Missense mutations account for more than 50% of the mutations known to be involved in human inherited diseases. Missense classification is a challenging task that involves sequencing of the genome, identifying the variations, and assessing their deleteriousness. This is a very laborious, time and cost intensive task to be carried out in the laboratory. Advancements in bioinformatics have led to several large-scale next-generation genome sequencing projects, and subsequently the identification of genome variations. Several studies have combined this data with information on established deleterious and neutral variants to develop machine learning based classifiers. There are significant issues with the missense classifiers due to which missense classification is still an open area of research. These issues can be classified under two broad categories: (a) Dataset overlap issue - where the performance estimates reported by the state-of-the-art classifiers are overly optimistic as they have often been evaluated on datasets that have significant overlaps with their training datasets. Also, there is no comparative analysis of these tools using a common benchmark dataset that contains no overlap with the training datasets, therefore making it impossible to identify the best classifier among them. Also, such a common benchmark dataset is not available. (b) Inadequate capture of vital biological information of the protein and mutations - such as conservation of long-range amino acid dependencies, changes in certain physico-chemical properties of the wild-type and mutant amino acids, due to the mutation. It is also not clear how to extract and use this information. Also, some classifiers use structural information that is not available for all proteins. In this study, we compiled a new dataset, containing around 2 - 15% overlap with the popularly used training datasets, with 18,036 mutations in 5,642 proteins. We reviewed and evaluated 15 state-of-the-art missense classifiers - SIFT, PANTHER, PROVEAN, PhD-SNP, Mutation Assessor, FATHMM, SNPs&GO, SNPs&GO3D, nsSNPAnalyzer, PolyPhen-2, SNAP, MutPred, PON-P2, CONDEL and MetaSNP, using the six metrics - accuracy, sensitivity, specificity, precision, NPV and MCC. When evaluated on our dataset, we observe huge performance drops from what has been claimed. Average drop in the performance for these 13 classifiers are around 15% in accuracy, 17% in sensitivity, 14% in specificity, 7% in NPV, 24% in precision and 30% in MCC. With this we show that the performance of these tools is not consistent on different datasets, and thus not reliable for practical use in a clinical setting. As we observed that the performance of the existing classifiers is poor in general, we tried to develop a new classifier that is robust and performs consistently across datasets, and better than the state-of-the-art classifiers. We developed a novel method of capturing long-range amino acid dependency conservation by boosting the conservation frequencies of substrings of amino acids of various lengths around the mutation position using AdaBoost learning algorithm. This score alone performed equivalently to the sequence conservation based tools in classifying missense mutations. Popularly used sequence conservation properties was combined with this boosted long-range dependency conservation scores using AdaBoost algorithm. This reduced the class bias, and improved the overall accuracy of the classifier. We trained a third classifier by incorporating changes in 21 important physico-chemical properties, due to the mutation. In this case, we observed that the overall performance further improved and the class bias further reduced. The performance of our final classifier is comparable with the state-of-the-art classifiers. We did not find any significant improvement, but the class-specific accuracies and precisions are marginally better by around 1-2% than those of the existing classifiers. In order to understand our classifier better, we dissected our benchmark dataset into: (a) seen and unseen proteins, and (b) pure and mixed proteins, and analysed the performance in detail. Finally we concluded that our classifier performs consistently across each of these categories of seen, unseen, pure and mixed protein.

One of the primary challenges in clinical genetics is the interpretation of the numerous genetic variants identified through sequencing applications. Assessing the impact of missense variants where only one amino acid is substituted is particularly difficult. In this study, we examined the structural characteristics of amino acids affected by missense substitutions in 26,690 pathogenic variants and compared them to 11,302 common variants found in the general population. This analysis was conducted across 6,747 protein structures. The residues were annotated using 7 protein features with a total of 35 feature subtypes. Subsequently, we assessed the burden of both common and pathogenic missense variants across these features. Additionally, we carried out separate analyses relative to protein function (with variants grouped in 24 protein functional classes) and relative to diseases (with variants grouped in 86 diseases). Through a comprehensive analysis of the entire dataset, we identified 25 pathogenic features that play a crucial role in the overall fitness and stability of proteins. Additionally, when we conducted individual analyses for 24 protein functional classes, we discovered specific features that are relevant to each function. For the disease analysis we identified 3 main clusters. Type I diseases primarily result from ordered mutations and are mainly affected by charge loss. This cluster is dominated by transporter protein class and includes diseases linked to X-chromosome. Type II diseases involve hydrolases and are characterized by enriched variants at the protein core, resulting in protein destabilization. Type III diseases involve extracellular matrix proteins (mainly collagen), are predominantly found in disordered regions, and are affected by charge gain and introduction of polar residues. Gly variants are particularly relevant in this cluster, as collagen proteins require Gly in every third residue in the collagen triple-helix. Considering the structural aspects when interpreting mutations associated with diseases offers valuable insights into their underlying mechanisms. Our work can serve as resource to delineate and understand variant pathogenicity by mapping a genetic variant into its structural context.

As Mucopolissacaridoses (MPS) são um grupo de doenças de armazenamento lisossômico causadas por deficiência de enzimas que catalisam a degradação gradual das glicosaminoglicanas (GAGs). GAGs (anteriormente chamadas de mucopolissacarídeos) são produtos de degradação das proteoglicanas que existem na matriz extracelular e tem efeito proteolítico. A classificação das MPS é baseada na deficiência enzimática específica. A MPS IVA é causada por mutações no gene que codifica a enzima GALNS (Nacetilgalactosamina-6-sulfatase), a qual desempenha um papel crucial na degradação do sulfato de queratano e condroitina-6-sulfatase. As mutações na enzima se resumem em três categorias: interrupção do sítio ativo, alterações no núcleo hidrofóbico e exposição da superfície, onde mutações missense na estrutura podem afetar gravemente a atividade da proteína GALNS, alterando seu núcleo hidrofóbico ou modificando seu enovelamento (folding). Com a falta de tratamentos efetivos, sendo em sua maioria paliativos, e tendo como base a estrutura já resolvida da GLANS selvagem, este trabalho teve como objetivo modelar 3 variantes da enzima GALNs, sendo uma mutação no sítio ativo, uma no núcleo hidrofóbico e uma na superfície. Foi usado o software MODELLER 9.12 para a modelagem comparativa, os softwares Prochek, PROSA II, ERRATv2, Verify3d, ProQ para a avaliação dos modelos, o software NAND 2.10, para simulação de dinâmica molecular e o software Chimera 1.10.1 para cálculo de superfícies eletrostáticas e hidrofobicidade da superfície. Os modelos apresentaram bons resultados segundo os softwares de avaliação e análise visual. Apresentaram poucas diferenças estruturais em relação à estrutura da GALNS selvagem, demonstraram estabilidade em simulação de dinâmica molecular. Entretanto, algumas diferenças foram observadas com relação à distribuição de cargas e hidrofobicidade no sítio ativo do modelo da variante com mutação no sítio ativo. Pôde ser concluído que as 3 mutações analisadas não causaram alterações estruturais significativas, não interferiram na estabilidade estrutural em simulação de dinâmica molecular, entretanto, foi demonstrado que mutações na região do sítio ativo podem interferir na função da enzima. / The Mucopolysaccharidoses (MPS) are a group of lysosomal storage diseases caused by deficiencies in enzymes that catalyze the gradual glycosaminoglycans (GAGs) degradation. GAGs (formerly called mucopolysaccharides) are products of proteoglycan degradation that exist in the extracellular matrix and have proteolytic effect. The classification of MPS is based on the specific enzyme deficiency. MPS IVA is caused by mutations in the gene that encodes the GALNS enzyme (Nacetilgalactosamina-6-sulfatase), which plays a crucial role in the degradation of keratan sulfate and chondroitin-6-sulfatase. Mutations in the enzyme can be summarized in three categories: interruption of the active site, changes in the hydrophobic core and display surface, where missense mutations in the structure can seriously affect the activity of GALNS protein, changing its hydrophobic core or modifying its folding. With the lack of effective treatments, in its most palliative, and based on the wild GALNS structure already determined, this study aimed to model 3 variants of GALNS enzyme, a mutation in the active site, one in the hydrophobic core and a on the surface. 9.12 MODELLER was used for comparative modeling software, the software Prochek, Prose II, ERRATv2, Verify3d, ProQ models for the evaluation of the NAND 2.10 software, for molecular dynamics simulation and software Chimera 1.10.1 calculates electrostatic and hydrophobic surface. The models showed good results according to the evaluation software and visual analysis. Presented few structural differences from the wild GALNS structure and showed stability in molecular dynamics simulation. However, some differences were observed with respect to the charge distribution and hydrophobicity in the active site of the variants of the model with a mutation in the active site. It might be concluded that the three mutations analyzed did not cause significant structural changes and did not affect the structural stability in molecular dynamics simulation, however, it has been shown that mutations in the active site region may interfere with the function of this enzyme.

As Mucopolissacaridoses (MPS) são um grupo de doenças de armazenamento lisossômico causadas por deficiência de enzimas que catalisam a degradação gradual das glicosaminoglicanas (GAGs). GAGs (anteriormente chamadas de mucopolissacarídeos) são produtos de degradação das proteoglicanas que existem na matriz extracelular e tem efeito proteolítico. A classificação das MPS é baseada na deficiência enzimática específica. A MPS IVA é causada por mutações no gene que codifica a enzima GALNS (Nacetilgalactosamina-6-sulfatase), a qual desempenha um papel crucial na degradação do sulfato de queratano e condroitina-6-sulfatase. As mutações na enzima se resumem em três categorias: interrupção do sítio ativo, alterações no núcleo hidrofóbico e exposição da superfície, onde mutações missense na estrutura podem afetar gravemente a atividade da proteína GALNS, alterando seu núcleo hidrofóbico ou modificando seu enovelamento (folding). Com a falta de tratamentos efetivos, sendo em sua maioria paliativos, e tendo como base a estrutura já resolvida da GLANS selvagem, este trabalho teve como objetivo modelar 3 variantes da enzima GALNs, sendo uma mutação no sítio ativo, uma no núcleo hidrofóbico e uma na superfície. Foi usado o software MODELLER 9.12 para a modelagem comparativa, os softwares Prochek, PROSA II, ERRATv2, Verify3d, ProQ para a avaliação dos modelos, o software NAND 2.10, para simulação de dinâmica molecular e o software Chimera 1.10.1 para cálculo de superfícies eletrostáticas e hidrofobicidade da superfície. Os modelos apresentaram bons resultados segundo os softwares de avaliação e análise visual. Apresentaram poucas diferenças estruturais em relação à estrutura da GALNS selvagem, demonstraram estabilidade em simulação de dinâmica molecular. Entretanto, algumas diferenças foram observadas com relação à distribuição de cargas e hidrofobicidade no sítio ativo do modelo da variante com mutação no sítio ativo. Pôde ser concluído que as 3 mutações analisadas não causaram alterações estruturais significativas, não interferiram na estabilidade estrutural em simulação de dinâmica molecular, entretanto, foi demonstrado que mutações na região do sítio ativo podem interferir na função da enzima. / The Mucopolysaccharidoses (MPS) are a group of lysosomal storage diseases caused by deficiencies in enzymes that catalyze the gradual glycosaminoglycans (GAGs) degradation. GAGs (formerly called mucopolysaccharides) are products of proteoglycan degradation that exist in the extracellular matrix and have proteolytic effect. The classification of MPS is based on the specific enzyme deficiency. MPS IVA is caused by mutations in the gene that encodes the GALNS enzyme (Nacetilgalactosamina-6-sulfatase), which plays a crucial role in the degradation of keratan sulfate and chondroitin-6-sulfatase. Mutations in the enzyme can be summarized in three categories: interruption of the active site, changes in the hydrophobic core and display surface, where missense mutations in the structure can seriously affect the activity of GALNS protein, changing its hydrophobic core or modifying its folding. With the lack of effective treatments, in its most palliative, and based on the wild GALNS structure already determined, this study aimed to model 3 variants of GALNS enzyme, a mutation in the active site, one in the hydrophobic core and a on the surface. 9.12 MODELLER was used for comparative modeling software, the software Prochek, Prose II, ERRATv2, Verify3d, ProQ models for the evaluation of the NAND 2.10 software, for molecular dynamics simulation and software Chimera 1.10.1 calculates electrostatic and hydrophobic surface. The models showed good results according to the evaluation software and visual analysis. Presented few structural differences from the wild GALNS structure and showed stability in molecular dynamics simulation. However, some differences were observed with respect to the charge distribution and hydrophobicity in the active site of the variants of the model with a mutation in the active site. It might be concluded that the three mutations analyzed did not cause significant structural changes and did not affect the structural stability in molecular dynamics simulation, however, it has been shown that mutations in the active site region may interfere with the function of this enzyme.

First published online: November 1, 2010 / 名古屋大学博士学位論文 学位の種類 : 博士(医学)(課程) 学位授与年月日:平成23年3月25日 久島周氏の博士論文として提出された

Abstract Breast cancer is a common malignancy affecting women around the world. Approximately 10 percent of breast cancer patients have a hereditary form of the disease. Women with an inherited alteration in one of the BRCA1 and BRCA2 genes have an increased risk of developing these cancers at a young age (before menopause), and often have multiple family members with the disease. A total of 6 families with multiple cases of breast cancer were identified from southern Taiwan, and five of these families were found to have missense mutations in the BRCA1 or BRCA2 genes. One novel missense mutation of A5885C (Gln1886Pro), as well as new silent mutation of A4806G (Thr1526), in the exon 11 of the BRCA2 gene was found in one(A) family. The second(F) family was found to have three missense mutations of C2731T (Pro871Leu), A3232G (Glu1038Gly) and A3667G (Lys1183Arg) in the exon 11 of the BRCA1 gene. It is very unusual to have three previously reported BRCA1 mutations in the same family and these three mutations are located on the same chromosome. Two missense mutations of A3232G (Glu1038Gly) in exon 11 and A4956G (Ser1613Gly) in exon 16, as well as silent mutations of T2430C (Leu771) and T4427C (Ser1436), of the BRCA1 gene were found in the third(E) family. The missense mutation of A4956G (Ser1613Gly) in exon 16, as well as silent mutation of T4427C (Ser1436), of the BRCA1 are found in the fourth(C) and fifth(D) family. The sixth(B) families were found to possess only one silent mutation of T4035C (Val1269) in the BRCA2 gene. The amino acid changes might cause the protein structure unstable and these could explain the moderate role of BRCA mutations in the pathogenesis of breast cancer.

Diss. (sammanfattning) Stockholm : Karol. inst., 2005. / Härtill 5 uppsatser.

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (leaves 76-87).

No description available.

The peroxisome is a subcellular organelle that carries out a diverse range of metabolic functions, including the b-oxidation of very long chain fatty acids, the breakdown of peroxide and the a-oxidation of fatty acids. Disruption of peroxisome metabolic functions leads to severe disease in humans. These diseases can be broadly grouped into two categories: those in which a single enzyme is defective, and those known as the peroxisome biogenesis disorders (PBDs), which result from a generalised failure to import peroxisomal matrix proteins (and consequently result in disruption of multiple metabolic pathways). The PBDs result from mutations in PEX genes, which encode protein products called peroxins, required for the normal biogenesis of the peroxisome. PEX1 encodes an AAA ATPase that is essential for peroxisome biogenesis, and mutations in PEX1 are the most common cause of PBDs worldwide. This study focused on the identification of mutations in PEX1 in an Australasian cohort of PBD patients, and the impact of these mutations on PEX1 function. As a result of the studies presented in this thesis, twelve mutations in PEX1 were identified in the Australasian cohort of patients. The identified mutations can be broadly grouped into three categories: missense mutations, mutations directly introducing a premature termination codon (PTC) and mutations that interrupt the reading frame of PEX1. The missense mutations that were identified were R798G, G843D, I989T and R998Q; all of these mutations affect amino acid residues located in the AAA domains of the PEX1 protein. Two mutations that directly introduce PTCs into the PEX1 transcript (R790X and R998X), and four frameshift mutations (A302fs, I370fs, I700fs and S797fs) were identified. There was also one mutation found in an intronic region (IVS22-19A>G) that is presumed to affect splicing of the PEX1 mRNA. Three of these mutations, G843D, I700fs and G973fs, were found at high frequency in this patient cohort. At the commencement of these studies, it was hypothesised that missense mutations would result in attenuation of PEX1 function, but mutations that introduced PTCs, either directly or indirectly, would have a deleterious effect on PEX1 function. Mutations introducing PTCs are thought to cause mRNA to be degraded by the nonsense-mediated decay of mRNA (NMD) pathway, and thus result in a decrease in PEX1 protein levels. The studies on the cellular impact of the identified PEX1 mutations were consistent with these hypotheses. Missense mutations were found to reduce peroxisomal protein import and PEX1 protein levels, but a residual level of function remained. PTC-generating mutations were found to have a major impact on PEX1 function, with PEX1 mRNA and protein levels being drastically reduced, and peroxisomal protein import capability abolished. Patients with two missense mutations showed the least impact on PEX1 function, patients with two PTC-generating mutations had a severe defect in PEX1 function, and patients carrying a combination of a missense mutation and a PTC-generating mutation showed levels of PEX1 function that were intermediate between these extremes. Thus, a correlation between PEX1 genotype and phenotype was defined for the Australasian cohort of patients investigated in these studies. For a number of patients, mutations in the coding sequence of one PEX1 allele could not be identified. Analysis of the 5' UTR of this gene was therefore pursued for potential novel mutations. The initial analyses demonstrated that the 5' end of PEX1 extended further than previously reported. Two co-segregating polymorphisms were also identified, termed –137 T>C and –53C>G. The -137T>C polymorphism resided in an upstream, in-frame ATG (termed ATG1), and the possibility that the additional sequence represented PEX1 coding sequence was examined. While both ATGs were found to be functional by virtue of in vitro and in vivo expression investigations, Western blot analysis of the PEX1 protein in patient and control cell extracts indicated that physiological translation of PEX1 was from the second ATG only. Using a luciferase reporter approach, the additional sequence was found to exhibit promoter activity. When examined alone the -137T>C polymorphism exerted a detrimental effect on PEX1 promoter activity, reducing activity to half that of wild-type levels, and the -53C>G polymorphism increased PEX1 promoter activity by 25%. When co-expressed (mimicking the physiological condition) these polymorphisms compensated for each other to bring PEX1 promoter activity to near wild-type levels. The PEX1 mutations identified in this study have been utilised by collaborators at the National Referral Laboratory for Lysosomal, Peroxisomal and Related Genetic Disorders (based at the Women's and Children's Hospital, Adelaide), in prenatal diagnosis of the PBDs. In addition, the identification of three common mutations in Australasian PBD patients has led to the implementation of screening for these mutations in newly referred patients, often enabling a precise diagnosis of a PBD to be made. Finally, the strong correlation between genotype and phenotype for the patient cohort investigated as part of these studies has generated a basis for the assessment of newly identified mutations in PEX1.