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Fasoulis R, Rigo MM, Lizée G, Antunes DA, Kavraki LE. APE-Gen2.0: Expanding Rapid Class I Peptide-Major Histocompatibility Complex Modeling to Post-Translational Modifications and Noncanonical Peptide Geometries. J Chem Inf Model 2024; 64:1730-1750. [PMID: 38415656 PMCID: PMC10936522 DOI: 10.1021/acs.jcim.3c01667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 02/29/2024]
Abstract
The recognition of peptides bound to class I major histocompatibility complex (MHC-I) receptors by T-cell receptors (TCRs) is a determinant of triggering the adaptive immune response. While the exact molecular features that drive the TCR recognition are still unknown, studies have suggested that the geometry of the joint peptide-MHC (pMHC) structure plays an important role. As such, there is a definite need for methods and tools that accurately predict the structure of the peptide bound to the MHC-I receptor. In the past few years, many pMHC structural modeling tools have emerged that provide high-quality modeled structures in the general case. However, there are numerous instances of non-canonical cases in the immunopeptidome that the majority of pMHC modeling tools do not attend to, most notably, peptides that exhibit non-standard amino acids and post-translational modifications (PTMs) or peptides that assume non-canonical geometries in the MHC binding cleft. Such chemical and structural properties have been shown to be present in neoantigens; therefore, accurate structural modeling of these instances can be vital for cancer immunotherapy. To this end, we have developed APE-Gen2.0, a tool that improves upon its predecessor and other pMHC modeling tools, both in terms of modeling accuracy and the available modeling range of non-canonical peptide cases. Some of the improvements include (i) the ability to model peptides that have different types of PTMs such as phosphorylation, nitration, and citrullination; (ii) a new and improved anchor identification routine in order to identify and model peptides that exhibit a non-canonical anchor conformation; and (iii) a web server that provides a platform for easy and accessible pMHC modeling. We further show that structures predicted by APE-Gen2.0 can be used to assess the effects that PTMs have in binding affinity in a more accurate manner than just using solely the sequence of the peptide. APE-Gen2.0 is freely available at https://apegen.kavrakilab.org.
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Affiliation(s)
- Romanos Fasoulis
- Department
of Computer Science, Rice University, Houston, Texas 77005, United States
| | - Mauricio M. Rigo
- Department
of Computer Science, Rice University, Houston, Texas 77005, United States
| | - Gregory Lizée
- Department
of Melanoma Medical Oncology—Research, The University of Texas MD Anderson Cancer Center, Houston, Texas 77054, United States
| | - Dinler A. Antunes
- Department
of Biology and Biochemistry, University
of Houston, Houston, Texas 77004, United States
| | - Lydia E. Kavraki
- Department
of Computer Science, Rice University, Houston, Texas 77005, United States
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Fasoulis R, Rigo MM, Antunes DA, Paliouras G, Kavraki LE. Transfer learning improves pMHC kinetic stability and immunogenicity predictions. Immunoinformatics (Amst) 2024; 13:100030. [PMID: 38577265 PMCID: PMC10994007 DOI: 10.1016/j.immuno.2023.100030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.
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Affiliation(s)
- Romanos Fasoulis
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Mauricio Menegatti Rigo
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Dinler Amaral Antunes
- Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Rd, Houston, 77004, TX, United States
| | - Georgios Paliouras
- Institute of Informatics and Telecommunications, NCSR Demokritos, Patr. Gregoriou E and 27 Neapoleos St, Athens, 15341, Greece
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
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Conev A, Fasoulis R, Hall-Swan S, Ferreira R, Kavraki LE. HLAEquity: Examining biases in pan-allele peptide-HLA binding predictors. iScience 2024; 27:108613. [PMID: 38188519 PMCID: PMC10770483 DOI: 10.1016/j.isci.2023.108613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 11/13/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024] Open
Abstract
Peptide-HLA (pHLA) binding prediction is essential in screening peptide candidates for personalized peptide vaccines. Machine learning (ML) pHLA binding prediction tools are trained on vast amounts of data and are effective in screening peptide candidates. Most ML models report the ability to generalize to HLA alleles unseen during training ("pan-allele" models). However, the use of datasets with imbalanced allele content raises concerns about biased model performance. First, we examine the data bias of two ML-based pan-allele pHLA binding predictors. We find that the pHLA datasets overrepresent alleles from geographic populations of high-income countries. Second, we show that the identified data bias is perpetuated within ML models, leading to algorithmic bias and subpar performance for alleles expressed in low-income geographic populations. We draw attention to the potential therapeutic consequences of this bias, and we challenge the use of the term "pan-allele" to describe models trained with currently available public datasets.
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Affiliation(s)
- Anja Conev
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Romanos Fasoulis
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Sarah Hall-Swan
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Rodrigo Ferreira
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, Houston, TX, USA
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Rigo MM, Fasoulis R, Conev A, Hall-Swan S, Antunes DA, Kavraki LE. SARS-Arena: Sequence and Structure-Guided Selection of Conserved Peptides from SARS-related Coronaviruses for Novel Vaccine Development. Front Immunol 2022; 13:931155. [PMID: 35903104 PMCID: PMC9315150 DOI: 10.3389/fimmu.2022.931155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/10/2022] [Indexed: 02/01/2023] Open
Abstract
The pandemic caused by the SARS-CoV-2 virus, the agent responsible for the COVID-19 disease, has affected millions of people worldwide. There is constant search for new therapies to either prevent or mitigate the disease. Fortunately, we have observed the successful development of multiple vaccines. Most of them are focused on one viral envelope protein, the spike protein. However, such focused approaches may contribute for the rise of new variants, fueled by the constant selection pressure on envelope proteins, and the widespread dispersion of coronaviruses in nature. Therefore, it is important to examine other proteins, preferentially those that are less susceptible to selection pressure, such as the nucleocapsid (N) protein. Even though the N protein is less accessible to humoral response, peptides from its conserved regions can be presented by class I Human Leukocyte Antigen (HLA) molecules, eliciting an immune response mediated by T-cells. Given the increased number of protein sequences deposited in biological databases daily and the N protein conservation among viral strains, computational methods can be leveraged to discover potential new targets for SARS-CoV-2 and SARS-CoV-related viruses. Here we developed SARS-Arena, a user-friendly computational pipeline that can be used by practitioners of different levels of expertise for novel vaccine development. SARS-Arena combines sequence-based methods and structure-based analyses to (i) perform multiple sequence alignment (MSA) of SARS-CoV-related N protein sequences, (ii) recover candidate peptides of different lengths from conserved protein regions, and (iii) model the 3D structure of the conserved peptides in the context of different HLAs. We present two main Jupyter Notebook workflows that can help in the identification of new T-cell targets against SARS-CoV viruses. In fact, in a cross-reactive case study, our workflows identified a conserved N protein peptide (SPRWYFYYL) recognized by CD8+ T-cells in the context of HLA-B7+. SARS-Arena is available at https://github.com/KavrakiLab/SARS-Arena.
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Affiliation(s)
| | - Romanos Fasoulis
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States
| | - Anja Conev
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States
| | - Sarah Hall-Swan
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States
| | - Dinler Amaral Antunes
- Antunes Lab, Center for Nuclear Receptors and Cell Signaling, Department of Biology and Biochemistry, University of Houston, Houston, TX, United States,*Correspondence: Lydia E. Kavraki, ; Dinler Amaral Antunes,
| | - Lydia E. Kavraki
- Kavraki Lab, Department of Computer Science, Rice University, Houston, TX, United States,*Correspondence: Lydia E. Kavraki, ; Dinler Amaral Antunes,
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Jackson KR, Antunes DA, Talukder AH, Maleki AR, Amagai K, Salmon A, Katailiha AS, Chiu Y, Fasoulis R, Rigo MM, Abella JR, Melendez BD, Li F, Sun Y, Sonnemann HM, Belousov V, Frenkel F, Justesen S, Makaju A, Liu Y, Horn D, Lopez-Ferrer D, Huhmer AF, Hwu P, Roszik J, Hawke D, Kavraki LE, Lizée G. Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules. PNAS Nexus 2022; 1:pgac124. [PMID: 36003074 PMCID: PMC9391200 DOI: 10.1093/pnasnexus/pgac124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide-HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.
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Affiliation(s)
- Kyle R Jackson
- University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Dinler A Antunes
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Amjad H Talukder
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Ariana R Maleki
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Kano Amagai
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Avery Salmon
- University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Immunology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Arjun S Katailiha
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Yulun Chiu
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Romanos Fasoulis
- Department of Computer Science, Rice University, Houston, TX, USA
| | | | - Jayvee R Abella
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Brenda D Melendez
- University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Fenge Li
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Yimo Sun
- University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Heather M Sonnemann
- University of Texas MD Anderson Cancer Center, UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | | | | | | | | | - Yang Liu
- ThermoFisher Scientific, San Jose, CA, USA
| | - David Horn
- ThermoFisher Scientific, San Jose, CA, USA
| | | | | | - Patrick Hwu
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Jason Roszik
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
| | - David Hawke
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, Houston, TX, USA
| | - Gregory Lizée
- Department of Melanoma, UT MD Anderson Cancer Center, Houston, TX, USA
- Department of Immunology, UT MD Anderson Cancer Center, Houston, TX, USA
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Fasoulis R, Paliouras G, Kavraki LE. Graph representation learning for structural proteomics. Emerg Top Life Sci 2021; 5:789-802. [PMID: 34665257 PMCID: PMC8786289 DOI: 10.1042/etls20210225] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 09/02/2021] [Accepted: 09/13/2021] [Indexed: 12/13/2022]
Abstract
The field of structural proteomics, which is focused on studying the structure-function relationship of proteins and protein complexes, is experiencing rapid growth. Since the early 2000s, structural databases such as the Protein Data Bank are storing increasing amounts of protein structural data, in addition to modeled structures becoming increasingly available. This, combined with the recent advances in graph-based machine-learning models, enables the use of protein structural data in predictive models, with the goal of creating tools that will advance our understanding of protein function. Similar to using graph learning tools to molecular graphs, which currently undergo rapid development, there is also an increasing trend in using graph learning approaches on protein structures. In this short review paper, we survey studies that use graph learning techniques on proteins, and examine their successes and shortcomings, while also discussing future directions.
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Affiliation(s)
- Romanos Fasoulis
- Department of Computer Science, Rice University, Houston, TX, U.S.A
| | - Georgios Paliouras
- Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, Houston, TX, U.S.A
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Papaioannou TG, Fasoulis R, Gialafos E, Tousoulis D. Methodological and computational insights on the assessment of arterial baroreflex sensitivity. Exp Physiol 2019; 104:779-780. [PMID: 31034118 DOI: 10.1113/ep087298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 01/23/2019] [Indexed: 11/08/2022]
Affiliation(s)
- Theodore G Papaioannou
- Biomedical Engineering Unit, First Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Romanos Fasoulis
- Biomedical Engineering Unit, First Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Elias Gialafos
- First Department of Neurology, Aeginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Dimitrios Tousoulis
- Biomedical Engineering Unit, First Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Athens, Greece
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Papaioannou TG, Fasoulis R, Toumpaniaris P, Tsioufis C, Dilaveris P, Soulis D, Koutsouris D, Tousoulis D. Assessment of arterial baroreflex sensitivity by different computational analyses of pressure wave signals alone. Comput Methods Programs Biomed 2019; 172:25-34. [PMID: 30902125 DOI: 10.1016/j.cmpb.2019.02.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Revised: 01/28/2019] [Accepted: 02/01/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Baroreflex sensitivity (BRS) is an important indicator of the functionality of the arterial baroreceptors, and its assessment may have major research and clinical implications. An important requirement for its quantification is the continuous recording of electrocardiography (ECG) signal, so as to extract the RR interval, in parallel with continuous beat-to-beat blood pressure recording. We aimed to accurately calculate the RR Interval from pressure wave recordings per se, namely, the Pulse Interval (PI) using various arterial pulse wave analysis algorithms and to evaluate the precision and accuracy of BRS values calculated with the PI compared to BRS values calculated with the RR Interval. METHODS We analyzed the open access data of the Eurobavar study, which contains a set of ECG and arterial blood pressure (BP) wave signals recorded at 11 European centers. Pressure waveforms were continuously recorded by the Finapres apparatus which uses a finger cuff. The cuff pressure around the finger is dynamically adjusted by a servo-system to equal intra-arterial pressure, thus allowing the continuous recording of beat-to-beat BP waves. RR Interval was calculated from the ECG, whereas, PI was extracted from the arterial pulse waveforms, using 4 different methods (minimum, maximum, maximum 1st derivative and intersecting tangents method). BRS values were estimated by time domain and frequency domain methods. In order to compare agreement, accuracy, precision, variability, and the association between the reference BRS using the RR Interval and the BRS values using PI, standard statistical methods (i.e. intraclass correlation coefficients, RMSE, regression analysis) and Bland-Altman methods were performed. RESULTS We found that analysis of pressure waves alone by frequency-based (i.e. spectral) methods, provides the most accurate results of BRS estimation compared to time-domain methods (ICC > 0.9, R > 0.9, RMSE > 0.8 ms/mmHg). Concerning the spectral method, any algorithm for PI calculation is sufficient, as all show excellent agreement with the respective RR-intervals determined by ECG time series. Only the intersecting tangents and the maximum 1st derivative methods for PI calculation produce the most accurate results in time domain BRS estimation. CONCLUSION BRS estimation by proper analysis of pressure wave signals alone is feasible and accurate. Further studies are needed to investigate the clinical validity and relevance of the different BRS estimations in diagnostic, prognostic and therapeutic levels.
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Affiliation(s)
- Theodore G Papaioannou
- First Department of Cardiology, Units of Biomedical Engineering (TGP, DS), Hypertension (KT), e-Cardiology (PD), Hippokration Hospital, Medical School, National and Kapodistrian University of Athens. 114 Vas. Sophias ave., Athens 11527, Greece.
| | - Romanos Fasoulis
- First Department of Cardiology, Units of Biomedical Engineering (TGP, DS), Hypertension (KT), e-Cardiology (PD), Hippokration Hospital, Medical School, National and Kapodistrian University of Athens. 114 Vas. Sophias ave., Athens 11527, Greece; Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens. 9, Iroon Polytechniou Str., Athens 15780, Greece
| | - Petros Toumpaniaris
- Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens. 9, Iroon Polytechniou Str., Athens 15780, Greece
| | - Constantinos Tsioufis
- First Department of Cardiology, Units of Biomedical Engineering (TGP, DS), Hypertension (KT), e-Cardiology (PD), Hippokration Hospital, Medical School, National and Kapodistrian University of Athens. 114 Vas. Sophias ave., Athens 11527, Greece
| | - Polychronis Dilaveris
- First Department of Cardiology, Units of Biomedical Engineering (TGP, DS), Hypertension (KT), e-Cardiology (PD), Hippokration Hospital, Medical School, National and Kapodistrian University of Athens. 114 Vas. Sophias ave., Athens 11527, Greece
| | - Dimitrios Soulis
- First Department of Cardiology, Units of Biomedical Engineering (TGP, DS), Hypertension (KT), e-Cardiology (PD), Hippokration Hospital, Medical School, National and Kapodistrian University of Athens. 114 Vas. Sophias ave., Athens 11527, Greece
| | - Dimitrios Koutsouris
- Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens. 9, Iroon Polytechniou Str., Athens 15780, Greece
| | - Dimitrios Tousoulis
- First Department of Cardiology, Units of Biomedical Engineering (TGP, DS), Hypertension (KT), e-Cardiology (PD), Hippokration Hospital, Medical School, National and Kapodistrian University of Athens. 114 Vas. Sophias ave., Athens 11527, Greece
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