Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

<p>Abstract</p> <p>Background</p> <p>All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences.</p> <p>Results</p> <p>The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the non-amyloidogenic prediction accuracy is 75.00%.</p> <p>Conclusions</p> <p>This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general.</p

Categorization of 77 dystrophin exons into 5 groups by a decision tree using indexes of splicing regulatory factors as decision markers

<p>Abstract</p> <p>Background</p> <p>Duchenne muscular dystrophy, a fatal muscle-wasting disease, is characterized by dystrophin deficiency caused by mutations in the <it>dystrophin </it>gene. Skipping of a target <it>dystrophin </it>exon during splicing with antisense oligonucleotides is attracting much attention as the most plausible way to express dystrophin in DMD. Antisense oligonucleotides have been designed against splicing regulatory sequences such as splicing enhancer sequences of target exons. Recently, we reported that a chemical kinase inhibitor specifically enhances the skipping of mutated <it>dystrophin </it>exon 31, indicating the existence of exon-specific splicing regulatory systems. However, the basis for such individual regulatory systems is largely unknown. Here, we categorized the <it>dystrophin </it>exons in terms of their splicing regulatory factors.</p> <p>Results</p> <p>Using a computer-based machine learning system, we first constructed a decision tree separating 77 authentic from 14 known cryptic exons using 25 indexes of splicing regulatory factors as decision markers. We evaluated the classification accuracy of a novel cryptic exon (exon 11a) identified in this study. However, the tree mislabeled exon 11a as a true exon. Therefore, we re-constructed the decision tree to separate all 15 cryptic exons. The revised decision tree categorized the 77 authentic exons into five groups. Furthermore, all nine disease-associated novel exons were successfully categorized as exons, validating the decision tree. One group, consisting of 30 exons, was characterized by a high density of exonic splicing enhancer sequences. This suggests that AOs targeting splicing enhancer sequences would efficiently induce skipping of exons belonging to this group.</p> <p>Conclusions</p> <p>The decision tree categorized the 77 authentic exons into five groups. Our classification may help to establish the strategy for exon skipping therapy for Duchenne muscular dystrophy.</p

Breast cancer vaccination comes to age: impacts of bioinformatics

Introduction: Breast cancer, as one of the major causes of cancer death among women, is the central focus of this study. The recent advances in the development and application of computational tools and bioinformatics in the field of immunotherapy of malignancies such as breast cancer have emerged the new dominion of immunoinformatics, and therefore, next generation of immunomedicines. Methods: Having reviewed the most recent works on the applications of computational tools, we provide comprehensive insights into the breast cancer incidence and its leading causes as well as immunotherapy approaches and the future trends. Furthermore, we discuss the impacts of bioinformatics on different stages of vaccine design for the breast cancer, which can be used to produce much more efficient vaccines through a rationalized time- and cost-effective in silico approaches prior to conducting costly experiments. Results: The tools can be significantly used for designing the immune system-modulating drugs and vaccines based on in silico approaches prior to in vitro and in vivo experimental evaluations. Application of immunoinformatics in the cancer immunotherapy has shown its success in the pre-clinical models. This success returns back to the impacts of several powerful computational approaches developed during the last decade. Conclusion: Despite the invention of a number of vaccines for the cancer immunotherapy, more computational and clinical trials are required to design much more efficient vaccines against various malignancies, including breast cancer

Preclinical Testing of Individualised Therapy Strategies for Pancreatic Cancer

Pancreatic cancer (PC) is a lethal disease with marked genetic heterogeneity. There is an immediate need to improve therapeutic options for patients in the metastatic setting. First, this thesis has summarised and pooled all available randomised data on the current chemotherapy regimens for PC and identified a need for a personalised approach using predictive biomarkers. Second, using the genomic data gained from the International Cancer Genome Consortium (ICGC), ROCK-1, a gene involved in cellular proliferation, metastasis formation and angiogenesis was identified as a potentially targetable aberration. The second and third chapters of this thesis outline the results of the work examining the effects of ROCK-1 inhibition in vitro and in vivo using unique patient-derived xenografts and cell lines including a model of metastatic disease. Two small-molecule inhibitors were examined, Y-27632 and fasudil. The results suggest that ROCK-1 inhibition has anti-cancer effects in vivo and can reduce tumour volume, prolong overall survival and prolong time to metastasis formation. ROCK-1 inhibition in combination with chemotherapy, is a promising therapeutic strategy in PC