1
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Wang Y, Li C, Li Z, Moalin M, den Hartog GJM, Zhang M. Computational Chemistry Strategies to Investigate the Antioxidant Activity of Flavonoids-An Overview. Molecules 2024; 29:2627. [PMID: 38893503 PMCID: PMC11173571 DOI: 10.3390/molecules29112627] [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: 04/28/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Despite several decades of research, the beneficial effect of flavonoids on health is still enigmatic. Here, we focus on the antioxidant effect of flavonoids, which is elementary to their biological activity. A relatively new strategy for obtaining a more accurate understanding of this effect is to leverage computational chemistry. This review systematically presents various computational chemistry indicators employed over the past five years to investigate the antioxidant activity of flavonoids. We categorize these strategies into five aspects: electronic structure analysis, thermodynamic analysis, kinetic analysis, interaction analysis, and bioavailability analysis. The principles, characteristics, and limitations of these methods are discussed, along with current trends.
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Affiliation(s)
- Yue Wang
- Department of Pharmacology and Personalized Medicine, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (C.L.); (G.J.M.d.H.)
| | - Chujie Li
- Department of Pharmacology and Personalized Medicine, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (C.L.); (G.J.M.d.H.)
| | - Zhengwen Li
- School of Pharmacy, Chengdu University, 2025 Chengluo Avenue, Chengdu 610106, China;
| | - Mohamed Moalin
- Research Centre Material Sciences, Zuyd University of Applied Science, 6400 AN Heerlen, The Netherlands;
| | - Gertjan J. M. den Hartog
- Department of Pharmacology and Personalized Medicine, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6200 MD Maastricht, The Netherlands; (Y.W.); (C.L.); (G.J.M.d.H.)
| | - Ming Zhang
- Hainan University-HSF/LWL Collaborative Innovation Laboratory, College of Food Sciences & Engineering, Hainan University, 58 People Road, Haikou 570228, China
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2
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Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints. J Mol Biol 2024; 436:168486. [PMID: 38336197 PMCID: PMC10942765 DOI: 10.1016/j.jmb.2024.168486] [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: 11/08/2023] [Revised: 01/17/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA.
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3
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Zhang Y, Wang X, Zhang Z, Huang Y, Kihara D. Assessment of Protein-Protein Docking Models Using Deep Learning. Methods Mol Biol 2024; 2780:149-162. [PMID: 38987469 DOI: 10.1007/978-1-0716-3985-6_10] [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] [Indexed: 07/12/2024]
Abstract
Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.
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Affiliation(s)
- Yuanyuan Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Zicong Zhang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Yunhan Huang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
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4
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Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes In and On the Membrane with Predicted Spatial Arrangement Constraints. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563303. [PMID: 37961264 PMCID: PMC10634698 DOI: 10.1101/2023.10.20.563303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA
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5
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Christoffer C, Kihara D. Modeling protein-nucleic acid complexes with extremely large conformational changes using Flex-LZerD. Proteomics 2023; 23:e2200322. [PMID: 36529945 PMCID: PMC10448949 DOI: 10.1002/pmic.202200322] [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: 11/01/2022] [Revised: 12/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Proteins and nucleic acids are key components in many processes in living cells, and interactions between proteins and nucleic acids are often crucial pathway components. In many cases, large flexibility of proteins as they interact with nucleic acids is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D atomic structures of such protein-nucleic acid complexes. When such structures are not yet experimentally determined, protein docking can be used to computationally generate useful structure models. However, such docking has long had the limitation that the consideration of flexibility is usually limited to small movements or to small structures. We previously developed a method of flexible protein docking which could model ordered proteins which undergo large-scale conformational changes, which we also showed was compatible with nucleic acids. Here, we elaborate on the ability of that pipeline, Flex-LZerD, to model specifically interactions between proteins and nucleic acids, and demonstrate that Flex-LZerD can model more interactions and types of conformational change than previously shown.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
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6
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Lang L, Frontera A, Perez A, Bauzá A. Computational Study of Driving Forces in ATSP, PDIQ, and P53 Peptide Binding: C═O···C═O Tetrel Bonding Interactions at Work. J Chem Inf Model 2023; 63:3018-3029. [PMID: 37014944 PMCID: PMC10207270 DOI: 10.1021/acs.jcim.3c00024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Indexed: 04/06/2023]
Abstract
Understanding the molecular interactions that drive peptide folding is crucial to chemistry and biology. In this study, we analyzed the role of CO···CO tetrel bonding (TtB) interactions in the folding mechanism of three different peptides (ATSP, pDIQ, and p53), which exhibit a different propensity to fold in an α helix motif. To achieve this goal, we used both a recently developed Bayesian inference approach (MELDxMD) and Quantum Mechanics (QM) calculations at the RI-MP2/def2-TZVP level of theory. These techniques allowed us to study the folding process and to evaluate the strength of the CO···CO TtBs as well as the synergies between TtBs and hydrogen-bonding (HB) interactions. We believe that the results derived from our study will be helpful for those scientists working in computational biology, peptide chemistry, and structural biology.
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Affiliation(s)
- Lijun Lang
- Chemistry
Department, University of Florida, Gainesville, Florida 32611, United States
| | - Antonio Frontera
- Department
of Chemistry, Universitat de les Illes Balears, Crta. de Valldemossa km 7.5, 07122 Palma, Baleares, Spain
| | - Alberto Perez
- Chemistry
Department, University of Florida, Gainesville, Florida 32611, United States
| | - Antonio Bauzá
- Department
of Chemistry, Universitat de les Illes Balears, Crta. de Valldemossa km 7.5, 07122 Palma, Baleares, Spain
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7
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Harini K, Kihara D, Michael Gromiha M. PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features. Methods 2023; 213:10-17. [PMID: 36924867 PMCID: PMC10563387 DOI: 10.1016/j.ymeth.2023.03.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/17/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States; Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan.
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8
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Rehman AU, Khurshid B, Ali Y, Rasheed S, Wadood A, Ng HL, Chen HF, Wei Z, Luo R, Zhang J. Computational approaches for the design of modulators targeting protein-protein interactions. Expert Opin Drug Discov 2023; 18:315-333. [PMID: 36715303 PMCID: PMC10149343 DOI: 10.1080/17460441.2023.2171396] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 01/18/2023] [Indexed: 01/31/2023]
Abstract
BACKGROUND Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery. AREAS COVERED In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes. EXPERT OPINION Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.
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Affiliation(s)
- Ashfaq Ur Rehman
- Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California Irvine, Irvine, California, USA
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, Zhejiang, China
| | - Beenish Khurshid
- Department of Biochemistry, Abdul Wali Khan University Mardan, Pakistan
| | - Yasir Ali
- National Center for Bioinformatics, Quaid-e-Azam University, Islamabad, Pakistan
| | - Salman Rasheed
- National Center for Bioinformatics, Quaid-e-Azam University, Islamabad, Pakistan
| | - Abdul Wadood
- Department of Biochemistry, Abdul Wali Khan University Mardan, Pakistan
| | - Ho-Leung Ng
- Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan, Kansas, USA
| | - Hai-Feng Chen
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, Zhejiang, China
| | - Zhiqiang Wei
- Medicinal Chemistry and Bioinformatics Center, Ocean University of China, Qingdao, Shandong, China
| | - Ray Luo
- Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California Irvine, Irvine, California, USA
| | - Jian Zhang
- Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, Zhejiang, China
- School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China
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9
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Esmaeeli R, Bauzá A, Perez A. Structural predictions of protein-DNA binding: MELD-DNA. Nucleic Acids Res 2023; 51:1625-1636. [PMID: 36727436 PMCID: PMC9976882 DOI: 10.1093/nar/gkad013] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/27/2022] [Accepted: 01/30/2023] [Indexed: 02/03/2023] Open
Abstract
Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein-DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein-DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein-DNA interactions).
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Affiliation(s)
- Reza Esmaeeli
- Department of Chemistry, Quantum theory project, University of Florida, Gainesville, FL 32611, USA
| | - Antonio Bauzá
- Department of Chemistry, Universitat de les Illes Balears, Palma de Mallorca (Baleares), 07122, Spain
| | - Alberto Perez
- Department of Chemistry, Quantum theory project, University of Florida, Gainesville, FL 32611, USA
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10
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Harini K, Christoffer C, Gromiha MM, Kihara D. Pairwise and Multi-chain Protein Docking Enhanced Using LZerD Web Server. Methods Mol Biol 2023; 2690:355-373. [PMID: 37450159 PMCID: PMC10561630 DOI: 10.1007/978-1-0716-3327-4_28] [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] [Indexed: 07/18/2023]
Abstract
Interactions of proteins with other macromolecules have important structural and functional roles in the basic processes of living cells. To understand and elucidate the mechanisms of interactions, it is important to know the 3D structures of the complexes. Proteomes contain numerous protein-protein complexes, for which experimentally determined structures often do not exist. Computational techniques can be a practical alternative to obtain useful complex structure models. Here, we present a web server that provides access to the LZerD and Multi-LZerD protein docking tools, which can perform both pairwise and multi-chain docking. The web server is user-friendly, with options to visualize the distribution and structures of binding poses of top-scoring models. The LZerD web server is available at https://lzerd.kiharalab.org . This chapter dictates the algorithm and step-by-step procedure to model the monomeric structures with AttentiveDist, and also provides the detail of pairwise LZerD docking, and multi-LZerD. This also provided case studies for each of the three modules.
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Affiliation(s)
- Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | | | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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11
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Christoffer C, Kihara D. Domain-Based Protein Docking with Extremely Large Conformational Changes. J Mol Biol 2022; 434:167820. [PMID: 36089054 PMCID: PMC9992458 DOI: 10.1016/j.jmb.2022.167820] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/31/2022] [Accepted: 09/03/2022] [Indexed: 11/17/2022]
Abstract
Proteins are key components in many processes in living cells, and physical interactions with other proteins and nucleic acids often form key parts of their functions. In many cases, large flexibility of proteins as they interact is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D structures of such protein complexes. When such structures are not yet experimentally determined, protein docking has long been present to computationally generate useful structure models. However, protein docking has long had the limitation that the consideration of flexibility is usually limited to very small movements or very small structures. Methods have been developed which handle minor flexibility via normal mode or other structure sampling, but new methods are required to model ordered proteins which undergo large-scale conformational changes to elucidate their function at the molecular level. Here, we present Flex-LZerD, a framework for docking such complexes. Via partial assembly multidomain docking and an iterative normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein-protein and protein-nucleic acid complexes.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA.
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12
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Chang L, Mondal A, Perez A. Towards rational computational peptide design. FRONTIERS IN BIOINFORMATICS 2022; 2:1046493. [PMID: 36338806 PMCID: PMC9634169 DOI: 10.3389/fbinf.2022.1046493] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 10/11/2022] [Indexed: 11/16/2022] Open
Abstract
Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available-and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.
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Affiliation(s)
- Liwei Chang
- Department of Chemistry, University of Florida, Gainesville, FL, United States,Quantum Theory Project, University of Florida, Gainesville, FL, United States
| | - Arup Mondal
- Department of Chemistry, University of Florida, Gainesville, FL, United States,Quantum Theory Project, University of Florida, Gainesville, FL, United States
| | - Alberto Perez
- Department of Chemistry, University of Florida, Gainesville, FL, United States,Quantum Theory Project, University of Florida, Gainesville, FL, United States,*Correspondence: Alberto Perez,
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13
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Kiruba Nesamalar E, SatheeshKumar J, Amudha T. Efficient DNA-ligand interaction framework using fuzzy C-means clustering based glowworm swarm optimization (FCMGSO) method. J Biomol Struct Dyn 2022:1-13. [PMID: 35930294 DOI: 10.1080/07391102.2022.2105958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Assessment of DNA and ligand interaction is a great challenge to the medical researchers and drug industries since the accurate mapping of DNA and ligand plays an important role in associating drugs for suitable diseases. The primary objective of this research work is to develop an efficient model for predicting the best DNA and Ligand mapping. In this research work, 500 instances of DNA and drugs used for cancer and non-cancer diseases from the National Centre for Biotechnology Information (NCBI) were considered for analysis. Binding energy is one of the important measures to predict and finalize the best DNA and ligand interaction. Existing methods used for the docking process such as Simulated Annealing (SA), Lamarckian Genetic Algorithm (LGA), Genetic Clustering (GC), Fuzzy C-means clustering (FCM), and Genetic Clustering with Multi swarm Optimization (GCMSO) were applied for all 500 instances. These algorithms failed to produce better binding energy due to a lack of optimization in the existing approaches. Optimization methods play a major role in predicting accurate DNA ligand docking. Hence, this research proposes an efficient architecture using Fuzzy C-Means Clustering with Glowworm Swarm (FCMGSO) optimization method for accurate analysis of the DNA-ligand docking process. Results are proving that the proposed FCMGSO algorithm shows less binding energy than other existing methods in all instances of samples considered from the NCBI dataset.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - J SatheeshKumar
- Department of Computer Applications, Bharathiar University, Coimbatore, India
| | - T Amudha
- Department of Computer Applications, Bharathiar University, Coimbatore, India
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14
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Tomaž Š, Gruden K, Coll A. TGA transcription factors-Structural characteristics as basis for functional variability. FRONTIERS IN PLANT SCIENCE 2022; 13:935819. [PMID: 35958211 PMCID: PMC9360754 DOI: 10.3389/fpls.2022.935819] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 07/04/2022] [Indexed: 06/15/2023]
Abstract
TGA transcription factors are essential regulators of various cellular processes, their activity connected to different hormonal pathways, interacting proteins and regulatory elements. Belonging to the basic region leucine zipper (bZIP) family, TGAs operate by binding to their target DNA sequence as dimers through a conserved bZIP domain. Despite sharing the core DNA-binding sequence, the TGA paralogues exert somewhat different DNA-binding preferences. Sequence variability of their N- and C-terminal protein parts indicates their importance in defining TGA functional specificity through interactions with diverse proteins, affecting their DNA-binding properties. In this review, we provide a short and concise summary on plant TGA transcription factors from a structural point of view, including the relation of their structural characteristics to their functional roles in transcription regulation.
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Affiliation(s)
- Špela Tomaž
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
- Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
| | - Anna Coll
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, Slovenia
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15
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Ding B, Yu Y, Geng S, Liu B, Hao Y, Liang G. Computational Methods for the Interaction between Cyclodextrins and Natural Compounds: Technology, Benefits, Limitations, and Trends. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:2466-2482. [PMID: 35170315 DOI: 10.1021/acs.jafc.1c07018] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cyclodextrins (CDs) have a hollow structure with a hydrophobic interior and hydrophilic exterior. Forming inclusion complexes with CDs will maximize the bioavailability of natural compounds and enable active components to be processed into functional foods, medicines, additives, and so forth. However, experimental methods cannot explain CD-guest binding at the atomic level. Different models have been recently developed to simulate the interaction between CDs and guests to study the binding conformation and analyze noncovalent forces. This review paper summarizes modeling methods of CD-natural compound complexes. The methods include quantitative structure-activity relationships, molecular docking, molecular dynamics simulations, and quantum-chemical calculations. The applications of these methods to enhance the solubility and bioactivities of guest molecules, assist material transportation, and promote compound extraction are also discussed. The purpose of this review is to explore interaction mechanisms of CDs and guests and to help expand new applications of CDs.
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Affiliation(s)
- Botian Ding
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Yuandong Yu
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Sheng Geng
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
| | - Benguo Liu
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 401331, China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China
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16
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Cao Y, Yang R, Wang W, Jiang S, Yang C, Liu N, Dai H, Lee I, Meng X, Yuan Z. Probing the Formation, Structure and Free Energy Relationships of M Protein Dimers of SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:573-582. [PMID: 35047128 PMCID: PMC8756865 DOI: 10.1016/j.csbj.2022.01.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 01/04/2022] [Accepted: 01/08/2022] [Indexed: 12/12/2022] Open
Abstract
The M protein of the novel coronavirus 2019 (SARS-CoV-2) is the major structural component of the viral envelope and is also the minimum requirement for virus particle budding. M proteins generally exist as dimers. In virus assembly, they are the main driving force for envelope formation through lateral interactions and interactions with other viral structural proteins that play a central role. We built 100 candidate models and finally analyzed the six most convincing structural features of the SARS-CoV-2 M protein dimer based on long-timescale molecular dynamics (MD) simulations, multiple free energy analyses (potential mean force (PMF) and molecular mechanics Poisson-Boltzmann surface area (MMPBSA)) and principal component analysis (PCA) to obtain the most reasonable structure. The dimer stability was found to depend on the Leu-Ile zipper motif and aromatic amino acids in the transmembrane domain (TMD). Furthermore, the C-terminal domain (CTD) effects were relatively small. These results highlight a model in which there is sufficient binding affinity between the TMDs of M proteins to form dimers through the residues at the interface of the three transmembrane helices (TMHs). This study aims to help find more effective inhibitors of SARS-CoV-2 M dimers and to develop vaccines based on structural information.
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Affiliation(s)
- Yipeng Cao
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060 PR China
- National Supercomputer Center in Tianjin, 300457 PR China
| | - Rui Yang
- Department of Infection and Immunity, Tianjin Union Medical Center, Nankai University Affiliated Hospital. 300031, PR China
| | - Wei Wang
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060 PR China
| | - Shengpeng Jiang
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060 PR China
| | - Chengwen Yang
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060 PR China
| | - Ningbo Liu
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060 PR China
| | - Hongji Dai
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060 PR China
- College of Physics, Nankai University, Tianjin 300071, PR China
| | - Imshik Lee
- Department of Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, PR China
| | - Xiangfei Meng
- National Supercomputer Center in Tianjin, 300457 PR China
- Corresponding authors.
| | - Zhiyong Yuan
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060 PR China
- Corresponding authors.
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17
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Verburgt J, Kihara D. Benchmarking of structure refinement methods for protein complex models. Proteins 2022; 90:83-95. [PMID: 34309909 PMCID: PMC8671191 DOI: 10.1002/prot.26188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/24/2021] [Accepted: 07/22/2021] [Indexed: 01/03/2023]
Abstract
Protein structure docking is the process in which the quaternary structure of a protein complex is predicted from individual tertiary structures of the protein subunits. Protein docking is typically performed in two main steps. The subunits are first docked while keeping them rigid to form the complex, which is then followed by structure refinement. Structure refinement is crucial for a practical use of computational protein docking models, as it is aimed for correcting conformations of interacting residues and atoms at the interface. Here, we benchmarked the performance of eight existing protein structure refinement methods in refinement of protein complex models. We show that the fraction of native contacts between subunits is by far the most straightforward metric to improve. However, backbone dependent metrics, based on the Root Mean Square Deviation proved more difficult to improve via refinement.
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Affiliation(s)
- Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA
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18
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Christoffer C, Bharadwaj V, Luu R, Kihara D. LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction. Front Mol Biosci 2021; 8:724947. [PMID: 34466411 PMCID: PMC8403062 DOI: 10.3389/fmolb.2021.724947] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/21/2021] [Indexed: 01/25/2023] Open
Abstract
Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Vijay Bharadwaj
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Ryan Luu
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, United States.,Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
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19
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Christoffer C, Chen S, Bharadwaj V, Aderinwale T, Kumar V, Hormati M, Kihara D. LZerD webserver for pairwise and multiple protein-protein docking. Nucleic Acids Res 2021; 49:W359-W365. [PMID: 33963854 PMCID: PMC8262708 DOI: 10.1093/nar/gkab336] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/13/2021] [Accepted: 04/19/2021] [Indexed: 12/13/2022] Open
Abstract
Protein complexes are involved in many important processes in living cells. To understand the mechanisms of these processes, it is necessary to solve the 3D structures of the protein complexes. When protein complex structures have not yet been determined by experiment, protein-protein docking tools can be used to computationally model the structures of these complexes. Here, we present a webserver which provides access to LZerD and Multi-LZerD protein docking tools. The protocol provided by the server have performed consistently among the top in the CAPRI blind evaluation. LZerD docks pairs of structures, while Multi-LZerD can dock three or more structures simultaneously. LZerD uses a soft protein surface representation with 3D Zernike descriptors and explores the binding pose space using geometric hashing. Multi-LZerD performs multi-chain docking by combining pairwise solutions by LZerD. Both methods output full-atom docked models of the input proteins. Users can also input distance constraints between interacting or non-interacting residues as well as residues that locate at the interface or far from the interface. The webserver is equipped with a user-friendly panel that visualizes the distribution and structures of binding poses of top scoring models. The LZerD webserver is available at https://lzerd.kiharalab.org.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Siyang Chen
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Vijay Bharadwaj
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Vidhur Kumar
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Matin Hormati
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA.,Department of Biological Sciences, Purdue University, West Lafayette IN, 47907, USA.,Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA
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20
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Wang X, Flannery ST, Kihara D. Protein Docking Model Evaluation by Graph Neural Networks. Front Mol Biosci 2021; 8:647915. [PMID: 34113650 PMCID: PMC8185212 DOI: 10.3389/fmolb.2021.647915] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 04/26/2021] [Indexed: 12/03/2022] Open
Abstract
Physical interactions of proteins play key functional roles in many important cellular processes. To understand molecular mechanisms of such functions, it is crucial to determine the structure of protein complexes. To complement experimental approaches, which usually take a considerable amount of time and resources, various computational methods have been developed for predicting the structures of protein complexes. In computational modeling, one of the challenges is to identify near-native structures from a large pool of generated models. Here, we developed a deep learning-based approach named Graph Neural Network-based DOcking decoy eValuation scorE (GNN-DOVE). To evaluate a protein docking model, GNN-DOVE extracts the interface area and represents it as a graph. The chemical properties of atoms and the inter-atom distances are used as features of nodes and edges in the graph, respectively. GNN-DOVE was trained, validated, and tested on docking models in the Dockground database and further tested on a combined dataset of Dockground and ZDOCK benchmark as well as a CAPRI scoring dataset. GNN-DOVE performed better than existing methods, including DOVE, which is our previous development that uses a convolutional neural network on voxelized structure models.
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Affiliation(s)
- Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Sean T. Flannery
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
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21
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Perez JJ, Perez RA, Perez A. Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering. Front Mol Biosci 2021; 8:681617. [PMID: 34095231 PMCID: PMC8173110 DOI: 10.3389/fmolb.2021.681617] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/05/2021] [Indexed: 12/13/2022] Open
Abstract
Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.
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Affiliation(s)
- Juan J Perez
- Department of Chemical Engineering, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Roman A Perez
- Bioengineering Institute of Technology, Universitat Internacional de Catalunya, Sant Cugat, Spain
| | - Alberto Perez
- The Quantum Theory Project, Department of Chemistry, University of Florida, Gainesville, FL, United States
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22
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Urso A, Fiannaca A, La Rosa M, La Paglia L, Lo Bosco G, Rizzo R. BITS2019: the sixteenth annual meeting of the Italian society of bioinformatics. BMC Bioinformatics 2020; 21:363. [PMID: 32938383 PMCID: PMC7493178 DOI: 10.1186/s12859-020-03708-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The 16th Annual Meeting of the Bioinformatics Italian Society was held in Palermo, Italy, on June 26-28, 2019. More than 80 scientific contributions were presented, including 4 keynote lectures, 31 oral communications and 49 posters. Also, three workshops were organised before and during the meeting. Full papers from some of the works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.
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Affiliation(s)
- Alfonso Urso
- ICAR-CNR, Institute for high performance computing and networking, National Research Council of Italy, Palermo, 90146, Italy.
| | - Antonino Fiannaca
- ICAR-CNR, Institute for high performance computing and networking, National Research Council of Italy, Palermo, 90146, Italy
| | - Massimo La Rosa
- ICAR-CNR, Institute for high performance computing and networking, National Research Council of Italy, Palermo, 90146, Italy
| | - Laura La Paglia
- ICAR-CNR, Institute for high performance computing and networking, National Research Council of Italy, Palermo, 90146, Italy
| | - Giosue' Lo Bosco
- Department of Mathematics and Computer Science, University of Palermo, Palermo, 90128, Italy
| | - Riccardo Rizzo
- ICAR-CNR, Institute for high performance computing and networking, National Research Council of Italy, Palermo, 90146, Italy
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