Abstract
We develop a knowledge-based statistical energy function on residual level for quantitatively predicting the affinity of protein-protein complexes by using 20 residue types and a distance-free reference state. The correlation coefficients between experimentally measured protein-protein binding affinities (PPIA) and the predicted affinities by our approach are 0.74 for 82 protein-protein (peptide) complexes. Compared to the published results of two other volume corrected knowledge-based scoring functions on atomic level, the proposed approach not only is the simplest but also yields the comparable correlation between theoretical and experimental binding affinities of the test sets with the reported best methods.
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Li, XL., Hou, ML., Wang, SL. (2010). A Residual Level Potential of Mean Force Based Approach to Predict Protein-Protein Interaction Affinity. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_85
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DOI: https://doi.org/10.1007/978-3-642-14922-1_85
Publisher Name: Springer, Berlin, Heidelberg
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