1
|
MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction. BMC Bioinformatics 2023; 24:21. [PMID: 36653789 PMCID: PMC9847203 DOI: 10.1186/s12859-023-05135-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 01/04/2023] [Indexed: 01/19/2023] Open
Abstract
N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: http://www.biolscience.cn/MultiScale-CNN-4mCPred/ .
Collapse
|
2
|
Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique. Int J Mol Sci 2022; 23:ijms23031251. [PMID: 35163174 PMCID: PMC8836036 DOI: 10.3390/ijms23031251] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 12/15/2022] Open
Abstract
4mC is a type of DNA alteration that has the ability to synchronize multiple biological movements, for example, DNA replication, gene expressions, and transcriptional regulations. Accurate prediction of 4mC sites can provide exact information to their hereditary functions. The purpose of this study was to establish a robust deep learning model to recognize 4mC sites in Geobacter pickeringii. In the anticipated model, two kinds of feature descriptors, namely, binary and k-mer composition were used to encode the DNA sequences of Geobacter pickeringii. The obtained features from their fusion were optimized by using correlation and gradient-boosting decision tree (GBDT)-based algorithm with incremental feature selection (IFS) method. Then, these optimized features were inserted into 1D convolutional neural network (CNN) to classify 4mC sites from non-4mC sites in Geobacter pickeringii. The performance of the anticipated model on independent data exhibited an accuracy of 0.868, which was 4.2% higher than the existing model.
Collapse
|
3
|
Bonilla B, Brown AJ, Hengel SR, Rapchak KS, Mitchell D, Pressimone CA, Fagunloye AA, Luong TT, Russell RA, Vyas RK, Mertz TM, Zaher HS, Mosammaparast N, Malc EP, Mieczkowski PA, Roberts SA, Bernstein KA. The Shu complex prevents mutagenesis and cytotoxicity of single-strand specific alkylation lesions. eLife 2021; 10:68080. [PMID: 34723799 PMCID: PMC8610418 DOI: 10.7554/elife.68080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 10/29/2021] [Indexed: 12/31/2022] Open
Abstract
Three-methyl cytosine (3meC) are toxic DNA lesions, blocking base pairing. Bacteria and humans express members of the AlkB enzymes family, which directly remove 3meC. However, other organisms, including budding yeast, lack this class of enzymes. It remains an unanswered evolutionary question as to how yeast repairs 3meC, particularly in single-stranded DNA. The yeast Shu complex, a conserved homologous recombination factor, aids in preventing replication-associated mutagenesis from DNA base damaging agents such as methyl methanesulfonate (MMS). We found that MMS-treated Shu complex-deficient cells exhibit a genome-wide increase in A:T and G:C substitutions mutations. The G:C substitutions displayed transcriptional and replicational asymmetries consistent with mutations resulting from 3meC. Ectopic expression of a human AlkB homolog in Shu-deficient yeast rescues MMS-induced growth defects and increased mutagenesis. Thus, our work identifies a novel homologous recombination-based mechanism mediated by the Shu complex for coping with alkylation adducts.
Collapse
Affiliation(s)
- Braulio Bonilla
- Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States
| | - Alexander J Brown
- Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, United States
| | - Sarah R Hengel
- Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States
| | - Kyle S Rapchak
- Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States
| | - Debra Mitchell
- Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, United States
| | - Catherine A Pressimone
- Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States
| | - Adeola A Fagunloye
- Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States
| | - Thong T Luong
- Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States
| | - Reagan A Russell
- University of Pittsburgh School of Medicine, Pittsburgh, United States
| | - Rudri K Vyas
- Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, United States
| | - Tony M Mertz
- Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, United States
| | - Hani S Zaher
- Biology, Washington University in St Louis, St. Louis, United States
| | | | - Ewa P Malc
- Genetics, University of North Carolina Chapel Hill, Chapel Hill, United States
| | - Piotr A Mieczkowski
- Genetics, University of North Carolina Chapel Hill, Chapel Hill, United States
| | - Steven A Roberts
- Molecular Biosciences and Center for Reproductive Biology, Washington State University, Pullman, United States
| | - Kara A Bernstein
- Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States
| |
Collapse
|
4
|
Zulfiqar H, Sun ZJ, Huang QL, Yuan SS, Lv H, Dao FY, Lin H, Li YW. Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli. Methods 2021; 203:558-563. [PMID: 34352373 DOI: 10.1016/j.ymeth.2021.07.011] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 07/22/2021] [Accepted: 07/29/2021] [Indexed: 10/20/2022] Open
Abstract
N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.
Collapse
Affiliation(s)
- Hasan Zulfiqar
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Jie Sun
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qin-Lai Huang
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Shi Yuan
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lv
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Lin
- Center for Informational Biology and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Yan-Wen Li
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; Key Laboratory of Intelligent Information Processing of Jilin Province, Northeast Normal University, Changchun 130117, China; Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
| |
Collapse
|
5
|
i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties. Genes (Basel) 2021; 12:genes12081117. [PMID: 34440291 PMCID: PMC8393747 DOI: 10.3390/genes12081117] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/15/2021] [Accepted: 07/16/2021] [Indexed: 01/26/2023] Open
Abstract
DNA is subject to epigenetic modification by the molecule N4-methylcytosine (4mC). N4-methylcytosine plays a crucial role in DNA repair and replication, protects host DNA from degradation, and regulates DNA expression. However, though current experimental techniques can identify 4mC sites, such techniques are expensive and laborious. Therefore, computational tools that can predict 4mC sites would be very useful for understanding the biological mechanism of this vital type of DNA modification. Conventional machine-learning-based methods rely on hand-crafted features, but the new method saves time and computational cost by making use of learned features instead. In this study, we propose i4mC-Deep, an intelligent predictor based on a convolutional neural network (CNN) that predicts 4mC modification sites in DNA samples. The CNN is capable of automatically extracting important features from input samples during training. Nucleotide chemical properties and nucleotide density, which together represent a DNA sequence, act as CNN input data. The outcome of the proposed method outperforms several state-of-the-art predictors. When i4mC-Deep was used to analyze G. subterruneus DNA, the accuracy of the results was improved by 3.9% and MCC increased by 10.5% compared to a conventional predictor.
Collapse
|
6
|
Zulfiqar H, Khan RS, Hassan F, Hippe K, Hunt C, Ding H, Song XM, Cao R. Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3348-3363. [PMID: 34198389 DOI: 10.3934/mbe.2021167] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/24/2023]
Abstract
N4-methylcytosine (4mC) is a kind of DNA modification which could regulate multiple biological processes. Correctly identifying 4mC sites in genomic sequences can provide precise knowledge about their genetic roles. This study aimed to develop an ensemble model to predict 4mC sites in the mouse genome. In the proposed model, DNA sequences were encoded by k-mer, enhanced nucleic acid composition and composition of k-spaced nucleic acid pairs. Subsequently, these features were optimized by using minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) and five-fold cross-validation. The obtained optimal features were inputted into random forest classifier for discriminating 4mC from non-4mC sites in mouse. On the independent dataset, our model could yield the overall accuracy of 85.41%, which was approximately 3.8% -6.3% higher than the two existing models, i4mC-Mouse and 4mCpred-EL respectively. The data and source code of the model can be freely download from https://github.com/linDing-groups/model_4mc.
Collapse
Affiliation(s)
- Hasan Zulfiqar
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Rida Sarwar Khan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiao-Ming Song
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Life Sciences, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| |
Collapse
|
7
|
Manavalan B, Hasan MM, Basith S, Gosu V, Shin TH, Lee G. Empirical Comparison and Analysis of Web-Based DNA N 4-Methylcytosine Site Prediction Tools. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 22:406-420. [PMID: 33230445 PMCID: PMC7533314 DOI: 10.1016/j.omtn.2020.09.010] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 09/11/2020] [Indexed: 12/12/2022]
Abstract
DNA N4-methylcytosine (4mC) is a crucial epigenetic modification involved in various biological processes. Accurate genome-wide identification of these sites is critical for improving our understanding of their biological functions and mechanisms. As experimental methods for 4mC identification are tedious, expensive, and labor-intensive, several machine learning-based approaches have been developed for genome-wide detection of such sites in multiple species. However, the predictions projected by these tools are difficult to quantify and compare. To date, no systematic performance comparison of 4mC tools has been reported. The aim of this study was to compare and critically evaluate 12 publicly available 4mC site prediction tools according to species specificity, based on a huge independent validation dataset. The tools 4mCCNN (Escherichia coli), DNA4mC-LIP (Arabidopsis thaliana), iDNA-MS (Fragaria vesca), DNA4mC-LIP and 4mCCNN (Drosophila melanogaster), and four tools for Caenorhabditis elegans achieved excellent overall performance compared with their counterparts. However, none of the existing methods was suitable for Geoalkalibacter subterraneus, Geobacter pickeringii, and Mus musculus, thereby limiting their practical applicability. Model transferability to five species and non-transferability to three species are also discussed. The presented evaluation will assist researchers in selecting appropriate prediction tools that best suit their purpose and provide useful guidelines for the development of improved 4mC predictors in the future.
Collapse
Affiliation(s)
- Balachandran Manavalan
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan.,Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea
| | - Vijayakumar Gosu
- Department of Animal Biotechnology, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Tae-Hwan Shin
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea.,Department of Molecular Science and Technology, Ajou University, Suwon 16499, Republic of Korea
| |
Collapse
|
8
|
4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-methylcytosine Sites in the Mouse Genome. Cells 2019; 8:cells8111332. [PMID: 31661923 PMCID: PMC6912380 DOI: 10.3390/cells8111332] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 10/21/2019] [Accepted: 10/24/2019] [Indexed: 12/24/2022] Open
Abstract
DNA N4-methylcytosine (4mC) is one of the key epigenetic alterations, playing essential roles in DNA replication, differentiation, cell cycle, and gene expression. To better understand 4mC biological functions, it is crucial to gain knowledge on its genomic distribution. In recent times, few computational studies, in particular machine learning (ML) approaches have been applied in the prediction of 4mC site predictions. Although ML-based methods are promising for 4mC identification in other species, none are available for detecting 4mCs in the mouse genome. Our novel computational approach, called 4mCpred-EL, is the first method for identifying 4mC sites in the mouse genome where four different ML algorithms with a wide range of seven feature encodings are utilized. Subsequently, those feature encodings predicted probabilistic values are used as a feature vector and are once again inputted to ML algorithms, whose corresponding models are integrated into ensemble learning. Our benchmarking results demonstrated that 4mCpred-EL achieved an accuracy and MCC values of 0.795 and 0.591, which significantly outperformed seven other classifiers by more than 1.5–5.9% and 3.2–11.7%, respectively. Additionally, 4mCpred-EL attained an overall accuracy of 79.80%, which is 1.8–5.1% higher than that yielded by seven other classifiers in the independent evaluation. We provided a user-friendly web server, namely 4mCpred-EL which could be implemented as a pre-screening tool for the identification of potential 4mC sites in the mouse genome.
Collapse
|
9
|
Manavalan B, Basith S, Shin TH, Wei L, Lee G. Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 16:733-744. [PMID: 31146255 PMCID: PMC6540332 DOI: 10.1016/j.omtn.2019.04.019] [Citation(s) in RCA: 162] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 04/16/2019] [Accepted: 04/22/2019] [Indexed: 11/19/2022]
Abstract
DNA N4-methylcytosine (4mC) is an important genetic modification and plays crucial roles in differentiation between self and non-self DNA and in controlling DNA replication, cell cycle, and gene-expression levels. Accurate 4mC site identification is fundamental to improve the understanding of 4mC biological functions and mechanisms. Hence, it is necessary to develop in silico approaches for efficient and high-throughput 4mC site identification. Although some bioinformatic tools have been developed in this regard, their prediction accuracy and generalizability require improvement to optimize their usability in practical applications. For this purpose, we here proposed Meta-4mCpred, a meta-predictor for 4mC site prediction. In Meta-4mCpred, we employed a feature representation learning scheme and generated 56 probabilistic features based on four different machine-learning algorithms and seven feature encodings covering diverse sequence information, including compositional, physicochemical, and position-specific information. Subsequently, the probabilistic features were used as an input to support vector machine and developed a final meta-predictor. To the best of our knowledge, this is the first meta-predictor for 4mC site prediction. Cross-validation results show that Meta-4mCpred achieved an overall average accuracy of 84.2% from six different species, which is ∼2%–4% higher than those attainable using the state-of-the-art predictors. Furthermore, Meta-4mCpred achieved an overall average accuracy of 86% on independent datasets evaluation, which is over 4% higher than those yielded by the state-of-the-art predictors. The user-friendly webserver employed to implement the proposed Meta-4mCpred is freely accessible at http://thegleelab.org/Meta-4mCpred.
Collapse
Affiliation(s)
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Tae Hwan Shin
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea; Institute of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea
| | - Leyi Wei
- School of Computer Science and Technology, Tianjin University, China.
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea; Institute of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea.
| |
Collapse
|
10
|
Tang Q, Cai A, Bian K, Chen F, Delaney JC, Adusumalli S, Bach AC, Akhlaghi F, Cho BP, Li D. Characterization of Byproducts from Chemical Syntheses of Oligonucleotides Containing 1-Methyladenine and 3-Methylcytosine. ACS OMEGA 2017; 2:8205-8212. [PMID: 29214236 PMCID: PMC5709782 DOI: 10.1021/acsomega.7b01482] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 11/01/2017] [Indexed: 06/07/2023]
Abstract
Oligonucleotides serve as important tools for biological, chemical, and medical research. The preparation of oligonucleotides through automated solid-phase synthesis is well-established. However, identification of byproducts generated from DNA synthesis, especially from oligonucleotides containing site-specific modifications, is sometimes challenging. Typical high-performance liquid chromatography (HPLC), mass spectrometry (MS), and gel electrophoresis methods alone are not sufficient for characterizing unexpected byproducts, especially for those having identical or very similar molecular weight (MW) to the products. We used a rigorous quality control procedure to characterize byproducts generated during oligonucleotide syntheses: (1) purify oligonucleotides by different HPLC systems; (2) determine exact MW by high-resolution MS; (3) locate modification position by MS/MS or exonuclease digestion with matrix-assisted laser desorption ionization-time of flight analysis; and (4) conduct, where applicable, enzymatic assays. We applied these steps to characterize byproducts in the syntheses of oligonucleotides containing biologically important methyl DNA adducts 1-methyladenine (m1A) and 3-methylcytosine (m3C). In m1A synthesis, we differentiated a regioisomeric byproduct 6-methyladenine, which possesses a MW identical to uncharged m1A. As for m3C, we identified a deamination byproduct 3-methyluracil, which is only 1 Da greater than uncharged m3C in the ∼4900 Da context. The detection of these byproducts would be very challenging if the abovementioned procedure was not adopted.
Collapse
Affiliation(s)
- Qi Tang
- Department
of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, 7 Greenhouse Road, Kingston, Rhode Island 02881, United States
| | - Ang Cai
- Department
of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, 7 Greenhouse Road, Kingston, Rhode Island 02881, United States
| | - Ke Bian
- Department
of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, 7 Greenhouse Road, Kingston, Rhode Island 02881, United States
| | - Fangyi Chen
- Department
of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, 7 Greenhouse Road, Kingston, Rhode Island 02881, United States
| | - James C. Delaney
- Visterra
Inc., One Kendall Square, Cambridge, Massachusetts 02139, United States
| | - Sravani Adusumalli
- Department
of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, 7 Greenhouse Road, Kingston, Rhode Island 02881, United States
| | - Alvin C. Bach
- Department
of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, 7 Greenhouse Road, Kingston, Rhode Island 02881, United States
| | - Fatemeh Akhlaghi
- Department
of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, 7 Greenhouse Road, Kingston, Rhode Island 02881, United States
| | - Bongsup P. Cho
- Department
of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, 7 Greenhouse Road, Kingston, Rhode Island 02881, United States
| | - Deyu Li
- Department
of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, 7 Greenhouse Road, Kingston, Rhode Island 02881, United States
| |
Collapse
|