Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) in the Human Interleukin 12B Gene: Identification and Structural Characterization

Background: Interleukin -12B (IL12B) polymorphism has been identified as a factor in the development of various Immunological disorders and cancer. The objective of this study was to identify the non-synonymous SNPs (nsSNPs) with the strongest predicted negative impact on the function of the IL12B protein.Methods: We employed a variety of computational methods, including SIFT, PolyPhen2, PROVEAN, SNAP2 to determine the functional impact of nsSNPs. Also, In order to investigate the potential association of nsSNPs in the IL12B gene with disease, a computational analysis was conducted using PhD-SNP, SNP&GO, and Pmut. Additionally, I-mutant and MuPro were employed to predict protein stability, while ConSurf was used to identify functional domains and conserved amino acid residues within the protein. Furthermore, SOPMA was used in combination with Project Hope and MutPred2 to predict the impact of mutations on both the structure and function of proteins. Finally, we used GeneMania to analyze the gene-gene interactions of the IL12B gene with other genes.Results: Our results indicate that nine nsSNPs (G72C, G86C, C90R, C131S, Y136D, P235L, V254G, Y258H and P259S) were found to be potentially deleterious in the IL12B gene.Conclusion: Our study emphasizes the significance of identifying functional and structural polymorphisms in the IL12B gene, as they may reveal potential therapeutic targets and provide insight into the underlying mechanisms of related diseases. Further experimental investigation is necessary to fully explore the role of these nsSNPs in disease pathogenesis.Keywords: Interleukin 12B; deleterious nsSNPs; Polymorphisms.; Computational analysis

From genes to behavior: placing cognitive models in the context of biological pathways.

Connecting neural mechanisms of behavior to their underlying molecular and genetic substrates has important scientific and clinical implications. However, despite rapid growth in our knowledge of the functions and computational properties of neural circuitry underlying behavior in a number of important domains, there has been much less progress in extending this understanding to their molecular and genetic substrates, even in an age marked by exploding availability of genomic data. Here we describe recent advances in analytical strategies that aim to overcome two important challenges associated with studying the complex relationship between genes and behavior: (i) reducing distal behavioral phenotypes to a set of molecular, physiological, and neural processes that render them closer to the actions of genetic forces, and (ii) striking a balance between the competing demands of discovery and interpretability when dealing with genomic data containing up to millions of markers. Our proposed approach involves linking, on one hand, models of neural computations and circuits hypothesized to underlie behavior, and on the other hand, the set of the genes carrying out biochemical processes related to the functioning of these neural systems. In particular, we focus on the specific example of value-based decision-making, and discuss how such a combination allows researchers to leverage existing biological knowledge at both neural and genetic levels to advance our understanding of the neurogenetic mechanisms underlying behavior

Prevalence and frequency spectra of single nucleotide polymorphisms at exon-intron junctions of human genes

Includes bibliographical references (leaves 92-112).In humans and other higher eukaryotes the observation of multiple splice isoforms for a given gene is common. However it is not clear whether all of these alternatively spliced isoforms are a product of true alternative splicing or some are due to DNA sequence variations in human populations. Genetic variations that affect splicing have been shown to cause variation in splicing patterns and potentially are an important source of phenotypic variability among humans. Furthermore, variation in disease susceptibility and manifestation between individuals is often associated with genetic polymorphisms that determine the way in which genes are spliced. Hence, identification of genetic polymorphisms that might affect the way in which pre-mRNAs are spliced is an area of great interest

Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene

Here we report an in-silico approach for identification, characterization and validation of deleterious non-synonymous SNPs (nsSNPs) in the interleukin-8 gene using three steps. In first step, sequence homology-based genetic analysis of a set of 50 coding SNPs associated with 41 rsIDs using SIFT (Sorting Intolerant from Tolerant) and PROVEAN (Protein Variation Effect Analyzer) identified 23 nsSNPs to be putatively damaging/deleterious in at least one of the two tools used. Subsequently, structure-homology based PolyPhen-2 (Polymorphism Phenotyping) analysis predicted 9 of 23 nsSNPs (K4T, E31A, E31K, S41Y, I55N, P59L, P59S, L70P and V88D) to be damaging. According to the conditional hypothesis for the study, only nsSNPs that score damaging/deleterious prediction in both sequence and structural homology-based approach will be considered as 'high-confidence' nsSNPs. In step 2, based on conservation of amino acid residues, stability analysis, structural superimposition, RSMD and docking analysis, the possible structural-functional relationship was ascertained for high-confidence nsSNPs. Finally, in a separate analysis (step 3), the IL-8 deregulation has also appeared to be an important prognostic marker for detection of patients with gastric and lung cancer. This study, for the first time, provided in-depth insights on the effects of amino acid substitutions on IL-8 protein structure, function and disease association

Computational molecular analysis of deleterious mutations in serum amyloid A3 gene in goats and cattle

Serum amyloid A3 (SAA3) protein found within caprine and bovine mammary epithelial cells is said to be important in disease conditions and tissue remodeling. The present investigation aimed at identifying deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in SAA3 gene of goats and cattle using an in silico assay. Amino acid sequence data of the protein of goats and SNPs of cattle were retrieved from the National Centre for Biotechnology Information (NCBI) database. Bioinformatics prediction tools used for the detection of deleterious nsSNPs were PROVEAN, SIFT, PolyPhe-2 and PANTHER. A total of eleven nsSNPs were obtained from the aligned sequences of goats, out of which two variants (R123G and G126D) were predicted to be deleterious by three out of the four algorithms. However, in cattle, four out of the eleven nsSNPs were found to be harmful to the transcribed protein. The two mutants in goats and R114Q in cattle were also found to decrease protein stability. Further confirmatory analysis however, revealed that variant R123G was highly deleterious as there were marked differences between it and the native protein in terms of total free energy, stabilizing residues, ordered and disordered regions of protein and secondary structure prediction. Similarly, Cmutant (a combination of R123G and G126D mutations) in goats and Dmutant (a combination of S77R, Q84K, S103W and R114Q mutations) in cattle also appeared to distort SAA3 protein structural landscape and function. The present deleterious nsSNPs when validated using wet lab experimental protocols could be important biological markers for disease detection and therapy in goats and cattle.Keywords: protein, variant, prediction, marker, ruminant

Insight into the evolution of a plant pathogen: Comparative genomic analysis of the fungal maize pathogen Colletotrichum graminicola

[ES] Uno de los mayores problemas para el desarrollo de estrategias efectivas frente a hongos fitopatógenos es que, al igual que la mayoría de los microorganismos, presentan una gran plasticidad fenotípica y una extraordinaria capacidad de adaptarse a nuevos ambientes y/o de infectar nuevos huéspedes. En este contexto, resulta imprescindible determinar qué genes (u otras regiones del genoma) muestran evidencias de evolución adaptativa con el objetivo de identificar secuencias involucradas en virulencia. La evolución adaptativa puede ser descripta como la retención de cambios en el genoma que, de alguna manera, confieren ventajas adaptativas los individuos que los poseen. Las evidencias de evolución adaptativa pueden ser de distintos tipos: selección positiva actuando en secuencias homólogas, ganancia y pérdida de genes y/o reordenaciones estructurales en el genoma. Entre las enfermedades vegetales causadas por los hongos, la antracnosis es una de las más destructivas, causando pérdidas significativas en cultivos en los cinco continentes. Los agentes etiológicos mejor conocidos de la enfermedad son ciertas especies del hongo filamentoso Colletotrichum, que infecta prácticamente todas las familias de plantas de interés agronómico u hortícola, con la consecuencia de pérdidas económicas significativas. Además, los hongos del género Colletotrichum representan importantes modelos experimentales en estudios relacionados con diversos aspectos de la fitopatología, como ser enzimas degradadoras de carbohidratos, procesos infectivos, resistencia del hospedador y biología molecular de las interacciones planta-patógeno. Muchas de las especies del género Colletotrichum presentan una forma de nutrición denominada hemibiotrofía, por lo que exhiben características tanto biotróficas como necrotróficas. En la presente Tesis Doctoral se han utilizado numerosas aproximaciones bioinformáticas para atender a diversas hipótesis relativas a la evolución y biología molecular de la patogenicidad en hongos filamentosos, haciendo hincapié en el patosistema C. graminicola-maíz. Para esto, nos hemos basado en el análisis de los genomas de ocho cepas de C. graminicola, una de ellas la recientemente secuenciada M1.001 y las restantes siete resecuenciadas por nuestro grupo. En el primer capítulo de la Tesis (Chapter I) se describen los procedimientos llevados a cabo para la secuenciación, ensamblaje y anotación de los nuevos genomas, como así también las características fenotípicas de dichos aislados. Por otra parte, por medio de análisis de los genomas, se presentan resultados concernientes a cuatro aspectos fundamentales de la genómica comparada de estos organismos: recombinación, relaciones filogenéticas, variaciones estructurales del genoma y ganancia-pérdida de genes. En el segundo capítulo (Chapter II) se analizan los patrones de selección natural actuando sobre secuencias codificantes y no codificantes de proteínas a nivel de todo el genoma. En el tercer capítulo (Chapter III) se detallan los resultados de la aplicación de una metodología bioinformática para la detección y anotación de ARNs no codificantes (ncRNAs) fúngicos a partir de secuencias públicamente disponibles. Finalmente, en el cuarto capítulo (Chapter IV), se analizan los patrones de selección positiva actuando sobre genes que codifican proteínas relacionadas con las defensas de las plantas entre miembros de las Poáceas. En general, la presente Tesis Doctoral ofrece un recurso importante para los fitopatólogos moleculares, ya que podría servir como guía para el análisis bioinformático de genomas de hongos fitopatógenos en busca de dianas para el desarrollo de estrategias de control. A su vez, el presente trabajo contribuye a mejorar nuestros conocimientos acerca de los procesos moleculares y evolutivos que tienen lugar durante las interacciones planta-patógeno

Computational and Experimental Approaches to Reveal the Effects of Single Nucleotide Polymorphisms with Respect to Disease Diagnostics

DNA mutations are the cause of many human diseases and they are the reason for natural differences among individuals by affecting the structure, function, interactions, and other properties of DNA and expressed proteins. The ability to predict whether a given mutation is disease-causing or harmless is of great importance for the early detection of patients with a high risk of developing a particular disease and would pave the way for personalized medicine and diagnostics. Here we review existing methods and techniques to study and predict the effects of DNA mutations from three different perspectives: in silico, in vitro and in vivo. It is emphasized that the problem is complicated and successful detection of a pathogenic mutation frequently requires a combination of several methods and a knowledge of the biological phenomena associated with the corresponding macromolecules

PRETICTIVE BIOINFORMATIC METHODS FOR ANALYZING GENES AND PROTEINS

Since large amounts of biological data are generated using various high-throughput technologies, efficient computational methods are important for understanding the biological meanings behind the complex data. Machine learning is particularly appealing for biological knowledge discovery. Tissue-specific gene expression and protein sumoylation play essential roles in the cell and are implicated in many human diseases. Protein destabilization is a common mechanism by which mutations cause human diseases. In this study, machine learning approaches were developed for predicting human tissue-specific genes, protein sumoylation sites and protein stability changes upon single amino acid substitutions. Relevant biological features were selected for input vector encoding, and machine learning algorithms, including Random Forests and Support Vector Machines, were used for classifier construction. The results suggest that the approaches give rise to more accurate predictions than previous studies and can provide valuable information for further experimental studies. Moreover, seeSUMO and MuStab web servers were developed to make the classifiers accessible to the biological research community. Structure-based methods can be used to predict the effects of amino acid substitutions on protein function and stability. The nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) located at the protein binding interface have dramatic effects on protein-protein interactions. To model the effects, the nsSNPs at the interfaces of 264 protein-protein complexes were mapped on the protein structures using homology-based methods. The results suggest that disease-causing nsSNPs tend to destabilize the electrostatic component of the binding energy and nsSNPs at conserved positions have significant effects on binding energy changes. The structure-based approach was developed to quantitatively assess the effects of amino acid substitutions on protein stability and protein-protein interaction. It was shown that the structure-based analysis could help elucidate the mechanisms by which mutations cause human genetic disorders. These new bioinformatic methods can be used to analyze some interesting genes and proteins for human genetic research and improve our understanding of their molecular mechanisms underlying human diseases