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      Quantifying Mineral-Ligand Structural Similarities: Bridging the Geological World of Minerals with the Biological World of Enzymes

      research-article
      1 , 2 , 1 , 3 , * , 1 , 4
      Life
      MDPI
      origin of life, minerals, enzymes, ligand, astrobiology

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          Abstract

          Metal compounds abundant on Early Earth are thought to play an important role in the origins of life. Certain iron-sulfur minerals for example, are proposed to have served as primitive metalloenzyme cofactors due to their ability to catalyze organic synthesis processes and facilitate electron transfer reactions. An inherent difficulty with studying the catalytic potential of many metal compounds is the wide range of data and parameters to consider when searching for individual minerals and ligands of interest. Detecting mineral-ligand pairs that are structurally analogous enables more relevant selections of data to study, since structural affinity is a key indicator of comparable catalytic function. However, current structure-oriented approaches tend to be subjective and localized, and do not quantify observations or compare them with other potential targets. Here, we present a mathematical approach that compares structural similarities between various minerals and ligands using molecular similarity metrics. We use an iterative substructure search in the crystal lattice, paired with benchmark structural similarity methods. This structural comparison may be considered as a first stage in a more advanced analysis tool that will include a range of chemical and physical factors when computing mineral-ligand similarity. This approach will seek relationships between the mineral and enzyme worlds, with applications to the origins of life, ecology, catalysis, and astrobiology.

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          Most cited references57

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          Measures of the Amount of Ecologic Association Between Species

          Lee Dice (1945)
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            The Protein Data Bank. A computer-based archival file for macromolecular structures.

            The Protein Data Bank is a computer-based archival file for macromolecular structures. The Bank stores in a uniform format atomic co-ordinates and partial bond connectivities, as derived from crystallographic studies. Text included in each data entry gives pertinent information for the structure at hand (e.g. species from which the molecule has been obtained, resolution of diffraction data, literature citations and specifications of secondary structure). In addition to atomic co-ordinates and connectivities, the Protein Data Bank stores structure factors and phases, although these latter data are not placed in any uniform format. Input of data to the Bank and general maintenance functions are carried out at Brookhaven National Laboratory. All data stored in the Bank are available on magnetic tape for public distribution, from Brookhaven (to laboratories in the Americas), Tokyo (Japan), and Cambridge (Europe and worldwide). A master file is maintained at Brookhaven and duplicate copies are stored in Cambridge and Tokyo. In the future, it is hoped to expand the scope of the Protein Data Bank to make available co-ordinates for standard structural types (e.g. alpha-helix, RNA double-stranded helix) and representative computer programs of utility in the study and interpretation of macromolecular structures.
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              Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

              Background Cheminformaticians are equipped with a very rich toolbox when carrying out molecular similarity calculations. A large number of molecular representations exist, and there are several methods (similarity and distance metrics) to quantify the similarity of molecular representations. In this work, eight well-known similarity/distance metrics are compared on a large dataset of molecular fingerprints with sum of ranking differences (SRD) and ANOVA analysis. The effects of molecular size, selection methods and data pretreatment methods on the outcome of the comparison are also assessed. Results A supplier database (https://mcule.com/) was used as the source of compounds for the similarity calculations in this study. A large number of datasets, each consisting of one hundred compounds, were compiled, molecular fingerprints were generated and similarity values between a randomly chosen reference compound and the rest were calculated for each dataset. Similarity metrics were compared based on their ranking of the compounds within one experiment (one dataset) using sum of ranking differences (SRD), while the results of the entire set of experiments were summarized on box and whisker plots. Finally, the effects of various factors (data pretreatment, molecule size, selection method) were evaluated with analysis of variance (ANOVA). Conclusions This study complements previous efforts to examine and rank various metrics for molecular similarity calculations. Here, however, an entirely general approach was taken to neglect any a priori knowledge on the compounds involved, as well as any bias introduced by examining only one or a few specific scenarios. The Tanimoto index, Dice index, Cosine coefficient and Soergel distance were identified to be the best (and in some sense equivalent) metrics for similarity calculations, i.e. these metrics could produce the rankings closest to the composite (average) ranking of the eight metrics. The similarity metrics derived from Euclidean and Manhattan distances are not recommended on their own, although their variability and diversity from other similarity metrics might be advantageous in certain cases (e.g. for data fusion). Conclusions are also drawn regarding the effects of molecule size, selection method and data pretreatment on the ranking behavior of the studied metrics. Graphical Abstract A visual summary of the comparison of similarity metrics with sum of ranking differences (SRD). Electronic supplementary material The online version of this article (doi:10.1186/s13321-015-0069-3) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Life (Basel)
                Life (Basel)
                life
                Life
                MDPI
                2075-1729
                10 December 2020
                December 2020
                : 10
                : 12
                : 338
                Affiliations
                [1 ]Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA; dxczhao20@ 123456gmail.com (D.Z.); yly@ 123456gps.caltech.edu (Y.L.Y.)
                [2 ]Department of Mathematics, Harvard University, Massachusetts Hall, Cambridge, MA 02138, USA
                [3 ]Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo 152-8550, Japan
                [4 ]NASA Jet Propulsion Laboratory, Oak Grove Dr, La Cañada Flintridge, CA 91011, USA
                Author notes
                [* ]Correspondence: sjbart@ 123456caltech.edu
                Author information
                https://orcid.org/0000-0001-5680-476X
                Article
                life-10-00338
                10.3390/life10120338
                7764262
                33321803
                e9335d5c-502c-4938-9568-156f239e5308
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 September 2020
                : 07 December 2020
                Categories
                Article

                origin of life,minerals,enzymes,ligand,astrobiology
                origin of life, minerals, enzymes, ligand, astrobiology

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