric or dimeric) P450 BM3 protein. However, structures are available for the FAD/NADPH-binding (ferredoxin reductase-like) FAD domain of the BM3 CPR , and theAside from X-ray crystallographic approaches, several other methods have been used to characterize the structural and spectroscopic properties of P450 BM3 and its component domains. These include techniques such as CD , Mössbauer spectroscopy (to probe ferryl species in BM3 and P450cam), resonance Raman , and EPR  –21. Howe–et al.  studies. In the EM structural model, the two CPR domains interact with one another in an extended conformation, with the more flexible N-terminal heme domains predicted to interact with the CPR FMN domains at the proximal face of the heme. In this model, the CPR domains are tightly associated whereas the heme domains can occupy multiple conformations. However, the exact positions of the heme domains could not be accurately determined and so specific residues or regions of interaction with the CPR FAD-/FMN–binding domains could not be defined . These det al.  and Girv  of the enzyme.In previous studies, we have used a variety of MS techniques including hydrogen-deuterium exchange mass spectrometry (HDX-MS), native MS, and CIU to analyze structural properties and dynamics within proteins that have been difficult to capture by alternative methods, such as X-ray crystallography . The HDXcam –32. In tAFig. S1). The monomeric heme domain exhibited the same three charged species in its ligand-free and NPG-bound states, with the 14+ state being the most prominent . For the CPR (diflavin reductase) domain, multiple species were observed, corresponding to one, two, three, and four molecules of NADP+ bound to the reductase domain .Native MS experiments confirmed that the full-length protein exists predominantly as a dimer in solution  and ligand-bound domains . The ligand-free, full-length dimeric P450 BM3 protein showed six discrete unfolding events (A). The heme domain showed three discrete unfolding intermediates (B), whereas the CPR domain undergoes unfolding across a narrow range of overlapping, unfolding intermediates (D). As stated previously, the CPR module contains two domains: the FAD domain and the FMN domain. However, the CPR domain exhibits an unfolding pattern more suggestive of a single domain structure. In contrast, the heme domain behaves more like a multidomain structure during CIU experiments, because of the presence of three discrete unfolding events. The full-length dimeric protein and its domains remain stable in their unfolded states up to 200 V as there is no loss of signal.Collision-induced unfolding (CIU) is a technique where external energy is applied to probe protein stability and follows protein unfolding, similar to methods in which increasing temperature is used to unfold the protein, such as differential scanning calorimetry (DSC) . The unf domains , C, E, Fg events A. The hemediates B, whereamediates D. As staTWCCSHe) values derived from IM-MS were 34.6 ± 0.2, 37.5 ± 1.1, and 99.7 ± 1.2 nm2 for the heme domain, CPR domain, and full-length dimeric BM3 protein, respectively (shown in Table S1). A CPR model structure was produced using the FMN-binding domain structure (PDB 1BVY) and the FAD-binding domain (PDB 4DQK) aligned to the NADP+-bound rat CPR (PDB 1AMO). This model was used throughout the work described here and will be referred to as the CPR model structure from this point forward. These structures gave CCS values of 39.1 nm2 for the heme domain  and 46.1 nm2 for the CPR domain model . Highly dynamic and flexible proteins were shown to have significantly smaller experimental CCS when compared with theoretical CCS based on crystal structures, because of compaction in the gas phase (The collision cross section (as phase .C). The undefined edges are because of the ligand dissociating at high voltages. The presence of NADP+ did not appear to have any significant effect on the unfolding energy barrier of the CPR domain (E and F).The binding of NPG to the heme domain slightly increased the unfolding energy barrier of the heme domain C. The unR domain , E and FFig. S2. green to blue) and solvent accessibility because of deshielding (yellow to red). A and B, show the differences between the heme domain of the full-length P450 BM3 and the heme domain mapped onto PDB 1BU7 and rotated by ∼180 degrees. Differences were calculated by subtracting the heme domain deuterium values from the full-length P450 BM3 deuterium values. A large area of Fig. 3B in the heme domain from the full-length P450 BM3 shows a slight increase in shielding (cyan), including residues 279–315 . Other areas that see changes include the F-helix (residues 178–185) and the β3/4 sheets in cyan. The area with the greatest shielding (dark blue) when the CPR domain is present is from residues 22–30 in the heme domain A-helix, as seen clearly in Fig. 3A. These areas are potential sites for forming the interface between the CPR and heme domains. Interestingly, there are certain areas  that have increased solvent accessibility when the CPR domain is present in the protein, in particular, glutamate 424 (red) and the residues surrounding it (yellow). Fig. 3C shows the butterfly difference plot for the HDX-MS data, highlighting areas of greatest change for the heme domain in blue (indicating increased shielding) and red (indicating increased deshielding). Similarly, the greatest changes observed for the CPR domain, by comparing this domain to the full-length protein, are highlighted in Fig. 3F.HDX-MS is a technique that enables visualization of the solvent accessibility of amino acid residues to determine protein structure, ligand binding, and protein dynamics. Here we have performed experiments comparing two states to each other, for example by contrasting a full-length protein with its component domains, or by comparing a ligand-free protein with a ligand-bound protein to determine which areas become more shielded (less deuterium uptake) or more solvent accessible/deshielded (more deuterium uptake) on conformational rearrangement. Some examples of deuterium plots showing the changes in deuterium uptake over time across several states are shown in D and E shows that the isolated CPR domain exhibits greater changes in the uptake of deuterium than for full-length P450 BM3 or the heme domain, as observed for the blue and red regions of the protein. Differences were calculated by subtracting the CPR domain deuterium values from the full-length P450 BM3 deuterium values. These data have been mapped onto the CPR model structure . Many areas within the full-length P450 BM3 protein are deshielded, leading to increased deuterium exchange (orange to red). In particular, a portion of the hinge domain , a region adjacent to the NADPH-binding site of the FAD domain (residues 967–974 in orange and residues 980–990 in yellow) and a portion of the FMN domain (residues 593–598 in red). These data show how flexible the CPR domain is and how much it moves in the presence of the heme domain within the full-length P450 BM3 protein. Examination of the shielded areas for the interaction sites of the two domains reveals a number of interesting regions, indicated in dark blue in D and E. In the FAD domain, three areas are identified in the hinge domain: residues 709–720, 766–767, and 773–781 in dark blue. In the FMN domain, the residues most affected are 559–566 (cyan), Thr-592 (dark blue) and 599–601 (dark blue). The combination of shielded and deshielded areas in the CPR hinge domain suggests a dramatic protein movement, allowing one side to become more solvent accessible and the other more shielded.1JPZ and the CPR model used previously . The ligands used for these experiments were the substrate NPG (which binds within the P450 active site) and NADP+, which binds adjacent to the FAD cofactor of the CPR domain). The NADP+-binding site is immediately adjacent to the FAD isoalloxazine ring, but the oxidized NADP+ cannot reduce the FAD and instead acts as a structural mimic of NADPH. In A and B, the ligand-free protein is compared with the NADP+-bound protein, revealing very little structural change in the heme domain. There is a slight increase in solvent accessibility visible around the N terminus of the heme domain. In particular, three residues of the A-helix, near the N terminus, exhibit deshielding in all ligand-bound states. The greatest change to the heme domain was exhibited on NPG binding or NADP+/NPG binding to the full-length P450 BM3 in C and D, and E and F, respectively. Both states exhibit deshielding around residue 138 and a portion of the J-helix (residues 294–305). However, these areas are more extensive for the NPG-bound protein. No significant shielding is observed for the NPG-bound protein. However, deshielding is observed across the protein upon NPG-binding in the B-, B′-, C-, C′-, D-, E-, F-, I-, J-, J′-, and K-helices. In addition, a β sheet at the C terminus of the heme domain exhibits significant deshielding. However, the greatest deshielding is observed in the E-helix (residues 149–155 in red). In contrast, the NADP+/NPG-bound protein exhibits fewer deshielded areas, many identical to the NPG-bound protein. This differs for the large deshielded area containing much of the L-helix, observed on NADP+/NPG-binding. Interestingly, shielding is observed for the NADP+/NPG-bound protein near the N terminus (residues 15–20 in purple) and the helices and random coil segments between the F- and G-helices (residues 187–197 in cyan).+-bound form reveals very little change in solvent accessibility across the CPR domain, shown in gray (A and B). There is also a slight increase in shielding in the FAD domain for residues 1007–1011 (cyan). Surprisingly, binding of NPG had a greater effect on the conformational changes exhibited in the CPR domain compared with those induced by the binding of the CPR domain ligand NADP+, as seen in C and D (NPG-bound) and E and F (NADP+/NPG-bound). The figures illustrate that the CPR domain has undergone many structural changes, including extensive deshielding of the FMN domain, which is further amplified in the NADP+/NPG-bound structure. This is shown, in particular, by orange segments observed in the FMN domain (residues 559–567) and in the FAD domain (residues 711–720 and 822–829). In contrast, shielding is observed for the FAD domain in two areas: residues 967–974 (in cyan) and residues 787–792. The latter area is located on the opposite face to the greatest deshielded area of the protein (residues 713–718). For the NADP+/NPG-bound CPR structure, there is an additional increase in solvent accessibility in two areas: residues 1029–1034 of the FAD domain (red) and residues 559–564 of the FMN domain.In  in gray , A and BComparison of the experimental CCS values to the CCS values calculated from the crystal structures showed slight differences for the heme and CPR domains. Because of the lack of a full-length crystal structure of P450 BM3, no CCS values could be predicted in this case. The isolated heme domain CCS value discrepancy is within the 14% range described as an acceptable error in the literature . HoweverFrom thermal denaturation (DSC) experiments, the full-length P450 BM3 was found to have three distinct unfolding events, with the isolated heme domain having two unfolding events and the CPR domain a single unfolding event . Our CIUet al.  domain interacts to catalyze electron transfer to the P450 heme iron. Gricman et al.  and othe al.  showed a slight deshielding on NPG binding. Also of interest is the consistent deshielding of Thr-149 in the E-helix observed on NADP+ binding. This residue is not well-represented in the literature and no mutagenesis studies have targeted it to date.For the full-length P450 BM3, the heme domain did not exhibit structural changes as dramatic as those observed for the CPR domain on ligand binding. Comparisons of the data described here to those for the human cholesterol 24-hydroxylase CYP46A1 (the only other published example of P450 HDX-MS data) revealed similar percentage changes in deuterium uptake , 29. Are+/NPG-bound forms compared with the ligand-free full-length P450 BM3. Examination of the raw data without state comparisons indicated that the linker region had very high deuterium uptake (8–27%) whether in its full-length form (ligand-free or ligand-bound) or in the CPR domain (ligand-free), which is unsurprising as the flexible linker region likely has very high solvent accessibility regardless of the protein conformation.Unfortunately, there are no crystal structures for the full-length P450 BM3 enzyme in which the catalytically important heme-FMN linker region is intact, and thus we could not map our HDX data accurately to this critically important, but highly dynamic, region of P450 BM3. The deuterium uptake changes observed for this linker region were relatively low; between 4.8 to 6.1% by comparing the CPR and heme domains to the full-length P450 BM3 protein, and between −1.5 to 2.7% for the NADP1BVY/4DQK aligned to PDB 1AMO), revealing a number of shielded areas throughout this domain. However, our focus was drawn toward the FMN domain, as this protein module is known to be responsible for shuttling NADPH-derived electrons between the BM3 CPR and heme domains . A C773A mutation was originally introduced to prevent dimerization of the FAD-binding domain (through formation of an intermolecular disulfide bridge) and to facilitate its crystallization, and is located in the CPR hinge domain region  to the heme domain induces major conformational changes that are transmitted through to the CPR domain, and that the binding of NADP+ produces other conformational changes that are conducive for catalysis. The areas of greatest deshielding are seen in (i) the hinge domain of the CPR, (ii) within the β sheets of the CPR FMN domain, and (iii) in a peripheral α helix within the FMN domain (Lα2). Studies have suggested that NADP+ binding to the CPR domain induces a closed conformation in which the FAD and FMN cofactors are a short distance apart (∼4 Å)  . On redut (∼4 Å) . An impocurately . The deue.g. conformational rearrangements associated with ligand binding in this multidomain dimeric enzyme. Important findings include HDX-MS studies that reveal the flexible nature of the CPR domain and its greater mobility in the presence of the heme domain in full-length P450 BM3 enzyme. Structural reorganization was also observed on the binding of NPG to the intact BM3 enzyme, resulting in deshielding in the heme domain. Furthermore, we have identified several additional unfolding intermediates for the full-length protein and heme domain during CIU experiments. In addition, modeling CCS values from X-ray crystallographic structures to our experimental values leads us to believe the CPR domain adopts a more compact structure than the CPR model would suggest. Our findings thus provide important new insights into the structural and conformational properties of this highly dynamic, industrially important protein.In conclusion, we have probed the surface of the biotechnologically important P450-CPR fusion protein P450 BM3. We have identified many areas of interest with respect to sites of protein dimerization and Escherichia coli BL21 (DE3) strain for gene expression. The BM3 heme domain and the intact P450 BM3 enzyme were expressed in TB medium  with cell growth for 24 h at 37 °C. Cells were harvested by centrifugation . The cell pellets were resuspended in ice-cold buffer A (50 mm potassium phosphate (potassium Pi) containing 350 mm KCl and 10% glycerol, pH 8) containing protease inhibitors , and DNase (10 μg ml−1) (Merck). Cells were lysed by sonication on ice using a Bandelin Sonopuls instrument at 37% amplitude with 12 pulses for 40 s, and with 60-s breaks between pulses. The cell extract was clarified by centrifugation . 30% w/v ammonium sulfate was added to the clarified extract and contaminant proteins were removed by incubation for 1 h at 4 °C using gentle agitation. Precipitated material was removed using centrifugation .WT P450 BM3 and its heme domain were expressed using pET14b (heme domain) and pET15b (full-length P450 BM3 protein) vectors, as described in our previous studies (m (300 ml), 20 mm (200 ml), and 200 mm (60 ml) imidazole was applied to elute the proteins. The eluted proteins were dialyzed into 25 mm potassium Pi, pH 6.5 (buffer B) and applied to separate 28-ml columns containing CHT hydroxyapatite type 1 resin (Bio-Rad). A linear gradient of 25–300 mm potassium Pi, pH 6.5 (600 ml) was applied in both cases to fractionate full-length BM3 and its heme domain.For the full-length P450 BM3 and its heme domain, the proteins were purified using affinity chromatography with a nickel-IDA column, followed by a further chromatography step using hydroxyapatite. The clarified cell extracts in both cases were incubated with nickel-IDA resin overnight at 4 °C in buffer A. The full-length BM3 and heme domain–bound resins were then applied to columns and a stepwise gradient of 10 mA600 reached 0.8 by addition of 500 μm isopropyl 1-thio-β-d-galactopyranoside . Thereafter, the growth temperature was lowered to 30 °C and cell culture was continued for 24 h. Cells were harvested and resuspended in 50 mm Tris containing 1 mm EDTA, pH 7.2 (buffer C). Subsequently, the cells were ruptured by sonication and the CPR domain partially purified using 30% w/v ammonium sulfate, as described above for the full-length BM3 and heme domain proteins. The CPR protein required dialysis before further purification to remove the ammonium sulfate. The dialysis was completed using buffer C. The CPR was purified using a 150 ml DEAE SepharoseTM fast flow resin  with a linear gradient of 0–300 mm KCl in buffer C (1500 ml). The protein was dialyzed into 50 mm potassium Pi, pH 7.2 (buffer D) for further purification on a Q-SepharoseTM Fast Flow column. The CPR protein was then eluted using a gradient of 0–300 mm KCl in buffer D. Finally, the protein was purified using a hydroxyapatite column as described above for the full-length P450 BM3 and heme domain proteins.The CPR domain was cloned into pET11a and expressed in BL21-Gold (DE3) cells. The BM3 CPR domain was expressed in 2× YT medium with cell growth at 37 °C. CPR gene expression was induced when the m ammonium acetate, pH 7 (buffer E) was the buffer chosen because of its volatility for use in native MS experiments.Before analysis of the purified proteins, the samples were desalted using a gel filtration column . 50 mm ammonium acetate (buffer E) using Micro Bio-Spin Chromatography columns , following the manufacturer's instructions. The buffer was exchanged three times for the full-length P450 BM3 enzyme and once for the heme and CPR domains to ensure adequate protein compatibility for native MS. Native MS and IM-MS data were acquired on a Synapt G2S HDMS instrument . NanoESI capillaries were prepared in house from thin-walled borosilicate capillaries   using a Flaming/Brown P-97 micropipette puller . A positive voltage was applied to the solution through a platinum wire  inserted into the capillary. Gentle source conditions were applied to preserve the native-like structure: capillary voltage 1.2–1.5 kV, sampling cone 40 V, source temperature 40 °C. Trap collision energy was 4 V and transfer collision energy was set to 0 V. IM-MS experiments were acquired using a modified Waters Synapt G2 with a 25.05 cm Radio-Frequency confining linear drift cell filled with ∼2 Torr helium, 298 K. A multifield approach (20 V drift voltage increments) was applied using WREnS and in-house software (ORIGAMI)  . CapillaExperiments were performed on a Waters Synapt G2S instrument using NanoESI and trap-activated ion mobility; capillary voltage was 1.2–1.5 kV, cone 40 V, and source temperature 40 °C. The helium cell and the IMS gas flows were 180 and 90 ml/min, respectively; the IMS wave velocity was 400 m/s, and the IMS wave height was 35 V. Nitrogen was the carrier gas. The most intense charge state for each protein species was mass selected using the quadrupole prior to the trap region. Activation was induced by elevating the trap collision energy. ORIGAMI was used to automatically acquire data for collision energies from 4 to 200 V in 2-V increments, as well as for data processing .1BVY (the BM3 FMN domain) and PDB 4DQK (the BM3 FAD domain) aligned to PDB 1AMO (the rat CPR structure), as no full-length BM3 CPR structure has been solved to date.Structures were first energy minimized in the gas phase (with an infinite nonbonded cut-off) using AmberTools14  and the 42–mN-palmitoylglycine (NPG) and/or 1 mm NADPH (NADP+), which bind in the P450 active site and to the FAD domain, close to the FAD cofactor, respectively. NPG and NADP+ were purchased from the Cayman Chemical Company  and Bio Basic Canada Inc. . Samples were frozen at −80 °C until required.The same protein stocks for native MS were also used for HDX-MS. The protein was incubated with appropriate ligands for a few hours on ice. The ligands used were 0.5 mm/z range 290–2500. 30 μm protein solutions were diluted 20-fold into 10 mm potassium Pi in either H2O or D2O, pH/pD 7, and the mixture incubated at 20 °C for 0 min (in H2O) or for 1, 10, 30, or 180 min (in D2O), before the quench step. HDX quenching was achieved by mixing the reaction solution 1:1 with cooled 100 mm potassium Pi . Approximately 37.5 pmol were then injected into the HDX module (0 °C) and washed over a pepsin column  with 0.1% HCOOH in H2O, pH 2.5, at 200 μl min−1. Resulting peptides were trapped on a VanGuard C18 trap column. Peptide separation was achieved on a C18 column  at 40 μl/min flow over 16 min with the following gradient: 0 min, 5% B; 7 min, 35% B; 8 min, 85% B; 11 min, 5% B; 12 min, 95% B; 13 min, 5% B; 14 min, 95% B; 15 min, 5% B . The mass spectrometer was operated in TOF mode (no IM), with MSe data acquisition (trap collision energy ramp 14–35 V). LeuEnk peptide was used as Lock Spray. Data were acquired using Waters MassLynx software v4.1, with the LEAP robot controlled by HDx Director 1.0.3.9. Data processing and analysis were carried out with Waters ProteinLynx Global Server 3.0.1 and Waters DynamX 3.0 software, respectively. Python scripts of the deuterium uptake were generated using the data collected at 1 h. Python scripts were mapped onto structures in PyMOL . High coverage was generated for each state; full-length BM3 versus heme domain (91.5%), full-length BM3 versus CPR domain (89.8%), full-length BM3/no ligands versus full-length BM3/plus ligands (99.1%).The HDX-MS set-up was comprised of a Waters nanoACQUITY UPLC system with ESI-MS detection coupled to a LEAP Technologies dual-armed robot for sample preparation, incubation, and inlet injection. A Waters Synapt G2S mass spectrometer was operated in positive ion/resolution mode, with data acquired over the All the data presented are available in this manuscript.L. N. J., P. E. B., M. W. V., and A. W. M. conceptualization; L. N. J., K. J. P., L. O. J., and H. M. G. data curation; L. N. J., K. J. P., L. O. J., and H. M. G. validation; L. N. J., K. J. P., H. M. G., and M. W. V. investigation; L. N. J., K. J. P., L. O. J., H. M. G., P. E. B., M. W. V., and A. W. M. methodology; L. N. J., H. M. G., and A. W. M. writing-original draft; L. N. J., K. J. P., H. M. G., P. E. B., M. W. V., and A. W. M. writing-review and editing; K. J. P., and P. E. B. resources; L. O. J. software; P. E. B., M. W. V., and A. W. M. supervision; A. W. M. funding acquisition; A. W. M. project administration.


Relative error estimation has been recently used in regression analysis. A crucial issue of the existing relative error estimation procedures is that they are sensitive to outliers. To address this issue, we employ the  In regression analysis, many analysts use the  least squares estimation, which aims at minimizing the mean squared prediction error . On the In relative error estimation, we minimize a loss function based on the relative error. An advantage of using such a loss function is that it is scale free or unit free. Recently, several researchers have proposed various loss functions based on relative error ,6,7,8,9. , and th , which implements our algorithm, along with a sample program of the rree function.To achieve a relative error estimation that is robust against outliers, this paper employs the Fujisawa , which i entropy . The est entropy . To findThe reminder of this paper is organized as follows: p-dimensional coefficient vector, and Suppose that We first remark that the condition To provide a simple expression of the loss functions based on the relative error, we writeChen et al. ,8 pointefined asG(β)=∑i=1max{a,b}  and g can havimation, ,6. HowevLeast product relative error estimation (LPRE) Chen et al.  re reγ-like