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Alexander LT, Durairaj J, Kryshtafovych A, Abriata LA, Bayo Y, Bhabha G, Breyton C, Caulton SG, Chen J, Degroux S, Ekiert DC, Erlandsen BS, Freddolino PL, Gilzer D, Greening C, Grimes JM, Grinter R, Gurusaran M, Hartmann MD, Hitchman CJ, Keown JR, Kropp A, Kursula P, Lovering AL, Lemaitre B, Lia A, Liu S, Logotheti M, Lu S, Markússon S, Miller MD, Minasov G, Niemann HH, Opazo F, Phillips GN, Davies OR, Rommelaere S, Rosas‐Lemus M, Roversi P, Satchell K, Smith N, Wilson MA, Wu K, Xia X, Xiao H, Zhang W, Zhou ZH, Fidelis K, Topf M, Moult J, Schwede T. Protein target highlights in CASP15: Analysis of models by structure providers. Proteins 2023; 91:1571-1599. [PMID: 37493353 PMCID: PMC10792529 DOI: 10.1002/prot.26545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 06/15/2023] [Indexed: 07/27/2023]
Abstract
We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three-dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.
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Affiliation(s)
- Leila T. Alexander
- BiozentrumUniversity of BaselBaselSwitzerland
- Computational Structural BiologySIB Swiss Institute of BioinformaticsBaselSwitzerland
| | - Janani Durairaj
- BiozentrumUniversity of BaselBaselSwitzerland
- Computational Structural BiologySIB Swiss Institute of BioinformaticsBaselSwitzerland
| | | | - Luciano A. Abriata
- School of Life SciencesÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Yusupha Bayo
- Department of BiosciencesUniversity of MilanoMilanItaly
- IBBA‐CNR Unit of MilanoInstitute of Agricultural Biology and BiotechnologyMilanItaly
| | - Gira Bhabha
- Department of Cell BiologyNew York University School of MedicineNew YorkNew YorkUSA
| | | | | | - James Chen
- Department of Cell BiologyNew York University School of MedicineNew YorkNew YorkUSA
| | | | - Damian C. Ekiert
- Department of Cell BiologyNew York University School of MedicineNew YorkNew YorkUSA
- Department of MicrobiologyNew York University School of MedicineNew YorkNew YorkUSA
| | - Benedikte S. Erlandsen
- Wellcome Centre for Cell BiologyInstitute of Cell Biology, University of EdinburghEdinburghUK
| | - Peter L. Freddolino
- Department of Biological Chemistry, Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMichiganUSA
| | - Dominic Gilzer
- Department of ChemistryBielefeld UniversityBielefeldGermany
| | - Chris Greening
- Department of Microbiology, Biomedicine Discovery InstituteMonash UniversityClaytonVictoriaAustralia
- Securing Antarctica's Environmental FutureMonash UniversityClaytonVictoriaAustralia
- Centre to Impact AMRMonash UniversityClaytonVictoriaAustralia
- ARC Research Hub for Carbon Utilisation and RecyclingMonash UniversityClaytonVictoriaAustralia
| | - Jonathan M. Grimes
- Division of Structural Biology, Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
| | - Rhys Grinter
- Department of Microbiology, Biomedicine Discovery InstituteMonash UniversityClaytonVictoriaAustralia
- Centre for Electron Microscopy of Membrane ProteinsMonash Institute of Pharmaceutical SciencesParkvilleVictoriaAustralia
| | - Manickam Gurusaran
- Wellcome Centre for Cell BiologyInstitute of Cell Biology, University of EdinburghEdinburghUK
| | - Marcus D. Hartmann
- Max Planck Institute for BiologyTübingenGermany
- Interfaculty Institute of Biochemistry, University of TübingenTübingenGermany
| | - Charlie J. Hitchman
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical BiologyUniversity of LeicesterLeicesterUK
| | - Jeremy R. Keown
- Division of Structural Biology, Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
| | - Ashleigh Kropp
- Department of Microbiology, Biomedicine Discovery InstituteMonash UniversityClaytonVictoriaAustralia
| | - Petri Kursula
- Department of BiomedicineUniversity of BergenBergenNorway
- Faculty of Biochemistry and Molecular Medicine & Biocenter OuluUniversity of OuluOuluFinland
| | | | - Bruno Lemaitre
- School of Life SciencesÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Andrea Lia
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical BiologyUniversity of LeicesterLeicesterUK
- ISPA‐CNR Unit of LecceInstitute of Sciences of Food ProductionLecceItaly
| | - Shiheng Liu
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCaliforniaUSA
- California NanoSystems InstituteUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Maria Logotheti
- Max Planck Institute for BiologyTübingenGermany
- Interfaculty Institute of Biochemistry, University of TübingenTübingenGermany
- Present address:
Institute of BiochemistryUniversity of GreifswaldGreifswaldGermany
| | - Shuze Lu
- Lanzhou University School of Life SciencesLanzhouChina
| | | | | | - George Minasov
- Department of Microbiology‐ImmunologyNorthwestern Feinberg School of MedicineChicagoIllinoisUSA
| | | | - Felipe Opazo
- NanoTag Biotechnologies GmbHGöttingenGermany
- Institute of Neuro‐ and Sensory PhysiologyUniversity of Göttingen Medical CenterGöttingenGermany
- Center for Biostructural Imaging of Neurodegeneration (BIN)University of Göttingen Medical CenterGöttingenGermany
| | - George N. Phillips
- Department of BiosciencesRice UniversityHoustonTexasUSA
- Department of ChemistryRice UniversityHoustonTexasUSA
| | - Owen R. Davies
- Wellcome Centre for Cell BiologyInstitute of Cell Biology, University of EdinburghEdinburghUK
| | - Samuel Rommelaere
- School of Life SciencesÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Monica Rosas‐Lemus
- Department of Microbiology‐ImmunologyNorthwestern Feinberg School of MedicineChicagoIllinoisUSA
- Present address:
Department of Molecular Genetics and MicrobiologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Pietro Roversi
- IBBA‐CNR Unit of MilanoInstitute of Agricultural Biology and BiotechnologyMilanItaly
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical BiologyUniversity of LeicesterLeicesterUK
| | - Karla Satchell
- Department of Microbiology‐ImmunologyNorthwestern Feinberg School of MedicineChicagoIllinoisUSA
| | - Nathan Smith
- Department of Biochemistry and the Redox Biology CenterUniversity of NebraskaLincolnNebraskaUSA
| | - Mark A. Wilson
- Department of Biochemistry and the Redox Biology CenterUniversity of NebraskaLincolnNebraskaUSA
| | - Kuan‐Lin Wu
- Department of ChemistryRice UniversityHoustonTexasUSA
| | - Xian Xia
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCaliforniaUSA
- California NanoSystems InstituteUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Han Xiao
- Department of BiosciencesRice UniversityHoustonTexasUSA
- Department of ChemistryRice UniversityHoustonTexasUSA
- Department of BioengineeringRice UniversityHoustonTexasUSA
| | - Wenhua Zhang
- Lanzhou University School of Life SciencesLanzhouChina
| | - Z. Hong Zhou
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCaliforniaUSA
- California NanoSystems InstituteUniversity of CaliforniaLos AngelesCaliforniaUSA
| | | | - Maya Topf
- University Medical Center Hamburg‐Eppendorf (UKE)HamburgGermany
- Centre for Structural Systems BiologyLeibniz‐Institut für Virologie (LIV)HamburgGermany
| | - John Moult
- Department of Cell Biology and Molecular Genetics, Institute for Bioscience and Biotechnology ResearchUniversity of MarylandRockvilleMarylandUSA
| | - Torsten Schwede
- BiozentrumUniversity of BaselBaselSwitzerland
- Computational Structural BiologySIB Swiss Institute of BioinformaticsBaselSwitzerland
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2
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Kretsch RC, Andersen ES, Bujnicki JM, Chiu W, Das R, Luo B, Masquida B, McRae EK, Schroeder GM, Su Z, Wedekind JE, Xu L, Zhang K, Zheludev IN, Moult J, Kryshtafovych A. RNA target highlights in CASP15: Evaluation of predicted models by structure providers. Proteins 2023; 91:1600-1615. [PMID: 37466021 PMCID: PMC10792523 DOI: 10.1002/prot.26550] [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: 04/27/2023] [Revised: 06/16/2023] [Accepted: 06/26/2023] [Indexed: 07/20/2023]
Abstract
The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA-only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x-ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA-protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.
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Affiliation(s)
- Rachael C. Kretsch
- Biophysics Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Ebbe S. Andersen
- Interdisciplinary Nanoscience Center and Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Janusz M. Bujnicki
- International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Wah Chiu
- Biophysics Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Bioengineering and James H. Clark Center, Stanford University, Stanford, CA, USA
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Rhiju Das
- Biophysics Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford, CA, USA
| | - Bingnan Luo
- The State Key Laboratory of Biotherapy, Frontiers Medical Center of Tianfu Jincheng Laboratory, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610044, Sichuan, China
| | - Benoît Masquida
- UMR 7156, CNRS – Universite de Strasbourg, Strasbourg, France
| | - Ewan K.S. McRae
- Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Griffin M. Schroeder
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
- Center for RNA Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Zhaoming Su
- The State Key Laboratory of Biotherapy, Frontiers Medical Center of Tianfu Jincheng Laboratory, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610044, Sichuan, China
| | - Joseph E. Wedekind
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
- Center for RNA Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Lily Xu
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kaiming Zhang
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Ivan N. Zheludev
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - John Moult
- Department of Cell Biology and Molecular Genetics, Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland, USA
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3
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Roterman I, Stapor K, Konieczny L. Role of environmental specificity in CASP results. BMC Bioinformatics 2023; 24:425. [PMID: 37950210 PMCID: PMC10638730 DOI: 10.1186/s12859-023-05559-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Recently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14 (Critical Assessment of Structure Prediction) competition. However, the question of the mechanism behind the protein folding process itself remains unanswered. Correctly predicting the structure also does not solve the problem of, for example, amyloid proteins, where a polypeptide chain with an unaltered sequence adopts a different 3D structure. RESULTS This work was an attempt at explaining the structural variation by considering the contribution of the environment to protein structuring. The application of the fuzzy oil drop (FOD) model to assess the validity of the selected models provided in the CASP13, CASP14 and CASP15 projects reveals the need for an environmental factor to determine the 3D structure of proteins. Consideration of the external force field in the form of polar water (Fuzzy Oil Drop) and a version modified by the presence of the hydrophobic compounds, FOD-M (FOD-Modified) reveals that the protein folding process is environmentally dependent. An analysis of selected models from the CASP competitions indicates the need for structure prediction as dependent on the consideration of the protein folding environment. CONCLUSIONS The conditions governed by the environment direct the protein folding process occurring in a certain environment. Therefore, the variation of the external force field should be taken into account in the models used in protein structure prediction.
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Affiliation(s)
- Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University - Medical College, Medyczna 7, 30-688, Krakow, Poland.
| | - Katarzyna Stapor
- Faculty of Automatic, Electronics and Computer Science, Department of Applied, Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
| | - Leszek Konieczny
- Jagiellonian University - Medical College, Kopernika 7, 31-034, Krakow, Poland
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4
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Alexander LT, Lepore R, Kryshtafovych A, Adamopoulos A, Alahuhta M, Arvin AM, Bomble YJ, Böttcher B, Breyton C, Chiarini V, Chinnam NB, Chiu W, Fidelis K, Grinter R, Gupta GD, Hartmann MD, Hayes CS, Heidebrecht T, Ilari A, Joachimiak A, Kim Y, Linares R, Lovering AL, Lunin VV, Lupas AN, Makbul C, Michalska K, Moult J, Mukherjee PK, Nutt W(S, Oliver SL, Perrakis A, Stols L, Tainer JA, Topf M, Tsutakawa SE, Valdivia‐Delgado M, Schwede T. Target highlights in CASP14: Analysis of models by structure providers. Proteins 2021; 89:1647-1672. [PMID: 34561912 PMCID: PMC8616854 DOI: 10.1002/prot.26247] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 09/13/2021] [Accepted: 09/16/2021] [Indexed: 12/11/2022]
Abstract
The biological and functional significance of selected Critical Assessment of Techniques for Protein Structure Prediction 14 (CASP14) targets are described by the authors of the structures. The authors highlight the most relevant features of the target proteins and discuss how well these features were reproduced in the respective submitted predictions. The overall ability to predict three-dimensional structures of proteins has improved remarkably in CASP14, and many difficult targets were modeled with impressive accuracy. For the first time in the history of CASP, the experimentalists not only highlighted that computational models can accurately reproduce the most critical structural features observed in their targets, but also envisaged that models could serve as a guidance for further studies of biologically-relevant properties of proteins.
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Affiliation(s)
- Leila T. Alexander
- Biozentrum, University of BaselBaselSwitzerland
- Computational Structural BiologySIB Swiss Institute of BioinformaticsBaselSwitzerland
| | | | | | - Athanassios Adamopoulos
- Oncode Institute and Division of BiochemistryNetherlands Cancer InstituteAmsterdamThe Netherlands
| | - Markus Alahuhta
- Bioscience Center, National Renewable Energy LaboratoryGoldenColoradoUSA
| | - Ann M. Arvin
- Department of PediatricsStanford University School of MedicineStanfordCaliforniaUSA
- Microbiology and ImmunologyStanford University School of MedicineStanfordCaliforniaUSA
| | - Yannick J. Bomble
- Bioscience Center, National Renewable Energy LaboratoryGoldenColoradoUSA
| | - Bettina Böttcher
- Biocenter and Rudolf Virchow Center, Julius‐Maximilians Universität WürzburgWürzburgGermany
| | - Cécile Breyton
- Univ. Grenoble Alpes, CNRS, CEA, Institute for Structural BiologyGrenobleFrance
| | - Valerio Chiarini
- Program in Structural Biology and BiophysicsInstitute of Biotechnology, University of HelsinkiHelsinkiFinland
| | - Naga babu Chinnam
- Department of Molecular and Cellular OncologyThe University of Texas M.D. Anderson Cancer CenterHoustonTexasUSA
| | - Wah Chiu
- Microbiology and ImmunologyStanford University School of MedicineStanfordCaliforniaUSA
- BioengineeringStanford University School of MedicineStanfordCaliforniaUSA
- Division of Cryo‐EM and Bioimaging SSRLSLAC National Accelerator LaboratoryMenlo ParkCaliforniaUSA
| | | | - Rhys Grinter
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of MicrobiologyMonash UniversityClaytonAustralia
| | - Gagan D. Gupta
- Radiation Biology & Health Sciences DivisionBhabha Atomic Research CentreMumbaiIndia
| | - Marcus D. Hartmann
- Department of Protein EvolutionMax Planck Institute for Developmental BiologyTübingenGermany
| | - Christopher S. Hayes
- Department of Molecular, Cellular and Developmental BiologyUniversity of California, Santa BarbaraSanta BarbaraCaliforniaUSA
- Biomolecular Science and Engineering ProgramUniversity of California, Santa BarbaraSanta BarbaraCaliforniaUSA
| | - Tatjana Heidebrecht
- Oncode Institute and Division of BiochemistryNetherlands Cancer InstituteAmsterdamThe Netherlands
| | - Andrea Ilari
- Institute of Molecular Biology and Pathology of the National Research Council of Italy (CNR)RomeItaly
| | - Andrzej Joachimiak
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
- Department of Biochemistry and Molecular BiologyUniversity of ChicagoChicagoIllinoisUSA
| | - Youngchang Kim
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
| | - Romain Linares
- Univ. Grenoble Alpes, CNRS, CEA, Institute for Structural BiologyGrenobleFrance
| | | | - Vladimir V. Lunin
- Bioscience Center, National Renewable Energy LaboratoryGoldenColoradoUSA
| | - Andrei N. Lupas
- Department of Protein EvolutionMax Planck Institute for Developmental BiologyTübingenGermany
| | - Cihan Makbul
- Biocenter and Rudolf Virchow Center, Julius‐Maximilians Universität WürzburgWürzburgGermany
| | - Karolina Michalska
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
| | - John Moult
- Department of Cell Biology and Molecular GeneticsInstitute for Bioscience and Biotechnology Research, University of MarylandRockvilleMarylandUSA
| | - Prasun K. Mukherjee
- Nuclear Agriculture & Biotechnology DivisionBhabha Atomic Research CentreMumbaiIndia
| | - William (Sam) Nutt
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
| | - Stefan L. Oliver
- Department of PediatricsStanford University School of MedicineStanfordCaliforniaUSA
| | - Anastassis Perrakis
- Oncode Institute and Division of BiochemistryNetherlands Cancer InstituteAmsterdamThe Netherlands
| | - Lucy Stols
- Center for Structural Genomics of Infectious Diseases, Consortium for Advanced Science and Engineering, University of ChicagoChicagoIllinoisUSA
- X‐ray Science DivisionArgonne National Laboratory, Structural Biology CenterArgonneIllinoisUSA
| | - John A. Tainer
- Department of Molecular and Cellular OncologyThe University of Texas M.D. Anderson Cancer CenterHoustonTexasUSA
- Department of Cancer BiologyUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Maya Topf
- Institute of Structural and Molecular Biology, Birkbeck, University College LondonLondonUK
- Centre for Structural Systems Biology, Leibniz‐Institut für Experimentelle VirologieHamburgGermany
| | - Susan E. Tsutakawa
- Molecular Biophysics and Integrated BioimagingLawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA
| | | | - Torsten Schwede
- Biozentrum, University of BaselBaselSwitzerland
- Computational Structural BiologySIB Swiss Institute of BioinformaticsBaselSwitzerland
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5
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Kryshtafovych A, Moult J, Albrecht R, Chang GA, Chao K, Fraser A, Greenfield J, Hartmann MD, Herzberg O, Josts I, Leiman PG, Linden SB, Lupas AN, Nelson DC, Rees SD, Shang X, Sokolova ML, Tidow H. Computational models in the service of X-ray and cryo-electron microscopy structure determination. Proteins 2021; 89:1633-1646. [PMID: 34449113 PMCID: PMC8616789 DOI: 10.1002/prot.26223] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/11/2021] [Accepted: 08/17/2021] [Indexed: 01/20/2023]
Abstract
Critical assessment of structure prediction (CASP) conducts community experiments to determine the state of the art in computing protein structure from amino acid sequence. The process relies on the experimental community providing information about not yet public or about to be solved structures, for use as targets. For some targets, the experimental structure is not solved in time for use in CASP. Calculated structure accuracy improved dramatically in this round, implying that models should now be much more useful for resolving many sorts of experimental difficulties. To test this, selected models for seven unsolved targets were provided to the experimental groups. These models were from the AlphaFold2 group, who overall submitted the most accurate predictions in CASP14. Four targets were solved with the aid of the models, and, additionally, the structure of an already solved target was improved. An a posteriori analysis showed that, in some cases, models from other groups would also be effective. This paper provides accounts of the successful application of models to structure determination, including molecular replacement for X-ray crystallography, backbone tracing and sequence positioning in a cryo-electron microscopy structure, and correction of local features. The results suggest that, in future, there will be greatly increased synergy between computational and experimental approaches to structure determination.
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Affiliation(s)
| | - John Moult
- Institute for Bioscience and Biotechnology Research, Department of Cell Biology and Molecular genetics, University of Maryland, 9600 Gudelsky Drive, Rockville, MD 20850, USA
| | - Reinhard Albrecht
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
| | - Geoffrey A. Chang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California-San Diego, La Jolla, CA, 92093, USA
- Department of Pharmacology, University of California-San Diego, La Jolla, CA, 92093, USA
| | - Kinlin Chao
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
| | - Alec Fraser
- Department of Biochemistry and Molecular Biology, Sealy Center for Structural Biology and Molecular Biophysics (SCSB), The University of Texas Medical Branch at Galveston, TX 77555, USA
| | - Julia Greenfield
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
| | - Marcus D. Hartmann
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
| | - Osnat Herzberg
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Inokentijs Josts
- The Hamburg Advanced Research Center for Bioorganic Chemistry (HARBOR) & Department of Chemistry, Institute for Biochemistry and Molecular Biology, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
| | - Petr G. Leiman
- Department of Biochemistry and Molecular Biology, Sealy Center for Structural Biology and Molecular Biophysics (SCSB), The University of Texas Medical Branch at Galveston, TX 77555, USA
| | - Sara B. Linden
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
| | - Andrei N. Lupas
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany
| | - Daniel C. Nelson
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
- Department of Veterinary Medicine, University of Maryland, College Park, MD 20742, USA
| | - Steven D. Rees
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California-San Diego, La Jolla, CA, 92093, USA
| | - Xiaoran Shang
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD 20850, USA
| | - Maria L. Sokolova
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Henning Tidow
- The Hamburg Advanced Research Center for Bioorganic Chemistry (HARBOR) & Department of Chemistry, Institute for Biochemistry and Molecular Biology, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
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6
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Prahlad J, Yuan Y, Lin J, Chang CW, Iwata-Reuyl D, Liu Y, de Crécy-Lagard V, Wilson MA. The DUF328 family member YaaA is a DNA-binding protein with a novel fold. J Biol Chem 2020; 295:14236-14247. [PMID: 32796037 PMCID: PMC7549036 DOI: 10.1074/jbc.ra120.015055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/11/2020] [Indexed: 01/07/2023] Open
Abstract
DUF328 family proteins are present in many prokaryotes; however, their molecular activities are unknown. The Escherichia coli DUF328 protein YaaA is a member of the OxyR regulon and is protective against oxidative stress. Because uncharacterized proteins involved in prokaryotic oxidative stress response are rare, we sought to learn more about the DUF328 family. Using comparative genomics, we found a robust association between the DUF328 family and genes involved in DNA recombination and the oxidative stress response. In some proteins, DUF328 domains are fused to other domains involved in DNA binding, recombination, and repair. Cofitness analysis indicates that DUF328 family genes associate with recombination-mediated DNA repair pathways, particularly the RecFOR pathway. Purified recombinant YaaA binds to dsDNA, duplex DNA containing bubbles of unpaired nucleotides, and Holliday junction constructs in vitro with dissociation equilibrium constants of 200-300 nm YaaA binds DNA with positive cooperativity, forming multiple shifted species in electrophoretic mobility shift assays. The 1.65-Å resolution X-ray crystal structure of YaaA reveals that the protein possesses a new fold that we name the cantaloupe fold. YaaA has a positively charged cleft and a helix-hairpin-helix DNA-binding motif found in other DNA repair enzymes. Our results demonstrate that YaaA is a new type of DNA-binding protein associated with the oxidative stress response and that this molecular function is likely conserved in other DUF328 family members.
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Affiliation(s)
- Janani Prahlad
- Department of Biochemistry and Redox Biology Center, University of Nebraska, Lincoln, Nebraska, USA
| | - Yifeng Yuan
- Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida, USA
| | - Jiusheng Lin
- Department of Biochemistry and Redox Biology Center, University of Nebraska, Lincoln, Nebraska, USA
| | - Chou-Wei Chang
- Department of Cancer Genetics and Epigenetics, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Dirk Iwata-Reuyl
- Department of Chemistry, Portland State University, Portland, Oregon, USA
| | - Yilun Liu
- Department of Cancer Genetics and Epigenetics, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Valérie de Crécy-Lagard
- Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida, USA,University of Florida Genetics Institute, Gainesville, Florida, USA,For correspondence: Valérie de Crécy-Lagard, ; Mark A. Wilson,
| | - Mark A. Wilson
- Department of Biochemistry and Redox Biology Center, University of Nebraska, Lincoln, Nebraska, USA,For correspondence: Valérie de Crécy-Lagard, ; Mark A. Wilson,
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7
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Liu T, Ish‐Shalom S, Torng W, Lafita A, Bock C, Mort M, Cooper DN, Bliven S, Capitani G, Mooney SD, Altman RB. Biological and functional relevance of CASP predictions. Proteins 2018; 86 Suppl 1:374-386. [PMID: 28975675 PMCID: PMC5820171 DOI: 10.1002/prot.25396] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 09/12/2017] [Accepted: 10/03/2017] [Indexed: 02/06/2023]
Abstract
Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.
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Affiliation(s)
- Tianyun Liu
- Department of BioengineeringStanford UniversityStanfordCalifornia
| | - Shirbi Ish‐Shalom
- Biomedical Informatics Training Program, Stanford UniversityStanfordCalifornia
| | - Wen Torng
- Department of BioengineeringStanford UniversityStanfordCalifornia
| | - Aleix Lafita
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- Department of Biosystems Science and EngineeringETH Zurich4058BaselSwitzerland
| | - Christian Bock
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleWashington
- Heidelberg UniversityHeidelbergGermany
| | - Matthew Mort
- Institute of Medical Genetics, Cardiff UniversityUnited Kingdom
| | - David N Cooper
- Institute of Medical Genetics, Cardiff UniversityUnited Kingdom
| | - Spencer Bliven
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- National Center for Biotechnology Information, National Library of MedicineNational Institutes of HealthBethesdaMaryland
| | - Guido Capitani
- Laboratory of Biomolecular ResearchPaul Scherrer InstituteVilligenSwitzerland
- Department of BiologyETH ZurichZurichSwitzerland
| | - Sean D. Mooney
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleWashington
| | - Russ B. Altman
- Department of BioengineeringStanford UniversityStanfordCalifornia
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8
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Kryshtafovych A, Albrecht R, Baslé A, Bule P, Caputo AT, Carvalho AL, Chao KL, Diskin R, Fidelis K, Fontes CMGA, Fredslund F, Gilbert HJ, Goulding CW, Hartmann MD, Hayes CS, Herzberg O, Hill JC, Joachimiak A, Kohring GW, Koning RI, Lo Leggio L, Mangiagalli M, Michalska K, Moult J, Najmudin S, Nardini M, Nardone V, Ndeh D, Nguyen TH, Pintacuda G, Postel S, van Raaij MJ, Roversi P, Shimon A, Singh AK, Sundberg EJ, Tars K, Zitzmann N, Schwede T. Target highlights from the first post-PSI CASP experiment (CASP12, May-August 2016). Proteins 2018; 86 Suppl 1:27-50. [PMID: 28960539 PMCID: PMC5820184 DOI: 10.1002/prot.25392] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 09/19/2017] [Accepted: 09/25/2017] [Indexed: 12/27/2022]
Abstract
The functional and biological significance of the selected CASP12 targets are described by the authors of the structures. The crystallographers discuss the most interesting structural features of the target proteins and assess whether these features were correctly reproduced in the predictions submitted to the CASP12 experiment.
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Affiliation(s)
- Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, California, 95616
| | - Reinhard Albrecht
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, 72076, Germany
| | - Arnaud Baslé
- Institute for Cell and Molecular Biosciences, University of Newcastle, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Pedro Bule
- CIISA - Faculdade de Medicina Veterinária, Universidade de Lisboa, Avenida da Universidade Técnica, 1300-477, Portugal, Lisboa
| | - Alessandro T Caputo
- Oxford Glycobiology Institute, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, England, United Kingdom
| | - Ana Luisa Carvalho
- UCIBIO, REQUIMTE, Departamento de Química, Faculdade de Cien⁁cias e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516, Portugal
| | - Kinlin L Chao
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland, 20850
| | - Ron Diskin
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, California, 95616
| | - Carlos M G A Fontes
- CIISA - Faculdade de Medicina Veterinária, Universidade de Lisboa, Avenida da Universidade Técnica, 1300-477, Portugal, Lisboa
| | - Folmer Fredslund
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark
| | - Harry J Gilbert
- Institute for Cell and Molecular Biosciences, University of Newcastle, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Celia W Goulding
- Department of Molecular Biology and Biochemistry/Pharmaceutical Sciences, University of California Irvine, Irvine, California, 92697
| | - Marcus D Hartmann
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, Tübingen, 72076, Germany
| | - Christopher S Hayes
- Department of Molecular, Cellular and Developmental Biology/Biomolecular Science and Engineering Program, University of California, Santa Barbara, Santa Barbara, California, 93106
| | - Osnat Herzberg
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland, 20850
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland, 20742
| | - Johan C Hill
- Oxford Glycobiology Institute, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, England, United Kingdom
| | - Andrzej Joachimiak
- Argonne National Laboratory, Midwest Center for Structural Genomics/Structural Biology Center, Biosciences Division, Argonne, Illinois, 60439
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, 60637
| | - Gert-Wieland Kohring
- Microbiology, Saarland University, Campus Building A1.5, Saarbrücken, Saarland, D-66123, Germany
| | - Roman I Koning
- Netherlands Centre for Electron Nanoscopy, Institute of Biology Leiden, Leiden University, 2333, CC Leiden, The Netherlands
- Department of Molecular Cell Biology, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Leila Lo Leggio
- Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark
| | - Marco Mangiagalli
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy
| | - Karolina Michalska
- Argonne National Laboratory, Midwest Center for Structural Genomics/Structural Biology Center, Biosciences Division, Argonne, Illinois, 60439
| | - John Moult
- Department of Cell Biology and Molecular genetics, University of Maryland, 9600 Gudelsky Drive, Institute for Bioscience and Biotechnology Research, Rockville, Maryland, 20850
| | - Shabir Najmudin
- CIISA - Faculdade de Medicina Veterinária, Universidade de Lisboa, Avenida da Universidade Técnica, 1300-477, Portugal, Lisboa
| | - Marco Nardini
- Department of Biosciences, University of Milano, Milano, 20133, Italy
| | - Valentina Nardone
- Department of Biosciences, University of Milano, Milano, 20133, Italy
| | - Didier Ndeh
- Institute for Cell and Molecular Biosciences, University of Newcastle, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Thanh-Hong Nguyen
- Department of Macromolecular Structures, Centro Nacional de Biotecnologia (CSIC), calle Darwin 3, Madrid, 28049, Spain
| | - Guido Pintacuda
- Université de Lyon, Centre de RMN à Très Hauts Champs, Institut des Sciences Analytiques (UMR 5280 - CNRS, ENS Lyon, UCB Lyon 1), Villeurbanne, 69100, France
| | - Sandra Postel
- University of Maryland School of Medicine, Institute of Human Virology, Baltimore, Maryland, 21201
| | - Mark J van Raaij
- Department of Macromolecular Structures, Centro Nacional de Biotecnologia (CSIC), calle Darwin 3, Madrid, 28049, Spain
| | - Pietro Roversi
- Oxford Glycobiology Institute, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, England, United Kingdom
- Leicester Institute of Structural and Chemical Biology, Department of Molecular and Cell Biology, University of Leicester, Henry Wellcome Building, University Road, Leicester, LE1 7RN, UK
| | - Amir Shimon
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Abhimanyu K Singh
- School of Biosciences, University of Kent, Canterbury, Kent, CT2 7NJ, United Kingdom
| | - Eric J Sundberg
- Department of Medicine and Department of Microbiology and Immunology, University of Maryland School of Medicine, Institute of Human Virology, Baltimore, Maryland, 21201
| | - Kaspars Tars
- Latvian Biomedical Research and Study Center, Rātsupītes 1, Riga, LV1067, Latvia
- Faculty of Biology, Department of Molecular Biology, University of Latvia, Jelgavas 1, Riga, LV-1004, Latvia
| | - Nicole Zitzmann
- Oxford Glycobiology Institute, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, England, United Kingdom
| | - Torsten Schwede
- Biozentrum/SIB Swiss Institute of Bioinformatics, Klingelbergstrasse 50, Basel, 4056, Switzerland
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9
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Baslé A, Hewitt L, Koh A, Lamb HK, Thompson P, Burgess JG, Hall MJ, Hawkins AR, Murray H, Lewis RJ. Crystal structure of NucB, a biofilm-degrading endonuclease. Nucleic Acids Res 2018; 46:473-484. [PMID: 29165717 PMCID: PMC5758888 DOI: 10.1093/nar/gkx1170] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 11/13/2017] [Indexed: 01/23/2023] Open
Abstract
Bacterial biofilms are a complex architecture of cells that grow on moist interfaces, and are held together by a molecular glue of extracellular proteins, sugars and nucleic acids. Biofilms are particularly problematic in human healthcare as they can coat medical implants and are thus a potential source of disease. The enzymatic dispersal of biofilms is increasingly being developed as a new strategy to treat this problem. Here, we have characterized NucB, a biofilm-dispersing nuclease from a marine strain of Bacillus licheniformis, and present its crystal structure together with the biochemistry and a mutational analysis required to confirm its active site. Taken together, these data support the categorization of NucB into a unique subfamily of the ββα metal-dependent non-specific endonucleases. Understanding the structure and function of NucB will facilitate its future development into an anti-biofilm therapeutic agent.
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Affiliation(s)
- Arnaud Baslé
- Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Lorraine Hewitt
- Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Alan Koh
- Centre for Bacterial Cell Biology, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Heather K Lamb
- Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Paul Thompson
- Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - J Grant Burgess
- Marine Biology, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Michael J Hall
- Chemistry, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Alastair R Hawkins
- Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Heath Murray
- Centre for Bacterial Cell Biology, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Richard J Lewis
- Institute for Cell and Molecular Biosciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK,To whom correspondence should be addressed. Tel: +44 191 208 5482;
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10
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Newman J, Sharp JA, Enjapoori AK, Bentley J, Nicholas KR, Adams TE, Peat TS. Structural characterization of a novel monotreme-specific protein with antimicrobial activity from the milk of the platypus. Acta Crystallogr F Struct Biol Commun 2018; 74:39-45. [PMID: 29372906 PMCID: PMC5947691 DOI: 10.1107/s2053230x17017708] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 12/12/2017] [Indexed: 11/10/2022] Open
Abstract
Monotreme lactation protein (MLP) is a recently identified protein with antimicrobial activity. It is present in the milk of monotremes and is unique to this lineage. To characterize MLP and to gain insight into the potential role of this protein in the evolution of lactation, the crystal structure of duck-billed platypus (Ornithorhynchus anatinus) MLP was determined at 1.82 Å resolution. This is the first structure to be reported for this novel, mammalian antibacterial protein. MLP was expressed as a FLAG epitope-tagged protein in mammalian cells and crystallized readily, with at least three space groups being observed (P1, C2 and P21). A 1.82 Å resolution native data set was collected from a crystal in space group P1, with unit-cell parameters a = 51.2, b = 59.7, c = 63.1 Å, α = 80.15, β = 82.98, γ = 89.27°. The structure was solved by SAD phasing using a protein crystal derivatized with mercury in space group C2, with unit-cell parameters a = 92.7, b = 73.2, c = 56.5 Å, β = 90.28°. MLP comprises a monomer of 12 helices and two short β-strands, with much of the N-terminus composed of loop regions. The crystal structure of MLP reveals no three-dimensional similarity to any known structures and reveals a heretofore unseen fold, supporting the idea that monotremes may be a rich source for the identification of novel proteins. It is hypothesized that MLP in monotreme milk has evolved to specifically support the unusual lactation strategy of this lineage and may have played a central role in the evolution of these mammals.
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Affiliation(s)
- Janet Newman
- Biomedical Manufacturing, CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia
| | - Julie A. Sharp
- Institute for Frontier Materials, Deakin University, Geelong, VIC 3217, Australia
| | | | - John Bentley
- Biomedical Manufacturing, CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia
| | - Kevin R. Nicholas
- Institute for Frontier Materials, Deakin University, Geelong, VIC 3217, Australia
| | - Timothy E. Adams
- Biomedical Manufacturing, CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia
| | - Thomas S. Peat
- Biomedical Manufacturing, CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia
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11
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Nealon JO, Philomina LS, McGuffin LJ. Predictive and Experimental Approaches for Elucidating Protein-Protein Interactions and Quaternary Structures. Int J Mol Sci 2017; 18:E2623. [PMID: 29206185 PMCID: PMC5751226 DOI: 10.3390/ijms18122623] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 11/29/2017] [Accepted: 11/30/2017] [Indexed: 11/17/2022] Open
Abstract
The elucidation of protein-protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development.
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Affiliation(s)
- John Oliver Nealon
- School of Biological Sciences, University of Reading, Reading RG6 6AS, UK.
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12
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Gadzała M, Kalinowska B, Banach M, Konieczny L, Roterman I. Determining protein similarity by comparing hydrophobic core structure. Heliyon 2017; 3:e00235. [PMID: 28217749 PMCID: PMC5300504 DOI: 10.1016/j.heliyon.2017.e00235] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Revised: 12/06/2016] [Accepted: 01/19/2017] [Indexed: 12/19/2022] Open
Abstract
Formal assessment of structural similarity is - next to protein structure prediction - arguably the most important unsolved problem in proteomics. In this paper we propose a similarity criterion based on commonalities between the proteins' hydrophobic cores. The hydrophobic core emerges as a result of conformational changes through which each residue reaches its intended position in the protein body. A quantitative criterion based on this phenomenon has been proposed in the framework of the CASP challenge. The structure of the hydrophobic core - including the placement and scope of any deviations from the idealized model - may indirectly point to areas of importance from the point of view of the protein's biological function. Our analysis focuses on an arbitrarily selected target from the CASP11 challenge. The proposed measure, while compliant with CASP criteria (70-80% correlation), involves certain adjustments which acknowledge the presence of factors other than simple spatial arrangement of solids.
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Affiliation(s)
- M. Gadzała
- AGH - Academic Computer Center − Cyfronet, Nawojki 11, Kraków 30-950, Poland
| | - B. Kalinowska
- Faculty of Physics, Astronomy, Applied Computer Science − Jagiellonian University, Łojasiewicza 11, Kraków 30-348, Poland
| | - M. Banach
- Department of Bioinformatics and Telemedicine, Jagiellonian University − Medical College, Łazarza 16, Krakow 31-530, Poland
| | - L. Konieczny
- Chair of Medical Biochemistry, Jagiellonian University − Medical College, Kopernika 7, Kraków 31-034, Poland
| | - I. Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University − Medical College, Łazarza 16, Krakow 31-530, Poland
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13
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Perez A, Morrone JA, Brini E, MacCallum JL, Dill KA. Blind protein structure prediction using accelerated free-energy simulations. SCIENCE ADVANCES 2016; 2:e1601274. [PMID: 27847872 PMCID: PMC5106196 DOI: 10.1126/sciadv.1601274] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 10/07/2016] [Indexed: 05/30/2023]
Abstract
We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition.
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Affiliation(s)
- Alberto Perez
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Joseph A. Morrone
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Justin L. MacCallum
- Department of Chemistry, University of Calgary, Calgary, Alberta T2N 1N4, Canada
| | - Ken A. Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
- Department of Physics Astronomy, Stony Brook University, Stony Brook, NY 11794, USA
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14
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Kryshtafovych A, Monastyrskyy B, Fidelis K. CASP11 statistics and the prediction center evaluation system. Proteins 2016; 84 Suppl 1:15-9. [PMID: 26857434 PMCID: PMC5479680 DOI: 10.1002/prot.25005] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 01/18/2016] [Accepted: 02/04/2016] [Indexed: 01/10/2023]
Abstract
We outline the role of the Protein Structure Prediction Center (predictioncenter.org) in conducting the CASP11 and CASP ROLL experiments, discuss the experiment statistics, and provide an overview of the present CASP infrastructure. The biggest changes compared to the previous CASPs are the implementation of the evaluation system incorporating practically all evaluation measures, statistical tests, and visualization tools historically used by the CASP assessors, the expansion of the infrastructure to incorporate new categories of contact-assisted and multimeric predictions, and the redesign of the assessors' web-workspace enabling assessments based on multiple measures for different group categories and target sets. Proteins 2016; 84(Suppl 1):15-19. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, 95616
| | - Bohdan Monastyrskyy
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, 95616
| | - Krzysztof Fidelis
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, 95616.
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15
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Li Q, Dahl DB, Vannucci M, Joo H, Tsai JW. KScons: a Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure. Bioinformatics 2016; 32:3774-3781. [PMID: 27559156 DOI: 10.1093/bioinformatics/btw553] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 07/15/2016] [Accepted: 08/18/2016] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION By simplifying the many-bodied complexity of residue packing into patterns of simple pairwise secondary structure interactions between a single knob residue with a three-residue socket, the knob-socket construct allows a more direct incorporation of structural information into the prediction of residue contacts. By modeling the preferences between the amino acid composition of a socket and knob, we undertake an investigation of the knob-socket construct's ability to improve the prediction of residue contacts. The statistical model considers three priors and two posterior estimations to better understand how the input data affects predictions. This produces six implementations of KScons that are tested on three sets: PSICOV, CASP10 and CASP11. We compare against the current leading contact prediction methods. RESULTS The results demonstrate the usefulness as well as the limits of knob-socket based structural modeling of protein contacts. The construct is able to extract good predictions from known structural homologs, while its performance degrades when no homologs exist. Among our six implementations, KScons MST-MP (which uses the multiple structure alignment prior and marginal posterior incorporating structural homolog information) performs the best in all three prediction sets. An analysis of recall and precision finds that KScons MST-MP improves accuracy not only by improving identification of true positives, but also by decreasing the number of false positives. Over the CASP10 and CASP11 sets, KScons MST-MP performs better than the leading methods using only evolutionary coupling data, but not quite as well as the supervised learning methods of MetaPSICOV and CoinDCA-NN that incorporate a large set of structural features. CONTACT qiwei.li@rice.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiwei Li
- Department of Statistics, Rice University, Houston, TX, USA
| | - David B Dahl
- Department of Statistics, Brigham Young University, Provo, UT, USA
| | | | - Hyun Joo
- Department of Chemistry, University of the Pacific, Stockton, CA, USA
| | - Jerry W Tsai
- Department of Chemistry, University of the Pacific, Stockton, CA, USA
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