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Zeng X, Meng FF, Li X, Zhong KY, Jiang B, Li Y. GHGPR-PPIS: A graph convolutional network for identifying protein-protein interaction site using heat kernel with Generalized PageRank techniques and edge self-attention feature processing block. Comput Biol Med 2024; 168:107683. [PMID: 37984202 DOI: 10.1016/j.compbiomed.2023.107683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/10/2023] [Accepted: 11/06/2023] [Indexed: 11/22/2023]
Abstract
Accurately pinpointing protein-protein interaction site (PPIS) on the molecular level is of utmost significance for annotating protein function and comprehending the mechanisms underpinning various diseases. While numerous computational methods for predicting PPIS have emerged, they have indeed mitigated the labor and time constraints associated with traditional experimental methods. However, the predictive accuracy of these methods has yet to reach the desired threshold. In this context, we proposed a groundbreaking graph-based computational model called GHGPR-PPIS. This innovative model leveraged a graph convolutional network using heat kernel (GraphHeat) in conjunction with Generalized PageRank techniques (GHGPR) to predict PPIS. Additionally, building upon the GHGPR framework, we devised an edge self-attention feature processing block, further augmenting the performance of the model. Experimental findings conclusively demonstrated that GHGPR-PPIS surpassed all competing state-of-the-art models when evaluated on the benchmark test set. Impressively, on two distinct independent test sets and a specific protein chain, GHGPR-PPIS consistently demonstrated superior generalization performance and practical applicability compared to the comparative model, AGAT-PPIS. Lastly, leveraging the t-SNE dimensionality reduction algorithm and clustering visualization technique, we delved into an interpretability analysis of the effectiveness of GHGPR-PPIS by meticulously comparing the outputs from different stages of the model.
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Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Fan-Fang Meng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Xin Li
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Kai-Yang Zhong
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Bei Jiang
- Yunnan Key Laboratory of Screening and Research on Anti-pathogenic Plant Resources from Western Yunnan, Dali University, Dali, 671000, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China.
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Fang Y, Jiang Y, Wei L, Ma Q, Ren Z, Yuan Q, Wei DQ. DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model. Bioinformatics 2023; 39:btad718. [PMID: 38015872 PMCID: PMC10723037 DOI: 10.1093/bioinformatics/btad718] [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: 07/21/2023] [Revised: 11/04/2023] [Accepted: 11/27/2023] [Indexed: 11/30/2023] Open
Abstract
MOTIVATION Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information. RESULTS In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite. AVAILABILITY AND IMPLEMENTATION The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.
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Affiliation(s)
- Yitian Fang
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200040, China
- Peng Cheng Laboratory, Shenzhen 518055, China
| | - Yi Jiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Leyi Wei
- School of Software, Shandong University, Jinan, Shandong 250100, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Qianmu Yuan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200040, China
- Peng Cheng Laboratory, Shenzhen 518055, China
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Soleymani F, Paquet E, Viktor H, Michalowski W, Spinello D. Protein-protein interaction prediction with deep learning: A comprehensive review. Comput Struct Biotechnol J 2022; 20:5316-5341. [PMID: 36212542 PMCID: PMC9520216 DOI: 10.1016/j.csbj.2022.08.070] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/15/2022] Open
Abstract
Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.
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Affiliation(s)
- Farzan Soleymani
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada
| | - Herna Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, Canada
| | | | - Davide Spinello
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
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Yang Y, Zhao J, Zeng L, Vihinen M. ProTstab2 for Prediction of Protein Thermal Stabilities. Int J Mol Sci 2022; 23:ijms231810798. [PMID: 36142711 PMCID: PMC9505338 DOI: 10.3390/ijms231810798] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022] Open
Abstract
The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.
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Affiliation(s)
- Yang Yang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
- Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210000, China
| | - Jianjun Zhao
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Lianjie Zeng
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Mauno Vihinen
- Department of Experimental Medical Science, BMC B13, Lund University, SE-22184 Lund, Sweden
- Correspondence:
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ProB-Site: Protein Binding Site Prediction Using Local Features. Cells 2022; 11:cells11132117. [PMID: 35805201 PMCID: PMC9266162 DOI: 10.3390/cells11132117] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 01/16/2023] Open
Abstract
Protein–protein interactions (PPIs) are responsible for various essential biological processes. This information can help develop a new drug against diseases. Various experimental methods have been employed for this purpose; however, their application is limited by their cost and time consumption. Alternatively, computational methods are considered viable means to achieve this crucial task. Various techniques have been explored in the literature using the sequential information of amino acids in a protein sequence, including machine learning and deep learning techniques. The current efficiency of interaction-site prediction still has growth potential. Hence, a deep neural network-based model, ProB-site, is proposed. ProB-site utilizes sequential information of a protein to predict its binding sites. The proposed model uses evolutionary information and predicted structural information extracted from sequential information of proteins, generating three unique feature sets for every amino acid in a protein sequence. Then, these feature sets are fed to their respective sub-CNN architecture to acquire complex features. Finally, the acquired features are concatenated and classified using fully connected layers. This methodology performed better than state-of-the-art techniques because of the selection of the best features and contemplation of local information of each amino acid.
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Yang X, Da Q, Qian P, Veeravalli B, Leong TW, Dai L, Nordlund P, Prabhu N, Zhao Z, Zeng Z. CETSA Feature Based Clustering for Protein Outlier Discovery by Protein-to-Protein Interaction Prediction. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1659-1662. [PMID: 36085889 DOI: 10.1109/embc48229.2022.9871558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The Cellular Thermal Shift Assay (CETSA) is a biophysical assay based on the principle of ligand-induced thermal stabilization of target proteins. This technology has revolutionized cell-based target engagement studies and has been used as guidance for drug design. Although many ap-plications of CETSA data have been explored, the correlations between CETSA data and protein-protein interactions (PPI) have barely been touched. In this study, we conduct the first exploration study applying CETSA data for PPI prediction. We use a machine learning method, Decision Tree, to predict PPI scores using proteins' CETSA features. It shows promising results that the predicted PPI scores closely match the ground-truth PPI scores. Furthermore, for a small number of protein pairs, whose PPI score predictions mismatch the ground truth, we use iterative clustering strategy to gradually reduce the number of these pairs. At the end of iterative clustering, the remaining protein pairs may have some unusual properties and are of scientific value for further biological investigation. Our study has demonstrated that PPI is a brand-new application of CETSA data. At the same time, it also manifests that CETSA data can be used as a new data source for PPI exploration study.
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Mahbub S, Bayzid MS. EGRET: edge aggregated graph attention networks and transfer learning improve protein-protein interaction site prediction. Brief Bioinform 2022; 23:6518045. [PMID: 35106547 DOI: 10.1093/bib/bbab578] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 11/25/2021] [Accepted: 12/16/2021] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Protein-protein interactions (PPIs) are central to most biological processes. However, reliable identification of PPI sites using conventional experimental methods is slow and expensive. Therefore, great efforts are being put into computational methods to identify PPI sites. RESULTS We present Edge Aggregated GRaph Attention NETwork (EGRET), a highly accurate deep learning-based method for PPI site prediction, where we have used an edge aggregated graph attention network to effectively leverage the structural information. We, for the first time, have used transfer learning in PPI site prediction. Our proposed edge aggregated network, together with transfer learning, has achieved notable improvement over the best alternate methods. Furthermore, we systematically investigated EGRET's network behavior to provide insights about the causes of its decisions. AVAILABILITY EGRET is freely available as an open source project at https://github.com/Sazan-Mahbub/EGRET. CONTACT shams_bayzid@cse.buet.ac.bd.
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Affiliation(s)
- Sazan Mahbub
- Department of Computer Science University of Maryland, College Park, Maryland 20742, USA
| | - Md Shamsuzzoha Bayzid
- Department of Computer Science and Engineering Bangladesh University of Engineering and Technology, Dhaka-1205, Bangladesh
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Zeng M, Zhang F, Wu FX, Li Y, Wang J, Li M. Protein-protein interaction site prediction through combining local and global features with deep neural networks. Bioinformatics 2020; 36:1114-1120. [PMID: 31593229 DOI: 10.1093/bioinformatics/btz699] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 07/25/2019] [Accepted: 09/04/2019] [Indexed: 12/21/2022] Open
Abstract
MOTIVATION Protein-protein interactions (PPIs) play important roles in many biological processes. Conventional biological experiments for identifying PPI sites are costly and time-consuming. Thus, many computational approaches have been proposed to predict PPI sites. Existing computational methods usually use local contextual features to predict PPI sites. Actually, global features of protein sequences are critical for PPI site prediction. RESULTS A new end-to-end deep learning framework, named DeepPPISP, through combining local contextual and global sequence features, is proposed for PPI site prediction. For local contextual features, we use a sliding window to capture features of neighbors of a target amino acid as in previous studies. For global sequence features, a text convolutional neural network is applied to extract features from the whole protein sequence. Then the local contextual and global sequence features are combined to predict PPI sites. By integrating local contextual and global sequence features, DeepPPISP achieves the state-of-the-art performance, which is better than the other competing methods. In order to investigate if global sequence features are helpful in our deep learning model, we remove or change some components in DeepPPISP. Detailed analyses show that global sequence features play important roles in DeepPPISP. AVAILABILITY AND IMPLEMENTATION The DeepPPISP web server is available at http://bioinformatics.csu.edu.cn/PPISP/. The source code can be obtained from https://github.com/CSUBioGroup/DeepPPISP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha 410083, People's Republic of China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, Changsha 410083, People's Republic of China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon SKS7N5A9, Canada
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, People's Republic of China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, People's Republic of China
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Liu H, Voss C, Li SSC. Using Reciprocal Protein-Peptide Array Screening to Unravel Protein Interaction Networks. Methods Mol Biol 2017; 1555:429-436. [PMID: 28092048 DOI: 10.1007/978-1-4939-6762-9_25] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Protein-protein interactions (PPIs) play a central role in almost all cellular processes. Recent technological advances have enabled the elucidation of an incredibly complex PPI network within the cell. However, protein interactions driven by posttranslational modifications (PTMs) such as phosphorylation, which comprises a significant part of the PPI network, have proven difficult to decipher systematically. Herein, we describe a reciprocal protein-peptide array strategy to uncover PPIs mediated by tyrosine phosphorylation and the Src homology 2 (SH2) domain. This strategy, namely combining peptide and protein domain arrays for PPI mapping, may be applicable for other peptide-binding modules.
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Affiliation(s)
- Huadong Liu
- Department of Biochemistry, Siebens-Drake Medical Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, N6A 5C1, Canada
| | - Courtney Voss
- Department of Biochemistry, Siebens-Drake Medical Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, N6A 5C1, Canada
| | - Shawn S C Li
- Department of Biochemistry, Siebens-Drake Medical Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, N6A 5C1, Canada.
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10
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Drift Barriers to Quality Control When Genes Are Expressed at Different Levels. Genetics 2016; 205:397-407. [PMID: 27838629 DOI: 10.1534/genetics.116.192567] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Accepted: 11/02/2016] [Indexed: 11/18/2022] Open
Abstract
Gene expression is imperfect, sometimes leading to toxic products. Solutions take two forms: globally reducing error rates, or ensuring that the consequences of erroneous expression are relatively harmless. The latter is optimal, but because it must evolve independently at so many loci, it is subject to a stringent "drift barrier"-a limit to how weak the effects of a deleterious mutation s can be, while still being effectively purged by selection, expressed in terms of the population size N of an idealized population such that purging requires s < -1/N In previous work, only large populations evolved the optimal local solution, small populations instead evolved globally low error rates, and intermediate populations were bistable, with either solution possible. Here, we take into consideration the fact that the effectiveness of purging varies among loci, because of variation in gene expression level, and variation in the intrinsic vulnerabilities of different gene products to error. The previously found dichotomy between the two kinds of solution breaks down, replaced by a gradual transition as a function of population size. In the extreme case of a small enough population, selection fails to maintain even the global solution against deleterious mutations, explaining the nonmonotonic relationship between effective population size and transcriptional error rate that was recently observed in experiments on Escherichia coli, Caenorhabditis elegans, and Buchnera aphidicola.
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11
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Fan GL, Zhang XY, Liu YL, Nang Y, Wang H. DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns. J Comput Chem 2015; 36:2317-27. [PMID: 26484844 DOI: 10.1002/jcc.24210] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Revised: 08/20/2015] [Accepted: 08/23/2015] [Indexed: 12/28/2022]
Abstract
Identification of the proteins secreted by the malaria parasite is important for developing effective drugs and vaccines against infection. Therefore, we developed an improved predictor called "DSPMP" (Discriminating Secretory Proteins of Malaria Parasite) to identify the secretory proteins of the malaria parasite by integrating several vector features using support vector machine-based methods. DSPMP achieved an overall predictive accuracy of 98.61%, which is superior to that of the existing predictors in this field. We show that our method is capable of identifying the secretory proteins of the malaria parasite and found that the amino acid composition for buried and exposed sequences, denoted by AAC(b/e), was the most important feature for constructing the predictor. This article not only introduces a novel method for detecting the important features of sample proteins related to the malaria parasite but also provides a useful tool for tackling general protein-related problems. The DSPMP webserver is freely available at http://202.207.14.87:8032/fuwu/DSPMP/index.asp.
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Affiliation(s)
- Guo-Liang Fan
- Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Xiao-Yan Zhang
- Department of Physics, Inner Mongolia University of Technology, Hohhot, 010051, China
| | - Yan-Ling Liu
- Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Yi Nang
- Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
| | - Hui Wang
- Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China
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Liu H, Li L, Voss C, Wang F, Liu J, Li SSC. A Comprehensive Immunoreceptor Phosphotyrosine-based Signaling Network Revealed by Reciprocal Protein-Peptide Array Screening. Mol Cell Proteomics 2015; 14:1846-58. [PMID: 25907764 PMCID: PMC4587333 DOI: 10.1074/mcp.m115.047951] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2015] [Revised: 04/22/2015] [Indexed: 01/18/2023] Open
Abstract
Cells of the immune system communicate with their environment through immunoreceptors. These receptors often harbor intracellular tyrosine residues, which, when phosphorylated upon receptor activation, serve as docking sites to recruit downstream signaling proteins containing the Src Homology 2 (SH2) domain. A systematic investigation of interactions between the SH2 domain and the immunoreceptor tyrosine-based regulatory motifs (ITRM), including inhibitory (ITIM), activating (ITAM), or switching (ITSM) motifs, is critical for understanding cellular signal transduction and immune function. Using the B cell inhibitory receptor CD22 as an example, we developed an approach that combines reciprocal or bidirectional phosphopeptide and SH2 domain array screens with in-solution binding assays to identify a comprehensive SH2-CD22 interaction network. Extending this approach to 194 human ITRM sequences and 78 SH2 domains led to the identification of a high-confidence immunoreceptor interactome containing 1137 binary interactions. Besides recapitulating many previously reported interactions, our study uncovered numerous novel interactions. The resulting ITRM-SH2 interactome not only helped to fill many gaps in the immune signaling network, it also allowed us to associate different SH2 domains to distinct immune functions. Detailed analysis of the NK cell ITRM-mediated interactions led to the identification of a network nucleated by the Vav3 and Fyn SH2 domains. We showed further that these SH2 domains have distinct functions in cytotoxicity. The bidirectional protein-peptide array approach described herein may be applied to the numerous other peptide-binding modules to identify potential protein-protein interactions in a systematic and reliable manner.
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Affiliation(s)
- Huadong Liu
- From the ‡Department of Biochemistry and the Siebens-Drake Medical Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5C1
| | - Lei Li
- From the ‡Department of Biochemistry and the Siebens-Drake Medical Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5C1
| | - Courtney Voss
- From the ‡Department of Biochemistry and the Siebens-Drake Medical Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5C1
| | - Feng Wang
- §Department of Chemistry, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | - Juewen Liu
- §Department of Chemistry, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | - Shawn Shun-Cheng Li
- From the ‡Department of Biochemistry and the Siebens-Drake Medical Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario N6A 5C1;
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Ma Y, Nagamune T, Kawahara M. Split focal adhesion kinase for probing protein–protein interactions. Biochem Eng J 2014. [DOI: 10.1016/j.bej.2014.06.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Morris M, Rogers SM. Integrating phenotypic plasticity within an Ecological Genomics framework: recent insights from the genomics, evolution, ecology, and fitness of plasticity. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2014; 781:73-105. [PMID: 24277296 DOI: 10.1007/978-94-007-7347-9_5] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
E.B. Ford's 1964 book Ecological Genetics was a call for biologists to engage in multidisciplinary work in order to elucidate the link between genotype, phenotype, and fitness for ecologically relevant traits. In this review, we argue that the integration of an ecological genomics framework in studies of phenotypic plasticity is a promising approach to elucidate the causal links between genes and the environment, particularly during colonization of novel environments, environmental change, and speciation. This review highlights some of the questions and hypotheses generated from a mechanistic, evolutionary, and ecological perspective, in order to direct the continued and future use of genomic tools in the study of phenotypic plasticity.
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Affiliation(s)
- Matthew Morris
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada,
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