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1. Binary Interactome Models of Inner- Versus Outer-Complexome Organization

Author

Thomas Rolland, Atina G. Cote, Marc Vidal, Michael E. Cusick, Alice Desbuleux, István Kovács, Michael A. Calderwood, Kerstin Spirohn, Sadie Schlabach, Jan Tavernier, Jüri Reimand, Irma Lemmens, Jean-Claude Twizere, Patrick Aloy, Pascal Falter-Braun, David E. Hill, Jennifer K. Knapp, Carles Pons, Noor Jailkhani, Yang Wang, Luke Lambourne, Yves Jacob, Tiziana M. Cafarelli, Marinella Gebbia, Nishka Kishore, Tong Hao, David De Ridder, Quan Zhong, Wenting Bian, Benoit Charloteaux, Mohamed Helmy, Katja Luck, Joseph C. Mellor, Dae-Kyum Kim, Frederick P. Roth, Anupama Yadav, Miles W. Mee, Yun Shen, Dana-Farber Cancer Institute [Boston], Université de Liège, University of Toronto, Institute for Research in Biomedicine [Barcelona, Spain] (IRB), University of Barcelona-Barcelona Institute of Science and Technology (BIST), Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Université Paris Diderot, Sorbonne Paris Cité, Paris, France, Université Paris Diderot - Paris 7 (UPD7), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)

Subjects
Physics, 0303 health sciences, 03 medical and health sciences, 0302 clinical medicine, Homogeneous, Structural plasticity, Binary number, Computational biology, Interactome, Functional similarity, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

SummaryHundreds of different protein complexes that perform important functions across all cellular processes, collectively comprising the “complexome” of an organism, have been identified1. However, less is known about the fraction of the interactome that exists outside the complexome, in the “outer-complexome”. To investigate features of “inner”- versus outer-complexome organisation in yeast, we generated a high-quality atlas of binary protein-protein interactions (PPIs), combining three previous maps2–4 and a new reference all-by-all binary interactome map. A greater proportion of interactions in our map are in the outer-complexome, in comparison to those found by affinity purification followed by mass spectrometry5–7 or in literature curated datasets8–11. In addition, recent advances in deep learning predictions of PPI structures12 mirror the existing experimentally resolved structures in being largely focused on the inner complexome and missing most interactions in the outer-complexome. Our new PPI network suggests that the outer-complexome contains considerably more PPIs than the inner-complexome, and integration with functional similarity networks13–15 reveals that interactions in the inner-complexome are highly detectable and correspond to pairs of proteins with high functional similarity, while proteins connected by more transient, harder-to-detect interactions in the outer-complexome, exhibit higher functional heterogeneity.

Published
2021

2. Natural variants suppress mutations in hundreds of essential genes

Author

Gianni Liti, Bryan-Joseph San Luis, Jolanda van Leeuwen, Leopold Parts, Meredith Laver, Michael W Yuen, Carles Pons, Elise Eray, Amandine Batté, Patrick Aloy, Maykel Lopes, Jia-Xing Yue, University of Toronto, Université de Lausanne (UNIL), Institut de Recherche sur le Cancer et le Vieillissement (IRCAN), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Institute for Research in Biomedicine [Barcelona, Spain] (IRB), University of Barcelona-Barcelona Institute of Science and Technology (BIST), Université de Lausanne = University of Lausanne (UNIL), Université Nice Sophia Antipolis (1965 - 2019) (UNS), and Liti, Gianni

Subjects
genetic interactions, Medicine (General), Saccharomyces cerevisiae Proteins, QH301-705.5, Mutant, Context (language use), Saccharomyces cerevisiae, [SDV.GEN] Life Sciences [q-bio]/Genetics, Biology, medicine.disease_cause, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, 0302 clinical medicine, R5-920, Gene Expression Regulation, Fungal, Genetic variation, medicine, [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], natural variation, Allele, Biology (General), Gene, 030304 developmental biology, Genetics, 0303 health sciences, Mutation, [SDV.GEN]Life Sciences [q-bio]/Genetics, Genes, Essential, Genes, Modifier, General Immunology and Microbiology, Applied Mathematics, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], compensatory evolution, Genetic Variation, Articles, genetic modifiers, Phenotype, Computational Theory and Mathematics, Gain of Function Mutation, genetic suppression, Genetics, Gene Therapy & Genetic Disease, General Agricultural and Biological Sciences, 030217 neurology & neurosurgery, Function (biology), Information Systems
Abstract

The consequence of a mutation can be influenced by the context in which it operates. For example, loss of gene function may be tolerated in one genetic background, and lethal in another. The extent to which mutant phenotypes are malleable, the architecture of modifiers and the identities of causal genes remain largely unknown. Here, we measure the fitness effects of ~ 1,100 temperature‐sensitive alleles of yeast essential genes in the context of variation from ten different natural genetic backgrounds and map the modifiers for 19 combinations. Altogether, fitness defects for 149 of the 580 tested genes (26%) could be suppressed by genetic variation in at least one yeast strain. Suppression was generally driven by gain‐of‐function of a single, strong modifier gene, and involved both genes encoding complex or pathway partners suppressing specific temperature‐sensitive alleles, as well as general modifiers altering the effect of many alleles. The emerging frequency of suppression and range of possible mechanisms suggest that a substantial fraction of monogenic diseases could be managed by modulating other gene products., A survey of ~ 1,100 temperature‐sensitive alleles of yeast essential genes in ten diverse strains shows that natural genetic variants frequently suppress deleterious mutations. Genetic mapping and allele replacement identify causal suppressor genes.

Published
2021
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3. Generation of level-k LGT Networks

Author

Celine Scornavacca, Joan Carles Pons, Gabriel Cardona, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226

Subjects
Theoretical computer science, Gene Transfer, Horizontal, level-k, blobbed tree, Stochastic modelling, Computer science, 0206 medical engineering, 02 engineering and technology, Set (abstract data type), Evolution, Molecular, MESH: Computer Simulation, Simple (abstract algebra), evolutionary model, Genetic algorithm, MESH: Evolution, MESH: Models, Genetics, Quantitative Biology::Populations and Evolution, MESH: Horizontal, Computer Simulation, [INFO]Computer Science [cs], MESH: Gene Transfer, MESH: Phylogeny, Phylogeny, Event (probability theory), Phylogenetic network, Phylogenetic tree, Models, Genetic, generator, Applied Mathematics, Computational Biology, LGT network, Quantitative Biology::Genomics, MESH: Genetic, MESH: Molecular, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 020602 bioinformatics, Biotechnology, Generator (mathematics), MESH: Computational Biology
Abstract

Phylogenetic networks provide a mathematical model to represent the evolution of a set of species where, apart from speciation, reticulate evolutionary events have to be taken into account. Among these events, lateral gene transfers need special consideration due to the asymmetry in the roles of the species involved in such an event. To take into account this asymmetry, LGT networks were introduced. Contrarily to the case of phylogenetic trees, the combinatorial structure of phylogenetic networks is much less known and difficult to describe. One of the approaches in the literature is to classify them according to their level and find generators of the given level that can be used to recursively generate all networks. In this paper, we adapt the concept of generators to the case of LGT networks. We show how these generators, classified by their level, give rise to simple LGT networks of the specified level, and how any LGT network can be obtained from these simple networks, that act as building blocks of the generic structure. The stochastic models of evolution of phylogenetic networks are also much less studied than those for phylogenetic trees. In this setting, we introduce a novel two-parameter model that generates LGT networks. Finally, we present some computer simulations using this model in order to investigate the complexity of the generated networks, depending on the parameters of the model.

Published
2020
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4. Fast algorithm for the reconciliation of gene trees and LGT networks

Author

Joan Carles Pons Mayol, Celine Scornavacca, Gabriel Cardona, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement [IRD] : UR226-Centre National de la Recherche Scientifique (CNRS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École pratique des hautes études (EPHE)

Subjects
0301 basic medicine, Statistics and Probability, Theoretical computer science, 0206 medical engineering, 02 engineering and technology, Biology, Polynomial algorithm, General Biochemistry, Genetics and Molecular Biology, Evolution, Molecular, 03 medical and health sciences, Highways of transfers, Reconciliation method, Phylogenomics, Gene Regulatory Networks, [INFO]Computer Science [cs], Gene, Phylogeny, Phylogenetic network, General Immunology and Microbiology, Phylogenetic tree, Models, Genetic, Ecology, Applied Mathematics, General Medicine, Fast algorithm, Tree (graph theory), 030104 developmental biology, Modeling and Simulation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], General Agricultural and Biological Sciences, Gene evolution, 020602 bioinformatics, Algorithms
Abstract

International audience; In phylogenomics, reconciliations aim at explaining the discrepancies between the evolutionary histories of genes and species. Several reconciliation models are available when the evolution of the species of interest is modelled via phylogenetic trees; the most commonly used are the DL model, accounting for duplications and losses in gene evolution and yielding polynomially-solvable problems, and the DTL model, which also accounts for gene transfers and implies NP-hard problems. However, when dealing with non-tree-like evolutionary events such as hybridisations, phylogenetic networks - and not phylogenetic trees - should be used to model species evolution. Reconciliation models involving phylogenetic networks are still at their early days. In this paper, we propose a new reconciliation model in which the evolution of species is modelled by a special kind of phylogenetic networks - the LGT networks. Our model considers duplications, losses and transfers of genes, but restricts transfers to happen through some specific arcs of the network, called secondary arcs. Moreover, we provide a polynomial algorithm to compute the most parsimonious reconciliation between a gene tree and an LGT network under this model. Our method, when combined with quartet decomposition methods to detect putative "highways" of transfers, permits to refine their analyses by allowing to examine the two possible directions of a highway and even consider combinations of highways.

Published
2017
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5. Community-Wide Assessment of Protein-Interface Modeling Suggests Improvements to Design Methodology

Author

Libin Cao, Anne Poupon, Brian G. Pierce, Howook Hwang, Ying Chen, Victor L. Hsu, Hasup Lee, Yangyu Huang, Daisuke Kihara, Juan Fernández-Recio, Vladimir Potapov, Aroop Sircar, Chaok Seok, Timothy A. Whitehead, Jérôme Azé, Nir Ben Tal, Seren Soner, Brian Kuhlman, P. Benjamin Stranges, Nobuyuki Uchikoga, Sanbo Qin, Xinqi Gong, Yi Xiao, Carlos J. Camacho, Yaoqi Zhou, Gideon Schreiber, Ora Schueler-Furman, Paul A. Bates, Krishna Praneeth Kilambi, Joël Janin, Mati Cohen, Julie C. Mitchell, Panwen Wang, Cunxin Wang, Raed Khashan, Mayuko Takeda-Shitaka, Lin Li, Martin Zacharias, Alexander Tropsha, Genki Terashi, Xiaofan Li, David Baker, Jian Zhan, Julie Bernauer, Zohar Itzhaki, Mainak Guharoy, Eva-Maria Strauch, Xiaoqin Zou, Thom Vreven, Hahnbeom Park, Sheng-You Huang, Stephen Bush, Daron M. Standley, Feng Yang, Yuko Tsuchiya, Fan Jiang, Jacob E. Corn, Takashi Ishida, Chunhua Li, Junsu Ko, Robert G. Hall, Thomas Bourquard, Iain H. Moal, Weiyi Zhang, C.M. Driggers, Nir London, Jessica L. Morgan, Ron Jacak, Haruki Nakamura, Laura Pérez-Cano, Denis Fouches, Bin Liu, Yutaka Akiyama, Omar N. A. Demerdash, Yuval Inbar, Xianjin Xu, Yuedong Yang, Dachuan Guo, Masahito Ohue, Turkan Haliloglu, Jeffrey J. Gray, Juan Esquivel-Rodríguez, Alexandre M. J. J. Bonvin, Pemra Ozbek, Sarel J. Fleishman, Şefik Kerem Ovali, Charles H. Robert, Huan-Xiang Zhou, Eiji Kanamori, Yuri Matsuzaki, Carles Pons, Zhiping Weng, Kengo Kinoshita, Shoshana J. Wodak, Shiyong Liu, Panagiotis L. Kastritis, University of Washington [Seattle], Institute of Molecular Biophysics [Tallahassee], Florida State University [Tallahassee] (FSU), University of Wisconsin Whitewater, Kitasato University, Biomolecular Modelling laboratory [London], Cancer Research UK London Research Institute, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Seoul National University [Seoul] (SNU), Knowledge representation, reasonning (ORPAILLEUR), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Algorithms and Models for Integrative Biology (AMIB ), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Physiologie de la reproduction et des comportements [Nouzilly] (PRC), Institut National de la Recherche Agronomique (INRA)-Institut Français du Cheval et de l'Equitation [Saumur]-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Department of Chemical Engineering [Bogazici] (ChE), Boǧaziçi üniversitesi = Boğaziçi University [Istanbul], Tel Aviv University [Tel Aviv], University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Chinese Academy of Sciences [Beijing] (CAS), Beijing University of Technology, Laboratoire de biochimie théorique [Paris] (LBT (UPR_9080)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Institut de biologie physico-chimique (IBPC (FR_550)), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Huazhong University of Science and Technology [Wuhan] (HUST), Hadassah Hebrew University Medical Center [Jerusalem], Weizmann Institute of Science [Rehovot, Israël], Institute for Protein Research [Osaka], Osaka University [Osaka], Japan Biological Informatics Consortium [Tokyo], WPI Immunology Frontier Research Center (IFREC), Graduate School of Information Sciences [Sendai], Tohoku University [Sendai], Oregon State University (OSU), Indiana University - Purdue University Indianapolis (IUPUI), Indiana University System, Bijvoet Center for Biomolecular Research [Utrecht], Utrecht University [Utrecht], University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Johns Hopkins University (JHU), Tokyo Institute of Technology [Tokyo] (TITECH), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Purdue University [West Lafayette], Dalton Cardiovascular Research Center [Columbia], University of Missouri [Columbia] (Mizzou), University of Missouri System-University of Missouri System, The Hospital for sick children [Toronto] (SickKids), Institut de biochimie et biophysique moléculaire et cellulaire (IBBMC), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), The authors thank Sameer Velankar and Marc Lensink for their help in coordinating this experiment and Raik Grunberg for many helpful suggestions on a draft. S.J.F. was supported by a long-term fellowship from the Human Frontier Science Program. S.J.W. is Canada Research Chair Tier 1, funded by the Canadian Institutes of Health Research. Research in the Baker laboratory was supported by the Howard Hughes Medical Institute, the Defense Advanced Research Projects Agency, the National Institutes of Health Yeast Resource Center, and the Defense Threat Reduction Agency., Technical University of Munich (TUM), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Laboratoire de Recherche en Informatique (LRI), Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Institut Français du Cheval et de l'Equitation [Saumur]-Institut National de la Recherche Agronomique (INRA), Boğaziçi University [Istanbul], Centre National de la Recherche Scientifique (CNRS)-Institut de biologie physico-chimique (IBPC), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Weizmann Institute of Science, University of Missouri [Columbia], Institut National de la Recherche Agronomique (INRA)-Institut Français du Cheval et de l'Equitation [Saumur] (IFCE)-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Tel Aviv University (TAU), Institut de biologie physico-chimique (IBPC (FR_550)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Institut Français du Cheval et de l'Equitation [Saumur]-Université de Tours-Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot - Paris 7 (UPD7)-Institut de biologie physico-chimique (IBPC), and Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Models, Molecular, biochemistry and molecular biology, Computer science, Protein design, Nanotechnology, Machine learning, computer.software_genre, Article, 03 medical and health sciences, Structural Biology, protein protein interactions, Taverne, conformational plasticity, Computational design, Macromolecular docking, CASP, Design methods, Molecular Biology, 030304 developmental biology, Protein interface, 0303 health sciences, Binding Sites, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Structural Biology [q-bio.BM], business.industry, 030302 biochemistry & molecular biology, Proteins, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM], negative design, Docking (molecular), computational protein design, Critical assessment, Artificial intelligence, business, computer, Protein Binding
Abstract

International audience; The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

Published
2011
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