Publications

@article{Lee2019,

title = {Pathogenic Protist Transmembranome database (PPTdb): A web-based platform for searching and analysis of protist transmembrane proteins},

author = {C. -C. Lee and P. -J. Huang and Y. -M. Yeh and S. -Y. Chen and C. -H. Chiu and W. -H. Cheng and P. Tang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069785098&doi=10.1186%2fs12859-019-2857-7&partnerID=40&md5=a8d0bc61439150c3b7d52174c56420c4},

doi = {10.1186/s12859-019-2857-7},

issn = {14712105},

year  = {2019},

date = {2019-01-01},

journal = {BMC Bioinformatics},

volume = {20},

publisher = {BioMed Central Ltd.},

abstract = {Background: Pathogenic protist membrane transporter proteins play important roles not only in exchanging molecules into and out of cells but also in acquiring nutrients and biosynthetic compounds from their hosts. Currently, there is no centralized protist membrane transporter database published, which makes system-wide comparisons and studies of host-pathogen membranomes difficult to achieve. Results: We analyzed over one million protein sequences from 139 protists with full or partial genome sequences. Putative transmembrane proteins were annotated by primary sequence alignments, conserved secondary structural elements, and functional domains. We have constructed the PPTdb (Pathogenic Protist Transmembranome database), a comprehensive membrane transporter protein portal for pathogenic protists and their human hosts. The PPTdb is a web-based database with a user-friendly searching and data querying interface, including hierarchical transporter classification (TC) numbers, protein sequences, functional annotations, conserved functional domains, batch sequence retrieving and downloads. The PPTdb also serves as an analytical platform to provide useful comparison/mining tools, including transmembrane ability evaluation, annotation of unknown proteins, informative visualization charts, and iterative functional mining of host-pathogen transporter proteins. Conclusions: The PPTdb collected putative protist transporter proteins and offers a user-friendly data retrieving interface. Moreover, a pairwise functional comparison ability can provide useful information for identifying functional uniqueness of each protist. Finally, the host and non-host protein similarity search can fulfill the needs of comprehensive studies of protists and their hosts. The PPTdb is freely accessible at http://pptdb.cgu.edu.tw. © 2019 The Author(s).},

note = {cited By 0},

keywords = {Ability evaluation; Functional annotation; Functional domains; Secondary structural elements; Trans-membrane proteins; Transporter proteins; Web based platform; WEB-BASED database, amino acid sequence; article; genome; human; infectious agent; membrane; mining; nonhuman; protein domain; protist; sequence alignment; structure activity relation; computer interface; factual database; fungus; genetics; metabolism; plant, carrier protein, Classification (of information); Database systems; Query processing; Websites, Databases, Factual; Fungi; Humans; Membrane Transport Proteins; Plants; User-Computer Interface, Proteins},

pubstate = {published},

tppubtype = {article}

}

@article{Lo2007,

title = {Protein structural similarity search by Ramachandran codes},

author = {W. -C. Lo and P. -J. Huang and C. -H. Chang and P. -C. Lyu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049165999&doi=10.1186%2f1471-2105-8-307&partnerID=40&md5=5fdeb7f56944fdecd0837c22cb720914},

doi = {10.1186/1471-2105-8-307},

issn = {14712105},

year  = {2007},

date = {2007-01-01},

journal = {BMC Bioinformatics},

volume = {8},

abstract = {Background: Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases. Results: We propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms. Conclusion: As a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era. © 2007 Lo et al; licensee BioMed Central Ltd.},

note = {cited By 42},

keywords = {Algorithms; Amino Acid Sequence; Database Management Systems; Databases, Amino Acid, Database searches; Expectation values; Functional annotation; Homologous structures; Protein structures; Sequence Alignment Methods; Sequence similarity; Structural similarity, Java programming language; Search engines; Tools; Web services, protein, Protein; Information Storage and Retrieval; Molecular Sequence Data; Proteins; Sequence Alignment; Sequence Analysis, Protein; Sequence Homology, Proteins},

pubstate = {published},

tppubtype = {article}

}