1
|
Grady SK, Dojcsak L, Harville EW, Wallace ME, Vilda D, Donneyong MM, Hood DB, Valdez RB, Ramesh A, Im W, Matthews-Juarez P, Juarez PD, Langston MA. Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:124201. [PMID: 38109119 PMCID: PMC10727037 DOI: 10.1289/ehp12901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways. OBJECTIVES The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar's focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics. DISCUSSION State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901.
Collapse
Affiliation(s)
- Stephen K. Grady
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, Tennessee, USA
| | - Levente Dojcsak
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
| | - Emily W. Harville
- Department Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Maeve E. Wallace
- Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | - Dovile Vilda
- Department of Social, Behavioral, and Population Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, USA
| | | | - Darryl B. Hood
- Division of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, Ohio, USA
| | - R. Burciaga Valdez
- Department of Economics, University of New Mexico, Albuquerque, New Mexico, USA
| | - Aramandla Ramesh
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, Meharry Medical College, Nashville, Tennessee, USA
| | - Wansoo Im
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee, USA
| | | | - Paul D. Juarez
- Department of Family and Community Medicine, Meharry Medical College, Nashville, Tennessee, USA
- Institute on Health Disparities, Equity, and the Exposome, Meharry Medical College, Nashville, Tennessee, USA
| | - Michael A. Langston
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee, USA
| |
Collapse
|
2
|
Kazmirchuk TDD, Bradbury-Jost C, Withey TA, Gessese T, Azad T, Samanfar B, Dehne F, Golshani A. Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing. Genes (Basel) 2023; 14:1194. [PMID: 37372372 DOI: 10.3390/genes14061194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics.
Collapse
Affiliation(s)
- Thomas David Daniel Kazmirchuk
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Calvin Bradbury-Jost
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Taylor Ann Withey
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Tadesse Gessese
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Taha Azad
- Department of Microbiology and Infectious Diseases, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, QC J1H 5N4, Canada
| | - Bahram Samanfar
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre (ORDC), Ottawa, ON K1A 0C6, Canada
| | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Ashkan Golshani
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| |
Collapse
|
3
|
Wang XW, Madeddu L, Spirohn K, Martini L, Fazzone A, Becchetti L, Wytock TP, Kovács IA, Balogh OM, Benczik B, Pétervári M, Ágg B, Ferdinandy P, Vulliard L, Menche J, Colonnese S, Petti M, Scarano G, Cuomo F, Hao T, Laval F, Willems L, Twizere JC, Vidal M, Calderwood MA, Petrillo E, Barabási AL, Silverman EK, Loscalzo J, Velardi P, Liu YY. Assessment of community efforts to advance network-based prediction of protein-protein interactions. Nat Commun 2023; 14:1582. [PMID: 36949045 PMCID: PMC10033937 DOI: 10.1038/s41467-023-37079-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/02/2023] [Indexed: 03/24/2023] Open
Abstract
Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.
Collapse
Affiliation(s)
- Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Lorenzo Madeddu
- Translational and Precision Medicine Department Sapienza University of Rome, Rome, Italy
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Leonardo Martini
- Department of Computer, Control, and Management Engineering "Antonio Rubert", Sapienza University of Rome, Rome, Italy
| | | | - Luca Becchetti
- Department of Computer, Control, and Management Engineering "Antonio Rubert", Sapienza University of Rome, Rome, Italy
| | - Thomas P Wytock
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA
| | - István A Kovács
- Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, 60208, USA
| | - Olivér M Balogh
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
| | - Bettina Benczik
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
- Pharmahungary Group, 6722, Szeged, Hungary
| | - Mátyás Pétervári
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
| | - Bence Ágg
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
- Pharmahungary Group, 6722, Szeged, Hungary
| | - Péter Ferdinandy
- Cardiometabolic and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
- Pharmahungary Group, 6722, Szeged, Hungary
| | - Loan Vulliard
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna, Austria
| | - Jörg Menche
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Department of Structural and Computational Biology, Max Perutz Labs, University of Vienna, Vienna, Austria
- Faculty of Mathematics, University of Vienna, Vienna, Austria
| | - Stefania Colonnese
- Department of Information Engineering, Electronics, and Telecommunications (DIET), University of Rome "Sapienza", Rome, Italy
| | - Manuela Petti
- Department of Computer, Control, and Management Engineering "Antonio Rubert", Sapienza University of Rome, Rome, Italy
| | - Gaetano Scarano
- Department of Information Engineering, Electronics, and Telecommunications (DIET), University of Rome "Sapienza", Rome, Italy
| | - Francesca Cuomo
- Department of Information Engineering, Electronics, and Telecommunications (DIET), University of Rome "Sapienza", Rome, Italy
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Florent Laval
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Laboratory of Molecular and Cellular Epigenetic, GIGA Institute, University of Liège, Liège, Belgium
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
| | - Luc Willems
- Laboratory of Molecular and Cellular Epigenetic, GIGA Institute, University of Liège, Liège, Belgium
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, GIGA Institute, University of Liège, Liège, Belgium
- TERRA Teaching and Research Centre, University of Liège, Gembloux, Belgium
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Enrico Petrillo
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Albert-László Barabási
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, 02115, USA
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Paola Velardi
- Translational and Precision Medicine Department Sapienza University of Rome, Rome, Italy.
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA.
| |
Collapse
|
4
|
Hajikarimlou M, Hooshyar M, Moutaoufik M, Aly K, Azad T, Takallou S, Jagadeesan S, Phanse S, Said K, Samanfar B, Bell J, Dehne F, Babu M, Golshani A. A computational approach to rapidly design peptides that detect SARS-CoV-2 surface protein S. NAR Genom Bioinform 2022; 4:lqac058. [PMID: 36004308 PMCID: PMC9394169 DOI: 10.1093/nargab/lqac058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/10/2022] [Accepted: 08/01/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
The coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted the development of diagnostic and therapeutic frameworks for timely containment of this pandemic. Here, we utilized our non-conventional computational algorithm, InSiPS, to rapidly design and experimentally validate peptides that bind to SARS-CoV-2 spike (S) surface protein. We previously showed that this method can be used to develop peptides against yeast proteins, however, the applicability of this method to design peptides against other proteins has not been investigated. In the current study, we demonstrate that two sets of peptides developed using InSiPS method can detect purified SARS-CoV-2 S protein via ELISA and Surface Plasmon Resonance (SPR) approaches, suggesting the utility of our strategy in real time COVID-19 diagnostics. Mass spectrometry-based salivary peptidomics shortlist top SARS-CoV-2 peptides detected in COVID-19 patients’ saliva, rendering them attractive SARS-CoV-2 diagnostic targets that, when subjected to our computational platform, can streamline the development of potent peptide diagnostics of SARS-CoV-2 variants of concern. Our approach can be rapidly implicated in diagnosing other communicable diseases of immediate threat.
Collapse
Affiliation(s)
- Maryam Hajikarimlou
- Ottawa Institute of Systems Biology, University of Ottawa , Health Science Campus, Ottawa , Ontario , Canada
- Department of Biology, Carleton University , Ottawa , Ontario , Canada
| | - Mohsen Hooshyar
- Ottawa Institute of Systems Biology, University of Ottawa , Health Science Campus, Ottawa , Ontario , Canada
- Department of Biology, Carleton University , Ottawa , Ontario , Canada
| | - Mohamed Taha Moutaoufik
- Department of Biochemistry, Research and Innovation Centre, University of Regina , Regina , Canada
| | - Khaled A Aly
- Department of Biochemistry, Research and Innovation Centre, University of Regina , Regina , Canada
| | - Taha Azad
- The Ottawa Hospital Research Institute 501 Smyth Road , Ottawa , Ontario , Canada
| | - Sarah Takallou
- Ottawa Institute of Systems Biology, University of Ottawa , Health Science Campus, Ottawa , Ontario , Canada
- Department of Biology, Carleton University , Ottawa , Ontario , Canada
| | - Sasi Jagadeesan
- Ottawa Institute of Systems Biology, University of Ottawa , Health Science Campus, Ottawa , Ontario , Canada
- Department of Biology, Carleton University , Ottawa , Ontario , Canada
| | - Sadhna Phanse
- Department of Biochemistry, Research and Innovation Centre, University of Regina , Regina , Canada
| | - Kamaledin B Said
- Department of Biology, Carleton University , Ottawa , Ontario , Canada
- Department of Pathology and Microbiology, College of Medicine, University of Hail , Saudi Arabia
| | - Bahram Samanfar
- Ottawa Institute of Systems Biology, University of Ottawa , Health Science Campus, Ottawa , Ontario , Canada
- Department of Biology, Carleton University , Ottawa , Ontario , Canada
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre (ORDC) , Ottawa , Ontario , Canada
| | - John C Bell
- The Ottawa Hospital Research Institute 501 Smyth Road , Ottawa , Ontario , Canada
| | - Frank Dehne
- School of Computer Science, Carleton University , Ottawa , Ontario , Canada
| | - Mohan Babu
- Department of Biochemistry, Research and Innovation Centre, University of Regina , Regina , Canada
| | - Ashkan Golshani
- Ottawa Institute of Systems Biology, University of Ottawa , Health Science Campus, Ottawa , Ontario , Canada
- Department of Biology, Carleton University , Ottawa , Ontario , Canada
| |
Collapse
|
5
|
Dick K, Pattang A, Hooker J, Nissan N, Sadowski M, Barnes B, Tan LH, Burnside D, Phanse S, Aoki H, Babu M, Dehne F, Golshani A, Cober ER, Green JR, Samanfar B. Human-Soybean Allergies: Elucidation of the Seed Proteome and Comprehensive Protein-Protein Interaction Prediction. J Proteome Res 2021; 20:4925-4947. [PMID: 34582199 DOI: 10.1021/acs.jproteome.1c00138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The soybean crop, Glycine max (L.) Merr., is consumed by humans, Homo sapiens, worldwide. While the respective bodies of literature and -omics data for each of these organisms are extensive, comparatively few studies investigate the molecular biological processes occurring between the two. We are interested in elucidating the network of protein-protein interactions (PPIs) involved in human-soybean allergies. To this end, we leverage state-of-the-art sequence-based PPI predictors amenable to predicting the enormous comprehensive interactome between human and soybean. A network-based analytical approach is proposed, leveraging similar interaction profiles to identify candidate allergens and proteins involved in the allergy response. Interestingly, the predicted interactome can be explored from two complementary perspectives: which soybean proteins are predicted to interact with specific human proteins and which human proteins are predicted to interact with specific soybean proteins. A total of eight proteins (six specific to the human proteome and two to the soy proteome) have been identified and supported by the literature to be involved in human health, specifically related to immunological and neurological pathways. This study, beyond generating the most comprehensive human-soybean interactome to date, elucidated a soybean seed interactome and identified several proteins putatively consequential to human health.
Collapse
Affiliation(s)
- Kevin Dick
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Arezo Pattang
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Julia Hooker
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Nour Nissan
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Michael Sadowski
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Bradley Barnes
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Le Hoa Tan
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Daniel Burnside
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Sadhna Phanse
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada S4S 0A2
| | - Hiroyuki Aoki
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada S4S 0A2
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, Saskatchewan, Canada S4S 0A2
| | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Ashkan Golshani
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Elroy R Cober
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| | - Bahram Samanfar
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, Canada K1A 0C6
- Department of Biology and Institute of Biochemistry, and Ottawa Institute of Systems Biology, Carleton University, Ottawa, Ontario, Canada K1S 5B6
| |
Collapse
|
6
|
Hagan RD, Langston MA. Molecular Subtyping and Outlier Detection in Human Disease Using the Paraclique Algorithm. ALGORITHMS 2021; 14:63. [PMID: 36092474 PMCID: PMC9455766 DOI: 10.3390/a14020063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Recent discoveries of distinct molecular subtypes have led to remarkable advances in treatment for a variety of diseases. While subtyping via unsupervised clustering has received a great deal of interest, most methods rely on basic statistical or machine learning methods. At the same time, techniques based on graph clustering, particularly clique-based strategies, have been successfully used to identify disease biomarkers and gene networks. A graph theoretical approach based on the paraclique algorithm is described that can easily be employed to identify putative disease subtypes and serve as an aid in outlier detection as well. The feasibility and potential effectiveness of this method is demonstrated on publicly available gene co-expression data derived from patient samples covering twelve different disease families.
Collapse
|
7
|
Hooshyar M, Jessulat M, Burnside D, Kluew A, Babu M, Golshani A. Deletion of yeast TPK1 reduces the efficiency of non-homologous end joining DNA repair. Biochem Biophys Res Commun 2020; 533:899-904. [DOI: 10.1016/j.bbrc.2020.09.083] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 09/20/2020] [Indexed: 12/12/2022]
|
8
|
Dick K, Samanfar B, Barnes B, Cober ER, Mimee B, Tan LH, Molnar SJ, Biggar KK, Golshani A, Dehne F, Green JR. PIPE4: Fast PPI Predictor for Comprehensive Inter- and Cross-Species Interactomes. Sci Rep 2020; 10:1390. [PMID: 31996697 PMCID: PMC6989690 DOI: 10.1038/s41598-019-56895-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 12/13/2019] [Indexed: 02/06/2023] Open
Abstract
The need for larger-scale and increasingly complex protein-protein interaction (PPI) prediction tasks demands that state-of-the-art predictors be highly efficient and adapted to inter- and cross-species predictions. Furthermore, the ability to generate comprehensive interactomes has enabled the appraisal of each PPI in the context of all predictions leading to further improvements in classification performance in the face of extreme class imbalance using the Reciprocal Perspective (RP) framework. We here describe the PIPE4 algorithm. Adaptation of the PIPE3/MP-PIPE sequence preprocessing step led to upwards of 50x speedup and the new Similarity Weighted Score appropriately normalizes for window frequency when applied to any inter- and cross-species prediction schemas. Comprehensive interactomes for three prediction schemas are generated: (1) cross-species predictions, where Arabidopsis thaliana is used as a proxy to predict the comprehensive Glycine max interactome, (2) inter-species predictions between Homo sapiens-HIV1, and (3) a combined schema involving both cross- and inter-species predictions, where both Arabidopsis thaliana and Caenorhabditis elegans are used as proxy species to predict the interactome between Glycine max (the soybean legume) and Heterodera glycines (the soybean cyst nematode). Comparing PIPE4 with the state-of-the-art resulted in improved performance, indicative that it should be the method of choice for complex PPI prediction schemas.
Collapse
Affiliation(s)
- Kevin Dick
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, K1S 5B6, Canada
| | - Bahram Samanfar
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, K1A 0C6, Canada
- Department of Biology, Carleton University, Ottawa, K1S 5B6, Ontario, Canada
| | - Bradley Barnes
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, K1S 5B6, Canada
| | - Elroy R Cober
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, K1A 0C6, Canada
| | - Benjamin Mimee
- Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu Research and Development Centre, Saint-Jean-sur-Richelieu, J3B 3E6, Quebec, Canada
| | - Le Hoa Tan
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, K1A 0C6, Canada
| | - Stephen J Molnar
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, Ontario, K1A 0C6, Canada
| | - Kyle K Biggar
- Department of Biology, Carleton University, Ottawa, K1S 5B6, Ontario, Canada
| | - Ashkan Golshani
- Department of Biology, Carleton University, Ottawa, K1S 5B6, Ontario, Canada
- Ottawa Institute of Systems Biology, Carleton University, 1125 Colonel By Drive, Ottawa, K1S 5B6, Canada
| | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, Ontario, K1S 5B6, Canada
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, K1S 5B6, Canada.
| |
Collapse
|
9
|
Graph Theoretical Analysis of Genome-Scale Data: Examination of Gene Activation Occurring in the Setting of Community-Acquired Pneumonia. Shock 2019; 50:53-59. [PMID: 29049138 DOI: 10.1097/shk.0000000000001029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
INTRODUCTION We have previously reported evidence that Black individuals appear to have a significantly higher incidence of infection-related hospitalizations compared with White individuals. It is possible that the host immune response is responsible for this vital difference. In support of such a hypothesis, the aim of this study was to determine whether Black and White individuals exhibit differential whole blood gene network activation. METHODS We examined whole blood network activation in a subset of patients (n = 22 pairs, propensity score matched (1:1) Black and White patients) with community-acquired pneumonia (CAP) from the Genetic and Inflammatory Markers of Sepsis study. We employed day one whole blood transcriptomic data generated from this cohort and constructed co-expression graphs for each racial group. Pearson correlation coefficients were used to weight edges. Spectral thresholding was applied to ascribe significance. Innovative graph theoretical methods were then invoked to detect densely connected gene networks and provide differential structural analysis. RESULTS Propensity matching was employed to reduce potential bias due to confounding variables. Although Black and White patients had similar socio- and clinical demographics, we identified novel differences in molecular network activation-dense subgraphs known as paracliques that displayed complete gene connection for both White (three paracliques) and Black patients (one paraclique). Specifically, the genes that comprised the paracliques in the White patients include circadian loop, cell adhesion, mobility, proliferation, tumor suppression, NFκB, and chemokine signaling. However, the genes that comprised the paracliques in the Black patients include DNA and messenger RNA processes, and apoptosis signaling. We investigated the distribution of Black paracliques across White paracliques. Black patients had five paracliques (with almost complete connection) comprised of genes that are critical for host immune response widely distributed across 22 parcliques in the White population. Anchoring the analysis on two critical inflammatory mediators, interleukin (IL)-6 and IL-10 identified further differential network activation among the White and Black patient populations. CONCLUSIONS These results demonstrate that, at the molecular level, Black and White individuals may experience different activation patterns with CAP. Further validation of the gene networks we have identified may help pinpoint genetic factors that increase host susceptibility to community-acquired pneumonia, and may lay the groundwork for personalized management of CAP.
Collapse
|
10
|
Li Y, Li LP, Wang L, Yu CQ, Wang Z, You ZH. An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model. Int J Mol Sci 2019; 20:ijms20143511. [PMID: 31319578 PMCID: PMC6679202 DOI: 10.3390/ijms20143511] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 07/04/2019] [Accepted: 07/15/2019] [Indexed: 01/03/2023] Open
Abstract
Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein–protein interactions (PPIs) data has been produced by high-throughput biotechnology, the disadvantage of biological experimental technique is time-consuming and costly. Thus, computational methods for predicting protein interactions have become a research hot spot. In this research, we propose an efficient computational method that combines Rotation Forest (RF) classifier with Local Binary Pattern (LBP) feature extraction method to predict PPIs from the perspective of Position-Specific Scoring Matrix (PSSM). The proposed method has achieved superior performance in predicting Yeast, Human, and H. pylori datasets with average accuracies of 92.12%, 96.21%, and 86.59%, respectively. In addition, we also evaluated the performance of the proposed method on the four independent datasets of C. elegans, H. pylori, H. sapiens, and M. musculus datasets. These obtained experimental results fully prove that our model has good feasibility and robustness in predicting PPIs.
Collapse
Affiliation(s)
- Yang Li
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Li-Ping Li
- School of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Zheng Wang
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Zhu-Hong You
- School of Information Engineering, Xijing University, Xi'an 710123, China
| |
Collapse
|
11
|
Yao Y, Du X, Diao Y, Zhu H. An integration of deep learning with feature embedding for protein–protein interaction prediction. PeerJ 2019; 7:e7126. [PMID: 31245182 PMCID: PMC6585896 DOI: 10.7717/peerj.7126] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 05/14/2019] [Indexed: 12/22/2022] Open
Abstract
Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew’s correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI.
Collapse
|
12
|
Insights into the suitability of utilizing brown rats (Rattus norvegicus) as a model for healing spinal cord injury with epidermal growth factor and fibroblast growth factor-II by predicting protein-protein interactions. Comput Biol Med 2019; 104:220-226. [DOI: 10.1016/j.compbiomed.2018.11.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 11/28/2018] [Accepted: 11/29/2018] [Indexed: 01/06/2023]
|
13
|
Burnside D, Schoenrock A, Moteshareie H, Hooshyar M, Basra P, Hajikarimlou M, Dick K, Barnes B, Kazmirchuk T, Jessulat M, Pitre S, Samanfar B, Babu M, Green JR, Wong A, Dehne F, Biggar KK, Golshani A. In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences. iScience 2018; 11:375-387. [PMID: 30660105 PMCID: PMC6348295 DOI: 10.1016/j.isci.2018.11.038] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 10/19/2018] [Accepted: 11/28/2018] [Indexed: 12/29/2022] Open
Abstract
Synthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS) was developed to design synthetic binding proteins (SBPs) that bind pre-determined targets while minimizing off-target interactions. InSiPS is a genetic algorithm that refines a pool of random sequences over hundreds of generations of mutation and selection to produce SBPs with pre-specified binding characteristics. As a proof of concept, we design SBPs against three yeast proteins and demonstrate binding and functional inhibition of two of three targets in vivo. Peptide SPOT arrays confirm binding sites, and a permutation array demonstrates target specificity. Our foundational approach will support the field of de novo design of small binding polypeptide motifs and has robust applicability while offering potential advantages over the limited number of techniques currently available. InSiPS engineers synthetic binding proteins (SBPs) using primary protein sequence SBPs are designed to a bind a target protein and avoid “off-target” interactions Binding and functional inhibition of two of three target proteins in yeast is demonstrated Our new approach offers advantages over alternative tools that rely on 3D models
Collapse
Affiliation(s)
- Daniel Burnside
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Andrew Schoenrock
- School of Computer Science, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Houman Moteshareie
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Mohsen Hooshyar
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Prabh Basra
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Maryam Hajikarimlou
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Kevin Dick
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Brad Barnes
- School of Computer Science, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Tom Kazmirchuk
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Matthew Jessulat
- Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, SK S4S 0A2, Canada
| | - Sylvain Pitre
- School of Computer Science, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Bahram Samanfar
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Research and Development Centre (ORDC), Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C5, Canada
| | - Mohan Babu
- Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, SK S4S 0A2, Canada
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Alex Wong
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Kyle K Biggar
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Institute of Biochemistry, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Ashkan Golshani
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Institute of Biochemistry, Carleton University, Ottawa, ON K1S5B6, Canada.
| |
Collapse
|
14
|
Omidi K, Jessulat M, Hooshyar M, Burnside D, Schoenrock A, Kazmirchuk T, Hajikarimlou M, Daniel M, Moteshareie H, Bhojoo U, Sanders M, Ramotar D, Dehne F, Samanfar B, Babu M, Golshani A. Uncharacterized ORF HUR1 influences the efficiency of non-homologous end-joining repair in Saccharomyces cerevisiae. Gene 2018; 639:128-136. [DOI: 10.1016/j.gene.2017.10.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 06/25/2017] [Accepted: 10/02/2017] [Indexed: 01/05/2023]
|
15
|
Gupta S, Verheggen K, Tavernier J, Martens L. Unbiased Protein Association Study on the Public Human Proteome Reveals Biological Connections between Co-Occurring Protein Pairs. J Proteome Res 2017; 16:2204-2212. [PMID: 28480704 PMCID: PMC5491052 DOI: 10.1021/acs.jproteome.6b01066] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
![]()
Mass-spectrometry-based, high-throughput
proteomics experiments
produce large amounts of data. While typically acquired to answer
specific biological questions, these data can also be reused in orthogonal
ways to reveal new biological knowledge. We here present a novel method
for such orthogonal data reuse of public proteomics data. Our method
elucidates biological relationships between proteins based on the
co-occurrence of these proteins across human experiments in the PRIDE
database. The majority of the significantly co-occurring protein pairs
that were detected by our method have been successfully mapped to
existing biological knowledge. The validity of our novel method is
substantiated by the extremely few pairs that can be mapped to existing
knowledge based on random associations between the same set of proteins.
Moreover, using literature searches and the STRING database, we were
able to derive meaningful biological associations for unannotated
protein pairs that were detected using our method, further illustrating
that as-yet unknown associations present highly interesting targets
for follow-up analysis.
Collapse
Affiliation(s)
- Surya Gupta
- VIB-UGent Center for Medical Biotechnology, VIB , A. Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , B-9000 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University , B-9000 Ghent, Belgium
| | - Kenneth Verheggen
- VIB-UGent Center for Medical Biotechnology, VIB , A. Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , B-9000 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University , B-9000 Ghent, Belgium
| | - Jan Tavernier
- VIB-UGent Center for Medical Biotechnology, VIB , A. Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , B-9000 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB , A. Baertsoenkaai 3, B-9000 Ghent, Belgium.,Department of Biochemistry, Ghent University , B-9000 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University , B-9000 Ghent, Belgium
| |
Collapse
|
16
|
Schoenrock A, Burnside D, Moteshareie H, Pitre S, Hooshyar M, Green JR, Golshani A, Dehne F, Wong A. Evolution of protein-protein interaction networks in yeast. PLoS One 2017; 12:e0171920. [PMID: 28248977 PMCID: PMC5382968 DOI: 10.1371/journal.pone.0171920] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 01/28/2017] [Indexed: 01/04/2023] Open
Abstract
Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.
Collapse
Affiliation(s)
| | | | | | - Sylvain Pitre
- School of Computer Science, Carleton University, Ottawa, Canada
| | | | - James R. Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
| | | | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, Canada
| | - Alex Wong
- Department of Biology, Carleton University, Ottawa, Canada
| |
Collapse
|
17
|
Samanfar B, Molnar SJ, Charette M, Schoenrock A, Dehne F, Golshani A, Belzile F, Cober ER. Mapping and identification of a potential candidate gene for a novel maturity locus, E10, in soybean. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2017; 130:377-390. [PMID: 27832313 DOI: 10.1007/s00122-016-2819-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Accepted: 10/27/2016] [Indexed: 05/04/2023]
Abstract
KEY MESSAGE E10 is a new maturity locus in soybean and FT4 is the predicted/potential functional gene underlying the locus. Flowering and maturity time traits play crucial roles in economic soybean production. Early maturity is critical for north and west expansion of soybean in Canada. To date, 11 genes/loci have been identified which control time to flowering and maturity; however, the molecular bases of almost half of them are not yet clear. We have identified a new maturity locus called "E10" located at the end of chromosome Gm08. The gene symbol E10e10 has been approved by the Soybean Genetics Committee. The e10e10 genotype results in 5-10 days earlier maturity than E10E10. A set of presumed E10E10 and e10e10 genotypes was used to identify contrasting SSR and SNP haplotypes. These haplotypes, and their association with maturity, were maintained through five backcross generations. A functional genomics approach using a predicted protein-protein interaction (PPI) approach (Protein-protein Interaction Prediction Engine, PIPE) was used to investigate approximately 75 genes located in the genomic region that SSR and SNP analyses identified as the location of the E10 locus. The PPI analysis identified FT4 as the most likely candidate gene underlying the E10 locus. Sequence analysis of the two FT4 alleles identified three SNPs, in the 5'UTR, 3'UTR and fourth exon in the coding region, which result in differential mRNA structures. Allele-specific markers were developed for this locus and are available for soybean breeders to efficiently develop earlier maturing cultivars using molecular marker assisted breeding.
Collapse
Affiliation(s)
- Bahram Samanfar
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON, K1A 0C6, Canada
| | - Stephen J Molnar
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON, K1A 0C6, Canada
| | - Martin Charette
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON, K1A 0C6, Canada
| | - Andrew Schoenrock
- School of Computer Science, Carleton University, Ottawa, ON, K1S 5B6, Canada
| | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, ON, K1S 5B6, Canada
| | - Ashkan Golshani
- Department of Biology and Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON, K1S 5B6, Canada
| | - François Belzile
- Département de Phytologie and Institut de Biologie Intégrative et des Systèmes, Université Laval, Quebec City, QC, G1V 0A6, Canada
| | - Elroy R Cober
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON, K1A 0C6, Canada.
| |
Collapse
|
18
|
Zhou M, Li Q, Wang R. Current Experimental Methods for Characterizing Protein-Protein Interactions. ChemMedChem 2016; 11:738-56. [PMID: 26864455 PMCID: PMC7162211 DOI: 10.1002/cmdc.201500495] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 01/08/2016] [Indexed: 12/14/2022]
Abstract
Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method.
Collapse
Affiliation(s)
- Mi Zhou
- State Key Laboratory of Bioorganic & Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Rd, Shanghai, 200032, People's Republic of China
| | - Qing Li
- State Key Laboratory of Bioorganic & Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Rd, Shanghai, 200032, People's Republic of China
| | - Renxiao Wang
- State Key Laboratory of Bioorganic & Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Rd, Shanghai, 200032, People's Republic of China.
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Macau, 999078, People's Republic of China.
| |
Collapse
|
19
|
Zhou M, Li Q, Wang R. Current Experimental Methods for Characterizing Protein-Protein Interactions. ChemMedChem 2016. [PMID: 26864455 DOI: 10.1002/cmdc.201500495.] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method.
Collapse
Affiliation(s)
- Mi Zhou
- State Key Laboratory of Bioorganic & Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Rd, Shanghai, 200032, People's Republic of China
| | - Qing Li
- State Key Laboratory of Bioorganic & Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Rd, Shanghai, 200032, People's Republic of China
| | - Renxiao Wang
- State Key Laboratory of Bioorganic & Natural Products Chemistry, Collaborative Innovation Center of Chemistry for Life Sciences, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Rd, Shanghai, 200032, People's Republic of China. .,State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Macau, 999078, People's Republic of China.
| |
Collapse
|
20
|
Esmaielbeiki R, Krawczyk K, Knapp B, Nebel JC, Deane CM. Progress and challenges in predicting protein interfaces. Brief Bioinform 2016; 17:117-31. [PMID: 25971595 PMCID: PMC4719070 DOI: 10.1093/bib/bbv027] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 03/18/2015] [Indexed: 12/31/2022] Open
Abstract
The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.
Collapse
|