FALCON2: a web server for high-quality prediction of protein tertiary structures
Abstract Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called P...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-09-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-021-04353-8 |
| _version_ | 1830343824000417792 |
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| author | Lupeng Kong Fusong Ju Haicang Zhang Shiwei Sun Dongbo Bu |
| author_facet | Lupeng Kong Fusong Ju Haicang Zhang Shiwei Sun Dongbo Bu |
| author_sort | Lupeng Kong |
| collection | DOAJ |
| description | Abstract Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions. |
| first_indexed | 2024-12-19T22:15:01Z |
| format | Article |
| id | doaj.art-8ebd7756e88a456bbbc1c44d3d008136 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-19T22:15:01Z |
| publishDate | 2021-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-8ebd7756e88a456bbbc1c44d3d0081362022-12-21T20:03:47ZengBMCBMC Bioinformatics1471-21052021-09-0122111410.1186/s12859-021-04353-8FALCON2: a web server for high-quality prediction of protein tertiary structuresLupeng Kong0Fusong Ju1Haicang Zhang2Shiwei Sun3Dongbo Bu4Key Lab of Intelligent Information Processing, Big-Data Academy, Institute of Computing Technology, Chinese Academy of SciencesKey Lab of Intelligent Information Processing, Big-Data Academy, Institute of Computing Technology, Chinese Academy of SciencesKey Lab of Intelligent Information Processing, Big-Data Academy, Institute of Computing Technology, Chinese Academy of SciencesKey Lab of Intelligent Information Processing, Big-Data Academy, Institute of Computing Technology, Chinese Academy of SciencesKey Lab of Intelligent Information Processing, Big-Data Academy, Institute of Computing Technology, Chinese Academy of SciencesAbstract Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions.https://doi.org/10.1186/s12859-021-04353-8Protein structure predictionTemplate-based modelingAb initio predictionProtein structure prediction web service |
| spellingShingle | Lupeng Kong Fusong Ju Haicang Zhang Shiwei Sun Dongbo Bu FALCON2: a web server for high-quality prediction of protein tertiary structures BMC Bioinformatics Protein structure prediction Template-based modeling Ab initio prediction Protein structure prediction web service |
| title | FALCON2: a web server for high-quality prediction of protein tertiary structures |
| title_full | FALCON2: a web server for high-quality prediction of protein tertiary structures |
| title_fullStr | FALCON2: a web server for high-quality prediction of protein tertiary structures |
| title_full_unstemmed | FALCON2: a web server for high-quality prediction of protein tertiary structures |
| title_short | FALCON2: a web server for high-quality prediction of protein tertiary structures |
| title_sort | falcon2 a web server for high quality prediction of protein tertiary structures |
| topic | Protein structure prediction Template-based modeling Ab initio prediction Protein structure prediction web service |
| url | https://doi.org/10.1186/s12859-021-04353-8 |
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