Using Support Vector Machine Combined with Post-processing Procedure to Improve Prediction of Interface Residues in Transient Complexes
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Reliable prediction of interface residues in transient complexes remains challenging, yet is highly desirable for the design of new drugs. The existing computational methods mainly rely on evolutionary information to identify these key residues, but evolutionary information may not be effective for the interface residues in all types of transient complexes, such as antigen–antibody complexes. Herein we combined B-factor with sequence profile and accessible surface area to predict these important residues using support vector machine (SVM). Furthermore, a post-processing method was developed to reduce the number of false positives recognized by SVM. The prediction results show that B-factor is an effective indicator for the interface residues in antigen–antibody complexes as well as those in other types of transient complexes. In addition, we found that the post-processing procedure made an important contribution to further improve the prediction performance. Consequently, the proposed approach could provide new insight into accurately predicting interface residues in different types of transient complexes.
KeywordsInterface residues Antigen–antibody complexes B-factor Support vector machine Post-processing procedure
Support vector machine
Accessible surface area
Matthew’s correlation coefficient
Receiver operating characteristic
Complementarity determining region
This work was supported by the National Natural Science Foundation of China (Grant No. 90608020) and NCET-060651.
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