A method for predicting the helical interaction relationship of α-transmembrane proteins based on random forest
A transmembrane protein and random forest technology, applied in the field of biological computing, can solve problems such as low accuracy, long time consumption, and few prediction methods, and achieve the effect of improving accuracy, reducing search space, and convenient and quick methods
Active Publication Date: 2018-08-14
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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[0006] The purpose of the present invention is to provide a method for predicting the helical interaction relationship of α-transmembrane protein based on random forest, aiming to solve the problem of few, slow, time-consuming and low-accuracy prediction methods for the three-dimensional structure of α-transmembrane protein question
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[0013] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention clearer, the present invention will be further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0014] The random forest method is a classifier that contains multiple decision trees. A forest is built in a random manner. The forest is composed of many decision trees, and each decision tree in the random forest is unrelated. After the forest is obtained, whenever a new sample is input, let each decision tree in the forest judge which category the sample should belong to (for the classification algorithm), and then predict the sample according to which category is selected the most For which category.
[0015] Based on the above theory, an embodiment of the present invention provides a...
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The invention belongs to the field of biocomputing and provides a method for predicating a helix interactive relationship of an alpha transmembrane protein based on a random forest. The method comprises the following steps that an alpha transmembrane protein chain of a specific three-dimensional structure is collected to establish a training set; based on the training set, the feature information of interactive residue pairs and the feature information of non-interactive residue pairs on helixes in the alpha transmembrane protein chain are extracted, and a predication model is established through a random forest algorithm; a target alpha transmembrane protein which is used for testing and is of a specific first-level structure is collected, the feature information of the residue pairs in the alpha helixes is extracted, and prediction is conducted based on the predication model; according to a prediction result, whether an interactive residue pair exists in the alpha helixes of the target alpha transmembrane protein is judged. By the adoption of the method for predicating the helix interactive relationship of the alpha transmembrane protein based on the random forest, the computing speed is high, and the accuracy rate is also high.
Description
technical field [0001] The invention belongs to the field of biological calculation, and in particular relates to a method for predicting the helical interaction relationship of alpha transmembrane protein based on random forest. Background technique [0002] Membrane proteins account for about 60% of the drug targets currently known or under study. The three-dimensional structure of membrane protein largely determines its physiological function, and the physiological function of membrane protein often determines its pharmacological function. Therefore, in order to speed up the research of membrane protein target drugs, it is very important to determine the three-dimensional structure of membrane protein. At present, the biological experimental methods for analyzing the three-dimensional structure of proteins mainly include X-RAY and NMR methods, but these methods are not only complicated, time-consuming, but also expensive. In view of this, it is particularly important to...
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IPC IPC(8): G06F19/18
Inventor 张慧玲贝振东魏彦杰
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI