【摘 要】
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Background: Since various diseases and therapeutic approaches are correlated with protein subcellular localization, effective medical approaches require delivery of the drug to the appropriate subcell
【机 构】
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Department of Chemistry, Tongji University, Shanghai, 200092, China
【出 处】
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第五届全国生物信息学与系统生物学学术大会
论文部分内容阅读
Background: Since various diseases and therapeutic approaches are correlated with protein subcellular localization, effective medical approaches require delivery of the drug to the appropriate subcellular localization to selectively interfere with protein function.Experimental research is both time consuming and costly.Rapid methods have recently been developed for the prediction of protein subcellular localization.However, the accuracy of prediction needs to be improved, and the gap between known subcellular localizations and the ever-increasing number of protein sequences remains to be filled.Methods: In our previous study, we published an algorithm that could extract local combinational variables with fixed locations from aligned sequences.The LM candidates were also generated and selected using the method of our previous study, with some modifications.The workflow of the LM algorithm consists of two steps.Detecting LM candidates and selecting LMs from LM candidates.Results: we present a novel approach that focuses on local sequence motifs encoding thereby improves the accuracy of prediction of the subcellular localization.Besides it also helps in identifying many local protein segments, which could be useful for studying the functions of viral proteins and in the design of antiviral drugs the main findings.Conclusions: We developed a LMs based method for subcellular localization.Compared with existing methods, ours greatly improved the accuracy subcellular localization prediction, which allowed us to detect useful protein segments and to select the most valuable motifs to find potential function region.Moreover, the LMs identified might help us increase the success rate and reduce the risk, cost, and time associated with screening candidates for drug discovery .
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