Exploring the Chemical Space of Antiparasitic Peptides and Discovery of New Promising Leads through a Novel Approach based on Network Science and Similarity Searching - Supporting Information
Authors/Creators
- 1. Universidad San Francisco de Quito, Grupo de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Quito, Ecuador
- 2. Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (<CICESE), Baja California 22860, Mexico
- 3. Colegio de Ciencias e Ingenierías "El Politécnico", Universidad San Francisco de Quito (USFQ), Quito, Ecuador
- 4. CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n 4450-208 Porto, Portugal
Description
Supporting Information for a draft of the article titled Exploring the Chemical Space of Antiparasitic Peptides and Discovery of New Promising Leads through a Novel Approach based on Network Science and Similarity Searching.
We explain the contents of this material below:
Supporting information 1 (SI1): FASTA files of 550, 415, and 405 antiparasitic peptides (APPs) to generate networks for this study. Also, this folder contains FASTA files of the five benchmarking datasets of APPs/non-APPs used to compare the performance of our multi-query similarity searching models (mQSSMs) between them and with the algorithms previously reported in the literature.
Supporting information 2 (SI2): MS word file with tables of parameters of similarity threshold analysis for chemical space networks (CSNs) and half-space proximal networks (HSPNs) of APPs; common APPs in the top 50 of most central nodes from CSN and HSPN retrieved by weighted degree, hub-bridge, harmonic, and betweenness centrality measures; the number and community membership percentage of the most central APPs by weighted degree, hub-bridge, harmonic, and betweenness centrality measures; and query sets and the number of queries from the mQSSMs to find novel APPs for CSN, HSPN, and CSN-HSPN.
Supporting information 3 (SI3): Graphml files of all the networks created in this study.
Supporting information 4 (SI4): Excel file with normalized weighted degree, hub-bridge, harmonic, and betweenness centrality measures for CSN and HSPN.
Supporting information 5 (SI5): FASTA files of the most central and non-redundant APPs by each type of network and centrality measures, or the query sets.
Supporting information 6 (SI6): Excel files with results of mQSSMs (Output Predictions).
Supporting information 7 (SI7): This folder has three kinds of files, SI7-A that contains three folders with original results for each mQSSM generated, as well an excel file with statistical parameters. SI7-B is an excel file with the performance parameter of the best 21 mQSSMs proposed, as well as the ranking of these models. Finally, SI7-C and SI7-D are pdf files with results of multiple comparisons of our mQSSMs and with literature algorithms, respectively.
Supporting information 8 (SI8): FASTA files of 95 lead compounds and communities obtained from the CSN of these compounds.
Supporting information 9 (SI9): PowerPoint file with alignments and sequence logos of 95 lead compounds and communities obtained from the CSN of these peptides.
Files
SI1_APPs_and_Validation_Datasets_Fasta_Files.zip
Files
(7.2 MB)
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