MAY acknowledges funding by the Carl-Zeiss-Foundation.&#160;All figures were generated using Microsoft Excel.

NOTE: See Figure 1 for a summary of the protocol and the Table of Materials for details about the software used.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63632/63632fig01.jpg" /> 
Figure 1: Summary flowchart of the protocol. <a href="https://www.jove.com/files/ftp_upload/63632/63632fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
1. Software and file downloads
NOTE: All programs are free for individual use and can be run on a personal computer.
<ol>
	<li>Create a new directory for this project. Place the files and executables here for easy access.</li>
	<li>Download and install the necessary software packages.
	<ol>
		<li>Download the latest version of MAYGEN as a .jar file. 
		NOTE: MAYGEN is freely available as a .jar file from https://github.com/MehmetAzizYirik/MAYGEN/releases</li>
		<li>Download and install the package management software Conda and the cheminformatics toolkit RDKit18. 
		NOTE: RDKit will filter the molecular structures produced by MAYGEN and runs best in a Conda environment. Instructions for downloading the Conda platform can be found at&#160;https://conda.io/projects/conda/en/latest/user-guide/install/index.html. RDKit installation and environment setup instructions can be found at https://www.rdkit.org/docs/Install.html.
		<ol>
			<li>Install RDKit in the main Conda environment instead of a separate RDKit environment via the Anaconda prompt. On Windows systems, search for &#34;Anaconda prompt&#34; and click on the resulting shortcut to run. On MacOS and Linux systems, interact with Conda through the terminal without running any additional programs. Next, type the following command and press Enter to run, and answer yes to any questions that come up during the installation: 
			conda install -c rdkit rdkit. 
			While there are many freely available descriptor calculation programs, this example uses PaDEL-Descriptor19, a free and fast calculator for molecular descriptors and fingerprints.</li>
		</ol>
		</li>
		<li>Download and save the .jar file in the project folder. 
		NOTE: PaDEL-Descriptor can be downloaded for free from http://www.yapcwsoft.com/dd/padeldescriptor/.</li>
	</ol>
	</li>
	<li>Download the Jupyter notebooks and text files of substructure patterns from Supplemental Files 1-5. 
	NOTE: Jupyter notebooks can also be downloaded from the following GitHub page: https://github.com/cmayerb1/AA-structure-manip.</li>
</ol>
2. Structure generation using MAYGEN
<ol>
	<li>In a command prompt, navigate to the directory containing the MAYGEN .jar executable file.</li>
	<li>For each chemical formula of interest, run MAYGEN using the following command: 
	java -jar [MAYGEN .jar file name] -f [chemical formula] -v -o [folder for MAYGEN output] -m -sdf. 
	NOTE: This will save a .sdf file in the designated folder, named after the formula used.
	<ol>
		<li>If the formula is a fuzzy formula instead of a discrete formula, replace the -f flag with a -fuzzy flag, and enclose any element intervals in brackets (e.g., use C[5-7]H[12-15] to ensure that all structures generated have between 5 and 7 carbon atoms and between 12 and 15 hydrogen atoms).</li>
	</ol>
	</li>
</ol>
3. Filter compounds with undesired substructures
<ol>
	<li>Open an Anaconda prompt (see step 1.2.2.1) and navigate to the folder containing the Jupyter notebooks downloaded from Supplemental File 1.</li>
	<li>Open the Jupyter notebook for substructure filtering using the following command: 
	jupyter notebook [notebook file name]</li>
	<li>In the designated cell at the start of the notebook, enter the full file path of the input .sdf file (generated by MAYGEN), full file path of the desired .sdf output file, and file path of the &#34;badlist&#34; file as strings (within quotes). See Supplemental File 2 for an example of a badlist.
	<ol>
		<li>If some substructures in the filtered library (a goodlist) are to be retained, create a .txt file of SMARTS patterns20 for those substructures (a goodlist) and put the goodlist file path in the designated line at the start of the notebook. See Supplemental File 3 for an example of a goodlist.</li>
	</ol>
	</li>
	<li>Restart the notebook kernel and run all cells (from the menu at the top, select Kernel, Restart &#38; Run All) to get a .sdf file with the desired name in the specified output folder.</li>
	<li>Repeat the previous two steps for each structure file generated by MAYGEN in step 2.</li>
</ol>
4. (Optional) Additional structure modifications
NOTE: These are performed in this example but may not be needed for curating other libraries.
<ol>
	<li>Pseudoatom replacement. 
	NOTE: Here, a pseudoatom is a unique atom used to represent a larger substructure shared by all generated structures, thus reducing MAYGEN&#39;s generation time. See Supplemental File 4 for an example of pseudoatom replacement.
	<ol>
		<li>Open an Anaconda prompt (see step 1.2.2.1) and navigate to the folder containing the Jupyter notebooks.</li>
		<li>Open the Jupyter notebook for pseudoatom replacement: 
		jupyter notebook [notebook file name]</li>
		<li>In the designated cell at the start of the notebook, enter the full file path of the input .sdf file and the full file path of the desired .sdf output file as strings (within quotes).</li>
		<li>Restart the notebook kernel and run all the cells to get a .sdf file with the desired name in the specified output folder.</li>
	</ol>
	</li>
	<li>Amino acid N- and C-termini capping 
	NOTE: This procedure is specific to alpha-amino acids, adding molecular caps to the N- and C-termini of alpha-amino acid backbones. See Supplemental File 5 for an example of amino acid capping.
	<ol>
		<li>Open an Anaconda prompt (see step 1.2.2.1) and navigate to the folder containing the Jupyter notebooks.</li>
		<li>Open the Jupyter notebook for amino acid capping: 
		jupyter notebook [notebook file name]</li>
		<li>In the designated cell at the start of the notebook, enter the full file path of the input .sdf file and the full file path of the desired .sdf output file as strings (within quotes).</li>
		<li>Restart the notebook kernel and run all the cells to get a .sdf file with the desired name in the specified output folder.</li>
	</ol>
	</li>
</ol>
5. Descriptor generation
<ol>
	<li>Prior to descriptor generation, place all .sdf files for which descriptors are to be calculated in a single folder. 
	NOTE: If not done already, give these files descriptive names for easy filtering after descriptor generation.</li>
	<li>Open a command prompt, and navigate to the folder containing the PaDEL-Descriptor .jar file.</li>
	<li>Run PaDEL-Descriptor for the collected .sdf files using the following command: 
	java -jar PaDEL-Descriptor.jar -dir [directory of the .sdf files] -file [file path of a .csv file for results] -2d -retainorder -usefilenameasmolname 
	NOTE: The results file will have the molecule name in the first column and each descriptor in the subsequent columns.</li>
	<li>Export these data to any spreadsheet software for further analysis.</li>
</ol>

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	<li>Ruddigkeit, L., van Deursen, R., Blum, L. C., Reymond, J. -. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enumeration+of+166+billion+organic+small+molecules+in+the+chemical+universe+database+GDB-17.">Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17.</a> Journal of Chemical Information and Modeling. 52 (11), 2864-2875 (2012).</li><li>Buchanan, B. G., Feigenbaum, E. A., Webber, B. L., Nilsson, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dendral+and+Meta-Dendral:+their+applications+dimension.">Dendral and Meta-Dendral: their applications dimension.</a> Readings in Artificial Intelligence. , 313-322 (1981).</li><li>Gugisch, R., Basak, S. C., Restrepo, G., Villaveces, J. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MOLGEN+5.0,+A+Molecular+Structure+Generator.">MOLGEN 5.0, A Molecular Structure Generator.</a> Advances in Mathematical Chemistry and Applications. , 113-138 (2015).</li><li>Yirik, M. A., Steinbeck, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemical+graph+generators.">Chemical graph generators.</a> PLOS Computational Biology. 17 (1), 1008504 (2021).</li><li>Jaghoori, M. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PMG:+multi-core+metabolite+identification.">PMG: multi-core metabolite identification.</a> Electronic Notes in Theoretical Computer Science. 299, 53-60 (2013).</li><li>Yirik, M. A., Sorokina, M., Steinbeck, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MAYGEN:+an+open-source+chemical+structure+generator+for+constitutional+isomers+based+on+the+orderly+generation+principle.">MAYGEN: an open-source chemical structure generator for constitutional isomers based on the orderly generation principle.</a> Journal of Cheminformatics. 13 (1), 48 (2021).</li><li>Sims, C. C., Leech, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+methods+in+the+study+of+permutation+groups.">Computational methods in the study of permutation groups.</a> Computational Problems in Abstract Algebra. , 169-183 (1970).</li><li>Mat, W. -. K., Xue, H., Wong, J. T. -. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+genomics+of+LUCA.">The genomics of LUCA.</a> Frontiers in Bioscience. 13, 5605-5613 (2008).</li><li>Fournier, G. P., Alm, E. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ancestral+reconstruction+of+a+pre-LUCA+aminoacyl-tRNA+synthetase+ancestor+supports+the+late+addition+of+Trp+to+the+genetic+code.">Ancestral reconstruction of a pre-LUCA aminoacyl-tRNA synthetase ancestor supports the late addition of Trp to the genetic code.</a> Journal of Molecular Evolution. 80 (3-4), 171-185 (2015).</li><li>Higgs, P. G., Pudritz, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Thermodynamic+basis+for+prebiotic+amino+acid+synthesis+and+the+nature+of+the+first+genetic+code.">A Thermodynamic basis for prebiotic amino acid synthesis and the nature of the first genetic code.</a> Astrobiology. 9 (5), 483-490 (2009).</li><li>Trifonov, E. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Consensus+temporal+order+of+amino+acids+and+evolution+of+the+triplet+code.">Consensus temporal order of amino acids and evolution of the triplet code.</a> Gene. 261 (1), 139-151 (2000).</li><li>Cleaves, H. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+origin+of+the+biologically+coded+amino+acids.">The origin of the biologically coded amino acids.</a> Journal of Theoretical Biology. 263 (4), 490-498 (2010).</li><li>Moosmann, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Redox+biochemistry+of+the+genetic+code.">Redox biochemistry of the genetic code.</a> Trends in Biochemical Sciences. 46 (2), 83-86 (2021).</li><li>Simkus, D. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methodologies+for+analyzing+soluble+organic+compounds+in+extraterrestrial+samples:+amino+acids,+amines,+monocarboxylic+acids,+aldehydes,+and+ketones.">Methodologies for analyzing soluble organic compounds in extraterrestrial samples: amino acids, amines, monocarboxylic acids, aldehydes, and ketones.</a> Life. 9 (2), 47 (2019).</li><li>Criado-Reyes, J., Bizzarri, B. M., Garc&#237;a-Ruiz, J. M., Saladino, R., Di Mauro, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+borosilicate+glass+in+Miller-Urey+experiment.">The role of borosilicate glass in Miller-Urey experiment.</a> Scientific Reports. 11 (1), 21009 (2021).</li><li>Parker, E. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Primordial+synthesis+of+amines+and+amino+acids+in+a+1958+Miller+H2S-rich+spark+discharge+experiment.">Primordial synthesis of amines and amino acids in a 1958 Miller H2S-rich spark discharge experiment.</a> Proceedings of the National Academy of Sciences of the United States of America. 108 (14), 5526-5531 (2011).</li><li>Bada, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+insights+into+prebiotic+chemistry+from+Stanley+Miller's+spark+discharge+experiments.">New insights into prebiotic chemistry from Stanley Miller's spark discharge experiments.</a> Chemical Society Reviews. 42 (5), 2186-2196 (2013).</li><li>Yap, C. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PaDEL-descriptor:+An+open+source+software+to+calculate+molecular+descriptors+and+fingerprints.">PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints.</a> Journal of Computational Chemistry. 32 (7), 1466-1474 (2011).</li><li>SMARTS - A language for describing molecular patterns. Daylight Chemical Information Systems, Inc Available from: <a target="_blank" href="https://www.daylight.com/html/doc/theory/theory.smarts.html">https://www.daylight.com/html/doc/theory/theory.smarts.html</a> (2019)</li><li>Meringer, M., Cleaves, H. J., Freeland, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Beyond+terrestrial+biology:+charting+the+chemical+universe+of+&#945;-amino+acid+structures.">Beyond terrestrial biology: charting the chemical universe of &#945;-amino acid structures.</a> Journal of Chemical Information and Modeling. 53 (11), 2851-2862 (2013).</li><li>Zherebker, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Speciation+of+organosulfur+compounds+in+carbonaceous+chondrites.">Speciation of organosulfur compounds in carbonaceous chondrites.</a> Scientific Reports. 11 (1), 7410 (2021).</li><li>Tanford, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+hydrophobic+effect+and+the+organization+of+living+matter.">The hydrophobic effect and the organization of living matter.</a> Science. 200 (4345), 1012-1018 (1978).</li><li>Grantham, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Amino+acid+difference+formula+to+help+explain+protein+evolution.">Amino acid difference formula to help explain protein evolution.</a> Science. 185 (4154), 862-864 (1974).</li><li>Cordero, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Covalent+radii+revisited.">Covalent radii revisited.</a> Dalton Transactions. (21), 2832-2838 (2008).</li><li>Cleaves, H. J., Butch, C., Burger, P. B., Goodwin, J., Meringer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=One+among+millions:+the+chemical+space+of+nucleic+acid-like+molecules.">One among millions: the chemical space of nucleic acid-like molecules.</a> Journal of Chemical Information and Modeling. 59 (10), 4266-4277 (2019).</li></ol>

The authors have no conflicts of interest to disclose.

One feature of the &#34;early&#34; amino acids is a lack of sulfur. The meta-analyses mentioned earlier generally consider the sulfur-containing coded amino acids (Cys and Met) to have been relatively late additions to the genetic code, conclusions supported by the lack of sulfur-containing amino acids in meteorites and spark tube experiments. However, organosulfur compounds are readily detected in comets and meteorites22, and reanalysis of spark tube experiments using H2S gas found amino acids and other organic compounds containing sulfur16. When considering an alternative amino acid alphabet, one enriched in sulfur is worth exploring.
In the above protocol, structure generation and substructure filtering are considered critical steps; depending on the composition of the finished structure library, a researcher may only need to perform those two steps. Instructions and software for additional actions (pseudoatom replacement and addition of substructures (in this case, amino acid capping)) are included for more relevant descriptor calculation (capping ensures that XLogP calculations are influenced by the sidechain and not the backbone amine or carboxyl groups) and faster structure generation via the use of a pseudoatom, which is discussed in more detail below. Additionally, descriptor calculation is done here as an easy way to visualize the diversity of the structures generated and compare the effects of sulfur enrichment in the finished libraries.
While PaDEL-Descriptor can calculate thousands of molecular properties, molecular volume (as calculated van der Waals volume) and partition coefficient (as XLogP) were used here for two distinct reasons. First, these two descriptors measure molecular properties (size and hydrophobicity, respectively) that are familiar to most chemists and biologists. Second, in the case of amino acids, these two properties are significant. For decades, amino acid size and hydrophobicity were known to influence the thermodynamics of protein folding23. These two properties help explain amino acid substitution frequencies that have been integral to understanding protein evolution24.
The above example shows that, in the two descriptors studied (molecular volume and hydrophobicity), substituting a divalent sulfur for a carbon and two hydrogens does not yield significant changes. The slight, nonsignificant increase in mean molecular volume from sulfur substitution (Figure 3) could be attributed to sulfur&#39;s larger covalent radius (~103 pm) compared to either sp3 (~75 pm) or sp2 (~73 pm) carbon25. Similarly, sulfur substitution has minimal effect on the mean XLogP (Figure 4). The largest effect was between the VAIL and VAIL_S libraries, likely due to a combination of the VAIL library being especially hydrophobic (the sidechains are only hydrocarbons) and sulfhydryl groups being much more acidic than the methyl groups they would replace. The minimal effect of sulfur substitution is apparent in Figure 2, where libraries with sulfur substitution occupy the same chemical space as analogous libraries without sulfur substitution.
The decrease in the number of structures (Figure 5A) and time needed to generate those structures (Figure 5B) when using a pseudoatom is unsurprising. Using a pseudoatom reduces the number of heavy atoms that need to be incorporated into a chemical graph, reducing the number of graph nodes and yielding exponential decreases in generation time and number of structures. Here, the choice of trivalent phosphorus as a pseudoatom stems from basic biochemistry (absent posttranslational addition of phosphate groups, no genetically coded amino acids contain phosphorus) and the valence of the atom that would replace it (a trivalent phosphorus can easily be replaced with a tetravalent carbon that is singly bonded to another atom or group of atoms). While the provided code for pseudoatom substitution is specific for replacing a trivalent phosphorus with an alanine substructure, users can customize the code to work with different pseudoatoms or replacement substructures, potentially using multiple pseudoatoms during initial structure generation followed by replacing each pseudoatom with a larger molecular substructure.
Structure generation methods similar to those employed by MAYGEN (and other methods such as neural networks) are already used in drug discovery to generate compound libraries for in silico screening; a recent review4 discusses these methods in more detail. As these methods are intended primarily for the creation of drug-like molecules, there are some limitations on their ability to generate molecules, such as using biological or pharmaceutical properties to limit the structures created (inverse QSPR/QSAR) or creating structures from a preset number of substructure building blocks. As astrobiology is focused more on the multitude of organic compounds that can form abiotically and less on any end products or their properties, MAYGEN&#39;s exhaustive structure generation is ideal for creating structure libraries to address astrobiological questions. The approach to substructure filtering described here (performed after structure generation via an external program) differs from the competitor program MOLGEN in that MOLGEN&#39;s substructure filtering occurs during structure generation. As MAYGEN is open-source, not only is it more accessible than MOLGEN due to MOLGEN&#39;s licensing cost, but individuals could implement new features such as substructure filtering during structure generation.
As written, the protocol described here is focused on generating and curating libraries of relatively small alpha-amino acids. To generate different libraries, users can give different molecular formulae to MAYGEN, change the substructure filtering by changing the maximum allowed ring size and bond valence, or edit the goodlist and badlist files to add or remove substructure patterns. Protocol modifications that involve changing how atoms and substructures are added or replaced (pseudoatom substitution and molecular capping) are feasible but will require more attention to valence restrictions to avoid RDKit errors about incorrect valences in modified structures.
The protocol detailed above is designed for small alpha-amino acids. However, the general format (comprehensive structure generation using pseudoatoms, followed by substructure filtering and molecular modifications) is highly flexible for compounds beyond small amino acids. Even in astrobiology, a similar recent procedure using MOLGEN was used to investigate constitutional isomers of nucleic acids26. In addition to the tools described above, MAYGEN can be paired with other open-source cheminformatics tools to make creating and analyzing novel chemical structures affordable and accessible to a broad array of research fields.

Molecular structure generation serves as a practical application of the general problem of exhaustive graph generation; given several nodes (atoms) and constraints on their connectivity (e.g., valences, bond multiplicities, desired/undesired substructures), how many connected graphs (molecules) are possible? Structure generators have seen extensive application in drug discovery and pharmaceutical development, where they can create vast libraries of novel structures for in silico screening1.
The first structure generator, CONGEN, was developed for the first artificial intelligence project in organic chemistry, DENDRAL2 (short for DENDRitic ALgorithm). Several software successors of DENDRAL were reported in the literature; however, not all of them were maintained or efficient. Currently, MOLGEN3 is the state-of-the-art molecular structure generator. Unfortunately for most potential users, it is closed-source and requires a licensing fee. Thus, there has been the need for an efficient open-source structure generator that can easily adapt to specific applications. One challenge for an efficient structure generator is managing combinatorial explosion; as the size of a molecular formula increases, the size of the chemical search space increases exponentially. A recent review further explores the history and challenges of molecular structure generation4.
Prior to 2021, the Parallel Molecule Generator (PMG)5 was the fastest open-source structure generator, but it was still slower than MOLGEN by orders of magnitude. MAYGEN6 is approximately 47 times faster than PMG and around 3 times slower than MOLGEN, making MAYGEN the fastest and most efficient open-source structure generator available. More detailed comparisons and benchmarking tests can be found in the paper introducing MAYGEN6. A key feature of the program is its lexicographical ordering-based test for canonical structures, an orderly graph-generation method based on the Schreier-Sims7 algorithm. The software can be easily integrated into other projects and enhanced for the needs of the users.
Like MOLGEN and PMG, MAYGEN takes a user-defined molecular formula and generates all structures possible for that formula. For example, if a user runs MAYGEN with the formula C5H12, MAYGEN will generate all possible structures containing five carbon atoms and twelve hydrogen atoms. Unlike its open-source counterpart PMG, MAYGEN can also accommodate &#34;fuzzy&#34; molecular formulae that use intervals instead of discrete numbers for the count of each element. For example, if a user runs MAYGEN with the formula C5-7H12-15, MAYGEN will generate all possible structures that contain between five and seven carbon atoms and twelve and fifteen hydrogen atoms, allowing for simple generation of structures with a wide range of atomic compositions.
Astrobiology is one such field that can benefit from molecular structure generators. A popular topic in astrobiology is the evolution of the amino acid alphabet shared by all extant life on Earth. One of the defining features of the Last Universal Common Ancestor (LUCA) is its use of twenty genetically coded amino acids for protein construction8,9. Based on meta-analyses of work in multiple fields10,11,12, approximately 10 of these amino acids (Gly, Ala, Val, Asp, Glu, Ser, Thr, Leu, Ile, Pro) readily form under abiotic conditions and likely made up the amino acid alphabet of pre-LUCA organisms. Over time, this &#34;early&#34; alphabet was expanded in response to different structural and functional needs. For example, a recent review from Moosmann13 claims that the addition of more recent members of the genetically coded amino acids (namely Met, Tyr, and Trp) allowed for survival in oxygen-rich environments by preventing the intracellular proliferation of reactive oxygen species.
An ever-growing suite of analytical chemistry techniques allows insight into the amino acid structures that can form under abiotic conditions. A recent review14 by Simkus and others details the methods used to detect numerous organic compounds in meteorites, as well as organic compounds from in vitro simulations of early Earth environments15,16,17. Systematic generation of chemical structures allows researchers to explore beyond the organic compounds detected via instrumentation, populating the structural space around structural &#34;islands&#34; identified by analytical chemistry. In the case of the &#34;early&#34; amino acids, this systematic structure generation shows possible protein chemistries available to early life without limiting exploration to structures that have been experimentally detected under abiotic synthesis conditions. With open-source cheminformatics toolkits and efficient structure generators such as MAYGEN, creating and exploring novel chemical structure libraries is now easier than ever before and can guide more detailed investigations into alternative chemistries of life.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>conda v. 4.10.3</td>
			<td></td>
			<td></td>
			<td>https://www.anaconda.com/products/individual</td>
		</tr>
		<tr>
			<td>Java 17</td>
			<td></td>
			<td></td>
			<td>https://java.com/en/download/help/download_options.html</td>
		</tr>
		<tr>
			<td>MAYGEN v. 1.8</td>
			<td></td>
			<td></td>
			<td>https://github.com/MehmetAzizYirik/MAYGEN/releases</td>
		</tr>
		<tr>
			<td>PaDEL-Descriptor v. 2.21</td>
			<td></td>
			<td></td>
			<td>http://www.yapcwsoft.com/dd/padeldescriptor/</td>
		</tr>
		<tr>
			<td>python v. 3.7.11</td>
			<td></td>
			<td></td>
			<td>included in Anaconda environment</td>
		</tr>
		<tr>
			<td>RDKit v. 2020.09.1.0</td>
			<td></td>
			<td></td>
			<td>https://www.rdkit.org/docs/Install.html, or installed via conda: https://anaconda.org/rdkit/rdkit</td>
		</tr>
		<tr>
			<td>*These specific versions were used for this manuscript; user can obtain more recent versions if available.</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

curation of computational chemical libraries demonstrated with alpha-amino acids

Exhaustive generation of molecular structures has numerous chemical and biochemical applications such as drug design, molecular database construction, exploration of alternative biochemistries, and many more. Mathematically speaking, these are graph generators with chemical constraints. In the field, the most efficient generator currently (MOLGEN) is a commercial product, limiting its use. Alternative to that, another molecular structure generator, MAYGEN, is a recent open-source tool with efficiency comparable to MOLGEN and the capacity for users to increase its performance by adding new features. One of the research fields that can benefit from this development is astrobiology; structure generators allow researchers to supplement experimental data with computational possibilities for alternative biochemistry. This protocol details one use case for structure generation in astrobiology, namely the generation and curation of alpha-amino acid libraries. Using open-source structure generators and cheminformatics tools, the practices described here can be implemented beyond astrobiology for the low-cost creation and curation of chemical structure libraries for any research question.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2"></td>
			<td rowspan="2">Library</td>
			<td colspan="2" rowspan="2">Formula</td>
			<td colspan="2" rowspan="2">Additional constraints</td>
			<td rowspan="2">&#34;Early&#34; coded amino acids</td>
			<td rowspan="2">Generation time (ms)</td>
			<td colspan="2">Structures</td>
		</tr>
		<tr>
			<td>Initial</td>
			<td>Final</td>
		</tr>
		<tr>
			<td>1</td>
			<td>Gly</td>
			<td colspan="2">C2H5NO2</td>
			<td colspan="2">include Gly substructure</td>
			<td>Gly</td>
			<td>192</td>
			<td>84</td>
			<td>1</td>
		</tr>
		<tr>
			<td>2</td>
			<td>VAIL</td>
			<td colspan="2">PC0-3H3-9</td>
			<td colspan="2"></td>
			<td>Val, Ala, Ile, Leu</td>
			<td>172</td>
			<td>70</td>
			<td>22</td>
		</tr>
		<tr>
			<td>3</td>
			<td>DEST</td>
			<td colspan="2">PC0-3O1-2H3-5</td>
			<td colspan="2"></td>
			<td>Asp, Glu, Ser, Thr</td>
			<td>481</td>
			<td>1928</td>
			<td>254</td>
		</tr>
		<tr>
			<td>4</td>
			<td>Pro</td>
			<td colspan="2">C2-5NO2H7-11</td>
			<td colspan="2">Include N-meGly or N-meAla substructure</td>
			<td>Pro</td>
			<td>4035</td>
			<td>79777</td>
			<td>16</td>
		</tr>
		<tr>
			<td>5</td>
			<td>VAIL_S</td>
			<td colspan="2">PSC0-2H3-7</td>
			<td colspan="2"></td>
			<td></td>
			<td>122</td>
			<td>65</td>
			<td>31</td>
		</tr>
		<tr>
			<td>6</td>
			<td>DEST_S</td>
			<td colspan="2">PSC0-2O1-2H3</td>
			<td colspan="2"></td>
			<td></td>
			<td>349</td>
			<td>1075</td>
			<td>79</td>
		</tr>
		<tr>
			<td>7</td>
			<td>Pro_S</td>
			<td colspan="2">C2-4SNO2H7-9</td>
			<td colspan="2">Include N-meGly or N-meAla substructure</td>
			<td></td>
			<td>3999</td>
			<td>75734</td>
			<td>10</td>
		</tr>
	</tbody>
</table>
Table 1: Compound libraries used in this example. Libraries built from formulae 1-4 (Gly, VAIL, DEST, and Pro) are based on previously published fuzzy formulae of the &#34;early&#34; coded amino acids21, while libraries built from formulae 5-7 (VAIL_S, DEST_S, and Pro_S) are based on variants of formulae 2-4 that imagine a divalent sulfur replacing one of the carbon atoms. Structure counts reflect the number of molecules generated by MAYGEN for each formula (&#34;Initial&#34;) and the number of molecules remaining after filtering out those with unwanted substructures (&#34;Final&#34;). Abbreviations: VAIL = valine, alanine, isoleucine, leucine; DEST = aspartic acid, glutamic acid, serine, threonine; X_S = Divalent sulfur replaces one of the carbons in library X; N-meX = N-methylX.
The general methods above were applied to formulae based on the &#34;early&#34; coded amino acids, following the procedure of Meringer et al.21 Badlist structures were taken from this same source and converted to SMARTS strings to easily represent substructural patterns. Two badlist substructures were not used in this example: structure 018 (CH3-CH-N) matched near-isomers of proline that were not themselves unstable; structure 106 (R-C-C-OH, where R=alanine substructure attaching at the beta-carbon) matched glutamic acid, a coded amino acid. In addition to these chemical formulae, variants with divalent sulfur taking the place of a carbon atom and two hydrogen atoms were created. For performance reasons, several of these formulae use a trivalent phosphorus atom (e.g., a &#34;pseudoatom&#34;) as a substitute for the beta-carbon of an alanine substructure. Table 1 lists the libraries generated in this example, the formulae used to generate them, and the number of compounds contained within. Library names are based on the coded amino acids from which they are derived: either using the 3-letter abbreviation (Gly = glycine, Pro = proline) or single-letter abbreviation (VAIL = Valine, Alanine, Isoleucine, Leucine; DEST = Aspartic acid, Glutamic acid, Serine, Threonine). The &#34;_S&#34; suffix indicates a sulfur was substituted for a carbon in the original library&#39;s formula (e.g., VAIL_S is built with the same fuzzy formula as VAIL, but with a divalent sulfur replacing one of the carbons).
After structure generation with MAYGEN, the resulting libraries were filtered of compounds containing at least one substructure contained in the badlist. Following this filtering, any phosphorus atoms were replaced with an alanine substructure. Next, &#34;capped&#34; versions of all structures were created, with an acetyl group added to the N-terminus and an N-methyl amide group added to the C-terminus. This was done to remove the effect on the hydrophobicity of the free amine and carboxylic acid groups in the alpha-amino acid backbone. PaDEL-Descriptor was used to calculate XLogP for all capped structures and calculated van der Waals volume (VABC) for all uncapped structures.
Figure 2 shows the chemical space of the filtered libraries, as defined by VABC and XLogP descriptors. Here, the range of possible logP values increases with molecular volume, even within libraries that lack explicitly hydrophilic sidechains (e.g., VAIL, Pro). Coded amino acids with hydrocarbon sidechains were more hydrophobic than most other amino acids of a comparable volume from their respective library. This also seems to be the case for Met and Cys compared to other members of the VAIL_S library with similar volumes. Coded amino acids with hydroxyl side chains (Ser and Thr) were among the smallest members of the DEST library, with Asp only slightly larger than Thr.
Figure 3 and Figure 4 show the impacts on volume and logP when a divalent sulfur replaces a carbon in an alpha-amino acid side chain. Sulfur substitution led to a slight increase in molecular volume in all libraries (Figure 3). The effect of sulfur substitution on logP is not as homogenous as for volume (Figure 4). The mean logP of the VAIL_S library is slightly lower than that of the VAIL library, but this effect is not seen in either of the other library pairs (DEST and DEST_S, Pro and Pro_S).
Figure 5 quantifies the effects on structure generation of a pseudoatom standing in for a common substructure; here, a trivalent P substituted for an alanine moiety during structure generation. Using a pseudoatom in structure generation greatly decreased the number of structures generated by ~3 orders of magnitude (Figure 5A) and the total time needed to generate those structures by 1-2 orders of magnitude (Figure 5B).
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63632/63632fig02.jpg" /> 
Figure 2: Chemical space of all filtered amino acid libraries. Black markers represent amino acids from libraries without sulfur; yellow markers represent amino acids from sulfur-enriched libraries. Circles: VAIL and VAIL_S; squares: DEST and DEST_S; triangles: Pro and Pro_S; stars: coded amino acids. Note that the two sulfur-containing coded amino acids (Met and Cys) are not considered &#34;early&#34; amino acids but are present in the VAIL_S library. Abbreviations: XLogP = partition coefficient; VAIL = valine, alanine, isoleucine, leucine; DEST = aspartic acid, glutamic acid, serine, threonine; X_S = Divalent sulfur replaces one of the carbons in library X. <a href="https://www.jove.com/files/ftp_upload/63632/63632fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63632/63632fig03.jpg" /> 
Figure 3: Mean van der Waals volumes (in &#197;3) of libraries with and without sulfur. Black bars represent the mean volumes of libraries without sulfur (VAIL, DEST, Pro), while yellow bars represent mean volumes of the sulfur-substituted versions of those libraries (VAIL_S, DEST_S, Pro_S). Error bars show standard deviation. Abbreviations: VAIL = valine, alanine, isoleucine, leucine; DEST = aspartic acid, glutamic acid, serine, threonine; X_S = Divalent sulfur replaces one of the carbons in library X. <a href="https://www.jove.com/files/ftp_upload/63632/63632fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63632/63632fig04.jpg" /> 
Figure 4: Mean XLogP values of libraries with and without sulfur. Black bars represent libraries without sulfur (VAIL, DEST, Pro), while yellow bars represent sulfur-substituted versions of those libraries (VAIL_S, DEST_S, Pro_S). Error bars show standard deviation. Abbreviations: XLogP = partition coefficient; VAIL = valine, alanine, isoleucine, leucine; DEST = aspartic acid, glutamic acid, serine, threonine; X_S = Divalent sulfur replaces one of the carbons in library X. <a href="https://www.jove.com/files/ftp_upload/63632/63632fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63632/63632fig05.jpg" /> 
Figure 5: Effects of a trivalent pseudoatom on MAYGEN structure generation. All tests were done on a PC with an Intel i7-7700HQ processor at 2.8 GHz, 16 GB of RAM, no saving structures to a file, and the -m option to use multithreading. Tests using a pseudoatom used the fuzzy formulae as described in Table 1. For tests without a pseudoatom, the fuzzy formulae used were the same as described in Table 1 with the following changes: P was replaced with N; carbon counts were increased by 3; hydrogen counts were increased by 7; oxygen counts were increased by 2. Black bars show libraries generated with a pseudoatom; gray bars show libraries generated without a pseudoatom. (A)&#160;Number of structures generated using the fuzzy formulae used to build the VAIL and DEST libraries with and without a trivalent phosphorus substituting for an alanine substructure. (B) Time (in ms) needed to build the VAIL and DEST libraries with and without a trivalent phosphorus substituting for an alanine substructure. Abbreviations: VAIL = valine, alanine, isoleucine, leucine; DEST = aspartic acid, glutamic acid, serine, threonine. <a href="https://www.jove.com/files/ftp_upload/63632/63632fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental File 1: Substructure screening notebook. <a href="https://www.jove.com/files/ftp_upload/63632/supple_1.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 2: Sample badlist. <a href="https://www.jove.com/files/ftp_upload/63632/supple_2.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 3: Sample goodlist. <a href="https://www.jove.com/files/ftp_upload/63632/supple_3.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 4: Pseudoatom replacement notebook. <a href="https://www.jove.com/files/ftp_upload/63632/supple_4.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 5: Amino acid capping notebook. <a href="https://www.jove.com/files/ftp_upload/63632/supple_5.zip" target="_blank">Please click here to download this File.</a>

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Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

The purpose of this protocol is to efficiently generate and curate small-molecule structure libraries using open-source software.

Exhaustive generation of molecular structures has numerous chemical and biochemical applications such as drug design, molecular database construction, ...

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2.5K Views.  University of Maryland-Baltimore County. The purpose of this protocol is to efficiently generate and curate small-molecule structure libraries using open-source software. 

Video: Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Free Radicals in Chemical Biology: from Chemical Behavior to Biomarker Development

The involvement of free radicals in life sciences has constantly increased with time and has been connected to several physiological and pathological processes. This subject embraces diverse scientific areas, spanning from physical, biological and bioorganic chemistry to biology and medicine, with applications to the amelioration of quality of life, health and aging. Multidisciplinary skills are required for the full investigation of the many facets of radical processes in the biological environment and chemical knowledge plays a crucial role in unveiling basic processes and mechanisms. We developed a chemical biology approach able to connect free radical chemical reactivity with biological processes, providing information on the mechanistic pathways and products. The core of this approach is the design of biomimetic models to study biomolecule behavior (lipids, nucleic acids and proteins) in aqueous systems, obtaining insights of the reaction pathways as well as building up molecular libraries of the free radical reaction products. This context can be successfully used for biomarker discovery and examples are provided with two classes of compounds: mono-trans isomers of cholesteryl esters, which are synthesized and used as references for detection in human plasma, and purine 5',8-cyclo-2'-deoxyribonucleosides, prepared and used as reference in the protocol for detection of such lesions in DNA samples, after ionizing radiations or obtained from different health conditions.

Radical-based biomimetic chemistry has been applied to building-up libraries necessary for biomarker development.

The involvement of free radicals in life sciences has constantly increased with time and has been connected to several physiological and pathological ...

Qualitative Identification of Carboxylic Acids, Boronic Acids, and Amines Using Cruciform Fluorophores

Molecular cruciforms are X-shaped systems in which two conjugation axes intersect at a central core. If one axis of these molecules is substituted with electron-donors, and the other with electron-acceptors, cruciforms&#39; HOMO will localize along the electron-rich and LUMO along the electron-poor axis. This spatial isolation of cruciforms&#39; frontier molecular orbitals (FMOs) is essential to their use as sensors, since analyte binding to the cruciform invariably changes its HOMO-LUMO gap and the associated optical properties. Using this principle, Bunz and Miljani&#263; groups developed 1,4-distyryl-2,5-bis(arylethynyl)benzene and benzobisoxazole cruciforms, respectively, which act as fluorescent sensors for metal ions, carboxylic acids, boronic acids, phenols, amines, and anions. The emission colors observed when these cruciform are mixed with analytes are highly sensitive to the details of analyte&#39;s structure and - because of cruciforms&#39; charge-separated excited states - to the solvent in which emission is observed. Structurally closely related species can be qualitatively distinguished within several analyte classes: (a) carboxylic acids; (b) boronic acids, and (c) metals. Using a hybrid sensing system composed from benzobisoxazole cruciforms and boronic acid additives, we were also able to discern among structurally similar: (d) small organic and inorganic anions, (e) amines, and (f) phenols. The method used for this qualitative distinction is exceedingly simple. Dilute solutions (typically 10-6 M) of cruciforms in several off-the-shelf solvents are placed in UV/Vis vials. Then, analytes of interest are added, either directly as solids or in concentrated solution. Fluorescence changes occur virtually instantaneously and can be recorded through standard digital photography using a semi-professional digital camera in a dark room. With minimal graphic manipulation, representative cut-outs of emission color photographs can be arranged into panels which permit quick naked-eye distinction among analytes. For quantification purposes, Red/Green/Blue values can be extracted from these photographs and the obtained numeric data can be statistically processed.

Cross-conjugated cruciform fluorophores based on 1,4-distyryl-2,5-bis(arylethynyl)benzene and benzobisoxazole nuclei can be used to qualitatively identify diverse Lewis acidic and Lewis basic analytes. This method relies on the differences in emission colors of the cruciforms that are observed upon analyte addition. Structurally closely related species can be distinguished from each other.

Molecular cruciforms are X-shaped systems in which two conjugation axes intersect at a central core. If one axis of these molecules is substituted with ...

A Simple, Low-cost, and Robust System to Measure the Volume of Hydrogen Evolved by Chemical Reactions with Aqueous Solutions

There is a growing research interest in the development of portable systems which can deliver hydrogen on-demand to proton exchange membrane (PEM) hydrogen fuel cells. Researchers seeking to develop such systems require a method of measuring the generated hydrogen. Herein, we describe a simple, low-cost, and robust method to measure the hydrogen generated from the reaction of solids with aqueous solutions. The reactions are conducted in a conventional one-necked round-bottomed flask placed in a temperature controlled water bath. The hydrogen generated from the reaction in the flask is channeled through tubing into a water-filled inverted measuring cylinder. The water displaced from the measuring cylinder by the incoming gas is diverted into a beaker on a balance. The balance is connected to a computer, and the change in the mass reading of the balance over time is recorded using data collection and spreadsheet software programs. The data can then be approximately corrected for water vapor using the method described herein, and parameters such as the total hydrogen yield, the hydrogen generation rate, and the induction period can also be deduced. The size of the measuring cylinder and the resolution of the balance can be changed to adapt the setup to different hydrogen volumes and flow rates.

The study of methods to generate on-demand hydrogen for fuel cells continues to grow in importance. However, systems to measure hydrogen evolution from the reaction of chemicals with water can be complicated and expensive. This article details a simple, low-cost, and robust method to measure the evolution of hydrogen gas.

There is a growing research interest in the development of portable systems which can deliver hydrogen on-demand to proton exchange membrane (PEM) ...

Sulfate Separation by Selective Crystallization with a Bis-iminoguanidinium Ligand

A simple and effective method for selective sulfate separation from aqueous solutions by crystallization with a bis-guanidinium ligand, 1,4-benzene-bis(iminoguanidinium) (BBIG), is demonstrated. The ligand is synthesized as the chloride salt (BBIG-Cl) by in situ imine condensation of terephthalaldehyde with aminoguanidinium chloride in water, followed by crystallization as the sulfate salt (BBIG-SO4). Alternatively, BBIG-Cl is synthesized ex situ in larger scale from ethanol. The sulfate separation ability of the BBIG ligand is demonstrated by selective and quantitative crystallization of sulfate from seawater. The ligand can be recycled by neutralization of BBIG-SO4 with aqueous NaOH and crystallization of the neutral bis-iminoguanidine, which can be converted back into BBIG-Cl with aqueous HCl and reused in another separation cycle. Finally, 35S-labeled sulfate and &#946; liquid scintillation counting are employed for monitoring the sulfate concentration in solution. Overall, this protocol will instruct the user in the necessary skills to synthesize a ligand, employ it in the selective crystallization of sulfate from aqueous solutions, and quantify the separation efficiency.

A protocol for in situ aqueous synthesis of a bis(iminoguanidinium) ligand and its utilization in selective separation of sulfate is presented.

A simple and effective method for selective sulfate separation from aqueous solutions by crystallization with a bis-guanidinium ligand, ...

Insights into the Interactions of Amino Acids and Peptides with Inorganic Materials Using Single-Molecule Force Spectroscopy

The interactions between proteins or peptides and inorganic materials lead to several interesting processes. For example, combining proteins with minerals leads to the formation of composite materials with unique properties. In addition, the undesirable process of biofouling is initiated by the adsorption of biomolecules, mainly proteins, on surfaces. This organic layer is an adhesion layer for bacteria and allows them to interact with the surface. Understanding the fundamental forces that govern the interactions at the organic-inorganic interface is therefore important for many areas of research and could lead to the design of new materials for optical, mechanical and biomedical applications. This paper demonstrates a single-molecule force spectroscopy technique that utilizes an AFM to measure the adhesion force between either peptides or amino acids and well-defined inorganic surfaces. This technique involves a protocol for attaching the biomolecule to the AFM tip through a covalent flexible linker and single-molecule force spectroscopy measurements by atomic force microscope. In addition, an analysis of these measurements is included.

Here we present a protocol to measure the force of interactions between a well-defined inorganic surface and either peptides or amino acids by single-molecule force spectroscopy measurements using an atomic force microscope (AFM). The information obtained from the measurement is important to better understand the peptide-inorganic material interphase.

The interactions between proteins or peptides and inorganic materials lead to several interesting processes. For example, combining proteins with minerals ...

Determining the Chemical Composition of Corrosion Inhibitor/Metal Interfaces with XPS: Minimizing Post Immersion Oxidation

An approach for acquiring more reliable X-ray photoelectron spectroscopy data from corrosion inhibitor/metal interfaces is described. More specifically, the focus is on metallic substrates immersed in acidic solutions containing organic corrosion inhibitors, as these systems can be particularly sensitive to oxidation following removal from solution. To minimize the likelihood of such degradation, samples are removed from solution within a glove box purged with inert gas, either N2 or Ar. The glove box is directly attached to the load-lock of the ultra-high vacuum X-ray photoelectron spectroscopy instrument, avoiding any exposure to the ambient laboratory atmosphere, and thus reducing the possibility of post immersion substrate oxidation. On this basis, one can be more certain that the X-ray photoelectron spectroscopy features observed are likely to be representative of the in situ submerged scenario, e.g. the oxidation state of the metal is not modified.

A protocol to avoid the oxidation of metallic substrates during sample transfer from an inhibited acidic solution to an X-ray photoelectron spectrometer is presented.

An approach for acquiring more reliable X-ray photoelectron spectroscopy data from corrosion inhibitor/metal interfaces is described. More specifically, ...

Facile Preparation of (2Z,4E)-Dienamides by the Olefination of Electron-deficient Alkenes with Allyl Acetate

Direct cross-coupling between two alkenes via vinylic C-H bond activation represents an efficient strategy for the synthesis of butadienes with high atomic and step economy. However, this functionality-directed cross-coupling reaction has not been developed, as there are still limited directing groups in practical use. In particular, a stoichiometric amount of oxidant is usually required, producing a large amount of waste. Due to our interest in novel 1,3-butadiene synthesis, we describe the ruthenium-catalyzed olefination of electron-deficient alkenes using allyl acetate and without external oxidant. The reaction of 2-phenyl acrylamide and allyl acetate was chosen as a model reaction, and the desired diene product was obtained in 80% isolated yield with good stereoselectivity (Z,E/Z,Z = 88:12) under optimal conditions: [Ru(p-cymene) Cl2]2 (3 mol %) and AgSbF6 (20 mol %) in DCE at 110 &#186;C for 16 h. With the optimized catalytic conditions in hand, representative &#945;- and/or &#946;-substituted acrylamides were investigated, and all reacted smoothly, regardless of aliphatic or aromatic groups. Also, differently N-substituted acrylamides have proven to be good substrates. Moreover, we examined the reactivity of different allyl derivatives, suggesting that the chelation of acetate oxygen to the metal is crucial for the catalytic process. Deuterium-labeled experiments were also conducted to investigate the reaction mechanism. Only Z-selective H/D exchanges on acrylamide were observed, indicating a reversible cyclometalation event. In addition, a kinetic isotope effect (KIE) of 3.2 was observed in the intermolecular isotopic study, suggesting that the olefinic C-H metalation step is probably involved in the rate-determining step.

Facile Preparation of 2Z,4E-Dienamides by the Olefination of Electron-deficient Alkenes with Allyl Acetate

The ruthenium-catalyzed olefination of electron-deficient alkenes with allyl acetate is described here. By using aminocarbonyl as a directing group, this external oxidant-free protocol has high efficiency and good stereo- and regioselectivity, opening a novel synthetic route to (Z,E)-butadiene skeletons.

Direct cross-coupling between two alkenes via vinylic C-H bond activation represents an efficient strategy for the synthesis of butadienes with high ...

Fizzy Extraction of Volatile Organic Compounds Combined with Atmospheric Pressure Chemical Ionization Quadrupole Mass Spectrometry

Chemical analysis of volatile and semivolatile compounds dissolved in liquid samples can be challenging. The dissolved components need to be brought to the gas phase, and efficiently transferred to a detection system. Fizzy extraction takes advantage of the effervescence phenomenon. First, a carrier gas (here, carbon dioxide) is dissolved in the sample by applying overpressure and stirring the sample. Second, the sample chamber is decompressed abruptly. Decompression leads to the formation of numerous carrier gas bubbles in the sample liquid. These bubbles assist the release of the dissolved analyte species from the liquid to the gas phase. The released analytes are immediately transferred to the atmospheric pressure chemical ionization interface of a triple quadrupole mass spectrometer. The ionizable analyte species give rise to mass spectrometric signals in the time domain. Because the release of the analyte species occurs over short periods of time (a few seconds), the temporal signals have high amplitudes and high signal-to-noise ratios. The amplitudes and areas of the temporal peaks can then be correlated with concentrations of the analytes in the liquid samples subjected to fizzy extraction, which enables quantitative analysis. The advantages of fizzy extraction include: simplicity, speed, and limited use of chemicals (solvents).

Fizzy extraction is a new laboratory technique for analysis of volatile and semivolatile compounds. A carrier gas is dissolved in the liquid sample by applying overpressure and stirring the sample. The sample chamber is then decompressed. The analyte species are liberated to the gas phase due to effervescence.

Chemical analysis of volatile and semivolatile compounds dissolved in liquid samples can be challenging. The dissolved components need to be brought to ...

Continuous Flow Chemistry: Reaction of Diphenyldiazomethane with p-Nitrobenzoic Acid

Continuous flow technology has been identified as instrumental for its environmental and economic advantages leveraging superior mixing, heat transfer and cost savings through the &#34;scaling out&#34; strategy as opposed to the traditional &#34;scaling up&#34;. Herein, we report the reaction of diphenyldiazomethane with p-nitrobenzoic acid in both batch and flow modes. To effectively transfer the reaction from batch to flow mode, it is essential to first conduct the reaction in batch. As a consequence, the reaction of diphenyldiazomethane was first studied in batch as a function of temperature, reaction time, and concentration to obtain kinetic information and process parameters. The glass flow reactor set-up is described and combines two types of reaction modules with &#34;mixing&#34; and &#34;linear&#34; microstructures. Finally, the reaction of diphenyldiazomethane with p-nitrobenzoic acid was successfully conducted in the flow reactor, with up to 95% conversion of the diphenyldiazomethane in 11 min. This proof of concept reaction aims to provide insight for scientists to consider flow technology&#39;s competitiveness, sustainability, and versatility in their research.

Continuous Flow Chemistry: Reaction of Diphenyldiazomethane with p-Nitrobenzoic Acid

Flow chemistry carries environmental and economic advantages by leveraging superior mixing, heat transfer and cost benefits. Herein, we provide a blueprint to transfer chemical processes from batch to flow mode. The reaction of diphenyldiazomethane (DDM) with p-nitrobenzoic acid, conducted in batch and flow, was chosen for proof of concept.

Continuous flow technology has been identified as instrumental for its environmental and economic advantages leveraging superior mixing, heat transfer and ...

Chemical Precipitation Method for the Synthesis of Nb2O5 Modified Bulk Nickel Catalysts with High Specific Surface Area

We demonstrate a method for the synthesis of NixNb1-xO catalysts with sponge-like and fold-like nanostructures. By varying the Nb:Ni ratio, a series of NixNb1-xO nanoparticles with different atomic compositions (x = 0.03, 0.08, 0.15, and 0.20) have been prepared by chemical precipitation. These NixNb1-xO catalysts are characterized by X-ray diffraction, X-ray photoelectron spectroscopy, and scanning electron microscopy. The study revealed the sponge-like and fold-like appearance of Ni0.97Nb0.03O and Ni0.92Nb0.08O on the NiO surface, and the larger surface area of these NixNb1-xO catalysts, compared with the bulk NiO. Maximum surface area of 173 m2/g can be obtained for Ni0.92Nb0.08O catalysts. In addition, the catalytic hydroconversion of lignin-derived compounds using the synthesized Ni0.92Nb0.08O catalysts have been investigated.

Chemical Precipitation Method for the Synthesis of Nb2O5 Modified Bulk Nickel Catalysts with High Specific Surface Area

A protocol for the synthesis of sponge-like and fold-like Ni1-xNbxO nanoparticles by chemical precipitation is presented.

We demonstrate a method for the synthesis of NixNb1-xO catalysts with sponge-like and fold-like nanostructures. By varying the Nb:Ni ratio, a series of ...