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Zhang Q, Zhu J, Ju F, Kong L, Sun S, Zheng WM, Bu D. ISSEC: inferring contacts among protein secondary structure elements using deep object detection. BMC Bioinformatics 2020; 21:503. [PMID: 33153432 PMCID: PMC7643357 DOI: 10.1186/s12859-020-03793-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/30/2020] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them. RESULTS We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions. CONCLUSIONS Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.
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Affiliation(s)
- Qi Zhang
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Jianwei Zhu
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Fusong Ju
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Lupeng Kong
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Shiwei Sun
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Wei-Mou Zheng
- Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dongbo Bu
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China.
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Abstract
Proteins are molecular machines whose function depends on their ability to achieve complex folds with precisely defined structural and dynamic properties. The rational design of proteins from first-principles, or de novo, was once considered to be impossible, but today proteins with a variety of folds and functions have been realized. We review the evolution of the field from its earliest days, placing particular emphasis on how this endeavor has illuminated our understanding of the principles underlying the folding and function of natural proteins, and is informing the design of macromolecules with unprecedented structures and properties. An initial set of milestones in de novo protein design focused on the construction of sequences that folded in water and membranes to adopt folded conformations. The first proteins were designed from first-principles using very simple physical models. As computers became more powerful, the use of the rotamer approximation allowed one to discover amino acid sequences that stabilize the desired fold. As the crystallographic database of protein structures expanded in subsequent years, it became possible to construct proteins by assembling short backbone fragments that frequently recur in Nature. The second set of milestones in de novo design involves the discovery of complex functions. Proteins have been designed to bind a variety of metals, porphyrins, and other cofactors. The design of proteins that catalyze hydrolysis and oxygen-dependent reactions has progressed significantly. However, de novo design of catalysts for energetically demanding reactions, or even proteins that bind with high affinity and specificity to highly functionalized complex polar molecules remains an importnant challenge that is now being achieved. Finally, the protein design contributed significantly to our understanding of membrane protein folding and transport of ions across membranes. The area of membrane protein design, or more generally of biomimetic polymers that function in mixed or non-aqueous environments, is now becoming increasingly possible.
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