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Combinatorial Protein Design Strategies 13backbone conformations (e.g., T = 300 K). Here, a backbone-dependentrotamer library is used throughout (60). Reference energies are measured withrespect to that of glycine (G), which has no side chain. The energy constrainton the sequences involving interatomic interactions then takes the form:∑E ≈ E = { ε [ α, r ( α)] −γ [ α, β ] w [ α, r ( α)]}c c i k ref ref i ki, α,k+ ∑ εij ,[α, rk(α) ; α′ , rk′( α′)] wi[α, rk (α)] wj[α′ , rk′(α′)]i, j>iα,α′kk , ′2.3.4. Rotamer and Identity ProbabilitiesThe theory maximizes the total conformational entropy, S c, yielding a probabilityw i[α,r(α)] that a particular amino acid is present at site i and is in sidechainconformation k. The amino acid probability, w i(α), can then be determinedusing:Using an analogy to statistical thermodynamics, the Lagrange multiplier thatarises from constraining the conformational energy, β c, may be considered aneffective inverse temperature, 1/β c= T c. The corresponding “heat capacity,” C v,is defined as:EcCv= ∂ 2 2 2= βc ( i−i )∂T∑ ε εc ilocε = ε [ α, r ( α)] w[ α, r ( α)]iα,kilocε [ α, r ( α)] = ε [ α, r ( α)] − γ ( α, β )i∑k i k+w ( α) = ∑ w[ α,r ( α)]i i kk∑j, α′ , k′kwhere ε i is termed a local mean field energy, which denotes the average localfield around a particular amino acid side, i. The effective heat capacity, C v, providesa quantitative measure of the fluctuations in the sequence–rotamer identitiesas values of the constraint conditions, such as the overall energy, aremodulated during a calculation.By way of example, the theory is applied to a particular protein, an SH3domain. The conformational entropy decreases with decreasing the effective temperatureT c(i.e., decreasing E c; Fig. 1). At high energies (high T c,low β c), there aremany unfavorable (high-energy) interactions between residues and a broad distributionof sequence–rotamer states at each site. On average, the number of probablerefikε [ α, r ( α); α′ , r ( α′ )] w [ α′ , ( α′ )]ij , k k′jcr k ′
14 Kono et al.Fig. 1. Sequence–conformation entropy, S c, of the SH3 domain is plotted againsteffective temperature, T c(upper panel). Effective heat capacities per residue C vfor allburied and exposed residues are plotted against effective temperature (lower panel).Temperatures are given in arbitrary units determined by the molecular potential used,here moles per kilocalorie.
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METHODS IN MOLECULAR BIOLOGY 352Pr
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M E T H O D S I N M O L E C U L A R
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PrefaceProtein engineering is a fas
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ContentsPreface ...................
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ContributorsKATJA M. ARNDT • Inst
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Contributors xiKAZUNARI TAIRA • D
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1Combinatorial Protein Design Strat
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Combinatorial Protein Design Strate
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Combinatorial Protein Design Strate
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Combinatorial Protein Design Strate
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Combinatorial Protein Design Strate
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Combinatorial Protein Design Strate
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Combinatorial Protein Design Strate
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Combinatorial Protein Design Strate
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Combinatorial Protein Design Strate
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2Global Incorporation of Unnatural
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Incorporation of Unnatural Amino Ac
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Incorporation of Unnatural Amino Ac
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Incorporation of Unnatural Amino Ac
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Incorporation of Unnatural Amino Ac
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Incorporation of Unnatural Amino Ac
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3Considerations in the Design and O
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Design of Coiled Coil Structures 37
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Design of Coiled Coil Structures 39
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Design of Coiled Coil Structures 41
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Design of Coiled Coil Structures 43
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Design of Coiled Coil Structures 45
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Design of Coiled Coil Structures 47
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Design of Coiled Coil Structures 49
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Design of Coiled Coil Structures 51
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Design of Coiled Coil Structures 53
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Design of Coiled Coil Structures 55
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Design of Coiled Coil Structures 57
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Design of Coiled Coil Structures 59
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Design of Coiled Coil Structures 61
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Design of Coiled Coil Structures 63
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Design of Coiled Coil Structures 65
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Design of Coiled Coil Structures 67
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Design of Coiled Coil Structures 69
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4Calcium Indicators Based on Calmod
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Protein-Based Ca 2+ Indicators 73Fi
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Protein-Based Ca 2+ Indicators 7512
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Protein-Based Ca 2+ Indicators 77Fi
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Protein-Based Ca 2+ Indicators 79Fi
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Protein-Based Ca 2+ Indicators 8145
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5Design and Synthesis of Artificial
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Design of Zinc Finger Proteins 853.
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Design of Zinc Finger Proteins 873.
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Design of Zinc Finger Proteins 89pr
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Design of Zinc Finger Proteins 91Fi
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Design of Zinc Finger Proteins 932.
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96 Koide and Koidewhile retaining t
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98 Koide and Koide5. M9-tryptone: M
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100 Koide and Koidetarget-binding s
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102 Koide and Koide4. Discard the s
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104 Koide and Koideup to 1 mM for h
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106 Koide and Koideplate. Incubate
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108 Koide and Koide1. Perform steps
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7Engineering Site-Specific Endonucl
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Engineering Site-Specific Endonucle
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115Fig. 1. Mapping group-specific r
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Engineering Site-Specific Endonucle
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Engineering Site-Specific Endonucle
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Engineering Site-Specific Endonucle
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Engineering Site-Specific Endonucle
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8Protein Library Design and Screeni
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Protein Library Design and Screenin
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Protein Library Design and Screenin
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Protein Library Design and Screenin
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135Fig. 2. Excel worksheet describi
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137Fig. 4. Excel worksheet describi
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Protein Library Design and Screenin
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Protein Library Design and Screenin
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Protein Library Design and Screenin
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Protein Library Design and Screenin
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Protein Library Design and Screenin
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Protein Library Design and Screenin
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Protein Library Design and Screenin
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Protein Library Design and Screenin
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9Protein Design by Binary Patternin
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Protein Design by Binary Patterning
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Protein Design by Binary Patterning
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Protein Design by Binary Patterning
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Protein Design by Binary Patterning
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Protein Design by Binary Patterning
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10Versatile DNA Fragmentation and D
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NExT DNA Shuffling 169This chapter
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NExT DNA Shuffling 1713. Methods3.1
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NExT DNA Shuffling 17372°C, 4 min.
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NExT DNA Shuffling 175Fig. 2. Varia
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NExT DNA Shuffling 1773.5.1. Direct
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NExT DNA Shuffling 179Fig. 3. Reass
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181
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NExT DNA Shuffling 183likelihood of
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NExT DNA Shuffling 1853.9.2. Calibr
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NExT DNA Shuffling 187staining. Bec
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NExT DNA Shuffling 1894. Zhao, H.,
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11Degenerate Oligonucleotide Gene S
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Degenerate Oligonucleotide Gene Shu
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Degenerate Oligonucleotide Gene Shu
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Degenerate Oligonucleotide Gene Shu
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Degenerate Oligonucleotide Gene Shu
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Degenerate Oligonucleotide Gene Shu
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Degenerate Oligonucleotide Gene Shu
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12M13 Bacteriophage Coat Proteins E
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Engineered M13 Bacteriophage Coat P
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Engineered M13 Bacteriophage Coat P
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Engineered M13 Bacteriophage Coat P
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Engineered M13 Bacteriophage Coat P
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Engineered M13 Bacteriophage Coat P
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Engineered M13 Bacteriophage Coat P
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Engineered M13 Bacteriophage Coat P
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222 Fujita et al.The methods that h
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224 Fujita et al.3. Streptavidin an
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226 Fujita et al.Fig. 2. (Continued
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228 Fujita et al.Fig. 3. (Continued
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230 Fujita et al.Fig. 3. (A) Schema
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232 Fujita et al.1. Add 2 µg mRNA
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234 Fujita et al.Innovative Bioscie
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236 Fujita et al.30. Kim, Y., Mlsa,
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238 Ghadessy and Holligeraffinity m
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240 Ghadessy and HolligerFig. 1. (A
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242 Ghadessy and HolligerFinally, t
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244 Ghadessy and Holliger2. Prepare
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246 Ghadessy and Holliger2. After 6
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248 Ghadessy and Holliger21. Oberho
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250 Campbell-Valois and Michnickscr
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252 Campbell-Valois and Michnickfol
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254 Campbell-Valois and Michnick7.
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256 Campbell-Valois and MichnickFig
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258 Campbell-Valois and Michnickres
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260Fig. 3. BL21 pREP4 cells were co
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262 Campbell-Valois and MichnickFig
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264 Campbell-Valois and Michnickvar
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266 Campbell-Valois and Michnick3.
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268 Campbell-Valois and Michnick7.
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270 Campbell-Valois and Michnick21.
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272 Campbell-Valois and Michnick16.
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274 Campbell-Valois and Michnick48.
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276 Hecky et al.improved half-life
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278 Hecky et al.Fig. 1. (A) Scheme
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280 Hecky et al.11. Reverse primer
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282 Hecky et al.high variability in
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284 Hecky et al.coding sequence for
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286 Hecky et al.4. Analyze the resu
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Table 1Amino Acid Substitutions Sel
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290 Hecky et al.Fig. 4. Structure o
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292 Hecky et al.Fig. 5. Expression
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294 Hecky et al.Table 2Kinetic Para
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296 Hecky et al.The thermoactivity
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298 Hecky et al.Fig. 8. Unfolding o
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300 Hecky et al.for the first trans
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302 Hecky et al.7. Thompson, M. J.
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304 Hecky et al.40. Wang, X., Minas
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306 IndexCCalcium indicators, see C
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308 Indexchemiocompetent cellprepar
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310 Indexmaterials, 169, 170mutatio
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312 IndexTerminal truncation, see a
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