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.

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.

The influence of formulation and manufacturing process parameters on the characteristics of lyophilized orally disintegrating tablets

Gelatin is a principal excipient used as a binder in the formulation of lyophilized orally disintegrating tablets. The current study focuses on exploiting the physicochemical properties of gelatin by varying formulation parameters to determine their influence on orally disintegrating tablet (ODT) characteristics. Process parameters, namely pH and ionic strength of the formulations, and ball milling were investigated to observe their effects on excipient characteristics and tablet formation. The properties and characteristics of the formulations and tablets which were investigated included: glass transition temperature, wettability, porosity, mechanical properties, disintegration time, morphology of the internal structure of the freeze-dried tablets, and drug dissolution. The results from the pH study revealed that adjusting the pH of the formulation away from the isoelectric point of gelatin, resulted in an improvement in tablet disintegration time possibly due to increase in gelatin swelling resulting in greater tablet porosity. The results from the ionic strength study revealed that the inclusion of sodium chloride influenced tablet porosity, tablet morphology and the glass transition temperature of the formulations. Data from the milling study showed that milling the excipients influenced formulation characteristics, namely wettability and powder porosity. The study concludes that alterations of simple parameters such as pH and salt concentration have a significant influence on formulation of ODT. © 2011 by the authors; licensee MDPI, Basel, Switzerland.

Through-thickness plasma modification of biodegradable and nonbiodegradable porous polymer constructs

Pure poly(lactide-co-glycolide) and polystyrene surfaces are not very suitable to support cell adhesion/ spreading owing to their hydrophobic nature and low surface energy. The interior surfaces of large porous 3D scaffolds were modified and activated using radio-frequency, low-pressure air plasma. An increase in the wettability of the surface was observed after exposure to air plasma, as indicated by the decrease in the contact angles of the wet porous system. The surface composition of the plasma-treated polymers was studied using X-ray photoelectron spectroscopy. pH-dependent zeta-potential measurements confirm the presence of an increased number of functional groups. However, the plasma-treated surfaces have a less acidic character than the original polymer surfaces as seen by a shift in their isoelectric point. Zeta-potential, as well as contact angle measurements, on 3D scaffolds confirm that plasma treatment is a useful tool to modify the surface properties throughout the interior of large scaffolds. © 2008 Wiley Periodicals, Inc.