Protein Fold Recognition Using Neural Networks

To predict accurately the three-dimensional (3D) structures of proteins from their amino acid sequences alone remains a challenging problem. However, using protein fold recognition tools, it is often possible to achieve good models or at least to gain some more information, to aid scientists in their research. This thesis describes development of TUNE (Threading Using Neural Networks), a fold recognition program using artificial neural network (ANN) models. A new method to generate amino acid substitution matrices is described in chapter two. It uses an ANN to generalise amino acid substitutions observed in protein structure alignments. Matrices for alignment scoring from this approach were compared with classic alignment scoring schemes. From these neural network models, a series of encoding schemes were constructed. These schemes describe the amino acid types with a few numbers. They were generated to replace the orthogonal encoding scheme, so that smaller, faster and more accurate neural network models can be applied on bioinformatic problems. The TUNE model was introduced in chapter four to measure protein sequence-structure compatibility. Given the integrated residue structural environment descriptions, the model predicts probabilities of observing amino acid types in such environments. Using this model, a scoring function to measure the fitness of a residue in a protein structure model can be made for protein threading programs. The model in chapter two was extended by including the residue structural environment descriptions for predictions. A simple protein fold recognition program with a dynamic programming algorithm was developed using this model. The program was then tested in the fourth round of the Critical Assessment of protein Structure Prediction methods (CASP4) and produced reasonably good results

MESSM: a framework for protein threading by neural networks and support vector machines

Protein threading, which is also referred to as fold recognition, aligns a probe amino acid sequence onto a library of representative folds of known structure to identify a structural similarity. Following the threading technique of the structural profile approach, this research focused on developing and evaluating a new framework - Mixed Environment Specific Substitution Mapping (MESSM) - for protein threading by artificial neural networks (ANNs) and support vector machines (SVMs). The MESSM presents a new process to develop an efficient tool for protein fold recognition. It achieved better efficiency while retained the effectiveness on protein prediction. The MESSM has three key components, each of which is a step in the protein threading framework. First, building the fold profile library-given a protein structure with a residue level environmental description, Neural Networks are used to generate an environment-specific amino acid substitution (3D-1D) mapping. Second, mixed substitution mapping--a mixed environment-specific substitution mapping is developed by combing the structural-derived substitution score with sequence profile from well-developed amino acid substitution matrices. Third, confidence evaluation--a support vector machine is employed to measure the significance of the sequence-structure alignment. Four computational experiments are carried out to verify the performance of the MESSM. They are Fischer, ProSup, Lindahl and Wallner benchmarks. Tested on Fischer, Lindahl and Wallner benchmarks, MESSM achieved a comparable performance on fold recognition to those energy potential based threading models. For Fischer benchmark, MESSM correctly recognise 56 out of 68 pairs, which has the same performance as that of COBLATH and SPARKS. The computational experiments show that MESSM is a fast program. It could make an alignment between probe sequence (150 amino acids) and a profile of 4775 template proteins in 30 seconds on a PC with IG memory Pentium IV. Also, tested on ProSup benchmark, the MESSM achieved alignment accuracy of 59.7%, which is better than current models. The research work was extended to develop a threading score following the threading technique of the contact potential approach. A TES (Threading with Environment-specific Score) model is constructed by neural networks

Machine learning-guided directed evolution for protein engineering

Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.Comment: Made significant revisions to focus on aspects most relevant to applying machine learning to speed up directed evolutio

Peptide classification using optimal and information theoretic syntactic modeling

We consider the problem of classifying peptides using the information residing in their syntactic representations. This problem, which has been studied for more than a decade, has typically been investigated using distance-based metrics that involve the edit operations required in the peptide comparisons. In this paper, we shall demonstrate that the Optimal and Information Theoretic (OIT) model of Oommen and Kashyap [22] applicable for syntactic pattern recognition can be used to tackle peptide classification problem. We advocate that one can model the differences between compared strings as a mutation model consisting of random substitutions, insertions and deletions obeying the OIT model. Thus, in this paper, we show that the probability measure obtained from the OIT model can be perceived as a sequence similarity metric, using which a support vector machine (SVM)-based peptide classifier can be devised. The classifier, which we have built has been tested for eight different substitution matrices and for two different data sets, namely, the HIV-1 Protease cleavage sites and the T-cell epitopes. The results show that the OIT model performs significantly better than the one which uses a Needleman-Wunsch sequence alignment score, it is less sensitive to the substitution matrix than the other methods compared, and that when combined with a SVM, is among the best peptide classification methods availabl

Development of a deep learning-based computational framework for the classification of protein sequences

Dissertação de mestrado em BioinformaticsProteins are one of the more important biological structures in living organisms, since they perform multiple biological functions. Each protein has different characteristics and properties, which can be employed in many industries, such as industrial biotechnology, clinical applications, among others, demonstrating a positive impact. Modern high-throughput methods allow protein sequencing, which provides the protein sequence data. Machine learning methodologies are applied to characterize proteins using information of the protein sequence. However, a major problem associated with this method is how to properly encode the protein sequences without losing the biological relationship between the amino acid residues. The transformation of the protein sequence into a numeric representation is done by encoder methods. In this sense, the main objective of this project is to study different encoders and identify the methods which yield the best biological representation of the protein sequences, when used in machine learning (ML) models to predict different labels related to their function. The methods were analyzed in two study cases. The first is related to enzymes, since they are a well-established case in the literature. The second used transporter sequences, a lesser studied case in the literature. In both cases, the data was collected from the curated database Swiss-Prot. The encoders that were tested include: calculated protein descriptors; matrix substitution methods; position-specific scoring matrices; and encoding by pre-trained transformer methods. The use of state-of-the-art pretrained transformers to encode protein sequences proved to be a good biological representation for subsequent application in state-of-the-art ML methods. Namely, the ESM-1b transformer achieved a Mathews correlation coefficient above 0.9 for any multiclassification task of the transporter classification system.As proteínas são estruturas biológicas importantes dos organismos vivos, uma vez que estas desempenham múltiplas funções biológicas. Cada proteína tem características e propriedades diferentes, que podem ser aplicadas em diversas indústrias, tais como a biotecnologia industrial, aplicações clínicas, entre outras, demonstrando um impacto positivo. Os métodos modernos de alto rendimento permitem a sequenciação de proteínas, fornecendo dados da sequência proteica. Metodologias de aprendizagem de máquinas tem sido aplicada para caracterizar as proteínas utilizando informação da sua sequência. Um problema associado a este método e como representar adequadamente as sequências proteicas sem perder a relação biológica entre os resíduos de aminoácidos. A transformação da sequência de proteínas numa representação numérica é feita por codificadores. Neste sentido, o principal objetivo deste projeto é estudar diferentes codificadores e identificar os métodos que produzem a melhor representação biológica das sequências proteicas, quando utilizados em modelos de aprendizagem mecânica para prever a classificação associada à sua função a sua função. Os métodos foram analisados em dois casos de estudo. O primeiro caso foi baseado em enzimas, uma vez que são um caso bem estabelecido na literatura. O segundo, na utilização de proteínas de transportadores, um caso menos estudado na literatura. Em ambos os casos, os dados foram recolhidos a partir da base de dados curada Swiss-Prot. Os codificadores testados incluem: descritores de proteínas calculados; métodos de substituição por matrizes; matrizes de pontuação específicas da posição; e codificação por modelos de transformadores pré-treinados. A utilização de transformadores de última geração para codificar sequências de proteínas demonstrou ser uma boa representação biológica para aplicação subsequente em métodos ML de última geração. Nomeadamente, o transformador ESM-1b atingiu um coeficiente de correlação de Matthews acima de 0,9 para multiclassificação do sistema de classificação de proteínas transportadoras

The Relative Importance of Input Encoding and Learning Methodology on Protein Secondary Structure Prediction

In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input encoding is shown to have a greater impact on prediction accuracy than learning methodology with a binary input encoding providing the highest training and test set prediction accuracy