Isoelectric Point Of Polypeptide

[Rozella Dibley]

Ourpeptide calculator is a convenient tool for scientists as a molecular weight peptide calculator, which can be used as an amino acid calculator as well. Additionally, the tool includes a hydrophobicity calculator, a net charge calculator at different pH, isoelectric point calculator and the hydrophilicity ratio.

To calculate the charge on a peptide you must add up the charges from all the positively charged components like the N-terminal amino group, arginine, lysine, and histidine residues, and then subtract the charges from all the negatively charged components like the C-terminal carboxyl group, aspartic acid, glutamic acid, cysteine, and tyrosine residues. The specific charge values depend on the pH and use dissociation constant (pKa) values for each ionizable group.

The peptide net charge calculator determines the charge of a peptide sequence at a given pH. It utilizes the Henderson-Hasselbalch equation and pKa values of the ionizable groups. The net charge (Z) sums the contributions from positive charges of the N-terminus, arginine, lysine, and histidine residues, and negative charges of the C-terminus, aspartic acid, glutamic acid, cysteine, and tyrosine residues.

The calculator is one of the most useful tool for the peptide chemist to calculate peptide molecular weight and more. With the calculator and its easy use, peptide chemists can have access to a molecular weight peptide calculator and amino acid calculator, the isoelectric point, a peptide net charge calculator at neutral pH, the average hydrophilicity, the percentage of hydrophilic amino acids, the plot of the net charge vs. pH and a hydrophobicity calculator displayed in a plot.

For the molecular weight amino acid calculator, you can enter the 1- or 3- letter code of the desired amino acid, and the tool will provide the value the same way it would calculate peptide molecular weight.

The calculation of the average hydrophilicity of a peptide is based on the data from Hopp&Woods. The hydrophilicity value for each amino acid in the peptide sequence is indicated in a bar graph. The ratio of hydrophilic residues to total number of amino acids is reported in %.

To use our peptide calculator mass properties, enter the sequence or the amino acid using 1-letter or 3-letter amino acid codes and our calculator will provide the following physico-chemical properties of the sequence:

The isoelectric point (pI) is the pH at which a molecule or a surface carries no net electrical charge. This means that the molecule or surface is electrically neutral, as the positive and negative charges present within it balance each other out. The concept of the isoelectric point is particularly important in biochemistry and chemistry, especially in the study of proteins, amino acids, and other biomolecules.

The isoelectric point (pI) stands as a critical concept in biochemistry, influencing various aspects of protein behavior, solubility, and pharmaceutical formulation. Its significance extends to protein purification techniques, such as chromatography and electrophoresis, where the manipulation of pH aids in separating proteins based on their charge differences. Moreover, the pI informs drug development strategies, guiding the formulation and optimization of pharmaceutical compounds for enhanced efficacy and stability. By comprehending the isoelectric point, researchers can navigate the complex interplay between pH, charge, and molecular structure, unlocking a multitude of applications across biotechnology, medicine, and chemistry.

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Motivation: Increased efficiency in initial crystallization screening reduces cost and material requirements in structural genomics. Because pH is one of the few consistently reported parameters in the Protein Data Bank (PDB), the isoelectric point (pI) of a protein has been explored as a useful indirect predictor for the optimal choice of range and distribution of the pH sampling in crystallization trials.

Results: We have analyzed 9596 unique protein crystal forms from the August 2003 PDB and have found a significant relationship between the calculated pI of successfully crystallized proteins and the difference between pI and reported pH at which they were crystallized. These preferences provide strong prior information for the design of crystallization screening experiments with significantly increased efficiency and corresponding reduction in material requirements, leading to potential cost savings of millions of US$ for structural genomics projects involving high-throughput crystallographic structure determination.

The isoelectric point (pI, pH(I), IEP), is the pH at which a molecule carries no net electrical charge or is electrically neutral in the statistical mean. The standard nomenclature to represent the isoelectric point is pH(I).[1] However, pI is also used.[2] For brevity, this article uses pI. The net charge on the molecule is affected by pH of its surrounding environment and can become more positively or negatively charged due to the gain or loss, respectively, of protons (H+).

The pI value can affect the solubility of a molecule at a given pH. Such molecules have minimum solubility in water or salt solutions at the pH that corresponds to their pI and often precipitate out of solution. Biological amphoteric molecules such as proteins contain both acidic and basic functional groups. Amino acids that make up proteins may be positive, negative, neutral, or polar in nature, and together give a protein its overall charge. At a pH below their pI, proteins carry a net positive charge; above their pI they carry a net negative charge. Proteins can, thus, be separated by net charge in a polyacrylamide gel using either preparative native PAGE, which uses a constant pH to separate proteins, or isoelectric focusing, which uses a pH gradient to separate proteins. Isoelectric focusing is also the first step in 2-D gel polyacrylamide gel electrophoresis.

The pH of an electrophoretic gel is determined by the buffer used for that gel. If the pH of the buffer is above the pI of the protein being run, the protein will migrate to the positive pole (negative charge is attracted to a positive pole). If the pH of the buffer is below the pI of the protein being run, the protein will migrate to the negative pole of the gel (positive charge is attracted to the negative pole). If the protein is run with a buffer pH that is equal to the pI, it will not migrate at all. This is also true for individual amino acids.

In the two examples (on the right) the isoelectric point is shown by the green vertical line. In glycine the pK values are separated by nearly 7 units. Thus in the gas phase, the concentration of the neutral species, glycine (GlyH), is effectively 100% of the analytical glycine concentration.[5] Glycine may exist as a zwitterion at the isoelectric point, but the equilibrium constant for the isomerization reaction in solution

Moreover, experimentally measured isoelectric point of proteins were aggregated into the databases.[12][13] Recently, a database of isoelectric points for all proteins predicted using most of the available methods had been also developed.[14]

In practice, a protein with an excess of basic aminoacids (arginine, lysine and/or histidine) will bear an isoelectric point roughly greater than 7 (basic), while a protein with an excess of acidic aminoacids (aspartic acid and/or glutamic acid) will often have an isoelectric point lower than 7 (acidic).The electrophoretic linear (horizontal) separation of proteins by Ip along a pH gradient in a polyacrylamide gel (also known as isoelectric focusing), followed by a standard molecular weight linear (vertical) separation in a second polyacrylamide gel (SDS-PAGE), constitutes the so called two-dimensional gel electrophoresis or PAGE 2D. This technique allows a thorough separation of proteins as distinct "spots", with proteins of high molecular weight and low Ip migrating to the upper-left part of the bidimensional gel, while proteins with low molecular weight and high Ip locate to the bottom-right region of the same gel.

Note: The following list gives the isoelectric point at 25 C for selected materials in water. The exact value can vary widely, depending on material factors such as purity and phase as well as physical parameters such as temperature. Moreover, the precise measurement of isoelectric points can be difficult, thus many sources often cite differing values for isoelectric points of these materials.

Motivation: In any macromolecular polyprotic system-for example protein, DNA or RNA-the isoelectric point-commonly referred to as the pI-can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge-and thus the electrophoretic mobility-of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods.

Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction.

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