The MULTICOM toolbox for protein structure prediction

BMC Bioinformatics

Xin Deng¹  Jesse Eickholt¹  Zheng Wang¹  Jilong Li¹  Jianlin Cheng² 

[1]Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA

[2]C. Bond Life Science Center, University of Missouri-Columbia, Columbia, MO, 65211, USA

关键词: Protein disorder;    Fold recognition;    Protein model quality assessment;    Tertiary structure;    Contact map;    Domain;    Solvent accessibility;    Secondary structure;    Bioinformatics tool;    Protein structure prediction;   

Others  :  1088305
DOI  :  10.1186/1471-2105-13-65

 received in 2012-01-20, accepted in 2012-04-30,  发布年份 2012

【 摘 要 】

Background

As genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental techniques. The gap between protein sequence and structure continues to enlarge rapidly as the throughput of genome sequencing techniques is much higher than that of protein structure determination techniques. Computational software tools for predicting protein structure and structural features from protein sequences are crucial to make use of this vast repository of protein resources.

Results

To meet the need, we have developed a comprehensive MULTICOM toolbox consisting of a set of protein structure and structural feature prediction tools. These tools include secondary structure prediction, solvent accessibility prediction, disorder region prediction, domain boundary prediction, contact map prediction, disulfide bond prediction, beta-sheet topology prediction, fold recognition, multiple template combination and alignment, template-based tertiary structure modeling, protein model quality assessment, and mutation stability prediction.

Conclusions

These tools have been rigorously tested by many users in the last several years and/or during the last three rounds of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7-9) from 2006 to 2010, achieving state-of-the-art or near performance. In order to facilitate bioinformatics research and technological development in the field, we have made the MULTICOM toolbox freely available as web services and/or software packages for academic use and scientific research. It is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/ webcite.

【 授权许可】

   
2012 Cheng et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】

• [1]Kendrew J, Dickerson R, Strandberg B, Hart R, Davies D, Phillips D, Shore V: Structure of myoglobin: a three-dimensional Fourier synthesis at 2å resolution. Nature 1960, 185(4711):422-427.
• [2]Perutz M, Rossmann M, Cullis A, Muirhead H, Will G, North A: Structure of haemoglobin: a three-dimensional Fourier synthesis at 5.5å resolution, obtained by X-ray analysis. Nature 1960, 185(4711):416-422.
• [3]Fox BG, Goulding C, Malkowski MG, Stewart L, Deacon A: Structural genomics: from genes to structures with valuable materials and many questions in between. Nat Methods 2008, 5(2):129-132.
• [4]Rost B, Liu J, Przybylski D, Nair R, Wrzeszczynski KO, Bigelow H, Ofran Y: Prediction of protein structure through evolution. Handbook of Chemoinformatics 2003, 1789-1811.
• [5]Pollastri G, Mclysaght A: Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 2005, 21(8):1719-1720.
• [6]Karplus K, Karchin R, Draper J, Casper J, Mandel-Gutfreund Y, Diekhans M, Hughey R: Combining local-structure, fold-recognition, and new fold methods for protein structure prediction. Proteins: Structure, Function, and Bioinformatics 2003, 53(S6):491-496.
• [7]Cheng J, Randall A, Sweredoski M, Baldi P: SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 2005, 33(Web Server Issue):W72-W76.
• [8]Vullo A, Bortolami O, Pollastri G, Tosatto SCE: Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines. Nucleic Acids Res 2006, 34:W164-W168.
• [9]McGuffin L, Bryson K, Jones D: The PSIPRED protein structure prediction server. Bioinformatics 2000, 16(4):404.
• [10]Adamczak R, Porollo A, Meller J: Accurate prediction of solvent accessibility using neural networks–based regression. Proteins: Structure, Function, and Bioinformatics 2004, 56(4):753-767.
• [11]Adamczak R, Porollo A, Meller J: Combining prediction of secondary structure and solvent accessibility in proteins. Proteins: Structure, Function, and Bioinformatics 2005, 59(3):467-475.
• [12]Wagner M, Adamczak R, Porollo A, Meller J: Linear regression models for solvent accessibility prediction in proteins. J Comput Biol 2005, 12(3):355-369.
• [13]Porollo A, Adamczak R, Wagner M, Meller J: Maximum feasibility approach for consensus classifiers: Applications to protein structure prediction. 2003, 2003:75-76.
• [14]Karypis G: YASSPP: better kernels and coding schemes lead to improvements in protein secondary structure prediction. Proteins: Structure, Function, and Bioinformatics 2006, 64(3):575-586.
• [15]Cole C, Barber JD, Barton GJ: The Jpred 3 secondary structure prediction server. Nucleic Acids Res 2008, 36(suppl 2):W197-W201.
• [16]Frishman D, Argos P: Incorporation of long-distance interactions into a secondary structure prediction algorithm. Protein Eng 1996, 9(2):133-142.
• [17]Frishman D, Argos P: Knowledge-based protein secondary structure assignment. Proteins: Structure, Function, and Bioinformatics 1995, 23(4):566-579.
• [18]Kabsch W, Sander C: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22(12):2577-2637.
• [19]Sen TZ, Jernigan RL, Garnier J, Kloczkowski A: GOR V server for protein secondary structure prediction. Bioinformatics 2005, 21(11):2787-2788.
• [20]Petersen B, Petersen TN, Andersen P, Nielsen M, Lundegaard C: A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct Biol 2009, 9(1):51. BioMed Central Full Text
• [21]Pollastri G, Baldi P, Fariselli P, Casadio R: Prediction of coordination number and relative solvent accessibility in proteins. Proteins: Structure, Function, and Bioinformatics 2002, 47(2):142-153.
• [22]Faraggi E, Xue B, Zhou Y: Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins: Structure, Function, and Bioinformatics 2009, 74(4):847-856.
• [23]Iakoucheva LM, Kimzey AL, Masselon CD, Bruce JE, Garner EC, Brown CJ, Dunker AK, Smith RD, Ackerman EJ: Identification of intrinsic order and disorder in the DNA repair protein XPA. Protein Sci 2001, 10(3):560-571.
• [24]Dunker AK, Cortese MS, Romero P, Iakoucheva LM, Uversky VN: Flexible nets. FEBS J 2005, 272(20):5129-5148.
• [25]Mizianty MJ, Stach W, Chen K, Kedarisetti KD, Disfani FM, Kurgan L: Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources. Bioinformatics 2010, 26(18):i489-i496.
• [26]Ward JJ, McGuffin LJ, Bryson K, Buxton BF, Jones DT: The DISOPRED server for the prediction of protein disorder. Bioinformatics 2004, 20(13):2138-2139.
• [27]Zhang T, Faraggi E, Xue B, Dunker A, Uversky VN, Zhou Y: SPINE-D: Accurate Prediction of Short and Long Disordered Regions by a Single Neural-Network Based Method. J Biomol Struct Dyn 2012, 29(4):799-813.
• [28]Ishida T, Kinoshita K: PrDOS: prediction of disordered protein regions from amino acid sequence. Nucleic Acids Res 2007, 35(suppl 2):W460-W464.
• [29]Shimizu K, Hirose S, Noguchi T: POODLE-S: web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix. Bioinformatics 2007, 23(17):2337-2338.
• [30]Hirose S, Shimizu K, Kanai S, Kuroda Y, Noguchi T: POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions. Bioinformatics 2007, 23(16):2046-2053.
• [31]Shimizu K, Muraoka Y, Hirose S, Tomii K, Noguchi T: Predicting mostly disordered proteins by using structure-unknown protein data. BMC Bioinforma 2007, 8(1):78. BioMed Central Full Text
• [32]Dosztányi Z, Csizmok V, Tompa P, Simon I: The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. J Mol Biol 2005, 347(4):827-839.
• [33]Dosztányi Z, Csizmok V, Tompa P, Simon I: IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics 2005, 21(16):3433-3434.
• [34]McGuffin L: The ModFOLD server for the quality assessment of protein structural models. Bioinformatics 2008, 24(4):586.
• [35]Roche DB, Buenavista MT, Tetchner SJ, McGuffin LJ: The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction. Nucleic Acids Res 2011, 39(suppl 2):W171-W176.
• [36]Marsden RL, McGuffin LJ, Jones DT: Rapid protein domain assignment from amino acid sequence using predicted secondary structure. Protein Sci 2002, 11(12):2814-2824.
• [37]Chen P, Liu C, Burge L, Li J, Mohammad M, Southerland W, Gloster C, Wang B: DomSVR: domain boundary prediction with support vector regression from sequence information alone. Amino Acids 2010, 39(3):713-726.
• [38]Sim J, Kim SY, Lee J: PPRODO: prediction of protein domain boundaries using neural networks. Proteins: Structure, Function, and Bioinformatics 2005, 59(3):627-632.
• [39]Liu J, Rost B: Sequence-based prediction of protein domains. Nucleic Acids Res 2004, 32(12):3522-3530.
• [40]Eickholt J, Deng X, Cheng J: DoBo: Protein domain boundary prediction by integrating evolutionary signals and machine learning. BMC Bioinforma 2011, 12:43. BioMed Central Full Text
• [41]Gewehr JE, Zimmer R: SSEP-Domain: protein domain prediction by alignment of secondary structure elements and profiles. Bioinformatics 2006, 22(2):181-187.
• [42]Rost B, Yachdav G, Liu J: The predictprotein server. Nucleic Acids Res 2004, 32(suppl 2):W321-W326.
• [43]Baú D, Martin A, Mooney C, Vullo A, Walsh I, Pollastri G: Distill: a suite of web servers for the prediction of one-, two-, and three-dimensional structural features of proteins. BMC Bioinforma 2006, 7(1):402. BioMed Central Full Text
• [44]Singh S, Hajela K, Ramani A: SVM-BetaPred: prediction of right-handed ß-helix fold from protein sequence using SVM. Pattern Recognition in Bioinformatics 2007, 108-119.
• [45]Punta M, Rost B: PROFcon: novel prediction of long-range contacts. Bioinformatics 2005, 21(13):2960-2968.
• [46]Fuchs A, Kirschner A, Frishman D: Prediction of helix–helix contacts and interacting helices in polytopic membrane proteins using neural networks. Proteins: Structure, Function, and Bioinformatics 2009, 74(4):857-871.
• [47]Ferre F, Clote P: DiANNA: a web server for disulfide connectivity prediction. Nucleic Acids Res 2005, 33(suppl 2):W230-W232.
• [48]O’Connor BD, Yeates TO: GDAP: a web tool for genome-wide protein disulfide bond prediction. Nucleic Acids Res 2004, 32(suppl 2):W360-W364.
• [49]Fariselli P, Riccobelli P, Casadio R: Role of evolutionary information in predicting the disulfide-bonding state of cysteine in proteins. Proteins: Structure, Function, and Bioinformatics 1999, 36(3):340-346.
• [50]Bradley P, Cowen L, Menke M, King J, Berger B: Betawrap: Successful prediction of parallel β-helices from primary sequence reveals an association with many microbial pathogens. Proc Natl Acad Sci 2001, 98(26):14819-14824.
• [51]Zimmermann O, Wang L, Hansmann UHE: BETTY: Prediction of β-Strand Type from Sequence. In Silico Biol 2007, 7(4):535-542.
• [52]Li Y, Fang Y, Fang J: Predicting residue–residue contacts using random forest models. Bioinformatics 2011, 27(24):3379-3384.
• [53]Björkholm P, Daniluk P, Kryshtafovych A, Fidelis K, Andersson R, Hvidsten TR: Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue–residue contacts. Bioinformatics 2009, 25(10):1264-1270.
• [54]Wu S, Zhang Y: A comprehensive assessment of sequence-based and template-based methods for protein contact prediction. Bioinformatics 2008, 24(7):924-931.
• [55]Shackelford G, Karplus K: Contact prediction using mutual information and neural nets. Proteins: Structure, Function, and Bioinformatics 2007, 69(S8):159-164.
• [56]Zhang Y, Skolnick J: The protein structure prediction problem could be solved using the current PDB library. Proc Natl Acad Sci 2005, 102(4):1029-1034.
• [57]Baker D, Sali A: Protein structure prediction and structural genomics. Science 2001, 294(5540):93-96.
• [58]Zhang Y: Progress and challenges in protein structure prediction. Curr Opin Struct Biol 2008, 18(3):342-348.
• [59]Zhou H, Zhou Y: SPEM: improving multiple sequence alignment with sequence profiles and predicted secondary structures. Bioinformatics 2005, 21(18):3615-3621.
• [60]Xu J, Li M, Kim D, Xu Y: RAPTOR: optimal protein threading by linear programming. J Bioinforma Comput Biol 2003, 1(1):95-117.
• [61]Simons K, Kooperberg C, Huang E, Baker D: Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 1997, 268(1):209-225.
• [62]Roy A, Kucukural A, Zhang Y: I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 2010, 5(4):725-738.
• [63]Zhang Y: I-TASSER: Fully automated protein structure prediction in CASP8. Proteins: Structure, Function, and Bioinformatics 2009, 77(S9):100-113.
• [64]Zhang Y: I-TASSER server for protein 3D structure prediction. BMC Bioinforma 2008, 9(1):40. BioMed Central Full Text
• [65]Šali A, Potterton L, Yuan F, van Vlijmen H, Karplus M: Evaluation of comparative protein modeling by MODELLER. Proteins: Structure, Function, and Bioinformatics 1995, 23(3):318-326.
• [66]Fiser A, Sali A: Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol 2003, 374:461-491.
• [67]Soding J, Biegert A, Lupas A: The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res 2005, 33(Web Server Issue):W244-W248.
• [68]Xu D, Zhang Y: Ab initio protein structure assembly using continuous structure fragments and optimized knowledge‐based force field. Proteins: Structure, Function, and Bioinformatics 2012.
• [69]Zhou H, Skolnick J: Ab initio protein structure prediction using chunk-TASSER. Biophys J 2007, 93(5):1510-1518.
• [70]Wallner B, Larsson P, Elofsson A: Pcons. net: protein structure prediction meta server. Nucleic Acids Res 2007, 35(suppl 2):W369-W374.
• [71]Karplus K, Barrett C, Hughey R: Hidden Markov models for detecting remote protein homologies. Bioinformatics 1998, 14(10):846-856.
• [72]Peng J, Xu J: Low-homology protein threading. Bioinformatics 2010, 26(12):i294-i300.
• [73]Yang Y, Faraggi E, Zhao H, Zhou Y: Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates. Bioinformatics 2011, 27(15):2076-2082.
• [74]Moult J, Fidelis K, Kryshtafovych A, Rost B, Hubbard T, Tramontano A: Critical assessment of methods of protein structure prediction-round VII. Proteins: Structure, Function, and Bioinformatics 2007, 69(Suppl 8):3-9.
• [75]Moult J, Fidelis K, Kryshtafovych A, Tramontano A: Critical assessment of methods of protein strucutre prediction – round IX. Protiens 2011, 79(S10):1-5.
• [76]Monastyrskyy B, Fidelis K, Moult J, Tramontano A, Kryshtafovych A: Evaluation of disorder predictions in CASP9. Proteins 2011, 79(S10):107-118.
• [77]Monastyrskyy B, Fidelis K, Tramontano A, Kryshtafovych A: Evaluation of residue-residue contact prediction in CASP9. Proteins 2011, 79(S10):119-125.
• [78]Cozzetto D, Kryshtafovych A, Fidelis K, Moult J, Rost B, Tramontano A: Evaluation of template-based models in CASP8 with standard measures. Proteins: Structure, Function, and Bioinformatics 2009, 77(Suppl 9):000-000.
• [79]Mariani V, Kiefer F, Schmidt T, Haas J, Schwede T: Assessment of template based protein structure predictions in CASP9. Proteins 2011, 79(S10):37-58.
• [80]Kinch L, Shi SY, Cong Q, Cheng H, Liao Y, Grishin NV: CASP9 assessment of free modeling target predictions. Proteins 2011, 79(S10):59-73.
• [81]Benkert P, Tosatto S, Schomburg D: QMEAN: a comprehensive scoring function for model quality assessment. Proteins 2008., 71(1)
• [82]Cozzetto D, Kryshtafovych A, Tramontano A: Evaluation of CASP8 model quality predictions. Proteins: Structure, Function, and Bioinformatics 2009, 77(S9):157-166.
• [83]Eisenberg D, Luthy R, Bowie J: VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol 1997, 277:396-404.
• [84]Larsson P, Skwark M, Wallner B, Elofsson A: Assessment of global and local model quality in CASP8 using Pcons and ProQ. Proteins 2009, 77(S9):167-172.
• [85]McGuffin L, Roche D: Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments. Bioinformatics 2010, 26(2):182-188.
• [86]Paluszewski M, Karplus K: Model Quality Assessment using Distance Constraints from Alignments. Proteins 2008, 75:540-549.
• [87]Kryshtafovych A, Fidelis K, Tramontano A: Evaluation of model quality predictions in CASP9. Proteins 2011, 79(S10):91-109.
• [88]Moult J, Fidelis K, Kryshtafovych A, Rost B, Tramontano A: Critical assessment of methods of protein structure prediction (CASP)-round VIII. 2009. (Accpeted)
• [89]MacCallum JL, Perez A, Schnieders MJ, Hua L, Jacobson MP, Dill KA: Assessment of protein structure refinement in CASP9. Proteins 2011, 79(S10):74-90.
• [90]Baldi P, Pollastri G: The principled design of large-scale recursive neural network architectures–DAG-RNNs and the protein structure prediction problem. J Mach Learn Res 2003, 4:575-602.
• [91]Bernstein FC, Koetzle TF, Williams GJB, Meyer EF: The protein data bank: A computer-based archival file for macromolecular structures*. J Mol Biol 1977, 112(3):535-542.
• [92]Deng X, Eickholt J, Cheng J: PreDisorder: ab initio sequence-based prediction of protein disordered regions. BMC Bioinforma 2009, 10(1):436. BioMed Central Full Text
• [93]Deng X, Eickholt J, Cheng J: A comprehensive overview of computational protein disorder prediction methods. Mol BioSyst 2011, 8.
• [94]Wu S, Szilagyi A, Zhang Y: Improving protein structure prediction using multiple sequence-based contact predictions. Structure 2011, 19(8):1182-1191.
• [95]Tegge AN, Wang Z, Eickholt J, Cheng J: NNcon: improved protein contact map prediction using 2D-recursive neural networks. Nucleic Acids Res 2009, 37(suppl 2):W515-W518.
• [96]Cheng J, Baldi P: Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinforma 2007, 8(1):113. BioMed Central Full Text
• [97]Ezkurdia I, Graña O, Izarzugaza JMG, Tress ML: Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8. Proteins: Structure, Function, and Bioinformatics 2009, 77(S9):196-209.
• [98]Izarzugaza JMG, Graña O, Tress ML, Valencia A, Clarke ND: Assessment of intramolecular contact predictions for CASP7. Proteins: Structure, Function, and Bioinformatics 2007, 69(S8):152-158.
• [99]Cheng J, Saigo H, Baldi P: Large scale prediction of disulphide bridges using kernel methods, two dimensional recursive neural networks, and weighted graph matching. Proteins: Structure, Function, and Bioinformatics 2006, 62(3):617-629.
• [100]Baldi P, Cheng J, Vullo A: Large-scale prediction of disulphide bond connectivity. The MIT Press, Cambridge, MA; 2004:97-104. [Advances in Neural Information Processing Systems 17: 2004]
• [101]Cheng J, Baldi P: Three-stage prediction of protein β-sheets by neural networks, alignments and graph algorithms. Bioinformatics 2005, 21(suppl 1):i75-i84.
• [102]Wang Z, Eickholt J, Cheng J: MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8. Bioinformatics 2010, 26(7):882-888.
• [103]Zhang Y, Skolnick J: Scoring function for automated assessment of protein structure template quality. Proteins: Structure, Function, and Bioinformatics 2004, 57(4):702-710.
• [104]Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov I, Bourne P: The protein data bank. Nucleic Acids Res 2000, 28(1):235-242.
• [105]Zemla A: LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res 2003, 31(13):3370-3374.
• [106]Wang Z, Eickholt J, Cheng J: APOLLO: a quality assessment service for single and multiple protein models. Bioinformatics 2011, 27(12):1715-1716.
• [107]Wang Z, Tegge AN, Cheng J: Evaluating the absolute quality of a single protein model using structural features and support vector machines. Proteins: Structure, Function, and Bioinformatics 2009, 75(3):638-647.
• [108]Cheng J, Wang Z, Tegge A, Eickholt J: Prediction of global and local quality of CASP8 models by MULTICOM series. Proteins 2009, 77(S9):181-184.
• [109]Wang Z, Cheng J: An iterative self-refining and self-evaluating approach for protein model quality estimation. Protein Sci 2012, 21(1):142-151.
• [110]Cheng J, Randall A, Baldi P: Prediction of protein stability changes for single site mutations using support vector machines. Proteins: Structure, Function, and Bioinformatics 2006, 62(4):1125-1132.
• [111]Gilis D, Rooman M: PoPMuSiC, an algorithm for predicting protein mutant stability changes. Application to prion proteins. Protein Engineering 2000, 13(12):849-856.
• [112]Worth CL, Preissner R, Blundell TL: SDM—a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Res 2011, 39(suppl 2):W215-W222.
• [113]Capriotti E, Fariselli P, Casadio R: I-Mutant2. 0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005, 33(suppl 2):W306-W310.
• [114]Parthiban V, Gromiha MM, Schomburg D: CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 2006, 34(suppl 2):W239-W242.
• [115]Lin G, Wang Z, Xu D, Cheng J: SeqRate: sequence-based protein folding type classification and rates prediction. BMC Bioinforma 2010, 11(Suppl 3):S1. BioMed Central Full Text
• [116]Deng X, Cheng J: MSACompro: Protein Multiple Sequence Alignment Using Predicted Secondary Structure, Solvent Accessibility, and Residue-Residue Contacts. BMC Bioinforma 2011, 12:472. BioMed Central Full Text
• [117]Thompson JD, Koehl P, Ripp R, Poch O: BAliBASE 3.0: latest developments of the multiple sequence alignment benchmark. Proteins: Structure, Function, and Bioinformatics 2005, 1:127-136.
• [118]Dai J, Cheng J: HMMEditor: a visual editing tool for profile hidden Markov model. BMC genomics 2008, 9(Suppl 1):S8. BioMed Central Full Text