Chinese Chemical Letters

【Chinese Chemical Letters】介绍
英文名称:Chinese Chemical Letters

本刊是由中国科协主管、中国化学会主办、中国医学科学院药物所承办的学术期刊,是由著名化学家梁晓天院士主编。是中国化学界通向世界的窗口,内容覆盖化学全领域。本刊的办刊宗旨是“新、快、准”,我们将坚持这个宗旨,力求及时反映化学研究中各个相关领域内的最新进展及热点问题,主要读者群是科研人员、研究生、大学教师。现已被国内外多家数据库收录,如SCI Search、Chemical Abstract、Research Alert、Chemistry Citation Index、《日本科技文献速报》、万方数据数字化期刊群、中国学术期刊过刊全文数据库、中国学术期刊(光盘版)、中国学术期刊文摘、中文期刊全文数据库、俄罗斯Рж期刊源等。

  1. A New Hybrid Model of Amino Acid Substitution for Protein Functional Classification

  2. Chinese Chemical Letters,Volume 29, Issue 3, June 2005, Pages 220-228

    Fu Sheng NIE  , Zhining Wen  , Ke Long Wang  , Menglong Li


    In this paper, a new hybrid model of amino acid substitution is developed and compared with the others in previous works. The results show that the new hybrid model can characterize
    the protein sequences very well by calculating Fisher weights, which can denote how much the variants contribute to the classification.

  3. Using pseudo amino acid composition to predict transmembrane regions in protein: cellular automata and Lempel-Ziv complexity

  4. Chinese Chemical Letters,Volume 34, Number 1,Jan,2008

    guangxuan Min  , Yanzhi Guo  , Menglong Li*  , Tuanfei Zhu 


    The knowledge of subnuclear localization in eukaryotic cells is indispensable for understanding the biological function of nucleus,
    genome regulation and drug discovery. In this study, a new feature representation was proposed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorporate the sequence-order information; PSSM describes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation accuracy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OETKNN
    (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at predict_SubNL/index.htm and the dataset used in our study is from Shen and Chou’s work by downloading at bioinf/Nuc-PLoc/Data.htm.