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      Analysis of High Molecular Mass Compounds from the Spider Pamphobeteus verdolaga Venom Gland. A Transcriptomic and MS ID Approach

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          Abstract

          Nowadays, spider venom research focuses on the neurotoxic activity of small peptides. In this study, we investigated high-molecular-mass compounds that have either enzymatic activity or housekeeping functions present in either the venom gland or venom of Pamphobeteus verdolaga. We used proteomic and transcriptomic-assisted approaches to recognize the proteins sequences related to high-molecular-mass compounds present in either venom gland or venom. We report the amino acid sequences (partial or complete) of 45 high-molecular-mass compounds detected by transcriptomics showing similarity to other proteins with either enzymatic activity (i.e., phospholipases A 2, kunitz-type, hyaluronidases, and sphingomyelinase D) or housekeeping functions involved in the signaling process, glucanotransferase function, and beta-N-acetylglucosaminidase activity. MS/MS analysis showed fragments exhibiting a resemblance similarity with different sequences detected by transcriptomics corresponding to sphingomyelinase D, hyaluronidase, lycotoxins, cysteine-rich secretory proteins, and kunitz-type serine protease inhibitors, among others. Additionally, we report a probably new protein sequence corresponding to the lycotoxin family detected by transcriptomics. The phylogeny analysis suggested that P. verdolaga includes a basal protein that underwent a duplication event that gave origin to the lycotoxin proteins reported for Lycosa sp. This approach allows proposing an evolutionary relationship of high-molecular-mass proteins among P. verdolaga and other spider species.

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            Interactive Tree Of Life (iTOL) v4: recent updates and new developments

            Abstract The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the display, manipulation and annotation of phylogenetic and other trees. It is freely available and open to everyone. The current version introduces four new dataset types, together with numerous new features. Annotation options have been expanded and new control options added for many display elements. An interactive spreadsheet-like editor has been implemented, providing dataset creation and editing directly in the web interface. Font support has been rewritten with full support for UTF-8 character encoding throughout the user interface. Google Web Fonts are now fully supported in the tree text labels. iTOL v4 is the first tool which supports direct visualization of Qiime 2 trees and associated annotations. The user account system has been streamlined and expanded with new navigation options, and currently handles >700 000 trees from more than 40 000 individual users. Full batch access has been implemented allowing programmatic upload and export of trees and annotations.
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              SignalP 5.0 improves signal peptide predictions using deep neural networks

              Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
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                Author and article information

                Journal
                Toxins (Basel)
                Toxins (Basel)
                toxins
                Toxins
                MDPI
                2072-6651
                29 June 2021
                July 2021
                : 13
                : 7
                : 453
                Affiliations
                [1 ]Grupo de Toxinología—Serpentario, Facultad de Ciencias Farmacéuticas y Alimentarias, Universidad de Antioquia UdeA, Carrera 53 No. 61-30, Medellín 050010, Colombia; cesar.segura@ 123456udea.edu.co
                [2 ]Facultad de Ciencias Farmacéuticas y Alimentarias, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín 050010, Colombia
                [3 ]Facultad de Medicina, Universidad Cooperativa de Colombia, Calle 50 A No. 41-20, Medellín 050012, Colombia; leidy.vargasmu@ 123456campusucc.edu.co
                [4 ]Unidad de Espectrometría de Masas, Sede de Investigación Universitaria, Universidad de Antioquia UdeA, Carrera 53 No. 61-30, Medellín 050010, Colombia
                [5 ]Centro de Investigación en Recursos Naturales y Sustentabilidad, Universidad Bernardo O’Higgins, Avenida Viel 1497, Santiago 7750000, Chile; msaldarriaga@ 123456docente.ubo.cl
                [6 ]Grupo de Génetica, Regeneración y Cáncer, Universidad de Antioquia UdeA, Carrera 53 No. 61-30, Medellín 050010, Colombia; claudia.arenas@ 123456udea.edu.co
                Author notes
                [* ]Correspondence: sebastian.estrada@ 123456udea.edu.co ; Tel.: +57-(4)-2192315; Fax: +57-(4)-2631914
                [†]

                Current address: Vidarium, Nutrition, Health and Wellness Research Center, Nutresa Business Group, Calle 8 Sur No. 50-67, Medellín 050023, Colombia.

                Author information
                https://orcid.org/0000-0003-1332-5106
                https://orcid.org/0000-0002-9979-8416
                https://orcid.org/0000-0002-4768-4919
                https://orcid.org/0000-0001-7905-5052
                Article
                toxins-13-00453
                10.3390/toxins13070453
                8309857
                34209760
                f2a6043c-c0ca-4bbb-89d4-cebd22f96116
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 21 April 2021
                : 08 June 2021
                Categories
                Article

                Molecular medicine
                theraphosidae,pamphobeteus,transcriptomic,high-molecular-mass compounds,phospholipases,kunitz-type,hyaluronidases,lycotoxins

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