The growing interest in two-dimensional imine-based covalent organic frameworks (COFs) is inspired by their crystalline porous structures and the potential for extensive π-electron delocalization. The intrinsic reversibility and strong polarization of imine linkages, however, leads to insufficient chemical stability and optoelectronic properties. Developing COFs with improved robustness and π-delocalization is highly desirable but remains an unsettled challenge. Here we report a facile strategy that transforms imine-linked COFs into ultrastable porous aromatic frameworks by kinetically fixing the reversible imine linkage via an aza-Diels-Alder cycloaddition reaction. The as-formed, quinoline-linked COFs not only retain crystallinity and porosity, but also display dramatically enhanced chemical stability over their imine-based COF precursors, rendering them among the most robust COFs up-to-date that can withstand strong acidic, basic and redox environment. Owing to the chemical diversity of the cycloaddition reaction and structural tunability of COFs, the pores of COFs can be readily engineered to realize pre-designed surface functionality. Despite recent success in developing new covalent organic frameworks (COFs) materials, synthesis of chemically stable COFs still remains challenging. Here the authors demonstrate a facile strategy that transforms imine-linked COFs into robust porous aromatic frameworks by kinetically fixing the reversible imine linkage via an aza-Diels-Alder cycloaddition reaction. Owing to their high surface area, chemical diversity, and tunable functionality, COFs have been adopted as an auspicious platform for a plethora of applications pertaining to gas adsorption2, separation3, chemical sensing4, catalysis5, optoelectronics6, and energy storage7. In order to establish a covalently linked periodic framework in COFs, current methods rely on the reversible covalent bond formation to connect multivalent monomers through thermodynamic equilibria9. This inherent reversibility of the linkages within COFs, however, severely limit fundamental materials properties such as environmental stability towards solvents and chemicals, thus constraining their practical applications. For example, COFs with boroxine linkages are susceptible to water or protic solvents12. COFs based on a more robust linkage, such as the C=N imine bond, represent the prevalent class of COFs that show improved hydrothermal stability13. The chemical stability of most imine COFs is however still far from satisfactory since they undergo hydrolysis under strongly acidic conditions or exchange with amines due to the reversible nature of imine14. As such, a facile methodology that enables the fabrication of chemically robust COFs is much desired19.Covalent organic frameworks (COFs) are porous, crystalline networks constructed by linking molecular organic building units with covalent bonds20. The imine linkage has been deemed as a better choice than other dynamic bonds as it serves as an sp2-hybridized unit readily inserted into a π-extended framework to open up conjugation pathways. The resulting framework, however, is inferior in facilitating π electron delocalization between the linked units on account of the strongly polarized nature of the C=N bonds21. Despite a few examples of crystalline π-extended 2D polymer layers obtained from the surface- or interface-assisted synthesis23, and a most recent sp2 carbon-conjugated COF24, a general strategy to reinforce strong covalent bonding and extended π-electron delocalization in the same framework remains largely unexplored.One specific perspective that motivates the field of COFs is the potential of constructing a periodic conjugated 2D framework with covalently linked aromatic ring systems, from which exceptional electronic and magnetic properties are predicted28 to give functionally diverse and topologically identical porous materials. The modification reaction happens almost exclusively on the linkers that bear chemically addressable pendant groups, with only one exception in which the reaction targets the imine linkages to transform them into more stable amide linkages29. Herein, we report a more versatile approach to kinetically fix the reversible linkage in imine COFs to afford crystalline and chemically robust porous 2D aromatic framework with extended π-electron delocalization . Owing to the structural tunability of starting imine-linked COFs and substrate diversity of the aza-DA reaction, a large variety of functional moieties can be introduced to selectively alter the pore surface and wettability of the resultant COFs.Herein, we explore a strategy that can transform the dynamic imine linkages in COFs into more robust and conductive bonds while preserving the topology, crystallinity, and porosity of COFs. Starting from an imine COF can bypass the crystallization problem encountered in irreversible crosslinking, while the linkage transformation can significantly boost the chemical stability and electron delocalization in the resultant COFs. A number of COFs have been modified postsyntheticallyion Fig. . Thanks 31. COF-1 was readily obtained following a reported protocol and was subjected to the reaction with phenylacetylene (1a) at 110 °C in the presence of BF3•Et2O  and chloranil in toluene for 72 h. The solid separated from the reaction mixture was washed with excess anhydrous THF and saturated NaHCO3, and then dried under vacuum to afford MF-1a as dark yellow solid.The viability of the Povarov reaction was first demonstrated on a prototype imine-linked COF, a highly crystalline and porous COF denoted as COF-1−1 that correspond to the stretch of aromatic quinoline core30 and blue shift of the C–C=N-C stretch (from 1202 to 1222 cm−1) after the reaction. As a control, the 1D imine-polymer . Moreover, the C-C=N-C stretching is close to the model compound, diphenyl quinoline (1238 cm−1). Another control experiment run under the identical reaction conditions except without phenylacetylene 1a confirmed that the Lewis acid catalyst (BF3•Et2O) and chloranil alone induced no chemical modification of COF-1, as revealed by the lack of evident changes in the FT-IR spectra of COF-1  to afford MF-1b bearing a characteristic chemical probe. FT-IR spectra of MF-1b revealed a peak at ~1725 cm−1, arising from the carbonyl moiety   NMR spectroscopy revealed an apparent up-field shift of the aromatic and -OMe resonances in the spectrum of MF-1a compared to that of COF-1. Such up-field shift is again observed in the spectra of MF-1b, together with two well-resolved but characteristic peaks at 165 and 50 ppm that could be assigned to the methyl ester groups  spectra of MF-1a and COF-1 Fig.  revealedety Fig. , which iups Fig. . These sThe powder X-ray diffraction (PXRD) pattern of COF-1 exhibited six prominent diffraction peaks, with the most intensive one at 2.73° and the five other peaks at 4.79°, 5.54°, 7.35°, 9.65°, and 25.2°, assigned to the (100), (110), (200), (210), (220), and (001) facets, respectively Fig. , indicat2 sorption analyses performed at 77 K revealed a Brunauer−Emmett−Teller (BET) surface area of 1760, 955, and 590 m2 g−1 for COF-1, MF-1a, and MF-1b, respectively , corresponding to the C=N in quinoline moieties33. Based on the integration of the peak areas, the degrees of functionalization in MF-1a and MF-1b are assessed to be 27% and 25%, respectively. Further XPS depth profiling analysis indicates that the quinoline/imine nitrogen ratio remains constant after repeated exposure to high energy argon ion beam irradiations, confirming a uniform material composition from the surface to the buried body of the bulk , using the low-dose TEM technique equipped with Gatan K2 Summit direct-detection electron-counting camera2O/MeOH at 60 °C,1 day), strong oxidant  and reducing agents , as revealed by the retention of major diffraction peaks in the PXRD patterns of the treated samples , superacid , strong base 26. The generality was demonstrated by reacting COF-1 with para-substituted arylalkynes 1c–e under similar reaction conditions to give MF-1c-e bearing fluorine, nitrile and trifluoromethyl groups on the pore surfaces. The PXRD analysis confirmed the retention of crystallinity after the Povarov reaction  and -CF3 stretch (~1325 cm–1) in the FT-IR spectra of MF-1b and MF-1c, which are different from these in the spectra of free 1d (2226 cm−1) and 1e (1320 cm−1) , unambiguously confirmed the chemical attachment of -PhCN and –PhCF3 groups to the framework , highlighting an effective way to systematically fine-tune COF surface wettability into the super-hydrophobic region. The methyl ester-bearing MF-1b has a similar contact angle of 122°. Upon treatment with NaHCO3, the contact angle (MF-1b′) decreases significantly to 35°, indicating the feasibility of imparting hydrophilicity via hydrolysis of ester moieties. Similar fine modulation of hydrophobicity is also observed in the series of MF-2s. MF-2a has a contact angle of 152° which is significantly higher than the 125° contact angle of the pristine COF-2 and the introduction of fluorinated functional groups  led to gradually increased hydrophobicity, evidenced by the contact angle change from –H  to –F  and –CF3  , toluene, and 1,4-dioxane were purchased from Fisher chemicals. 1-ethynyl-4-fluorobenzene and 1,3,5-tri-(4-aminophenyl)benzene were purchased from TCI. 1-ethynyl-4-(trifluoromethyl)benzene as purchased from 1Click Chemistry Stock Products. Methyl 4-ethynyl benzoate was purchased from AK scientific. 2,5-Dimethoxyterephthalaldehyde were purchased from Carbosynth chemicals. 2,4-diphenyl-quinoline were purchased from EnamineStore. Phenylacetylene, 4-ethynyl benzonitrile, boron trifluoride diethyl etherate, triethylamine were purchased from Alfa Aesar chemicals. Chloranil, triflic acid, 1,3,5-triformylbenzene, benzidine, mesitylene, λ = 1.5406 Å). Nitrogen sorption isotherms were obtained at 77 K with a Micromeritics Instrument Corporation model 3Flex surface characterization analyzer. The Brunauer-Emmett-Teller (BET) method was utilized to calculate the specific surface areas. By using the non-local density functional theory (NLDFT) model, the pore size distribution was derived from the sorption curve. TGA measurements were performed on a TA Instruments Q5000IR TGA under Argon, by heating to 600 °C at a rate of 10 °C min–1. Solid-state 13C CP-MAS NMR spectra were recorded on a BrukerAvance500 I (MF-1d sample only) and BrukerAvance500 II. The UV-vis diffuse reflectance measurement was performed on Cary 5000 UV-Vis-NIR spectrometer. X-ray photoelectron spectroscopy (XPS) measurement was carried out on a Thermo Scientific K-Alpha XPS apparatus equipped with a monochromatic Al K source and food gun for charge compensation. To remove the acid residues, the COFs samples were treated with triethylamine methanolic solution, followed by washing with copious amount of methanol and degas at 200 °C under dynamic vacuum for 1 day prior to XPS measurement. Low-dose TEM images of COF-1 were acquired on the TEAM I FEI Titan-class microscope at 300 kV, equipped with both geometric aberrations corrected to third order and chromatic aberrations corrected to the first order. Imaging data were collected in the Gatan K2 direct-detection camera operated in electron-counting mode (camera counting frame rate of 400 fps (frames per second) at 4 k × 4 k resolution) with a final image output rate of 40 fps at 4 k × 4 k resolution. The HRTEM image of MF-1a was acquired with an FEI TitanX 60 300 microscope at 200 kV. The COF sample was sonicated in toluene with a sonication probe for 15 min and drop-casted onto a copper grid . SEM images were obtained with a Zeiss Gemini Ultra-55 Analytical Field Emission Scanning Electron Microscope operated at 15 kV using an in-lens detector. Water contact angle measurements were carried out using a Kruss easy drop optical contact angle meter (Model: FM41) under ambient conditions.Fourier transform infrared (FT-IR) spectra were recorded on a Perkin Elmer Spectrum One FT-IR system. Powder X-ray diffraction (PXRD) patterns were recorded on a Bruker Discovery D8 X-ray diffractometer and Rigaku MiniFLex 6G Benchtop XRD with Cu Kα1 radiation . Unit cell and sample parameter were refined simultaneously. The Pawley refinement results including unit cell parameters and final related refinement factors for MF-1a and MF-2a were listed in Supplementary Table o-dichlorobenzene/n-butanol (1 mL/1 mL) mixture of 1,3,5-tri-(4-aminophenyl) benzene  and 2,5-dimethoxy terephthalaldehyde  in the presence of acetic acid  in a Biotage microwave vial (5 mL) was degassed through 3 freeze–pump–thaw cycles. The vial was sealed and heated at 120 °C for 3 days. The precipitate was collected via centrifugation, washed times with anhydrous THF and subjected to Soxhlet extraction using THF as the solvent for 1 day. The powder collected was dried at 120 °C under vacuum overnight to give yellow colored COF-1 in an isolated yield of ~80%.An Benzidine  and 1,3,5-triformylbenzene  were dissolved via sonication in a mixture of 1,4-dioxane/mesitylene (1 mL/1 mL) in a 5 mL-Biotage microwave vial. Afterwards, aqueous acetic acid  was added to the mixture. The vial was degassed by three freeze–pump–thaw cycles. Finally, the vial was sealed and heated in an oven at 120 °C for 3 days. The precipitates were isolated by centrifugation, washed with anhydrous THF for three times, and dried at 120 °C under vacuum overnight to give a yellow colored powder in ~90 % yield.3•OEt2 , chloranil , and 2 mL of toluene were added to a 5 mL-Biotage microwave vial. The vial was sealed and heated under N2 at 110 °C in an oil bath. After 1–3 days, the mixture was cooled to room temperature and the precipitate was isolated via centrifugation. The reaction mixture was then washed with THF and quenched with saturated aqueous sodium bicarbonate (2 mL × 3). Subsequently, the solids were washed with THF using a Soxhlet extractor for 12 h and dried under vacuum.COFs (4 mg), phenylacetylene , BF2O) at ambient temperature, 0.5 mL HCl (1 and 12 M) at ambient temperature, 50 °C or 100 °C, NaOH (1 and 14 M) in H2O/methanol solution at 60 °C or 100 °C, NaBH4  in methanol at 60 °C and KMnO4  in H2O/CH3CN solution. The samples were washed with THF for three times, dried under vacuum at ambient temperature and subjected to PXRD measurements.The MF-1a sample (~2 mg) was kept for pre-designed time under static condition in 0.5 mL of 98% triflic acid (2% catalytic HAll relevant data supporting the findings of this study are available from the corresponding author on request.Supplementary InformationPeer Review File


Sulfolobus acidocaldarius, where they act synergistically and negatively modulate motility. The structure of the FHA domain of ArnA at 1.75 Å resolution revealed that it belongs to the subclass of FHA domains, which recognizes double-pSer/pThr motifs. We also solved the 1.5 Å resolution crystal structure of the ArnB paralog vWA2, disclosing a complex topology comprising the vWA domain, a β-sandwich fold, and a C-terminal helix bundle. We further show that ArnA binds to the C terminus of ArnB, which harbors all the phosphorylation sites identified to date and is important for the function of ArnB in archaellum regulation. We also observed that expression levels of the archaellum components in response to changes in nutrient conditions are independent of changes in ArnA and ArnB levels and that a strong interaction between ArnA and ArnB observed during growth on rich medium sequentially diminishes after nutrient limitation. In summary, our findings unravel the structural features in ArnA and ArnB important for their interaction and functional archaellum expression and reveal how nutrient conditions affect this interaction.Phosphorylation-dependent interactions play crucial regulatory roles in all domains of life. Forkhead-associated (FHA) and von Willebrand type A (vWA) domains are involved in several phosphorylation-dependent processes of multiprotein complex assemblies. Although well-studied in eukaryotes and bacteria, the structural and functional contexts of these domains are not yet understood in Archaea. Here, we report the structural base for such an interacting pair of FHA and vWA domain-containing proteins, ArnA and ArnB, in the thermoacidophilic archaeon  Expression of the archaellum is regulated by a complex network of positive and negative regulators , whereas, as previously described, the levels of FlaB were strongly increased under starvation conditions (A)  and ArnA and ArnB have been shown to interact in vivo   (A and B). After 0.5 h of starvation, ArnA still co-eluted with ArnB, whereas after 1.5 h the levels of ArnA in the elution fractions were already decreased, and strikingly, after 4 h of starvation ArnA did not co-elute with ArnB anymore (A and B), indicating a complete loss of the interaction between ArnA and ArnB under prolonged starvation conditions.Because the levels of ArnA and ArnB do not change significantly after nutrient depletion , A and B in vivo , we set chaellum A 23, 24A and B aged ArnB A 13), d, dA and ArnB (A) , A and B anymore , A and BBecause FHA domain–containing proteins like ArnA interact generally via phosphorylated threonine residues on their interaction partner, it appears plausible that dephosphorylation of ArnB under nutrient limiting conditions abolishes the interaction. Indeed, several phosphorylated residues have been identified in ArnB previously , 25, and5A8I), which represents the first archaeal FHA crystal structure, was solved by molecular replacement at 1.75 Å resolution (Table S4). The structure lacked the N terminus, including the zinc-finger domain . The FHA domain of ArnA adopted a monomeric 10-stranded β-sandwich fold with four and six β-strands on both sides, respectively (C). The N- and C-terminal β-strands of the sandwich, which are the longest β-strands of ArnA, align in an antiparallel fashion, thus bringing the N and C terminus in close proximity. All β-strands are connected by short loop regions on the side of the termini and by longer loops on the opposite side. Here, two sulfate ions are found that originate from the sulfate-containing crystallization condition of ArnA. In the crystal structure of Rad53p–FHA1 from S. cerevisiae (D), this area complexes a phosphothreonine peptide (E) (E) (E)  allows the binding of a second phosphorylated residue (E)  . Thtide (E) E 1, 3)..5A8I), w (E) (E) . In the  (E) (E) E 4). In. In5A8I) residue E (5).S. acidocaldarius and paralog of ArnB . S. acidocaldarius vWA2 and ArnB are not only homologs of each other (sequence identity 31% for 350 residues) but also encoded in close proximity in the genome (upstream and downstream of ArnA)  deletion of vWA2 did not affect motility on plate, suggesting that vWA2 is not directly involved in motility regulation , thus excluding the possibility that vWA2 is directly involved in the regulation of motility. However, given their homology, the structures of vWA2 and ArnB are expected to resemble each other and make vWA2 to a valid model for ArnB apart from a missing 20-amino acid C-terminal extension that is exclusively part of ArnB . The complete structure of vWA2 (Ala-2–Gly-366) (PDB code 5A8J) was determined from an orthorhombic crystal form at a resolution of 1.45 Å via MAD phasing. The vWA2 structure represents the first archaeal example of a vWA-containing gene product  and harbors in addition to the vWA-domain a β-sandwich and a four-helix bundle. The vWA domain is built of a central, twisted six-stranded β-sheet flanked by four helices with largely alternating α-helices and β-strands. Interestingly, the complex topology of vWA2/ArnB implies that the vWA domain is inserted within the eight-stranded β-sandwich. Here, six of the eight β-strands (strands CDD′EFG) are formed by a stretch next to the C terminus of the vWA domain, whereas the other two β-strands (AB) are derived from the very N terminus of vWA2 (A and D). With its two antiparallel β-sheets, ABED and D′CFG, this β-sandwich is topologically related to a distorted C-type immunoglobulin domain. Here, at the C terminus, the vWA2 structure is completed by a four-helix bundle that spatially connects the vWA domain and the β-sandwich structure which are edge-to-edge oriented toward each other. The same tripartite structure of vWA2 is found for the structure of a von Willebrand factor type A–like gene product from Catenulispora acidiphila, a bacterium belonging to the actinobacterial class . Although the overall sequence identity is marginal, i.e. only 20% for amino acids Ile-57–Asn-222, which represent the vWA domain, the structural similarity between vWA2 and the vWA-like gene product from C. acidiphila is high including the 8-stranded β-sandwich domain and the C-terminal four-helical bundle (C). Furthermore, ArnB, vWA2, and the vWA-like gene product from C. acidiphila harbor an intact MIDAS (E) in the vWA domain. Although no Mg2+ or Ca2+ ion is found in the vWA2 structure, the structure of the C. acidiphila ortholog harbors here a bound sodium ion (E). Overall, we solved the first structures of an archaeal FHA domain–containing protein, as well as of an archaeal vWA2 domain–containing gene product.Because several attempts to crystallize ArnB were unsuccessful, we set out to crystallize Saci_1209 (vWA2), another vWA domain–containing protein of of ArnA) . Previougulation . Indeed, of vWA2 , A and Dl bundle C. Furthect MIDAS E in the dium ion E. OveralS. acidocaldarius was cultured to early exponential phase in nutrient-rich conditions (0 h) and transferred to medium lacking nutrients and analyzed at 0.5, 1.5, and 4 h after nutrient depletion. Then iTRAQ analysis was performed to identify and quantify phosphorylated peptides from the ArnB protein. As a result, three unique phosphorylated peptides derived from ArnB  were detected and quantified (A). These three consecutive peptides  were also identified as three of the four highest phosphorylated peptides by in vitro phosphorylation experiments, when ArnB was phosphorylated by ArnC or ArnD, respectively  to assess whether the phosphorylation of these residues is (i) important for the function of ArnB in regulation of motility and (ii) involved in the interaction of ArnA with ArnB.As noted, upon nutrient depletion, the ArnA–ArnB interaction is gradually abolished when the expression levels of the archaellum increases . Our strFig. 5eviously . The thrΔ316, which no longer contained most of the phosphorylated serine and threonine residues (In vitro phosphorylation assays were performed with [γ-32P]ATP and the S/T kinases ArnC and ArnD (A). Upon incubation with [γ-32P]ATP, autophosphorylation of the respective kinase, and upon addition of ArnB, phosphotransfer to ArnB was detected. In contrast, no phosphotransfer was found to occur to ArnBΔ316 (A), supporting the previous notion that phosphorylated residues are predominantly located at the C terminus of ArnB . All ArnB variants were expressed in S. acidocaldarius ΔArnB using a maltose-inducible promoter . However, unexpectedly ArnB T343A/T344A did not bind to the StrepMag beads , which hindered the analysis of the interaction with ArnA in this mutant. Interaction of ArnA with the other alanine mutants was observed, because ArnA was detected in elution fractions obtained from all pulldown experiments of cells grown to early exponential phase. (B). Strikingly, co-elution of ArnA with ArnBΔ316 was strongly reduced compared with co-elution of ArnA with WT ArnB, demonstrating that the C terminus of ArnB is important for the interaction with ArnA. To test the influence of the mutations and the C-terminal deletion of ArnB on motility of S. acidocaldarius, motility assays were performed (C). As observed previously, deletion of arnB resulted in hypermotility, a phenotype that could be complemented by the expression of plasmid-encoded ArnB . Although plasmid-based expression of all alanine mutant variants, including the T343A/T344A mutant, which we were unable to test in the interaction assay above, showed swimming behavior similar to that of the in trans complemented arnB deletion mutant, expression of plasmid-encoded ArnBΔ316 failed to fully complement the hypermotility phenotype . Thus, our data demonstrate that the C-terminal four-helix bundle of ArnB is essential for the interaction with ArnA. This C-terminal domain can be phosphorylated by ArnC in vitro and is found phosphorylated in vivo, suggesting that phosphorylation of this domain might influence its interaction with ArnA. However, mutation of the single identified phosphorylated residues did not affect motility, suggesting that multiple residues might play a role in this processBecause it was previously observed that the l phase. B. Strikierformed C. As obsded ArnB  . Indeed, several of the components of this network in S. acidocaldarius, like ArnA, ArnB h medium , (ii) deh medium , (iii) Aaldarius , (iv) rein vitro .316), could only partly complement the ΔarnB strain, further suggesting that the ArnA–ArnB interaction plays an important role in the regulation of archaellum expression.Indeed, we found that the strong interaction between ArnA and ArnB, which was observed during growth on rich medium, was sequentially diminished after nutrient limitation. Furthermore, a mutant lacking the C-terminal domain, in which the phosphorylated residues were identified and in which this interaction was strongly reduced . Most FHA domain–containing proteins studied so far usually recognize only a single pThr residue. Interestingly, several FHA domains like that one in the Dun1 kinase of S. cerevisiae bind to a duplicated threonine–phosphate recognition motif (pTXXpTXXS) with higher affinity than the monophosphorylated motif , suggesting that conformational changes must occur before these motifs can be phosphorylated and that phosphorylation of these residues will also lead to conformational changes within ArnB. Indeed, the C-terminal helix bundle of ArnB includes a helix-turn-helix motif that forms only weak, secondary interactions with the adjacent β-sandwich like a salt bridge between Arg-24 and Glu-299 and weak hydrophobic interactions in the small interface. This supports a signaling switch model, in which phosphorylation of the C terminus of ArnB is coupled to conformational changes.The crystal structure of the ArnB homolog vWA2 consists of three domains: a vWA domain, a β-sandwich domain, and a four-helix bundle domain. The vWA domain revealed an intact MIDAS site, which might be involved in interaction with a glutamate residue coming from the interacting partner. The C-terminal domain four-helix bundle contained almost all the previously identified phosphorylated residues . Indeed,Fig. 5Overall, our structures indicate that ArnA contains a functional phospho-recognition motif that might recognize several phosphorylated motifs in the highly phosphorylated C-terminal four-helix bundle domain of ArnB. Further possible interactions might occur via the MIDAS site in ArnB and the N-terminal zinc-finger domain of ArnA. Together with our other data, these results suggest that under rich conditions ArnA and ArnB might interact via the phospho-recognition module of ArnA and the phosphorylated four-helix bundle domain of ArnB and that under nutrient depleting conditions ArnB becomes dephosphorylated at these residues, and in consequence the interaction between ArnA and ArnB is lost. To assess whether the phosphorylation pattern of ArnB is altered after nutrient depletion, we performed iTRAQ analysis and quantified several phosphorylated peptides of ArnB. However, no peptides could be detected, where such a decrease in the phosphorylation levels was found, which can be correlated with the observed loss of ArnA–ArnB interaction during nutrient depletion. Furthermore, mutation of pairs of the identified phosphorylated residues did not affect motility or the interaction between ArnA and ArnB.Fig. S2). For example, ArnB exhibits patches of negative potentials at the vWA domain and a positive potential patch on the side of the β sandwich, as well as a negative deepening on top of it. In the case of an interaction, it will be interesting to know whether the different phosphorylation patterns of ArnB allow interaction with different partners necessary for motility regulation. The identification of these interaction partners for example by immune-precipitation and furthermore the roles of the MIDAS and zinc-finger domains in the interaction of ArnA and ArnB will require further studies of this motility repression module.Accordingly, either multiple phosphorylated residues might play a role in this process or under starvation conditions a currently unidentified protein may become phosphorylated and recruit ArnA by displacing ArnB. In this context it is also plausible that ArnB may interact with other cellular components given its prominent electrostatic properties  supplemented with 0.1% NZ-amine, 0.2% sucrose, and 10 μg/ml uracil RIL and Rosetta(DE3)pLysS strains, 30 μg/ml chloramphenicol was added to the medium.All Δ316 was performed in E. coli BL21 RIL. vWA2 (saci1209) and ArnA (saci1210) was expressed and purified essentially as described . The supernatant was applied for His-tag affinity purification on His-select nickel-affinity gel (Sigma). Proteins were eluted in 20 mm HEPES/NaOH, pH 7, 100 mm KCl, 100 mm imidazole. Strep-tag affinity purification of ArnA was performed using a Strep-tactin superflow column (IBA) in 100 mm Tris, pH 8.0, 150 mm NaCl, 2.5 mm desthiobiotin. Finally, affinity-purified vWA2 and ArnA were subjected to size-exclusion chromatography  in 20 mm HEPES, pH 7.0, 100 mm KCl.Expression of ArnB and ArnBescribed . Heterolescribed , includim Tris, 100 mm NaCl, pH 8.0, was crystallized using the hanging-drop vapor-diffusion method. 1 μl of protein was added to 1 μl of reservoir solution . After 2 months at 18 °C a crystal appeared, which was frozen in reservoir buffer supplemented with 30% (v/v) glycerol for data collection. The data set was recorded at Beamline 14.1 . Initial phasing was performed with molecular replacement using the CCP4 package , as template structure. Structural refinement using the 1.75 Å data set was performed with PHENIX 2SO4). The crystal for the MAD data sets appeared in a drop containing 0.3 μl of the protein in HEPES buffer at a concentration of 4.0 mg/ml and 0.3 μl of reservoir solution 2SO4). The crystals were soaked for 1 h with 50 mm Na2WO4. For data collection, native and heavy metal–supplemented crystals were frozen in reservoir solution containing 30% (v/v) glycerol. All data sets were recorded at the European Synchrotron Radiation Facility in Grenoble, France, with the native data set at Beamline ID29 and the 2.0-Å MAD data sets at Beamline ID14-4. Initial MAD phasing attributable to a Keggin cluster formed by tungstate-phosphate was performed with the CCP4 package, and the structural refinement of the 1.45-Å native data set was performed with PHENIX. The homology model of ArnB based on the vWA2 structure was generated with SWISS-MODEL  in 20 m package , 37. The package  using Emh PHENIX  and COOTh PHENIX . Structuh PHENIX . The crySS-MODEL , 43.Motility assays were performed as described , 24. TheIn vitro phosphorylation assays using [γ-32P]ATP  were performed as described  overnight. Screens were scanned using a phosphorimaging device .escribed . For pho600 of 10 in 1× PBS, and supplemented with SDS-loading buffer. After incubation at 100 °C for 10 min 10 μl/sample were subjected to SDS-PAGE analysis according to the method of Laemmli using 11% gels  using the semi-dry method . FlaB waA600 = ∼0.4) or cultures that were grown in nutrient-depleted medium for either 0.5, 1.5, or 4 h were collected as described  according to the manufacturer's protocol. For each assay 200-ml samples of cultures either grown to exponential phase  and at 0.5, 1.5, and 4 h after starvation were washed with ice-cold water before being resuspended in 1 ml of protein extraction buffer consisting of 0.05% SDS in 0.5 m triethylammonium bicarbonate, pH 8.5. Protein extraction was carried out using an ultrasonicator  for 8 times at 70% duty cycle . Extracted proteins were collected by centrifugation at 21,000 × g at 4 °C for 30 min , and protein concentrations were determined using a Bradford assay (Sigma). A total of 100 μg of proteins from each sample  was used for an 8-plex iTRAQ analysis with 8 iTRAQ tags used, and the analysis was performed based on the manufacturer's instruction. Briefly, these proteins were reduced by 2 μl of 50 mm tris-(2-carboxyethyl) phosphine at 60 °C for 1 h, and alkylated by 1 μl of 200 mm methyl methanethiosulfonate for 10 min at room temperature before being digested by trypsin MS grade (Promega) with the ratio of trypsin:proteins 1:20. Digested proteins from four different sampling points were then labeled with iTRAQ reagents as follows: 0 h, 113 and 114 (control); 0.5 h, 115 and 116; 1.5 h, 117 and 118; and 4 h, 119 and 121, and incubated at room temperature for 2 h. All labeled peptides were then combined before being dried in a vacuum concentrator (Eppendorf Concentrator 5301).Frozen cells of m ammonium formate in 80% acetonitrile (ACN), pH 3.0, before being loading onto a 4.6 × 200-mm PolyHYDROXYETHYL-A column  coupled with an ultra-high performance liquid chromatography 3000 system (Dionex). An UV detector was used to monitor peptides' abundance at a wavelength of 280 nm. Peptides were fractionated at a flow rate of 0.5 ml/min using a gradient with HILIC buffer B containing 10 mm ammonium formate in 5% ACN, pH 5.0; 10 min of 2% buffer B before ramping up to 20% of buffer B for 5 min, and to 60% of buffer B for 50 min, and hen ramped up to 100% of buffer B for 10 min and kept for 10 min, and finally 0% of buffer B for 5 min. Eluted peptides were collected every 2 min and then dried in a vacuum concentrator before being cleaned using C18 spinning tips (Nest Group) before submitting to a mass spectrometer.Hydrophilic interaction chromatography (HILIC) was used for cleaning and fractionation of dried iTRAQ-labeled peptides, in which peptides were resuspended in 100 μl of HILIC buffer A containing 10 mTM hybrid quadrupole-Orbitrap mass spectrometer  coupled with a nano ultra-high performance liquid chromatography 3000 system (Dinonex) operated at a flow rate of 0.3 μl/min. Peptides were separated using a C18 column with a 105-min gradient of buffer B (0.1% formic acid in 97% ACN) as follow: 3% for 5 min, then ramped up to 10% for 5 min, 50% for 75 min, 90% for 1 min, then kept at 90% for 4 min before being ramped back to 3% buffer B for 1 min, and then maintained at 3% for 14 min. The MS was operated in positive mode with resolutions of full MS and ddMS set at 60,000 and 15,000, respectively. AGC targets were set at 3.106 and 5.104 for full MS and ddMS, respectively; maximum IT times were set at 100 and 20 ms for full MS and ddMS, respectively; a full mass scan ranging from 375 to 1500 m/z was applied for MS, whereas the mass scan of 100–1500 m/z was applied for ddMS; default charge state of ion was set at 2.Selected cleaned peptides (from different fractions) were redissolved in 20 μl of buffer A consisting of 0.1% formic acid in 3% ACN then combined into six different fractions before 3 μl of sample was withdrawn and submitted onto a Q ExactiveS. acidocaldarius MW001 database . Modifications of iTRAQ reagents  and methylmethanethiosulfate were set as fixed modifications, whereas methionine oxidation and phosphorylation  were set as variable modification; trypsin digestion used with a maximum missed cleavages of 2; a minimum peptide length of 6, and a maximum peptide mass of 4600 Da were set; tolerances of 20 and 4.5 ppm were applied for MS and MS/MS, respectively. A false discovery rate of 0.01 was used for identification of both peptides and proteins; a minimum score of 40 was used for modified peptides. All detected peptides containing intensities of iTRAQ reagents, from MaxQuant, were then submitted to an in-house proteomic pipeline for quantitation of protein and determination of regulated proteins (t tests (α = 0.01) were then performed at the peptide level (peptides corresponding to an identified protein) to determine regulated proteins for each phenotype comparison. Phosphorylated peptides were manually examined and calculated for their relative ratios with time, t = 0 h used as a control.All raw data files from MS analysis were submitted to MaxQuant version 1.5.3.8 for protein identification against the proteins . First, L. H., K. A., L. F. B., J. R., S. K., and T. K. P. data curation; L. H., K. A., L. F. B., X. Y., J. R., S. K., T. K. P., and C. v. d. D. formal analysis; L. H., K. A., L. F. B., T. K. P., and S.-V. A. writing-original draft; K. A., L. F. B., X. Y., J. R., T. K. P., and C. v. d. D. investigation; X. Y. funding acquisition; T. K. P., C. v. d. D., and P. C. W. methodology; C. v. d. D., P. C. W., L.-O. E., and S.-V. A. supervision; C. v. d. D., P. C. W., L.-O. E., and S.-V. A. writing-review and editing; L.-O. E. and S.-V. A. resources; L.-O. E. and S.-V. A. project administration; S.-V. A. conceptualization; S.-V. A. visualization.


HNAC1, a single-domain intracellular antibody (intrabody) from the camelid family that was selected for its specific binding to the nonamyloid component (NAC) of human α-synuclein (α-syn), a natively disordered protein, implicated in the pathogenesis of Parkinson’s disease (PD) and related neurological disorders. Specifically, we performed ab initio modeling that revealed several possible modes of VHNAC1 binding to the NAC region of α-syn as well as mutations that potentially enhance the affinity between these interacting proteins. While our initial design strategy did not lead to improved affinity, it ultimately guided us towards a model that aligned more closely with experimental observations, revealing a key residue on the paratope and the participation of H4 loop residues in binding, as well as confirming the importance of electrostatic interactions. The binding activity of the best intrabody mutant, which involved just a single amino acid mutation compared to parental VHNAC1, was significantly enhanced primarily through a large increase in association rate. Our results indicate that structure-based computational design can be used to successfully improve the affinity of antibodies against natively disordered and weakly immunogenic antigens such as α-syn, even in cases such as ours where crystal structures are unavailable.Improving the affinity of protein-protein interactions is a challenging problem that is particularly important in the development of antibodies for diagnostic and clinical use. Here, we used structure-based computational methods to optimize the binding affinity of V While the exact ways that α-syn contributes to PD pathogenesis remain unclear, it is generally believed that aberrant soluble oligomeric α-syn conformations, termed protofibrils, are the toxic species that disrupt cellular homeostasis and cause neuronal death. Because α-syn may play a central role in pathogenesis, reducing intracellular levels to prevent the abnormal misfolding, aggregation, and toxicity in vulnerable cells may serve as a potential therapeutic strategy for slowing the progression of PD and other synucleinopathies where misfolded proteins and subsequent protein aggregation appears to be an underlying factor4.α-Synuclein (α-syn) is a presynaptic neuronal protein that is pathologically linked to a number of neurodegenerative disorders collectively known as synucleinopathies, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA)8. Intrabodies are antibody fragments that have been engineered to be expressed intracellularly where they bind their cognate target antigens, and have shown promise in infectious diseases10 and cancer12. Intrabodies have also been used to target molecular features of protein misfolding and aggregation in Huntington’s disease15, with follow-on studies providing in vivo proof-of-concept of the protective effects of such intrabodies18. More recently, intrabodies have been developed that have affinity for different conformations of α-syn 21 or specific residues of α-syn 24. One of these, the single-chain Fv (scFv) intrabody NAC32 that is specific for amino acids 53–87 of α-syn comprising part of the nonamyloid component (NAC) region, was found to reduce the toxicity caused by the A53T mutant of α-syn in cultured cells22.To achieve such an outcome, intracellular antibodies (intrabodies) have been proposed as a strategy for targeting and/or neutralizing different aberrant α-syn species25, and are prone to misfolding and aggregation due to the redox potential and macromolecular crowding of the intracellular environment28. Because solubility remains a difficult property to predict, library-based strategies are currently the most effective tool for identifying solubility-enhanced intrabody variants31. Regarding affinity for antigen, suboptimal affinity may allow kinetic escape and accumulation of the target in a manner that prevents phenotypic inhibition or knockout. For example, an initial panel of eight huntingtin (htt)-specific scFv clones isolated from a synthetic, non-immune library using yeast surface display, was found to be completely inactive in preventing aggregation of htt in a yeast model of HD32. The affinity of the hits (in the micromolar range) was deemed to be insufficient for biological activity. In support of this notion, a tighter binding clone (Kd ≈ 30 nM) was isolated by affinity maturation using yeast display and found to efficiently block htt aggregation in cultured mammalian cells. However, while this finding illuminates the importance of strong binding affinity for intrabody performance, the process by which affinity was improved was labor intensive and time consuming, requiring construction of two combinatorial antibody libraries and a total of eight rounds of fluorescence-activated cell sorting (FACS)-based library screening. Likewise, we recently employed a directed evolution strategy to enhance the affinity of NAC32 and, while successful, it required multiple labor-intensive rounds of library construction and selection to yield a clone exhibiting a modest ~8-fold increase in binding affinity31.Unfortunately, it remains a significant challenge to isolate intrabodies that exhibit sufficient stability and binding affinity when expressed in living cells, specifically in the reducing environment of the cytoplasm. Regarding intracellular stability, only a small fraction of intrabodies are intrinsically soluble in the cytoplasm. Indeed, many promising intrabodies suffer from poor cytoplasmic solubility40 sequences in just a few days, which is in contrast to experimental library search strategies  that interrogate ~1010 sequences per experiment over a timeframe of weeks to months. Computational strategies take advantage of algorithms such as Monte Carlo-based searches33 that permit large numbers of residue types and conformations to be screened in silico using fast evaluations of energetic properties. This is made possible by the compatibility of exhaustive search algorithms with the highest-quality methods for energy evaluations, especially the treatment of the solvent and electrostatic interactions, such as numerical solution of the Poisson-Boltzmann equation34. Indeed, progress in computing performance and force-field parameterization has made it possible to efficiently perform in silico affinity maturation whereby accurate biophysical models are leveraged to effectively guide the identification of antibody residues and motifs that maximize affinity for a target antigen epitope. Such approaches have been used by several groups to design mutations in antibodies that significantly improve their affinity towards antigen41.To overcome some of the shortcomings associated with experimental affinity maturation, structure-based computational techniques have emerged as an important complement for achieving high-affinity binding. The advantage of computational techniques stems from their ability to screen optimal designs within a virtual library containing ~10HNAC1, a recombinant single-domain camelid antibody (VHH) specific for the NAC domain of α-syn. A coherent picture of the interaction between VHNAC1 and NAC was constructed using a combination of advanced molecular dynamics (MD) techniques 42, umbrella sampling (US)43, and weighted histogram method (WHAM)44) together with numerical techniques ). This undertaking was non-trivial because, unlike the earlier in silico efforts discussed above, high-resolution structures for VHNAC1, the NAC antigen, and the VHNAC1-NAC complex do not exist. Moreover, we had to resolve the issue of the multitude of conformations that can be adopted by α-syn. Importantly, our ab initio modeling results allowed us to propose: (i) possible conformations of the VHNAC1 binding region within the NAC domain; (ii) possible modes of VHNAC1 binding to the NAC region; and (iii) mutations that enhance VHNAC1 binding to NAC, specifically by alleviating electrostatically suboptimal contacts at the binding interface. Unexpectedly, a single point mutation (N77D) was sufficient to enhance the affinity of VHNAC1 by more than an order of magnitude, with the resulting N77D mutant exhibiting high nanomolar binding affinity as confirmed by surface plasmon resonance (SPR) experiments. The improved affinity of this mutant was achieved primarily through an order-of-magnitude increase in association rate. Our results demonstrate that structure-based computational design can be used to significantly improve the affinity of intracellular antibodies even when crystal structures are not available.Inspired by these earlier efforts, here we describe a strategy for computational affinity maturation of V46. The protein has an amphipathic N-terminus and acidic C-terminus that are separated by a central hydrophobic domain known as the NAC region , for isolating binding proteins from combinatorial libraries50. This strategy leverages the inbuilt ability of the twin-arginine translocation (Tat) system to efficiently localize non-covalently assembled protein complexes to the periplasm of Escherichia coli cells51. The assay involves two engineered fusion proteins: (i) a Tat signal peptide  fused to the N-terminus of a designer binding protein , and (ii) the corresponding antigen  fused to the reporter enzyme, TEM-1 β-lactamase (Bla)  Fig. . Co-tranE. coli cells along with the α-syn(A53T)-Bla fusion, resulting in the outgrowth of bacterial colonies that were resistant to carbenicillin (Carb). Six colonies were chosen for further analysis; however, following sequencing only two clones were confirmed to be full-length, unique VHH sequences. These two VHH clones exhibited Carb resistance that was greater than that conferred by a non-specific green fluorescent protein (GFP)-binding VHH (cAbGFP4)52 or a randomly chosen non-binding VHH clone, VHNB10, and on par with that conferred by the positive control NAC32  above background in ELISA experiments 54. While homology models (and other knowledge-based methods) are very efficient at predicting the framework regions due to high sequence similarity, these methods can produce loop conformations with considerable deviations from the correct or native conformations due to high sequence and length variability in the variable regions of VHHs55. Hence, we performed all atomistic REMD simulations in implicit solvent to obtain the equilibrium conformations of the binding loops of VHNAC1. All simulations employed GROMACS56 using the CHARMM27 force field in implicit solvent57. All residues except those in the hypervariable loops H1, H2, H3, and H4 were backbone-restrained. This technique was previously used to successfully predict the conformation of VHH loops59. Furthermore, we retained all of the VHNAC1 residues  a 19-residue α-helical peptide (residues 61–79) observed with solution NMR spectroscopy of α-syn in the micelle-bound form (PDB: 1XQ8)61; and (ii) a 13-residue random coil peptide generated by piecing together the amino acids (residues 66–78) one-by-one  and 13-residue random coil (18 structures) NAC peptides were obtained from four docking runs in ClusPro: (i) VHNAC1 (N conformation) with 19-mer α-helical peptide; (ii) VHNAC1 (N conformation) with 13-mer random coil peptide; (iii) VHNAC1 (ST conformation) with 19-mer; (iv) VHNAC1 (ST conformation) with 13-mer  are flanked on both sides by hydrophilic residues (57-EKTKEQVTN-65 and 79-QKTVE-83). For the Model III complex, the hydrophobic core fits into a paratope on VHNAC1 and the NAC region for two versions of the antigen: (i) the 13-residue hydrophobic random coil peptide (residues 66–78); and (ii) a 24-residue peptide (residues 57–80)  simulations to obtain an estimate for the potential of mean force (PMF) as a function of separation between V69. We obtained the PBEQ surface for VHNAC1 and NAC antigen corresponding to Model III, which revealed two primary residues on VHNAC1 that were interacting unfavorably with the antigen. These residues were lysine at position 76 (K76) and lysine at position 99 (K99). The unfavorable energetics were most likely due to the proximity of VHNAC1 residue K76 to NAC residues K58 and K60 , it is computationally much faster than US simulation (~1000 ns of cumulative simulation time), and thus was used as a first step for scanning multiple mutants for improved affinity. We thus proposed four VHNAC1 variants – K76A, K76E, K76A/M78T, and K76E/M78T – each of which was aimed at alleviating electrostatic repulsion at the binding interface for Model III. Following equilibration simulations and PBEQ analysis of the equilibrated structures, mutants K76A and K76A/M78T were found to have an improved electrostatic free energy of binding over parental VHNAC1 while mutants K76E and K76E/M78T exhibited the opposite behavior , and thus stronger affinity, with kon values in the following order: VHNAC1 > K76A/M78T > K76E. Taken together, while the designed mutations did not improve the affinity, they did reveal the importance of long-range electrostatic interactions.To test these findings, we expressed and purified Vnts Fig. , step 8,HNAC1 (N conformation) in complex with the 19-residue α-helical NAC peptide according to our ClusPro analysis  that was significantly stronger than that measured for parental VHNAC1 (Table Kd = 46 nM) measured for NAC3231. Importantly, this large improvement in binding affinity was evident based on (i) approximate Kd values derived by plotting the plateau levels against α-syn(A53T) concentration and fitting the equilibrium binding responses  are indicative of a modulation of electrostatic interactions at the binding interface driven by computational design.To test these findings, we compared the α-syn(A53T)-binding activity of the N77D mutant with that of VAC1 Fig. , step 8.C1 Table –8 and on38 because no high-resolution structures for any of the components of our system – the intrabody, the antigen, or the intrabody-antigen complex – were available a priori. Instead, we used the amino acid sequence of the intrabody and its antigen to develop a model of binding in silico, and then used the model to predict affinity-enhancing mutations. Specifically, advanced MD techniques were used to obtain two unique equilibrium conformations of the binding loops of VHNAC1, one in which H3 exhibited a neutral conformation and another in which it adopted a stretched-twist conformation. For the antigen, we considered two models for the NAC region, a 19-residue α-helical peptide and a 13-residue random coil peptide. This was particularly important because if the α-syn molecule is in a state of dynamic equilibrium between multiple transient conformations60, binding might be specific to only one conformation. Alternatively, binding might result in arresting NAC in a specific conformation. Altogether, by considering two distinct VHNAC1 structures along with specific secondary conformations of the NAC peptide, we were able to generate a wide spectrum of possibilities for the final structure of the VHNAC1-NAC complex. In total, 41 such structures were generated and subsequently ranked from the most stable to the least stable as determined by MD simulations, which enabled enhanced conformational sampling for each complex.Here, we have demonstrated that it is possible to use structure-based computational design to significantly enhance the affinity of a single-domain camelid intrabody against the NAC domain of α-syn. Our work represented a significant departure from previous computational affinity maturation studies71. A distinct advantage that our methodology has over most ensemble docking methodologies is its effectiveness in sampling diverse conformations. For example, we are able to sample conformations as diverse as ST and N for loops H3 using REMD simulations in implicit solvent, which allows fast conformational sampling. Such diversity is difficult when ensembles are generated primarily in the neighborhood of initial conformations. This is especially important in the absence of a solved starting structure for VHNAC1.Our approach is similar to ensemble dock approaches wherein ensembles of conformations are generated for one or both binding partners before using docking methods to predict the structure of the complexHNAC1 from REMD simulations (N conformation of H3 loop) and the top model for its complex with the α-helical NAC peptide predicted by ClusPro. This model led to the N77D design that was experimentally confirmed to bind antigen with greater affinity than its progenitor VHNAC1. These observations further boost confidence in the capacity of current modeling tools and techniques for generating experimentally relevant predictions.From these models, electrostatic optimization methods were used to identify potential affinity-enhancing mutations with a particular focus on alleviating electrostatically suboptimal contacts at the binding interface. While our initial design strategy did not lead to improved affinity, it revealed a key residue on the paratope and the participation of H4 loop residues in binding, confirmed the importance of electrostatic interactions, and ultimately guided us towards a model that was better aligned with experimental observations. Notably, the final successful model for binding was comprised of the top model for V38 is that, in principle, a small, experimentally tractable set of putative affinity-enhancing mutations can be identified. As a result, subsequent confirmation by site-directed mutagenesis or even a focused library approach can be carried out significantly faster than would be required for iterative random mutagenesis approaches. Moreover, for the design strategy to be considered robust, it should yield a considerable fraction of designs that are successful when tested experimentally. Along these lines, we designed six unique VHNAC1 variants each containing 1–3 mutations total, which is well within the range of experimental techniques for manual mutagenesis and affinity determination. Three of these designs  were produced recombinantly and evaluated for antigen binding by SPR, and one (N77D) exhibited affinity for α-syn(A53T) that was significantly increased compared to the parental VHNAC1 intrabody (a success hit rate of 33%). This enhancement could be attributed to an order-of-magnitude increase in the association rate. Altogether, the results described here represent an important step towards developing rational design strategies based on ab initio modeling and bottom-up design approaches that provide a deeper understanding of protein surfaces and interactions, and facilitate the design of mutations that modulate these interactions.A key advantage of computational design strategies such as the one described here and elsewhereet al. reported an initial success hit rate of 12% (10 mutants with measured affinities better than wild-type out of 83 constructed mutants), but upon refining their protocol for predicting ΔΔGs of binding using information from all constructed designs, they retroactively improved their success rate to 26% 38. Unsuccessful designs are also important reminders that computational design relies heavily on the accuracy of the structure; hence, the lack of a high-resolution structure for VHNAC1-NAC contributed to the difficulty in choosing an accurate model and making reliable predictions. However, structure alone is unlikely to be the sole factor given that the rather low 12% hit rate reported by Clark et al. involved computational predictions based on a moderate resolution (2.8 Å) crystal structure of the antibody-antigen complex38. Indeed, better treatments for entropic contributions, explicit water molecules, hydrogen bonding networks, and solvation effects, among others, are needed. With continued improvements in computational methodology, we anticipate that structure-based design will become an important tool for optimizing critical intrabody properties such as affinity, stability, and solubility.Even though the K76A/M78T and K76E designs were not successful, they proved to be very useful in refining the search methodology in a manner that guided us directly to the affinity-matured N77D design. Indeed, failed designs have proven incredibly useful in learning about deficiencies in the energy functions, search procedures, or other methodology in previous structure-guided computational design efforts. For example, Clark E. coli strain DH5α was used for plasmid cloning and library construction, strain MC4100 was used for genetic selection using FLI-TRAP50, and strain BL21(DE3) was used to express proteins for ELISA and SPR analysis. Cultures were typically grown in Luria Bertani (LB) medium supplemented with the appropriate antibiotic, and protein expression was induced with isopropyl β-D-1-thiogalactopyranoside  or arabinose (0.2–1.0% w/v) depending on the plasmid used. Antibiotics were supplemented at the following concentrations: ampicillin ; chloramphenicol ; tetracycline , and kanamycin .https://www.addgene.org/pooled-library/messer-synuclein-vhh/) was generated previously by alpaca immunization with a fusion protein between biotin and a NAC-containing peptide derived from α-syn -Bla, which encodes additional copies of the Tat machinery for efficient protein export and a fusion protein between the α-syn A53T mutant and TEM-1 Bla31. To express unfused versions of different binding proteins for ELISA and SPR analysis, we cloned genes encoding NAC32 scFv, cAbGFP4, VHNAC1 and the designed VHNAC1 mutants between the NdeI and NotI sites of plasmid pET-21a(+), which introduced a 6x -His tag to the C-terminus of each gene product. During the cloning procedure, a C-terminal FLAG tag was also introduced.The immune-focused VHH library -Bla plasmid were transformed with library plasmid DNA and incubated at 37 °C for 1 h without any antibiotics. These transformed cells were then subcultured into fresh LB containing 25 µg/ml Cm and 10 µg/ml Tet to ensure that cells contained both plasmids and grown overnight. The next day, cells were spun down and normalized to OD600 ≈ 2.5 in fresh LB. 100-µL aliquots of serially diluted cells (including the dilution previously determined by spot plating) were then directly plated on LB agar supplemented 25–100 μg/ml Carb and 1.0% arabinose. Following incubation at 30 °C for ~48 h, hits were randomly picked and screened by spot plating to confirm Carb resistance. All hits deemed to be positive were then sequenced.For library selection, electrocompetent DH5α cells were transformed with the VHH library in plasmid pDD18-spTorA-VHHlib-FLAG. Clones containing library plasmids were recovered by selection on LB agar supplemented with Cm. The library, which was determined to contain ~1 × 10HNB10, VHNAC1 or one of the designed VHNAC1 mutants) and plasmid pDD322-TatABC::α-syn(A53T)-Bla. Briefly, cells were grown overnight at 37 °C in LB medium supplemented with appropriate antibiotics. The next day, β-lactam resistance was determined by spot plating 5 µL of serially-diluted overnight cells that had been normalized in fresh LB to OD600 ≈ 2.5 onto LB agar plates supplemented with varying amounts of Carb (10–100 μg/ml) or onto control plates supplemented with 25 μg/ml Cm. Following plating, bacteria were cultured at 30 °C for ~48 h.Spot plating of bacteria was performed using MC4100 cells carrying plasmid pDD18-spTorA-X-FLAG  with EDTA (1 mM) and resuspended in 500 μL PBS with 2% SDS. The resuspended pellets were boiled for 10 min and then centrifuged for 10 min at 16000 rcf. The supernatant was recovered as the insoluble fraction. Proteins in recovered fractions were electrophoretically separated using 12% SDS-polyacrylamide gels (BioRad), and then subjected to Western blot analysis according to standard protocols. Briefly, proteins were transferred onto polyvinylidene fluoride (PVDF) membranes, and intrabodies were detected by probing membranes with mouse anti-FLAG antibody conjugated to horseradish peroxidase (HRP) (Sigma-Aldrich).Soluble whole cell lysates were prepared by pelleting 20–25 mL of induced cell culture and resuspending in 500 μL PBS to achieve normalized final OD2SO4, and absorbance in each well was measured at 450 nm.To evaluate binding to α-syn, ELISA plates were coated with 1 μg/ul purified α-syn(A53T) peptide (Genway) in bicarbonate buffer overnight at 4 °C. Plates were then blocked with 5% non-fat milk in TBS for 2 h at room temperature and then washed using TBS supplemented with 0.1% Tween 20 (TBST). Whole cell lysate samples derived from BL21(DE3) cells expressing intrabodies from plasmid pET-21a were serially diluted in TBS with 1% BSA (TBS-BSA) and added to the plates (40 μL/well). Plates were incubated for 1 h at room temperature and then washed with TBST. After washing, plates were incubated with anti-FLAG antibody conjugated to HRP in TBS-BSA (50 μL/well) for 1 h at room temperature. After washing, plates were incubated with TMB HRP substrate (Thermo Fisher Scientific) for 10–20 min. The reaction was quenched with 2 M H2PO4, 300 mM NaCl, and 90 mM imidazole (pH 7.5). Proteins were eluted using a buffer containing 20 mM NaH2PO4, 300 mM NaCl, and 250 mM imidazole (pH 7.5). After affinity purification, eluted proteins were concentrated followed by size exclusion chromatography (SEC). Briefly, 2.5 ml of concentrated protein was injected (1 ml at a time) into a Superdex 75 column  attached to an FPLC system  and samples were run at a flow-rate of 0.5 mL/min at 4 °C in buffer containing 15 mM Tris-HCl, 500 mM NaCl. The high salt concentration was used to prevent aggregation. The migration of intrabodies was monitored by absorbance at 280 nm, and 250 μL fractions were collected.Intrabodies were purified from soluble whole cell lysates by Ni-NTA chromatography (Qiagen) using gravity columns according to standard protocols. Soluble lysates were incubated with the Ni-NTA resin for 1 h at 4 °C on a rotating platform. The lysates with the resin were poured into columns, until all the lysate and resin flowed into the column. The flow-through was poured again over the resin and allowed to flow-through again. The columns were washed four times with a buffer containing 20 mM NaH72. Briefly, purified VHH proteins were concentrated to >0.1 mg/ml and 400 μl was added to disposable plastic cuvettes (BrandTech Scientific) for DLS measurements using a Zetasizer Nano ZS  at a backscatter angle of 173°. Analysis was performed using Malvern Zetasizer software version 7.10. Dispersant viscosity was approximated using the Zetasizer software dispersants manager for complex solvents. The measurement position and attenuator were set to automatic and the temperature was set to 4.0 °C. The protein analysis model was used with the display range set between 2 nm and 6,000 nm and thresholds set to their default values of 0.05 and 0.01, respectively. For each sample, quadruplicate measurements consisting of 20 runs of 30 s were performed. Intensity size distributions were obtained using the autocorrelation function.Analysis of protein aggregation using DLS was performed as described previouslyN-hydroxysuccinimide/N-ethyl-N′(dimethyl-aminopropyl) (NHS-EDC). For antigen immobilization, 0.15 mg/ml of α-syn(A53T) peptide (Genway) in 10 mM sodium acetate buffer, pH 4.0 was coupled to CM5 chips using NHS-EDC chemistry to a level of ~1500 RU. Serial dilutions of intrabodies  in HBS-EP buffer  were injected at 30 μl/min for 2 min and 25 °C. Flow cells were regenerated by injection of 10 mM glycine (pH 4.0) followed by thorough washing with the running buffer. Additionally, a blank cell on the same sensor chip was used as a reference to correct for non-specific binding. Zero-concentration samples (blanks) were also included for double referencing. BIAevaluation software  was used to calculate values for kon, koff, and Kd by curve-fitting the data to a 1:1 Langmuir binding model using a simultaneous non-linear program. For comparison, the data were also evaluated by fitting the equilibrium binding responses to obtain Kd values using Prism 7 software.Antigen binding was evaluated by SPR using a Biacore 3000 instrument with CM-5 sensor chips that were activated with HNAC1 were generated using the homology model engine of Swiss-Model53 and was based on the solved crystal structure for an anti-β2 adrenergic receptor nanobody (PDB: 3P0G)54. The homology model was generated for 121 out of 122 amino acid residues of VHNAC1 (Q residue at N-terminus was not present in the template structure). All simulations employed GROMACS56 using the CHARMM27 force field with CMAP and the OBC implicit solvent model57. The SD integrator of the GROMACS package was used with a timestep of 2 fs. The LINCS algorithm in GROMACS was used to constrain the length of all bonds73. The inverse friction constant of the SD integration was set to 91 ps−1. All interactions were run with without cutoffs. The homology model was subjected to a short steepest descent energy minimization followed by a short equilibration at a constant temperature of 300 K. For the REMD simulations, all residues except those in the hypervariable loops H1, H2, H3 and H4 were backbone-restrained59. The Cα, N and C backbone atoms that do not belong to the hypervariable loops were restrained with the harmonic force constant of 2000 kJ/nm, while backbone atoms in H1-H4 along with side chains from all the residues were free to move. These restraints were imposed to simulate the limited motion of these residues in the folded domain. A total of 24 replicas at temperatures ranging from 300 K to 900 K were simulated for 20 ns (per replica) for the REMD simulations with an exchange attempt every 1 ps (500 steps). The g_cluster program in GROMACS with the linkage algorithm was used to cluster structures at the lowest temperature replica (300 K) to obtain the top 8 conformations. These conformations were used to start a new REMD, and simulated for another 8 ns to obtain the top 2 replicas in a similar manner.The starting structures for V61; and (ii) a 13-residue random coil peptide generated by piecing together amino acids one-by-one. However, peptides of different lengths were used for modeling at different stages. For example, a 19-residue peptide (amino acids 61–79) was initially employed for docking calculations, but after the top complexes were obtained for the 19-residue peptide, the peptide was extended to 24 residues (amino acids 57–80) or to 27 residues (amino acids 57–83).In general, two models were considered for the NAC region: (i) a 19-residue α-helical peptide based on PDB: 1XQ8HNAC1 (N and ST) and 2 models of NAC  were used as starting structures to generate docked structures using the ClusPro docking software65. A total of 41 structures were generated (4 possible permutations and ~10 structures per VHNAC1-NAC pair). To rank these structures from the most stable to the least stable, all structures were energy minimized and equilibrated for 5 ns in the NVT ensemble at 300 K. Only the complexes that were stable in the equilibration runs were retained for further simulations. For each VHNAC1-NAC pair, a separate REMD was conducted. REMD was used as a technique to rank the complexes from the most stable  to the least stable (highest temperature 450 K). Several stable structures were obtained for the α-helical NAC peptide with either the N or ST conformations of VHNAC1; however, none of the complexes generated for the random coil model from ClusPro were stable in the equilibration simulations. To generate candidate structures involving the random coil peptide, the 20 complexes obtained for the α-helical NAC peptide with either the N or ST conformations of VHNAC1 were equilibrated at high temperature (550 K). The VHNAC1 was position restrained in the simulations to maintain its native structure while the α-helical NAC peptide was equilibrated to a higher temperature (to relax to a random coil conformation). The conformations thus obtained were equilibrated at 300 K for 5 ns followed by REMD simulations. The conformations sampled at 300 K were obtained for each case.The two conformations of VHNAC1 and the NAC peptide antigen and (ii) residues on VHNAC1 and the antigen. The energy of VHNAC1-NAC interaction for the top 5 complexes was determined. Umbrella sampling (US) simulations were used to obtain PMF of binding as a function of the radial distance between VHNAC1 and the NAC peptide antigen. US simulations were used to compare the change in PMF between the bound (NAC peptide in complex with VHNAC1) and unbound state (NAC peptide far away from VHNAC1). PMFs were compared for complexes with the same starting models for VHNAC1 and NAC peptide. Our PMF calculation is intended to describe the free energy changes associated with a transition between two “basins” in conformation space, where such basins may only represent metastable states  as opposed to thermodynamic states. This is particularly relevant for the initial complex state for which we make specific choices. We use the US method to probe conformations along an order parameter that gradually moves the system from the bound basin to the unbound basin (in our case the order parameter is the distance between antibody and antigen). In US, molecules are unconstrained and free to sample all degrees of freedom “orthogonal” to the main order parameter. The initial configurations for the US windows were obtained by using the Pulling code in GROMACS. A total of ~80–96 windows were placed at a distance of 0.03 nm for windows at small separations (<2.5 nm) to 0.10 nm for windows at large separations (>4 nm) between VHNAC1 and the NAC peptide. Each window was run for 5–15 ns depending on the time it took for the free energy profiles to converge within each window. Overall, the simulations were run for a cumulative time of 500 ns to 1.5 μs. The g_wham program was used to obtain the PMF as a function of distance using WHAM, which estimates the statistical uncertainty of the unbiased probability distribution given the umbrella histograms, and subsequently computes the PMF that corresponds to the smallest uncertainty74.To obtain the energy terms of interest, energy-groups were defined within GROMACS to calculate the energy of interaction between (i) V69. It incorporates the effects of ionic strength mediated screening by evaluating the PBEQ at a finite number of points within a three-dimensional grid box. The electrostatic free energy of binding is calculated as:DelPhi calculates electrostatic potentials in and around macromolecules and the corresponding electrostatic energieset al.35: (i) PBEQ was solved for the complex (and not the encounter complex), this method has been previously implemented with success; (ii) PARSE parameters were used for partial atomic charges and radii75; and (iii) a dielectric constant of 4 was used for protein and explicit water, and 80 for implicit solvent regions; ionic strength was set at 0 M  and 0.15 M , and modeled with a 2.0-Å Stern Layer and a molecular surface generated with a 1.4-Å probe sphere. The number of grid points per angstrom (gpa) was set to 1.5. For all calculations, electrostatic free energy was calculated for 50–100 structures and the estimated value was obtained by averaging over them.For all our calculations using DelPhi, we used the following inputs according to Lippow Supplementary Information


OPA1 is a dynamin-related GTPase that controls mitochondrial fusion, cristae remodeling, energetics and mtDNA maintenance. However, the molecular architecture of OPA1 is poorly understood. Here we modeled the structure of human OPA1 by the threading approach. We found that the C-terminal region of the OPA1 protein had multiple functional domains, while the N-terminal region was rich in alpha helices and did not include specific domains. For the short soluble forms of OPA1, we observed that there were obvious hydrophobic regions near the two cleavage sites and the N-terminal was positively charged after cleavage. The blue native analysis revealed that the protein could form stable homodimers. In addition, the evolutionary conservation of the C-terminal region, where most of the known mutated disease-related sites were located, was significantly higher than that of the N-terminal region. These findings provided new insights into the structure and biochemical function of OPA1. Mitochondria undergo a constant dynamic balance between organelles fusion and fission to maintain their network morphology and functions . The funMany evidences indicate that OPA1 is associated with other important mitochondrial functions, including mitochondrial DNA (mtDNA) maintenance, which is probably anchored to the IMM, and the oxidative phosphorylation efficiency . In mousOPA1 cause the disease dominant optic atrophy (DOA), one inherited optic neuropathy that is characterized by selective degeneration of retinal ganglion cells and classically presents in early childhood with progressive visual failure  that are associated with stimulation of GTPase activity. Several dynamins contain a domain that can be a transmembrane domain, a sequence or a pleckstrin-homology domain (PH domain) for interacting with lipid membranes , mitofusins, Drp1, OPA1, Mx proteins, guanylate-binding proteins (GBPs) and atlastins in eukaryotic cells . The mitembranes . HoweverOPA1 gene is built from 30 exons and has been reported to generate at least eight mRNA variants by the alternative splicing of exons 4, 4b and 5b  was expressed in Escherichia coli BL21 (DE3) cells as N-terminal His6-SUMO-tagged fusion proteins using the pET28a plasmid (His6-SUMO-OPA1). Cells were grown in LB medium at 37 °C to an OD600 of ∼0.6. Protein expression was induced by addition of 100 µM IPTG and cultures were incubated overnight at 16 °C. The cell pellet was resuspended in 10 mM Tris-HCl pH 8.0, 20 mM imidazole and 500 mM NaCl buffer. Cells were lysed using a microfluidizer (Microfluidics). After centrifugation at 12,000 rpm for 1 h at 4 °C, the soluble extract was filtered and combined with Ni-NTA agarose  in batch. After beads were packed into a column, the combination was washed with resuspension buffer supplemented with 40 mM imidazole, and protein was eluted with an imidazole gradient to a final concentration of 500 mM. Peak fractions were pooled, and the His6-SUMO-tag was cleaved by ULP1 peptidase in the 4 °C overnight to generate the OPA1 (exons 6–28) protein. Then diluted the NaCl concentration to 50 mM and applied to Resource Q column . Peak fractions were concentrated and loaded to a Superdex 200 10/300 gel filtration column  using the buffer with 10 mM Tris-HCl, pH 8.0 and 150 mM NaCl. The protein was concentrated to ∼10 mg/ml prior to freezing. All the samples were analyzed by SDS-PAGE electrophoresis. The procedure for SDS-PAGE was based on a general protocol as previously described  protein in phoresis . From thFurther, we predicted the possible mode of dimerization of the C-terminal region by using the human OPA1 protein modeled structure. Protein–protein docking results showed that the C-terminal region of the human OPA1 protein could form two stable homodimers modes. In the first mode of homodimer, the GTPase domains of the two monomers were approximately 130 Å apart, and the dimeric interface involved in 23 pairs of interaction among 15 amino acid sites, 10 pairs of which were from the interaction between the linker domain and middle domain, 12 pairs of which were from the interaction between the middle domain and the GED, while one pair of sites was between the two same GED domains . In the Furthermore, we collected all the vertebrate OPA1 proteins by sequence similarity search. Totally, more than 900 of OPA1 protein sequences were discovered from 205 species including several spliced variants. The sequence alignments was shown in the form of sequence logo map . Using tThe results showed that the N-terminal region of OPA1 protein was significantly less conservative than the C-terminal region . For thoHuman OPA1 mutations have been associated to a large spectrum of neurodegenerations. 171 amino acid site variants in the human OPA1 had been analyzed by the locus-specific database dedicated to OPA1 . The posThe N-terminal region of OPA1 protein occupied 34% of the total length, but the disease-related mutation sites located in this region accounted for only 15% of the total disease-related mutation sites in the OPA1 while the mutations unrelated to diseases in this region occupied 70% of the total mutation sites unrelated to diseases. The disease-related variant sites in the N-terminal region were concentrated in the exons 1–2 coding region (8 variants) and the exons 6-8 coding region (11 variants), while the variant sites unrelated to disease were concentrated on the exons 4, 4b and 5b (five variants). For the C-terminal region, more than 25% of the sites in the GTPase domain and the linker region encoded by exons 17–18 were found to have disease-related mutations, respectively. And about 20% of sites in the GED were also found to present disease-related variation. Therefore, most of the disease-related mutations in the OPA1 protein were located in the C-terminal region, while in the alternative splicing region, almost no disease-related mutation occurred.OPA1 is a member of dynamin superfamily, which is essential for shaping the cristae morphology and IMM fusion . In humaThe C-terminal region of OPA1 protein was a dense structure comprising a GTPase domain, a middle domain, a PH domain and a GED. The structure of this region was different from mitofusins and atlastins that mediated membrane fusion, while it was similar to dynamin 1 and MxA. The OPA1 protein contained a PH domain that interacted with phospholipid molecules, while mitofusins and atlastins did not include. In addition, similar to dynamin 1 and MxA, OPA1 could self-assemble to form dimers through the middle domain and GED, while this was also different from mitofusins and atlastins that dimerized by GTPase-GTPase domains. All these findings indicated that OPA1 might have a specific fusion mechanism.in vitro membrane fusion assay unveiled that OPA1 l-form on one side of the membrane and cardiolipin on the other side, are the minimal components sufficient and necessary for fusion  was more than 90% for the protein structure of all predicted OPA1 domains. While, the ratio of all disallowed residues  was less than 5%. According to quality assessment, the accuracy of the OPA1 protein structural models were acceptable.Click here for additional data file.10.7717/peerj.7285/supp-2Figure S2(A) The OPA1 (exons 6-28) elution profiles of a S200 size-exclusion column. (B) SDS-PAGE and Blue native PAGE analysis of purified human OPA1 (exons 6-28) protein. Purification was carried out more than 10 times for OPA1 with similar purity and yield.Click here for additional data file.10.7717/peerj.7285/supp-3Figure S3Sequence logos were generated as graphical representations of the multiple sequence alignments of the amino acids. The positions of the amino acid sites in vertebrates OPA1 protein are in accord with those of human OPA1 isoform 8.Click here for additional data file.10.7717/peerj.7285/supp-4Data S1Click here for additional data file.


Genetic sequence database retrieval benchmarks play an essential role in evaluating the performance of sequence searching tools. To date, all phylogenetically diverse benchmarks known to the authors include only query sequences with single protein domains. Domains are the primary building blocks of protein structure and function. Independently, each domain can fulfill a single function, but most proteins (>80% in Metazoa) exist as multi-domain proteins. Multiple domain units combine in various arrangements or architectures to create different functions and are often under evolutionary pressures to yield new ones. Thus, it is crucial to create gold standards reflecting the multi-domain complexity of real proteins to more accurately evaluate sequence searching tools.This work introduces MultiDomainBenchmark (MDB), a database suite of 412 curated multi-domain queries and 227,512 target sequences, representing at least 5108 species and 1123 phylogenetically divergent protein families, their relevancy annotation, and domain location. Here, we use the benchmark to evaluate the performance of two commonly used sequence searching tools, BLAST/PSI-BLAST and HMMER. Additionally, we introduce a novel classification technique for multi-domain proteins to evaluate how well an algorithm recovers a domain architecture.http://csc.columbusstate.edu/carroll/MDB/.MDB is publicly available at The online version of this article (10.1186/s12859-019-2660-5) contains supplementary material, which is available to authorized users. Genetic sequence database searching is a foundational tool in bioinformatics commonly used to make new discoveries, guide annotation, and direct downstream analysis, among many other tasks. Therefore, the performance of database searching tools is crucial to high quality results in many biomedical applications. Benchmarking such tools provides a systematic comparison to aid developers and researchers to understand the strengths of each tool. Here, we introduce the first phylogenetically diverse benchmark of multi-domain protein sequences.Decades ago, the first benchmarks for genetic sequence database retrieval were comprised of single domain sequences. With less supporting evidence then we now enjoy, benchmark designers used just single domain sequences to provide a robust standard and to simplify homology evaluation. Databases such as Pfam , SCOPe , and othOther databases have also been used for benchmarking sequence searching tools. The OMA (“Orthologous MAtrix”) database  providesn false positive records) (ROC n) [k (TAP-k) metric [k, “k” imposes a threshold to fix the median number of irrelevant  records per query. This threshold is applied to all the queries. The TAP-k is based on the average precision : Central to assessment of sequence searching tools is the evaluation metric. For the past two decades, the normalized area under a receiver operating characteristic curve (up to  (ROC n)  has been (ROC n) , 16, 18. (ROC n) , 20. We Tq is the total number of relevant records for a query q, j(E0) is the rank of the last relevant record with a statistical score of E0 or lower and p(x) is the precision of the record at rank x. Notice that there could be irrelevant records with a score lower than E0  but do not affect in the average precision. The TAP-k remedies this situation by penalizing irrelevant records occurring before the threshold E0 and normalizing to account for the extra precision term: Here, k scores are in the range of 0.0 to 1.0. A TAP-k score is 0.0 if no relevant records are retrieved before the cutoff. Conversely, a TAP-k score is 1.0 when all the relevant records and no others are retrieved before the cutoff.Due to the normalization, TAP-k and timing performance results here. To determine relevancy, we use a novel approach that accounts for the domain architecture within a protein.In this study, we introduce MultiDomainBenchmark (MDB), the first phylogenetically-broad database retrieval benchmark with multi-domain queries. We anticipate that the primary use of this benchmark will be to evaluate the retrieval performance of searching tools. Namely, the MDB will allow for assessments using multi-domain sequences. Along those lines, and to illustrate the utility of MDB, we benchmarked two sequence searching tools, BLAST/PSI-BLAST , 22 and e−18.To illustrate the importance of accounting for multiple domains when using a searching tool, we constructed single-domain queries and database from our multi-domain database by creating a new sequence for each domain and its flanking amino acids up to the next domain (or edge of the sequence). While we could use dozens of examples that illustrate the same point, we arbitrarily choose up |Q1L5Y1 |Q1L5Y1_9FILI  as the query and up |Q76IJ5 |Q76IJ5_9FUNG  as the target. The query has three domains: PF00623, PF04983 and PF04998. The target has four domains: PF00623, PF04983, PF05000 and PF04998. Using each of the three (single-domain) sequences from the original multi-domain query, we searched using PSI-BLAST against the 337,199 single-domain sequences. Each of the searches listed a hit for the correct single-domain sequence from up |Q76IJ5 |Q76IJ5_FUNG, however, each of the e-values were above the default cut-off of 0.001 . Conversely, we when search with PSI-BLAST, using the original multiple-domain sequence as a query, it lists the match to up |Q76IJ5 |Q76IJ5_9FUNG with an e-value of 21, d2, d3) and sequenceB have DA . Here, although the sequences contain the same domains, the domains appear in a different order. Consequently, each sequence has a different DA and therefore, we classify the match of sequenceA and sequenceB in a retrieval list as irrelevant . As another example, pfam21 |Q3GCI4 |Q3GCI4_9FIRM (RefSeq WP_011640391) contains the HAMP domain and the MCPsignal domain. These domains, in this order, constitute da00101  . We define a DA as an ordered set of domains . Furthermore, we use DAs to perform classification. As a theoretical example, let sequenceA have DA d, d2, d3 1, d2, d2, d2, d3, d2 would have a DA of d1, d2, d3, d2. We sorted the sequences based on the number of domains (counting collapsed domains as a single domain). We assigned a new (ascending) number to the first occurrence of each DA. In all, there are 2525 unique DAs among the multi-domain sequences (with 32.0% having collapsed domains).We created MDB to evaluate genetic database retrieval under realistic conditions, namely, ones using multi-domain queries. Stemming from our familiarity with the curation of the RefProtDom benchmark, we applied several additional filtering steps to RefProtDom and some novel classification concepts to produce MDB. As a starting point, RefProtDom v1.2 has 234,505 sequences. First, we ignored each sequence that had one or more amino acids with multiple domain annotations. Current evaluation measures assume that each amino acid belongs to at most one protein domain. We removed the 6993 sequences with overlapping domains to simplify analyses. We formed the target (or subject) database from the resulting 227,512 (single- and multi-domain) sequences  . Second, we filtered out DAs that did not have at least one sequence shorter or equal to 1800 amino acids (to reduce execution time). This resulted in 1179 DAs see Fig. b. FurtheThe sequences and DAs in the MDB can be characterized by 1) length of each query sequence, 2) number of sequences in each DA and 3) number of domains per sequences. First, the query sequences range from 170 to 1800 residues long (with an average of 759.7 residues). Figure As is common with sequence searching benchmarks, the data are contained in flat-text files (readable by any text editor). The target sequences (which include the query sequences) are in a FASTA formatted file. Domain locations and relevancy information are contained in tab-delimited files.With the explosion of sequence data and more sophisticated tools than ever before, we now have more annotated sequences and genomes available. Multiple databases now include domain annotations . For exSeveral derivative works of the Pfam database exist, with RefProtDom being of special interest. RefProtDom applies several additional filters to the Pfam database to create a homology evaluation benchmark. Although RefProtDom version 2 has been released , it did Relevancy is more clearly defined for single domain matches. Consequently, if a researcher is primarily concerned with just a single domain, then the results of the evaluation of searching tools using existing single-domain benchmarks are probably adequate for that use case. If however, the protein(s) of interest have multiple domains or are being compared against multi-domain proteins, then the evaluation results from a multi-domain benchmark may prove more valuable. Furthermore, although many protocols for manipulating domain architectures collapse adjacent repeated domains into one, the consequences of the collapse are not fully understood. Researchers exploring the relevancy of retrieved proteins with repeated domains should therefore inspect the corresponding results carefully. Finally, most search tools do not try to detect domain rearrangements. Accordingly, we do not try to capture domain rearrangements with this benchmark.Although other multi-domain databases and benchmarks do exist, they are not structured as general-purpose benchmarks. For example, the gold-standard benchmark introduced by Song et al. is noticeably different from MultiDomainBenchmark. First, it only comprises human and mouse sequences. Second, it is much smaller with only 0.8% of the number of sequences in MDB (and therefore fewer relationships defined).On one hand, MultiDomainBenchmark places heavy restrictions on domain architecture, namely, it insists that retrieved proteins should match all query domains, matching the query order though not the multiplicity (because it collapses multiple domains into one). On the other hand, many domain benchmarks count a single domain match as correct, while yet others could count multiple domain matches with omissions as correct. The difference reflects the intent of MultiDomainBenchmark: to evaluate tools for retrieving proteins whose functions overlap very tightly with the query protein.Consider for example, the inhibitor of apoptosis (IAP) family, whose members c-IAP1 and c-IAP2 contain the domain architectures BIR-BIR-BIR-UBA-CARD-RING, and whose member XIAP contains the slightly different architecture BIR-BIR-BIR-UBA-RING, omitting the CARD domain. For the query c-IAP2, most domain benchmarks would count both c-IAP1 and XIAP as correct hits, whereas MultiDomainBenchmark insists on a more precise structural overlap, so with query c-IAP2 it would count c-IAP1 as a correct hit, but not XIAP.Because of their widespread use, we chose two sequence searching tools to illustrate the usefulness of MultiDomainBenchmark: BLAST/PSI-BLAST and HMMER. We evaluated each tool with both non-iterative and iterative protocols. For non-iterative evaluations, we searched against the collection of 227,512 sequences (with non-overlapping domains) in the MDB target database using each of the 206 MDB Test queries. Figure k (TAP-k) metric [Due to ambiguities inherent with classifying the homology of multi-domain searches, we focused instead on capturing domain architectures. In addition to the criterion for a match to be classified as relevant (a “true positive”) described in the “) metric .k scores for both searching tools span the full range from 0.0 to 1.0. Figure k scores from BLAST’s for each data set . Note, for each value of k={1,3,5,20}, the x-axis is sorted independently to provide a visually discernible graph. The most common difference is exactly 0.0, as one might expect. For k=20, 7.2% of the TAP scores were the same. This percentage increases as k decreases with k=1 having 19.4% of its scores being the same. The average difference varies from 0.12 (for k=1), to 0.16 (for k=3) (larger averages indicate BLAST performed better than HMMER). Figure k scores for iterative searches show much more discord than for the non-iterative ones. For example, the percentage of searches that have the same TAP-k score varied from 3.8% (k=20) to 15.0% (k=1). Additionally, the averages ranged from 0.16 (k=1) to 0.18 (k=3). The distribution of TAP-k scores is illustrated in the Additional file Given the phylogenetically diverse set of queries in the MDB Test subset, the TAP-Additionally, we gathered timing results. We executed the programs on a shared environment system and therefore the timing results are just first approximations to the actual execution times. Figure p=0.0114.Researchers have been benchmarking sequence searching tools for decades. With just the exceptions mentioned previously, these benchmarks have only had single-domain sequences. As one would expect, sequence searching tools perform differently on single- and multi-domain benchmarks. To quantify this, we divided the ASTRAL database into two halves, each with 5162 sequences (as has been done elsewhere ). We comIn this study, we presented MultiDomainBenchmark, the first phylogenetically diverse benchmark with multi-domain queries. MDB has a target database with 227,512 single- and multi-domain sequences. The 66,602 multi-domain sequences have 2525 unique DAs. We applied additional filters yielding 412 phylogenetically diverse DAs and from each one we randomly selected a query sequence. We designed this benchmark on the one hand, to bring attention to the issue of evaluation of searches with multiple domains, and on the other, to perform such analyses. Here, we also provided the initial use of MDB by assessing BLAST/PSI-BLAST’s and HMMER’s ability to capture domain architectures and their execution times. While many other sequence searching tool exist, our case study here simply demonstrates the use of MDB.We invite other developers and researchers to also use MDB. To this end (and for reproducibility), we provide the scripts on our website that we used to perform the case study.Additional file 1k scores from the case study. (PDF 259 kb)Supplementary material. Supplementary material detailing multi-domain proteins in UniProt-SwissProt and the distribution of TAP-