title: Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles.
abstract: Locating sequences compatible with a protein structural fold is the well-known inverse protein-folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy-optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment-derived sequence profiles and structure-derived energy profiles. SPIN improves over the fragment-derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild-type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single-body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks-lab.org.
authors.affiliation: School of Informatics and Computing, Indiana University-Purdue University, Indianapolis, Indiana, 46202.
authors.forename: Zhixiu
authors.initials: Z
authors.lastname: Li
authors.forename: Yuedong
authors.initials: Y
authors.lastname: Yang
authors.forename: Eshel
authors.initials: E
authors.lastname: Faraggi
authors.forename: Jian
authors.initials: J
authors.lastname: Zhan
authors.forename: Yaoqi
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000595:Amino Acid Sequence; D019943:Amino Acid Substitution; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D020407:Internet; D008958:Models, Molecular; D050505:Mutant Proteins / Q000737:chemistry / Q000378:metabolism; D016571:Neural Networks, Computer; D010446:Peptide Fragments / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D019151:Peptide Library; D011487:Protein Conformation; D015202:Protein Engineering / Q000379:methods*; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D017386:Sequence Homology, Amino Acid; D012984:Software; D013499:Surface Properties
chemical_list: D050505:Mutant Proteins; D010446:Peptide Fragments; D019151:Peptide Library; D011506:Proteins
keywords: inverse protein folding problem; knowledge-based energy function; neural network; protein design; sequence profiles