1 2 3 NHLBI - AbDesigner : An online tool for 4 design of peptide - directed antibodies 5 6 by 7 8

25 Investigation of physiological mechanisms at a cellular level often requires production of high 26 quality antibodies, frequently using synthetic peptides as immunogens. Here we describe a new, web27 based software tool called NHLBI-AbDesigner that allows the user to visualize the information needed to 28 choose optimal peptide sequences for peptide-directed antibody production 29 (http://helixweb.nih.gov/AbDesigner/). The choice of an immunizing peptide is generally based on a 30 need to optimize immunogenicity, antibody specificity, multi-species conservation, and robustness in 31 the face of post-translational modifications (PTMs). AbDesigner displays information relevant to these 32 criteria as follows: 1) Immunogenicity Score, based on hydropathy and secondary structure prediction; 2) 33 Uniqueness Score, a predictor of specificity of an antibody against all proteins expressed in the same 34 species; 3) Conservation Score, a predictor of ability of the antibody to recognize orthologs in other 35 animal species; and 4) Protein Features that show structural domains, variable regions, and annotated 36 PTMs that may affect antibody performance. AbDesigner displays the information online in an 37 interactive graphical user interface, which allows the user to recognize the trade-offs that exist for 38 alternative synthetic peptide choices and to choose the one that is best for a proposed application. 39 Several examples of the use of AbDesigner for the display of such trade-offs are presented, including 40 production of a new antibody to Slc9a3. We also used the program in large-scale mode to create a 41 database listing the 15-amino acid peptides with the highest Immunogenicity Scores for all known 42 proteins in five animal species, one plant species (A. thaliana), and S. cerevisiae. 43 44 45 INTRODUCTION 46 The advent of genome sequencing projects for multiple animal and plant species at the 47 beginning of this century has led to a broadened view of physiological and biochemical mechanisms at a 48 cellular level, owing to the recognition of many poorly studied proteins that may play key roles in 49 cellular regulation. The data from these sequencing projects also provides the information needed for 50 facile generation of reagents, including antibodies, necessary for investigation of these proteins and the 51 cellular pathways that they are involved with. Such antibodies are typically used for identification of 52 protein localization in cells (e.g. by immunocytochemistry), purification of protein complexes (e.g. by 53 immunoprecipitation), and routine quantification (e.g. by immunoblotting). However, the acquisition or 54 production of antibodies for such investigations remains a trial-and-error undertaking in many cases. 55 The antibody-design task involves the choice of an immunogen that is predicted to evoke a 56 vigorous immunogenic response in the host species. Frequently, the immunogen consists of a short 57 synthetic peptide that is conjugated to a carrier protein. In this setting, the initial task involves the 58 choice of a potentially immunogenic peptide sequence that corresponds to a portion of the target 59 protein. In many cases, suitable antibodies have been obtained using the relative hydropathy of 60 candidate peptides as the sole predictor of immunogenicity (e.g. via Kyte-Doolittle hydropathy index 61 (22). Jameson and Wolf (14) have presented an objective function, the so-called antigenic index, that 62 has also been widely used as a predictor of immunogenicity. The Jameson-Wolf antigenic index is a 63 weighted sum of several determinants including hydropathy, secondary structure predictors (e.g. via the 64 Chou-Fasman method (4), and surface probability (via Janin method (15)). However the decision of 65 what immunizing peptides to use often depends on factors other than imputed antigenicity (or more 66 properly 'immunogenicity'). For example, an immunizing peptide that is identical to or similar to 67 sequences in other proteins is likely to produce an antibody that is not specific, recognizing not only the 68 target protein but also these other proteins. Thus, there may be a trade-off between immunogenicity 69 and “uniqueness” of a given synthetic peptide that may complicate the choice. Other trade-offs can also 70 be recognized. For example, an investigator may wish to produce an antibody that recognizes a given 71 protein in more than one species. Under this circumstance, he or she may wish to choose an 72 immunizing peptide that is common to all species of interest. Another potential problem with selecting 73 the immunizing peptide comes when post-translational modifications occur within the corresponding 74 region of the target protein. Under this circumstance, the antibody may recognize the protein in the 75 absence of the post-translational modification but not in the presence of the modification if a key 76 epitope is obliterated. When multiple trade-offs must be considered in the production of the synthetic 77 peptide to be used for immunization, it can be difficult to take all of the relevant information into 78 consideration, since such information must be culled from multiple sources using multiple software 79 applications. 80 To address this task, we present a fully integrated online software tool, NHLBI-AbDesigner, for 81 the design of peptide-directed antibodies. This program is implemented as a web application 82 (http://helixweb.nih.gov/AbDesigner/) that displays information relevant to the choice of the optimal 83 immunizing peptide for a given biological application, including predicted immunogenicity, uniqueness 84 (predictor of specificity), conservation (predictor of multi-species cross-reactivity), and relevant protein 85 features such as post-translational modifications, domain architecture, sites of sequence variation due 86 to alternative splicing, and other regions or sites of interest culled from the corresponding Swiss-Prot 87 record. AbDesigner was also employed in batch mode to generate a genome wide list of top scoring 88 immunizing peptides for selected animal and plant species (viz. Homo sapiens, Rattus norvegicus, Mus 89 musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, and Arabidopsis 90 thaliana) as a resource for large-scale antibody design. 91 EXPERIMENTAL PROCEDURES 92 Peptide synthesis and antibody production. Based on analysis of rat NHE3 (gene symbol: Slc9a3) 93 using AbDesigner, a 20-aa synthetic peptide corresponding to amino acids 621-640 of the COOH94 terminal tail of NHE3 was produced by standard solid-phase peptide synthesis techniques followed by 95 HPLC purification to >95% (sequence: YLYKPRQEYKHLYSRHELTP with an added amino-terminal cysteine 96 for conjugation, Lofstrand, Gaithersburg, MD). The peptide was purified by HPLC and was conjugated to 97 maleimide-activated keyhole limpet hemocyanin via covalent linkage to the N-terminal cysteine. Two 98 rabbits were immunized (Antibodies, Inc., Davis, CA) with this conjugate using a combination of Freund’s 99 complete and incomplete adjuvants. One of these antisera (7644) was used for the present studies after 100 affinity purification on a column made with the same synthetic peptide used for immunizations 101 (SulfoLink Immobilization Kit, Pierce, Rockford, IL). 102 Immunoblotting. Immunoblotting was carried out as described (31) to assess the ability of 103 antibodies to recognize the target proteins using whole homogenates (or membrane fractions) from the 104 kidney cortex and outer medulla of rats or humans. Rat kidneys were resected from pathogen-free male 105 Sprague–Dawley rats (Taconic Farms) weighing 180–220 g after euthanization (Animal protocol #H106 0110, approved by the Animal Care and Use Committee, NHLBI). Human kidney tissue was obtained 107 from a nephrectomy specimen (approved as exempt from review by the National Institutes of Health 108 Office of Human Subjects Research) (32). In brief, kidney tissue was homogenized using a tissue 109 homogenizer (Omni 1000 fitted with a micro-sawtooth generator) in ice-cold isolation solution (pH 7.6) 110 containing 250 mM sucrose, 10 mM triethanolamine, 1 μg/ml leupeptin, and 0.1 mg/ml 111 phenylmethylsulfonyl fluoride, and total protein concentration (BCA Protein Assay Kit, Pierce) was 112 adjusted to 1-2 μg/μl with isolation solution. The samples were solubilized in 5× Laemmli sample buffer 113 (1 vol per 4 vol of sample) followed by heating to 60°C for 15 minutes. After solubilization in Laemmli 114 buffer, protein samples (15–20 μg) were resolved by SDS-PAGE gel electrophoresis on 10 or 4–15% 115 polyacrylamide gels (BioRad, Hercules, CA) and transferred electrophoretically onto nitrocellulose 116 membranes. The membranes were then blocked with Odyssey blocking buffer (Li-Cor, Lincoln, NE), 117 rinsed, and probed with primary antibody overnight at room temperature. After a washing, blots were 118 incubated with species-specific fluorescently labeled secondary antibodies Alexa Fluor 680 (Molecular 119 Probes, Eugene, OR) used at 1:5,000 for detection of all primary antibodies. Fluorescence was imaged 120 using an Odyssey Imaging System (Li-Cor). 121 Software implementation. The main workflow of AbDesigner is illustrated in Figure 1. 122 AbDesigner was written in Java (Java Development Kit 6 Update 23, Oracle) using NetBeans IDE 7.0 as an 123 integrated development environment. The web application of AbDesigner was developed using Java 124 Servlet as a controller and using Java Applet and JavaServer Pages for presentation. The web application 125 was implemented using Apache Tomcat 7.0.12 hosted by the NIH Biowulf cluster 126 (http://biowulf.nih.gov/) and the NHLBI Center for Biomedical Informatics 127 (http://www.nhlbi.nih.gov/about/cbi/index.htm). A mirror site has been established at 128 https://javaapps.nhlbi.nih.gov/AbDesigner83134205a7d57d3c59dba2ae1cdbe9567ef043e7c89ddfcdb31 129 a46bc4ad4efd5/. 130 To run the web version of AbDesigner, a user needs the Java SE Runtime Environment Version 6 131 (http://www.java.com/en/download/). Earlier version may work with reduced functionality. The web 132 version of AbDesigner has been tested on multiple operating systems and web browsers. The operating 133 systems tested are Microsoft Windows XP [Version 5.1.2600], Microsoft Windows 7 [Version 6.1.7600], 134 and Mac OS X 10.6.4 (10F569). The web browsers tested are Firefox 4 (Windows), Internet Explorer 9 135 (Windows), and Safari 5 (Macintosh and Windows). 136 The calculation of Immunogenicity Score (Ig Score), Uniqueness Score, and Conservation Score is 137 described in the Appendix. To display Protein Features (including structural domains, variable regions, 138 and annotated PTMs), Abdesigner extracts the relevant information from the Swiss-Prot Protein 139 Database downloaded from the National Center for Biotechnology Information (NCBI, 140 http://www.ncbi.nlm.nih.gov/protein). 141 142 RESULTS 143 Software description: submission page. The object of AbDesigner is to display the features of a 144 protein relevant to the choice of a synthetic peptide sequence to be used as an immunogen in antibody 145 production. It does so in a manner that allows the user to judge trade-offs for candidate peptide 146 sequences with respect to multiple factors including hydropathy, secondary structure, uniqueness, 147 conservation among species, and the presence or absence of post-translational modifications. The 148 online submission page can be found at http://helixweb.nih.gov/AbDesigner/ and is illustrated in Figure 149 2. To specify a protein for analysis, the program accepts the following types of input: Gene Symbol, 150 Swiss-Prot Accession Number, or Swiss-Prot Entry Name from any of the following seven species: Homo 151 sapiens, Rattus norvegicus, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, 152 Saccharomyces cerevisiae, or Arabidopsis thaliana (see part A and B of Figure 2). Proteins from other 153 species can be analyzed by entering the sequence of that protein FASTA format (This should be avoided 154 when analyzing proteins from the above seven species because of the limitations associated with 155 submitting a FASTA amino acid sequence as described in the Appendix). The amino acid sequence and 156 protein annotations for the input protein are extracted from the Swiss-Prot Protein Database 157 downloaded from the National Center for Biotechnology Information (NCBI). A user-defined sequence 158 length for production of synthetic peptides for immunization can be set from 5 to 50 amino acids 159 (‘Peptide Length’, see part C of Figure 2). (A peptide of 10-25 amino acids is typically used. A longer 160 sequence can provide a greater likelihood of producing a potent antibody by virtue of the fact that it 161 contains more potential epitopes, but by the same token there is a greater chance that it will produce 162 IgG clones with lower specificity. Furthermore, the cost of peptide synthesis often increases 163 substantially with larger peptides.) Finally, a user-defined linear ‘Epitope Length’ can be set from 5 164 amino acids to the full length of the peptide (to be used for determination of uniqueness and 165 conservation of a peptide, see part D of Figure 2). In the example in Figure 2, Gene Symbol (Rat) is 166 selected as an input type, AQP2 (the gene symbol for water channel aquaporin-2) is the input, a peptide 167 length of 15 amino acids is entered, and an epitope length of 7 amino acids is selected. 168 Software description: output page. After the input values are entered, AbDesigner calculates 169 and displays (Figure 3) the Immunogenicity Score (Ig Score) [modified from the principle of the Jameson170 Wolf antigenic index (14)], which is aligned with the calculated Uniqueness Score, the calculated 171 Conservation Score, and Protein Features extracted from the corresponding Swiss-Prot record. 172 Uniqueness Score allows the user to predict the specificity of an antibody produced by a peptide. 173 Conservation Score allows the user to predict the likelihood that the ortholog of the target protein (i.e. 174 from alternative species) will be recognized by the antibody. How the scores are calculated from the 175 sequence data is described in the Appendix. The Protein Features reported are position-dependent 176 annotations of regions or sites of interest such as post-translational modifications, binding sites, enzyme 177 active sites, local secondary structure, sequence conflicts, and other characteristics culled from the 178 appropriate Swiss-Prot protein record. The parallel linear display shown in Figure 3 allows the user to 179 evaluate the various trade-offs among factors that may impinge on the choice of an immunizing peptide 180 sequence, according to the intended user-specific purpose. In the following, we describe the features of 181 the output page in greater detail. 182 The online graphical output of AbDesigner consists of an interactive Java Applet embedded in an 183 html page (Figure 3). The main panel (the Applet) displays a variety of information and allows users to 184 ‘mouse-over’ each element to obtain further data. Annotations in Figure 3 are designated by letters A-J 185 (not part of the actual output). In the main panel, the upper line (A) shows the input amino acid 186 sequence. When users mouse-over the main panel, the peptide of interest of the appropriate length is 187 highlighted in yellow with the central amino acid underlined. The low complexity regions of a protein 188 identified by the segmasker program (based on the SEG filtering algorithm (36), obtained from the NCBI 189 C++ Toolkit, ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/) are displayed in red font and 190 on mouse-over along the protein sequence row. 191 The next line (B) of the main panel displays Chou-Fasman secondary structure prediction. The 192 Chou-Fasman prediction is performed as described (4) except for slight modifications (see Appendix). 193 The secondary structure predicted, i.e. alpha helix, beta sheet, strong beta turn, or weak beta turn is 194 displayed by different colors and on mouse-over. High immunogenicity correlates with a lack of alpha 195 helices or beta sheets, and presence of beta turns. The chief practical value of this analysis is that it 196 identifies locations of prolines (the chief determinant of a prediction of ‘beta-turn’), which aid 197 immunogenicity by interfering with α-helix formation. 198 The next line (C) of the main panel displays the Kyte-Doolittle hydropathy index (KDHI) of each 199 peptide along a protein sequence. The KDHI is displayed in 8-bit scale (0 255) RGB color heat map and 200 on mouse-over. Given that lower KDHI values correlate with greater immunogenicity, the 8-bit 201 transformation of the KDHI is performed in the reverse direction so that the lowest value (-4.5, most 202 hydrophilic) is transformed to 255 (displayed in the brightest cyan, by default) while the highest value 203 (4.5, most hydrophobic) is transformed to 0. 204 Parts D-F of the main panel display Immunogenicity (Ig) Score, Uniqueness Score, and 205 Conservation Score, respectively, using the desired peptide length as the window for these calculations. 206 These scores are displayed in 8-bit RGB color heat maps. By default, the highest Immunogenicity Score, 207 Uniqueness Score, and Conservation Score are displayed in the brightest green, yellow, and red, 208 respectively. However, user-defined colors can be selected from the menu bar. In addition, for each 209 peptide, the Immunogenicity Score value and rank are displayed on mouse-over in the Immunogenicity 210 Score heat map, whereas multiple sequence alignments are displayed on mouse-over in the Uniqueness 211 and Conservation heat maps. 212 Part G of the main panel displays various useful Protein Features. The position-dependent 213 annotations of regions or sites of interest in the sequence (including known sites of post-translational 214 modifications), extracted from the Swiss-Prot database, are displayed by various distinct colors and on 215 mouse-over along a protein sequence. Figure 4 illustrates the mouse-over feature of the output page. 216 At the bottom of the results page (H-J) are three separate lists of peptides sorted by 217 Immunogenicity Score rank, Uniqueness-optimized rank, and Conservation-optimized rank (Figure 3). 218 “Ig Score rank” shows peptides from most immunogenic to least. “Uniqueness-optimized rank” shows 219 primary ranking of peptides from most “unique” (i.e. specific for target protein versus all other proteins 220 in that species) to least unique, with secondary ranking based on Immunogenicity Score. “Conservation221 optimized rank” shows primary ranking of peptides from most “conserved” (i.e. sequence is conserved 222 among multiple orthologous species) to least conserved, with secondary ranking based on 223 Immunogenicity Score. These lists provide an alternative to the heat maps for identification of 224 candidate peptides for immunization. However, use of these lists alone has the pitfall that they do not 225 take into account the protein annotations provided in the main display. 226 Analysis of previously produced antibodies. Our laboratory has made extensive use of the 227 principles incorporated into AbDesigner to produce a substantial number of antibodies that have been 228 successfully used for research applications over the past 15 years (Table 1). These peptide sequences 229 were chosen based on calculation of the various protein characteristics by a series of analyses using the 230 GCG suite of protein analysis programs (3), BLAST (1), and manual inspection of protein records available 231 on GenBank as described by Knepper and Masilamani (21). The main advantage of AbDesigner is in the 232 visualization of this information in a parallel arrangement, allowing facile recognition of the trade-offs 233 involved in the consideration of alternative peptide sequences for immunization. In most cases, viewed 234 retrospectively, peptides chosen for immunization were within the top 10 percentile of AbDesigner 235 Immunogenicity Scores (Table 1). However, as illustrated in the examples described in Figures 5-8, 236 predicted immunogenicity cannot be practically employed alone in the choice of immunizing peptides. 237 Maximizing multi-species coverage. The first example comes from the production of antibodies 238 to the thiazide-sensitive Na-Cl cotransporter (NCC; Gene symbol: Slc12a3), which is expressed in the 239 renal distal convoluted tubule and is important for regulation of blood pressure and extracellular fluid 240 volume. An initial antibody made to a 23 amino acid peptide corresponding to rat NCC produced an 241 effective antibody that has been utilized for studies in rat (18). However this region is poorly conserved 242 among mammalian species and the rat antibody only poorly recognized human NCC. Figure 5 shows the 243 AbDesigner output for rat NCC at low magnification at the top (A), and magnified views of the amino244 terminal region at the bottom (B and C). The original antibody was made from amino acids 104-126 245 (Figure 5C), which can be seen to have a relatively low Conservation Score (dark region on heat map). A 246 region of high conservation and with reasonable predicted immunogenicity was identified in the amino 247 acid range 74-95 of rat NCC (Figure 5B). To obtain an antibody that would efficiently recognize human 248 NCC, a peptide from this region was utilized to make a new antibody to NCC (2). Figure 5D shows an 249 immunoblot demonstrating that the antibody against the highly conserved peptide (lower panel) 250 recognizes an ~160 kDa band in both rat and human kidney cortex (human > rat). In contrast, as seen 251 previously, the original antibody (upper panel) recognized rat NCC much more strongly than human 252 NCC. 253 Avoiding non-unique regions and special protein domains. The next example comes from the 254 antibodies targeting SNARE proteins, i.e. syntaxins and VAMPs (synaptobrevins) (Table 1). These are 255 type II integral membrane proteins with a single membrane span near the COOH-terminus and an 256 extracellular COOH-terminal tail. The AbDesigner output for syntaxin-4 is illustrated in Figure 6 with a 257 low magnification view at the top, and a magnified view of the amino-terminal region at the bottom. 258 The best Immunogenicity Scores were found amid amino acids 105-128 (Region A). However, this region 259 was within a coiled-coil region (purple bar) that forms very stable SNARE complexes with other SNARE 260 proteins. This factor may limit the use of the antibody to situations that would require exhaustive 261 denaturation of protein samples prior to the analysis to effectively break up the coiled-coil interactions 262 with other SNARE proteins. As seen in Figure 6, the second best Immunogenicity Score region where 263 these circumstances could be reasonably avoided is the N-terminal 23 amino acids (Region B). This 264 region also exhibited a high degree of specificity (high Uniqueness Score as displayed in brightest 265 yellow). Using a syntaxin-4 peptide from this region, an antibody was successfully produced and utilized 266 for immunoblotting and immunocytochemistry (24). 267 Another example comes from the production of antibodies to sodium hydrogen exchanger 3 268 (NHE3; gene symbol: Slc9a3). To produce an effective antibody for NHE3, a region of high 269 Immunogenicity Score, high Uniqueness Score (specificity), and high Conservation Score was identified 270 (Figure 7A, low magnification view; Figure 7B, magnified view) in the carboxyl-terminal region. We have 271 now utilized a peptide from this region (sequence: YLYKPRQEYKHLYSRHELTP, amino acids 621-640 with 272 an added N-terminal cysteine) to make a new antibody to NHE3 (previously unpublished). Figure 7C 273 shows an immunoblot demonstrating that the antibody successfully recognizes an 84 kDa band in rat 274 kidney cortical and outer medullary tissues, matching the mass seen previously with other NHE3 275 antibodies. As anticipated from the high Uniqueness Score, the new antibody appears to be highly 276 specific, i.e. it labeled no major bands other than that expected for NHE3. 277 Avoiding post-translational modifications. Another example comes from two antibodies 278 targeting aquaporin-2 (gene symbol: Aqp2) (Table 1), a water channel expressed in the kidney and 279 responsible for regulation of water excretion. Figure 8 shows the AbDesigner output at low 280 magnification at the top, and a magnified view of the carboxyl-terminal region of aquaporin-2 at the 281 bottom. The original antibodies to aquaporin-2 arbitrarily targeted the carboxyl-terminal 22 amino acids 282 of the protein (amino acids 250-271), producing antibodies that have been extensively used in the 283 localization of the protein (5);(27). Other laboratories made antibodies to the same region (10);(33). 284 Subsequent studies, however, revealed that this region encompasses several serines that are variably 285 phosphorylated (13);(12). Phosphorylation at these sites is likely to alter the ability of the antibody to 286 recognize the aquaporin-2 protein if one or more of the phosphorylated serines lies within the major 287 epitopes recognized by the antibody. This led us to produce a new antibody (12) upstream from this 288 poly-phosphorylated region (amino acids 237-255, in an area that is also highly conserved among rat, 289 mouse, and human), allowing the use of the antibody for precise quantification of aquaporin-2 290 abundance (37). 291 Large-scale implementation. We used a command line version of AbDesigner running on the 292 NIH Biowulf cluster (http://biowulf.nih.gov/) to identify the top scoring 15 amino acid peptides against 293 all known members of the proteomes of Homo sapiens, Rattus norvegicus, Mus musculus, Drosophila 294 melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, and Arabidopsis thaliana. A database 295 of the top Immunogenicity Score peptide, the top Uniqueness-optimized peptide (top Immunogenicity 296 Score among top ranked Uniqueness Scores), and the top Conservation-optimized peptide (top 297 Immunogenicity Score among top ranked Conservation Scores) for each protein is included as Dataset 1 298 (available for download at http://helixweb.nih.gov/ESBL/Database/AbDesignerPeptides/). An analysis of 299 the amino acid composition of these top candidate peptides in comparison to the background 300 composition of all 67,660 proteins is shown in Figure 9. As expected (21), the top ranking peptides have 301 higher percentages of charged amino acids (D, E, R, K and H), polar amino acids (S, T, N, Q, Y, and C), 302 glycines, and prolines compared to the background (all amino acids in all input proteins). 303 304 305 306 307 DISCUSSION 308 In this paper, we describe a web-based software tool, AbDesigner, created by biologists to help 309 biologists design peptide-directed antibodies. Its design is based on our own experience in peptide310 directed antibody design (Table 1) using principles discussed by Knepper and Masilamani (21) to obtain 311 peptide sequences that are most likely to be highly immunogenic, while producing antibodies that are 312 specific for the target protein (based on the calculated Uniqueness Score). Beyond this, AbDesigner 313 reports Conservation Scores for all candidate peptides, which reflect the likelihood that an antibody 314 made in a given mammalian species will recognize the ortholog in other species. Additional Protein 315 Features are displayed, which allow the user to avoid regions in the target protein that undergo post316 translational modifications (possibly obliterating the epitope) or splicing variations that may make some 317 isoforms of the target protein invisible to the antibody. The web interface is designed to provide the 318 user with a ‘minimalist’, intuitive tool that will allow successful use without having to read complicated 319 instructions or to install files on the user’s computer (other than perhaps Java, which is usually pre320 installed on most computers). The multiple ‘mouse-over’ features that were included in the AbDesigner 321 display provide a great deal of ancillary information without a cluttered presentation. Overall, the 322 software allows the user to display the information needed to recognize the trade-offs that may exist for 323 alternative choices of synthetic peptide sequences to use in antibody production. Software with a 324 similar goal called Bishop has been previously reported (23); it shares some features of AbDesigner but is 325 not available as an online tool. Although AbDesigner is designed for peptide-directed antibody 326 production in which the immunogen is a synthetic peptide conjugated to a carrier protein, it can also be 327 used to visualize the same type of information for candidate recombinant fusion proteins, an approach 328 that is employed by many investigators for creation of immunogens for antibody production. 329 AbDesigner is also well-suited for researchers who wish to evaluate existing custom made or purchased 330 antibodies. Before purchase, the user can predict whether or not a given commercial antibody will work 331 for his or her purpose. 332 We emphasize that the online version of AbDesigner is not designed for unsupervised choices of 333 immunizing peptides. The final sequence chosen in a particular context depends on subjective factors 334 that must be furnished by the user, reflecting the nature of the intended experimental use of the 335 antibody. It is clear from the analysis of the successfully produced antibodies described in Table 1 that 336 excellent antibodies can be produced from sequences that do not necessarily rank highest in terms of 337 Immunogenicity Score. Thus, the purpose of AbDesigner is to organize and display information, not to 338 make predictions. In that regard, AbDesigner may find broader use as a means of displaying an overview 339 of the properties of a given protein, for example, that users may encounter in their reading or may 340 newly identify in discovery studies. Future development should include addition of other types of 341 information including three-dimensional protein structure and location of epitopes for existing 342 antibodies. 343 344 345 346 347 APPENDIX 348 Immunogenicity Score 349 Immunogenicity Score is a predictor of immunogenicity. The higher the score, the greater the 350 predicted immunogenicity. The Immunogenicity Score of a peptide is calculated from the following 351 formula: 352 Ig Score = (-KDHI + 4.5) * Pt (average) * Tail bonus 353 Where: KDHI = Kyte-Doolittle hydropathy index, Pt (average) = average Chou-Fasman conformational 354 parameters of beta turn, and Tail bonus = a value ranging from 1.0 to 1.5. 355 KDHI is an average value of the hydropathy indices of consecutive amino acids in a peptide. It is 356 calculated using the hydropathy scale (a range of -4.5 to 4.5) (22), with a negative KDHI predicted to be 357 more immunogenic than a positive KDHI. Thus, the negative value of the KDHI is used in the formula. A 358 value of 4.5 is added in order to keep the Immunogenicity Score in a positive range. Pt (average) is an 359 average value of the Chou-Fasman conformational parameters of a beta turn (Pt) of amino acids in a 360 peptide. It is calculated using Pt values from a reference database of 29 proteins as described (4). The 361 “topological domain” information of a protein extracted from the Swiss-Prot database is used for 362 determining the Tail bonus. Tail bonus is only given to a peptide that resides in NH2or COOH-terminal 363 tail of an integral membrane protein. Tail bonus values can range from 1.0 to 1.5. A Tail bonus value of 364 1.5 corresponds to the full length of a peptide being present in a tail region, while a peptide whose full 365 length is present in a non-tail region is given a tail bonus value of 1.0. Values are linearly correlated with 366 the number of amino acids contained in the tail compared with the entire length of the peptide. 367 368 Uniqueness Score 369 A typical immunogenic peptide is assumed to contain multiple linear, overlapping potential 370 epitopes (~ 5 amino acids (11)), each of which can theoretically invoke an immune response. The 371 uniqueness of these linear epitope sequences compared with other proteins of the same species helps 372 determine the specificity of an antibody produced against that peptide. In AbDesigner, the sequence of 373 each successive linear epitope (shifting by one amino acid) along a peptide sequence is compared 374 against the entire protein sequence database of that species. The length of a linear epitope can be set 375 from 5 amino acids to the full length of the peptide. The total number of linear epitopes in other 376 proteins that have sequences identical to the linear epitopes in a given peptide is calculated as follows: 377 n 378 M = ∑ mi 379 i = 1 380 381 Where: M = the total number of linear epitope matches, n = the number of successive linear epitopes 382 along a peptide sequence, and mi = the number of the linear epitopes in the other proteins that have 383 sequences identical to the linear epitope i. Redundant linear epitopes in a protein are counted only 384 once. A higher value of M corresponds to a less unique peptide and vice versa. The Uniqueness Score of 385 a peptide is then calculated based on the following formula: 386 Mc – M, M < Mc 387 0, M ≥ Mc 388 389 Where: Mc = the cutoff value for M. The highest Uniqueness Score is equal to Mc (when there is no 390 match to any linear epitopes in a peptide, M = 0) and the lowest Uniqueness Score is equal to 0 (when 391 the total number of linear epitope matches is equal to or above the cutoff value, M ≥ Mc). Mc is 392 arbitrarily set to three times of n. 393 394 Conservation Score 395 Conservation Score predicts the likelihood that the target protein will be detectable by the 396 antibody in multiple orthologous species. The higher the score, the greater the predicted conservation. 397 The Conservation Score of a peptide is determined in a comparable manner to the Uniqueness Score. To 398 begin with, the sequence of each successive linear epitope (shifting by one amino acid) along a peptide 399 in a protein is compared against the sequences of the orthologous proteins, based on a mnemonic 400 protein identification code in the Swiss-Prot entry name (http://www.uniprot.org/manual/entry_name), 401 Uniqueness Score = among three commonly studied species (i.e. human, rat, and mouse). The Conservation Score of a 402 peptide is then calculated from the total number of linear epitopes in orthologous proteins that have 403 sequences identical to the linear epitopes in a given peptide as follows: 404 n 405 Conservation Score = ∑ ci 406 i = 1 407 408 Where: n = the number of successive linear epitopes along a peptide sequence and ci = the number of 409 the linear epitopes in the orthologous proteins that have sequences identical to the linear epitope i. 410 Redundant linear epitopes in a protein are counted only once. The highest Conservation Score is 411 reached when a peptide is completely conserved across all three species and is equal to the total 412 number of the orthologous species evaluated multiplied by n. The lowest Conservation Score is equal to 413 0 (when there is no conservation). 414 415 Heat Maps 416 The Immunogenicity Score, Uniqueness Score, and Conservation Score of each peptide are 417 displayed in 8-bit RGB color heat maps. The transformation of those values into a density 418 representation (D) on an 8-bit scale (0 255) is performed using linear scaling: D = 255 * (X Xmin)/(Xmax 419 Xmin), where X is the value, Xmin is the minimum value and Xmax is the maximum value as defined in Table 420 2. 421 422 Limitations associated with submitting a FASTA amino acid sequence 423 By submitting just a FASTA amino acid sequence, the program assumes that the input protein is 424 not present in the local Swiss-Prot protein database (from the following seven species: Homo sapiens, 425 Rattus norvegicus, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces 426 cerevisiae, or Arabidopsis thaliana). Thus, the following processes that make use of the database cannot 427 be executed: 1) extraction of Protein Features; 2) calculation of Tail bonus; 3) calculation of Uniqueness 428 Score; and 4) calculation of Conservation Score. This leads to the following results: 1) Protein Features 429 will not be displayed in the graphical output; 2) Immunogenicity Score will be calculated without 430 factoring in Tail bonus; 3) Uniqueness Score will not be calculated, the graphical output of Uniqueness 431 Score will be the default brightest yellow, and Uniqueness-optimized rank will be the same as 432 Immunogenicity Score rank; and 4) Conservation Score will not be calculated, the graphical output of 433 Conservation Score will be the default black, and Conservation-optimized rank will be the same as 434 Immunogenicity Score rank. 435 436 437 438 Acknowledgments 439 This work was supported by the Intramural Budget of the National Heart, Lung and Blood Institute 440 (Project Z01-HL-001285). 441 442 443 References 444 445 1. Altschul SF, Gish W, Miller W, Myers EW and Lipman DJ. Basic local alignment search 446 tool. J Mol Biol 215: 403-410, 1990. 447 2. 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A list of the peptide-directed antibodies and the synthetic peptides used for the generation of 548 these antibodies by our laboratory 549 550 Target protein (reference) Swiss-Prot No. Peptide sequence Position Length Ig Score Ig Score percentile rank NHE3 (16) P26433 DSFLQADGPEEQLQPASPESTHM 809-831 23 8.69 9.52 NHE3 (this paper) P26433 YLYKPRQEYKHLYSRHELTP 621-640 20 9.18 6.40 NaPi-2a (17) Q06496 LEELPPATPSPRLALPAHHNATRL 614-637 24 7.17 10.75 NKCC2 (8) P55016 EYYRNTGSVSGPKVNRPSLQE 109-129 21 9.9 1.49 NKCC2 (28) P55016 SDSTDPPHYEETSFGDEAQNRLK 33-55 23 10.5 0.09 NCC (18) P55018 DGRPGHELTDGLVEDETGANSEK 104-126 23 9.67 0.41 NCC (2) P55018 PGEPRKVRPTLADLHSFLKQEG 74-95 22 8.32 5.50 αENaC (26) P37089 LGKGDKREEQGLGPEPSAPRQPT 45-67 23 10.47 3.55 βENaC (26) P37090 NYDSLRLQPLDTMESDSEVEAI 617-638 22 7.76 12.48 γENaC (26) P37091 NTLRLDRAFSSQLTDTQLTNEL 629-650 22 7.57 14.94 Na,K-ATPase, α1-subunit (30) P06685 DEVRKLIIRRRPGGWVEKETYY 1002-1023 22 7.43 5.99 Aquaporin-1 (35) P29975 GQVEEYDLDADDINSRVEMKPK 248-269 22 8.84 0.81 Aquaporin-2 (5) P34080 EVRRRQSVELHSPQSLPRGSKA 250-271 22 8.81 5.20 Aquaporin-2 (12) P34080 LKGLEPDTDWEEREVRRRQ 237-255 19 9.43 1.19 Aquaporin-3 (7) P47862 HLEQPPPSTEAENVKLAHMKHKEQI 268-292 25 8.01 1.12 Aquaporin-4 (34) P47863 IDIDRGDEKKGKDSSGE 302-318 17 11.03 0.65 Aquaporin-4 (34) P47863 TKGSYMEVEDNRSQVETED 273-291 19 9.4 2.62 P2Y2 purinoceptor (19) P41232 SISSDDSRRTESTPAGSETKDIRL 351-374 24 9.46 9.12 NSF (9) Q9QUL6 DPEYRVRKFLALMREEGASPLDFD 721-744 24 5.01 19.28 VAMP-2 (24) P63045 SATAATVPPAAPAGEGG 2-18 17 6.77 61.00 Syntaxin-3 (25) Q08849 KDRLEQLKAKQLTQDDDTDEVE 2-23 22 9.09 3.73 Syntaxin-4 (24) Q08850 RDRTHELRQGDNISDDEDEVRV 2-23 22 9.93 1.81 Synaptotagmin VIII (20) Q9R0N6 PREVDRVLALQPRLPLLRPRS 375-395 21 7.19 46.13 ROMK1 (6) P35560 KRGYDNPNFVLSEVDETDDTQM 370-391 22 9.39 1.62 UT-A1 (29) Q62668 QEKNRRASMITKYQAYDVS 911-929 19 8.56 7.94