Peptide-fluorescent bacteria complex as luminescent reagents for cancer diagnosis

Currently in clinic, people use hematoxylin and eosin stain (H&E stain) and immunohistochemistry methods to identify the generation and genre of cancers for human pathological samples. Since these methods are inaccurate and time consuming, developing a rapid and accurate method to detect cancer is urgently demanded. In our study, binding peptides for lung cancer cell line A549 were identified using bacteria surface display method. With those binding peptides for A549 cells on the surface, the fluorescent bacteria (Escherichia coli with stably expressed green fluorescent protein) were served as specific detecting reagents for the diagnosis of cancers. The binding activity of peptide-fluorescent bacteria complex was confirmed by detached cancer cells, attached cancer cells and mice tumor xenograft samples. A unique fixation method was developed for peptide-bacteria complex in order to make this complex more feasible for the clinic use. This peptide-fluorescent bacteria complex has great potential to become a new diagnostic tool for clinical application.

A framework for identifying affinity classes of inorganic materials binding peptide sequences

With the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.