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Sora V, Tiberti M, Beltrame L, Dogan D, Robbani SM, Rubin J, Papaleo E. PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles. J Chem Inf Model 2023; 63:4237-4245. [PMID: 37437128 DOI: 10.1021/acs.jcim.3c00574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.
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Affiliation(s)
- Valentina Sora
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
- Cancer Systems Biology, Section of Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Ludovica Beltrame
- Cancer Systems Biology, Section of Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Deniz Dogan
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Shahriyar Mahdi Robbani
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Joshua Rubin
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Structural Biology, Danish Cancer Institute, Strandboulevarden 49, 2100 Copenhagen, Denmark
- Cancer Systems Biology, Section of Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800 Lyngby, Denmark
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Nygaard M, Terkelsen T, Vidas Olsen A, Sora V, Salamanca Viloria J, Rizza F, Bergstrand-Poulsen S, Di Marco M, Vistesen M, Tiberti M, Lambrughi M, Jäättelä M, Kallunki T, Papaleo E. The Mutational Landscape of the Oncogenic MZF1 SCAN Domain in Cancer. Front Mol Biosci 2016; 3:78. [PMID: 28018905 PMCID: PMC5156680 DOI: 10.3389/fmolb.2016.00078] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 11/17/2016] [Indexed: 11/24/2022] Open
Abstract
SCAN domains in zinc-finger transcription factors are crucial mediators of protein-protein interactions. Up to 240 SCAN-domain encoding genes have been identified throughout the human genome. These include cancer-related genes, such as the myeloid zinc finger 1 (MZF1), an oncogenic transcription factor involved in the progression of many solid cancers. The mechanisms by which SCAN homo- and heterodimers assemble and how they alter the transcriptional activity of zinc-finger transcription factors in cancer and other diseases remain to be investigated. Here, we provide the first description of the conformational ensemble of the MZF1 SCAN domain cross-validated against NMR experimental data, which are probes of structure and dynamics on different timescales. We investigated the protein-protein interaction network of MZF1 and how it is perturbed in different cancer types by the analyses of high-throughput proteomics and RNASeq data. Collectively, we integrated many computational approaches, ranging from simple empirical energy functions to all-atom microsecond molecular dynamics simulations and network analyses to unravel the effects of cancer-related substitutions in relation to MZF1 structure and interactions.
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Affiliation(s)
- Mads Nygaard
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Thilde Terkelsen
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - André Vidas Olsen
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Valentina Sora
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Juan Salamanca Viloria
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Fabio Rizza
- Department of Biomedical Sciences, University of Padua Padua, Italy
| | - Sanne Bergstrand-Poulsen
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Miriam Di Marco
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Mette Vistesen
- Cell Stress and Survival Unit and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Matteo Tiberti
- Department of Chemistry and Biochemistry, School of Biological and Chemical Sciences, Queen Mary University of London London, UK
| | - Matteo Lambrughi
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Marja Jäättelä
- Unit of Cell Death and Metabolism and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Tuula Kallunki
- Unit of Cell Death and Metabolism and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory and Center for Autophagy, Recycling and Disease, Danish Cancer Society Research Center Copenhagen, Denmark
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