Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms

Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques

Protein family classification using multiple-class neural networks.

The objective of genomic sequence analysis is to retrieve important information from the vast amount of genomic sequence data, such as DNA, RNA and protein sequences. The main task includes the interpretation of the function of DNA sequence on a genomic scale, the comparisons among genomes to gain insight into the universality of biological mechanisms and into the details of gene structure and function, the determination of the structure of all proteins and protein family classification. With its many features and capabilities for recognition, generalization and classification, artificial neural network technology is well suited for sequence analysis. At the state of the art, many methods have been devised to determine if a given protein sequence is member of a given protein superfamily. This is a binary classification problem, and efficient neural network techniques are mentioned in literature for solving such problem. In this Master\u27s thesis, we consider the problem of classifying given protein sequences into one among at least three protein families using neural networks, and, propose two methods: Pair-wise Multiple Classification Approach and Single Network Approach for this problem. In Pair-wise Multiple Classification Approach , several sub-networks are employed to perform the task whereas a compact network system is used in Single Network Approach . We performed experiments, using SNNS and UOWNNS neural network simulator on our NNs with different input/output representation, and reported accuracies as high as 95%. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Z54. Source: Masters Abstracts International, Volume: 43-01, page: 0248. Adviser: Alioune Ngom. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Improving protein succinylation sites prediction using embeddings from protein language model

Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease