SMET: Systematic multiple enzyme targeting – a method to rationally design optimal strains for target chemical overproduction

Identifying multiple enzyme targets for metabolic engineering is very critical for redirecting cellular metabolism to achieve desirable phenotypes, e.g., overproduction of a target chemical. The challenge is to determine which enzymes and how much of these enzymes should be manipulated by adding, deleting, under-, and/or over-expressing associated genes. In this study, we report the development of a systematic multiple enzyme targeting method (SMET), to rationally design optimal strains for target chemical overproduction. The SMET method combines both elementary mode analysis and ensemble metabolic modeling to derive SMET metrics including l-values and c-values that can identify rate-limiting reaction steps and suggest which enzymes and how much of these enzymes to manipulate to enhance product yields, titers, and productivities. We illustrated, tested, and validated the SMET method by analyzing two networks, a simple network for concept demonstration and an Escherichia coli metabolic network for aromatic amino acid overproduction. The SMET method could systematically predict simultaneous multiple enzyme targets and their optimized expression levels, consistent with experimental data from the literature, without performing an iterative sequence of single-enzyme perturbation. The SMET method was much more efficient and effective than single-enzyme perturbation in terms of computation time and finding improved solutions.     

Lysine acetylation sites prediction using an ensemble of support vector machine classifiers

Lysine acetylation is an essentially reversible and high regulated post-translational modification which regulates diverse protein properties. Experimental identification of acetylation sites is laborious and expensive. Hence, there is significant interest in the development of computational methods for reliable prediction of acetylation sites from amino acid sequences. In this paper we use an ensemble of support vector machine classifiers to perform this work. The experimentally determined acetylation lysine sites are extracted from Swiss-Prot database and scientific literatures. Experiment results show that an ensemble of support vector machine classifiers outperforms single support vector machine classifier and other computational methods such as PAIL and LysAcet on the problem of predicting acetylation lysine sites. The resulting method has been implemented in EnsemblePail, a web server for lysine acetylation sites prediction available at http://www.aporc.org/EnsemblePail/.     

Effective stomatal and boundary-layer resistances of heterogeneous surfaces

In nature surfaces are rarely uniform, so terms such as ‘surface’, ‘stomatal’ or ‘canopy’ resistance usually indicate some kind of average over a population of sub-areas, each with its own separate resistance. Questions then arise as to how gross measurements of these resistances should be interpreted in terms of the components, or how components should be aggregated into representative single values. Aggregation schemes have been published by Raupach (1991, Vegetatio 91, 105–120) and Lhomme (1992, Agriculture and Forest Meteorology 61, 11–21), but these are different for reasons that were not explained. This paper develops the idea that averaging schemes should be designed to serve particular purposes, and that they can be varied to suit these purposes. It is shown that the ‘effective’ resistances defined by Raupach and Lhomme preserve different quantities. A further averaging scheme is developed which preserves both correct transpiration rate and CO₂ flux when used in the Penman-Monteith equation and an equation describing assimilation. All of these schemes are fairly complex, so the work provides a warning against naive use of effective variables.     

A Structural Analysis of the Regularly Arranged Porin on the Outer Membrane of Campylobacter jejuni Based on Correlation Averaging

A negatively stained electron micrograph of regularly arranged porin proteins of Campylobacter jejuni on the isolated outer membrane of bacteria was analyzed in detail by the correlation averaging method using a computer-assisted program. The results showed that the porin of C. jejuni had a trimeric structure separated by about 10.4±0.15 nm. In addition, the pores in the trimers were also separated by about 4.3 ±0.1 nm.