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On Simplified Global Nonlinear Function for Fitness Landscape: A Case Study of Inverse Protein Folding

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journal contribution
posted on 12.09.2016, 00:00 by Yun Xu, Changyu Hu, Yang Dai, Jie Liang
The construction of fitness landscape has broad implication in understanding molecular evolution, cellular epigenetic state, and protein structures. We studied the problem of constructing fitness landscape of inverse protein folding or protein design, with the aim to generate amino acid sequences that would fold into an a priori determined structural fold which would enable engineering novel or enhanced biochemistry. For this task, an effective fitness function should allow identification of correct sequences that would fold into the desired structure. In this study, we showed that nonlinear fitness function for protein design can be constructed using a rectangular kernel with a basis set of proteins and decoys chosen a priori. The full landscape for a large number of protein folds can be captured using only 480 native proteins and 3,200 non-protein decoys via a finite Newton method. A blind test of a simplified version of fitness function for sequence design was carried out to discriminate simultaneously 428 native sequences not homologous to any training proteins from 11 million challenging protein-like decoys. This simplified function correctly classified 408 native sequences (20 misclassifications, 95% correct rate), which outperforms several other statistical linear scoring function and optimized linear function. Our results further suggested that for the task of global sequence design of 428 selected proteins, the search space of protein shape and sequence can be effectively parametrized with just about 3,680 carefully chosen basis set of proteins and decoys, and we showed in addition that the overall landscape is not overly sensitive to the specific choice of this set. Our results can be generalized to construct other types of fitness landscape.


This work was supported by NSF grants DBI 1062328 and DMS-0800257, and NIH grants GM079804 and GM086145. This work was also funded by the Chicago Biomedical Consortium with support from the Searle Funds at the Chicago Community Trust and the UIC Research Open Access Article Publishing (ROAAP) Fund.


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This is a copy of an article published in the PLoS ONE. © 2014 Xu et al.


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