No abstract

The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, "Peptide Prediction with Abundance" (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set. Post-translational modification (PTM)1 of proteins is a key regulatory mechanism in the vast majority of biological processes. Historically, to follow PTMs, site-specific antibodies had to be generated in a time-consuming and laborious process associated with high failure rates. Mass spectrometry (MS) holds enormous promise in PTM analysis as it is currently the only technique that has the ability to both discover, localize, and quantify proteome-wide modifications (1). Recent advances in instrumentation and method optimization makes it possible to detect the complete yeast proteome within one hour (2), an ever increasing proportion of the human proteome (3-6), and more than 10,000 phosphorylation sites in a single MS experiment (7,8). As a result one of the major publicly available databases (www.phosphosite.org (9)) has curated Ͼ200,000 phosphorylation sites.Although the number of proteins and PTMs that can be identified is impressive, many modifications have still not been identified in any MS-based experiment. The identification and quantification of biologically relevant modifications is challenging for three reasons: (1) many proteins of interest are of very low abundance rendering them difficult to detect and quantify; (2) many modifications sites are present at substoichiometric quantities, further reducing their detectability; and (3) as large scale proteomics is based on the detect...

The dead-end filtration characteristics of the dimorphic yeast Kluyveromyces marxianus var. marxianus (formerly fragilis) NRRLy2415 were investigated for a range of mean cell morphologies, ranging from predominantly yeast-like to predominantly filamentous. Semiautomated image analysis was used to measure the mean cell specific surface area, Sv, and the mean ratio of cell length to equivalent cylindrical diameter, Ldm, in each broth. The method of Ju and Ho (Biotechnol. Bioeng. 1988, 32, 95-99) was used to show that for broths with Ldm values between 1.72 and 10.03, the voidage of cell pellets formed by centrifugation increased with increasing Ldm. In the pressure range 30-180 kPa, the specific filter cake resistance, alpha, was found to be related to pressure, DeltaP, through the equation alpha = alpha0(1 + kcDeltaP). The dependence of alpha0/Sv2 on Ldm was found to be qualitatively consistent with the pellet voidage data and the Carman-Kozeny equation. Considerably better agreement with the experimental data was obtained when the Kozeny constant, K, was treated as variable and related to Ldm through the equation K = 4.83 + 7.08 log10 Ldm. The cake compressibility constant, kc, was found to increase with increasing Ldm, a phenomenon consistent with the wide range of voidages that can be displayed by beds of long cylinders.

No abstract

No abstract

The specific cake resistance in dead-end filtration is a complex function of suspension properties and operating conditions. In this study, the specific resistance of resuspended dried bakers yeast suspensions was measured in a series of 150 experiments covering a range of pressures, cell concentrations, pHs, ionic strengths and membrane resistances. The specific resistance was found to increase linearly with pressure and exhibited a complex dependence on pH and ionic strength. The specific resistance data were correlated using an artificial neural network containing a single hidden layer with nine neurons employing the sigmoidal activation function. The network was trained with 104 training points, 13 validation points and 33 test points. Excellent agreement was obtained between the neural network and the test data with average errors of less than 10%. In addition, a network was trained for prediction of the filtrate flux directly from the system inputs and this approach is easily extended to crossflow filtration by adding inputs such as the crossflow velocity and channel height. An attempt was made to interpret the network weights for both the specific resistance and flux networks. The effective contribution of each input to the system output was computed in each case and showed trends that were as expected. Although network weights, and consequently the computed effect of each parameter, is different each time a network is changed (depending on the initial weights used in the training process), the variation was low enough for information contained in the network to be interpreted in a meaningful way.Keywords: Dead-end filtration; Specific cake resistance; Artificial neural network; Bakers yeast

This work addresses the optimal control strategy of diafiltration buffer utilisation in discontinuous membrane processes that are designed to fulfil the twin aims of concentration and fractionation. The problem of optimal process operation is formulated using a general membrane response model that encounters concentration-dependent flux and rejections. We consider two problems, operation time minimisation and diluant consumption minimisation, and we apply theory of optimal control and derive necessary conditions of optimality. Through selected case studies from literature, we demonstrate how to apply the proposed methodology to determine optimal time-dependent wash-water feeding policy. The analytical results are confirmed by numerical computations, using numerical methods of dynamic optimisation. The presented methodology allows decision makers to analyse suboptimality of conventional diafiltration strategies in terms of processing time and diluant consumption. Results show that depending on the complexity of the membrane response model, it may be attractive to implement optimal trajectory.

No abstract