Recombinant Passenger Proteins Can Be Conveniently Purified by One-Step Affinity Chromatography.

Fusion tag is one of the best available tools to date for enhancement of the solubility or improvement of the expression level of recombinant proteins in Escherichia coli. Typically, two consecutive affinity purification steps are often necessitated for the purification of passenger proteins. As a fusion tag, acyl carrier protein (ACP) could greatly increase the soluble expression level of Glucokinase (GlcK), α-Amylase (Amy) and GFP. When fusion protein ACP-G2-GlcK-Histag and ACP-G2-Amy-Histag, in which a protease TEV recognition site was inserted between the fusion tag and passenger protein, were coexpressed with protease TEV respectively in E. coli, the efficient intracellular processing of fusion proteins was achieved. The resulting passenger protein GlcK-Histag and Amy-Histag accumulated predominantly in a soluble form, and could be conveniently purified by one-step Ni-chelating chromatography. However, the fusion protein ACP-GFP-Histag was processed incompletely by the protease TEV coexpressed in vivo, and a large portion of the resulting target protein GFP-Histag aggregated in insoluble form, indicating that the intracellular processing may affect the solubility of cleaved passenger protein. In this context, the soluble fusion protein ACP-GFP-Histag, contained in the supernatant of E. coli cell lysate, was directly subjected to cleavage in vitro by mixing it with the clarified cell lysate of E. coli overexpressing protease TEV. Consequently, the resulting target protein GFP-Histag could accumulate predominantly in a soluble form, and be purified conveniently by one-step Ni-chelating chromatography. The approaches presented here greatly simplify the purification process of passenger proteins, and eliminate the use of large amounts of pure site-specific proteases.

Integrated application of uniform design and least-squares support vector machines to transfection optimization.

BACKGROUND: Transfection in mammalian cells based on liposome presents great challenge for biological professionals. To protect themselves from exogenous insults, mammalian cells tend to manifest poor transfection efficiency. In order to gain high efficiency, we have to optimize several conditions of transfection, such as amount of liposome, amount of plasmid, and cell density at transfection. However, this process may be time-consuming and energy-consuming. Fortunately, several mathematical methods, developed in the past decades, may facilitate the resolution of this issue. This study investigates the possibility of optimizing transfection efficiency by using a method referred to as least-squares support vector machine, which requires only a few experiments and maintains fairly high accuracy. RESULTS: A protocol consists of 15 experiments was performed according to the principle of uniform design. In this protocol, amount of liposome, amount of plasmid, and the number of seeded cells 24 h before transfection were set as independent variables and transfection efficiency was set as dependent variable. A model was deduced from independent variables and their respective dependent variable. Another protocol made up by 10 experiments was performed to test the accuracy of the model. The model manifested a high accuracy. Compared to traditional method, the integrated application of uniform design and least-squares support vector machine greatly reduced the number of required experiments. What's more, higher transfection efficiency was achieved. CONCLUSION: The integrated application of uniform design and least-squares support vector machine is a simple technique for obtaining high transfection efficiency. Using this novel method, the number of required experiments would be greatly cut down while higher efficiency would be gained. Least-squares support vector machine may be applicable to many other problems that need to be optimized.

Using random forest for reliable classification and cost-sensitive learning for medical diagnosis.

BACKGROUND: Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. RESULTS: In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. CONCLUSION: The method of using RF outlier measure to design a nonconformity measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.