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Gucwa M, Bijak V, Zheng H, Murzyn K, Minor W. CheckMyMetal (CMM): validating metal-binding sites in X-ray and cryo-EM data. IUCRJ 2024; 11:871-877. [PMID: 39141478 PMCID: PMC11364027 DOI: 10.1107/s2052252524007073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/18/2024] [Indexed: 08/16/2024]
Abstract
Identifying and characterizing metal-binding sites (MBS) within macromolecular structures is imperative for elucidating their biological functions. CheckMyMetal (CMM) is a web based tool that facilitates the interactive validation of MBS in structures determined through X-ray crystallography and cryo-electron microscopy (cryo-EM). Recent updates to CMM have significantly enhanced its capability to efficiently handle large datasets generated from cryo-EM structural analyses. In this study, we address various challenges inherent in validating MBS within both X-ray and cryo-EM structures. Specifically, we examine the difficulties associated with accurately identifying metals and modeling their coordination environments by considering the ongoing reproducibility challenges in structural biology and the critical importance of well annotated, high-quality experimental data. CMM employs a sophisticated framework of rules rooted in the valence bond theory for MBS validation. We explore how CMM validation parameters correlate with the resolution of experimentally derived structures of macromolecules and their complexes. Additionally, we showcase the practical utility of CMM by analyzing a representative cryo-EM structure. Through a comprehensive examination of experimental data, we demonstrate the capability of CMM to advance MBS characterization and identify potential instances of metal misassignment.
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Affiliation(s)
- Michal Gucwa
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesville22908USA
- Department of Computational Biophysics and BioinformaticsJagiellonian UniversityKrakowPoland
- Doctoral School of Exact and Natural SciencesJagiellonian UniversityKrakowPoland
| | - Vanessa Bijak
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesville22908USA
| | - Heping Zheng
- Bioinformatics CenterHunan University College of BiologyChangshaHunan410082People’s Republic of China
| | - Krzysztof Murzyn
- Department of Computational Biophysics and BioinformaticsJagiellonian UniversityKrakowPoland
| | - Wladek Minor
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesville22908USA
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Wang CR, McFarlane LO, Pukala TL. Exploring snake venoms beyond the primary sequence: From proteoforms to protein-protein interactions. Toxicon 2024; 247:107841. [PMID: 38950738 DOI: 10.1016/j.toxicon.2024.107841] [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: 04/22/2024] [Revised: 06/26/2024] [Accepted: 06/28/2024] [Indexed: 07/03/2024]
Abstract
Snakebite envenomation has been a long-standing global issue that is difficult to treat, largely owing to the flawed nature of current immunoglobulin-based antivenom therapy and the complexity of snake venoms as sophisticated mixtures of bioactive proteins and peptides. Comprehensive characterisation of venom compositions is essential to better understanding snake venom toxicity and inform effective and rationally designed antivenoms. Additionally, a greater understanding of snake venom composition will likely unearth novel biologically active proteins and peptides that have promising therapeutic or biotechnological applications. While a bottom-up proteomic workflow has been the main approach for cataloguing snake venom compositions at the toxin family level, it is unable to capture snake venom heterogeneity in the form of protein isoforms and higher-order protein interactions that are important in driving venom toxicity but remain underexplored. This review aims to highlight the importance of understanding snake venom heterogeneity beyond the primary sequence, in the form of post-translational modifications that give rise to different proteoforms and the myriad of higher-order protein complexes in snake venoms. We focus on current top-down proteomic workflows to identify snake venom proteoforms and further discuss alternative or novel separation, instrumentation, and data processing strategies that may improve proteoform identification. The current higher-order structural characterisation techniques implemented for snake venom proteins are also discussed; we emphasise the need for complementary and higher resolution structural bioanalytical techniques such as mass spectrometry-based approaches, X-ray crystallography and cryogenic electron microscopy, to elucidate poorly characterised tertiary and quaternary protein structures. We envisage that the expansion of the snake venom characterisation "toolbox" with top-down proteomics and high-resolution protein structure determination techniques will be pivotal in advancing structural understanding of snake venoms towards the development of improved therapeutic and biotechnology applications.
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Affiliation(s)
- C Ruth Wang
- Discipline of Chemistry, School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, 5005, Australia
| | - Lewis O McFarlane
- Discipline of Chemistry, School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, 5005, Australia
| | - Tara L Pukala
- Discipline of Chemistry, School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, 5005, Australia.
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Majorek KA, Gucwa M, Murzyn K, Minor W. Metal ions in biomedically relevant macromolecular structures. Front Chem 2024; 12:1426211. [PMID: 39246722 PMCID: PMC11378719 DOI: 10.3389/fchem.2024.1426211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 06/28/2024] [Indexed: 09/10/2024] Open
Abstract
Understanding the functions of metal ions in biological systems is crucial for many aspects of research, including deciphering their roles in diseases and potential therapeutic use. Structural information about the molecular or atomic details of these interactions, generated by methods like X-ray crystallography, cryo-electron microscopy, or nucleic magnetic resonance, frequently provides details that no other method can. As with any experimental method, they have inherent limitations that sometimes lead to an erroneous interpretation. This manuscript highlights different aspects of structural data available for metal-protein complexes. We examine the quality of modeling metal ion binding sites across different structure determination methods, where different kinds of errors stem from, and how they can impact correct interpretations and conclusions.
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Affiliation(s)
- Karolina A Majorek
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, United States
| | - Michal Gucwa
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, United States
- Department of Computational Biophysics and Bioinformatics, Jagiellonian University, Krakow, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland
| | - Krzysztof Murzyn
- Department of Computational Biophysics and Bioinformatics, Jagiellonian University, Krakow, Poland
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, United States
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Kairys V, Baranauskiene L, Kazlauskiene M, Zubrienė A, Petrauskas V, Matulis D, Kazlauskas E. Recent advances in computational and experimental protein-ligand affinity determination techniques. Expert Opin Drug Discov 2024; 19:649-670. [PMID: 38715415 DOI: 10.1080/17460441.2024.2349169] [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: 03/18/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved. AREAS COVERED In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein - small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design. EXPERT OPINION The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.
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Affiliation(s)
- Visvaldas Kairys
- Department of Bioinformatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Lina Baranauskiene
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | | | - Asta Zubrienė
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Vytautas Petrauskas
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Daumantas Matulis
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Egidijus Kazlauskas
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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Banayan NE, Loughlin BJ, Singh S, Forouhar F, Lu G, Wong K, Neky M, Hunt HS, Bateman LB, Tamez A, Handelman SK, Price WN, Hunt JF. Systematic enhancement of protein crystallization efficiency by bulk lysine-to-arginine (KR) substitution. Protein Sci 2024; 33:e4898. [PMID: 38358135 PMCID: PMC10868448 DOI: 10.1002/pro.4898] [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: 06/18/2023] [Revised: 01/01/2024] [Accepted: 01/02/2024] [Indexed: 02/16/2024]
Abstract
Structural genomics consortia established that protein crystallization is the primary obstacle to structure determination using x-ray crystallography. We previously demonstrated that crystallization propensity is systematically related to primary sequence, and we subsequently performed computational analyses showing that arginine is the most overrepresented amino acid in crystal-packing interfaces in the Protein Data Bank. Given the similar physicochemical characteristics of arginine and lysine, we hypothesized that multiple lysine-to-arginine (KR) substitutions should improve crystallization. To test this hypothesis, we developed software that ranks lysine sites in a target protein based on the redundancy-corrected KR substitution frequency in homologs. This software can be run interactively on the worldwide web at https://www.pxengineering.org/. We demonstrate that three unrelated single-domain proteins can tolerate 5-11 KR substitutions with at most minor destabilization, and, for two of these three proteins, the construct with the largest number of KR substitutions exhibits significantly enhanced crystallization propensity. This approach rapidly produced a 1.9 Å crystal structure of a human protein domain refractory to crystallization with its native sequence. Structures from Bulk KR-substituted domains show the engineered arginine residues frequently make hydrogen-bonds across crystal-packing interfaces. We thus demonstrate that Bulk KR substitution represents a rational and efficient method for probabilistic engineering of protein surface properties to improve crystallization.
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Affiliation(s)
- Nooriel E. Banayan
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Blaine J. Loughlin
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Shikha Singh
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Farhad Forouhar
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Guanqi Lu
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
| | - Kam‐Ho Wong
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
- Present address:
Vaccine Research and DevelopmentPfizer Inc.Pearl RiverNew YorkUSA
| | - Matthew Neky
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
- Present address:
Columbia UniversityNew YorkNew YorkUSA
| | - Henry S. Hunt
- Department of PhysicsStanford UniversityStanfordCaliforniaUSA
| | | | | | - Samuel K. Handelman
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
- Present address:
Department of Pain & Neuronal HealthEli Lily & Co.893 Delaware StIndianapolisIndianaUSA
| | - W. Nicholson Price
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
- Present address:
University of Michigan Law SchoolAnn ArborMichiganUSA
| | - John F. Hunt
- Department of Biological Sciences702A Sherman Fairchild Center, MC2434, Columbia UniversityNew YorkNew YorkUSA
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Ng TK, Ji J, Liu Q, Yao Y, Wang WY, Cao Y, Chen CB, Lin JW, Dong G, Cen LP, Huang C, Zhang M. Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2. Biomolecules 2023; 14:14. [PMID: 38275755 PMCID: PMC10813463 DOI: 10.3390/biom14010014] [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] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 01/27/2024] Open
Abstract
Deep neural network-based programs can be applied to protein structure modeling by inputting amino acid sequences. Here, we aimed to evaluate the AlphaFold2-modeled myocilin wild-type and variant protein structures and compare to the experimentally determined protein structures. Molecular dynamic and ligand binding properties of the experimentally determined and AlphaFold2-modeled protein structures were also analyzed. AlphaFold2-modeled myocilin variant protein structures showed high similarities in overall structure to the experimentally determined mutant protein structures, but the orientations and geometries of amino acid side chains were slightly different. The olfactomedin-like domain of the modeled missense variant protein structures showed fewer folding changes than the nonsense variant when compared to the predicted wild-type protein structure. Differences were also observed in molecular dynamics and ligand binding sites between the AlphaFold2-modeled and experimentally determined structures as well as between the wild-type and variant structures. In summary, the folding of the AlphaFold2-modeled MYOC variant protein structures could be similar to that determined by the experiments but with differences in amino acid side chain orientations and geometries. Careful comparisons with experimentally determined structures are needed before the applications of the in silico modeled variant protein structures.
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Affiliation(s)
- Tsz Kin Ng
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Jie Ji
- Network & Information Centre, Shantou University, Shantou 515041, China
| | - Qingping Liu
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
- Key Laboratory of Carbohydrate and Lipid Metabolism Research, College of Life Science and Technology, Dalian University, Dalian 116622, China
| | - Yao Yao
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
- Shantou University Medical College, Shantou 515041, China
| | - Wen-Ying Wang
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
- Shantou University Medical College, Shantou 515041, China
| | - Yingjie Cao
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Chong-Bo Chen
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Jian-Wei Lin
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Geng Dong
- Shantou University Medical College, Shantou 515041, China
| | - Ling-Ping Cen
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Chukai Huang
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Mingzhi Zhang
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
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