Chemical Diversity of Metal Sulfide Minerals and Its Implications for the Origin of Life.
Chemical Diversity of Metal Sulfide Minerals and Its Implications for the Origin of Life.
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DOI：
10.3390/life8040046
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发表时间：
2018-10-10
期刊：
影响因子：
--
通讯作者：
Nakamura R
中科院分区：
文献类型：
--
作者：
Li Y;Kitadai N;Nakamura R
由地球上丰富的金属硫化物催化的前生物有机合成是理解生物化学从无机分子进化的关键过程，然而硫化物的催化功能在生命起源的背景下仍然很少探索。过去对益生元化学的研究主要集中在几种类型的金属硫化物催化剂上，如FeS或NiS，它们形成的产物类型有限，活性和选择性较差。为了探索金属硫化物催化生物前化学反应的潜力，本文对矿物学数据库中304种天然金属硫化物矿物的化学多样性（化学组成和相结构的变化）进行了调查。基于电催化的先进理论和分析工具，如质子耦合电子转移，酶和矿物之间的结构比较，以及原位光谱，讨论了合理预测金属硫化物的催化功能的方法。为此，我们引入了一个模型的地球电化学驱动的化学进化的益生元合成，因为它可以帮助我们预测动力学和选择性的目标益生元化学在“化学混乱的条件下”。我们期望将矿物数据库的数据挖掘与电催化领域开发的实验方法，理论和机器学习方法相结合，将有助于在广泛的pH和Eh条件下预测和验证催化性能，并有助于合理筛选生命起源中涉及的矿物催化剂。
Prebiotic organic synthesis catalyzed by Earth-abundant metal sulfides is a key process for understanding the evolution of biochemistry from inorganic molecules, yet the catalytic functions of sulfides have remained poorly explored in the context of the origin of life. Past studies on prebiotic chemistry have mostly focused on a few types of metal sulfide catalysts, such as FeS or NiS, which form limited types of products with inferior activity and selectivity. To explore the potential of metal sulfides on catalyzing prebiotic chemical reactions, here, the chemical diversity (variations in chemical composition and phase structure) of 304 natural metal sulfide minerals in a mineralogy database was surveyed. Approaches to rationally predict the catalytic functions of metal sulfides are discussed based on advanced theories and analytical tools of electrocatalysis such as proton-coupled electron transfer, structural comparisons between enzymes and minerals, and in situ spectroscopy. To this end, we introduce a model of geoelectrochemistry driven prebiotic synthesis for chemical evolution, as it helps us to predict kinetics and selectivity of targeted prebiotic chemistry under “chemically messy conditions”. We expect that combining the data-mining of mineral databases with experimental methods, theories, and machine-learning approaches developed in the field of electrocatalysis will facilitate the prediction and verification of catalytic performance under a wide range of pH and Eh conditions, and will aid in the rational screening of mineral catalysts involved in the origin of life.
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影响因子：
3.6
作者：
Ooka H;Hashimoto K;Nakamura R
通讯作者：
Nakamura R
影响因子：
2.9
作者：
Anderson, LJ;Richardson, DJ;Butt, JN
通讯作者：
Butt, JN
DOI：
10.1088/0022-3719/15/30/010
发表时间：
1982-01-01
期刊：
JOURNAL OF PHYSICS C-SOLID STATE PHYSICS
影响因子：
--
作者：
BULLETT, DW
通讯作者：
BULLETT, DW
影响因子：
4.6
作者：
Calderazzo, Faust;Cotton, F. Albert
通讯作者：
Cotton, F. Albert
影响因子：
11.4
作者：
Bostick, BC;Fendorf, S;Helz, GR
通讯作者：
Helz, GR