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      Computational Analysis and Low-Scale Constitutive Expression of Laccases Synthetic Genes GlLCC1 from Ganoderma lucidum and POXA 1B from Pleurotus ostreatus in Pichia pastoris

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          Abstract

          Lacasses are multicopper oxidases that can catalyze aromatic and non-aromatic compounds concomitantly with reduction of molecular oxygen to water. Fungal laccases have generated a growing interest due to their biotechnological potential applications, such as lignocellulosic material delignification, biopulping and biobleaching, wastewater treatment, and transformation of toxic organic pollutants. In this work we selected fungal genes encoding for laccase enzymes GlLCC1 in Ganoderma lucidum and POXA 1B in Pleurotus ostreatus. These genes were optimized for codon use, GC content, and regions generating secondary structures. Laccase proposed computational models, and their interaction with ABTS [2, 2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)] substrate was evaluated by molecular docking. Synthetic genes were cloned under the control of Pichia pastoris glyceraldehyde-3-phosphate dehydrogenase (GAP) constitutive promoter. P. pastoris X-33 was transformed with pGAPZαA- LaccGluc-Stop and pGAPZαA- LaccPost-Stop constructs. Optimization reduced GC content by 47 and 49% for LaccGluc-Stop and LaccPost-Stop genes, respectively. A codon adaptation index of 0.84 was obtained for both genes. 3D structure analysis using SuperPose revealed LaccGluc-Stop is similar to the laccase crystallographic structure 1GYC of Trametes versicolor. Interaction analysis of the 3D models validated through ABTS, demonstrated higher substrate affinity for LaccPost-Stop, in agreement with our experimental results with enzymatic activities of 451.08 ± 6.46 UL -1 compared to activities of 0.13 ± 0.028 UL -1 for LaccGluc-Stop. This study demonstrated that G. lucidum GlLCC1 and P. ostreatus POXA 1B gene optimization resulted in constitutive gene expression under GAP promoter and α-factor leader in P. pastoris. These are important findings in light of recombinant enzyme expression system utility for environmentally friendly designed expression systems, because of the wide range of substrates that laccases can transform. This contributes to a great gamut of products in diverse settings: industry, clinical and chemical use, and environmental applications.

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          CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues

          Cavities on a proteins surface as well as specific amino acid positioning within it create the physicochemical properties needed for a protein to perform its function. CASTp () is an online tool that locates and measures pockets and voids on 3D protein structures. This new version of CASTp includes annotated functional information of specific residues on the protein structure. The annotations are derived from the Protein Data Bank (PDB), Swiss-Prot, as well as Online Mendelian Inheritance in Man (OMIM), the latter contains information on the variant single nucleotide polymorphisms (SNPs) that are known to cause disease. These annotated residues are mapped to surface pockets, interior voids or other regions of the PDB structures. We use a semi-global pair-wise sequence alignment method to obtain sequence mapping between entries in Swiss-Prot, OMIM and entries in PDB. The updated CASTp web server can be used to study surface features, functional regions and specific roles of key residues of proteins.
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            Protein structure prediction and structural genomics.

            Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics.
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              QMEAN: A comprehensive scoring function for model quality assessment.

              In protein structure prediction, a considerable number of alternative models are usually produced from which subsequently the final model has to be selected. Thus, a scoring function for the identification of the best model within an ensemble of alternative models is a key component of most protein structure prediction pipelines. QMEAN, which stands for Qualitative Model Energy ANalysis, is a composite scoring function describing the major geometrical aspects of protein structures. Five different structural descriptors are used. The local geometry is analyzed by a new kind of torsion angle potential over three consecutive amino acids. A secondary structure-specific distance-dependent pairwise residue-level potential is used to assess long-range interactions. A solvation potential describes the burial status of the residues. Two simple terms describing the agreement of predicted and calculated secondary structure and solvent accessibility, respectively, are also included. A variety of different implementations are investigated and several approaches to combine and optimize them are discussed. QMEAN was tested on several standard decoy sets including a molecular dynamics simulation decoy set as well as on a comprehensive data set of totally 22,420 models from server predictions for the 95 targets of CASP7. In a comparison to five well-established model quality assessment programs, QMEAN shows a statistically significant improvement over nearly all quality measures describing the ability of the scoring function to identify the native structure and to discriminate good from bad models. The three-residue torsion angle potential turned out to be very effective in recognizing the native fold. (c) 2007 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2015
                22 January 2015
                : 10
                : 1
                : e0116524
                Affiliations
                [1 ]Laboratorio de Biotecnología Molecular, Grupo de Biotecnología Ambiental e Industrial (GBAI), Departamento de Microbiología, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, D.C., Colombia
                [2 ]Laboratorio de Microbiología Ambiental y Suelos, Grupo de Biotecnología Ambiental e Industrial (GBAI), Departamento de Microbiología, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, D.C., Colombia
                [3 ]Departamento de Química y Grupo de Investigación en Genética, Biodiversidad y Manejo de Ecosistemas (GEBIOME), Facultad de Ciencias Exactas y Naturales, Universidad de Caldas, Manizales-Caldas, Colombia
                [4 ]Escuela de Ciencias Biológicas, Facultad de Ciencias Básicas, Universidad Pedagógica y Tecnológica de Colombia (UPTC), Tunja-Boyacá, Colombia
                [5 ]Departamento de Química, Facultad de Ciencias, Universidad Nacional de Colombia (UNAL), Bogotá, D.C., Colombia
                [6 ]Departamento de Biotecnología y Bioingeniería, Centro de Investigaciones y de Estudios Avanzados del Instituto Politécnico Nacional (IPN), México, D.F., México
                National Centre for Cell Science, INDIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: RAPP AMPR EARG. Performed the experiments: CMRH EDMA SAPC EARG AMCB. Analyzed the data: CMRH EDMA SAPC EARG RAPP EARM RRV EARM AMPR. Wrote the paper: CMRH EDMA RAPP.

                Article
                PONE-D-14-13173
                10.1371/journal.pone.0116524
                4303304
                25611746
                6426f45c-0b86-4cbe-a795-f28204b023c6
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 28 March 2014
                : 24 November 2014
                Page count
                Figures: 6, Tables: 4, Pages: 21
                Funding
                This work was supported by Pontificia Universidad Javeriana, Bogotá, D.C. Colombia (Grants 00004334 and 00004335) and by Universidad Nacional de Colombia, Bogotá, D.C. Colombia (grant DIB 12929). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are within the paper and Supporting Information files.

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