Networking Products, Servers, Switches manufacturer / supplier in China, offering New in Stock for HP -24-Poe+ Switch, Look, New Server--Thinkserver Sr670V2 3u Rack GPU Ai Server Win Server Datacenter Xeon CPU Server, New and Original CL-Stack-Kit Network 10g SFP Optical Transceiver Stack Module and so on.
If you are looking for more details, kindly visit qunfeng.
Circular dichroism (CD) is an excellent tool for rapid determination of the secondary structure and folding properties of proteins that have been obtained using recombinant techniques or purified from tissues. The most widely used applications of protein CD are to determine whether an expressed, purified protein is folded, or if a mutation affects its conformation or stability. In addition, it can be used to study protein interactions. This protocol details the basic steps of obtaining and interpreting CD data and methods for analyzing spectra to estimate the secondary structural composition of proteins. CD has the advantage that it is that measurements may be made on multiple samples containing 20 µg or less of proteins in physiological buffers in a few hours. However, it does not give the residue-specific information that can be obtained by X-ray crystallography or NMR.
The rapid characterization of new proteins is of great importance for the fields of proteomics and structural genomics. Circular dichroism (CD) is an excellent method for rapidly evaluating the secondary structure, folding and binding properties of proteins. Briefly, circular dichroism is defined as the unequal absorption of left-handed and right-handed circularly polarized light. A beam of light has time dependent electric and magnetic fields associated with it. If the light is polarized by passing through suitable prisms or filters its electric field, E, will oscillate sinusoidally in a single plane. When viewed from the front, the sinusoidal wave can be visualized as the resultant of two vectors of equal length, which trace out circles, one which rotates clockwise (ER) and the other which rotates counterclockwise (EL). The two circularly polarized waves have physical existence. The waves are 90 degrees out of phase with each other and can be separated using a variety of prisms or electronic devices which utilize Pockel's effect1. When asymmetric molecules interact with light, they may absorb right and left handed circularly polarized light to different extents (hence the term circular dichroism) and also have different indices of refraction for the two waves. The result is that the plane of the light wave is rotated and that the addition of the ER and EL vectors results in a vector that traces out an ellipse and the light is said to be elliptically polarized. CD is reported either in units of ΔE, the difference in absorbance of ER and EL by an asymmetric molecule, or in degrees ellipticity, which is defined as the angle whose tangent is the ratio of the minor to the major axis of the ellipse. [θ], the molar ellipticity in deg. · cm2/dmol = ΔE. For illustrations of the phenomena of CD see Beychok2. There is also a website that illustrates the production of circularly polarized light and the circular dichroism of optically active molecules with animated graphics http://www.enzim.hu/~szia/cddemo/.
CD is an excellent method of determining the secondary structure of proteins. When the chromophores of the amides of the polypeptide backbone of proteins are aligned in arrays, their optical transitions are shifted or split into multiple transitions due to 'exciton' interactions (See Sreerama and Woody3 for a recent review). The result is that different structural elements have characteristic CD spectra ( ). For example, α-helical proteins have negative bands at 222 nm and 208 nm and a positive band at 193 nm4. Proteins with well-defined antiparallel β-pleated sheets (β-helices) have negative bands at 218 nm and positive bands at 195 nm5, while disordered proteins have very low ellipticity above 210 nm and negative bands near 195 nm6. The collagens are a unique class of proteins, which have three chains that wrap together in a triple helix. Each strand has a conformation resembling that of poly-L-proline7 in a extended helical conformation where all of the bonds are trans to each other (poly-L-proline II). Charged polypeptides, such as poly-L-glutamate or poly-L-lysine at neutral pH (originally thought to be in random coil conformation) have a similar extended poly-L-proline II-like conformation8'10. The spectra of some representative proteins, with widely varying conformations, are shown in . Because the spectra of proteins are so dependent on their conformation, CD can be used to estimate the structure of unknown proteins and monitor conformational changes due to temperature, mutations, heat, denaturants or binding interactions. While CD does not give the secondary structure of specific residues, as do X-ray crystallographic and NMR structural determinations, the method has the advantage that data can be collected and analyzed in a few hours on solutions of samples containing 20 µg or less of protein in aqueous buffers under physiological conditions. Secondary structure can also be estimated from Fourier transform infrared spectroscopy of proteins (FTIR)11'13 and Raman spectroscopy14 but in some cases the measurement must be made on protein films or in deuterium oxide.
In addition to the intrinsic circular dichroism of the protein backbone, when ligands with chromophores bind to proteins they may develop strong extrinsic CD bands that can also be used to follow binding. The aromatic chromophores of proteins, which have bands in the near ultraviolet region, are often in asymmetric environments and can be used to examine whether mutations change the tertiary structure of proteins.
Key considerations in designing and implementing CD experiments are discussed below.
CD spectra are collected in high transparency quartz cuvettes (cells). Both rectangular and cylindrical cells are available, with path lengths ranging from 0.01 to 1 cm. Water jacketed cylindrical cells are available for CD machines that do not have temperature regulated cell holders. Most cylindrical cells and 0.1 cm rectangular cells have relatively low birefringence and give reasonably straight baselines, but all cells are different and baseline spectra must always be collected. Rectangular cells with path lengths greater than 0.2 cm may have high birefringence due to strain. Cells designed for fluorescence, in which all four sides are made of the same material usually have lower birefringence than cells where two sides are frosted. Rectangular cells with path lengths less than 0.1 cm often have a very small total sample volume and a very small surface area facing the light beam of the CD machine. It is important that the light beam be very tightly focused if these cells are used, since large artifacts are produced if the light does not go directly through the sample. Dismountable short path length cells are available, but the path length can be affected by the viscosity of the sample, and they tend to leak if temperatures are changed. Their use is not recommended. If they are necessary to collect data at low wavelengths, the scale should be checked by comparing data at higher wavelengths with that collected under the same conditions in cells with 0.1 cm or higher path lengths.
Buffers for CD spectroscopy must not contain any materials that are optically active and should be as transparent as possible. The total absorbance of the sample, including the buffer and cell, should be below one for high quality data. Samples where the protein is dissolved in water alone have the highest transparency, but some proteins denature in the absence of salt. The low wavelength cutoffs of several buffers commonly used for CD measurements (buffer + 0.1 mg/ml protein in 0.1 cm cuvettes) are given in . Oxygen absorbs light below 200 nm; for optimum transparency, buffers should therefore be prepared with glass-distilled water or the water should be degassed before use. Samples for CD analysis should be free of particulate matter. They can be filtered through filters (e.g. 0.1'0.2 micron) such as those sold by Millipore (http://www.millipore.com) or centrifuged at 100,000 g.
Samples for CD spectroscopy must be at least 95% pure by the criteria of HPLC, mass spectroscopy or gel electrophoresis. For secondary structure measurements, sample concentrations may range from 0.005 to 5 mg/ml depending on the path length of the cell. The hardest part of obtaining high quality CD data is the correct determination of protein concentration. The method of Lowry15 and the Bradford dye binding methods16 give different results for different proteins and must not be used for determining protein concentrations for CD.
The most accurate method of determining protein concentration is quantitative amino acid analysis, using the concentrations of the stable amino acids, e.g. alanine and lysine to calculate the concentration of the intact protein. This method is very sensitive and can be performed on aliquots of the actual CD samples; however many laboratories do not have the equipment to do their own measurements. Concentration can be determined using published molar extinction coefficients if they are available; the protein should be dialyzed or desalted into the CD buffer immediately before the spectrum is obtained and filtered though 0.1 to 0.2 micron filters to reduce light scattering. The spectrum of the CD buffer alone must be subtracted from the spectrum of the sample.
Three different methods of rapid and accurate protein quantification, which are independent of protein composition, are described in this protocol. (Step 1, Options A'C). Since CD is usually carried out on samples with relatively low concentrations, it is best to prepare a filtered stock solution of the protein of ~1 mg/ml in the CD buffer and determine its protein concentration and dilute the stock for the CD measurements.
None of these rapid methods will work if the protein is highly stable and the protein is not fully unfolded in 6 M guanidine-HCl or in 3% NaOH. In these cases, the protein may be hydrolyzed and the amino acid composition quantified using ninhydrin17; the protein may be subjected to Kjeldhal digestion followed by determination of ammonium sulfate using Nessler's reagent18; or the protein may be ashed with 72% HCLO4 in water at 210 °C and the nitrogen determined with Berthelot phenol-hypochorite reagent as described by Jaenicke19. The phenol-hypochorite method has high sensitivity, needing only 0.05'10 ug of protein in the sample and has an error within of 0.01 µg, but it and the Micro Kjeldhal method cannot be used for samples dissolved in buffers containing nitrogen, such as Tris-HCl. A protocol for the Kjeldhal method is provided in SUPPLEMENTAL MATERIALS.
Some proteins, such as collagen and collagen fragments, take a long time to fold. If the folding properties are not known, the proteins may be pre-folded for several days on ice in the refrigerator, or for several hours at 25 °C before performing CD experiments.
There are many different methods to analyze CD spectra to estimate secondary structure; the most commonly used are described below. Links to sites where the software can be obtained and descriptions of the sites are given in . In addition, some of the software is available in the file CD.Zip in SUPPLEMENTAL MATERIALS.
All methods of analyzing CD spectra assume that the spectrum of a protein can be represented by a linear combination of the spectra of its secondary structural elements, plus a noise term, which includes the contribution of aromatic chromophores and prosthetic groups.
θλ = ''εiSλi + noise
Want more information on Cd Screen Structure From China? Feel free to contact us.
(1)
where θλis the CD of the protein as a function of wavelength, εi is the fraction of each secondary structure, i, and Sλi is the ellipticity at each wavelength of each ith secondary structural element. In constrained fits, the sum of all the fractional weights, εi, must equal one and all of the fractional contributions must be greater than or equal to zero.
There are two general classes of methods to evaluate protein conformation. The first uses standards of polypeptides, with defined compositions in known conformations, which have been determined by X-ray scattering of films or by IR in solution5,20,21. The second uses the spectra of proteins which have been characterized by X-ray crystallography as standards. These are then compared to the spectra of unknown proteins using least squares analysis22'26, ridge regression27, singular value decomposition17, single value decomposition with variable selection28'31; the self consistent method32'36; or neural network analysis37'39.
All of the listed methods described below are useful for determining the α-helical content of globular proteins. The protein-based analyses are superior when analyzing the conformation of globular, well-folded proteins (see below). However, most programs using data based on protein spectra do not correctly analyze the conformation of proteins with a majority of pure β-helices, such as found in some synthetic polypeptides and in amyloids, or many fibrous proteins such as collagen or coiled-coil proteins. For analysis of the spectra of these samples, more accurate results will be obtained using constrained least squares fitting analysis programs (e.g. LINCOMB40) with polypeptide based reference sets5,20,21, or the programs K2D38 and CONTIN27. compares the results obtained using various methods for the conformational analyses of the proteins shown in , and for the analysis of the conformation of two polypeptides, one with a pure α-helical conformation and one with a pure β-pleated sheet conformation. The effects of wavelength range on the goodness of fits for the programs listed in have been compared previously26. The effect of the nature of the protein database on the goodness of the estimates for SELCON3, CONTIN and CDSSTR have been described in detail36.
Linear Regression: Linear regression fits the spectrum of an unknown protein by comparison to the spectra of a set of fixed standards. It is useful for evaluating the effects of mutations, ligands and solvents on protein conformation. Suitable standards include: polypeptides with known conformations, which are essential for the analysis of non-globular polypeptides and fibrous proteins; standard spectra extracted from a database of known proteins using the method of least squares, which are useful for analyzing globular proteins; and standard spectra extracted from a database of protein with known conformations using the convex constraint algorithm. Either Constrained Least Squares or Non-Constrained Multilinear Regression (MLR) can be used.
In constrained least squares fits, the sum of the contribution of each spectrum is constrained to equal one: θλ=ΣεiSλi ΣεI = 1 and εi ' 0. Least squares analysis can be performed using the program LINCOMB40, which is available in the file CD.Zip in SUPPLEMENTAL MATERIAL. It can also be done by loading a database of reference spectra into a commercial graphics program, e.g. SigmaPlot (Systat Software Inc. Richmond, CA) http://www.systat.com/products/sigmaplot/), with the wavelength in the first column, the ellipticity values of the references, e.g., α-helix, antiparallel β-sheet, parallel β-sheet, turn and disordered, in columns 2'6 and the unknown protein in column 7 and fitting the data in column 7 to the equation θλι = ΣειSi where ΣSi is constrained to equal 1 and εi is constrained to be ' 0. An example of the needed equations is given in SigmaPlot format in the file LS1. doc in SUPPLEMENTAL MATERIALS. The use of commercial graphics programs is recommended because they quickly find the best fit to the data with all of the fractional components being positive and one can easily plot the best fit to the unknown data. The major advantages of this approach are the use of an invariant database, which is useful for direct comparisons, and that better fits are obtained than with non-constrained least squares methods. However, there are no good single standards for β-turns.
In non-constrained least squares fit the sum of the conformations is not constrained to equal one20.
θλ = ''εiSλi + C
(2)
A DOS based program for calculating non-constrained least squares fits is MLR. It is available in the program CD1.EXE in SUPPLEMENTAL MATERIALS. It also can be evaluated using graphics programs with curve fitting routines. An example of such a routine is also given in the file LS2.doc in SUPPLEMENTAL MATERIALS. The advantages of this approach are that there is no need to know protein concentration, invariant standards are used and it is useful for analyzing difference spectra. However, it is the least accurate method.
Ridge Regression (CONTIN): CONTINfits the CD of unknown proteins by comparison to a linear combination of the spectra of a large database of proteins with known conformations. In this method the contribution of each reference spectrum is kept small unless it contributes to a good agreement between the theoretical best fit curve and the raw data. This method results in relatively good estimates of α-helices and β-sheets. Different references are used for every fit, which is an advantage for obtaining the best fits of the data, but complicates the quantitative analysis of the effect of a mutations or denaturant, since a different set of standards is used for each analysis.
Singular Value Decomposition (SVD): SVDextracts basis curves with unique nodes from a set of spectra of proteins with known structures. The basis curves are each characterized by a mixture of secondary structures, and are then used to analyze the conformation of unknown proteins. The sum of weights is not constrained to equal 1. This method provides the best estimates of α-helical content of proteins. However, it provides terrible estimates of β-sheets and turns if data are not collected to at least 184 nm. It is also unsuitable for the analysis of polypeptides and protein fragments.
Variable Selection (VARSLC, CDSSTR): In the variable selection methodan initial large database of standard spectra from proteins with known spectra and secondary structures is created. The protein structure is then determined using SVD. Some of the protein spectra are then eliminated systematically to create new databases with a smaller number of standards. SVD is performed using all of the reduced data sets and those fulfilling selection criteria for a good fit are averaged. Two versions of the Program are available, VARSLC (also called Varselec) and CDSSTR. This method provides superior fits of the conformation of globular proteins. However, it does not always give fractional content of various conformations that add up to 100% and it can be very slow if a large number of reference sets are used and more than two spectra are eliminated from the combinations. It is also unsuitable for the analysis of polypeptides and protein fragments.
Self Consistent Method (SELCON): In SELCON, the spectrum of the protein to be analyzed is included in the basis set and an initial guess is made for the unknown structure as a first approximation. The resulting matrix equation is solved using the SVD algorithm and the initial guess is replaced by the solution. The process is repeated until self-consistency is attained. The program works well for globular proteins. The original program, SELCON, evaluates α-helix, antiparallel and parallel β sheets, turns and remainder. SELCON2is modified to use a data set where the poly-L-proline II conformation is evaluated in addition to α-helix, total β-sheets and turns. In the version on the CDpro website (see ), SELCON3, the α-helix and β-sheet conformations in globular protein structures are divided into regular and distorted fractions by considering a certain number of terminal residues in a given helical or strand segment to be distorted. The number of α-helical and β-strand segments and their average length in a given protein are estimated from the fraction of distorted helical and strand conformations relative to the total helix and strand content. The main advantage of this method is that it provides very good estimates of the structure of globular proteins. However, SELCON3 gives poor estimates of turns compared to SELCON1 and SELCON2, VARSLC and CDNN. All the versions of SELCON are unsuitable for the analysis of polypeptides and protein fragments.
Neural Networks (CDNN, K2D, SOMCD): A neural network is an artificial intelligence program used to find correlations in data. Two widely used programs are CDNNand K2D. CDNN analyzes data to determine helix, anti and parallel β-structure, turns and remainder and K2D determines helix, total β-structure and remainder. SOMCD is a variation of K2D that uses a larger reference set to train the network and also analyzes turns. A neural network is first trained using a set of known proteins so that the input of the CD at each wavelength results in the output of the correct secondary structure. The trained network is then used to analyze unknown proteins. K2D gives a good estimate of the helical and sheet contents of both proteins and polypeptides. However, the K2D program does not estimate turns. CDNN is not suitable for the analysis of polypeptides and it currently is not being distributed.
Convex Constraint Algorithm (CCA): The CCA algorithmdeconvolutes a set of spectra into a desired number of basis spectra, which, when recombined, generate the entire data set with a minimum deviation between the original data set and the reconstructed curves. It is very useful to determine how many different states contribute to the changes in CD as a function of ligand or temperature. The method was developed to estimate protein conformation, but is poorer than least squares, SVD or neural net analysis.
The protocol described here will cover: setting up CD machines to collect data; procedures for obtaining high quality, reproducible data; and methods to analyze CD spectra to estimate the secondary structures of proteins. For more detailed information, there are several reviews which discuss CD spectroscopy in detail, including the preparations of buffers, protein samples and cuvettes and converting raw data to molar and mean residue elllipticity48'53 and analysis of data to yield secondary structure information3,26,50,54. Three further protocols will cover (1) the determination of the thermodynamics of protein folding from CD data collected as a function of temperature47; (2) determination of the free energy of folding and binding constants from CD data collected as a function of denaturants, osmolytes or ligands55 and (3) determination of the kinetics of folding from CD data collected as a function of time56.
For more information, please visit Smart Waste Sorting Equipment China.