Abstract
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.
Highlights
Protein design has been the focus of many experimental, theoretical, and computational studies [1,2,3,4,5,6,7,8,9]
To obtain such a nonlinear function, our goal is to find a set of parameters faD,aN g such that H(c) has fitness value close to {1 for native proteins, and has fitness values close to z1 for decoys
Because we are unaware of any other development of design fitness functions amenable for high-throughput tests, and frequently no distinctions were made between protein folding potential and protein design fitness function, we compared our fitness function with several well-established scoring functions developed for protein folding
Summary
Protein design has been the focus of many experimental, theoretical, and computational studies [1,2,3,4,5,6,7,8,9]. We studied the problem of designing a protein sequence that is compatible with an a priori specified three-dimensional template protein fold. This problem was first formulated 30 years ago [16,17]. Known as the inverse protein folding problem, it addresses the fundamental problem of designing proteins to facilitate engineering of proteins with enhanced or novel biochemical functions. An ideal fitness function can characterize the properties of fitness landscape of many proteins simultaneously. Such a fitness function would be useful for designing novel proteins and novel functions, as well as for studying the global evolution of protein structure and protein functions
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