scholarly journals Spontaneous Formation of Autocatalytic Sets with Self-Replicating Inorganic Metal Oxide Clusters

2019 ◽  
Author(s):  
Haralampos N. Miras ◽  
Cole Mathis ◽  
Weimin Xuan ◽  
De-Liang Long ◽  
Robert Pow ◽  
...  
Keyword(s):  
Molecular Recognition ◽  
Fundamental Problem ◽  
Sequence Information ◽  
Formation Kinetic ◽  
Molybdenum Blue ◽  
Autocatalytic Sets ◽  
Kinetic Investigations ◽  
Inorganic Molecules ◽  
Small Clusters ◽  
Self Replication

Biological self-replication is driven by complex machinery requiring large amounts of sequence information too complex to have formed spontaneously. This presents a fundamental problem for understanding the origins self-replication and by extension, life. One route for the emergence of self-replicators is via autocatalytic sets, but experimentally these have been based on RNA and require sequence information. Showing an example outside of biology, would give insights into how the universal ‘life-like’ chemistry can be. Here we show how a simple inorganic salt can spontaneously form information-rich, autocatalytic sets of replicating inorganic molecules that work via molecular recognition based on the {PMo12} Keggin ion, and {Mo36} cluster. These small clusters are involved in an autocatalytic network, where the assembly of gigantic molybdenum blue wheel (Mo154-blue), {Mo132} ball containing 154 and 132 molybdenum atoms, and a new {PMo12}Ì{Mo124 Ce4} nanostructure are templated by the smaller clusters which are themselves able to catalyse their own formation. Kinetic investigations revealed key traits of autocatalytic systems including molecular recognition and kinetic saturation. A stochastic model confirms the presence of an autocatalytic network driven by molecular recognition, where the larger clusters are the only products stabilised by information contained in the cycle, isolated due to a critical transition in the network. This study demonstrates how information-rich autocatalytic sets, based on simple inorganic salts, can spontaneously emerge which are capable of collective self-reproduction outside of biology.<br>

scholarly journals Spontaneous Formation of Autocatalytic Sets with Self-Replicating Inorganic Metal Oxide Clusters

2019 ◽  
Author(s):  
Haralampos N. Miras ◽  
Cole Mathis ◽  
Weimin Xuan ◽  
De-Liang Long ◽  
Robert Pow ◽  
...  
Keyword(s):  
Molecular Recognition ◽  
Fundamental Problem ◽  
Sequence Information ◽  
Formation Kinetic ◽  
Molybdenum Blue ◽  
Autocatalytic Sets ◽  
Kinetic Investigations ◽  
Inorganic Molecules ◽  
Small Clusters ◽  
Self Replication

Biological self-replication is driven by complex machinery requiring large amounts of sequence information too complex to have formed spontaneously. This presents a fundamental problem for understanding the origins self-replication and by extension, life. One route for the emergence of self-replicators is via autocatalytic sets, but experimentally these have been based on RNA and require sequence information. Showing an example outside of biology, would give insights into how the universal ‘life-like’ chemistry can be. Here we show how a simple inorganic salt can spontaneously form information-rich, autocatalytic sets of replicating inorganic molecules that work via molecular recognition based on the {PMo12} Keggin ion, and {Mo36} cluster. These small clusters are involved in an autocatalytic network, where the assembly of gigantic molybdenum blue wheel (Mo154-blue), {Mo132} ball containing 154 and 132 molybdenum atoms, and a new {PMo12}Ì{Mo124 Ce4} nanostructure are templated by the smaller clusters which are themselves able to catalyse their own formation. Kinetic investigations revealed key traits of autocatalytic systems including molecular recognition and kinetic saturation. A stochastic model confirms the presence of an autocatalytic network driven by molecular recognition, where the larger clusters are the only products stabilised by information contained in the cycle, isolated due to a critical transition in the network. This study demonstrates how information-rich autocatalytic sets, based on simple inorganic salts, can spontaneously emerge which are capable of collective self-reproduction outside of biology.<br>

scholarly journals Spontaneous formation of autocatalytic sets with self-replicating inorganic metal oxide clusters

2020 ◽  
Vol 117 (20) ◽  
pp. 10699-10705 ◽  
Author(s):  
Haralampos N. Miras ◽  
Cole Mathis ◽  
Weimin Xuan ◽  
De-Liang Long ◽  
Robert Pow ◽  
...  
Keyword(s):  
Molecular Recognition ◽  
Inorganic Salt ◽  
Molybdenum Blue ◽  
Keggin Ion ◽  
Metal Oxide Clusters ◽  
Spontaneous Formation ◽  
Autocatalytic Sets ◽  
Kinetic Investigations ◽  
Inorganic Molecules ◽  
Small Clusters

Here we show how a simple inorganic salt can spontaneously form autocatalytic sets of replicating inorganic molecules that work via molecular recognition based on the {PMo12} ≡ [PMo12O40]3– Keggin ion, and {Mo36} ≡ [H3Mo57M6(NO)6O183(H2O)18]22– cluster. These small clusters are able to catalyze their own formation via an autocatalytic network, which subsequently template the assembly of gigantic molybdenum-blue wheel {Mo154} ≡ [Mo154O462H14(H2O)70]14–, {Mo132} ≡ [MoVI72MoV60O372(CH3COO)30(H2O)72]42– ball-shaped species containing 154 and 132 molybdenum atoms, and a {PMo12}⊂{Mo124Ce4} ≡ [H16MoVI100MoV24Ce4O376(H2O)56 (PMoVI10MoV2O40)(C6H12N2O4S2)4]5– nanostructure. Kinetic investigations revealed key traits of autocatalytic systems including molecular recognition and kinetic saturation. A stochastic model confirms the presence of an autocatalytic network involving molecular recognition and assembly processes, where the larger clusters are the only products stabilized by the cycle, isolated due to a critical transition in the network.

scholarly journals The macroscopic shape of assemblies formed from microparticles based on host–guest interaction dependent on the guest content

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Takahiro Itami ◽  
Akihito Hashidzume ◽  
Yuri Kamon ◽  
Hiroyasu Yamaguchi ◽  
Akira Harada
Keyword(s):  
Molecular Recognition ◽  
Smart Materials ◽  
Self Assembly ◽  
Early Stage ◽  
Contact Point ◽  
Sodium Acrylate ◽  
Contact Points ◽  
Assembly Behavior ◽  
Small Clusters ◽  
Macroscopic Shape

AbstractBiological macroscopic assemblies have inspired researchers to utilize molecular recognition to develop smart materials in these decades. Recently, macroscopic self-assemblies based on molecular recognition have been realized using millimeter-scale hydrogel pieces possessing molecular recognition moieties. During the study on macroscopic self-assembly based on molecular recognition, we noticed that the shape of assemblies might be dependent on the host–guest pair. In this study, we were thus motivated to study the macroscopic shape of assemblies formed through host–guest interaction. We modified crosslinked poly(sodium acrylate) microparticles, i.e., superabsorbent polymer (SAP) microparticles, with β-cyclodextrin (βCD) and adamantyl (Ad) residues (βCD(x)-SAP and Ad(y)-SAP microparticles, respectively, where x and y denote the mol% contents of βCD and Ad residues). Then, we studied the self-assembly behavior of βCD(x)-SAP and Ad(y)-SAP microparticles through the complexation of βCD with Ad residues. There was a threshold of the βCD content in βCD(x)-SAP microparticles for assembly formation between x = 22.3 and 26.7. On the other hand, the shape of assemblies was dependent on the Ad content, y; More elongated assemblies were formed at a higher y. This may be because, at a higher y, small clusters formed in an early stage can stick together even upon collisions at a single contact point to form elongated aggregates, whereas, at a smaller y, small clusters stick together only upon collisions at multiple contact points to give rather circular assemblies. On the basis of these observations, the shape of assembly formed from microparticles can be controlled by varying y.

scholarly journals FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

2020 ◽  
Author(s):  
Jiangyan Feng ◽  
Diwakar Shukla
Keyword(s):  
Ligand Binding ◽  
Structure Prediction ◽  
De Novo ◽  
Three Dimensional ◽  
Sequence Information ◽  
Structural Constraints ◽  
Complex Signals ◽  
Residue Contacts ◽  
Small Clusters ◽  
Functional Mechanisms

AbstractProteins are dynamic molecules which perform diverse molecular functions by adopting different three-dimensional structures. Recent progress in residue-residue contacts prediction opens up new avenues for the de novo protein structure prediction from sequence information. However, it is still difficult to predict more than one conformation from residue-residue contacts alone. This is due to the inability to deconvolve the complex signals of residue-residue contacts, i.e. spatial contacts relevant for protein folding, conformational diversity, and ligand binding. Here, we introduce a machine learning based method, called FingerprintContacts, for extending the capabilities of residue-residue contacts. This algorithm leverages the features of residue-residue contacts, that is, (1) a single conformation outperforms the others in the structural prediction using all the top ranking residue-residue contacts as structural constraints, and (2) conformation specific contacts rank lower and constitute a small fraction of residue-residue contacts. We demonstrate the capabilities of FingerprintContacts on eight ligand binding proteins with varying conformational motions. Furthermore, FingerprintContacts identifies small clusters of residue-residue contacts which are preferentially located in the dynamically fluctuating regions. With the rapid growth in protein sequence information, we expect FingerprintContacts to be a powerful first step in structural understanding of protein functional mechanisms.

scholarly journals Control of the macroscopic shape of assemblies formed from microparticles based on host–guest interaction

2020 ◽  
Author(s):  
Takahiro Itami ◽  
Akihito Hashidzume ◽  
Yuri Kamon ◽  
Hiroyasu Yamaguchi ◽  
Akira Harada
Keyword(s):  
Molecular Recognition ◽  
Smart Materials ◽  
Self Assembly ◽  
Early Stage ◽  
Contact Point ◽  
Sodium Acrylate ◽  
Contact Points ◽  
Assembly Behavior ◽  
Small Clusters ◽  
Macroscopic Shape

Abstract Biological macroscopic assemblies have inspired researchers to utilize molecular recognition to develop smart materials in these decades. Recently, macroscopic self-assemblies based on molecular recognition have been realized using millimeter-scale hydrogel pieces possessing molecular recognition moieties. During the study on macroscopic self-assembly based on molecular recognition, we noticed that the shape of assemblies might be dependent on the host–guest pair. In this study, we were thus motivated to study the macroscopic shape of assemblies formed through host–guest interaction. We modified crosslinked poly(sodium acrylate) microparticles, i.e., superabsorbent polymer (SAP) microparticles, with β-cyclodextrin (βCD) and adamantyl (Ad) residues (βCD(x)-SAP and Ad(y)-SAP microparticles, respectively, where x and y denote the mol % contents of βCD and Ad residues). Then, we studied the self-assembly behavior of βCD(x)-SAP and Ad(y)-SAP microparticles through the complexation of βCD with Ad residues. There was a threshold of the βCD content in βCD(x)-SAP microparticles for assembly formation between x = 22.3 and 26.7. On the other hand, the shape of assemblies was dependent on the Ad content, y; More elongated assemblies were formed at a higher y. This may be because, at a higher y, small clusters formed in an early stage can stick together even upon collisions at a single contact point to form elongated aggregates, whereas, at a smaller y, small clusters stick together only upon collisions at multiple contact points to give rather circular assemblies. On the basis of these observations, the shape of assembly formed from microparticles can be controlled by varying y.

scholarly journals Autocatalytic Networks at the Basis of Life’s Origin and Organization

Life ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 62 ◽  
Author(s):  
Wim Hordijk ◽  
Mike Steel
Keyword(s):  
Sufficient Condition ◽  
Autocatalytic Sets ◽  
Autopoietic Systems ◽  
Mathematical Formalization ◽  
The Origin Of Life ◽  
1960S And 1970S ◽  
The 1960S ◽  
Inorganic Molecules ◽  
Minimal Life ◽  
Autocatalytic Networks

Life is more than the sum of its constituent molecules. Living systems depend on a particular chemical organization, i.e., the ways in which their constituent molecules interact and cooperate with each other through catalyzed chemical reactions. Several abstract models of minimal life, based on this idea of chemical organization and also in the context of the origin of life, were developed independently in the 1960s and 1970s. These models include hypercycles, chemotons, autopoietic systems, (M,R)-systems, and autocatalytic sets. We briefly compare these various models, and then focus more specifically on the concept of autocatalytic sets and their mathematical formalization, RAF theory. We argue that autocatalytic sets are a necessary (although not sufficient) condition for life-like behavior. We then elaborate on the suggestion that simple inorganic molecules like metals and minerals may have been the earliest catalysts in the formation of prebiotic autocatalytic sets, and how RAF theory may also be applied to systems beyond chemistry, such as ecology, economics, and cognition.

SOME INTRIGUING HIGH-THROUGHPUT DNA SEQUENCE VARIANTS PREDICTION OVER PROTEIN FUNCTIONALITY

2016 ◽  
Vol 78 (6-4) ◽  
Author(s):  
Atabak Kheirkhah ◽  
Salwani Mohd Daud ◽  
Noor Azurati Ahmad @ Salleh ◽  
Suriani Mohd Sam ◽  
Hafiza Abas ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Dna Sequence ◽  
High Throughput ◽  
Protein Function ◽  
Sequence Data ◽  
Fundamental Problem ◽  
Sequence Information ◽  
Sequence Variants ◽  
Sequence Prediction ◽  
Dna Sequence Variants

This paper intends to review computational methods and high throughput automated tools for precisely prediction various functionalities of uncharacterized proteins based on their desired DNA sequence information alone. Then proposes a hybrid weighted network and Genetic Algorithm to improve prediction purpose. The main advantage of the method is the protein function and DNA sequence prediction can be computed precisely using best fitness parent in genetic algorithm. With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased exponentially and the pace is much slower in determining their biological attributes. The gap between DNA sequence variants and their functionalities has become increasingly large. However, detection of sequences based on protein data bank has become benchmark for many researchers. As amount of DNA sequence data continues to increase, the fundamental problem stay at the front of genome analysis. In the course of developing these methods, the following matters were often needed to consider: benchmark dataset construction, gene sequence prediction, operating algorithm, anticipated accuracy, gene recommender and functional integrations. In this review, we are to discuss each of them, with a different focus on operational algorithms and how to increase the accuracy of DNA sequence variants prediction.

scholarly journals The macroscopic shape of assemblies formed from microparticles based on host–guest interaction dependent on the guest content

2020 ◽  
Author(s):  
Takahiro Itami ◽  
Akihito Hashidzume ◽  
Yuri Kamon ◽  
Hiroyasu Yamaguchi ◽  
Akira Harada
Keyword(s):  
Molecular Recognition ◽  
Smart Materials ◽  
Self Assembly ◽  
Early Stage ◽  
Contact Point ◽  
Sodium Acrylate ◽  
Contact Points ◽  
Assembly Behavior ◽  
Small Clusters ◽  
Macroscopic Shape

Abstract Biological macroscopic assemblies have inspired researchers to utilize molecular recognition to develop smart materials in these decades. Recently, macroscopic self-assemblies based on molecular recognition have been realized using millimeter-scale hydrogel pieces possessing molecular recognition moieties. During the study on macroscopic self-assembly based on molecular recognition, we noticed that the shape of assemblies might be dependent on the host–guest pair. In this study, we were thus motivated to study the macroscopic shape of assemblies formed through host–guest interaction. We modified crosslinked poly(sodium acrylate) microparticles, i.e., superabsorbent polymer (SAP) microparticles, with β-cyclodextrin (βCD) and adamantyl (Ad) residues (βCD(x)-SAP and Ad(y)-SAP microparticles, respectively, where x and y denote the mol % contents of βCD and Ad residues). Then, we studied the self-assembly behavior of βCD(x)-SAP and Ad(y)-SAP microparticles through the complexation of βCD with Ad residues. There was a threshold of the βCD content in βCD(x)-SAP microparticles for assembly formation between x = 22.3 and 26.7. On the other hand, the shape of assemblies was dependent on the Ad content, y; More elongated assemblies were formed at a higher y. This may be because, at a higher y, small clusters formed in an early stage can stick together even upon collisions at a single contact point to form elongated aggregates, whereas, at a smaller y, small clusters stick together only upon collisions at multiple contact points to give rather circular assemblies. On the basis of these observations, the shape of assembly formed from microparticles can be controlled by varying y.

scholarly journals The RNA world and the origin of metabolic enzymes

2014 ◽  
Vol 42 (4) ◽  
pp. 985-988 ◽  
Author(s):  
Markus Ralser
Keyword(s):  
Metal Ions ◽  
Metabolic Network ◽  
Environmental Chemistry ◽  
Rna World ◽  
Chemical Constituents ◽  
Pentose Phosphate ◽  
Rna Molecules ◽  
The Origin Of Life ◽  
Inorganic Molecules ◽  
Self Replication

An RNA world has been placed centre stage for explaining the origin of life. Indeed, RNA is the most plausible molecule able to form both a (self)-replicator and to inherit information, necessities for initiating genetics. However, in parallel with self-replication, the proto-organism had to obtain the ability to catalyse supply of its chemical constituents, including the ribonucleotide metabolites required to replicate RNA. Although the possibility of an RNA-catalysed metabolic network has been considered, it is to be questioned whether RNA molecules, at least on their own, possess the required catalytic capacities. An alternative scenario for the origin of metabolism involves chemical reactions that are based on environmental catalysts. Recently, we described a non-enzymatic glycolysis and pentose phosphate pathway-like reactions catalysed by metal ions [mainly Fe(II)] and phosphate, simple inorganic molecules abundantly found in Archaean sediments. While the RNA world can serve to explain the origin of genetics, the origin of the metabolic network might thus date back to constraints of environmental chemistry. Interestingly, considering a metal-catalysed origin of metabolism gives rise to an attractive hypothesis about how the first enzymes could have formed: simple RNA or (poly)peptide molecules could have bound the metal ions, and thus increased their solubility, concentration and accessibility. In a second step, this would have allowed substrate specificity to evolve.

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