36 research outputs found

    Overlapping Clusters and Support Vector Machines Based Interval Type-2 Fuzzy System for the Prediction of Peptide Binding Affinity

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    In the post-genome era, it is becoming more complex to process high dimensional, low-instance available, and nonlinear biological datasets. This paper aims to address these characteristics as they have adverse effects on the performance of predictive models in bioinformatics. In this paper, an interval type-2 Takagi Sugeno fuzzy predictive model is proposed in order to manage high-dimensionality and nonlinearity of such datasets which is the common feature in bioinformatics. A new clustering framework is proposed for this purpose to simplify antecedent operations for an interval type-2 fuzzy system. This new clustering framework is based on overlapping regions between the clusters. The cluster analysis of partitions and statistical information derived from them has identified the upper and lower membership functions forming the premise part. This is further enhanced by adapting the regression version of support vector machines in the consequent part. The proposed method is used in experiments to quantitatively predict affinities of peptide bindings to biomolecules. This case study imposes a challenge in post-genome studies and remains an open problem due to the complexity of the biological system, diversity of peptides, and curse of dimensionality of amino acid index representation characterizing the peptides. Utilizing four different peptide binding affinity datasets, the proposed method resulted in better generalization ability for all of them yielding an improved prediction accuracy of up to 58.2% on unseen peptides in comparison with the predictive methods presented in the literature. Source code of the algorithm is available at https://github.com/sekerbigdatalab

    Overlapping Clusters and Support Vector Machines based Interval Type-2 Fuzzy System for the Prediction of Peptide Binding Affinity

    Get PDF
    In the post-genome era, it is becoming more complex to process high-dimensional, low-instance available and nonlinear biological datasets. This study aims at addressing these characteristics as they have adverse effects on the performance of predictive models in bioinformatics. In this paper, an interval type-2 Takagi Sugeno fuzzy predictive model is proposed in order to manage high-dimensionality and nonlinearity of such datasets which is the common feature in bioinformatics. A new clustering framework is proposed for this purpose to simplify antecedent operations for an interval type-2 fuzzy system. This new clustering framework is based on overlapping regions between the clusters. The cluster analysis of partitions and statistical information derived from them have identified the upper and lower membership functions forming the premise part. This is further enhanced by adapting the regression version of support vector machines in the consequent part. The proposed method is used in experiments to quantitatively predict affinities of peptide bindings to biomolecules. This case study imposes a challenge in post-genome studies and remains an open problem due to the complexity of the biological system, diversity of peptides and curse of dimensionality of amino acid index representation characterising the peptides. Utilizing four different peptide binding affinity datasets, the proposed method resulted in better generalisation ability for all of them yielding an improved prediction accuracy of up to 58.2% on unseen peptides in comparison with the predictive methods presented in the literature

    Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction

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    Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a cell that is otherwise obscured. The objective of this dissertation is to develop machine learning based effective tools to predict disordered protein, its properties and dynamics, and interaction paradigm by systematically mining and analyzing large-scale biological data. In this dissertation, we propose a robust framework to predict disordered proteins given only sequence information, using an optimized SVM with RBF kernel. Through appropriate reasoning, we highlight the structure-like behavior of IDPs in disease-associated complexes. Further, we develop a fast and effective predictor of Accessible Surface Area (ASA) of protein residues, a useful structural property that defines protein’s exposure to partners, using regularized regression with 3rd-degree polynomial kernel function and genetic algorithm. As a key outcome of this research, we then introduce a novel method to extract position specific energy (PSEE) of protein residues by modeling the pairwise thermodynamic interactions and hydrophobic effect. PSEE is found to be an effective feature in identifying the enthalpy-gain of the folded state of a protein and otherwise the neutral state of the unstructured proteins. Moreover, we study the peptide-protein transient interactions that involve the induced folding of short peptides through disorder-to-order conformational changes to bind to an appropriate partner. A suite of predictors is developed to identify the residue-patterns of Peptide-Recognition Domains from protein sequence that can recognize and bind to the peptide-motifs and phospho-peptides with post-translational-modifications (PTMs) of amino acid, responsible for critical human diseases, using the stacked generalization ensemble technique. The involved biologically relevant case-studies demonstrate possibilities of discovering new knowledge using the developed tools

    Development of an intrabody capable of activating interferon regulatory factor-1 (IRF-1) and identification of IRF-1-binding peptide motifs

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    Interferon regulatory factor 1 (IRF-1) is a tumour suppressor protein and transcription factor. It has been shown to modulate target gene expression in response to stimuli, which include viral infection and DNA damage, and to be down-regulated in several forms of cancer. This thesis details the development of an intrabody, an intracellular antibody, that binds specifically to endogenous IRF-1. The binding of the intrabody to IRF-1 enhanced transcription from IRF-1-responsive reporter gene constructs and endogenous promoters, thus it was shown to activate IRF-1. Intrabody binding also increased the rate at which IRF-1 was degraded, suggesting that the intrabody epitope may be regulating both IRF-1 activity and turnover. These results were supported point mutation within the intrabody epitope (P325 to A) as the resultant mutant also displayed both a higher transcriptional activity and increased rate of degradation. In an effort to understand the mechanisms which regulate IRF-1 activity a search for novel IRF-1-interacting proteins was carried out using phage peptide display. This in vitro technique enables the identification of peptides able to bind a specific target protein. The sequence of these peptides can then be used to search protein databases for homologous, full-length proteins that could also bind the target protein. This led to the identification of an IRF-1-binding peptide that held sequence similar to a region of Zinc Finger 350 (ZNF350), a transcription factor involved in regulating the DNA damage response. Subsequently, endogenous ZNF350 and IRF-1 were co-immunoprecipitated from a human cancer cell line. The extreme C-terminus of IRF-1 was shown to be sufficient for an interaction with ZNF350, although a second, more N-terminal site was also shown to be essential for a stable intracellular interaction. This data sheds new light on the role of the extreme C-terminus of IRF-1 in modulating the protein‟s activity. This study also provides new and IRF-1-specific molecular tools, in the form of intrabodies and IRF-1-binding peptides, which could be used in the future to further characterise the activity and regulation of this tumour suppressor protein

    Genetic studies of hereditary myeloproliferative disorders

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    More than 300 billion blood cells are being replaced daily in a process called hematopoiesis. Hematopoiesis is orchestrated by hematopoietic stem cells (HSCs) residing in the bone marrow. HSCs produce multipotent and lineage-restricted progenitors, that are responsible for the supply of mature blood cells. Production of blood cells is governed by hematopoietic growth factors that are required for the survival and proliferation of blood cells at all stages of development. Mutations in genes responsible for the regulation of this fine-tuned system cause aberrant proliferation of different blood compartments. Myeloproliferative neoplasms (MPN) are characterized by the abnormal expansion of erythroid, megakaryocytic, and myeloid lineages, that is caused either by somatic mutations or by germline mutations transmitted through Mendelian inheritance within the family. The main topic of my doctoral research was the investigation of two distinct pedigrees diagnosed with erythrocytosis and thrombocytosis, respectively. Erythrocytosis occurred in ten individuals of Norwegian family that presented elevated hemoglobin and erythropoietin (EPO) serum levels. We performed genome-wide linkage analysis using SNP arrays coupled with targeted sequencing and identified a heterozygous single base deletion (ΔG) in exon 2 of the EPO gene as the sole candidate gene mutation in affected family members. EPO stimulates the proliferation of erythrocyte progenitors and prevents their apoptosis in order to produce mature erythrocytes. Surprisingly, ΔG introduces a frame-shift that generates a novel, 51-residue long polypeptide, which would predict a loss of erythropoietin function, and is at odds with the erythrocytosis phenotype. To elucidate the mechanism by which the loss-of-function mutation causes gain-off function phenotype, we utilized the CRISPR/Cas9 genome editing to introduce the ΔG mutation into Hep3B cells, a human hepatoma cell line that expresses EPO. We found that cells with ΔG mutation produce excessive amounts of biologically active EPO and reproduces the observation form the affected family members. On the molecular level, in addition to the known transcript originating from the physiologic promoter (P1), we identified novel transcripts that initiate in intron 1 of EPO from a putative alternative promoter (P2). Further functional analysis of P2 mRNAs revealed an alternative translational start site in exon 2 that P2 transcripts use to produce a biologically active EPO protein, by fusing a novel N-terminus to the EPO coding sequence through the ΔG single base deletion. Our data demonstrate for the first time, that a mutation in EPO cause familial erythrocytosis and explain how the ΔG mutation results in a gain-function phenotype. I also investigated a pedigree with autosomal-dominant. Targeted sequencing identified a novel activating mutation in exon 3 of the thrombopoietin (THPO) gene, a single nucleotide G->T substitution. Thrombopoietin stimulates the production of platelets from megakaryocytes. THPO expression is regulated on the translational level by seven upstream open reading frames (uORF1-7) in the exons 1-3 of THPO mRNA, that are interfering with the translation of TPO. G>T mutation maps to the Kozak sequence of the uORF7, the most critical negative regulator of TPO translation. We performed TPO overexpression and in vitro translation experiments to demonstrate that the G>T mutation disrupts the negative regulation governed by uORF7 and allows for increased translation of THPO protein coding sequence, ultimately causing thrombocytosis. Collectively, in both studies we identified novel gain-of-function mutations in hematopoietic growth factors, that act at different steps of gene expression and result in the dysregulated production of EPO and TPO, causing erythrocytosis and thrombocytosis in respective pedigrees

    Transcriptomic And Pharmacological Characterization Of A Murine Line Harboring An Antidepressant-Insensitive Serotonin Transporter

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    Perinatal depression is a complex mood disorder often treated with Selective Serotonin Reuptake Inhibitor (SSRI) antidepressants, which block the serotonin transporter (SERT) and alter synaptic serotonin concentrations. The serotonergic system is highly plastic and serotonin regulates the growth and maturation of neurons and signaling systems from conception through end of life. Notably, interference with serotonin levels during an SSRI sensitive window (SSW) of development in humans and rodents has been linked to neurovegetative effects which increase susceptibility for the manifestation of anxiety- and depressive-like behaviors and can be persistent throughout life. We hypothesize the persistent behavioral perturbations resulting from acute SSRI challenge during the SSW are the culmination of gene expression changes altering epigenetic modifications in the genome. Historically, inferences have been drawn from comparing postnatal SSRI treated wild- type (WT) mice to those that lack a functioning SERT. This model is problematic, as SERT knock-out (KO) mice display numerous physiological deficits prior to the SSW due to the trophic developmental role of serotonin. In this study, we attempt to characterize a more appropriate mouse model by utilizing RNA-Seq analysis to compare midbrain and hippocampus transcriptomes of WT and KO mice to the transgenic SERT I172M mouse that harbors a fully functioning transporter while rendering SERT resistant to many SSRIs. We further characterized pertinent metabolites to determine brain levels, and if I172M selectivity was maintained and revealed, desmethylcitalopram accumulates in the brain over time and not all SSRI metabolites maintain I172M selectivity. The primary focus of this study was the transcriptomic characterization of the I172M mouse, which is necessary before beginning postnatal SSRI treatment studies. Overall our findings unveil KO mice have significant aberrant neuronal signaling and neurotransmitter metabolism. Moreover, KO mice display dysregulated protein and RNA processing due to midbrain aggregation of misfolded truncated SERT protein and ectopic hippocampal Sert mRNA expression, respectively. Interestingly, comparison of differentially expressed genes in the midbrain and hippocampus of WT/I172M and WT/KO revealed that the Sert locus may share expression quantitative trait loci with Neurexin 1 (Nrxn1) and the transcription factor D- Box Binding PAR BZIP (Dbp), which is independent of Sert expression. Collectively, our data demonstrate WT and I172M mice share similar transcriptomes and validate the ability of the I172M model, over SERT KO, to appropriately detect SERT-SSRI mediated versus non-SSRI SERT mediated changes in response to postnatal SSRI exposure

    Insights into alpha-synuclein and TorsinA biology

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2013.Cataloged from PDF version of thesis. Vita.Includes bibliographical references.The yeast Saccharomyces cerevisiae has long been used to model complex cellular processes. As a eukaryote, much of its fundamental biology is conserved with higher organisms. As a single-celled, genetically tractable organism, it can easily be utilized for both high-throughput screening and hypothesis-driven analysis. Therefore, many groups use yeast to model disease-related proteins. One such model utilizes heterologous expression of [alpha]-synuclein ([alpha]-syn), a protein implicated in the progression of Parkinson's disease and other synucleinopathies. [alpha]-Syn expression in yeast is associated with many phenotypes that are recapitulated in higher organisms. Here, I used yeast to characterize two naturally occurring splice isoforms of [alpha]-syn, [alpha]-syn[delta]4 and [alpha]-syn[delta]6. Levels of these isoforms vary between synucleinopathies but little is known about their biology. I found that these splice isoforms display different localization patterns than full-length [alpha]-syn ([alpha]-synFL) and are less toxic in yeast. However, when expressed at a high level, both splice isoforms can exert toxicity and affect similar processes to [alpha]-synFL. Interestingly, the splice isoforms show differential responses to perturbations in sterol homeostasis. Studies concerning the relationship between sterol levels and synucleinopathy progression have been contradictory. Our findings reveal that [alpha]-syn[delta]4 is less sensitive to changes in sterol levels than [alpha]-synFL and [alpha]-syn[delta]6, suggesting that change in [alpha]-syn splice isoforms levels is a potential mechanism for these conflicting results. I also describe an attempt to model torsinA pathobiology in yeast. Mutations in torsinA cause early onset torsion dystonia, a devastating motor disorder. This protein has been described to function in regulating endoplasmic reticulum (ER) stress through the unfolded protein response (UPR). While I was unable to recapitulate a role for torsinA in the UPR in yeast, this model can serve as a platform for discovery of torsinA cofactors that enable it to act in this capacity, especially as more is uncovered concerning torsinA's role in the UPR. This thesis highlights both the strengths and limitations of modeling disease proteins in yeast. More specifically, my success with [alpha]-syn splice isoforms may provide insight into synucleinopathy etiology, while my inability to model torsinA-induced toxicity can inform subsequent attempts to study disease related proteins in yeast.by Julie S. Valastyan.Ph.D

    In silico molecular modelling and design of heme-containing peroxidases for industrial applications

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    It is widely known that the development of modern chemistry and the consequent world industrialization have improved our quality of life to unimaginable levels. However, these advances have come with a high cost, causing environmental, health and societal concerns. As a consequence, during the past two decades a growing need has appeared to update the traditional chemistry industry processes towards greener and efficient alternatives. Along these lines, the use of enzymes has shown to be a suitable alternative to conventional industrial chemical processes. Enzymes are life-essential proteins that catalyze biochemical reaction and show several advantages over conventional chemical catalysts: they work under milder conditions, which decrease the energy requirements and consequently the capital costs of reactions; They show a high degree of selectivity and catalytic efficiency; and in addition, they are inherently non-hazardous, reusable and biodegradable catalysts, making them ideal environmentally friendly reagents. However, the main bottleneck for taking more benefit of enzymes in an industrial context is the lack of biocatalysts with the required selectivity, availability, and compatibility with industrial rigorous process conditions, and because of this, the development of enhanced enzymes by means of enzyme engineering is a main research field nowadays. Along these lines, in silico methodologies have progressively turned into highly valuable tools for the study and design of enzymatic systems, due to their unique potential to offer atomic and electronic-level insights into biocatalysts’ activity. Moreover, the continuous software and hardware improvements, and the cost-effectiveness and rapidness generally associated with these methods, make them very appealing for their application to the real problems that face the industry. Motivated by the advances on computational techniques and by the ease of obtaining valuable experimental data, which has been provided by our collaborators, the main goal of this thesis has been to understand the mechanisms of reaction of the heme-containing peroxidases under study (Auricularia auricula-judae DyP and Agrocybe aegerita UPO). Moreover, the acquired knowledge has been used to evaluate experimentally obtained enzyme variants and to guide the design of new ones towards desired properties. In this way, distinct computational techniques at different levels of accuracy (e.g. PELE, QM/MM or MD calculations) have been used to unravel the atomic and electronic mechanistic details under peroxidases mechanisms (e.g. long range electron transfer pathways, peroxidation and peroxygenation mechanisms) and to rationalize the molecular determinants that guide yield and selectivity in both natural occurring and experimentally designed peroxidases. Furthermore, the better understanding of the molecular principles under enzyme activity, along with the use of in silico semi-rational redesign methods, has enabled us to tailor UPO enzyme towards the enhanced production of high-value chemicals.A causa del desenvolupament de la química moderna i de la consegüent industrialització del món, la nostra qualitat de vida ha millorat a uns nivells que creiem inimaginables. Malauradament, tots aquests avenços han vingut acompanyats de repercussions mediambientals, socials i de salut. Per això, en les dues últimes dècades s'ha percebut una creixent necessitat de reemplaçar els processos químics tradicionals per alternatives més ecològiques i eficients. En aquest sentit, els enzims han demostrat ser una alternativa molt plausible als processos químics convencionals que s'usen avui en dia en la indústria. Els enzims són proteïnes essencials per a la vida que catalitzen reaccions bioquímiques, i l'ús dels quals aporta múltiples avantatges en comparació a les tècniques convencionals: permeten treballar en condicions suaus, fet que disminueix els requisits energètics i conseqüentment els costos de les reaccions a nivells industrials; en general són molt selectius i eficients; i a més a més, són inherentment reutilitzables, segurs i biodegradables, fet que els converteix en reactius respectuosos amb el medi ambient. Tot i això, les seves aplicacions a nivells industrials encara són limitades degut a la baixa tolerància a substrats, la poca disponibilitat d'enzims i a l'escassa resistència a les severes condicions industrials. Per aquesta raó, avui en dia el desenvolupament d'enzims millorats és un camp d'investigació important. Particularment, les tècniques in silico de modelització molecular s'estan convertint cada vegada més en eines clau per a l'estudi i el disseny de biocatalitzadors degut al seu potencial per a obtenir informació, tant a escala electrònica com molecular, sobre els mecanismes d'acció enzimàtics. A més a més, millores en el software i el hardware, i la rapidesa i bona relació cost-qualitat que mostren aquests mètodes, els fan molt atractius per a resoldre els problemes reals de la indústria. Motivada pels avenços en les tècniques computacionals i per la facilitat d'obtenir dades experimentals, que han estat proporcionades pels nostres col·laboradors, l'objectiu principal d'aquesta tesi ha sigut entendre els mecanismes de reacció de les peroxidases (en particular la DyP del fong Auricularia aurícula-judae i la UPO del fong Agrocybe aegerita). D'altra banda, els coneixements adquirits durant aquest procés de racionalització s'han utilitzat per avaluar variants millorats d'enzims obtinguts experimentalment i per guiar el disseny de nous biocatalitzadors cap a les propietats desitjades. Així doncs, s'han utilitzat diferents tècniques computacionals, a diferents nivells de precisió (p. ex. PELE, QM/MM o MD), per tal de comprendre els mecanismes electrònics i moleculars responsables de diferents mecanismes en les peroxidases (p. ex., mecanismes de transferència electrònica de llarg abast, peroxidació o peroxigenació), i racionalitzar els determinants moleculars que guien el rendiment i la selectivitat tant en les peroxidases naturals com en aquelles millorades experimentalment. A banda d'això, la millor comprensió dels principis moleculars responsables de l'activitat enzimàtica, juntament amb l'ús de mètodes computacionals per al disseny d'enzims, ens ha permès adaptar l'enzim UPO cap a la producció de productes químics valuosos

    In silico molecular modelling and design of heme-containing peroxidases for industrial applications

    Get PDF
    [eng] It is widely known that the development of modern chemistry and the consequent world industrialization have improved our quality of life to unimaginable levels. However, these advances have come with a high cost, causing environmental, health and societal concerns. As a consequence, during the past two decades a growing need has appeared to update the traditional chemistry industry processes towards greener and efficient alternatives. Along these lines, the use of enzymes has shown to be a suitable alternative to conventional industrial chemical processes. Enzymes are life-essential proteins that catalyze biochemical reaction and show several advantages over conventional chemical catalysts: they work under milder conditions, which decrease the energy requirements and consequently the capital costs of reactions; They show a high degree of selectivity and catalytic efficiency; and in addition, they are inherently non-hazardous, reusable and biodegradable catalysts, making them ideal environmentally friendly reagents. However, the main bottleneck for taking more benefit of enzymes in an industrial context is the lack of biocatalysts with the required selectivity, availability, and compatibility with industrial rigorous process conditions, and because of this, the development of enhanced enzymes by means of enzyme engineering is a main research field nowadays. Along these lines, in silico methodologies have progressively turned into highly valuable tools for the study and design of enzymatic systems, due to their unique potential to offer atomic and electronic-level insights into biocatalysts’ activity. Moreover, the continuous software and hardware improvements, and the cost-effectiveness and rapidness generally associated with these methods, make them very appealing for their application to the real problems that face the industry. Motivated by the advances on computational techniques and by the ease of obtaining valuable experimental data, which has been provided by our collaborators, the main goal of this thesis has been to understand the mechanisms of reaction of the heme-containing peroxidases under study (Auricularia auricula-judae DyP and Agrocybe aegerita UPO). Moreover, the acquired knowledge has been used to evaluate experimentally obtained enzyme variants and to guide the design of new ones towards desired properties. In this way, distinct computational techniques at different levels of accuracy (e.g. PELE, QM/MM or MD calculations) have been used to unravel the atomic and electronic mechanistic details under peroxidases mechanisms (e.g. long range electron transfer pathways, peroxidation and peroxygenation mechanisms) and to rationalize the molecular determinants that guide yield and selectivity in both natural occurring and experimentally designed peroxidases. Furthermore, the better understanding of the molecular principles under enzyme activity, along with the use of in silico semi-rational redesign methods, has enabled us to tailor UPO enzyme towards the enhanced production of high-value chemicals.[cat] A causa del desenvolupament de la química moderna i de la consegüent industrialització del món, la nostra qualitat de vida ha millorat a uns nivells que creiem inimaginables. Malauradament, tots aquests avenços han vingut acompanyats de repercussions mediambientals, socials i de salut. Per això, en les dues últimes dècades s'ha percebut una creixent necessitat de reemplaçar els processos químics tradicionals per alternatives més ecològiques i eficients. En aquest sentit, els enzims han demostrat ser una alternativa molt plausible als processos químics convencionals que s'usen avui en dia en la indústria. Els enzims són proteïnes essencials per a la vida que catalitzen reaccions bioquímiques, i l'ús dels quals aporta múltiples avantatges en comparació a les tècniques convencionals: permeten treballar en condicions suaus, fet que disminueix els requisits energètics i conseqüentment els costos de les reaccions a nivells industrials; en general són molt selectius i eficients; i a més a més, són inherentment reutilitzables, segurs i biodegradables, fet que els converteix en reactius respectuosos amb el medi ambient. Tot i això, les seves aplicacions a nivells industrials encara són limitades degut a la baixa tolerància a substrats, la poca disponibilitat d'enzims i a l'escassa resistència a les severes condicions industrials. Per aquesta raó, avui en dia el desenvolupament d'enzims millorats és un camp d'investigació important. Particularment, les tècniques in silico de modelització molecular s'estan convertint cada vegada més en eines clau per a l'estudi i el disseny de biocatalitzadors degut al seu potencial per a obtenir informació, tant a escala electrònica com molecular, sobre els mecanismes d'acció enzimàtics. A més a més, millores en el software i el hardware, i la rapidesa i bona relació cost-qualitat que mostren aquests mètodes, els fan molt atractius per a resoldre els problemes reals de la indústria. Motivada pels avenços en les tècniques computacionals i per la facilitat d'obtenir dades experimentals, que han estat proporcionades pels nostres col·laboradors, l'objectiu principal d'aquesta tesi ha sigut entendre els mecanismes de reacció de les peroxidases (en particular la DyP del fong Auricularia aurícula-judae i la UPO del fong Agrocybe aegerita). D'altra banda, els coneixements adquirits durant aquest procés de racionalització s'han utilitzat per avaluar variants millorats d'enzims obtinguts experimentalment i per guiar el disseny de nous biocatalitzadors cap a les propietats desitjades. Així doncs, s'han utilitzat diferents tècniques computacionals, a diferents nivells de precisió (p. ex. PELE, QM/MM o MD), per tal de comprendre els mecanismes electrònics i moleculars responsables de diferents mecanismes en les peroxidases (p. ex., mecanismes de transferència electrònica de llarg abast, peroxidació o peroxigenació), i racionalitzar els determinants moleculars que guien el rendiment i la selectivitat tant en les peroxidases naturals com en aquelles millorades experimentalment. A banda d'això, la millor comprensió dels principis moleculars responsables de l'activitat enzimàtica, juntament amb l'ús de mètodes computacionals per al disseny d'enzims, ens ha permès adaptar l'enzim UPO cap a la producció de productes químics valuosos
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