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Journal articles on the topic 'Molecular Interaction Maps'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Barbuti, Roberto, Andrea Maggiolo-Schettini, Paolo Milazzo, Giovanni Pardini, and Aureliano Rama. "A Process Calculus for Molecular Interaction Maps." Electronic Proceedings in Theoretical Computer Science 11 (November 30, 2009): 35–49. http://dx.doi.org/10.4204/eptcs.11.3.

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2

Stower, Hannah. "Interaction maps for mammals." Nature Reviews Genetics 14, no. 4 (2013): 240. http://dx.doi.org/10.1038/nrg3451.

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3

de Souza, Natalie. "Transcription factor interaction maps." Nature Methods 7, no. 5 (2010): 344–45. http://dx.doi.org/10.1038/nmeth0510-344b.

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4

Kohn, Kurt W., Mirit I. Aladjem, John N. Weinstein, and Yves Pommier. "Molecular Interaction Maps of Bioregulatory Networks: A General Rubric for Systems Biology." Molecular Biology of the Cell 17, no. 1 (2006): 1–13. http://dx.doi.org/10.1091/mbc.e05-09-0824.

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A standard for bioregulatory network diagrams is urgently needed in the same way that circuit diagrams are needed in electronics. Several graphical notations have been proposed, but none has become standard. We have prepared many detailed bioregulatory network diagrams using the molecular interaction map (MIM) notation, and we now feel confident that it is suitable as a standard. Here, we describe the MIM notation formally and discuss its merits relative to alternative proposals. We show by simple examples how to denote all of the molecular interactions commonly found in bioregulatory networks. There are two forms of MIM diagrams. “Heuristic” MIMs present the repertoire of interactions possible for molecules that are colocalized in time and place. “Explicit” MIMs define particular models (derived from heuristic MIMs) for computer simulation. We show also how pathways or processes can be highlighted on a canonical heuristic MIM. Drawing a MIM diagram, adhering to the rules of notation, imposes a logical discipline that sharpens one's understanding of the structure and function of a network.

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5

Pommier, Y., O. Sordet, A. Rao, H. Zhang, and K. Kohn. "Targeting Chk2 Kinase: Molecular Interaction Maps and Therapeutic Rationale." Current Pharmaceutical Design 11, no. 22 (2005): 2855–572. http://dx.doi.org/10.2174/1381612054546716.

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6

Olsson, Tjelvar S. G., Peter A. Wood, and Colin R. Groom. "Evaluation of molecular crystal structures using full interaction maps." Acta Crystallographica Section A Foundations of Crystallography 69, a1 (2013): s75. http://dx.doi.org/10.1107/s0108767313099352.

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7

Kohn, Kurt W. "Molecular interaction maps as information organizers and simulation guides." Chaos: An Interdisciplinary Journal of Nonlinear Science 11, no. 1 (2001): 84. http://dx.doi.org/10.1063/1.1338126.

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8

Wood, Peter A., Tjelvar S. G. Olsson, Jason C. Cole, et al. "Evaluation of molecular crystal structures using Full Interaction Maps." CrystEngComm 15, no. 1 (2013): 65–72. http://dx.doi.org/10.1039/c2ce25849h.

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9

Walhout, Albertha J. M., and Marc Vidal. "Protein interaction maps for model organisms." Nature Reviews Molecular Cell Biology 2, no. 1 (2001): 55–63. http://dx.doi.org/10.1038/35048107.

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10

Griffin, James D. "Interaction maps for kinase inhibitors." Nature Biotechnology 23, no. 3 (2005): 308–9. http://dx.doi.org/10.1038/nbt0305-308.

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11

Aladjem, M. I., S. Pasa, S. Parodi, J. N. Weinstein, Y. Pommier, and K. W. Kohn. "Molecular Interaction Maps--A Diagrammatic Graphical Language for Bioregulatory Networks." Science Signaling 2004, no. 222 (2004): pe8. http://dx.doi.org/10.1126/stke.2222004pe8.

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12

Pommier, Yves, Olivier Sordet, Smitha Antony, Richard L. Hayward, and Kurt W. Kohn. "Apoptosis defects and chemotherapy resistance: molecular interaction maps and networks." Oncogene 23, no. 16 (2004): 2934–49. http://dx.doi.org/10.1038/sj.onc.1207515.

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13

Gardiner, K. "Building protein interaction maps for Down's syndrome." Briefings in Functional Genomics and Proteomics 3, no. 2 (2004): 142–56. http://dx.doi.org/10.1093/bfgp/3.2.142.

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14

Aghamiri, Sara Sadat, Vidisha Singh, Aurélien Naldi, Tomáš Helikar, Sylvain Soliman, and Anna Niarakis. "Automated inference of Boolean models from molecular interaction maps using CaSQ." Bioinformatics 36, no. 16 (2020): 4473–82. http://dx.doi.org/10.1093/bioinformatics/btaa484.

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Abstract Motivation Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner. Results We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability. Availability and implementation The present tool is available online: https://lifeware.inria.fr/∼soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq. Supplementary information Supplementary data are available at Bioinformatics online.

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15

Chaurasia, Gautam, Yasir Iqbal, Christian Hänig, Hanspeter Herzel, Erich E. Wanker, and Matthias E. Futschik. "Flexible web-based integration of distributed large-scale human protein interaction maps." Journal of Integrative Bioinformatics 4, no. 1 (2007): 40–50. http://dx.doi.org/10.1515/jib-2007-51.

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Summary Protein-protein interactions constitute the backbone of many molecular processes. This has motivated the recent construction of several large-scale human protein-protein interaction maps [1-10]. Although these maps clearly offer a wealth of information, their use is challenging: complexity, rapid growth, and fragmentation of interaction data hamper their usability. To overcome these hurdles, we have developed a publicly accessible database termed UniHI (Unified Human Interactome) for integration of human protein-protein interaction data. This database is designed to provide biomedical researchers a common platform for exploring previously disconnected human interaction maps. UniHI offers researchers flexible integrated tools for accessing comprehensive information about the human interactome. Several features included in the UniHI allow users to perform various types of network-oriented and functional analysis. At present, UniHI contains over 160,000 distinct interactions between 17,000 unique proteins from ten major interaction maps derived by both computational and experimental approaches [1-10]. Here we describe the details of the implementation and maintenance of UniHI and discuss the challenges that have to be addressed for a successful integration of interaction data.

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16

Uetz, Peter, and Michael J. Pankratz. "Protein interaction maps on the fly." Nature Biotechnology 22, no. 1 (2004): 43–44. http://dx.doi.org/10.1038/nbt0104-43.

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17

Nieddu, Erika, and Stefania Pasa. "Interfering with Protein-Protein Contact: Molecular Interaction Maps and Peptide Modulators." Current Topics in Medicinal Chemistry 7, no. 1 (2007): 21–32. http://dx.doi.org/10.2174/156802607779318271.

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18

Mercadante, Davide, Frauke Gräter, and Csaba Daday. "CONAN: A Tool to Decode Dynamical Information from Molecular Interaction Maps." Biophysical Journal 114, no. 6 (2018): 1267–73. http://dx.doi.org/10.1016/j.bpj.2018.01.033.

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19

Sciskala, Barbara, and Ralf Kölling. "Interaction Maps of the Saccharomyces cerevisiae ESCRT-III Protein Snf7." Eukaryotic Cell 12, no. 11 (2013): 1538–46. http://dx.doi.org/10.1128/ec.00241-13.

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ABSTRACT The Saccharomyces cerevisiae ESCRT-III protein Snf7 is part of an intricate interaction network at the endosomal membrane. Interaction maps of Snf7 were established by measuring the degree of binding of individual binding partners to putative binding motifs along the Snf7 sequence by glutathione S -transferase (GST) pulldown. For each interaction partner, distinct binding profiles were obtained. The following observations were made. The ESCRT-III subunits Vps20 and Vps24 showed a complementary binding pattern, suggesting a model for the series of events in the ESCRT-III functional cycle. Vps4 bound to individual Snf7 motifs but not to full-length Snf7. This suggests that Vps4 does not bind to the closed conformation of Snf7. We also demonstrate for the first time that the ALIX/Bro1 homologue Rim20 binds to the α6 helix of Snf7. Analysis of a Snf7 α6 deletion mutant showed that the α6 helix is crucial for binding of Bro1 and Rim20 in vivo and is indispensable for the multivesicular body (MVB)-sorting and Rim-signaling functions of Snf7. The Snf7Δα6 protein still appeared to be incorporated into ESCRT-III complexes at the endosomal membrane, but disassembly of the complex seemed to be defective. In summary, our study argues against the view that the ESCRT cycle is governed by single one-to-one interactions between individual components and emphasizes the network character of the ESCRT interactions.

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20

Stanyon, Clement A., and Russell L. Finley. "Progress and potential of Drosophila protein interaction maps." Pharmacogenomics 1, no. 4 (2000): 417–31. http://dx.doi.org/10.1517/14622416.1.4.417.

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21

Portilho, Débora M., Roger Persson, and Nathalie Arhel. "Role of non-motile microtubule-associated proteins in virus trafficking." Biomolecular Concepts 7, no. 5-6 (2016): 283–92. http://dx.doi.org/10.1515/bmc-2016-0018.

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AbstractViruses are entirely dependent on their ability to infect a host cell in order to replicate. To reach their site of replication as rapidly and efficiently as possible following cell entry, many have evolved elaborate mechanisms to hijack the cellular transport machinery to propel themselves across the cytoplasm. Long-range movements have been shown to involve motor proteins along microtubules (MTs) and direct interactions between viral proteins and dynein and/or kinesin motors have been well described. Although less well-characterized, it is also becoming increasingly clear that non-motile microtubule-associated proteins (MAPs), including structural MAPs of the MAP1 and MAP2 families, and microtubule plus-end tracking proteins (+TIPs), can also promote viral trafficking in infected cells, by mediating interaction of viruses with filaments and/or motor proteins, and modulating filament stability. Here we review our current knowledge on non-motile MAPs, their role in the regulation of cytoskeletal dynamics and in viral trafficking during the early steps of infection.

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22

Pommier, Y., M. Aladjem, and K. W. Kohn. "417 The ATM-Chk2 kinase pathway: molecular interaction maps and therapeutic rationale." European Journal of Cancer Supplements 2, no. 8 (2004): 125. http://dx.doi.org/10.1016/s1359-6349(04)80425-7.

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23

Luna, Augustin, Margot L. Sunshine, Martijn P. van Iersel, Mirit I. Aladjem, and Kurt W. Kohn. "PathVisio-MIM: PathVisio plugin for creating and editing Molecular Interaction Maps (MIMs)." Bioinformatics 27, no. 15 (2011): 2165–66. http://dx.doi.org/10.1093/bioinformatics/btr336.

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24

Jaimovich, A., R. Rinott, M. Schuldiner, H. Margalit, and N. Friedman. "Modularity and directionality in genetic interaction maps." Bioinformatics 26, no. 12 (2010): i228—i236. http://dx.doi.org/10.1093/bioinformatics/btq197.

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25

Wojcik, Jérôme, Ivo G. Boneca, and Pierre Legrain. "Prediction, Assessment and Validation of Protein Interaction Maps in Bacteria." Journal of Molecular Biology 323, no. 4 (2002): 763–70. http://dx.doi.org/10.1016/s0022-2836(02)01009-4.

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26

Wysocki, Kenneth, and Leslie Ritter. "Diseasome." Annual Review of Nursing Research 29, no. 1 (2011): 55–72. http://dx.doi.org/10.1891/0739-6686.29.55.

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Using bioinformatics computational tools, network maps that integrate the complex interactions of genetics and diseases have been developed. The purpose of this review is to introduce the reader to new approaches in understanding disease–gene associations using network maps, with an emphasis on how the human disease network (HDN) map (or diseasome) was constructed. A search was conducted in PubMed using the years 1999–2011 and using key words diseasome, molecular interaction, interactome, protein–protein interaction, and gene. The information reviewed included journal reviews, open source and webbased databases, and open source computational tools.

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27

Snitkin, Evan S., and Daniel Segrè. "Epistatic Interaction Maps Relative to Multiple Metabolic Phenotypes." PLoS Genetics 7, no. 2 (2011): e1001294. http://dx.doi.org/10.1371/journal.pgen.1001294.

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28

Bélemlilga, Dominique, Jean-Michel Gillet, and Pierre J. Becker. "Charge and momentum densities of cubic tetracyanoethylene and its insertion compounds." Acta Crystallographica Section B Structural Science 55, no. 2 (1999): 192–202. http://dx.doi.org/10.1107/s0108768198012166.

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Charge and momentum electron densities provide complementary views of cohesive forces in solids. This is particularly true for molecular crystals. The examples of cubic tetracyanoethylene (1,1,2,2-ethenetetracarbonitrile) and its alkali-metal insertion compounds are analyzed from a theoretical point of view. Besides the usual deformation density maps and anisotropy of Compton profiles, it is shown that interaction charge density and interaction Compton profiles can be defined and reveal the subtleties of the intermolecular interactions. It is shown that owing to the large cavities in the crystal, alkali-metal atoms can be inserted, leading to a strong charge transfer to the molecules and to a metallic character; the mechanism of insertion is revealed well by the combination of charge and momentum density studies. The combination of the two techniques of X-ray diffraction and Compton scattering should be of great help in the study of rather weak interactions present in molecular solids.

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29

Beyer, Andreas, Sourav Bandyopadhyay, and Trey Ideker. "Integrating physical and genetic maps: from genomes to interaction networks." Nature Reviews Genetics 8, no. 9 (2007): 699–710. http://dx.doi.org/10.1038/nrg2144.

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30

Spiriti, E., M. De Napoli, and F. Romano. "The FIRST experiment: interaction region and MAPS vertex detector." Nuclear Physics B - Proceedings Supplements 215, no. 1 (2011): 157–61. http://dx.doi.org/10.1016/j.nuclphysbps.2011.03.164.

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31

Owens, Melinda T., David A. Feldheim, Michael P. Stryker, and Jason W. Triplett. "Stochastic Interaction between Neural Activity and Molecular Cues in the Formation of Topographic Maps." Neuron 87, no. 6 (2015): 1261–73. http://dx.doi.org/10.1016/j.neuron.2015.08.030.

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32

Li, Jiao, Xiaoyan Zhu, and Jake Yue Chen. "Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts." PLoS Computational Biology 5, no. 7 (2009): e1000450. http://dx.doi.org/10.1371/journal.pcbi.1000450.

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33

Doki, Chihiro, Kohei Nishida, Shoma Saito, et al. "Microtubule elongation along actin filaments induced by microtubule-associated protein 4 contributes to the formation of cellular protrusions." Journal of Biochemistry 168, no. 3 (2020): 295–303. http://dx.doi.org/10.1093/jb/mvaa046.

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Abstract Actin-microtubule crosstalk is implicated in the formation of cellular protrusions, but the mechanism remains unclear. In this study, we examined the regulation of cell protrusion involving a ubiquitously expressed microtubule-associated protein (MAP) 4, and its superfamily proteins, neuronal MAP2 and tau. Fluorescence microscopy revealed that these MAPs bound to F-actin and microtubules simultaneously, and formed F-actin/microtubule hybrid bundles. The hybrid bundle-forming activity was in the order of MAP2 &gt; MAP4 ≫ tau. Interestingly, the microtubule assembly-promoting activity of MAP4 and MAP2, but not of tau, was upregulated by their interaction with F-actin. When MAP4 was overexpressed in NG108-15 cells, the number of cell processes and maximum process length of each cell increased significantly by 28% and 30%, respectively. Super-resolution microscopy revealed that 95% of microtubules in cell processes colocalized with F-actin, and MAP4 was always found in their vicinity. These results suggest that microtubule elongation along F-actin induced by MAP4 contributes to the formation of cellular protrusions. Since MAP4, MAP2 and tau had different crosstalk activity between F-actin and microtubules, it is likely that the functional differentiation of these MAPs is a driving force for neural evolution, causing significant changes in cell morphology.

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34

Huang, Hailiang, Bruno M. Jedynak, and Joel S. Bader. "Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps." PLoS Computational Biology 3, no. 11 (2007): e214. http://dx.doi.org/10.1371/journal.pcbi.0030214.

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35

Huang, Hailiang, Bruno Jedynak, and Joel S. Bader. "Where have all the interactions gone? Estimating the coverage of two-hybrid protein interaction maps." PLoS Computational Biology preprint, no. 2007 (2005): e214. http://dx.doi.org/10.1371/journal.pcbi.0030214.eor.

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36

Korlyukov, Alexander A., Maura Malinska, Anna V. Vologzhanina, Mikhail S. Goizman, Damian Trzybinski, and Krzysztof Wozniak. "Charge density view on bicalutamide molecular interactions in the monoclinic polymorph and androgen receptor binding pocket." IUCrJ 7, no. 1 (2020): 71–82. http://dx.doi.org/10.1107/s2052252519014416.

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High-resolution single-crystal X-ray measurements of the monoclinic polymorph of bicalutamide and the aspherical atom databank approach have served as a basis for a reconstruction of the charge density distribution of the drug and its androgen receptor (AR) and albumin complexes. The contributions of various types of intermolecular interactions to the total crystal energy or ligand:AR energy were estimated. The cyan and amide groups secured the ligand placement in the albumin (Lys-137) and the AR binding pocket (Leu-704, Asn-705, Arg-752), and also determined the packing of the small-molecule crystals. The total electrostatic interaction energy on average was −230 kJ mol−1, comparable with the electrostatic lattice energy of the monoclinic bicalutamide polymorph. This is the result of similar distributions of electropositive and electronegative regions on the experimental and theoretical molecular electrostatic potential maps despite differences in molecular conformations. In general, bicalutamide interacted with the studied proteins with similar electrostatic interaction energies and adjusted its conformation and electrostatic potential to fit the binding pocket in such a way as to enhance the interactions, e.g. hydrogen bonds and π...π stacking.

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37

Kontijevskis, Aleksejs, Peteris Prusis, Ramona Petrovska, et al. "A Look Inside HIV Resistance through Retroviral Protease Interaction Maps." PLoS Computational Biology 3, no. 3 (2007): e48. http://dx.doi.org/10.1371/journal.pcbi.0030048.

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Kontijevskis, Aleksejs, Peteris Prusis, Ramona Petrovska, et al. "A Look inside HIV Resistance through Retroviral Protease Interaction Maps." PLoS Computational Biology preprint, no. 2007 (2005): e48. http://dx.doi.org/10.1371/journal.pcbi.0030048.eor.

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39

Coulombe, Benoit, Mathieu Blanchette, and Célia Jeronimo. "Steps towards a repertoire of comprehensive maps of human protein interaction networks: the Human Proteotheque Initiative (HuPI)This paper is one of a selection of papers published in this Special Issue, entitled CSBMCB — Systems and Chemical Biology, and has undergone the Journal's usual peer review process." Biochemistry and Cell Biology 86, no. 2 (2008): 149–56. http://dx.doi.org/10.1139/o08-006.

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Defining human protein interaction networks has become essential to develop an overall, systems-based understanding of the molecular events that sustain cell growth in normal and disease conditions. To characterize protein interaction networks from human cells, we have undertaken the development of a systematic, unbiased technology pipeline that couples experimental and computational approaches. This discovery engine is central to the Human Proteotheque Initiative (HuPI), a multidisciplinary project aimed at building a repertoire of comprehensive maps of human protein interaction networks, the Human Proteotheque. The information contained in the Proteotheque is made publicly available through an interactive web site that can be consulted to visualize some of the fundamental molecular connections formed in human cells and to determine putative functions of previously uncharacterized proteins based on guilt by association. The process governing the evolution of HuPI towards becoming a repository of accurate and complete protein interaction maps is described.

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40

Kuperstein, Inna, David PA Cohen, Stuart Pook, et al. "NaviCell: a web-based environment for navigation, curation and maintenance of large molecular interaction maps." BMC Systems Biology 7, no. 1 (2013): 100. http://dx.doi.org/10.1186/1752-0509-7-100.

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41

Baniasadi, F., M. M. Tehranchi, M. B. Fathi, N. Safari, and V. Amani. "Intra-molecular magnetic exchange interaction in the tripyridinium bis[tetrachloroferrate(iii)] chloride molecular magnet: a broken symmetry-DFT study." Physical Chemistry Chemical Physics 17, no. 29 (2015): 19119–25. http://dx.doi.org/10.1039/c5cp02770e.

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A superexchange interaction path between Fe–Fe in (FeCl<sub>4</sub>)<sub>2</sub>(py·H)<sub>3</sub>Cl is illustrated making use of electronic spin density maps (ESDM) and the magnetic coupling constant is calculated using the BS-DFT method as J<sub>Fe–Fe</sub> = 13.2062 kJ mol<sup>−1</sup>.

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42

Pérez-Beaupuits, J. P., J. Stutzki, R. Güsten, V. Ossenkopf, and H. Wiesemeyer. "Atomic and Molecular Gas in M17 SW." Proceedings of the International Astronomical Union 8, S292 (2012): 55. http://dx.doi.org/10.1017/s1743921313000367.

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AbstractWe probe the spatial distribution of the [C ii] μm fine-structure emission and its association with neutral and molecular gas in a 5′.7 × 3′.7 (∼3.3 × 2.1 pc2) region of the M17 SW nebula. Comparison of velocity-resolved [C ii] emission maps with other atomic and molecular tracers is possible for the first time with the dual band receiver GREAT on board the SOFIA airborne telescope. We detected [C ii] emission in a much broader velocity range than the CO-lines (Pérez-Beaupuits et al. 2012). Only [C ii] narrow channel maps at intermediate velocities (between 10 and 24 kms) show correlations with other molecular gas components, supporting a clumpy cloud scenario. At lower (< 10 kms) and higher (>24 kms) velocities instead, we see more than 60% of the region mapped in [C ii] that is not associated with other tracers of star-forming material, the so called “CO-dark” gas. Interaction with winds and outflows lead to substantial excitation of [C ii] emitting gas, so that ablation and shock-interaction have to be taken into account to model the observed [C ii] emission.

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43

Das, A., J. H. Park, C. B. Hagen, and M. Parsons. "Distinct domains of a nucleolar protein mediate protein kinase binding, interaction with nucleic acids and nucleolar localization." Journal of Cell Science 111, no. 17 (1998): 2615–23. http://dx.doi.org/10.1242/jcs.111.17.2615.

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Nopp44/46 is a phosphoprotein of the protozoan parasite Trypanosoma brucei that is localized to the nucleolus. Based on the primary sequence, Nopp44/46 appears to be a protein composed of distinct domains. This communication describes the relationship of these domains to the known functional interactions of the molecule and suggests that the amino-terminal region defines a novel homology region that functions in nucleolar targeting. We have previously shown that Nopp44/46 is capable of interacting with nucleic acids and associating with a protein kinase. Using in vitro transcription and translation, we now demonstrate that the nucleic acid binding function maps to the carboxy-terminal domain of the molecule, a region rich in arginine-glycine-glycine motifs. Our experiments reveal that a central region containing a high proportion of acidic residues is required for association with the protein kinase. Analysis of transfectants expressing epitope-tagged Nopp44/46 deletion constructs showed that the amino-terminal 96 amino acids allowed nuclear and nucleolar accumulation of the protein. This region of the molecule shows homology to several recently described nucleolar proteins. Deletion of a 27-amino-acid region within this domain abrogated nucleolar, but not nuclear, localization. These studies show that Nopp44/46 is composed of distinct modules, each of which plays a different role in molecular interactions. We suggest that this protein could facilitate interactions between sets of nucleolar molecules.

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44

Bai, Xiaobo, Jonathan R. Bowen, Tara K. Knox, et al. "Novel septin 9 repeat motifs altered in neuralgic amyotrophy bind and bundle microtubules." Journal of Cell Biology 203, no. 6 (2013): 895–905. http://dx.doi.org/10.1083/jcb.201308068.

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Septin 9 (SEPT9) interacts with microtubules (MTs) and is mutated in hereditary neuralgic amyotrophy (HNA), an autosomal-dominant neuropathy. The mechanism of SEPT9 interaction with MTs and the molecular basis of HNA are unknown. Here, we show that the N-terminal domain of SEPT9 contains the novel repeat motifs K/R-x-x-E/D and R/K-R-x-E, which bind and bundle MTs by interacting with the acidic C-terminal tails of β-tubulin. Alanine scanning mutagenesis revealed that the K/R-R/x-x-E/D motifs pair electrostatically with one another and the tails of β-tubulin, enabling septin–septin interactions that link MTs together. SEPT9 isoforms lacking repeat motifs or containing the HNA-linked mutation R88W, which maps to the R/K-R-x-E motif, diminished intracellular MT bundling and impaired asymmetric neurite growth in PC-12 cells. Thus, the SEPT9 repeat motifs bind and bundle MTs, and thereby promote asymmetric neurite growth. These results provide the first insight into the mechanism of septin interaction with MTs and the molecular and cellular basis of HNA.

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45

Ferreira, João, Antonio Figueiredo, Jardel Barbosa, et al. "A study of new antimalarial artemisinins through molecular modeling and multivariate analysis." Journal of the Serbian Chemical Society 75, no. 11 (2010): 1533–48. http://dx.doi.org/10.2298/jsc100126124f.

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Artemisinin and 18 derivatives with antimalarial activity against W-2 strains of Plasmodium falciparum were studied through quantum chemistry and multivariate analysis. The geometry optimization of the structures was realized with the Hartree-Fock (HF) theory and 3-21G basis set. Maps of molecular electrostatic potential (MEP) and molecular docking were used to investigate the interaction between the ligands and the receptor (heme). Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were employed to select the most important descriptors related to activity. A predictive model was generated by the Partial Least Square (PLS) method through 15 molecules and 4 used as an external validation set, which were selected in the training set, the validation parameters of which are Q2 = 0.85 and R2 = 0.86. The model included as molecular parameters, the radial distribution function, RDF060e, the hydration energy, HE, and the distance between the O1 atom from the ligand and the iron atom from heme, d(Fe-O1). Thus, the synthesis of new derivatives may follow the results of the MEP maps and the PLS analysis.

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46

Kanthe, Ankit, Andrew Ilott, Mary Krause, et al. "No ordinary proteins: Adsorption and molecular orientation of monoclonal antibodies." Science Advances 7, no. 35 (2021): eabg2873. http://dx.doi.org/10.1126/sciadv.abg2873.

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The interaction of monoclonal antibodies (mAbs) with air/water interfaces plays a crucial role in their overall stability in solution. We aim to understand this behavior using pendant bubble measurements to track the dynamic tension reduction and x-ray reflectivity to obtain the electron density profiles (EDPs) at the surface. Native immunoglobulin G mAb is a rigid molecule with a flat, “Y” shape, and simulated EDPs are obtained by rotating a homology construct at the surface. Comparing simulations with experimental EDPs, we obtain surface orientation probability maps showing mAbs transition from flat-on Y-shape configurations to side-on or end-on configurations with increasing concentration. The modeling also shows the presence of β sheets at the surface. Overall, the experiments and the homology modeling elucidate the orientational phase space during different stages of adsorption of mAbs at the air/water interface. These finding will help define new strategies for the manufacture and storage of antibody-based therapeutics.

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Lindén, M., B. D. Nelson, and J. F. Leterrier. "The specific binding of the microtubule-associated protein 2 (MAP2) to the outer membrane of rat brain mitochondria." Biochemical Journal 261, no. 1 (1989): 167–73. http://dx.doi.org/10.1042/bj2610167.

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Purified mitochondria from rat brain contain microtubule-associated proteins (MAPs) bound to the outer membrane. Studies of binding in vitro performed with microtubules and with purified microtubule proteins showed that mitochondria preferentially interact with the high-molecular-mass MAPs (and not with Tau protein). Incubation of intact mitochondria with Taxol-stabilized microtubules resulted in the selective trapping of both MAPs 1 and 2 on mitochondria, indicating that an interaction between the two organelles occurred through a site on the arm-like projection of MAPs. Two MAP-binding sites were located on intact mitochondria. The lower-affinity MAP2-binding site (Kd = 2 x 10(-7) M) was preserved and enriched in the outer-membrane fraction, whereas the higher-affinity site (Kd = 1 x 10(-9) M) was destroyed after removing the outer membrane with digitonin. Detergent fractionation of mitochondrial outer membranes saturated with MAP2 bound in vitro showed that MAPs are associated with membrane fragments which contain the pore-forming protein (porin). MAP2 also partially prevents the solubilization of porin from outer membrane, indicating a MAP-induced change in the membrane environment of porin. These observations demonstrate the presence of specific MAP-binding sites on the outer membrane, suggesting an association between porin and the membrane domain involved in the cross-linkage between microtubules and mitochondria.

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Lappe, M., J. Park, O. Niggemann, and L. Holm. "Generating protein interaction maps from incomplete data: application to fold assignment." Bioinformatics 17, Suppl 1 (2001): S149—S156. http://dx.doi.org/10.1093/bioinformatics/17.suppl_1.s149.

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Meluzzi, Dario, and Gaurav Arya. "53 2D to 3D: interaction frequency maps to chromatin higher-order folded conformations." Journal of Biomolecular Structure and Dynamics 31, sup1 (2013): 33. http://dx.doi.org/10.1080/07391102.2013.786487.

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Kampmann, Martin, Michael C. Bassik, and Jonathan S. Weissman. "Functional genomics platform for pooled screening and generation of mammalian genetic interaction maps." Nature Protocols 9, no. 8 (2014): 1825–47. http://dx.doi.org/10.1038/nprot.2014.103.

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