1
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Xu G, Luo Z, Zhou R, Wang Q, Ma J. OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow. Brief Bioinform 2023; 24:bbad365. [PMID: 37833840 DOI: 10.1093/bib/bbad365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/29/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.
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Affiliation(s)
- Gang Xu
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China
- Shanghai AI Laboratory, Shanghai, 200030, China
| | - Zhenwei Luo
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China
- Shanghai AI Laboratory, Shanghai, 200030, China
| | - Ruhong Zhou
- Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
- Shanghai Institute for Advanced Study, Zhejiang University, Shanghai, 201203, China
| | - Qinghua Wang
- Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai, 200131, China
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 200433, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China
- Shanghai AI Laboratory, Shanghai, 200030, China
- Shanghai Institute for Advanced Study, Zhejiang University, Shanghai, 201203, China
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2
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DiIorio MC, Kulczyk AW. Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy. MICROMACHINES 2023; 14:1674. [PMID: 37763837 PMCID: PMC10534518 DOI: 10.3390/mi14091674] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023]
Abstract
Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.
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Affiliation(s)
- Megan C. DiIorio
- Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
| | - Arkadiusz W. Kulczyk
- Institute for Quantitative Biomedicine, Rutgers University, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Department of Biochemistry & Microbiology, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08901, USA
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3
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Giri N, Roy RS, Cheng J. Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions. Curr Opin Struct Biol 2023; 79:102536. [PMID: 36773336 PMCID: PMC10023387 DOI: 10.1016/j.sbi.2023.102536] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/20/2022] [Accepted: 01/03/2023] [Indexed: 02/11/2023]
Abstract
Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field.
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Affiliation(s)
- Nabin Giri
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA; NextGen Precision Health, University of Missouri, Columbia, 65211, Missouri, USA. https://twitter.com/@nvngiri
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA. https://twitter.com/@rajshekhorroy
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, Missouri, USA; NextGen Precision Health, University of Missouri, Columbia, 65211, Missouri, USA.
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4
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Nakamura A, Meng H, Zhao M, Wang F, Hou J, Cao R, Si D. Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps. Brief Bioinform 2023; 24:bbac632. [PMID: 36682003 PMCID: PMC10399284 DOI: 10.1093/bib/bbac632] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/15/2022] [Accepted: 12/29/2022] [Indexed: 01/23/2023] Open
Abstract
Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.
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Affiliation(s)
- Andrew Nakamura
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA
| | - Hanze Meng
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Minglei Zhao
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL 60637, USA
| | - Fengbin Wang
- Department of Biochemistry and Molecular Genetics, University of Alabama Birmingham, Heersink School of Medicine, Birmingham, AL 35233, USA
| | - Jie Hou
- Department of Computer Science, Saint Louis University, Saint Louis, MO 63103, USA
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA
| | - Dong Si
- Corresponding author: Dong Si, Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA. E-mail:
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5
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Lee S, Seok C, Park H. Benchmarking applicability of medium-resolution cryo-EM protein structures for structure-based drug design. J Comput Chem 2023; 44:1360-1368. [PMID: 36847771 DOI: 10.1002/jcc.27091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/18/2023] [Accepted: 02/05/2023] [Indexed: 03/01/2023]
Abstract
Cryo-electron microscopy (cryo-EM) is gaining large attention for high-resolution protein structure determination in solutions. However, a very high percentage of cryo-EM structures correspond to resolutions of 3-5 Å, making the structures difficult to be used in in silico drug design. In this study, we analyze how useful cryo-EM protein structures are for in silico drug design by evaluating ligand docking accuracy. From realistic cross-docking scenarios using medium resolution (3-5 Å) cryo-EM structures and a popular docking tool Autodock-Vina, only 20% of docking succeeded, when the success rate doubles in the same kind of cross-docking but using high-resolution (<2 Å) crystal structures instead. We decipher the reason for failures by decomposing the contribution from resolution-dependent and independent factors. The heterogeneity in the protein side-chain and backbone conformations is identified as the major resolution-dependent factor causing docking difficulty from our analysis, while intrinsic receptor flexibility mainly comprises the resolution-independent factor. We demonstrate the flexibility implementation in current ligand docking tools is able to rescue only a portion of failures (10%), and the limited performance was majorly due to potential structural errors than conformational changes. Our work suggests the strong necessity of more robust method developments on ligand docking and EM modeling techniques in order to fully utilize cryo-EM structures for in silico drug design.
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Affiliation(s)
- Seho Lee
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea.,Galux Inc., Seoul, Republic of Korea
| | - Hahnbeom Park
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
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6
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Zhu Z, Deng Z, Wang Q, Wang Y, Zhang D, Xu R, Guo L, Wen H. Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design. Front Pharmacol 2022; 13:939555. [PMID: 35837274 PMCID: PMC9275593 DOI: 10.3389/fphar.2022.939555] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
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Affiliation(s)
- Zhengdan Zhu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing Institute of Big Data Research, Beijing, China
| | - Zhenfeng Deng
- DP Technology, Beijing, China
- School of Pharmaceutical Sciences, Peking University, Beijing, China
| | | | | | - Duo Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- DP Technology, Beijing, China
| | - Ruihan Xu
- DP Technology, Beijing, China
- National Engineering Research Center of Visual Technology, Peking University, Beijing, China
| | | | - Han Wen
- DP Technology, Beijing, China
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7
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Singh S, Sahadevan R, Roy R, Biswas M, Ghosh P, Kar P, Sonawane A, Sadhukhan S. Structure-based design and synthesis of a novel long-chain 4''-alkyl ether derivative of EGCG as potent EGFR inhibitor: in vitro and in silico studies. RSC Adv 2022; 12:17821-17836. [PMID: 35765335 PMCID: PMC9201511 DOI: 10.1039/d2ra01919a] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/07/2022] [Indexed: 11/27/2022] Open
Abstract
Herein, we report the discovery of a novel long-chain ether derivative of (−)-epigallocatechin-3-gallate (EGCG), a major green tea polyphenol as a potent EGFR inhibitor. A series of 4′′-alkyl EGCG derivatives have been synthesized via regio-selectively alkylating the 4′′ hydroxyl group in the D-ring of EGCG and tested for their antiproliferative activities against high (A431), moderate (HeLa), and low (MCF-7) EGFR-expressing cancer cell lines. The most potent compound, 4′′-C14 EGCG showed the lowest IC50 values across all the tested cell lines. 4′′-C14 EGCG was also found to be significantly more stable than EGCG under physiological conditions (PBS at pH 7.4). Further western blot analysis and imaging data revealed that 4′′-C14 EGCG induced cell death in A431 cells with shrunken nuclei, nuclear fragmentation, membrane blebbing, and increased population of apoptotic cells where BAX upregulation and BCLXL downregulation were observed. In addition, autophosphorylation of EGFR and its downstream signalling proteins Akt and ERK were markedly inhibited by 4′′-C14 EGCG. MD simulation and the MM/PBSA analysis disclosed the binding mode of 4′′-C14 EGCG in the ATP-binding site of EGFR kinase domain. Taken together, our findings demonstrate that 4′′-C14 EGCG can act as a promising potent EGFR inhibitor with enhanced stability. Among the synthesized 4′′-alkyl EGCG derivatives, 4′′-C14 EGCG inhibited EGF stimulated phosphorylation of EGFR and its downstream signaling pathways, ERK and Akt. 4′′-C14 EGCG showed significantly improved stability than EGCG and induced apoptosis.![]()
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Affiliation(s)
- Satyam Singh
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore Madhya Pradesh 453 552 India
| | - Revathy Sahadevan
- Department of Chemistry, Indian Institute of Technology Palakkad Kerala 678 623 India
| | - Rajarshi Roy
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore Madhya Pradesh 453 552 India
| | - Mainak Biswas
- School of Biotechnology, KIIT Deemed to be University Bhubaneswar Orissa 751 024 India
| | - Priya Ghosh
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore Madhya Pradesh 453 552 India
| | - Parimal Kar
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore Madhya Pradesh 453 552 India
| | - Avinash Sonawane
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore Madhya Pradesh 453 552 India
| | - Sushabhan Sadhukhan
- Department of Chemistry, Indian Institute of Technology Palakkad Kerala 678 623 India .,Physical & Chemical Biology Laboratory, Indian Institute of Technology Palakkad Kerala 678 623 India.,Department of Biological Sciences & Engineering, Indian Institute of Technology Palakkad Kerala 678 623 India
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8
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Behkamal B, Naghibzadeh M, Saberi MR, Tehranizadeh ZA, Pagnani A, Al Nasr K. Three-Dimensional Graph Matching to Identify Secondary Structure Correspondence of Medium-Resolution Cryo-EM Density Maps. Biomolecules 2021; 11:1773. [PMID: 34944417 PMCID: PMC8698881 DOI: 10.3390/biom11121773] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/18/2021] [Accepted: 11/20/2021] [Indexed: 01/15/2023] Open
Abstract
Cryo-electron microscopy (cryo-EM) is a structural technique that has played a significant role in protein structure determination in recent years. Compared to the traditional methods of X-ray crystallography and NMR spectroscopy, cryo-EM is capable of producing images of much larger protein complexes. However, cryo-EM reconstructions are limited to medium-resolution (~4-10 Å) for some cases. At this resolution range, a cryo-EM density map can hardly be used to directly determine the structure of proteins at atomic level resolutions, or even at their amino acid residue backbones. At such a resolution, only the position and orientation of secondary structure elements (SSEs) such as α-helices and β-sheets are observable. Consequently, finding the mapping of the secondary structures of the modeled structure (SSEs-A) to the cryo-EM map (SSEs-C) is one of the primary concerns in cryo-EM modeling. To address this issue, this study proposes a novel automatic computational method to identify SSEs correspondence in three-dimensional (3D) space. Initially, through a modeling of the target sequence with the aid of extracting highly reliable features from a generated 3D model and map, the SSEs matching problem is formulated as a 3D vector matching problem. Afterward, the 3D vector matching problem is transformed into a 3D graph matching problem. Finally, a similarity-based voting algorithm combined with the principle of least conflict (PLC) concept is developed to obtain the SSEs correspondence. To evaluate the accuracy of the method, a testing set of 25 experimental and simulated maps with a maximum of 65 SSEs is selected. Comparative studies are also conducted to demonstrate the superiority of the proposed method over some state-of-the-art techniques. The results demonstrate that the method is efficient, robust, and works well in the presence of errors in the predicted secondary structures of the cryo-EM images.
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Affiliation(s)
- Bahareh Behkamal
- Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran;
| | - Mahmoud Naghibzadeh
- Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran;
| | - Mohammad Reza Saberi
- Medicinal Chemistry Department, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad 9177899191, Iran; (M.R.S.); (Z.A.T.)
- Bioinformatics Research Group, Mashhad University of Medical Sciences, Mashhad 9177899191, Iran
| | - Zeinab Amiri Tehranizadeh
- Medicinal Chemistry Department, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad 9177899191, Iran; (M.R.S.); (Z.A.T.)
| | - Andrea Pagnani
- Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy;
- Italian Institute for Genomic Medicine, IRCCS Candiolo, SP-142, I-10060 Candiolo, Italy
- INFN, Sezione di Torino, I-10125 Torino, Italy
| | - Kamal Al Nasr
- Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA
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9
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Alshammari M, He J. Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies. Molecules 2021; 26:7049. [PMID: 34834140 PMCID: PMC8624718 DOI: 10.3390/molecules26227049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 11/01/2021] [Accepted: 11/15/2021] [Indexed: 11/23/2022] Open
Abstract
Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information.
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Affiliation(s)
| | - Jing He
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA;
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10
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Seffernick JT, Canfield SM, Harvey SR, Wysocki VH, Lindert S. Prediction of Protein Complex Structure Using Surface-Induced Dissociation and Cryo-Electron Microscopy. Anal Chem 2021; 93:7596-7605. [PMID: 33999617 DOI: 10.1021/acs.analchem.0c05468] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
A variety of techniques involving the use of mass spectrometry (MS) have been developed to obtain structural information on proteins and protein complexes. One example of these techniques, surface-induced dissociation (SID), has been used to study the oligomeric state and connectivity of protein complexes. Recently, we demonstrated that appearance energies (AE) could be extracted from SID experiments and that they correlate with structural features of specific protein-protein interfaces. While SID AE provides some structural information, the AE data alone are not sufficient to determine the structures of the complexes. For this reason, we sought to supplement the data with computational modeling, through protein-protein docking. In a previous study, we demonstrated that the scoring of structures generated from protein-protein docking could be improved with the inclusion of SID data; however, this work relied on knowledge of the correct tertiary structure and only built full complexes for a few cases. Here, we performed docking using input structures that require less prior knowledge, using homology models, unbound crystal structures, and bound+perturbed crystal structures. Using flexible ensemble docking (to build primarily subcomplexes from an ensemble of backbone structures), the RMSD100 of all (15/15) predicted structures using the combined Rosetta, cryo-electron microscopy (cryo-EM), and SID score was less than 4 Å, compared to only 7/15 without SID and cryo-EM. Symmetric docking (which used symmetry to build full complexes) resulted in predicted structures with RMSD100 less than 4 Å for 14/15 cases with experimental data, compared to only 5/15 without SID and cryo-EM. Finally, we also developed a confidence metric for which all (26/26) proteins flagged as high confidence were accurately predicted.
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Affiliation(s)
- Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Shane M Canfield
- Department of Chemistry, Kenyon College, Gambier, Ohio 43022, United States
| | - Sophie R Harvey
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Vicki H Wysocki
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 West 18th Avenue, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 West 18th Avenue, Columbus, Ohio 43210, United States
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11
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Kulik M, Mori T, Sugita Y. Multi-Scale Flexible Fitting of Proteins to Cryo-EM Density Maps at Medium Resolution. Front Mol Biosci 2021; 8:631854. [PMID: 33842541 PMCID: PMC8025875 DOI: 10.3389/fmolb.2021.631854] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 01/26/2021] [Indexed: 11/13/2022] Open
Abstract
Structure determination using cryo-electron microscopy (cryo-EM) medium-resolution density maps is often facilitated by flexible fitting. Avoiding overfitting, adjusting force constants driving the structure to the density map, and emulating complex conformational transitions are major concerns in the fitting. To address them, we develop a new method based on a three-step multi-scale protocol. First, flexible fitting molecular dynamics (MD) simulations with coarse-grained structure-based force field and replica-exchange scheme between different force constants replicas are performed. Second, fitted Cα atom positions guide the all-atom structure in targeted MD. Finally, the all-atom flexible fitting refinement in implicit solvent adjusts the positions of the side chains in the density map. Final models obtained via the multi-scale protocol are significantly better resolved and more reliable in comparison with long all-atom flexible fitting simulations. The protocol is useful for multi-domain systems with intricate structural transitions as it preserves the secondary structure of single domains.
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Affiliation(s)
- Marta Kulik
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, Japan
| | - Takaharu Mori
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, Japan
| | - Yuji Sugita
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, Japan.,RIKEN Center for Computational Science, Kobe, Japan.,RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
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12
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Zhang B, Zhang W, Pearce R, Zhang Y, Shen HB. Fitting Low-Resolution Protein Structures into Cryo-EM Density Maps by Multiobjective Optimization of Global and Local Correlations. J Phys Chem B 2021; 125:528-538. [PMID: 33397114 DOI: 10.1021/acs.jpcb.0c09903] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The rigid-body fitting of predicted structural models into cryo-electron microscopy (cryo-EM) density maps is a necessary procedure for density map-guided protein structure determination and prediction. We proposed a novel multiobjective optimization protocol, MOFIT, which performs a rigid-body density-map fitting based on particle swarm optimization (PSO). MOFIT was tested on a large set of 292 nonhomologous single-domain proteins. Starting from structural models predicted by I-TASSER, MOFIT achieved an average coordinate root-mean-square deviation of 2.46 Å, which was 1.57, 2.79, and 3.95 Å lower than three leading single-objective function-based methods, where the differences were statistically significant with p-values of 1.65 × 10-6, 6.36 × 10-8, and 6.44 × 10-11 calculated using two-tail Student's t tests. Detailed analyses showed that the major advantages of MOFIT lie in the multiobjective protocol and the extensive PSO search simulations guided by the composite objective functions, which integrates complementary correlation coefficients from the global structure, local fragments, and individual residues with the cryo-EM density maps.
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Affiliation(s)
- Biao Zhang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Wenyi Zhang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Robin Pearce
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
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13
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Seffernick JT, Lindert S. Hybrid methods for combined experimental and computational determination of protein structure. J Chem Phys 2020; 153:240901. [PMID: 33380110 PMCID: PMC7773420 DOI: 10.1063/5.0026025] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/10/2020] [Indexed: 02/04/2023] Open
Abstract
Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.
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Affiliation(s)
- Justin T. Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
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14
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Alshammari M, He J. Combine Cryo-EM Density Map and Residue Contact for Protein Structure Prediction - A Case Study. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2020; 2020:110. [PMID: 35838376 PMCID: PMC9279007 DOI: 10.1145/3388440.3414708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Cryo-electron microscopy is a major structure determination technique for large molecular machines and membrane-associated complexes. Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. When combined with secondary structure sequence segments predicted from a protein sequence, it is possible to generate a set of likely topologies of α-traces and β-sheet traces. A topology describes the overall folding relationship among secondary structures; it is a critical piece of information for deriving the corresponding atomic structure. We propose a method for protein structure prediction that combines three sources of information: the secondary structure traces detected from the cryo-EM density map, predicted secondary structure sequence segments, and amino acid contact pairs predicted using MULTICOM. A case study shows that using amino acid contact prediction from MULTICOM improves the ranking of the true topology. Our observations convey that using a small set of highly voted secondary structure contact pairs enhances the ranking in all experiments conducted for this case.
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15
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Zhang B, Zhang X, Pearce R, Shen HB, Zhang Y. A New Protocol for Atomic-Level Protein Structure Modeling and Refinement Using Low-to-Medium Resolution Cryo-EM Density Maps. J Mol Biol 2020; 432:5365-5377. [PMID: 32771523 DOI: 10.1016/j.jmb.2020.07.027] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 07/14/2020] [Accepted: 07/31/2020] [Indexed: 12/19/2022]
Abstract
The rapid progress of cryo-electron microscopy (cryo-EM) in structural biology has raised an urgent need for robust methods to create and refine atomic-level structural models using low-resolution EM density maps. We propose a new protocol to create initial models using I-TASSER protein structure prediction, followed by EM density map-based rigid-body structure fitting, flexible fragment adjustment and atomic-level structure refinement simulations. The protocol was tested on a large set of 285 non-homologous proteins and generated structural models with correct folds for 260 proteins, where 28% had RMSDs below 2 Å. Compared to other state-of-the-art methods, the major advantage of the proposed pipeline lies in the uniform structure prediction and refinement protocol, as well as the extensive structural re-assembly simulations, which allow for low-to-medium resolution EM density map-guided structure modeling starting from amino acid sequences. Interestingly, the quality of both the image fitting and subsequent structure refinement was found to be strongly correlated with the correctness of the initial I-TASSER models; this is mainly due to the different correlation patterns observed between force field and structural quality for the models with template modeling score (or TM-score, a metric quantifying the similarity of models to the native) above and below a threshold of 0.5. Overall, the results demonstrate a new avenue that is ready to use for large-scale cryo-EM-based structure modeling and atomic-level density map-guided structure refinement.
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Affiliation(s)
- Biao Zhang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xi Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robin Pearce
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China.
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
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16
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McCafferty CL, Verbeke EJ, Marcotte EM, Taylor DW. Structural Biology in the Multi-Omics Era. J Chem Inf Model 2020; 60:2424-2429. [PMID: 32129623 PMCID: PMC7254829 DOI: 10.1021/acs.jcim.9b01164] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Indexed: 12/12/2022]
Abstract
Rapid developments in cryogenic electron microscopy have opened new avenues to probe the structures of protein assemblies in their near native states. Recent studies have begun applying single -particle analysis to heterogeneous mixtures, revealing the potential of structural-omics approaches that combine the power of mass spectrometry and electron microscopy. Here we highlight advances and challenges in sample preparation, data processing, and molecular modeling for handling increasingly complex mixtures. Such advances will help structural-omics methods extend to cellular-level models of structural biology.
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Affiliation(s)
- Caitlyn L. McCafferty
- Department
of Molecular Biosciences, University of
Texas at Austin, Austin, Texas 78712, United States
| | - Eric J. Verbeke
- Department
of Molecular Biosciences, University of
Texas at Austin, Austin, Texas 78712, United States
| | - Edward M. Marcotte
- Department
of Molecular Biosciences, University of
Texas at Austin, Austin, Texas 78712, United States
- Institute
for Cellular and Molecular Biology, University
of Texas at Austin, Austin, Texas 78712, United States
- Center
for Systems and Synthetic Biology, University
of Texas at Austin, Austin, Texas 78712, United States
| | - David W. Taylor
- Department
of Molecular Biosciences, University of
Texas at Austin, Austin, Texas 78712, United States
- Institute
for Cellular and Molecular Biology, University
of Texas at Austin, Austin, Texas 78712, United States
- Center
for Systems and Synthetic Biology, University
of Texas at Austin, Austin, Texas 78712, United States
- LIVESTRONG
Cancer Institutes, Dell Medical School, Austin, Texas 78712, United States
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17
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Leelananda SP, Lindert S. Using NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD Protein Structure Refinement. J Chem Inf Model 2020; 60:2522-2532. [PMID: 31872764 PMCID: PMC7262651 DOI: 10.1021/acs.jcim.9b00932] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Cryo-EM has become one of the prime methods for protein structure elucidation, frequently yielding density maps with near-atomic or medium resolution. If protein structures cannot be deduced unambiguously from the density maps, computational structure refinement tools are needed to generate protein structural models. We have previously developed an iterative Rosetta-MDFF protocol that used cryo-EM densities to refine protein structures. Here we show that, in addition to cryo-EM densities, incorporation of other experimental restraints into the Rosetta-MDFF protocol further improved refined structures. We used NMR chemical shift (CS) data integrated with cryo-EM densities in our hybrid protocol in both the Rosetta step and the molecular dynamics (MD) simulations step. In 15 out of 18 cases for all MD rounds, the refinement results obtained when density maps and NMR chemical shift data were used in combination outperformed those of density map-only refinement. Notably, the improvement in refinement was highest when medium and low-resolution density maps were used. With our hybrid method, the RMSDs of final models obtained were always better than the RMSDs obtained by our previous protocol with just density refinement for both medium (6.9 Å) and low (9 Å) resolution maps. For all the six test proteins with medium resolution density maps (6.9 Å), the final refined structure RMSDs were lower for the hybrid method than for the cryo-EM only refinement. The final refined RMSDs were less than 1.5 Å when our hybrid protocol was used with 4 Å density maps. For four out of the six proteins the final RMSDs were even less than 1 Å. This study demonstrates that by using a combination of cryo-EM and NMR restraints, it is possible to refine structures to atomic resolution, outperforming single restraint refinement. This hybrid protocol will be a valuable tool when only low-resolution cryo-EM density data and NMR chemical shift data are available to refine structures.
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Affiliation(s)
- Sumudu P. Leelananda
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210
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18
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Srivastava A, Tiwari SP, Miyashita O, Tama F. Integrative/Hybrid Modeling Approaches for Studying Biomolecules. J Mol Biol 2020; 432:2846-2860. [DOI: 10.1016/j.jmb.2020.01.039] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/20/2020] [Accepted: 01/24/2020] [Indexed: 12/12/2022]
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19
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Terwilliger TC, Adams PD, Afonine PV, Sobolev OV. Cryo-EM map interpretation and protein model-building using iterative map segmentation. Protein Sci 2019; 29:87-99. [PMID: 31599033 PMCID: PMC6933853 DOI: 10.1002/pro.3740] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 09/30/2019] [Accepted: 10/01/2019] [Indexed: 11/17/2022]
Abstract
A procedure for building protein chains into maps produced by single‐particle electron cryo‐microscopy (cryo‐EM) is described. The procedure is similar to the way an experienced structural biologist might analyze a map, focusing first on secondary structure elements such as helices and sheets, then varying the contour level to identify connections between these elements. Since the high density in a map typically follows the main‐chain of the protein, the main‐chain connection between secondary structure elements can often be identified as the unbranched path between them with the highest minimum value along the path. This chain‐tracing procedure is then combined with finding side‐chain positions based on the presence of density extending away from the main path of the chain, allowing generation of a Cα model. The Cα model is converted to an all‐atom model and is refined against the map. We show that this procedure is as effective as other existing methods for interpretation of cryo‐EM maps and that it is considerably faster and produces models with fewer chain breaks than our previous methods that were based on approaches developed for crystallographic maps.
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Affiliation(s)
- Thomas C Terwilliger
- Los Alamos National Laboratory, Los Alamos, New Mexico.,New Mexico Consortium, Los Alamos, New Mexico
| | - Paul D Adams
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California.,Department of Bioengineering, University of California Berkeley, Berkeley, California
| | - Pavel V Afonine
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California
| | - Oleg V Sobolev
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, California
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20
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Saltzberg DJ, Hepburn M, Pilla KB, Schriemer DC, Lees-Miller SP, Blundell TL, Sali A. SSEThread: Integrative threading of the DNA-PKcs sequence based on data from chemical cross-linking and hydrogen deuterium exchange. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2019; 147:92-102. [PMID: 31570166 DOI: 10.1016/j.pbiomolbio.2019.09.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 08/09/2019] [Accepted: 09/10/2019] [Indexed: 01/19/2023]
Abstract
X-ray crystallography and electron microscopy maps resolved to 3-8 Å are generally sufficient for tracing the path of the polypeptide chain in space, while often insufficient for unambiguously registering the sequence on the path (i.e., threading). Frequently, however, additional information is available from other biophysical experiments, physical principles, statistical analyses, and other prior models. Here, we formulate an integrative approach for sequence assignment to a partial backbone model as an optimization problem, which requires three main components: the representation of the system, the scoring function, and the optimization method. The method is implemented in the open source Integrative Modeling Platform (IMP) (https://integrativemodeling.org), allowing a number of different terms in the scoring function. We apply this method to localizing the sequence assignment within a 199-residue disordered region of three structured and sequence unassigned helices in the DNA-PKcs crystallographic structure, using chemical crosslinks, hydrogen deuterium exchange, and sequence connectivity. The resulting ensemble of threading models provides two major solutions, one of which suggests that the crucial ABCDE cluster of phosphorylation sites cannot undergo intra-molecular autophosphorylation without a conformational rearrangement. The ensemble of solutions embodies the most accurate and precise sequence threading given the available information.
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Affiliation(s)
- Daniel J Saltzberg
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA.
| | - Morgan Hepburn
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
| | - Kala Bharath Pilla
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA
| | - David C Schriemer
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
| | - Susan P Lees-Miller
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA
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21
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Seffernick J, Harvey SR, Wysocki VH, Lindert S. Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data. ACS CENTRAL SCIENCE 2019; 5:1330-1341. [PMID: 31482115 PMCID: PMC6716128 DOI: 10.1021/acscentsci.8b00912] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Indexed: 05/23/2023]
Abstract
Recently, mass spectrometry (MS) has become a viable method for elucidation of protein structure. Surface-induced dissociation (SID), colliding multiply charged protein complexes or other ions with a surface, has been paired with native MS to provide useful structural information such as connectivity and topology for many different protein complexes. We recently showed that SID gives information not only on connectivity and topology but also on relative interface strengths. However, SID has not yet been coupled with computational structure prediction methods that could use the sparse information from SID to improve the prediction of quaternary structures, i.e., how protein subunits interact with each other to form complexes. Protein-protein docking, a computational method to predict the quaternary structure of protein complexes, can be used in combination with subunit structures from X-ray crystallography and NMR in situations where it is difficult to obtain an experimental structure of an entire complex. While de novo structure prediction can be successful, many studies have shown that inclusion of experimental data can greatly increase prediction accuracy. In this study, we show that the appearance energy (AE, defined as 10% fragmentation) extracted from SID can be used in combination with Rosetta to successfully evaluate protein-protein docking poses. We developed an improved model to predict measured SID AEs and incorporated this model into a scoring function that combines the RosettaDock scoring function with a novel SID scoring term, which quantifies agreement between experiments and structures generated from RosettaDock. As a proof of principle, we tested the effectiveness of these restraints on 57 systems using ideal SID AE data (AE determined from crystal structures using the predictive model). When theoretical AEs were used, the RMSD of the selected structure improved or stayed the same in 95% of cases. When experimental SID data were incorporated on a different set of systems, the method predicted near-native structures (less than 2 Å root-mean-square deviation, RMSD, from native) for 6/9 tested cases, while unrestrained RosettaDock (without SID data) only predicted 3/9 such cases. Score versus RMSD funnel profiles were also improved when SID data were included. Additionally, we developed a confidence measure to evaluate predicted model quality in the absence of a crystal structure.
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22
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Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning. Nat Methods 2019; 16:911-917. [PMID: 31358979 PMCID: PMC6717539 DOI: 10.1038/s41592-019-0500-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 06/24/2019] [Indexed: 02/05/2023]
Abstract
An increasing number of protein structures have been solved by cryo-electron microscopy (cryo-EM). Although structures determined at near-atomic resolution are now routinely reported, many density maps are still determined at an intermediate resolution, where extracting structure information is still a challenge. We have developed a computational method, Emap2sec, which identifies the secondary structures of proteins (α helices, β sheets, and other structures) in an EM map of 5 to 10 Å resolution. Emap2sec uses a 3D deep convolutional neural network to assign secondary structure to each grid point in an EM map. We tested Emap2sec on 6.0 and 10.0 Å resolution EM maps simulated from 34 structures, as well as on 43 maps determined experimentally at 5.0 to 9.5 Å resolution. Emap2sec was able to clearly identify the secondary structures in many maps tested, and showed substantially better performance than existing methods.
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23
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Bonomi M, Vendruscolo M. Determination of protein structural ensembles using cryo-electron microscopy. Curr Opin Struct Biol 2019; 56:37-45. [DOI: 10.1016/j.sbi.2018.10.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 10/24/2018] [Accepted: 10/26/2018] [Indexed: 10/27/2022]
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24
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Aprahamian ML, Lindert S. Utility of Covalent Labeling Mass Spectrometry Data in Protein Structure Prediction with Rosetta. J Chem Theory Comput 2019; 15:3410-3424. [PMID: 30946594 DOI: 10.1021/acs.jctc.9b00101] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Covalent labeling mass spectrometry experiments are growing in popularity and provide important information regarding protein structure. Information obtained from these experiments correlates with residue solvent exposure within the protein in solution. However, it is impossible to determine protein structure from covalent labeling data alone. Incorporation of sparse covalent labeling data into the protein structure prediction software Rosetta has been shown to improve protein tertiary structure prediction. Here, covalent labeling techniques were analyzed computationally to provide insight into what labeling data is needed to optimize tertiary protein structure prediction in Rosetta. We have successfully implemented a new scoring functionality that provides improved predictions. We developed two new covalent labeling based score terms that use a "cone"-based neighbor count to quantify the relative solvent exposure of each amino acid. To test our method, we used a set of 20 proteins with structures deposited in the Protein Data Bank. Decoy model sets were generated for each of these 20 proteins, and the normalized covalent labeling score versus RMSD distributions were evaluated. On the basis of these distributions, we have determined an optimal subset of residues to use when performing covalent labeling experiments in order to maximize the structure prediction capabilities of the covalent labeling data. We also investigated how much false negative and false positive data can be tolerated without meaningfully impacting protein structure prediction. Using these new covalent labeling score terms, protein models were rescored and the resulting models improved by 3.9 Å RMSD on average. New models were also generated using Rosetta's AbinitioRelax program under the guidance of covalent labeling information, and improvement in model quality was observed.
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Affiliation(s)
- Melanie L Aprahamian
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
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25
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Simultaneous Determination of Protein Structure and Dynamics Using Cryo-Electron Microscopy. Biophys J 2019; 114:1604-1613. [PMID: 29642030 PMCID: PMC5954442 DOI: 10.1016/j.bpj.2018.02.028] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 02/05/2018] [Accepted: 02/20/2018] [Indexed: 11/21/2022] Open
Abstract
Cryo-electron microscopy is rapidly emerging as a powerful technique to determine the structures of complex macromolecular systems elusive to other techniques. Because many of these systems are highly dynamical, characterizing their movements is also a crucial step to unravel their biological functions. To achieve this goal, we report an integrative modeling approach to simultaneously determine structure and dynamics of macromolecular systems from cryo-electron microscopy density maps. By quantifying the level of noise in the data and dealing with their ensemble-averaged nature, this approach enables the integration of multiple sources of information to model ensembles of structures and infer their populations. We illustrate the method by characterizing structure and dynamics of the integral membrane receptor STRA6, thus providing insights into the mechanisms by which it interacts with retinol binding protein and translocates retinol across the membrane.
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26
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Terwilliger TC, Adams PD, Afonine PV, Sobolev OV. Map segmentation, automated model-building and their application to the Cryo-EM Model Challenge. J Struct Biol 2018; 204:338-343. [PMID: 30063987 PMCID: PMC6163059 DOI: 10.1016/j.jsb.2018.07.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 07/11/2018] [Accepted: 07/27/2018] [Indexed: 11/27/2022]
Abstract
A recently-developed method for identifying a compact, contiguous region representing the unique part of a density map was applied to 218 Cryo-EM maps with resolutions of 4.5 Å or better. The key elements of the segmentation procedure are (1) identification of all regions of density above a threshold and (2) choice of a unique set of these regions, taking symmetry into consideration, that maximize connectivity and compactness. This segmentation approach was then combined with tools for automated map sharpening and model-building to generate models for the 12 maps in the 2016 Cryo-EM Model Challenge in a fully automated manner. The resulting models have completeness from 24% to 82% and RMS distances from reference interpretations of 0.6 Å-2.1 Å.
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Affiliation(s)
- Thomas C Terwilliger
- Los Alamos National Laboratory, Los Alamos, NM 87545, USA; New Mexico Consortium, Los Alamos, NM 87544, USA.
| | - Paul D Adams
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720-8235, USA; Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
| | - Pavel V Afonine
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720-8235, USA; Department of Physics and International Centre for Quantum and Molecular Structures, Shanghai University, Shanghai 200444, People's Republic of China
| | - Oleg V Sobolev
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720-8235, USA
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27
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Bonomi M, Hanot S, Greenberg CH, Sali A, Nilges M, Vendruscolo M, Pellarin R. Bayesian Weighing of Electron Cryo-Microscopy Data for Integrative Structural Modeling. Structure 2018; 27:175-188.e6. [PMID: 30393052 DOI: 10.1016/j.str.2018.09.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 08/07/2018] [Accepted: 09/19/2018] [Indexed: 10/28/2022]
Abstract
Cryo-electron microscopy (cryo-EM) has become a mainstream technique for determining the structures of complex biological systems. However, accurate integrative structural modeling has been hampered by the challenges in objectively weighing cryo-EM data against other sources of information due to the presence of random and systematic errors, as well as correlations, in the data. To address these challenges, we introduce a Bayesian scoring function that efficiently and accurately ranks alternative structural models of a macromolecular system based on their consistency with a cryo-EM density map as well as other experimental and prior information. The accuracy of this approach is benchmarked using complexes of known structure and illustrated in three applications: the structural determination of the GroEL/GroES, RNA polymerase II, and exosome complexes. The approach is implemented in the open-source Integrative Modeling Platform (http://integrativemodeling.org), thus enabling integrative structure determination by combining cryo-EM data with other sources of information.
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Affiliation(s)
| | - Samuel Hanot
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France
| | - Charles H Greenberg
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, CA 94158, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, CA 94158, USA
| | - Michael Nilges
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France
| | | | - Riccardo Pellarin
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France.
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28
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Travers T, Wang KJ, López CA, Gnanakaran S. Sequence- and structure-based computational analyses of Gram-negative tripartite efflux pumps in the context of bacterial membranes. Res Microbiol 2018; 169:414-424. [DOI: 10.1016/j.resmic.2018.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 12/28/2017] [Accepted: 01/21/2018] [Indexed: 01/12/2023]
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29
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Tiemann JK, Rose AS, Ismer J, Darvish MD, Hilal T, Spahn CM, Hildebrand PW. FragFit: a web-application for interactive modeling of protein segments into cryo-EM density maps. Nucleic Acids Res 2018; 46:W310-W314. [PMID: 29788317 PMCID: PMC6030921 DOI: 10.1093/nar/gky424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 05/10/2018] [Indexed: 11/20/2022] Open
Abstract
Cryo-electron microscopy (cryo-EM) is a standard method to determine the three-dimensional structures of molecular complexes. However, easy to use tools for modeling of protein segments into cryo-EM maps are sparse. Here, we present the FragFit web-application, a web server for interactive modeling of segments of up to 35 amino acids length into cryo-EM density maps. The fragments are provided by a regularly updated database containing at the moment about 1 billion entries extracted from PDB structures and can be readily integrated into a protein structure. Fragments are selected based on geometric criteria, sequence similarity and fit into a given cryo-EM density map. Web-based molecular visualization with the NGL Viewer allows interactive selection of fragments. The FragFit web-application, accessible at http://proteinformatics.de/FragFit, is free and open to all users, without any login requirements.
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Affiliation(s)
- Johanna Ks Tiemann
- Institute of Medical Physics and Biophysics, Charité University Medicine Berlin, Berlin 10117, Germany.,Institute of Medical Physics and Biophysics, Medical University Leipzig, Leipzig, Sachsen 04107, Germany
| | - Alexander S Rose
- Institute of Medical Physics and Biophysics, Charité University Medicine Berlin, Berlin 10117, Germany
| | - Jochen Ismer
- Institute of Medical Physics and Biophysics, Charité University Medicine Berlin, Berlin 10117, Germany
| | - Mitra D Darvish
- Institute of Medical Physics and Biophysics, Charité University Medicine Berlin, Berlin 10117, Germany
| | - Tarek Hilal
- Institute of Medical Physics and Biophysics, Charité University Medicine Berlin, Berlin 10117, Germany
| | - Christian Mt Spahn
- Institute of Medical Physics and Biophysics, Charité University Medicine Berlin, Berlin 10117, Germany
| | - Peter W Hildebrand
- Institute of Medical Physics and Biophysics, Charité University Medicine Berlin, Berlin 10117, Germany.,Institute of Medical Physics and Biophysics, Medical University Leipzig, Leipzig, Sachsen 04107, Germany
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Aprahamian ML, Chea EE, Jones LM, Lindert S. Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data. Anal Chem 2018; 90:7721-7729. [PMID: 29874044 DOI: 10.1021/acs.analchem.8b01624] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In recent years mass spectrometry-based covalent labeling techniques such as hydroxyl radical footprinting (HRF) have emerged as valuable structural biology techniques, yielding information on protein tertiary structure. These data, however, are not sufficient to predict protein structure unambiguously, as they provide information only on the relative solvent exposure of certain residues. Despite some recent advances, no software currently exists that can utilize covalent labeling mass spectrometry data to predict protein tertiary structure. We have developed the first such tool, which incorporates mass spectrometry derived protection factors from HRF labeling as a new centroid score term for the Rosetta scoring function to improve the prediction of protein tertiary structures. We tested our method on a set of four soluble benchmark proteins with known crystal structures and either published HRF experimental results or internally acquired data. Using the HRF labeling data, we rescored large decoy sets of structures predicted with Rosetta for each of the four benchmark proteins. As a result, the model quality improved for all benchmark proteins as compared to when scored with Rosetta alone. For two of the four proteins we were even able to identify atomic resolution models with the addition of HRF data.
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Affiliation(s)
- Melanie L Aprahamian
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| | - Emily E Chea
- Department of Pharmaceutical Sciences , University of Maryland , Baltimore , Maryland 21201 , United States
| | - Lisa M Jones
- Department of Pharmaceutical Sciences , University of Maryland , Baltimore , Maryland 21201 , United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
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31
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Cassidy CK, Himes BA, Luthey-Schulten Z, Zhang P. CryoEM-based hybrid modeling approaches for structure determination. Curr Opin Microbiol 2018; 43:14-23. [PMID: 29107896 PMCID: PMC5934336 DOI: 10.1016/j.mib.2017.10.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 10/04/2017] [Accepted: 10/09/2017] [Indexed: 12/21/2022]
Abstract
Recent advances in cryo-electron microscopy (cryoEM) have dramatically improved the resolutions at which vitrified biological specimens can be studied, revealing new structural and mechanistic insights over a broad range of spatial scales. Bolstered by these advances, much effort has been directed toward the development of hybrid modeling methodologies for the construction and refinement of high-fidelity atomistic models from cryoEM data. In this brief review, we will survey the key elements of cryoEM-based hybrid modeling, providing an overview of available computational tools and strategies as well as several recent applications.
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Affiliation(s)
- C Keith Cassidy
- Department of Physics, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Benjamin A Himes
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Zaida Luthey-Schulten
- Department of Chemistry, Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Peijun Zhang
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK; Electron Bio-Imaging Centre, Diamond Light Sources, Harwell Science and Innovation Campus, Didcot OX11 0DE, UK.
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32
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Xia Y, Fischer AW, Teixeira P, Weiner B, Meiler J. Integrated Structural Biology for α-Helical Membrane Protein Structure Determination. Structure 2018; 26:657-666.e2. [PMID: 29526436 PMCID: PMC5884713 DOI: 10.1016/j.str.2018.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 06/14/2017] [Accepted: 02/05/2018] [Indexed: 01/12/2023]
Abstract
While great progress has been made, only 10% of the nearly 1,000 integral, α-helical, multi-span membrane protein families are represented by at least one experimentally determined structure in the PDB. Previously, we developed the algorithm BCL::MP-Fold, which samples the large conformational space of membrane proteins de novo by assembling predicted secondary structure elements guided by knowledge-based potentials. Here, we present a case study of rhodopsin fold determination by integrating sparse and/or low-resolution restraints from multiple experimental techniques including electron microscopy, electron paramagnetic resonance spectroscopy, and nuclear magnetic resonance spectroscopy. Simultaneous incorporation of orthogonal experimental restraints not only significantly improved the sampling accuracy but also allowed identification of the correct fold, which is demonstrated by a protein size-normalized transmembrane root-mean-square deviation as low as 1.2 Å. The protocol developed in this case study can be used for the determination of unknown membrane protein folds when limited experimental restraints are available.
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Affiliation(s)
- Yan Xia
- Department of Chemistry, Vanderbilt University, Stevenson Center, Station B 351822, Room 7330, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Axel W Fischer
- Department of Chemistry, Vanderbilt University, Stevenson Center, Station B 351822, Room 7330, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Pedro Teixeira
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Brian Weiner
- Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Stevenson Center, Station B 351822, Room 7330, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37232, USA.
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33
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Kim SS, Seffernick JT, Lindert S. Accurately Predicting Disordered Regions of Proteins Using Rosetta ResidueDisorder Application. J Phys Chem B 2018; 122:3920-3930. [PMID: 29595057 DOI: 10.1021/acs.jpcb.8b01763] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Although many proteins necessitate well-folded structures to properly instigate their biological functions, a large fraction of functioning proteins contain regions-known as intrinsically disordered protein regions-where stable structures are not likely to form. Notable functional roles of intrinsically disordered proteins are in transcriptional regulation, translation, and cellular signal transduction. Moreover, intrinsically disordered protein regions are highly abundant in many proteins associated with various human diseases, therefore these segments have become attractive drug targets for potential therapeutics. Over the past decades, numerous computational methods have been developed to accurately predict disordered regions of proteins. Here we introduce a user-friendly and reliable approach for the prediction of disordered protein regions using the structure prediction software Rosetta. Using 245 proteins from a benchmark data set (16 DisProt database proteins) and a test data set (229 proteins with NMR data), we use Rosetta to predict the global protein structures and then show that there is a statistically significant difference between Rosetta scores in disordered and ordered regions, with scores being less favorable in disordered regions. Furthermore, the difference in scores between ordered and disordered protein regions is sufficient to accurately identify disordered protein regions. As a result, our Rosetta ResidueDisorder method (benchmark data set prediction accuracy of 71.77% and independent test data set prediction accuracy of 65.37%) outperformed other established disorder prediction tools and did not exhibit a biased prediction toward either ordered or disordered regions. To facilitate usage, a Rosetta application has been developed for the Rosetta ResidueDisorder method.
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Affiliation(s)
- Stephanie S Kim
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry , Ohio State University , Columbus , Ohio 43210 , United States
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34
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Abstract
Structural studies of biocomplexes using single-particle cryo-electron microscopy (cryo-EM) is now a well-established technique in structural biology and has become competitive with X-ray crystallography. The latest advances in EM enable us to determine structures of protein complexes at 3-5 Å resolution for an extremely broad range of sizes from ~200 kDa up to hundreds of megadaltons (Bartesaghi et al., Science 348(6239):1147-1151, 2051; Bai et al., Nature 525(7568):212-217, 2015; Vinothkumar et al., Nature 515(7525):80-84, 2014; Grigorieff and Harrison, Curr Opin Struct Biol 21(2):265-273, 2011). The majority of biocomplexes comprise a number of different components and are not amenable to crystallisation. Secretion systems are typical examples of such multi-protein complexes, and structural studies of them are extremely challenging. The only feasible approach to revealing their spatial organisation and functional modification is cryo-EM. The development of systems for digital registration of images and algorithms for the fast and efficient processing of recorded images and subsequent analysis facilitated the determination of structures at near-atomic resolution. In this review we will describe sample preparation for cryo-EM, how data are collected by new detectors, and the logistics of image analysis through the basic steps required for reconstructions of both small and large biological complexes and their refinement to nearly atomic resolution. The processing workflow is illustrated using examples of EM analysis of a Type IV Secretion System.
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35
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Li B, Fooksa M, Heinze S, Meiler J. Finding the needle in the haystack: towards solving the protein-folding problem computationally. Crit Rev Biochem Mol Biol 2018; 53:1-28. [PMID: 28976219 PMCID: PMC6790072 DOI: 10.1080/10409238.2017.1380596] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 08/22/2017] [Accepted: 09/13/2017] [Indexed: 12/22/2022]
Abstract
Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
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Affiliation(s)
- Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Michaela Fooksa
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, USA
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
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36
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Al Nasr K, Yousef F, Jebril R, Jones C. Analytical Approaches to Improve Accuracy in Solving the Protein Topology Problem. Molecules 2018; 23:E28. [PMID: 29360779 PMCID: PMC6017786 DOI: 10.3390/molecules23020028] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 01/19/2018] [Accepted: 01/19/2018] [Indexed: 11/17/2022] Open
Abstract
To take advantage of recent advances in genomics and proteomics it is critical that the three-dimensional physical structure of biological macromolecules be determined. Cryo-Electron Microscopy (cryo-EM) is a promising and improving method for obtaining this data, however resolution is often not sufficient to directly determine the atomic scale structure. Despite this, information for secondary structure locations is detectable. De novo modeling is a computational approach to modeling these macromolecular structures based on cryo-EM derived data. During de novo modeling a mapping between detected secondary structures and the underlying amino acid sequence must be identified. DP-TOSS (Dynamic Programming for determining the Topology Of Secondary Structures) is one tool that attempts to automate the creation of this mapping. By treating the correspondence between the detected structures and the structures predicted from sequence data as a constraint graph problem DP-TOSS achieved good accuracy in its original iteration. In this paper, we propose modifications to the scoring methodology of DP-TOSS to improve its accuracy. Three scoring schemes were applied to DP-TOSS and tested: (i) a skeleton-based scoring function; (ii) a geometry-based analytical function; and (iii) a multi-well potential energy-based function. A test of 25 proteins shows that a combination of these schemes can improve the performance of DP-TOSS to solve the topology determination problem for macromolecule proteins.
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Affiliation(s)
- Kamal Al Nasr
- Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA.
| | - Feras Yousef
- Department of Mathematics, The University of Jordan, Amman 11942, Jordan.
| | - Ruba Jebril
- Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA.
| | - Christopher Jones
- Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA.
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37
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Al Nasr K, Jones C, Yousef F, Jebril R. PEM-fitter: A Coarse-Grained Method to Validate Protein Candidate Models. J Comput Biol 2017; 25:21-32. [PMID: 29140718 DOI: 10.1089/cmb.2017.0191] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The volumetric images produced by Cryo-Electron Microscopy (cryo-EM) technique are used to model macromolecular assemblies and machines. De novo protein modeling uses these images to computationally model the structure of the molecules. Many candidate conformations are usually generated during the intermediate step. Conventionally, each of these candidates is evaluated by time-consuming approaches such as potential energy. We introduce an initial version of a geometrical screening method that uses the skeleton of the cryo-EM images to evaluate candidate structures. The aim of this method is to reduce the number of native-like candidate conformations and, therefore, reduce the time required for structural evaluation by energy calculations. A test of two datasets was performed. The first dataset contains 10 proteins and shows that our method can successfully detect the correct native structure for the given skeleton among a set of different protein structures. The second dataset contains 12 proteins and shows that our method can filter slightly modified decoy conformations of the same protein. The efficiency of the method is also reported.
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Affiliation(s)
- Kamal Al Nasr
- 1 Department of Computer Science, Tennessee State University , Nashville, Tennessee
| | - Christopher Jones
- 1 Department of Computer Science, Tennessee State University , Nashville, Tennessee
| | - Feras Yousef
- 2 Department of Mathematics, The University of Jordan , Amman, Jordan
| | - Ruba Jebril
- 1 Department of Computer Science, Tennessee State University , Nashville, Tennessee
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38
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Leelananda SP, Lindert S. Iterative Molecular Dynamics-Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities. J Chem Theory Comput 2017; 13:5131-5145. [PMID: 28949136 DOI: 10.1021/acs.jctc.7b00464] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Knowing atomistic details of proteins is essential not only for the understanding of protein function but also for the development of drugs. Experimental methods such as X-ray crystallography, NMR, and cryo-electron microscopy (cryo-EM) are the preferred forms of protein structure determination and have achieved great success over the most recent decades. Computational methods may be an alternative when experimental techniques fail. However, computational methods are severely limited when it comes to predicting larger macromolecule structures with little sequence similarity to known structures. The incorporation of experimental restraints in computational methods is becoming increasingly important to more reliably predict protein structure. One such experimental input used in structure prediction and refinement is cryo-EM densities. Recent advances in cryo-EM have arguably revolutionized the field of structural biology. Our previously developed cryo-EM-guided Rosetta-MD protocol has shown great promise in the refinement of soluble protein structures. In this study, we extended cryo-EM density-guided iterative Rosetta-MD to membrane proteins. We also improved the methodology in general by picking models based on a combination of their score and fit-to-density during the Rosetta model selection. By doing so, we have been able to pick models superior to those with the previous selection based on Rosetta score only and we have been able to further improve our previously refined models of soluble proteins. The method was tested with five membrane spanning protein structures. By applying density-guided Rosetta-MD iteratively we were able to refine the predicted structures of these membrane proteins to atomic resolutions. We also showed that the resolution of the density maps determines the improvement and quality of the refined models. By incorporating high-resolution density maps (∼4 Å), we were able to more significantly improve the quality of the models than when medium-resolution maps (6.9 Å) were used. Beginning from an average starting structure root mean square deviation (RMSD) to native of 4.66 Å, our protocol was able to refine the structures to bring the average refined structure RMSD to 1.66 Å when 4 Å density maps were used. The protocol also successfully refined the HIV-1 CTD guided by an experimental 5 Å density map.
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Affiliation(s)
- Sumudu P Leelananda
- Department of Chemistry and Biochemistry, Ohio State University , Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University , Columbus, Ohio 43210, United States
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39
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Abstract
RosettaES, an algorithm that uses a fragment-based sampling strategy, improves macromolecular structure modeling from cryo-EM data at 3–5-Å resolution. Accurate atomic modeling of macromolecular structures into cryo-electron microscopy (cryo-EM) maps is a major challenge, as the moderate resolution makes accurate placement of atoms difficult. We present Rosetta enumerative sampling (RosettaES), an automated tool that uses a fragment-based sampling strategy for de novo model completion of macromolecular structures from cryo-EM density maps at 3–5-Å resolution. On a benchmark set of nine proteins, RosettaES was able to identify near-native conformations in 85% of segments. RosettaES was also used to determine models for three challenging macromolecular structures.
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40
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Zhou N, Wang H, Wang J. EMBuilder: A Template Matching-based Automatic Model-building Program for High-resolution Cryo-Electron Microscopy Maps. Sci Rep 2017; 7:2664. [PMID: 28572576 PMCID: PMC5453991 DOI: 10.1038/s41598-017-02725-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Accepted: 04/18/2017] [Indexed: 01/17/2023] Open
Abstract
The resolution of electron-potential maps in single-particle cryo-electron microscopy (cryoEM) is approaching atomic or near- atomic resolution. However, no program currently exists for de novo cryoEM model building at resolutions exceeding beyond 3.5 Å. Here, we present a program, EMBuilder, based on template matching, to generate cryoEM models at high resolution. The program identifies features in both secondary-structure and Cα stages. In the secondary structure stage, helices and strands are identified with pre-computed templates, and the voxel size of the entire map is then refined to account for microscopic magnification errors. The identified secondary structures are then extended from both ends in the Cα stage via a log-likelihood (LLK) target function, and if possible, the side chains are also assigned. This program can build models of large proteins (~1 MDa) in a reasonable amount of time (~1 day) and thus has the potential to greatly decrease the manual workload required for model building of high-resolution cryoEM maps.
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Affiliation(s)
- Niyun Zhou
- MOE Key Laboratory of Protein Science, Tsinghua University, Beijing, 100084, China.,School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Hongwei Wang
- MOE Key Laboratory of Protein Science, Tsinghua University, Beijing, 100084, China. .,School of Life Sciences, Tsinghua University, Beijing, 100084, China.
| | - Jiawei Wang
- State Key Laboratory of Membrane Biology, Tsinghua University, Beijing, 100084, China.
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41
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Li R, Si D, Zeng T, Ji S, He J. Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2017; 2016:41-46. [PMID: 29770260 DOI: 10.1109/bibm.2016.7822490] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The detection of secondary structure of proteins using three dimensional (3D) cryo-electron microscopy (cryo-EM) images is still a challenging task when the spatial resolution of cryo-EM images is at medium level (5-10Å ). Prior researches focused on the usage of local features that may not capture the global information of image objects. In this study, we propose to use deep learning methods to extract high representative global features and then automatically detect secondary structures of proteins. In particular, we build a convolutional neural network (CNN) classifier that predicts the probability of label for every individual voxel in 3D cryo-EM image with respect to the secondary structure elements of proteins such as α-helix, β-sheet and background. To effectively incorporate the 3D spatial information in protein structures, we propose to perform 3D convolutions in the convolutional layers of CNNs. We show that the proposed CNN classifier can outperform existing SVM method on identifying the secondary structure elements of proteins from 3D cryo-EM medium resolution images.
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Affiliation(s)
- Rongjian Li
- Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529
| | - Dong Si
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011
| | - Tao Zeng
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164
| | - Shuiwang Ji
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164
| | - Jing He
- Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529
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42
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Si D, He J. Modeling Beta-Traces for Beta-Barrels from Cryo-EM Density Maps. BIOMED RESEARCH INTERNATIONAL 2017; 2017:1793213. [PMID: 28164115 PMCID: PMC5259677 DOI: 10.1155/2017/1793213] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Accepted: 12/08/2016] [Indexed: 01/09/2023]
Abstract
Cryo-electron microscopy (cryo-EM) has produced density maps of various resolutions. Although α-helices can be detected from density maps at 5-8 Å resolutions, β-strands are challenging to detect at such density maps due to close-spacing of β-strands. The variety of shapes of β-sheets adds the complexity of β-strands detection from density maps. We propose a new approach to model traces of β-strands for β-barrel density regions that are extracted from cryo-EM density maps. In the test containing eight β-barrels extracted from experimental cryo-EM density maps at 5.5 Å-8.25 Å resolution, StrandRoller detected about 74.26% of the amino acids in the β-strands with an overall 2.05 Å 2-way distance between the detected β-traces and the observed ones, if the best of the fifteen detection cases is considered.
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Affiliation(s)
- Dong Si
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA
| | - Jing He
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
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43
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Poteat M, He J. An Iterative Bézier Method for Fitting Beta-sheet Component of a Cryo-EM Density Map. MOLECULAR BASED MATHEMATICAL BIOLOGY 2017; 5:31-39. [PMID: 34350231 PMCID: PMC8329936 DOI: 10.1515/mlbmb-2017-0003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cryo-electron microscopy (Cryo-EM) is a powerful technique to produce 3-dimensional density maps for large molecular complexes. Although many atomic structures have been solved from cryo-EM density maps, it is challenging to derive atomic structures when the resolution of density maps is not sufficiently high. Geometrical shape representation of secondary structural components in a medium-resolution density map enhances modeling of atomic structures. We compare two methods in producing surface representation of the β-sheet component of a density map. Given a 3-dimensional volume of β-sheet that is segmented from a density map, the performance of a polynomial fitting was compared with that of an iterative Bézier fitting. The results suggest that the iterative Bézier fitting is more suitable for β-sheets, since it provides more accurate representation of the corners that are naturally twisted in a β-sheet.
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Affiliation(s)
- Michael Poteat
- Department of Computer Science, Old Dominion University, Norfolk, VA, 23529
| | - Jing He
- Department of Computer Science, Old Dominion University, Norfolk, VA, 23529
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44
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Zeil S, Kovacs J, Wriggers W, He J. Comparing an Atomic Model or Structure to a Corresponding Cryo-electron Microscopy Image at the Central Axis of a Helix. J Comput Biol 2017; 24:52-67. [PMID: 27936925 PMCID: PMC5220566 DOI: 10.1089/cmb.2016.0145] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
Three-dimensional density maps of biological specimens from cryo-electron microscopy (cryo-EM) can be interpreted in the form of atomic models that are modeled into the density, or they can be compared to known atomic structures. When the central axis of a helix is detectable in a cryo-EM density map, it is possible to quantify the agreement between this central axis and a central axis calculated from the atomic model or structure. We propose a novel arc-length association method to compare the two axes reliably. This method was applied to 79 helices in simulated density maps and six case studies using cryo-EM maps at 6.4-7.7 Å resolution. The arc-length association method is then compared to three existing measures that evaluate the separation of two helical axes: a two-way distance between point sets, the length difference between two axes, and the individual amino acid detection accuracy. The results show that our proposed method sensitively distinguishes lateral and longitudinal discrepancies between the two axes, which makes the method particularly suitable for the systematic investigation of cryo-EM map-model pairs.
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Affiliation(s)
- Stephanie Zeil
- Department of Computer Science, Old Dominion University, Norfolk, Virginia
| | - Julio Kovacs
- Department of Mechanical and Aerospace Engineering and Institute of Biomedical Engineering, Old Dominion University, Norfolk, Virginia
| | - Willy Wriggers
- Department of Mechanical and Aerospace Engineering and Institute of Biomedical Engineering, Old Dominion University, Norfolk, Virginia
| | - Jing He
- Department of Computer Science, Old Dominion University, Norfolk, Virginia
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Leelananda SP, Lindert S. Computational methods in drug discovery. Beilstein J Org Chem 2016; 12:2694-2718. [PMID: 28144341 PMCID: PMC5238551 DOI: 10.3762/bjoc.12.267] [Citation(s) in RCA: 285] [Impact Index Per Article: 35.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 11/22/2016] [Indexed: 12/11/2022] Open
Abstract
The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
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Affiliation(s)
- Sumudu P Leelananda
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, USA
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Lindert S, McCammon JA. Improved cryoEM-Guided Iterative Molecular Dynamics--Rosetta Protein Structure Refinement Protocol for High Precision Protein Structure Prediction. J Chem Theory Comput 2016; 11:1337-46. [PMID: 25883538 PMCID: PMC4393324 DOI: 10.1021/ct500995d] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Indexed: 12/13/2022]
Abstract
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Many excellent methods exist that
incorporate cryo-electron microscopy
(cryoEM) data to constrain computational protein structure prediction
and refinement. Previously, it was shown that iteration of two such
orthogonal sampling and scoring methods – Rosetta and molecular
dynamics (MD) simulations – facilitated exploration of conformational
space in principle. Here, we go beyond a proof-of-concept study and
address significant remaining limitations of the iterative MD–Rosetta
protein structure refinement protocol. Specifically, all parts of
the iterative refinement protocol are now guided by medium-resolution
cryoEM density maps, and previous knowledge about the native structure
of the protein is no longer necessary. Models are identified solely
based on score or simulation time. All four benchmark proteins showed
substantial improvement through three rounds of the iterative refinement
protocol. The best-scoring final models of two proteins had sub-Ångstrom
RMSD to the native structure over residues in secondary structure
elements. Molecular dynamics was most efficient in refining secondary
structure elements and was thus highly complementary to the Rosetta
refinement which is most powerful in refining side chains and loop
regions.
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Constrained cyclic coordinate descent for cryo-EM images at medium resolutions: beyond the protein loop closure problem. ROBOTICA 2016; 34:1777-1790. [DOI: 10.1017/s0263574716000242] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
SUMMARYThe cyclic coordinate descent (CCD) method is a popular loop closure method in protein structure modeling. It is a robotics algorithm originally developed for inverse kinematic applications. We demonstrate an effective method of building the backbone of protein structure models using the principle of CCD and a guiding trace. For medium-resolution 3-dimensional (3D) images derived using cryo-electron microscopy (cryo-EM), it is possible to obtain guiding traces of secondary structures and their skeleton connections. Our new method, constrained cyclic coordinate descent (CCCD), builds α-helices, β-strands, and loops quickly and fairly accurately along predefined traces. We show that it is possible to build the entire backbone of a protein fairly accurately when the guiding traces are accurate. In a test of 10 proteins, the models constructed using CCCD show an average of 3.91 Å of backbone root mean square deviation (RMSD). When the CCCD method is incorporated in a simulated annealing framework to sample possible shift, translation, and rotation freedom, the models built with the true topology were ranked high on the list, with an average backbone RMSD100 of 3.76 Å. CCCD is an effective method for modeling atomic structures after secondary structure traces and skeletons are extracted from 3D cryo-EM images.
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McGreevy R, Teo I, Singharoy A, Schulten K. Advances in the molecular dynamics flexible fitting method for cryo-EM modeling. Methods 2016; 100:50-60. [PMID: 26804562 PMCID: PMC4848153 DOI: 10.1016/j.ymeth.2016.01.009] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Revised: 01/16/2016] [Accepted: 01/20/2016] [Indexed: 02/02/2023] Open
Abstract
Molecular Dynamics Flexible Fitting (MDFF) is an established technique for fitting all-atom structures of molecules into corresponding cryo-electron microscopy (cryo-EM) densities. The practical application of MDFF is simple but requires a user to be aware of and take measures against a variety of possible challenges presented by each individual case. Some of these challenges arise from the complexity of a molecular structure or the limited quality of available structural models and densities to be interpreted, while others stem from the intricacies of MDFF itself. The current article serves as an overview of the strategies that have been developed since MDFF's inception to overcome common challenges and successfully perform MDFF simulations.
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Affiliation(s)
- Ryan McGreevy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA
| | - Ivan Teo
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Abhishek Singharoy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA
| | - Klaus Schulten
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA; Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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Fischer AW, Heinze S, Putnam DK, Li B, Pino JC, Xia Y, Lopez CF, Meiler J. CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline. PLoS One 2016; 11:e0152517. [PMID: 27046050 PMCID: PMC4821492 DOI: 10.1371/journal.pone.0152517] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Accepted: 03/15/2016] [Indexed: 11/18/2022] Open
Abstract
In silico prediction of a protein's tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three 'assisted' protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.
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Affiliation(s)
- Axel W. Fischer
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Daniel K. Putnam
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - James C. Pino
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Yan Xia
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Carlos F. Lopez
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
- Department of Cancer Biology and Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
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50
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Structure and mechanism of the phage T4 recombination mediator protein UvsY. Proc Natl Acad Sci U S A 2016; 113:3275-80. [PMID: 26951671 DOI: 10.1073/pnas.1519154113] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The UvsY recombination mediator protein is critical for efficient homologous recombination in bacteriophage T4 and is the functional analog of the eukaryotic Rad52 protein. During T4 homologous recombination, the UvsX recombinase has to compete with the prebound gp32 single-stranded binding protein for DNA-binding sites and UvsY stimulates this filament nucleation event. We report here the crystal structure of UvsY in four similar open-barrel heptameric assemblies and provide structural and biophysical insights into its function. The UvsY heptamer was confirmed in solution by centrifugation and light scattering, and thermodynamic analyses revealed that the UvsY-ssDNA interaction occurs within the assembly via two distinct binding modes. Using surface plasmon resonance, we also examined the binding of UvsY to both ssDNA and the ssDNA-gp32 complex. These analyses confirmed that ssDNA can bind UvsY and gp32 independently and also as a ternary complex. They also showed that residues located on the rim of the heptamer are required for optimal binding to ssDNA, thus identifying the putative ssDNA-binding surface. We propose a model in which UvsY promotes a helical ssDNA conformation that disfavors the binding of gp32 and initiates the assembly of the ssDNA-UvsX filament.
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