Improving the thermostability of alpha-amylase by combinatorial coevolving-site saturation mutagenesis
© Wang et al.; licensee BioMed Central Ltd. 2012
Received: 21 March 2012
Accepted: 11 September 2012
Published: 11 October 2012
The generation of focused mutant libraries at hotspot residues is an important strategy in directed protein evolution. Existing methods, such as combinatorial active site testing and residual coupling analysis, depend primarily on the evolutionary conserved information to find the hotspot residues. Hardly any attention has been paid to another important functional and structural determinants, the functionally correlated variation information--coevolution.
In this paper, we suggest a new method, named combinatorial coevolving-site saturation mutagenesis (CCSM), in which the functionally correlated variation sites of proteins are chosen as the hotspot sites to construct focused mutant libraries. The CCSM approach was used to improve the thermal stability of α-amylase from Bacillus subtilis CN7 (Amy7C). The results indicate that the CCSM can identify novel beneficial mutation sites, and enhance the thermal stability of wild-type Amy7C by 8°C (), which could not be achieved with the ordinarily rational introduction of single or a double point mutation.
Our method is able to produce more thermostable mutant α-amylases with novel beneficial mutations at new sites. It is also verified that the coevolving sites can be used as the hotspots to construct focused mutant libraries in protein engineering. This study throws new light on the active researches of the molecular coevolution.
Directed protein evolution is invaluable in engineering biocatalysts for better properties, such as enhancements in activity, stability, and enzyme selectivity[1, 2]. However, it is usually limited by its inability to generate high-quality mutant libraries containing more beneficial variants. This is especially problematic considering the combinatorial complexity of mutant libraries and the huge sequence space[3, 4]. Constructing focused mutant libraries at defined hotspot residues is considered to be one of the most promising ways of improving directed protein evolution[3–5]. Much of pioneering work has been complemented by Reetz’s team[6–8].
All existing focused mutant library methods can be essentially classified into two categories: structure-based approaches and sequence-based approaches. The former includes combinatorial active site testing (CAST), B-factors, and knowledge-based potential analysis[6–10]. The latter includes protein design automation (PDA), residual coupling analysis (RCA), and ConSurf. While the aforementioned methods depend primarily on the evolutionary conservation information to find out the hotspot residues, there are some other important functional and structural determinants desirable to be taken into consideration, such as the functionally correlated variation information--coevolution.
Co-evolution is the correlated variation of protein sites promoted by selective pressures. The cooperation between residues at the coevolving sites, which usually takes the form of compensatory interactions, synergistic effects, allosteric interactions, and epistatic interactions[15–19], determines the structure and function of proteins[20, 21]. In recent years much attention has been paid to find the coevolving residues, as well as the reasons why residues co-evolve[14, 21–28], but few experimental design methods based on the coevolution and successful examples of using them have been reported.
In this study, we propose a method, combinatorial coevolving-site saturation mutagenesis (CCSM), which chooses the coevolving sites of proteins as hotspot residues to construct focused mutant libraries. We also describe the successful use of the CCSM method to improve the thermostability of α-amylase.
Results and discussion
α-Amylase is an important industrial biocatalyst in starch liquefaction processes and a valuable model enzyme for studies of thermal adaptation in proteins. We used the CCSM approach to improve the thermostability of α-amylase (Amy7C) to demonstrate the feasibility of this method.
Spotting the coevolving sites in Amy7C
The average distance between all coevolving sites in Amy7C is in the range 17.3 ± 7.31 Å, which is much greater than that reported by other research teams[26, 32]. The distance between coevolving sites are significantly greater than the distance used to define hotspot sites in previous studies, which is usually about 5 Å[6, 11]. The differences between the coevolving sites in this study and the hotspot sites found by previous studies must be attributed to the prediction methods, because the previous studies identified hotspots by evolutionary conservative information-based methods, such as the sequence alignment-based method and distance-based method[6–8], which could not usually find the coevolving sites located as distant as >17 Å apart.
Construction and screening of CCSM libraries
Ten CCSM libraries were constructed at coevolving-sites and explored using the HTS method, which is based on the starch-iodine method and DNS method[33, 34] (see Additional file1 for details). All possible combinations and permutations of amino acid residues are explored in the CCSM library through simultaneous and random mutation of the coevolving-sites using the NNK(G/T) degenerate primers (see Table SA1 in Additional file4: Table SA1).
A total of 10,010 clones were randomly selected and screened using the starch-iodine method in the first screening. The majority of the variants displayed impaired activities, and only about 10% retained any obvious starch hydrolytic ability relative to parental Amy7C. The active variants made up less than 5% of the three libraries of G89H100, G89D144, and G89T147. Active variants of the other seven libraries made up around 12.5%.
Rescreening of CCSM libraries
Sequence analysis of the CCSM mutants
The distribution of amino acids and codons at the 6 coevolving sites in the sequenced 92 amylase variants
C GT3,C GC3
TCG5, AG T1
All the coevolving sites showed dramatic variation in either single or double mutations, except D95 showed only two double mutations, i.e., D95HT147S (CATTCT) and G89FD95R (TTTCGG). G89 and N197 were found to be the most diverse mutation sites, which displayed 11 and 9 different kinds of amino acids respectively (Table1). Previous studies have shown that the eight strands and eight helixes of the TIM barrel of domain A are vital to the stability of the structure[35, 36], and few beneficial mutations can exist there. In this study, both D95HT147S and G89FD95R were found to involve changes to the residue D95 of the β3 in the TIM barrel of the domain A. The detrimental effects caused by D95 site mutation must be compensated by the covariation at the other coevolving site, like the T147S in D95HT147S. The similar but beneficial cooperation may also take place between coevolving residues in improved variants. The positions and interactions between coevolving residues in some example variants are shown in Figure A1 (see Additional file5: Figure SA1).
The aforementioned “false positive” phenomenon of high percentages of same sense mutations (28.6%) and single mutants (35.7%) upon rescreening should probably be attributed to the relatively lenient criteria adopted in our library construction and screening procedures. NNK degeneracy in the primers offers a variety of 32 codons and encodes all possible 20 amino acids, so it will inevitably produce same sense mutations in the library construction. Meanwhile, the selection criteria for the sequenced 98 variants were set at above 58°C and at more than 10% residual relative activity, which are far below that (about 64.8°C and 50%) of the wild-type enzyme (Figure3).
Validation of the representative improved variants
To evaluate the effects of CCSM in improving the thermal stability of Amy7C, the wild-type Amy7C and four representative variants of N197C, H100I, T147P and H100MD144R (denoted by “1”, “2”, “3” and “4” in Figure3), were purified to homogeneity and characterized [see Addition file1. There appeared to be a tradeoff between thermal stability and catalytic activity of Amy7C variants. Amy7C showed avalue of 1260.55 s-1 and avalue of 62.3°C. N197C showed a reducedvalue of 58.3°C and a slightly higher catalytic activityvalue of 1298.37 s-1. From the H100I, to T147P, to H100MD144R, thevalues increased by 4.5°C, 7°C, and 8°C, while the catalytic activities range from 1.04-fold, to 0.74-fold, to 0.31-fold, respectively.
Due to both the academic and industrial values, amylase has been extensively studied in different laboratories, and numerous engineering work has been done to increase the thermostabiliy. Among the most excellent works, Machius et al. have successfully identified some beneficial amino acid substitutions in an amylase BLA from Bacillus licheniformis[39–44], and even created a hyperthermostable variant with 23°C higher than the wild-type enzyme by substituting 7 amino acids[31, 38, 44]. However, to the best of our knowledge, if the test conditions and sources of α-amylases are not considered, the increase of 8°C observed in this study is the largest ever achieved with a single round by introducing up to two point mutations into wild-type α-amylases.
As a coevolving strategy, our method also identified stabilizing variants with only single mutations at certain coevolving sites, such as H100I and T147P mutations (see above). From time to time, there is no difference between our coevolving method and traditional mutation methods such as error-prone PCR and DNA shuffling in generation of stabilizing single mutations, but in fact our single mutation should be regarded as same as other coevolving double mutations since the newly introduced single amino acid has somehow improved the coordination between two residues at the coevolving sites, and made them perfectly match in certain performances such as thermostability.
So, the above validation results clearly indicate that the screened beneficial variants changed at the coevolving sites, and the new amino acid combinations and the cooperation between them at coevolving sites brought greater thermal stability than the wild-type enzyme. It also indicates that CSSM may be more effective in generating desired mutations because it involves at least two coevolving sites that may be located in some far-away positions in protein sequences but more likely in the proximity to each other on the three dimensional structure of the proteins, and since it involves the coordinate changes in both amino acid positions they will then be more likely to co-evolve towards some direction we desired, which could be imagined as coordinated “directed evolution”, in sharp contrast to the ordinary “directed evolution”. The method proposed here only uses the protein sequence to detect coevolving sites, then employs combinatorial saturation mutagenesis to create mutations changing at both coevolving sites, and then screens out beneficial variants. So, it seems promising that the CSSM method should be applicable to many interesting enzymes other than α-amylase.
This study shows that the new method of choosing the coevolving sites as the hotspots for constructing focused mutant libraries leads to improved variants with novel beneficial mutations at new sites. The successful application of CCSM in improving the thermostability of α-amylase in this study also throws new light on the active researches of the molecular coevolution.
The CCSM approach combines coevolving site identification with combinatorial saturation mutagenesis and high throughput screening method. The CCSM approach is carried out in three steps.
Step 1: Identification of coevolving sites
Where, MI(A:B) is the mutual information between two sites A and B, and i and j run through all the occurring amino acids in each site. The base 20 for the logarithm is the number of letters in the protein alphabet. P(a i ), P(b j ) and P(a i, b j ) are the observed frequencies of amino acids a i, , b j and (a i, , b j ), respectively.
Where, RCW(A:B) is the row and column weighted mutual information between A and B sites, MI ij represents the mutual information between sites i and j, MI i. stands for the summation over all sites in row i, MI .j denotes the sum of the Mutual Information matrix over all lines in column j, n is the number of alignment sequences.
The coevolving sites prediction in this research was carried out by the above method via InterMap3D server, which is an available server to the general community for predicting and visualizing co-evolving proteins residues.
Step 2: Construction of combinatorial saturation mutagenesis library at coevolving sites
The CCSM libraries are constructed by simultaneously and randomly mutating the coevolving sites using the protocol of QuickChange® XL Site-Directed Mutagenesis Kit from Stratagene (La Jolla, CA). Complementary primers 33–35 nucleotides in length, which include NNK (G/T) degenerate codons exactly matching the coevolving sites, were designed. For each pair of coevolving sites, PCR reactions were performed using two pairs of complementary primers, each pair corresponding to a coevolving site. After removal of the methylated template plasmid with DpnI enzyme, PCR products were transformed into E. coli XL1-Blue competent cells by chemical transformation. The transformed cells harboring the CCSM libraries were plated on LB agar supplemented with antibiotics.
Step 3: Screening of the improved mutants
We used high throughput screening method to identify improved mutants from the CCSM library in a statistically significant way. In this study, mutant enzymes are assayed for residual activity relative to the wild-type strain after heat treatment and assayed for thermo-stability with respect to the half-inactivation temperature (). Clones demonstrating the highest thermostability and survival relative activity are rescreened, and the genes of rescreened variants are sequenced to identify the mutations. The identified mutant enzymes are purified, and thevalue and catalytic activity are further characterized to confirm the initial screening results.
The α-amylase Amy7C [GenBank: JN980090], derived from Bacillus subtilis CN7, was used to demonstrate the utility of our CCSM method. Amy7C is Ca2+-independent and is relatively stable at a wide range of pH values. However, its thermostability is not sufficient for use in starch simultaneous saccharification and liquefaction processes. Bacillus subtilis CN7 was screened and deposited in our laboratory. The plate plasmid pSA7C, the host strains E. coli XL1-Blue and E. coli JM109, and nucleotide primers are listed in Table A1 (see Additional file4: Table SA1). The primers were synthesized by Generay (Shanghai, China) and gene sequencing was performed by Shanghai DNA Biotechnologies (Shanghai, China). All the detailed materials and methods can be found in supporting materials (Additional file1).
Combinatorial Coevolving-site Saturation Mutagenesis
Row and Column Weighting of Mutual Information
This work was supported by the Chinese National Basic Research Program(“973”)[grant 2009CB724703] and National Science and Technology Support Program [grant 2007BAD75B05].
- Yuan L, Kurek I, English J, Keenan R: Laboratory-directed protein evolution. Microbiol Mol Biol Rev 2005, 69(3):373–392. 10.1128/MMBR.69.3.373-392.2005PubMed CentralView ArticlePubMedGoogle Scholar
- Turner NJ: Directed evolution drives the next generation of biocatalysts. Nat Chem Biol 2009, 5(8):567–573. 10.1038/nchembio.203View ArticlePubMedGoogle Scholar
- Wong TS, Roccatano D, Schwaneberg U: Steering directed protein evolution: strategies to manage combinatorial complexity of mutant libraries. Environ Microbiol 2007, 9(11):2645–2659. 10.1111/j.1462-2920.2007.01411.xView ArticlePubMedGoogle Scholar
- Reetz MT, Kahakeaw D, Lohmer R: Addressing the numbers problem in directed evolution. Chembiochem 2008, 9(11):1797–1804. 10.1002/cbic.200800298View ArticlePubMedGoogle Scholar
- Reetz MT, Soni P, Acevedo JP, Sanchis J: Creation of an amino acid network of structurally coupled residues in the directed evolution of a thermostable enzyme. Angew Chem Int Ed Engl 2009, 48(44):8268–8272. 10.1002/anie.200904209View ArticlePubMedGoogle Scholar
- Reetz MT, Wang LW, Bocola M: Directed evolution of enantioselective enzymes: iterative cycles of CASTing for probing protein-sequence space. Angew Chem Int Ed Engl 2006, 45(8):1236–1241. 10.1002/anie.200502746View ArticlePubMedGoogle Scholar
- Reetz MT, Carballeira JD, Vogel A: Iterative saturation mutagenesis on the basis of B factors as a strategy for increasing protein thermostability. Angew Chem 2006, 118(46):7909–7915. 10.1002/ange.200602795View ArticleGoogle Scholar
- Reetz MT, Carballeira JD: Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes. Nat Protoc 2007, 2(4):891–903. 10.1038/nprot.2007.72View ArticlePubMedGoogle Scholar
- Reetz MT, Bocola M, Carballeira JD, Zha D, Vogel A: Expanding the range of substrate acceptance of enzymes: combinatorial active-site saturation test. Angew Chem Int Ed Engl 2005, 44(27):4192–4196. 10.1002/anie.200500767View ArticlePubMedGoogle Scholar
- Wiederstein M, Sippl MJ: Protein sequence randomization: efficient estimation of protein stability using knowledge-based potentials. J Mol Biol 2005, 345(5):1199–1212. 10.1016/j.jmb.2004.11.012View ArticlePubMedGoogle Scholar
- Hayes RJ, Bentzien J, Ary ML, Hwang MY, Jacinto JM, Vielmetter J, Kundu A, Dahiyat BI: Combining computational and experimental screening for rapid optimization of protein properties. Proc Natl Acad Sci U S A 2002, 99(25):15926–15931. 10.1073/pnas.212627499PubMed CentralView ArticlePubMedGoogle Scholar
- Voigt CA, Mayo SL, Arnold FH, Wang ZG: Computational method to reduce the search space for directed protein evolution. Proc Natl Acad Sci U S A 2001, 98(7):3778–3783. 10.1073/pnas.051614498PubMed CentralView ArticlePubMedGoogle Scholar
- Armon A, Graur D, Ben-Tal N: ConSurf: an algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information. J Mol Biol 2001, 307(1):447–463. 10.1006/jmbi.2000.4474View ArticlePubMedGoogle Scholar
- Pollock DD, Taylor WR, Goldman N: Coevolving protein residues: maximum likelihood identification and relationship to structure. J Mol Biol 1999, 287(1):187–198. 10.1006/jmbi.1998.2601View ArticlePubMedGoogle Scholar
- Yeang CH, Haussler D: Detecting coevolution in and among protein domains. PLoS Comput Biol 2007, 3(11):e211. 10.1371/journal.pcbi.0030211PubMed CentralView ArticlePubMedGoogle Scholar
- Ferrer-Costa C, Orozco M, de la Cruz X: Characterization of compensated mutations in terms of structural and physico-chemical properties. J Mol Biol 2007, 365(1):249–256. 10.1016/j.jmb.2006.09.053View ArticlePubMedGoogle Scholar
- Lockless SW, Ranganathan R: Evolutionarily conserved pathways of energetic connectivity in protein families. Science 1999, 286(5438):295–299. 10.1126/science.286.5438.295View ArticlePubMedGoogle Scholar
- Hatley ME, Lockless SW, Gibson SK, Gilman AG, Ranganathan R: Allosteric determinants in guanine nucleotide-binding proteins. Proc Natl Acad Sci U S A 2003, 100(24):14445–14450. 10.1073/pnas.1835919100PubMed CentralView ArticlePubMedGoogle Scholar
- Chakrabarti S, Panchenko AR: Coevolution in defining the functional specificity. Proteins 2009, 75(1):231–240. 10.1002/prot.22239PubMed CentralView ArticlePubMedGoogle Scholar
- Gobel U, Sander C, Schneider R, Valencia A: Correlated mutations and residue contacts in proteins. Proteins 1994, 18(4):309–317. 10.1002/prot.340180402View ArticlePubMedGoogle Scholar
- Lee BC, Park K, Kim D: Analysis of the residue-residue coevolution network and the functionally important residues in proteins. Proteins 2008, 72(3):863–872. 10.1002/prot.21972View ArticlePubMedGoogle Scholar
- Gouveia-Oliveira R, Pedersen AG: Finding coevolving amino acid residues using row and column weighting of mutual information and multi-dimensional amino acid representation. Algorithms Mol Biol 2007, 2: 12. 10.1186/1748-7188-2-12PubMed CentralView ArticlePubMedGoogle Scholar
- Dutheil J, Galtier N: Detecting groups of coevolving positions in a molecule: a clustering approach. BMC Evol Biol 2007, 7: 242. 10.1186/1471-2148-7-242PubMed CentralView ArticlePubMedGoogle Scholar
- Fares MA, McNally D: CAPS: coevolution analysis using protein sequences. Bioinformatics 2006, 22(22):2821–2822. 10.1093/bioinformatics/btl493View ArticlePubMedGoogle Scholar
- Gao H, Dou Y, Yang J, Wang J: New methods to measure residues coevolution in proteins. BMC Bioinformatics 2011, 12: 206. 10.1186/1471-2105-12-206PubMed CentralView ArticlePubMedGoogle Scholar
- Gloor GB, Martin LC, Wahl LM, Dunn SD: Mutual information in protein multiple sequence alignments reveals two classes of coevolving positions. Biochemistry 2005, 44(19):7156–7165. 10.1021/bi050293eView ArticlePubMedGoogle Scholar
- Pollock DD: Genomic biodiversity, phylogenetics and coevolution in proteins. Appl Bioinformatics 2002, 1(2):81–92.PubMed CentralPubMedGoogle Scholar
- Codoñer FM, Fares MA: Why should we care about molecular coevolution? Evol Bioinform 2008, 4: 29–38.Google Scholar
- Prakash O, Jaiswal N: Alpha-Amylase: an ideal representative of thermostable enzymes. Appl Biochem Biotechnol 2010, 160(8):2401–2414. 10.1007/s12010-009-8735-4View ArticlePubMedGoogle Scholar
- Hocker B, Jurgens C, Wilmanns M, Sterner R: Stability, catalytic versatility and evolution of the (beta alpha)(8)-barrel fold. Curr Opin Biotechnol 2001, 12(4):376–381. 10.1016/S0958-1669(00)00230-5View ArticlePubMedGoogle Scholar
- Declerck N, Machius M, Joyet P, Wiegand G, Huber R, Gaillardin C: Engineering the thermostability of Bacillus licheniformis a-amylase. Biologia, Bratislava 2002, 57(Suppl. 11):203–211.Google Scholar
- Little DY, Chen L: Identification of coevolving residues and coevolution potentials emphasizing structure, bond formation and catalytic coordination in protein evolution. PLoS One 2009, 4(3):e4762. 10.1371/journal.pone.0004762PubMed CentralView ArticlePubMedGoogle Scholar
- Fuwa H: A new method for microdetermination of amylase activity by the use of amylose as the substrate. J Biochem 1954, 41(5):583–603.Google Scholar
- Miller GL: Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 1959, 31(3):426–428. 10.1021/ac60147a030View ArticleGoogle Scholar
- Sterner R, Hocker B: Catalytic versatility, stability, and evolution of the (betaalpha)8-barrel enzyme fold. Chem Rev 2005, 105(11):4038–4055. 10.1021/cr030191zView ArticlePubMedGoogle Scholar
- Gromiha MM, Pujadas G, Magyar C, Selvaraj S, Simon I: Locating the stabilizing residues in (alpha/beta)8 barrel proteins based on hydrophobicity, long-range interactions, and sequence conservation. Proteins 2004, 55(2):316–329. 10.1002/prot.20052View ArticlePubMedGoogle Scholar
- Nagatani RA, Gonzalez A, Shoichet BK, Brinen LS, Babbitt PC: Stability for function trade-offs in the enolase superfamily "catalytic module". Biochemistry 2007, 46(23):6688–6695. 10.1021/bi700507dView ArticlePubMedGoogle Scholar
- Declerck N, Machius M, Joyet P, Wiegand G, Huber R, Gaillardin C: Hyperthermostabilization of Bacillus licheniformis alpha-amylase and modulation of its stability over a 50 degrees C temperature range. Protein Eng 2003, 16(4):287–293. 10.1093/proeng/gzg032View ArticlePubMedGoogle Scholar
- Declerck N, Joyet P, Gaillardin C, Masson JM: Use of amber suppressors to investigate the thermostability of Bacillus licheniformis alpha-amylase. Amino acid replacements at 6 histidine residues reveal a critical position at His-133. J Biol Chem 1990, 265(26):15481–15488.PubMedGoogle Scholar
- Joyet P, Declerck N, Gaillardin C: Hyperthermostable variants of a highly thermostable alpha-amylase. Biotechnology (N Y) 1992, 10(12):1579–1583. 10.1038/nbt1292-1579View ArticleGoogle Scholar
- Declerck N, Joyet P, Trosset JY, Garnier J, Gaillardin C: Hyperthermostable mutants of Bacillus licheniformis alpha-amylase: multiple amino acid replacements and molecular modelling. Protein Eng 1995, 8(10):1029–1037. 10.1093/protein/8.10.1029View ArticlePubMedGoogle Scholar
- Declerck N, Machius M, Chambert R, Wiegand G, Huber R, Gaillardin C: Hyperthermostable mutants of Bacillus licheniformis alpha-amylase: thermodynamic studies and structural interpretation. Protein Eng 1997, 10(5):541–549. 10.1093/protein/10.5.541View ArticlePubMedGoogle Scholar
- Declerck N, Machius M, Wiegand G, Huber R, Gaillardin C: Probing structural determinants specifying high thermostability in Bacillus licheniformis alpha-amylase. J Mol Biol 2000, 301(4):1041–1057. 10.1006/jmbi.2000.4025View ArticlePubMedGoogle Scholar
- Machius M, Declerck N, Huber R, Wiegand G: Kinetic stabilization of Bacillus licheniformis alpha-amylase through introduction of hydrophobic residues at the surface. J Biol Chem 2003, 278(13):11546–11553. 10.1074/jbc.M212618200View ArticlePubMedGoogle Scholar
- Whittle E, Shanklin J: Engineering delta 9–16:0-acyl carrier protein (ACP) desaturase specificity based on combinatorial saturation mutagenesis and logical redesign of the castor delta 9–18:0-ACP desaturase. J Biol Chem 2001, 276(24):21500–21505. 10.1074/jbc.M102129200View ArticlePubMedGoogle Scholar
- Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, et al.: UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 2004, 32: D115-D119. 10.1093/nar/gkh131PubMed CentralView ArticlePubMedGoogle Scholar
- Katoh K, Kuma K, Toh H, Miyata T: MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res 2005, 33(2):511–518. 10.1093/nar/gki198PubMed CentralView ArticlePubMedGoogle Scholar
- Gouveia-Oliveira R, Sackett PW, Pedersen AG: MaxAlign: maximizing usable data in an alignment. BMC Bioinformatics 2007, 8: 312. 10.1186/1471-2105-8-312PubMed CentralView ArticlePubMedGoogle Scholar
- Gouveia-Oliveira R, Roque FS, Wernersson R, Sicheritz-Ponten T, Sackett PW, Molgaard A, Pedersen AG: InterMap3D: predicting and visualizing co-evolving protein residues. Bioinformatics 2009, 25(15):1963–1965. 10.1093/bioinformatics/btp335View ArticlePubMedGoogle Scholar
- InterMap3D server. . http://www.cbs.dtu.dk/services/InterMap3D/ .
- Hogrefe HH, Cline J, Youngblood GL, Allen RM: Creating randomized amino acid libraries with the QuikChange multi site-directed mutagenesis kit. Biotechniques 2002, 33(5):1158–1160. 1162, 1164–1155 1162, 1164–1155PubMedGoogle Scholar
- Sambrook J, Russell D: Molecular Cloning: A Laboratory Manual third edn. New York: Cold Spring Harbor Laboratory Press; 2001.Google Scholar
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