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Pereira J, Simpkin AJ, Hartmann MD, Rigden DJ, Keegan RM, Lupas AN. High-accuracy protein structure prediction in CASP14. Proteins 2021; 89:1687-1699. [PMID: 34218458 DOI: 10.1002/prot.26171] [Citation(s) in RCA: 182] [Impact Index Per Article: 60.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/16/2021] [Accepted: 06/23/2021] [Indexed: 12/25/2022]
Abstract
The application of state-of-the-art deep-learning approaches to the protein modeling problem has expanded the "high-accuracy" category in CASP14 to encompass all targets. Building on the metrics used for high-accuracy assessment in previous CASPs, we evaluated the performance of all groups that submitted models for at least 10 targets across all difficulty classes, and judged the usefulness of those produced by AlphaFold2 (AF2) as molecular replacement search models with AMPLE. Driven by the qualitative diversity of the targets submitted to CASP, we also introduce DipDiff as a new measure for the improvement in backbone geometry provided by a model versus available templates. Although a large leap in high-accuracy is seen due to AF2, the second-best method in CASP14 out-performed the best in CASP13, illustrating the role of community-based benchmarking in the development and evolution of the protein structure prediction field.
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Affiliation(s)
- Joana Pereira
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Adam J Simpkin
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Marcus D Hartmann
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Daniel J Rigden
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Ronan M Keegan
- Department of Scientific Computing, Science and Technologies Facilities Council, UK Research and Innovation, Didcot, Oxfordshire, UK
| | - Andrei N Lupas
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, Germany
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2
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PSS-3D1D: an improved 3D1D profile method of protein fold recognition for the annotation of twilight zone sequences. ACTA ACUST UNITED AC 2011; 12:181-9. [DOI: 10.1007/s10969-011-9119-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2011] [Accepted: 11/24/2011] [Indexed: 10/14/2022]
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3
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Known Bioactive Small Molecules Probe the Function of a Widely Conserved but Enigmatic Bacterial ATPase, YjeE. ACTA ACUST UNITED AC 2008; 15:1287-95. [DOI: 10.1016/j.chembiol.2008.10.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2008] [Revised: 09/30/2008] [Accepted: 10/14/2008] [Indexed: 11/22/2022]
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4
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Liu S, Zhang C, Liang S, Zhou Y. Fold recognition by concurrent use of solvent accessibility and residue depth. Proteins 2007; 68:636-45. [PMID: 17510969 DOI: 10.1002/prot.21459] [Citation(s) in RCA: 78] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Recognizing the structural similarity without significant sequence identity (called fold recognition) is the key for bridging the gap between the number of known protein sequences and the number of structures solved. Previously, we developed a fold-recognition method called SP(3) which combines sequence-derived sequence profiles, secondary-structure profiles and residue-depth dependent, structure-derived sequence profiles. The use of residue-depth-dependent profiles makes SP(3) one of the best automatic predictors in CASP 6. Because residue depth (RD) and solvent accessible surface area (solvent accessibility) are complementary in describing the exposure of a residue to solvent, we test whether or not incorporation of solvent-accessibility profiles into SP(3) could further increase the accuracy of fold recognition. The resulting method, called SP(4), was tested in SALIGN benchmark for alignment accuracy and Lindahl, LiveBench 8 and CASP7 blind prediction for fold recognition sensitivity and model-structure accuracy. For remote homologs, SP(4) is found to consistently improve over SP(3) in the accuracy of sequence alignment and predicted structural models as well as in the sensitivity of fold recognition. Our result suggests that RD and solvent accessibility can be used concurrently for improving the accuracy and sensitivity of fold recognition. The SP(4) server and its local usage package are available on http://sparks.informatics.iupui.edu/SP4.
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Affiliation(s)
- Song Liu
- Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology and Biophysics, State University of New York at Buffalo, Buffalo, New York 14214, USA
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5
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Zhou H, Zhou Y. Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 2006; 58:321-8. [PMID: 15523666 PMCID: PMC1408319 DOI: 10.1002/prot.20308] [Citation(s) in RCA: 195] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recognizing structural similarity without significant sequence identity has proved to be a challenging task. Sequence-based and structure-based methods as well as their combinations have been developed. Here, we propose a fold-recognition method that incorporates structural information without the need of sequence-to-structure threading. This is accomplished by generating sequence profiles from protein structural fragments. The structure-derived sequence profiles allow a simple integration with evolution-derived sequence profiles and secondary-structural information for an optimized alignment by efficient dynamic programming. The resulting method (called SP(3)) is found to make a statistically significant improvement in both sensitivity of fold recognition and accuracy of alignment over the method based on evolution-derived sequence profiles alone (SP) and the method based on evolution-derived sequence profile and secondary structure profile (SP(2)). SP(3) was tested in SALIGN benchmark for alignment accuracy and Lindahl, PROSPECTOR 3.0, and LiveBench 8.0 benchmarks for remote-homology detection and model accuracy. SP(3) is found to be the most sensitive and accurate single-method server in all benchmarks tested where other methods are available for comparison (although its results are statistically indistinguishable from the next best in some cases and the comparison is subjected to the limitation of time-dependent sequence and/or structural library used by different methods.). In LiveBench 8.0, its accuracy rivals some of the consensus methods such as ShotGun-INBGU, Pmodeller3, Pcons4, and ROBETTA. SP(3) fold-recognition server is available on http://theory.med.buffalo.edu.
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Affiliation(s)
| | - Yaoqi Zhou
- *Correspondence to: Dr. Yaoqi Zhou, Howard Hughes Medical Institute, Center for Single Molecule Biophysics and Department of Physiology & Biophysics, State University of New York at Buffalo, 124 Sherman Hall, Buffalo, NY 14214. E-mail:
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6
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Abstract
Two single-method servers, SPARKS 2 and SP3, participated in automatic-server predictions in CASP6. The overall results for all as well as detailed performance in comparative modeling targets are presented. It is shown that both SPARKS 2 and SP3 are able to recognize their corresponding best templates for all easy comparative modeling targets. The alignment accuracy, however, is not always the best among all the servers. Possible factors are discussed. SPARKS 2 and SP3 fold recognition servers, as well as their executables, are freely available for all academic users on http://theory.med.buffalo.edu.
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Affiliation(s)
- Hongyi Zhou
- Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology and Biophysics, State University of New York, Buffalo, New York 14214, USA
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7
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Shah PK, Aloy P, Bork P, Russell RB. Structural similarity to bridge sequence space: finding new families on the bridges. Protein Sci 2005; 14:1305-14. [PMID: 15840833 PMCID: PMC2253280 DOI: 10.1110/ps.041187405] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Structures for protein domains have increased rapidly in recent years owing to advances in structural biology and structural genomics projects. New structures are often similar to those solved previously, and such similarities can give insights into function by linking poorly understood families to those that are better characterized. They also allow the possibility of combing information to find still more proteins adopting a similar structure and sometimes a similar function, and to reprioritize families in structural genomics pipelines. We explore this possibility here by preparing merged profiles for pairs of structurally similar, but not necessarily sequence-similar, domains within the SMART and Pfam database by way of the Structural Classification of Proteins (SCOP). We show that such profiles are often able to successfully identify further members of the same superfamily and thus can be used to increase the sensitivity of database searching methods like HMMer and PSI-BLAST. We perform detailed benchmarks using the SMART and Pfam databases with four complete genomes frequently used as annotation benchmarks. We quantify the associated increase in structural information in Swissprot and discuss examples illustrating the applicability of this approach to understand functional and evolutionary relationships between protein families.
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8
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Lee J, Kim SY, Lee J. Protein structure prediction based on fragment assembly and parameter optimization. Biophys Chem 2005; 115:209-14. [PMID: 15752606 DOI: 10.1016/j.bpc.2004.12.046] [Citation(s) in RCA: 35] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2004] [Revised: 11/09/2004] [Accepted: 12/10/2004] [Indexed: 11/28/2022]
Abstract
We propose a novel method for ab-initio prediction of protein tertiary structures based on the fragment assembly and global optimization. Fifteen residue long fragment libraries are constructed using the secondary structure prediction method PREDICT, and fragments in these libraries are assembled to generate full-length chains of a query protein. Tertiary structures of 50 to 100 conformations are obtained by minimizing an energy function for proteins, using the conformational space annealing method that enables one to sample diverse low-lying local minima of the energy. Then in order to enhance the performance of the prediction method, we optimize the linear parameters of the energy function, so that the native-like conformations become energetically more favorable than the non-native ones for proteins with known structures. We test the feasibility of the parameter optimization procedure by applying it to the training set consisting of three proteins: the 10-55 residue fragment of staphylococcal protein A (PDB ID 1bdd), a designed protein betanova, and 1fsd.
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Affiliation(s)
- Julian Lee
- Department of Bioinformatics and Life Science, Computer Aided Molecular Design Research Center, Bioinformatics and Molecular Design Technology Innovation Center, Soongsil University, Seoul 156-743, South Korea.
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Lee J, Kim SY, Joo K, Kim I, Lee J. Prediction of protein tertiary structure using PROFESY, a novel method based on fragment assembly and conformational space annealing. Proteins 2004; 56:704-14. [PMID: 15281124 DOI: 10.1002/prot.20150] [Citation(s) in RCA: 66] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A novel method for ab initio prediction of protein tertiary structures, PROFESY (PROFile Enumerating SYstem), is proposed. This method utilizes the secondary structure prediction information of a query sequence and the fragment assembly procedure based on global optimization. Fifteen-residue-long fragment libraries are constructed using the secondary structure prediction method PREDICT, and fragments in these libraries are assembled to generate full-length chains of a query protein. Tertiary structures of 50 to 100 conformations are obtained by minimizing an energy function for proteins, using the conformational space annealing method that enables one to sample diverse low-lying local minima of the energy. We apply PROFESY for benchmark tests to proteins with known structures to demonstrate its feasibility. In addition, we participated in CASP5 and applied PROFESY to four new-fold targets for blind prediction. The results are quite promising, despite the fact that PROFESY was in its early stages of development. In particular, PROFESY successfully provided us the best model-one structure for the target T0161.
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Affiliation(s)
- Julian Lee
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea
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10
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Zhou H, Zhou Y. Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition. Proteins 2004; 55:1005-13. [PMID: 15146497 DOI: 10.1002/prot.20007] [Citation(s) in RCA: 163] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
An elaborate knowledge-based energy function is designed for fold recognition. It is a residue-level single-body potential so that highly efficient dynamic programming method can be used for alignment optimization. It contains a backbone torsion term, a buried surface term, and a contact-energy term. The energy score combined with sequence profile and secondary structure information leads to an algorithm called SPARKS (Sequence, secondary structure Profiles and Residue-level Knowledge-based energy Score) for fold recognition. Compared with the popular PSI-BLAST, SPARKS is 21% more accurate in sequence-sequence alignment in ProSup benchmark and 10%, 25%, and 20% more sensitive in detecting the family, superfamily, fold similarities in the Lindahl benchmark, respectively. Moreover, it is one of the best methods for sensitivity (the number of correctly recognized proteins), alignment accuracy (based on the MaxSub score), and specificity (the average number of correctly recognized proteins whose scores are higher than the first false positives) in LiveBench 7 among more than twenty servers of non-consensus methods. The simple algorithm used in SPARKS has the potential for further improvement. This highly efficient method can be used for fold recognition on genomic scales. A web server is established for academic users on http://theory.med.buffalo.edu.
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Affiliation(s)
- Hongyi Zhou
- Howard Hughes Medical Institute Center for Single Molecule Biophysics, Department of Physiology & Biophysics, State University of New York at Buffalo, New York 14214, USA
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11
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Przybylski D, Rost B. Improving Fold Recognition Without Folds. J Mol Biol 2004; 341:255-69. [PMID: 15312777 DOI: 10.1016/j.jmb.2004.05.041] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2004] [Revised: 05/18/2004] [Accepted: 05/18/2004] [Indexed: 11/21/2022]
Abstract
The most reliable way to align two proteins of unknown structure is through sequence-profile and profile-profile alignment methods. If the structure for one of the two is known, fold recognition methods outperform purely sequence-based alignments. Here, we introduced a novel method that aligns generalised sequence and predicted structure profiles. Using predicted 1D structure (secondary structure and solvent accessibility) significantly improved over sequence-only methods, both in terms of correctly recognising pairs of proteins with different sequences and similar structures and in terms of correctly aligning the pairs. The scores obtained by our generalised scoring matrix followed an extreme value distribution; this yielded accurate estimates of the statistical significance of our alignments. We found that mistakes in 1D structure predictions correlated between proteins from different sequence-structure families. The impact of this surprising result was that our method succeeded in significantly out-performing sequence-only methods even without explicitly using structural information from any of the two. Since AGAPE also outperformed established methods that rely on 3D information, we made it available through. If we solved the problem of CPU-time required to apply AGAPE on millions of proteins, our results could also impact everyday database searches.
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Affiliation(s)
- Dariusz Przybylski
- CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, New York, NY 10032, USA.
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12
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Affiliation(s)
- Robert H Kretsinger
- Department of Biology, University of Virginia, Charlottesville, Virginia 22903, USA
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13
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Sandhya S, Kishore S, Sowdhamini R, Srinivasan N. Effective detection of remote homologues by searching in sequence dataset of a protein domain fold. FEBS Lett 2003; 552:225-30. [PMID: 14527691 DOI: 10.1016/s0014-5793(03)00929-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Profile matching methods are commonly used in searches in protein sequence databases to detect evolutionary relationships. We describe here a sensitive protocol, which detects remote similarities by searching in a specialized database of sequences belonging to a fold. We have assessed this protocol by exploring the relationships we detect among sequences known to belong to specific folds. We find that searches within sequences adopting a fold are more effective in detecting remote similarities and evolutionary connections than searches in a database of all sequences. We also discuss the implications of using this strategy to link sequence and structure space.
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Affiliation(s)
- S Sandhya
- Molecular Biophysics Unit, Indian Institute of Science, 560 012 Bangalore, India
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14
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Abstract
Central issues concerning protein structure prediction have been highlighted by the recently published summary of the fourth community-wide protein structure prediction experiment (CASP4). Although sequence/structure alignment remains the bottleneck in comparative modeling, there has been substantial progress in fully automated remote homolog detection and in de novo structure prediction. Significant further progress will probably require improvements in high-resolution modeling.
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Affiliation(s)
- Jack Schonbrun
- Howard Hughes Medical Institute and Department of Biochemistry, Box 357350, University of Washington, Seattle, Washington 98165, USA
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