Physical characterization of ZnO precursors

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2020, Tutora: Anna Maria Vilà ArbonèsZinc oxide (ZnO) is an important semiconductor material with multiple applications in piezo-electric transducers, spin functional devices and gas sensors, for instance. In this article, we study optical and structural properties of ZnO thin films obtained from 4 precursors deposited by sol-gel on glass substrates and heated to 4 different temperatures each one. The absorption coefficient and energy band gap were determined from UV-visible absorption spectrum (200 – 1100 nm). The luminescence properties were evaluated from photoluminescence spectrum (350 – 850 nm). The amount of impurities in the final material were obtained from elemental analysis. The energy band gap was found to vary between 3.00 – 3.25 eV, depending on the precursor and the annealing temperature. We discuss and relate the different processes with the impurities and structure of the samples. SEM images of the thin films were added to complete the stud

Preservation of protein clefts in comparative models

Additional material: 5 supplementary files.[Background] Comparative, or homology, modelling of protein structures is the most widely used prediction method when the target protein has homologues of known structure. Given that the quality of a model may vary greatly, several studies have been devoted to identifying the factors that influence modelling results. These studies usually consider the protein as a whole, and only a few provide a separate discussion of the behaviour of biologically relevant features of the protein. Given the value of the latter for many applications, here we extended previous work by analysing the preservation of native protein clefts in homology models. We chose to examine clefts because of their role in protein function/structure, as they are usually the locus of protein-protein interactions, host the enzymes' active site, or, in the case of protein domains, can also be the locus of domain-domain interactions that lead to the structure of the whole protein.[Results] We studied how the largest cleft of a protein varies in comparative models. To this end, we analysed a set of 53507 homology models that cover the whole sequence identity range, with a special emphasis on medium and low similarities. More precisely we examined how cleft quality – measured using six complementary parameters related to both global shape and local atomic environment, depends on the sequence identity between target and template proteins. In addition to this general analysis, we also explored the impact of a number of factors on cleft quality, and found that the relationship between quality and sequence identity varies depending on cleft rank amongst the set of protein clefts (when ordered according to size), and number of aligned residues.[Conclusion] We have examined cleft quality in homology models at a range of seq.id. levels. Our results provide a detailed view of how quality is affected by distinct parameters and thus may help the user of comparative modelling to determine the final quality and applicability of his/her cleft models. In addition, the large variability in model quality that we observed within each sequence bin, with good models present even at low sequence identities (between 20% and 30%), indicates that properly developed identification methods could be used to recover good cleft models in this sequence range.XdC acknowledges funding from the Spanish government (Grants BIO2003-09327, BIO2006-15557) and the Wellcome Trust (Research Collaboration Grant 069878/Z/02/Z). DP acknowledges economical support from the Government of Catalonia and SL from the Consejo Superior de Investigaciones Científicas.Peer reviewe

Alternative Splicing of Transcription Factors' Genes: Beyond the Increase of Proteome Diversity

Functional modification of transcription regulators may lead to developmental changes and phenotypical differences between species. In this work, we study the influence of alternative splicing on transcription factors in human and mouse. Our results show that the impact of alternative splicing on transcription factors is similar in both species, meaning that the ways to increase variability should also be similar. However, when looking at the expression patterns of transcription factors, we observe that they tend to diverge regardless of the role of alternative splicing. Finally, we hypothesise that transcription regulation of alternatively spliced transcription factors could play an important role in the phenotypical differences between species, without discarding other phenomena or functional families

Contributions of Structure Comparison Methods to the Protein Structure Prediction Field

Nowadays it is difficult to imagine an area of knowledge that can continue developing without the use of computers and informatics. It is not different with biology, that has seen an unpredictable growth in recent decades, with the rise of a new discipline, bioinformatics, bringing together molecular biology, biotechnology and information technology. More recently, the development of high throughput techniques, such as microarray, mass spectrometry and DNA sequencing, has increased the need of computational support to collect, store, retrieve, analyze, and correlate huge data sets of complex information. On the other hand, the growth of the computational power for processing and storage has also increased the necessity for deeper knowledge in the field. The development of bioinformatics has allowed now the emergence of systems biology, the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behavior of a living being. This book presents some theoretical issues, reviews, and a variety of bioinformatics applications. For better understanding, the chapters were grouped in two parts. In Part I, the chapters are more oriented towards literature review and theoretical issues. Part II consists of application-oriented chapters that report case studies in which a specific biological problem is treated with bioinformatics tools

A procedure for identifying homologous alternative splicing events

<p>Abstract</p> <p>Background</p> <p>The study of the functional role of alternative splice isoforms of a gene is a very active area of research in biology. The difficulty of the experimental approach (in particular, in its high-throughput version) leaves ample room for the development of bioinformatics tools that can provide a useful first picture of the problem. Among the possible approaches, one of the simplest is to follow classical protein function annotation protocols and annotate target alternative splice events with the information available from conserved events in other species. However, the application of this protocol requires a procedure capable of recognising such events. Here we present a simple but accurate method developed for this purpose.</p> <p>Results</p> <p>We have developed a method for identifying homologous, or equivalent, alternative splicing events, based on the combined use of neural networks and sequence searches. The procedure comprises four steps: (i) BLAST search for homologues of the two isoforms defining the target alternative splicing event; (ii) construction of all possible candidate events; (iii) scoring of the latter with a series of neural networks; and (iv) filtering of the results. When tested in a set of 473 manually annotated pairs of homologous events, our method showed a good performance, with an accuracy of 0.99, a precision of 0.98 and a sensitivity of 0.93. When no candidates were available, the specificity of our method varied between 0.81 and 0.91.</p> <p>Conclusion</p> <p>The method described in this article allows the identification of homologous alternative splicing events, with a good success rate, indicating that such method could be used for the development of functional annotation of alternative splice isoforms.</p

Development of pathogenicity predictors specific for variants that do not comply with clinical guidelines for the use of computational evidence

Predictors de patogenicitat in silico; Variants missense; Seqüenciació de nova generacióPredictores de patogenicidad in silico; Variantes missense; Secuenciación de nueva generaciónIn silico pathogenicity predictors; Missense variants; Next-generation sequencingBackground Strict guidelines delimit the use of computational information in the clinical setting, due to the still moderate accuracy of in silico tools. These guidelines indicate that several tools should always be used and that full coincidence between them is required if we want to consider their results as supporting evidence in medical decision processes. Application of this simple rule certainly decreases the error rate of in silico pathogenicity assignments. However, when predictors disagree this rule results in the rejection of potentially valuable information for a number of variants. In this work, we focus on these variants of the protein sequence and develop specific predictors to help improve the success rate of their annotation. Results We have used a set of 59,442 protein sequence variants (15,723 pathological and 43,719 neutral) from 228 proteins to identify those cases for which pathogenicity predictors disagree. We have repeated this process for all the possible combinations of five known methods (SIFT, PolyPhen-2, PON-P2, CADD and MutationTaster2). For each resulting subset we have trained a specific pathogenicity predictor. We find that these specific predictors are able to discriminate between neutral and pathogenic variants, with a success rate different from random. They tend to outperform the constitutive methods but this trend decreases as the performance of the constitutive predictor improves (e.g. with PON-P2 and PolyPhen-2). We also find that specific methods outperform standard consensus methods (Condel and CAROL). Conclusion Focusing development efforts on the case of variants for which known methods disagree we may obtain pathogenicity predictors with improved performances. Although we have not yet reached the success rate that allows the use of this computational evidence in a clinical setting, the simplicity of the approach indicates that more advanced methods may reach this goal in a close future.This work has been supported by the spanish Ministerio de Economía y Competitividad (BIO2012–40133; SAF2016–80255-R). It has also been supported, and the publication costs have been defrayed, by the European Regional Development Fund (ERDF), through the Interreg V-A Spain-France-Andorra programme (POCTEFA 2014–2020), research grant PIREPRED (EFA086/15)

The functional modulation of epigenetic regulators by alternative splicing

<p>Abstract</p> <p>Background</p> <p>Epigenetic regulators (histone acetyltransferases, methyltransferases, chromatin-remodelling enzymes, etc) play a fundamental role in the control of gene expression by modifying the local state of chromatin. However, due to their recent discovery, little is yet known about their own regulation. This paper addresses this point, focusing on alternative splicing regulation, a mechanism already known to play an important role in other protein families, e.g. transcription factors, membrane receptors, etc.</p> <p>Results</p> <p>To this end, we compiled the data available on the presence/absence of alternative splicing for a set of 160 different epigenetic regulators, taking advantage of the relatively large amount of unexplored data on alternative splicing available in public databases. We found that 49 % (70 % in human) of these genes express more than one transcript. We then studied their alternative splicing patterns, focusing on those changes affecting the enzyme's domain composition. In general, we found that these sequence changes correspond to different mechanisms, either repressing the enzyme's function (e.g. by creating dominant-negative inhibitors of the functional isoform) or creating isoforms with new functions.</p> <p>Conclusion</p> <p>We conclude that alternative splicing of epigenetic regulators can be an important tool for the function modulation of these enzymes. Considering that the latter control the transcriptional state of large sets of genes, we propose that epigenetic regulation of gene expression is itself strongly regulated by alternative splicing.</p

Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2: In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants

Endofenotip; Prediccions de patogenicitat; Predictor específic de proteïnaEndofenotipo; Predicciones de patogenicidad; Predictor específico de proteínaEndophenotype; Pathogenicity predictions; Protein-specific predictorThe present limitations in the pathogenicity prediction of BRCA1 and BRCA2 (BRCA1/2) missense variants constitute an important problem with negative consequences for the diagnosis of hereditary breast and ovarian cancer. However, it has been proposed that the use of endophenotype predictions, i.e., computational estimates of the outcomes of functional assays, can be a good option to address this bottleneck. The application of this idea to the BRCA1/2 variants in the CAGI 5-ENIGMA international challenge has shown promising results. Here, we developed this approach, exploring the predictive performances of the regression models applied to the BRCA1/2 variants for which the values of the homology-directed DNA repair and saturation genome editing assays are available. Our results first showed that we can generate endophenotype estimates using a few molecular-level properties. Second, we show that the accuracy of these estimates is enough to obtain pathogenicity predictions comparable to those of many standard tools. Third, endophenotype-based predictions are complementary to, but do not outperform, those of a Random Forest model trained using variant pathogenicity annotations instead of endophenotype values. In summary, our results confirmed the usefulness of the endophenotype approach for the pathogenicity prediction of the BRCA1/2 missense variants, suggesting different options for future improvements.This research was funded by the EU European Regional Development Fund (ERDF) through the Program Interreg V-A Spain-France-Andorra (POCTEFA), grant number EFA086/15-PIREPRED, by the Spanish Ministerio de Ciencia e Innovación, grant number PID2019-111217RB-I00, and by the Spanish Ministerio de Economía y Competitividad, grant number SAF2016-80255-R

Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2 : In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants

The present limitations in the pathogenicity prediction of BRCA1 and BRCA2 (BRCA1/2) missense variants constitute an important problem with negative consequences for the diagnosis of hereditary breast and ovarian cancer. However, it has been proposed that the use of endophenotype predictions, i.e., computational estimates of the outcomes of functional assays, can be a good option to address this bottleneck. The application of this idea to the BRCA1/2 variants in the CAGI 5-ENIGMA international challenge has shown promising results. Here, we developed this approach, exploring the predictive performances of the regression models applied to the BRCA1/2 variants for which the values of the homology-directed DNA repair and saturation genome editing assays are available. Our results first showed that we can generate endophenotype estimates using a few molecular-level properties. Second, we show that the accuracy of these estimates is enough to obtain pathogenicity predictions comparable to those of many standard tools. Third, endophenotype-based predictions are complementary to, but do not outperform, those of a Random Forest model trained using variant pathogenicity annotations instead of endophenotype values. In summary, our results confirmed the usefulness of the endophenotype approach for the pathogenicity prediction of the BRCA1/2 missense variants, suggesting different options for future improvement

PMut2: a web-based tool for predicting pathological mutations on proteins

Amino acid substitutions in proteins can result in an altered phenotype which might lead to a disease. PMut2 is a method that can predict whether a mutation has a pathological effect on the protein function. It uses current machine learning algorithms based on protein sequence derived information. The accuracy of PMut2 is as high as 82%, with a Matthews correlation coefficient of 0,62. PMut2 predictions can be obtained through a modern website which also allows to apply the same machine learning methodology that is used to train PMut2 to custom training sets, allowing users to build their own tailor-made predictors