Why is protein cyclisation desirable?

Why is protein cyclisation desirable?

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There are a number of methods to "cyclize" an existing peptide:

  • Disulphide bond as described in Disulfide Bond Mimetics: Strategies and Challenges by Gori et al.
  • "Linchpin" based (linker chemistry) as described in Synthetic Cross-linking of Peptides: Molecular Linchpins for Peptide Cyclization by Derda et al.

Why would the "cyclization" of a protein be a desirable goal?

As described in the abstract of Synthetic Cross-linking of Peptides: Molecular Linchpins for Peptide Cyclization by Derda et al., protein cyclization improves:

resistance to proteolytic degradation and conformational stability. The latter property leads to an increase in binding potency and increased bioavailability due to increased permeation through biological membranes.

In simpler terms, a protein that is cyclized is less likely to:

  1. Fold into an unhelpful shape.
  2. Be broken up by an enzyme.

Thus, they are more likely to:

  1. Act as better binders for a given target
  2. Make it across a cell membrane

Purification of proteins fused to glutathione S-transferase

This chapter describes the use of glutathione S-transferase (GST) gene fusion proteins as a method for inducible, high-level protein expression and purification from bacterial cell lysates. The protein is expressed in a pGEX vector, with the GST moiety located at the N terminus followed by the target protein. The use of GST as a fusion tag is desirable because it can act as a chaperone to facilitate protein folding, and frequently the fusion protein can be expressed as a soluble protein rather than in inclusion bodies. Additionally, the GST fusion protein can be affinity purified facilely without denaturation or use of mild detergents. The fusion protein is captured by immobilized glutathione and impurities are washed away. The fusion protein then is eluted under mild, non-denaturing conditions using reduced glutathione. If desired, the removal of the GST affinity tag is accomplished by using a site-specific protease recognition sequence located between the GST moiety and the target protein. Purified proteins have been used successfully in immunological studies, structure determinations, vaccine production, protein-protein, and protein-DNA interaction studies and other biochemical analyses.


Flow diagram illustrating the key…

Flow diagram illustrating the key decision-making steps for designing and executing a successful…

GST fusion proteins may be…

GST fusion proteins may be constructed using one of 10 different pGEX vectors.…

Desirable Specifications for Total Error, Imprecision, and Bias, derived from intra- and inter-individual biologic variation

This most recent and extensive listing of biologic goals has been provided by Ricos C, Alvarez V, Cava F, Garcia-Lario JV, Hernandez A, Jimenez CV, Minchinela J, Perich C, Simon M. "Current databases on biologic variation: pros, cons and progress." Scand J Clin Lab Invest 199959:491-500. This database was last updated in 2014.

PLEASE NOTE: The EFLM now hosts the latest database on biological variation!

Annex I, Part I: Within-subject and between-subject CV values of analytes and Desirable Analytical Quality Specifications for imprecision, bias and total error

Note on abbreviations:
CVI = within-subject biologic variation
CVG = between-subject biologic variation
I = desirable specification for imprecision
B = desirable specification for inaccuracy
TE = desirable specification for allowable total error

2. General approach  

Macromolecular crystallization, which includes the crystallization of proteins, nucleic acids and larger macromolecular assemblies such as viruses and ribosomes, is based on a rather diverse set of principles, experiences and ideas. There is no comprehensive theory, or even a very good base of fundamental data, to guide our efforts, although they are being accumulated at this time. As a consequence, macromolecular crystal growth is largely empirical in nature, and demands patience, perseverance and intuition.

Complicating the entire process, in addition to our limited understanding of the phenomena involved, is the astonishing complexity and range of the macromolecules before us. Even in the case of rather small proteins, such as cytochrome c or myoglobin for example, there are roughly a thousand atoms with thousands of bonds and thousands of degrees of freedom. For viruses or enzyme complexes having molecular weights measured in the millions of daltons, the possibilities for conformation, interaction and mobility are almost uncountable.

Only now are we beginning to develop rational approaches to macromolecular crystallization based on an understanding of the fundamental properties of the systems. We are only now using, in a serious and systematic manner, the classical methods of physical chemistry to determine the characteristics of those mechanisms responsible for the self-organization of large biological molecules into crystal lattices. As an alternative to the precise and reasoned strategies that we commonly apply to scientific problems, we continue to rely, for the time being at least, on what is fundamentally a trial-and-error approach. Macromolecular crystallization is generally a matter of searching, as systematically as possible, the ranges of the individual parameters that influence crystal formation, finding a set, or multiple sets of factors that yield some kind of crystals, and then optimizing the individual variables to obtain the best possible crystals. This is usually achieved by carrying out an extensive series, or establishing a vast matrix, of crystallization trials, evaluating the results and using the information that is obtained to improve conditions in successive rounds of trials. Because the number of variables is so large, and because the ranges are so broad, experience and insight in designing and evaluating the individual and collective trials becomes an important consideration.

Bioengineering of FGFs and New Drug Developments

Xiaokun Li , . Xiaokun Li , in Fibroblast Growth Factors , 2018

3.4 Isolation of Recombinant rFGF23 From the Cleavage Mixture and Further Characterization of rFGF23 by Western Blot and HPLC

When the target protein was fused directly to the C-terminus of SUMO, cleavage by SUMO protease 1 resulted in the release of the target protein with the desired N-terminal amino acid sequence (Malakhov et al., 2004 Marblestone et al., 2006). In our studies, the prepurified fusion protein was diluted and cleaved by SUMO protease. The lysate was purified by Ni-NTA resin. SUMO, SUMO protease and SUMO-FGF23 fusion protein containing His tags that were affiliated by Ni-NTA resin, but only rFGF23 flows through the column with digestion buffer. The results showed that rFGF23 was highly purified by SDS-PAGE ( Fig. 5A ) and could react with the human FGF23 polyclonal antibody by western blot ( Fig. 5B ). HPLC analysis of the target protein showed a major peak of rFGF23, with a retention time of 14.770 min the purity exceeded 90% ( Fig. 5C ).

Figure 5 . SDS-PAGE analysis of purified rFGF23 and its characterization by western blot and the purity by HPLC.

(A) Lane 1: Protein molecular weight marker. Lane 2: purified rFGF23 (B) Western blot analysis of the rFGF23 Lane 1: FGF23 (GenWay Comp) as control Lane 2: purified rFGF23 (C) HPLC analysis of the rFGF23.


Efficiently engineering proteins to enhance their thermostability and other features hinges on the correct understanding and prediction of the effects of protein backbone topology and dynamics. Here, we fused a Catcher module to the C-terminus of luciferase and a Tag module to the N-terminus, so that the spontaneous reaction between the Catcher and Tag covalently cyclized the luciferase. The thermal stability of cyclized luciferase improved, with T50, t1/2, and Tm values higher by 8.9 °C, 2.1-fold and 7.3 °C respectively compared with the linear enzyme. Furthermore, MD simulations suggested that the cyclizations resulted in a tight docking between Catcher/ Tag and luciferase. A series of interactions on the interface between Catcher/Tag and the C-domain of luciferase were identified. Compared with the uncyclized enzyme, these interactions appear to impose remarkable changes to the backbone dynamics of the enzyme. Our data showed that the cyclization increased the gyration radii, and decreased both RMSD values and Cα-fluctuations of the enzyme, which was consistent with experimental observations and indicates that the cyclized luciferase adopted a more stable conformation enssemble. Taken together, this study provides a theoretical understanding of how Catcher/Tag cyclization can enhance the thermal resilience of enzymes, and might be useful in guiding protein design.

Running the Experiment, Resolving Peaks

The following represents an example of a low pressure liquid chromatography (ion exchange resin) experiment.

  • DE-52 (diethylaminoethyl cellulose anion exchange)
  • Size = 1.0 x 12.7 cm
  • Volume = = 40 mL

The chromatogram for this experiment looked like this:

Figure 4.1.13: Chromatogram

The following events took place during this chromatography run:

1. Note the tick marks on the chromatogram.

  • The "event" marker from the fraction collector notifies the chart recorder when a tube change takes place.
  • The experiment begins with the tick next to the '0' on the x-axis ("tick 0") this indicates the startof fraction (tube) number 1.
  • The next tick mark ("tick 1") indicates the end of fraction number 1, and the start of fraction number 2.
  • Thus, fractions span the gap betweenthe tick marks.

2. The sample loading is begun at tick 0.

3. Sometime during fraction 5 we begin to notice the absorbence of the column effluent increasing

  • It has taken about (5 fractions x 10mls per fraction) or 50 mls from the start of loading until the detector notes any absorbance.
  • This compares well with the fact that the column volume is about 40 mls and there is some volume associated with the tubing going in and out of the column.
  • Thus, this 'delay' from sample load to sample detection is the dead volume of the system

4. Obviously, some material is not binding to the resin during the loading step. This is the flow-through . Is this some component of the sample which does not have affinity for the resin, or, does it represent that we have exceeded the capacity of the resin?

  • If we have exceeded the capacity of the resin, then the flow-through will have an A280 similar to the sample being loaded
  • Also, prior to exceeding the capacity, the flow-through will have some characteristic A280 which will then transition to another A280 (that of the loaded sample), resulting in a double-plateau chromatogram.
  • In the above experiment the flow-through plateaus around A280=0.5 or about 25% of the absorbance of the load. This would seem to indicate that a component, or component(s), representing one quarter of our sample, does not have affinity for the resin in the column

5. Around fraction 9 we begin to wash the column

  • This makes sense because 9 fractions x 10mls per fraction = 90 mls have loaded and this is equivalent to our original sample volume (i.e. all the sample has loaded)
  • The column is typically washed using the same buffer conditions in the protein sample

6. Around fraction 14 we note the A280 begins to decrease

  • This makes sense given that we determined the dead volume of the system to be approximately 50 mls or 5 fractions. Thus, a wash which was begun at fraction 9 is observed to decrease the absorbance around fraction 14
  • We continue washing the column until the A280 approaches 0 ( baseline ). In other words, all of the non-binding material in the sample has been washed away

7. After the A280 comes back down to baseline we begin our elution protocol. In this particular experiment we will use a linear gradient of increasing salt (NaCl) concentration (in wash buffer) to compete off the material bound to the ion exchange resin.

8. Our elution has produced two peaks: a small peak centered around fraction 42 and a larger peak centered around fraction 50

  • We will have to assay each peak (and the flow through) to find out where our protein of interest has gone
  • The two elution peaks are fairly well resolved. We could combine fractions 40-44 and call that "peak 1", and combine fractions 46-55 and call that "peak 2".

9. Is there any material left on the column? The integrated areas (i.e. summing the A280's of each fraction in a pool) of the flow-through, peak 1 and peak 2 are as follows

  • Flow through: 4
  • Peak 1: 2
  • Peak 2: 10
  • This gives a total integrated area of 16. Each fraction is 10 mls, so this gives a total A280 = 16 x 10 = 160 which is quite close to the total A280 of our loaded sample.
  • In other words, it looks like our chromatogram is accounting for all the components in our original sample.

10. If our protein of interest was actually peak 1 (and if our yield was 100%), then this column has provided an eight fold purification ( 2 x 10 / 162).

Resolving peaks

  • Contaminating peaks will not necessarily be completely separated from the peak which contains our protein of interest
  • In the following picture there are two components being resolved, and they are present in equimolar amounts (thus, the starting purity is 50%). The yield and purity are listed for the situation where we were to pool each peak by splitting at the midpoint between them (in this particular example the yield and purity are identical in each case)

Figure 4.1.14:Contaminating peaks

  • This gives you some idea of the amount of cross-contamination in each peak as a function of their separation from one another.
  • Software to fit gaussians to a chromatogram can provide this type of information

Pooling for purity verses yield

Usually, you will probably be pooling fractions in such a way as to maximize the recovery of your protein of interest. However, you always have the option of pooling to increase purity, and if you have lots of protein to work with this may allow you to achieve the desired purity with fewer steps. Here's an example of how it's done:

Figure 4.1.15:Yield vs. purity

  • These are all the same chromatogram, however, we can pool them differently to get better purity (at the expense of yield
  • The blue peak is the peak of interest and it is not resolved from a contaminating peak (in red).
  • The vertical line represents the left-most fraction we use to pool the peak (we pool all fractions to the right of the vertical line to get our protein of interest)
  • In the last panel we see that we can achieve about 98.8% purity if we are willing to part with half our protein!

Monitoring the purification

How do you know when you are finished purifying a protein?

There are several criteria. One criteria is that we cannot improve upon the specific activity of our sample. This value refers to the functional activity of our sample in relationship to the total protein concentration of the sample.

  • In the initial stages of purification this value will be low (not much activity in relationship to the total amount of protein).
  • This value will increase after each purification step as we remove other proteins from the sample.
  • At some point the specific activity will plateau, and by definition, if it is pure we cannot increase the specific activity .
  • There may be a published value for the specific activity which we can compare ours to.

Also, each step of the purification should be monitored by gel electrophoresis.

  • In the initial stages of purification we will probably see a variety of bands, of various molecular weights, on our gel.
  • After the different purification steps, we should see the disappearance of certain bands concomitant with the increasing concentration of a certain band (or bands) representing our protein.
  • If we have successfully purified our protein (and if it is a single polypeptide) we should arrive at a constant specific activity and a single band on a gel.
  • Analytical methods like HPLC or densitometer scanning of a stained gel can give us a quantitative idea of the purity of our final sample.

The following chart represents the typical data one would monitor during a purification:

Chemical Reactions That Use Amino Acids

Carbohydrates & Respiration

Both methods of using amino acids for fuel involve a series of chemical reactions known as the Krebs cycle, a series of chemical reactions your body uses to generate energy. When blood glucose levels get very low, the body can use amino acids to make more glucose. This is necessary because some cells, such as neurons and red blood cells, can use only glucose for fuel. The glucogenic amino acids can be converted into large molecules that contain four or five carbon atoms that, when used as a substrate for the Krebs cycle, can be converted into glucose. Ketogenic amino acids form smaller molecules that, though unable to be turned into glucose, can still be used in the Krebs cycle for energy.

  • Both methods of using amino acids for fuel involve a series of chemical reactions known as the Krebs cycle, a series of chemical reactions your body uses to generate energy.
  • Ketogenic amino acids form smaller molecules that, though unable to be turned into glucose, can still be used in the Krebs cycle for energy.

Targeted Editing of Zebrafish Genes to Understand Gene Function and Human Disease Pathology

Alberto Rissone , . Shawn M. Burgess , in The Zebrafish in Biomedical Research , 2020


Zinc Finger Nucleases are chimeric proteins, including DNA-binding domains typical of zinc finger-containing transcription factors and a bacterial FokI endonuclease domain ( Urnov, Rebar, Holmes, Zhang, & Gregory, 2010 ) (see Fig. 49.1A ). Researchers showed that each individual “zinc finger” was able to recognize and bind to a specific sequence of three nucleotides (nt) in the major groove of DNA, and they developed a library of specific zinc-fingers. Combining several fingers in tandem allowed the targeting of unique 9–12 base pair sequences within the genome. Two different ZFN “arms” are created to recognize a 10–18 base pair target with a spacer of 5–7 nt between each half, which is where the FokI-induced double-strand break will occur. Researchers increased the specificity of the ZFNs system by the use of recombinant versions of FokI enzymes able to cut the DNA only when in dimeric form. The two different halves needed to be brought together (one on each side of the spacer) in order to cut at the locus.

ZFNs first became popular in cell culture systems, and eventually, in 2008, they became the first genome-editing tool to be used in zebrafish ( Doyon et al., 2008 Meng et al., 2008 ). However, ZFNs adoption in zebrafish was limited by low efficiency and flexibility, the complex assembly of their arms, significant off-target activity, and lastly, by the advent of TALENs (2011) and CRISPR/Cas9 (2013) that represented more efficient, faster systems with higher mutagenic throughput.

After several years of ZFN use, TALENs emerged as a simpler-to-use alternative ( Joung & Sander, 2013 ). TALEN technology is conceptually identical to ZFNs: the coupling of FokI nucleases to DNA binding motifs working as dimers to increase specificity (see Fig. 49.1B ). In TALENs, the DNA binding motif is from the bacterial TALE repeats, which consist of 34 amino acids with the 12th and 13th residues consisting of a variable region (RVD) containing a simple code for binding to specific nucleotides. In contrast to ZFN domains, which bind to specific triplets of bases, each TALE repeat recognizes a single nucleotide. By changing only two amino acids in each RVD, researchers can generate a combination of motifs specific for any DNA sequence. While ZFN cutting induces insertion and deletion with the same frequency, TALENs rarely introduce insertions ( Kim et al., 2013 Sood et al., 2013 Varshney, Pei, et al., 2015 ). Compared to ZFNs, TALEN technology represented a huge step forward in flexibility, efficiency, and design with similar, or potentially lower, off-target activity. However, compared to the CRISPR/Cas9 system, TALENs still present a reduced mutagenic throughput because of a time-consuming cloning process.

Biologically Inspired and Biomolecular Materials

R.A. Hortensius , B.A.C. Harley , in Comprehensive Biomaterials II , 2017 Crosslinking

Crosslinking improves the mechanical competence (eg, Young’s modulus, yield stress) and decreases the degradation rate of CG scaffolds independent of the chemical and microstructural characteristics ( Harley et al., 2004, 2007b Yannas and Tobolsky, 1967 ). The three most common crosslinking techniques are the physically based dehydrothermal (DHT) and ultraviolet (UV) processes and the chemically based carbodiimide (EDAC) process. DHT and EDAC in particular have been used extensively for in vitro and in vivo applications ( Yannas et al., 1989 Harley et al., 2007b ). DHT processing involves heating of the CG material under vacuum for a specified amount of time (typically 105–120°C, <50 mTorr, 24–48 h) in order to remove residual moisture and introduce covalent crosslinks within the CG struts. Increasing the DHT crosslinking temperature and duration has been shown to increase compressive modulus by up to 2-fold and tensile modulus by up to 3.8-fold ( Harley et al., 2007b Haugh et al., 2009 ). While increased crosslink density was correlated with increasing compressive modulus, higher denaturation levels led to higher tensile modulus, but also led to reduced ultimate stress and strain to failure ( Haugh et al., 2009 ). EDAC processing utilizes carbodiimide chemistry to translate carboxyl (COOH) groups into unstable amine-reactive esters. This intermediate can be stabilized by N-hydroxysulfosuccinimide (NHS), leading to the formation of stable amide crosslinks ( Olde Damink et al., 1996 ). Increasing the ratio of EDAC and NHS to COOH increases the degree of crosslinking ( Olde Damink et al., 1996 Harley et al., 2007b ). Both DHT and EDAC methods maintain the integrity of the open pore structure of the material by introducing crosslinks within the CG struts only ( Harley et al., 2007b ).

Watch the video: Conformational stability: Protein folding and denaturation. MCAT. Khan Academy (February 2023).