Protein complementation assay as a display system for screening protein libraries in the intracellular environment

A wide range of screening strategies have been employed to isolate antibodies and other proteins with specific attributes, including binding affinity, specificity, stability and improved expression. However, there remains no high-throughput system to screen for target-binding proteins in a mammalian, intracellular environment. Such a system would allow binding reagents to be isolated against intracellular clinical targets such as cell signalling proteins associated with tumour formation (p53, ras, cyclin E), proteins associated with neurodegenerative disorders (huntingtin, betaamyloid precursor protein), and various proteins crucial to viral replication (e.g. HIV-1 proteins such as Tat, Rev and Vif-1), which are difficult to screen by phage, ribosome or cell-surface display. This study used the β-lactamase protein complementation assay (PCA) as the display and selection component of a system for screening a protein library in the cytoplasm of HEK 293T cells. The colicin E7 (ColE7) and Immunity protein 7 (Imm7) *Escherichia coli* proteins were used as model interaction partners for developing the system. These proteins drove effective β-lactamase complementation, resulting in a signal-to-noise ratio (9:1 – 13:1) comparable to that of other β-lactamase PCAs described in the literature. The model Imm7-ColE7 interaction was then used to validate protocols for library screening. Single positive cells that harboured the Imm7 and ColE7 binding partners were identified and isolated using flow cytometric cell sorting in combination with the fluorescent β-lactamase substrate, CCF2/AM. A single-cell PCR was then used to amplify the Imm7 coding sequence directly from each sorted cell. With the screening system validated, it was then used to screen a protein library based the Imm7 scaffold against a proof-of-principle target. The wild-type Imm7 sequence, as well as mutants with wild-type residues in the ColE7- binding loop were enriched from the library after a single round of selection, which is consistent with other eukaryotic screening systems such as yeast and mammalian cell-surface display. In summary, this thesis describes a new technology for screening protein libraries in a mammalian, intracellular environment. This system has the potential to complement existing screening technologies by allowing access to intracellular proteins and expanding the range of targets available to the pharmaceutical industry.

Improvement to Chinese information retrieval by incorporating word segmentation and query expansion

The increasing diversity of the Internet has created a vast number of multilingual resources on the Web. A huge number of these documents are written in various languages other than English. Consequently, the demand for searching in non-English languages is growing exponentially. It is desirable that a search engine can search for information over collections of documents in other languages. This research investigates the techniques for developing high-quality Chinese information retrieval systems. A distinctive feature of Chinese text is that a Chinese document is a sequence of Chinese characters with no space or boundary between Chinese words. This feature makes Chinese information retrieval more difficult since a retrieved document which contains the query term as a sequence of Chinese characters may not be really relevant to the query since the query term (as a sequence Chinese characters) may not be a valid Chinese word in that documents. On the other hand, a document that is actually relevant may not be retrieved because it does not contain the query sequence but contains other relevant words. In this research, we propose two approaches to deal with the problems. In the first approach, we propose a hybrid Chinese information retrieval model by incorporating word-based techniques with the traditional character-based techniques. The aim of this approach is to investigate the influence of Chinese segmentation on the performance of Chinese information retrieval. Two ranking methods are proposed to rank retrieved documents based on the relevancy to the query calculated by combining character-based ranking and word-based ranking. Our experimental results show that Chinese segmentation can improve the performance of Chinese information retrieval, but the improvement is not significant if it incorporates only Chinese segmentation with the traditional character-based approach. In the second approach, we propose a novel query expansion method which applies text mining techniques in order to find the most relevant words to extend the query. Unlike most existing query expansion methods, which generally select the highly frequent indexing terms from the retrieved documents to expand the query. In our approach, we utilize text mining techniques to find patterns from the retrieved documents that highly correlate with the query term and then use the relevant words in the patterns to expand the original query. This research project develops and implements a Chinese information retrieval system for evaluating the proposed approaches. There are two stages in the experiments. The first stage is to investigate if high accuracy segmentation can make an improvement to Chinese information retrieval. In the second stage, a text mining based query expansion approach is implemented and a further experiment has been done to compare its performance with the standard Rocchio approach with the proposed text mining based query expansion method. The NTCIR5 Chinese collections are used in the experiments. The experiment results show that by incorporating the text mining based query expansion with the hybrid model, significant improvement has been achieved in both precision and recall assessments.