CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks

BACKGROUND: One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes. RESULTS: We implemented a program CRNPRED which predicts secondary structures, contact numbers and residue-wise contact orders. This program is based on a novel machine learning scheme called critical random networks. Unlike most conventional one-dimensional structure prediction methods which are based on local windows of an amino acid sequence, CRNPRED takes into account the whole sequence. CRNPRED achieves, on average per chain, Q(3 )= 81% for secondary structure prediction, and correlation coefficients of 0.75 and 0.61 for contact number and residue-wise contact order predictions, respectively. CONCLUSION: CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions

BERTDom: Protein Domain Boundary Prediction Using BERT

The domains of a protein provide an insight on the functions that the protein can perform. Delineation of proteins using high-throughput experimental methods is difficult and a time-consuming task. Template-free and sequence-based computational methods that mainly rely on machine learning techniques can be used. However, some of the drawbacks of computational methods are low accuracy and their limitation in predicting different types of multi-domain proteins. Biological language modeling and deep learning techniques can be useful in such situations. In this study, we propose BERTDom for segmenting protein sequences. BERTDOM uses BERT for feature representation and stacked bi-directional long short term memory for classification. We pre-train BERT from scratch on a corpus of protein sequences obtained from UniProt knowledge base with reference clusters. For comparison, we also used two other deep learning architectures: LSTM and feed-forward neural networks. We also experimented with protein-to-vector (Pro2Vec) feature representation that uses word2vec to encode protein bio-words. For testing, three other bench-marked datasets were used. The experimental results on benchmarks datasets show that BERTDom produces the best F-score as compared to other template-based and template-free protein domain boundary prediction methods. Employing deep learning architectures can significantly improve domain boundary prediction. Furthermore, BERT used extensively in NLP for feature representation, has shown promising results when used for encoding bio-words. The code is available at https://github.com/maryam988/BERTDom-Code

Deep Robust Framework for Protein Function Prediction using Variable-Length Protein Sequences

Amino acid sequence portrays most intrinsic form of a protein and expresses primary structure of protein. The order of amino acids in a sequence enables a protein to acquire a particular stable conformation that is responsible for the functions of the protein. This relationship between a sequence and its function motivates the need to analyse the sequences for predicting protein functions. Early generation computational methods using BLAST, FASTA, etc. perform function transfer based on sequence similarity with existing databases and are computationally slow. Although machine learning based approaches are fast, they fail to perform well for long protein sequences (i.e., protein sequences with more than 300 amino acid residues). In this paper, we introduce a novel method for construction of two separate feature sets for protein sequences based on analysis of 1) single fixed-sized segments and 2) multi-sized segments, using bi-directional long short-term memory network. Further, model based on proposed feature set is combined with the state of the art Multi-lable Linear Discriminant Analysis (MLDA) features based model to improve the accuracy. Extensive evaluations using separate datasets for biological processes and molecular functions demonstrate promising results for both single-sized and multi-sized segments based feature sets. While former showed an improvement of +3.37% and +5.48%, the latter produces an improvement of +5.38% and +8.00% respectively for two datasets over the state of the art MLDA based classifier. After combining two models, there is a significant improvement of +7.41% and +9.21% respectively for two datasets compared to MLDA based classifier. Specifically, the proposed approach performed well for the long protein sequences and superior overall performance

Improved general regression network for protein domain boundary prediction

Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

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

Homology modeling in the time of collective and artificial intelligence

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.O