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>

<ol><li>Ruddigkeit, L., van Deursen, R., Blum, L. C., Reymond, J. -L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enumeration+of+166+billion+organic+small+molecules+in+the+chemical+universe+database+GDB-17%5BTitle%5D)+AND+%22Journal+of+Chemical+Information+and+Modeling%22%5BJournal%5D)">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. Dendral and Meta-Dendral: their applications dimension. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aBuchanan%2C+BG+%22Readings+in+Artificial+Intelligence%22">Readings in Artificial Intelligence</a>. Webber, B. L., Nilsson, N. J. , Morgan Kaufmann. 313-322 (1981).</li>
<li>Gugisch, R., et al. MOLGEN 5.0, A Molecular Structure Generator. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aGugisch%2C+R+%22Advances+in+Mathematical+Chemistry+and+Applications%22">Advances in Mathematical Chemistry and Applications</a>. Basak, S. C., Restrepo, G., Villaveces, J. L. , Bentham Science Publishers. 113-138 (2015).</li>
<li>Yirik, M. A., Steinbeck, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Chemical+graph+generators%5BTitle%5D)+AND+%22PLOS+Computational+Biology%22%5BJournal%5D)">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/pubmed?term=((PMG%3A+multi-core+metabolite+identification%5BTitle%5D)+AND+%22Electronic+Notes+in+Theoretical+Computer+Science%22%5BJournal%5D)">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/pubmed?term=((MAYGEN%3A+an+open-source+chemical+structure+generator+for+constitutional+isomers+based+on+the+orderly+generation+principle%5BTitle%5D)+AND+%22Journal+of+Cheminformatics%22%5BJournal%5D)">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. Computational methods in the study of permutation groups. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aSims%2C+CC+%22Computational+Problems+in+Abstract+Algebra%22">Computational Problems in Abstract Algebra</a>. Leech, J. , Pergamon. 169-183 (1970).</li>
<li>Mat, W. -K., Xue, H., Wong, J. T. -F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+genomics+of+LUCA%5BTitle%5D)+AND+%22Frontiers+in+Bioscience%22%5BJournal%5D)">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/pubmed?term=((Ancestral+reconstruction+of+a+pre-LUCA+aminoacyl-tRNA+synthetase+ancestor+supports+the+late+addition+of+Trp+to+the+genetic+code%5BTitle%5D)+AND+%22Journal+of+Molecular+Evolution%22%5BJournal%5D)">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/pubmed?term=((A+Thermodynamic+basis+for+prebiotic+amino+acid+synthesis+and+the+nature+of+the+first+genetic+code%5BTitle%5D)+AND+%22Astrobiology%22%5BJournal%5D)">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/pubmed?term=((Consensus+temporal+order+of+amino+acids+and+evolution+of+the+triplet+code%5BTitle%5D)+AND+%22Gene%22%5BJournal%5D)">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/pubmed?term=((The+origin+of+the+biologically+coded+amino+acids%5BTitle%5D)+AND+%22Journal+of+Theoretical+Biology%22%5BJournal%5D)">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/pubmed?term=((Redox+biochemistry+of+the+genetic+code%5BTitle%5D)+AND+%22Trends+in+Biochemical+Sciences%22%5BJournal%5D)">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/pubmed?term=((Methodologies+for+analyzing+soluble+organic+compounds+in+extraterrestrial+samples%3A+amino+acids%2C+amines%2C+monocarboxylic+acids%2C+aldehydes%2C+and+ketones%5BTitle%5D)+AND+%22Life%22%5BJournal%5D)">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., García-Ruiz, J. M., Saladino, R., Di Mauro, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+borosilicate+glass+in+Miller-Urey+experiment%5BTitle%5D)+AND+%22Scientific+Reports%22%5BJournal%5D)">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/pubmed?term=((Primordial+synthesis+of+amines+and+amino+acids+in+a+1958+Miller+H2S-rich+spark+discharge+experiment%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">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/pubmed?term=((New+insights+into+prebiotic+chemistry+from+Stanley+Miller%27s+spark+discharge+experiments%5BTitle%5D)+AND+%22Chemical+Society+Reviews%22%5BJournal%5D)">New insights into prebiotic chemistry from Stanley Miller's spark discharge experiments.</a> Chemical Society Reviews. 42 (5), 2186-2196 (2013).</li>
<li>RDKit: Open-source cheminformatics. , Available from: 
 <a target="_blank" href="http://www.rdkit.org">http://www.rdkit.org</a> (2021).</li>
<li>Yap, C. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((PaDEL-descriptor%3A+An+open+source+software+to+calculate+molecular+descriptors+and+fingerprints%5BTitle%5D)+AND+%22Journal+of+Computational+Chemistry%22%5BJournal%5D)">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/pubmed?term=((Beyond+terrestrial+biology%3A+charting+the+chemical+universe+of+%CE%B1-amino+acid+structures%5BTitle%5D)+AND+%22Journal+of+Chemical+Information+and+Modeling%22%5BJournal%5D)">Beyond terrestrial biology: charting the chemical universe of α-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/pubmed?term=((Speciation+of+organosulfur+compounds+in+carbonaceous+chondrites%5BTitle%5D)+AND+%22Scientific+Reports%22%5BJournal%5D)">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/pubmed?term=((The+hydrophobic+effect+and+the+organization+of+living+matter%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">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/pubmed?term=((Amino+acid+difference+formula+to+help+explain+protein+evolution%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">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/pubmed?term=((Covalent+radii+revisited%5BTitle%5D)+AND+%22Dalton+Transactions%22%5BJournal%5D)">Covalent radii revisited.</a> Dalton Transactions. (21), 2832-2838 (2008).</li>
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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><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>

Watch this Scientific Journal Video about Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids at JoVE.com

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, ...

curation-computational-chemical-libraries-demonstrated-with-alpha

Research

JoVE Journal

Chemistry

2.6K Views.  University of Maryland-Baltimore County. Ons protocol laat zien hoe open source software elke onderzoeker in staat kan stellen om een computationele structuurbibliotheek te maken en te beheren. Dit protocol-appèl komt voort uit de openheid en flexibiliteit. Iedereen kan het gebruiken en aanpassen aan hun specifieke onderzoeksvraag.Versies van dit protocol kunnen worden toegepast op toepassingen voor het ontdekken van geneesmiddelen, waardoor snel specifieke structuurbibliotheken worden gemaakt voor in silico-screening. Hoewe...

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

Split-and-pool Synthesis and Characterization of Peptide Tertiary Amide Library

Peptidomimetics are great sources of protein ligands. The oligomeric nature of these compounds enables us to access large synthetic libraries on solid phase by using combinatorial chemistry. One of the most well studied classes of peptidomimetics is peptoids. Peptoids are easy to synthesize and have been shown to be proteolysis-resistant and cell-permeable. Over the past decade, many useful protein ligands have been identified through screening of peptoid libraries. However, most of the ligands identified from peptoid libraries do not display high affinity, with rare exceptions. This may be due, in part, to the lack of chiral centers and conformational constraints in peptoid molecules. Recently, we described a new synthetic route to access peptide tertiary amides (PTAs). PTAs are a superfamily of peptidomimetics that include but are not limited to peptides, peptoids and N-methylated peptides. With side chains on both &#945;-carbon and main chain nitrogen atoms, the conformation of these molecules are greatly constrained by sterical hindrance and allylic 1,3 strain. (Figure 1) Our study suggests that these PTA molecules are highly structured in solution and can be used to identify protein ligands. We believe that these molecules can be a future source of high-affinity protein ligands. Here we describe the synthetic method combining the power of both split-and-pool and sub-monomer strategies to synthesize a sample one-bead one-compound (OBOC) library of PTAs.

Peptide tertiary amides (PTAs) are a superfamily of peptidomimetics that include but are not limited to peptides, peptoids and N-methylated peptides. Here we describe a synthetic method which combines&#160;both split-and-pool and sub-monomer strategies to synthesize a one-bead one-compound library of PTAs.

Peptidomimetics are great sources of protein ligands. The oligomeric nature of these compounds enables us to access large synthetic libraries on solid ...

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Biopanning bacterial display libraries is a proven technique for peptide affinity reagent discovery for recognition of both biotic and abiotic targets. Peptide affinity reagents can be used for similar applications to antibodies, including sensing and therapeutics, but are more robust and able to perform in more extreme environments. Specific enrichment of peptide capture agents to a protein target of interest is enhanced using semi-automated sorting methods which improve binding and wash steps and therefore decrease the occurrence of false positive binders. A semi-automated sorting method is described herein for use with a commercial automated magnetic-activated cell sorting device with an unconstrained bacterial display sorting library expressing random 15-mer peptides. With slight modifications, these methods are extendable to other automated devices, other sorting libraries, and other organisms. A primary goal of this work is to provide a comprehensive methodology and expound the thought process applied in analyzing and minimizing the resulting pool of candidates. These techniques include analysis of on-cell binding using fluorescence-activated cell sorting (FACS), to assess affinity and specificity during sorting and in comparing individual candidates, and the analysis of peptide sequences to identify trends and consensus sequences for understanding and potentially improving the affinity to and specificity for the target of interest.

Biopanning bacterial display libraries is a proven technique for discovery of peptide affinity reagents, a robust alternative to antibodies. The semi-automated sorting method herein has streamlined biopanning to decrease the occurrence of false positives. Here we illustrate the thought process and techniques applied in evaluating candidates and minimizing downstream analysis.

Biopanning bacterial display libraries is a proven technique for peptide affinity reagent discovery for recognition of both biotic and abiotic targets. ...

Biochemistry

Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library

Protein dimerization events that occur only in the presence of a small-molecule ligand enable the development of small-molecule biosensors for the dissection and manipulation of biological pathways. Currently, only a limited number of chemically induced dimerization (CID) systems exist and engineering new ones with desired sensitivity and selectivity for specific small-molecule ligands remains a challenge in the field of protein engineering. We here describe a high throughput screening method, combinatorial binders-enabled selection of CID (COMBINES-CID), for the de novo engineering of CID systems applicable to a large variety of ligands. This method uses the two-step selection of a phage-displayed combinatorial nanobody library to obtain 1) &#34;anchor binders&#34; that first bind to a ligand of interest and then 2) &#34;dimerization binders&#34; that only bind to anchor binder-ligand complexes. To select anchor binders, a combinatorial library of over 109 complementarity-determining region (CDR)-randomized nanobodies is screened with a biotinylated ligand and hits are validated with the unlabeled ligand by bio-layer interferometry (BLI). To obtain dimerization binders, the nanobody library is screened with anchor binder-ligand complexes as targets for positive screening and the unbound anchor binders for negative screening. COMBINES-CID is broadly applicable to select CID binders with other immunoglobulin, non-immunoglobulin, or computationally designed scaffolds to create biosensors for in vitro and in vivo detection of drugs, metabolites, signaling molecules, etc.

Creating chemically induced protein dimerization systems with desired affinity and specificity for any given small molecule ligand would have many biological sensing and actuation applications. Here, we describe an efficient, generalizable method for de novo engineering of chemically induced dimerization systems via the stepwise selection of a phage-displayed combinatorial single-domain antibody library.

Protein dimerization events that occur only in the presence of a small-molecule ligand enable the development of small-molecule biosensors for the ...

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

In fragment-based drug discovery, hundreds or often thousands of compounds smaller than ~300 Da are tested against the protein of interest to identify chemical entities that can be developed into potent drug candidates. Since the compounds are small, interactions are weak, and the screening method must therefore be highly sensitive; moreover, structural information tends to be crucial for elaborating these hits into lead-like compounds. Therefore, protein crystallography has always been a gold-standard technique, yet historically too challenging to find widespread use as a primary screen.
Initial XChem experiments were demonstrated in 2014 and then trialed with academic and industrial collaborators to validate the process. Since then, a large research effort and significant beamtime have streamlined sample preparation, developed a fragment library with rapid follow-up possibilities, automated and improved the capability of I04-1 beamline for unattended data collection, and implemented new tools for data management, analysis and hit identification.
XChem is now a facility for large-scale crystallographic fragment screening, supporting the entire crystals-to-deposition process, and accessible to academic and industrial users worldwide. The peer-reviewed academic user program has been actively developed since 2016, to accommodate projects from as broad a scientific scope as possible, including well-validated as well as exploratory projects. Academic access is allocated through biannual calls for peer-reviewed proposals, and proprietary work is arranged by Diamond's Industrial Liaison group. This workflow has already been routinely applied to over a hundred targets from diverse therapeutic areas, and effectively identifies weak binders (1%-30% hit rate), which both serve as high-quality starting points for compound design and provide extensive structural information on binding sites. The resilience of the process was demonstrated by continued screening of SARS-CoV-2 targets during the COVID-19 pandemic, including a 3-week turn-around for the main protease.

This paper describes the complete XChem process for crystal-based fragment screening, starting from applying for access and all subsequent steps to data dissemination.

In fragment-based drug discovery, hundreds or often thousands of compounds smaller than ~300 Da are tested against the protein of interest to identify ...

Predicting Amino Acid Preferences of Protein-Protein Binding Interfaces

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the recognition of linear motifs with variable conservation. The combination of highly conserved and more variable regions in the same ligands often contributes to the multispecificity of binding, a common property of enzymes and cell signaling proteins. Characterization of amino acid preferences of peptide-binding domains is important for the design of mediators of protein-protein interactions (PPIs). Computational methods are an efficient alternative to the often costly and cumbersome experimental techniques, enabling the design of potential mediators that can be later validated in downstream experiments. Here, we described a methodology using the Pepspec application of the Rosetta molecular modeling package to predict the amino acid preferences of peptide-binding domains. This methodology is useful when the structure of the receptor protein and the nature of the peptide ligand are both known or can be inferred. The methodology starts with a well-characterized anchor from the ligand, which is extended by randomly adding amino acid residues. The binding affinity of peptides generated this way is then evaluated by flexible-backbone peptide docking in order to select the peptides with the best predicted binding scores. These peptides are then used to calculate amino acid preferences and to optionally compute a position-weight matrix (PWM) that can be used in further studies. To illustrate the application of this methodology, we used the interaction between subunits of human interferon regulatory factor 5 (IRF5), previously known to be multispecific but globally guided by a short conserved motif called pLxIS. The estimated amino acid preferences were consistent with previous knowledge about the IRF5 binding surface. Positions occupied by phosphorylatable serine residues exhibited a high frequency of aspartate and glutamate, likely because their negatively charged side chains are similar to phosphoserine.

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

We describe a methodology based on sequence diversification to estimate the amino acid preferences of multispecific binding sites in protein-protein interactions (PPIs). In this strategy, thousands of potential peptide ligands are generated and screened in silico, thus overcoming some limitations of available experimental methods.

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the ...

Visualizing and Analyzing the Chemical Space of Natural Product Databases for Drug Discovery

Chemical space is a multidimensional descriptor space that encloses all possible molecules, and at least 1 x 1060 organic substances with a molecular weight below 500 Da are thought to be potentially relevant for drug discovery. Natural products have been the primary source of the new pharmacological entities marketed during the past forty years and continue to be one of the most productive sources for the creation of innovative medications. Chemoinformatics-based computational tools accelerate the drug development process for natural products. Methods including estimating bioactivities, safety profiles, ADME, and natural product likeness measurement have been used. Here, we go over recent developments in chemoinformatic tools designed to visualize, characterize, and expand the chemical space of natural compound data sets using various molecular representations, create visual representations of such spaces, and investigate structure-property relationships within chemical spaces. With an emphasis on drug discovery applications, we evaluate the open-source databases BIOFACQUIM and PeruNPDB as proof of concept.

Here, we provide a methodology that uses different molecular representations to display and analyze the chemical space of natural compound data sets, with a focus on applications related to drug discovery.

Chemical space is a multidimensional descriptor space that encloses all possible molecules, and at least 1 x 1060 organic substances with a molecular ...

In Silico Structural Analysis of Protein Carbonylation by Lipid Derivatives

Synthesizing Amino Acids Modified with Reactive Carbonyls in Silico to Assess Structural Effects Using Molecular Dynamics Simulations

Protein carbonylation by reactive aldehydes derived from lipid peroxidation leads to cross-linking, oligomerization, and aggregation of proteins, causing intracellular damage, impaired cell functions, and, ultimately, cell death. It has been described in aging and several age-related chronic conditions. However, the basis of structural changes related to the loss of function in protein targets is still not well understood. Hence, a route to the in silico construction of new parameters for amino acids carbonylated with reactive carbonyl species derived from fatty acid oxidation is described. The Michael adducts for Cys, His, and Lys with 4-hydroxy-2-nonenal (HNE), 4-hydroxy-2-hexenal (HHE), and a furan ring form for 4-Oxo-2-nonenal (ONE), were built, while malondialdehyde (MDA) was directly attached to each residue. The protocol describes details for the construction, geometry optimization, assignment of charges, missing bonds, angles, dihedral angles parameters, and its validation for each modified residue structure. As a result, structural effects induced by the carbonylation with these lipid derivatives have been measured by molecular dynamics simulations on different protein systems such as the thioredoxin enzyme, bovine serum albumin and the membrane Zu-5-ankyrin domain employing root-mean-square deviation (RMSD), root mean square fluctuation (RMSF), structural secondary prediction (DSSP) and the solvent-accessible surface area analysis (SASA), among others.

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Here, we describe a protocol for the optimization and parameterization of amino acid residues modified with reactive carbonyl species, adaptable to protein systems. The protocol steps include structure design and optimization, charge assignments, parameter construction, and preparation of protein systems.

Protein carbonylation by reactive aldehydes derived from lipid peroxidation leads to cross-linking, oligomerization, and aggregation of proteins, causing ...

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Receptors and enzyme proteins are important biomolecules that act as binding targets for bioactive small molecules. Thus, the rapid and global validation of the drug-protein interactions is highly desirable for not only understanding the molecular mechanisms underlying therapeutic efficacy but also for assessing drug characteristics, such as adsorption, distribution, metabolism, excretion, and toxicity (ADMET) for clinical use. Here, we present a biosensor-based high throughput strategy for the biopanning of T7 phage-displayed short peptides that can be easily displayed on the phage capsid. Subsequent analysis of the amino acid sequences of peptides containing short segments, as &#34;broken relics&#34;, of the drug-binding sites using bioinformatics programs in receptor ligand contact (RELIC) suite, is also shown. By applying this method to two clinically approved drugs, an anti-tumor irinotecan, and an anti-flu oseltamivir, the detailed process for collecting the drug-recognizing peptide sequences and highlighting the drug-binding sites of the target proteins are explained in this paper. The strategy described herein can be applied for any small molecules of interest.

This study aimed to present a strategy for identifying drug-peptide interactions. The strategy involves the biopanning of drug-recognizing short peptides based on a quartz-crystal microbalance (QCM) biosensor, followed by bioinformatics analysis for quantitatively assessing the information obtained for the drug recognition and annotation of the drug-binding sites on proteins.

Receptors and enzyme proteins are important biomolecules that act as binding targets for bioactive small molecules. Thus, the rapid and global validation ...

Bioengineering

Residue-Specific Exchange of Proline by Proline Analogs in Fluorescent Proteins: How "Molecular Surgery" of the Backbone Affects Folding and Stability

Replacement of proline (Pro) residues in proteins by the traditional site-directed mutagenesis by any of the remaining 19 canonical amino acids is often detrimental to protein folding and, in particular, chromophore maturation in green fluorescent proteins and related variants. A reasonable alternative is to manipulate the translation of the protein so that all Pro residues are replaced residue-specifically by analogs, a method known as selective pressure incorporation (SPI). The built-in chemical modifications can be used as a kind of &#34;molecular surgery&#34; to finely dissect measurable changes or even rationally manipulate different protein properties. Here, the study demonstrates the usefulness of the SPI method to study the role of prolines in the organization of the typical &#946;-barrel structure of spectral variants of the green fluorescent protein (GFP) with 10-15 prolines in their sequence: enhanced green fluorescent protein (EGFP), NowGFP, and KillerOrange. Pro residues are present in connecting sections between individual &#946;-strands and constitute the closing lids of the barrel scaffold, thus being responsible for insulation of the chromophore from water, i.e., fluorescence properties. Selective pressure incorporation experiments with (4R)-fluoroproline (R-Flp), (4S)-fluoroproline (S-Flp), 4,4-difluoroproline (Dfp), and 3,4-dehydroproline (Dhp) were performed using a proline-auxotrophic E. coli strain as expression host. We found that fluorescent proteins with S-Flp and Dhp are active (i.e., fluorescent), while the other two analogs (Dfp and R-Flp) produced dysfunctional, misfolded proteins. Inspection of UV-Vis absorption and fluorescence emission profiles showed few characteristic alterations in the proteins containing Pro analogs. Examination of the folding kinetic profiles in EGFP variants showed an accelerated refolding process in the presence of S-Flp, while the process was similar to wild-type in the protein containing Dhp. This study showcases the capacity of the SPI method to produce subtle modifications of protein residues at an atomic level (&#34;molecular surgery&#34;), which can be adopted for the study of other proteins of interest. It illustrates the outcomes of proline replacements with close chemical analogs on the folding and spectroscopic properties in the class of &#946;-barrel fluorescent proteins.

To overcome the limitations of classical site-directed mutagenesis, proline analogs with specific modifications were incorporated into several fluorescent proteins. We show how the replacement of hydrogen by fluorine or of the single by double bonds in proline residues ("molecular surgery") affects fundamental protein properties, including their folding and interaction with light.

Replacement of proline (Pro) residues in proteins by the traditional site-directed mutagenesis by any of the remaining 19 canonical amino acids is often ...

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

REPLACE is a unique strategy developed to more effectively target protein-protein interactions (PPIs). It aims to expand available drug target space by providing improved methodology for the identification of inhibitors for such binding sites and which represent the majority of potential drug targets. The main goal of this paper is to provide a methodological overview of the use and application of the REPLACE strategy which involves computational and synthetic chemistry approaches. REPLACE is exemplified through its application to the development of non-ATP competitive cyclin dependent kinases (CDK) inhibitors as anti-tumor therapeutics. CDKs are frequently deregulated in cancer and hence are considered as important targets for drug development. Inhibition of CDK2/cyclin A in S phase has been reported to promote selective apoptosis of cancer cells in a p53 independent manner through the E2F1 pathway. Targeting the protein-protein interaction at the cyclin binding groove (CBG) is an approach which will allow the specific inhibition of cell cycle over transcriptional CDKs. The CBG is recognized by a consensus sequence derived from CDK substrates and tumor suppressor proteins termed the cyclin binding motif (CBM). The CBM has previously been optimized to an octapeptide from p21Waf (HAKRRIF) and then further truncated to a pentapeptide retaining sufficient activity (RRLIF). Peptides in general are not cell permeable, are metabolically unstable and therefore the REPLACE (REplacement with Partial Ligand Alternatives through Computational Enrichment) strategy has been applied in order to generate more drug-like inhibitors. The strategy begins with the design of Fragment ligated inhibitory peptides (FLIPs) that selectively inhibit cell cycle CDK/cyclin complexes. FLIPs were generated by iteratively replacing residues of HAKRRLIF/RRLIF with fragment like small molecules (capping groups), starting from the N-terminus (Ncaps), followed by replacement on the C-terminus. These compounds are starting points for the generation of non-ATP competitive CDK inhibitors as anti-tumor therapeutics.

We describe implementation of the REPLACE strategy for targeting protein-protein interactions. REPLACE is an iterative strategy involving synthetic and computational approaches for the conversion of optimized peptidic inhibitors into drug like molecules.

REPLACE is a unique strategy developed to more effectively target protein-protein interactions (PPIs). It aims to expand available drug target space by ...

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

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