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Ali S, Chourasia P, Patterson M. From PDB files to protein features: a comparative analysis of PDB bind and STCRDAB datasets. Med Biol Eng Comput 2024:10.1007/s11517-024-03074-3. [PMID: 38622438 DOI: 10.1007/s11517-024-03074-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 03/13/2024] [Indexed: 04/17/2024]
Abstract
Understanding protein structures is crucial for various bioinformatics research, including drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification is a critical aspect of structural biology, where supervised machine learning algorithms classify structures based on data from databases such as Protein Data Bank (PDB). However, the challenge lies in designing numerical embeddings for protein structures without losing essential information. Although some effort has been made in the literature, researchers have not effectively and rigorously combined the structural and sequence-based features for efficient protein classification to the best of our knowledge. To this end, we propose numerical embeddings that extract relevant features for protein sequences fetched from PDB structures from popular datasets such as PDB Bind and STCRDAB. The features are physicochemical properties such as aromaticity, instability index, flexibility, Grand Average of Hydropathy (GRAVY), isoelectric point, charge at pH, secondary structure fracture, molar extinction coefficient, and molecular weight. We also incorporate scaling features for the sliding windows (e.g., k-mers), which include Kyte and Doolittle (KD) hydropathy scale, Eisenberg hydrophobicity scale, Hydrophilicity scale, Flexibility of the amino acids, and Hydropathy scale. Multiple-feature selection aims to improve the accuracy of protein classification models. The results showed that the selected features significantly improved the predictive performance of existing embeddings.
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Affiliation(s)
- Sarwan Ali
- Georgia State University, Atlanta, GA, USA.
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Ali S, Chourasia P, Patterson M. When Protein Structure Embedding Meets Large Language Models. Genes (Basel) 2023; 15:25. [PMID: 38254915 PMCID: PMC10815811 DOI: 10.3390/genes15010025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/16/2023] [Accepted: 12/21/2023] [Indexed: 01/24/2024] Open
Abstract
Protein structure analysis is essential in various bioinformatics domains such as drug discovery, disease diagnosis, and evolutionary studies. Within structural biology, the classification of protein structures is pivotal, employing machine learning algorithms to categorize structures based on data from databases like the Protein Data Bank (PDB). To predict protein functions, embeddings based on protein sequences have been employed. Creating numerical embeddings that preserve vital information while considering protein structure and sequence presents several challenges. The existing literature lacks a comprehensive and effective approach that combines structural and sequence-based features to achieve efficient protein classification. While large language models (LLMs) have exhibited promising outcomes for protein function prediction, their focus primarily lies on protein sequences, disregarding the 3D structures of proteins. The quality of embeddings heavily relies on how well the geometry of the embedding space aligns with the underlying data structure, posing a critical research question. Traditionally, Euclidean space has served as a widely utilized framework for embeddings. In this study, we propose a novel method for designing numerical embeddings in Euclidean space for proteins by leveraging 3D structure information, specifically employing the concept of contact maps. These embeddings are synergistically combined with features extracted from LLMs and traditional feature engineering techniques to enhance the performance of embeddings in supervised protein analysis. Experimental results on benchmark datasets, including PDB Bind and STCRDAB, demonstrate the superior performance of the proposed method for protein function prediction.
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Affiliation(s)
| | | | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA; (S.A.); (P.C.)
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Ali S, Chourasia P, Tayebi Z, Bello B, Patterson M. ViralVectors: compact and scalable alignment-free virome feature generation. Med Biol Eng Comput 2023; 61:2607-2626. [PMID: 37395885 DOI: 10.1007/s11517-023-02837-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 03/29/2023] [Indexed: 07/04/2023]
Abstract
The amount of sequencing data for SARS-CoV-2 is several orders of magnitude larger than any virus. This will continue to grow geometrically for SARS-CoV-2, and other viruses, as many countries heavily finance genomic surveillance efforts. Hence, we need methods for processing large amounts of sequence data to allow for effective yet timely decision-making. Such data will come from heterogeneous sources: aligned, unaligned, or even unassembled raw nucleotide or amino acid sequencing reads pertaining to the whole genome or regions (e.g., spike) of interest. In this work, we propose ViralVectors, a compact feature vector generation from virome sequencing data that allows effective downstream analysis. Such generation is based on minimizers, a type of lightweight "signature" of a sequence, used traditionally in assembly and read mapping - to our knowledge, the first use minimizers in this way. We validate our approach on different types of sequencing data: (a) 2.5M SARS-CoV-2 spike sequences (to show scalability); (b) 3K Coronaviridae spike sequences (to show robustness to more genomic variability); and (c) 4K raw WGS reads sets taken from nasal-swab PCR tests (to show the ability to process unassembled reads). Our results show that ViralVectors outperforms current benchmarks in most classification and clustering tasks. Graphical Abstract showing the all steps of proposed approach. We start by collecting the sequence-based data. Then Data cleaning and preprocessing is applied. After that, we generate the feature embeddings using minimizer based approach. Then Classification and clustering algorithms are applied on the resultant data and predictions are made on the test set.
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Affiliation(s)
- Sarwan Ali
- Georgia State University, Atlanta, GA, USA.
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Murad T, Ali S, Patterson M. Exploring the Potential of GANs in Biological Sequence Analysis. BIOLOGY 2023; 12:854. [PMID: 37372139 DOI: 10.3390/biology12060854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/03/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, such as viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become global pandemics. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, such as the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on generative adversarial networks (GANs), which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles real data, thus, these generated data can be employed to enhance the ML models' performance by eradicating the class imbalance problem for biological sequence analysis. We perform four distinct classification tasks by using four different sequence datasets (Influenza A Virus, PALMdb, VDjDB, Host) and our results illustrate that GANs can improve the overall classification performance.
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Affiliation(s)
- Taslim Murad
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
| | - Sarwan Ali
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
| | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
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Sahoo B, Ali S, Chen PY, Patterson M, Zelikovsky A. Assessing the Resilience of Machine Learning Classification Algorithms on SARS-CoV-2 Genome Sequences Generated with Long-Read Specific Errors. Biomolecules 2023; 13:934. [PMID: 37371514 PMCID: PMC10296223 DOI: 10.3390/biom13060934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/19/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
The emergence of third-generation single-molecule sequencing (TGS) technology has revolutionized the generation of long reads, which are essential for genome assembly and have been widely employed in sequencing the SARS-CoV-2 virus during the COVID-19 pandemic. Although long-read sequencing has been crucial in understanding the evolution and transmission of the virus, the high error rate associated with these reads can lead to inadequate genome assembly and downstream biological interpretation. In this study, we evaluate the accuracy and robustness of machine learning (ML) models using six different embedding techniques on SARS-CoV-2 error-incorporated genome sequences. Our analysis includes two types of error-incorporated genome sequences: those generated using simulation tools to emulate error profiles of long-read sequencing platforms and those generated by introducing random errors. We show that the spaced k-mers embedding method achieves high accuracy in classifying error-free SARS-CoV-2 genome sequences, and the spaced k-mers and weighted k-mers embedding methods are highly accurate in predicting error-incorporated sequences. The fixed-length vectors generated by these methods contribute to the high accuracy achieved. Our study provides valuable insights for researchers to effectively evaluate ML models and gain a better understanding of the approach for accurate identification of critical SARS-CoV-2 genome sequences.
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Affiliation(s)
- Bikram Sahoo
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA; (S.A.); (M.P.)
| | - Sarwan Ali
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA; (S.A.); (M.P.)
| | - Pin-Yu Chen
- IBM Research, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA;
| | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA; (S.A.); (M.P.)
| | - Alexander Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA; (S.A.); (M.P.)
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Chourasia P, Ali S, Ciccolella S, Vedova GD, Patterson M. Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data. JOURNAL OF COMPUTATIONAL BIOLOGY : A JOURNAL OF COMPUTATIONAL MOLECULAR CELL BIOLOGY 2023; 30:469-491. [PMID: 36730750 DOI: 10.1089/cmb.2022.0424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The massive amount of genomic data appearing for SARS-CoV-2 since the beginning of the COVID-19 pandemic has challenged traditional methods for studying its dynamics. As a result, new methods such as Pangolin, which can scale to the millions of samples of SARS-CoV-2 currently available, have appeared. Such a tool is tailored to take as input assembled, aligned, and curated full-length sequences, such as those found in the GISAID database. As high-throughput sequencing technologies continue to advance, such assembly, alignment, and curation may become a bottleneck, creating a need for methods that can process raw sequencing reads directly. In this article, we propose Reads2Vec, an alignment-free embedding approach that can generate a fixed-length feature vector representation directly from the raw sequencing reads without requiring assembly. Furthermore, since such an embedding is a numerical representation, it may be applied to highly optimized classification and clustering algorithms. Experiments on simulated data show that our proposed embedding obtains better classification results and better clustering properties contrary to existing alignment-free baselines. In a study on real data, we show that alignment-free embeddings have better clustering properties than the Pangolin tool and that the spike region of the SARS-CoV-2 genome heavily informs the alignment-free clusterings, which is consistent with current biological knowledge of SARS-CoV-2.
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Affiliation(s)
- Prakash Chourasia
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Sarwan Ali
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Simone Ciccolella
- Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Milan, Italy
| | - Gianluca Della Vedova
- Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Milan, Italy
| | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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Avila Cartes J, Anand S, Ciccolella S, Bonizzoni P, Della Vedova G. Accurate and fast clade assignment via deep learning and frequency chaos game representation. Gigascience 2022; 12:6963321. [PMID: 36576129 PMCID: PMC9795481 DOI: 10.1093/gigascience/giac119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/17/2022] [Accepted: 11/14/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Since the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus; most notably, the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade. RESULTS In this article, we leverage the frequency chaos game representation (FCGR) and convolutional neural networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieved an $96.29\%$ overall accuracy, while a similar tool, Covidex, obtained a $77,12\%$ overall accuracy. As far as we know, our method is the first using deep learning and FCGR for intraspecies classification. Furthermore, by using some feature importance methods, CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants. CONCLUSIONS By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on random forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. AVAILABILITY The trained models can be tested online providing a FASTA file (with 1 or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.
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Affiliation(s)
- Jorge Avila Cartes
- Department of Computer Science, Systems and Communications, University of Milano–Bicocca, Milan 20125, Italy
| | - Santosh Anand
- Department of Computer Science, Systems and Communications, University of Milano–Bicocca, Milan 20125, Italy
| | - Simone Ciccolella
- Correspondence address. Simone Ciccolella, Department of Computer Science, Systems and Communications, University of Milano–Bicocca, Milan 20125, Italy. E-mail:
| | - Paola Bonizzoni
- Department of Computer Science, Systems and Communications, University of Milano–Bicocca, Milan 20125, Italy
| | - Gianluca Della Vedova
- Correspondence address. Gianluca Della Vedova, Department of Computer Science, Systems and Communications, University of Milano–Bicocca, Milan 20125, Italy. E-mail:
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Artificial Intelligence Models for Zoonotic Pathogens: A Survey. Microorganisms 2022; 10:microorganisms10101911. [PMID: 36296187 PMCID: PMC9607465 DOI: 10.3390/microorganisms10101911] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/19/2022] [Accepted: 09/22/2022] [Indexed: 11/22/2022] Open
Abstract
Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.
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