Cell Surface Transmembrane Protein Database for Detecting Potential Targets of Antibody Drug in Application of Cancer Therapies

Background： Antibody drug conjugated （ADC） is one kind of very important method of therapy to cancer diseases. In this research, the authors introduce BLAST and some other important algorithms to create transmembrane protein databases. These databases are acquired from well-known databases such as NCBI or Swiss-Prot as template, and then collect all possible transmembrane protein by using BLAST or physical character. After collect these databases, the authors will aim at each nucleotide sequences to design the probes of oligonucleotide microarray, which can detect the high express transmembrane proteins very efficiently. Finally, the authors can accelerate the anti-cancer drug discovery by using these databases. Result： This study constructed a web service, the Transmembrane Protein Database, to researchers that are interested in or need to oligonucleotide microarray probe design for detecting potential targets of antibody drug. With user friendly web based windows containing each necessary selections, users can easily choose the parameters and get the suitable probe design suggestions. Conclusion： Transmembrane protein database is very important and powerful in detecting cancers or other human disease. By using this database, the authors offer a good strategy in transmembrane protein research as well.

An overview of human protein databases and their application to functional proteomics in health and disease

Functional proteomics can be defined as a strategy to couple proteomic information with biochemical and physiological analyses with the aim of understanding better the functions of proteins in normal and diseased organs.In recent years,a variety of publicly available bioinformatics databases have been developed to support protein-related information management and biological knowledge discovery.In addition to being used to annotate the proteome,these resources also offer the opportunity to develop global approaches to the study of the functional role of proteins both in health and disease.Here,we present a comprehensive review of the major human protein bioinformatics databases.We conclude this review by discussing a few examples that illustrate the importance of these databases in functional proteomics research.

Differentially expressed proteins on postoperative 3 days healing in rabbit Achilles tendon rupture model after early kinesitherapy

Objectives： Surgical repair of Achilles tendon （AT） rupture should immediately be followed by active tendon mobilization. The optimal time as to when the mobilization should begin is important yet controversial. Early kinesitherapy leads to reduced rehabilitation period. However, an insight into the detailed mechanism of this process has not been gained. Proteomic technique can be used to separate and purify the proteins by differential expression profile which is related to the function of different proteins, but research in the area of proteomic analysis of AT 3 days after repair has not been studied so far. Methods： Forty-seven New Zealand white rabbits were randomized into 3 groups. Group A （immobilization group, n=l 6） received postoperative cast immobilization; Group B （early motion group, n= 16） received early active motion treatments immediately following the repair of AT rupture from tenotomy. Another 15 rabbits served as control group （Group C）. The AT samples were prepared 3 days following the microsurgery. The proteins were separated employing two- dimensional polyacrylamide gel electrophoresis （2D-PAGE）. PDQuest software version 8.0 was used to identify differentially expressed proteins, followed by peptide mass fingerprint （PMF） and tandem mass spectrum analysis, using the National Center for Biotechnology Information （NCBI） protein database retrieval and then for bioinformatics analysis. Results： Amean of 446.33,436.33 and 462.67 protein spots on Achilles tendon samples of 13 rabbits in Group A, 14 rabbits in Group B and 13 rabbits in Group C were suc- cessfully detected in the 2D-PAGE. There were 40, 36 and 79 unique proteins in Groups A, B and C respectively. Some differentially expressed proteins were enzyme with the gel, matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry （MALDI-TOF MS）. We successfully identified 9 and 11 different proteins in Groups A and B, such as GAPDH, phosphoglycerate kinase 1, pro-alpha-1 type 1 collagen, peroxiredoxin 1, alpha-l-antiproteinase E a-1 and MAD2L1 binding protein, etc. And some with the molecular chaperone, oxidative stress, energy metabolism, signal transduetion, coupled with the tendon cell expression and protein synthesis, proliferate, differentiate and are closely related to the AT healing. The GAPDH protein was further validated through Western blotting. It was indicated that some differentially expressed proteins were involved in various metabolism pathways and may play an important role in initial healing of AT rupture. Conclusion： Differentially expressed proteins in rabbit healing AT model may contribute to 3 days healing of AT rupture through a new mechanobiological mechanism due to the application of postoperative early kinesitherapy.

pepgrep:A Tool for Peptide MS/MS Pattern Matching

Typically, detection of protein sequences in collision-induced dissociation （CID） tandem MS （MS2） dataset is performed by mapping identified peptide ions back to protein sequence by using the protein database search （PDS） engine. Finding a particular peptide sequence of interest in CID MS2 records very often requires manual evaluation of the spectrum, regardless of whether the peptide-associated MS2 scan is identified by PDS algorithm or not. We have developed a com- pact cross-platform database-free command-line utility, pepgrep, which helps to find an MS2 finger- print for a selected peptide sequence by pattern-matching of modelled MS2 data using Peptide-to- MS2 scoring algorithm, pepgrep can incorporate dozens of mass offsets corresponding to a variety of post-translational modifications （PTMs） into the algorithm. Decoy peptide sequences are used with the tested peptide sequence to reduce false-positive results. The engine is capable of screening an MS2 data file at a high rate when using a cluster computing environment. The matched MS2 spectrum can be displayed by using built-in graphical application programming interface （API） or optionally recorded to file. Using this algorithm, we were able to find extra peptide sequences in studied CID spectra that were missed by PDS identification. Also we found pepgrep especially useful for examining a CID of small fractions of peptides resulting from, for example, affinity puri- fication techniques. The peptide sequences in such samples are less likely to be positively identified by using routine protein-centric algorithm implemented in PDS. The software is freely available at http：//bsproteomics.essex.ac.uk：8080/data/download/pepgrep- 1.4.tgz.