Isoelectric point, electric charge, and nomenclature of the acid-base residues of proteins

Adsorption Behavior of the L-Theanine onto Cation Exchange Resin ZGSPC106Na and D001SD

Adsorption is an important technology for the separation of different tea components. The adsorption behavior of L-theanine onto adsorbents was comprehensively studied in this paper. Among tested adsorbents, cation exchange resin ZGSPC106Na and D001SD were suitable for separating L-theanine, PVPP and PA-6 for catechins and macroporous resin HPD-400 for caffeine. Adsorption of L-theanine onto the cation resins was significantly influenced by the acidity, contact time and temperature. The adsorption behavior could be described by the pseudo-second-order rate equation and fitted to Langmuir and Freundlich models. ZGSPC106Na exhibited higher adsorption capacity, while D001SD showed higher adsorption selectivity. These might be attributed to the distinctive structure of the two resins and different ionization of the adsorbates. A method for simultaneous preparation of decaffeinated polyphenols, caffeine-enriched extract and decaffeinated L-theanine was established through successive separation on the columns fulfilled with PA-6, HPD-400 and D001SD, respectively.

Protein pI and Intracellular Localization

The protein isoelectric point (pI) can be calculated from an amino acid sequence using computational analysis in a good agreement with experimental data. Availability of whole-genome sequences empowers comparative studies of proteome-wide pI distributions. It was found that the whole-proteome distributions of protein pI values are multimodal in different species. It was further hypothesized that the observed multimodality is associated with subcellular localization-specific differences in local pI distributions. Here, we overview the multimodality of proteome-wide pI distributions in different organisms focusing on the relationships between protein pI and subcellular localization. We also discuss the probable factors responsible for variation of the intracellular localization-specific pI profiles.

Hydrophobicity, amphilicity, and flexibility: Relation between molecular protein properties and the macroscopic effects of surface activity

Their surface activity enables proteins to form and stabilize foam, which can be used for in situ product separation or foam fractionation. Thus, it would be highly desirable to predict the surface activity of proteins based on their molecular properties like hydrophobicity, amphilicity, or structure on primary, secondary, and tertiary level. Ionic strength and pH were adjusted to gain maximum surface activity. The surface activity decreased in the order α lactalbumin > β‑lactoglobulin > trypsinogen > papain. For the theoretical analysis, the database was extended by including 2 hydrophobins into the investigation, since they are known to exhibit an outstanding surface activity. No relation to the macroscopic behavior was found considering the hydrophobicity. I.e., the non-hydrophobins did not differ significantly from each other, and from the hydrophobins, one was significantly hydrophobic, and the other was significantly hydrophilic. Also, no relations were found considering the amphilicity of the secondary structure elements. However, taking into account the tertiary protein structure, it was found that for most of the proteins investigated, the presence of non-buried amphiphilic secondary structure elements in combination with a certain amount of flexibility correlates with the surface activity.

Design of a software for calculating isoelectric point of a polypeptide according to their net charge using the graphical programming language LabVIEW

A software to calculate the net charge and to predict the isoelectric point (pI) of a polypeptide is developed in this work using the graphical programming language LabVIEW. Through this instrument the net charges of the ionizable residues of the polypeptide chains of the proteins are calculated at different pH values, tabulated, pI is predicted and an Excel (-xls) type file is generated. In this work, the experimental values of the pIs (pI) of different proteins are compared with the values of the pIs (pI) calculated graphically, achieving a correlation coefficient (R) of 0.934746 which represents a good reliability for a p < 0.01. In this way the generated program can constitute an instrument applicable in the laboratory, facilitating the calculation to graduate students and junior researchers. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(1):39-46, 2018.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

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.

Contact: yperez@ebi.ac.uk

Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR.

Supplementary information:Supplementary data are available at Bioinformatics online.

PIP-DB: the Protein Isoelectric Point database

A protein's isoelectric point or pI corresponds to the solution pH at which its net surface charge is zero. Since the early days of solution biochemistry, the pI has been recorded and reported, and thus literature reports of pI abound. The Protein Isoelectric Point database (PIP-DB) has collected and collated these data to provide an increasingly comprehensive database for comparison and benchmarking purposes. A web application has been developed to warehouse this database and provide public access to this unique resource. PIP-DB is a web-enabled SQL database with an HTML GUI front-end. PIP-DB is fully searchable across a range of properties.

AVAILABILITY AND IMPLEMENTATION

The PIP-DB database and documentation are available at http://www.pip-db.org.