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Su Z, Dhusia K, Wu Y. Encoding the space of protein-protein binding interfaces by artificial intelligence. Comput Biol Chem 2024; 110:108080. [PMID: 38643609 DOI: 10.1016/j.compbiolchem.2024.108080] [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: 12/15/2023] [Revised: 04/03/2024] [Accepted: 04/17/2024] [Indexed: 04/23/2024]
Abstract
The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space. After training, new conformations of interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligence, we were able to guide the assembly of protein complexes into their native conformations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Features in this degenerate space can be well characterized by artificial intelligence. In summary, our machine learning method will be potentially useful to search for and predict the conformations of unknown protein-protein interactions.
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Affiliation(s)
- Zhaoqian Su
- Data Science Institute, Vanderbilt University, 1001 19th Ave S, Nashville, TN 37212, USA
| | - Kalyani Dhusia
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
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2
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Cong H, Liu H, Cao Y, Liang C, Chen Y. Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention. BMC Bioinformatics 2023; 24:456. [PMID: 38053020 DOI: 10.1186/s12859-023-05592-7] [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: 12/18/2022] [Accepted: 11/30/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Protein-protein interactions (PPIs) are crucial in various biological functions and cellular processes. Thus, many computational approaches have been proposed to predict PPI sites. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in sequences. Many feature extraction methods rely on the sliding window technique, which simply merges all the features of residues into a vector. The importance of some key residues may be weakened in the feature vector, leading to poor performance. RESULTS We propose a novel sequence-based method for PPI sites prediction. The new network model, PPINet, contains multiple feature processing paths. For a residue, the PPINet extracts the features of the targeted residue and its context separately. These two types of features are processed by two paths in the network and combined to form a protein representation, where the two types of features are of relatively equal importance. The model ensembling technique is applied to make use of more features. The base models are trained with different features and then ensembled via stacking. In addition, a data balancing strategy is presented, by which our model can get significant improvement on highly unbalanced data. CONCLUSION The proposed method is evaluated on a fused dataset constructed from Dset186, Dset_72, and PDBset_164, as well as the public Dset_448 dataset. Compared with current state-of-the-art methods, the performance of our method is better than the others. In the most important metrics, such as AUPRC and recall, it surpasses the second-best programmer on the latter dataset by 6.9% and 4.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model, especially, the hybrid feature. We share our code for reproducibility and future research at https://github.com/CandiceCong/StackingPPINet .
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Affiliation(s)
- Hanhan Cong
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
- Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China
| | - Hong Liu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China.
- Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan, China.
| | - Yi Cao
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Yuehui Chen
- School of Information Science and Engineering, University of Jinan, Jinan, China
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China
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3
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Haque S, Kumar P, Mathkor DM, Bantun F, Jalal NA, Mufti AH, Prakash A, Kumar V. In silico evaluation of the inhibitory potential of nucleocapsid inhibitors of SARS-CoV-2: a binding and energetic perspective. J Biomol Struct Dyn 2023; 41:9797-9807. [PMID: 36379684 DOI: 10.1080/07391102.2022.2146752] [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: 08/17/2022] [Accepted: 11/07/2022] [Indexed: 11/17/2022]
Abstract
The COVID-19 outbreak brought on by the SARS-CoV-2 virus continued to infect a sizable population worldwide. The SARS-CoV-2 nucleocapsid (N) protein is the most conserved RNA-binding structural protein and is a desirable target because of its involvement in viral transcription and replication. Based on this aspect, this study focused to repurpose antiviral compounds approved or in development for treating COVID-19. The inhibitors chosen are either FDA-approved or are currently being studied in clinical trials against COVID-19. Initially, they were designed to target stress granules and other RNA biology. We have utilized structure-based molecular docking and all-atom molecular dynamics (MD) simulation approach to investigate in detail the binding energy and binding modes of the different anti-N inhibitors to N protein. The result showed that five drugs including Silmitasterib, Ninetanidinb, Ternatin, Luteolin, Fedratinib, PJ34, and Zotatafin were found interacting with RNA binding sites as well as to predicted protein interface with higher binding energy. Overall, drug binding increases the stability of the complex with maximum stability found in the order, Silmitasertib > PJ34 > Zotatatafin. In addition, the frustration changes due to drug binding brings a decrease in local frustration and this decrease is mainly observed in α-helix, β3, β5, and β6 strands and are important for drug binding. Our in-silico data suggest that an effective interaction occurs for some of the tested drugs and prompt their further validation to reduce the rapid outspreading of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
| | - Pawan Kumar
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Darin Mansor Mathkor
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
| | - Farkad Bantun
- Department of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Naif A Jalal
- Department of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Ahmad Hasan Mufti
- Medical Genetics Department, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Amresh Prakash
- Amity Institute of Integrative Sciences and Health, Amity University Haryana, Gurgaon, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences, Amity University, Noida, Uttar Pradesh, India
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4
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Yue Y, Li S, Wang L, Liu H, Tong HHY, He S. MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions. Brief Bioinform 2023; 24:bbad310. [PMID: 37651610 PMCID: PMC10516393 DOI: 10.1093/bib/bbad310] [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: 05/27/2023] [Revised: 07/12/2023] [Accepted: 08/04/2023] [Indexed: 09/02/2023] Open
Abstract
The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.
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Affiliation(s)
- Yang Yue
- School of Computer Science from the University of Birmingham, UK
| | - Shu Li
- Centre for Artificial Intelligence Driven Drug Discovery at Macao Polytechnic University
| | - Lingling Wang
- Centre for Artificial Intelligence Driven Drug Discovery at Macao Polytechnic University
| | - Huanxiang Liu
- Centre for Artificial Intelligence Driven Drug Discovery at Macao Polytechnic University
| | - Henry H Y Tong
- Centre for Artificial Intelligence Driven Drug Discovery at Macao Polytechnic University
| | - Shan He
- School of Computer Science, the University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
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5
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Roche R, Moussad B, Shuvo MH, Bhattacharya D. E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction. PLoS Comput Biol 2023; 19:e1011435. [PMID: 37651442 PMCID: PMC10499216 DOI: 10.1371/journal.pcbi.1011435] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 09/13/2023] [Accepted: 08/15/2023] [Indexed: 09/02/2023] Open
Abstract
Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures. Recent studies have demonstrated benefits of employing graph convolution for PPI site prediction, but ignore symmetries naturally occurring in 3-dimensional space and act only on experimental coordinates. Here we present EquiPPIS, an E(3) equivariant graph neural network approach for PPI site prediction. EquiPPIS employs symmetry-aware graph convolutions that transform equivariantly with translation, rotation, and reflection in 3D space, providing richer representations for molecular data compared to invariant convolutions. EquiPPIS substantially outperforms state-of-the-art approaches based on the same experimental input, and exhibits remarkable robustness by attaining better accuracy with predicted structural models from AlphaFold2 than what existing methods can achieve even with experimental structures. Freely available at https://github.com/Bhattacharya-Lab/EquiPPIS, EquiPPIS enables accurate PPI site prediction at scale.
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Affiliation(s)
- Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Bernard Moussad
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
| | - Debswapna Bhattacharya
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America
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6
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Slough MM, Li R, Herbert AS, Lasso G, Kuehne AI, Monticelli SR, Bakken RR, Liu Y, Ghosh A, Moreau AM, Zeng X, Rey FA, Guardado-Calvo P, Almo SC, Dye JM, Jangra RK, Wang Z, Chandran K. Two point mutations in protocadherin-1 disrupt hantavirus recognition and afford protection against lethal infection. Nat Commun 2023; 14:4454. [PMID: 37488123 PMCID: PMC10366084 DOI: 10.1038/s41467-023-40126-y] [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: 04/04/2023] [Accepted: 07/06/2023] [Indexed: 07/26/2023] Open
Abstract
Andes virus (ANDV) and Sin Nombre virus (SNV) are the etiologic agents of severe hantavirus cardiopulmonary syndrome (HCPS) in the Americas for which no FDA-approved countermeasures are available. Protocadherin-1 (PCDH1), a cadherin-superfamily protein recently identified as a critical host factor for ANDV and SNV, represents a new antiviral target; however, its precise role remains to be elucidated. Here, we use computational and experimental approaches to delineate the binding surface of the hantavirus glycoprotein complex on PCDH1's first extracellular cadherin repeat domain. Strikingly, a single amino acid residue in this PCDH1 surface influences the host species-specificity of SNV glycoprotein-PCDH1 interaction and cell entry. Mutation of this and a neighboring residue substantially protects Syrian hamsters from pulmonary disease and death caused by ANDV. We conclude that PCDH1 is a bona fide entry receptor for ANDV and SNV whose direct interaction with hantavirus glycoproteins could be targeted to develop new interventions against HCPS.
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Affiliation(s)
- Megan M Slough
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Rong Li
- Department of Animal, Dairy and Veterinary Sciences, Utah State University, Logan, UT, USA
| | - Andrew S Herbert
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD, USA
| | - Gorka Lasso
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ana I Kuehne
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD, USA
| | - Stephanie R Monticelli
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD, USA
- The Geneva Foundation, Tacoma, WA, USA
| | - Russell R Bakken
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD, USA
| | - Yanan Liu
- Department of Animal, Dairy and Veterinary Sciences, Utah State University, Logan, UT, USA
| | - Agnidipta Ghosh
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Alicia M Moreau
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD, USA
| | - Xiankun Zeng
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD, USA
| | - Félix A Rey
- Institut Pasteur, Université Paris Cité, CNRS UMR3569, Structural Virology Unit, F-75015, Paris, France
| | - Pablo Guardado-Calvo
- Institut Pasteur, Université Paris Cité, CNRS UMR3569, Structural Virology Unit, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Structural Biology of Infectious Diseases Unit, F-75015, Paris, France
| | - Steven C Almo
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
| | - John M Dye
- United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD, USA
| | - Rohit K Jangra
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA.
- Microbiology and Immunology, Louisiana State University Health Sciences Center-Shreveport, Shreveport, LA, USA.
| | - Zhongde Wang
- Department of Animal, Dairy and Veterinary Sciences, Utah State University, Logan, UT, USA.
| | - Kartik Chandran
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA.
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7
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Li B, Altelaar M, van Breukelen B. Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy. Int J Mol Sci 2023; 24:ijms24097884. [PMID: 37175590 PMCID: PMC10178578 DOI: 10.3390/ijms24097884] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/23/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein-protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein-protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein-protein interactions and a unique perspective on possible novel protein complexes.
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Affiliation(s)
- Bohui Li
- Biomolecular Mass Spectrometry and Proteomics, Padualaan 8, 3584 CH Utrecht, The Netherlands
- Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, The Netherlands
| | - Maarten Altelaar
- Biomolecular Mass Spectrometry and Proteomics, Padualaan 8, 3584 CH Utrecht, The Netherlands
- Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, The Netherlands
- Mass Spectrometry and Proteomics Facility, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Bas van Breukelen
- Biomolecular Mass Spectrometry and Proteomics, Padualaan 8, 3584 CH Utrecht, The Netherlands
- Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, The Netherlands
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8
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Soleymani F, Paquet E, Viktor HL, Michalowski W, Spinello D. ProtInteract: A deep learning framework for predicting protein-protein interactions. Comput Struct Biotechnol J 2023; 21:1324-1348. [PMID: 36817951 PMCID: PMC9929211 DOI: 10.1016/j.csbj.2023.01.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.
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Affiliation(s)
- Farzan Soleymani
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada,Corresponding author.
| | - Herna Lydia Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON K1N 6N5, Canada
| | | | - Davide Spinello
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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9
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Interplay between C1-inhibitor and group IIA secreted phospholipase A 2 impairs their respective function. Immunol Res 2023; 71:70-82. [PMID: 36385678 PMCID: PMC9845149 DOI: 10.1007/s12026-022-09331-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/14/2022] [Indexed: 11/18/2022]
Abstract
High levels of human group IIA secreted phospholipase A2 (hGIIA) have been associated with various inflammatory disease conditions. We have recently shown that hGIIA activity and concentration are increased in the plasma of patients with hereditary angioedema due to C1-inhibitor deficiency (C1-INH-HAE) and negatively correlate with C1-INH plasma activity. In this study, we analyzed whether the presence of both hGIIA and C1-INH impairs their respective function on immune cells. hGIIA, but not recombinant and plasma-derived C1-INH, stimulates the production of IL-6, CXCL8, and TNF-α from peripheral blood mononuclear cells (PBMCs). PBMC activation mediated by hGIIA is blocked by RO032107A, a specific hGIIA inhibitor. Interestingly, C1-INH inhibits the hGIIA-induced production of IL-6, TNF-α, and CXCL8, while it does not affect hGIIA enzymatic activity. On the other hand, hGIIA reduces the capacity of C1-INH at inhibiting C1-esterase activity. Spectroscopic and molecular docking studies suggest a possible interaction between hGIIA and C1-INH but further experiments are needed to confirm this hypothesis. Together, these results provide evidence for a new interplay between hGIIA and C1-INH, which may be important in the pathophysiology of hereditary angioedema.
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10
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Kumar N, Mukhtar S. Building Protein-Protein Interaction Graph Database Using Neo4j. Methods Mol Biol 2023; 2690:469-479. [PMID: 37450167 DOI: 10.1007/978-1-0716-3327-4_36] [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: 07/18/2023]
Abstract
A cell's various components interact with each other in a coordinated manner to respond to environmental cues and intracellular signals. Compared to the other biological networks, the protein-protein interaction (PPI) is mostly responsible for maintaining signaling pathways. Increasing numbers of experimentally verified and predicted PPIs in plants demand a scalable platform to deal with large and complex datasets. Network/graph data can be organized and analyzed using different tools. This chapter uses Neo4j, a graph database management system, to store and analyze plant PPI networks. To make the graph database and analyze network centrality, we used Arabidopsis interactome-1 main (AI-1MAIN) PPI network.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
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11
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Walder M, Edelstein E, Carroll M, Lazarev S, Fajardo JE, Fiser A, Viswanathan R. Integrated structure-based protein interface prediction. BMC Bioinformatics 2022; 23:301. [PMID: 35879651 PMCID: PMC9316365 DOI: 10.1186/s12859-022-04852-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/18/2022] [Indexed: 11/29/2022] Open
Abstract
Background Identifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein’s interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins’ intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model. Results We describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces. Conclusions The integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04852-2.
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Affiliation(s)
- M Walder
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA
| | - E Edelstein
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA
| | - M Carroll
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA
| | - S Lazarev
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA
| | - J E Fajardo
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - A Fiser
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - R Viswanathan
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA.
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12
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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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13
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Multi-task learning to leverage partially annotated data for PPI interface prediction. Sci Rep 2022; 12:10487. [PMID: 35729253 PMCID: PMC9213449 DOI: 10.1038/s41598-022-13951-2] [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: 01/17/2022] [Accepted: 05/31/2022] [Indexed: 11/29/2022] Open
Abstract
Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem. In addition, structure-based functional annotations, such as the PPI interface annotations, are scarce: only for about one-third of all protein structures residue-based PPI interface annotations are available. If we want to use a deep learning strategy, we have to overcome the problem of limited data availability. Here we use a multi-task learning strategy that can handle missing data. We start with the multi-task model architecture, and adapted it to carefully handle missing data in the cost function. As related learning tasks we include prediction of secondary structure, solvent accessibility, and buried residue. Our results show that the multi-task learning strategy significantly outperforms single task approaches. Moreover, only the multi-task strategy is able to effectively learn over a dataset extended with structural feature data, without additional PPI annotations. The multi-task setup becomes even more important, if the fraction of PPI annotations becomes very small: the multi-task learner trained on only one-eighth of the PPI annotations—with data extension—reaches the same performances as the single-task learner on all PPI annotations. Thus, we show that the multi-task learning strategy can be beneficial for a small training dataset where the protein’s functional properties of interest are only partially annotated.
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14
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Hephzibah Cathryn R, Udhaya Kumar S, Younes S, Zayed H, George Priya Doss C. A review of bioinformatics tools and web servers in different microarray platforms used in cancer research. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:85-164. [PMID: 35871897 DOI: 10.1016/bs.apcsb.2022.05.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.
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Affiliation(s)
- R Hephzibah Cathryn
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - S Udhaya Kumar
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - Salma Younes
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - Hatem Zayed
- Department of Biomedical Sciences, College of Health and Sciences, Qatar University, QU Health, Doha, Qatar
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India.
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15
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In Silico Identification of Potential Inhibitors of the SARS-CoV-2 Nucleocapsid Through Molecular Docking-Based Drug Repurposing. DR. SULAIMAN AL HABIB MEDICAL JOURNAL 2022. [PMCID: PMC9153216 DOI: 10.1007/s44229-022-00004-z] [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] [Indexed: 10/24/2022] Open
Abstract
AbstractSARS-CoV-2 is the virus responsible for the COVID-19 pandemic, and its effects on people worldwide continue to grow. Protein-targeted therapeutics are currently unavailable for this virus. As with other coronaviruses, the nucleocapsid (N) protein is the most conserved RNA-binding structural protein of SARS-CoV-2. The N protein is an appealing target because of its functional role in viral transcription and replication. Therefore, molecular docking method for structure-based drug design was used to investigate the binding energy and binding modes of various anti-N inhibitors in depth. The inhibitors selected were originally developed to target stress granules and other molecules involved in RNA biology, and were either FDA-approved or in the process of clinical trials for COVID-19. We aimed at targeting the N-terminal RNA binding domain (NTD) for molecular docking-based screening, on the basis of the first resolved crystal structure of SARS-CoV-2 N protein (PDB ID: 6M3M) and C-terminal domain (CTD) dimerization of the nucleocapsid phosphoprotein of SARS-COV-2 (PDB ID: 6WJI). Silmitasertib, nintedanib, ternatin, luteolin, and fedratinib were found to interact with RNA binding sites and to form a predicted protein interface with high binding energy. Similarly, silmitasertib, sirolimus-rapamycin, dovitinib, nintedanib, and fedratinib were found to interact with the SARS-CoV-2 N protein at its CTD dimerization sites, according to previous studies. In addition, we investigated an information gap regarding the relationships among the energetic landscape and stability and drug binding of the SARS-CoV-2 N NTD and CTD. Our in silico results clearly indicated that several tested drugs as potent putative inhibitors for COVID-19 therapeutics, thus indicating that they should be further validated as treatments to slow the spread of SARS-CoV-2.
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16
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Wang S, Wu R, Lu J, Jiang Y, Huang T, Cai YD. Protein-protein interaction networks as miners of biological discovery. Proteomics 2022; 22:e2100190. [PMID: 35567424 DOI: 10.1002/pmic.202100190] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 03/28/2022] [Accepted: 04/29/2022] [Indexed: 11/12/2022]
Abstract
Protein-protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein-complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two-hybrid, mass spectrometry, co-localization, and co-immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Steven Wang
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Runxin Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jiaqi Lu
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA
| | - Yijia Jiang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tao Huang
- Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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17
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Ameerul A, Almasmoum H, Pavanello L, Dominguez C, Sebastiaan Winkler G. Structural model of the human BTG2–PABPC1 complex by combining mutagenesis, NMR chemical shift perturbation data and molecular docking. J Mol Biol 2022; 434:167662. [DOI: 10.1016/j.jmb.2022.167662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/20/2022] [Accepted: 05/24/2022] [Indexed: 11/28/2022]
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18
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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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19
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Ranade H, Paliwal P, Chaudhary AA, Piplani S, Rudayni HA, Al-Zharani M, Niraj RR, Datta M. Predicting Diagnostic Potential of Cathepsin in Epithelial Ovarian Cancer: A Design Validated by Computational, Biophysical and Electrochemical Data. Biomolecules 2021; 12:biom12010053. [PMID: 35053201 PMCID: PMC8774009 DOI: 10.3390/biom12010053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 12/15/2022] Open
Abstract
Background: Epithelial ovarian cancer remains one of the leading variants of gynecological cancer with a high mortality rate. Feasibility and technical competence for screening and detection of epithelial ovarian cancer remain a major obstacle and the development of point of care diagnostics (POCD) may offer a simple solution for monitoring its progression. Cathepsins have been implicated as biomarkers for cancer progression and metastasis; being a protease, it has an inherent tendency to interact with Cystatin C, a cysteine protease inhibitor. This interaction was assessed for designing a POCD module. Methods: A combinatorial approach encompassing computational, biophysical and electron-transfer kinetics has been used to assess this protease-inhibitor interaction. Results: Calculations predicted two cathepsin candidates, Cathepsin K and Cathepsin L based on their binding energies and structural alignment and both predictions were confirmed experimentally. Differential pulse voltammetry was used to verify the potency of Cathepsin K and Cathepsin L interaction with Cystatin C and assess the selectivity and sensitivity of their electrochemical interactions. Electrochemical measurements indicated selectivity for both the ligands, but with increasing concentrations, there was a marked difference in the sensitivity of the detection. Conclusions: This work validated the utility of dry-lab integration in the wet-lab technique to generate leads for the design of electrochemical diagnostics for epithelial ovarian cancer.
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Affiliation(s)
- Hemangi Ranade
- Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur 303002, India; (H.R.); (P.P.); (R.R.N.)
| | - Priya Paliwal
- Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur 303002, India; (H.R.); (P.P.); (R.R.N.)
| | - Anis Ahmad Chaudhary
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia; (A.A.C.); (H.A.R.); (M.A.-Z.)
| | - Sakshi Piplani
- Vaxine Pty Ltd., Flinders University, Bedford Park, SA 5042, Australia;
| | - Hassan Ahmed Rudayni
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia; (A.A.C.); (H.A.R.); (M.A.-Z.)
| | - Mohammed Al-Zharani
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia; (A.A.C.); (H.A.R.); (M.A.-Z.)
| | - Ravi Ranjan Niraj
- Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur 303002, India; (H.R.); (P.P.); (R.R.N.)
| | - Manali Datta
- Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur 303002, India; (H.R.); (P.P.); (R.R.N.)
- Correspondence: ; Tel.: +91-7742889287
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20
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Wang P, Zhang G, Yu ZG, Huang G. A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites. Front Genet 2021; 12:752732. [PMID: 34764983 PMCID: PMC8576272 DOI: 10.3389/fgene.2021.752732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 09/20/2021] [Indexed: 11/29/2022] Open
Abstract
Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites.
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Affiliation(s)
- Pan Wang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
| | - Guiyang Zhang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China
| | - Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
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21
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Das S, Scholes HM, Sen N, Orengo C. CATH functional families predict functional sites in proteins. Bioinformatics 2021; 37:1099-1106. [PMID: 33135053 PMCID: PMC8150129 DOI: 10.1093/bioinformatics/btaa937] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/30/2020] [Accepted: 10/27/2020] [Indexed: 01/12/2023] Open
Abstract
MOTIVATION Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein-protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams). RESULTS FunSite's prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed other publicly available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite's performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyze which structural and evolutionary features are most predictive for functional sites. AVAILABILITYAND IMPLEMENTATION https://github.com/UCL/cath-funsite-predictor. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sayoni Das
- PrecisionLife Ltd., Long Hanborough, OX29 8LJ Oxford, UK
| | - Harry M Scholes
- Institute of Structural and Molecular Biology, University College London, WC1E 6BT, London, UK
| | - Neeladri Sen
- Institute of Structural and Molecular Biology, University College London, WC1E 6BT, London, UK
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, WC1E 6BT, London, UK
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22
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Souza SA, Held A, Lu WJ, Drouhard B, Avila B, Leyva-Montes R, Hu M, Miller BR, Ng HL. Mechanisms of allosteric and mixed mode aromatase inhibitors. RSC Chem Biol 2021; 2:892-905. [PMID: 34458816 PMCID: PMC8341375 DOI: 10.1039/d1cb00046b] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/17/2021] [Indexed: 11/21/2022] Open
Abstract
Aromatase (CYP19) catalyzes the last biosynthetic step of estrogens in mammals and is a primary drug target for hormone-related breast cancer. However, treatment with aromatase inhibitors is often associated with adverse effects and drug resistance. In this study, we used virtual screening targeting a predicted cytochrome P450 reductase binding site on aromatase to discover four novel non-steroidal aromatase inhibitors. The inhibitors have potencies comparable to the noncompetitive tamoxifen metabolite, endoxifen. Our two most potent inhibitors, AR11 and AR13, exhibit both mixed-type and competitive-type inhibition. The cytochrome P450 reductase-CYP19 coupling interface likely acts as a transient binding site. Our modeling shows that our inhibitors bind better at different sites near the catalytic site. Our results predict the location of multiple ligand binding sites on aromatase. The combination of modeling and experimental results supports the important role of the reductase binding interface as a low affinity, promiscuous ligand binding site. Our new inhibitors may be useful as alternative chemical scaffolds that may show different adverse effects profiles than current clinically used aromatase inhibitors.
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Affiliation(s)
- Samson A Souza
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
| | - Abby Held
- Department of Chemistry, Truman State University Kirksville MO USA
| | - Wenjie J Lu
- Department of Chemistry, University of Hawai'i at Mānoa Honolulu HI USA
| | - Brendan Drouhard
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
| | - Bryant Avila
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
| | - Raul Leyva-Montes
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
| | - Michelle Hu
- Department of Chemistry, University of Hawai'i at Mānoa Honolulu HI USA
| | - Bill R Miller
- Department of Chemistry, Truman State University Kirksville MO USA
| | - Ho Leung Ng
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
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23
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Kadam A, Abuthakir MHS, Jubin T, Vaishnav J, Garg A, Balaji C, Suthar D, Begum R. Identification and characterization of Poly(ADP-ribose) polymerase-1 interacting proteins during development of Dictyostelium discoideum. Protein Expr Purif 2021; 186:105923. [PMID: 34062238 DOI: 10.1016/j.pep.2021.105923] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 05/06/2021] [Accepted: 05/25/2021] [Indexed: 01/17/2023]
Abstract
Poly (ADP-ribose) polymerase-1 (PARP-1) is a multifunctional protein that is associated with various biological processes like chromatin remodeling, DNA damage, cell death etc. In Dictyostelium discoideum, PARP-1 has also been implicated in cellular differentiation and development. However, its interacting proteins during multicellular development are not yet explored. Hence, the present study aims to identify PARP-1 interacting proteins during multicellular development of D. discoideum. BRCA1 C-terminus (BRCT) domain of PARP-1, which is mainly involved in protein-protein interactions was cloned in pGEX4T1 vector and developmental interactome of PARP-1 were analyzed by affinity purification-mass spectrometry. These interactions were further confirmed by in-silico protein-protein docking analysis, which led to identification of the proteins that show high affinity for BRCT domain. Initially, the protein structures were modeled on SWISS MODEL and PHYRE2 servers, refined by 3Drefine and validated by PROCHECK. Further, interaction sites of BRCT and the conserved regions in all interacting proteins were predicted using cons-PPISP and ConSurf, respectively. Finally, protein-protein docking analysis was done by HADDOCK. Our results identified 19 possible BRCT interacting proteins during D. discoideum development. Furthermore, interacting residues involved in the interactions and functional regions were explored. This is the first report where PARP-1's developmental interactome in D. discoideum is well established. The current findings demonstrate PARP-1's developmental interactome in D. discoideum and provide the groundwork to understand its regulated functions in developmental biology which would undoubtedly extend our perception towards developmental diseases in higher complex organisms and their treatment.
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Affiliation(s)
- Ashlesha Kadam
- Department of Biochemistry, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara- 390002, Gujarat, India.
| | | | - Tina Jubin
- Department of Biochemistry, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara- 390002, Gujarat, India.
| | - Jayvadan Vaishnav
- Department of Biochemistry, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara- 390002, Gujarat, India.
| | - Abhishek Garg
- Department of Biochemistry, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara- 390002, Gujarat, India.
| | - Chinthapalli Balaji
- Department of Biological Sciences, Tata Institute of Fundamental Research (TIFR), Mumbai, 400005, Maharashtra, India.
| | - Devesh Suthar
- Department of Biochemistry, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara- 390002, Gujarat, India.
| | - Rasheedunnisa Begum
- Department of Biochemistry, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara- 390002, Gujarat, India.
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24
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Hashemi ZS, Zarei M, Fath MK, Ganji M, Farahani MS, Afsharnouri F, Pourzardosht N, Khalesi B, Jahangiri A, Rahbar MR, Khalili S. In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions. Front Mol Biosci 2021; 8:669431. [PMID: 33996914 PMCID: PMC8113820 DOI: 10.3389/fmolb.2021.669431] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/06/2021] [Indexed: 01/01/2023] Open
Abstract
Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.
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Affiliation(s)
- Zahra Sadat Hashemi
- ATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, Academic Center for Education, Culture and Research, Tehran, Iran
| | - Mahboubeh Zarei
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohsen Karami Fath
- Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Mahmoud Ganji
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mahboube Shahrabi Farahani
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Fatemeh Afsharnouri
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Navid Pourzardosht
- Cellular and Molecular Research Center, Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran
- Department of Biochemistry, Guilan University of Medical Sciences, Rasht, Iran
| | - Bahman Khalesi
- Department of Research and Production of Poultry Viral Vaccine, Razi Vaccine and Serum Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Abolfazl Jahangiri
- Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Rahbar
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Saeed Khalili
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
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25
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Slater O, Miller B, Kontoyianni M. Decoding Protein-protein Interactions: An Overview. Curr Top Med Chem 2021; 20:855-882. [PMID: 32101126 DOI: 10.2174/1568026620666200226105312] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 11/27/2019] [Accepted: 11/27/2019] [Indexed: 12/24/2022]
Abstract
Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.
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Affiliation(s)
- Olivia Slater
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Bethany Miller
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Maria Kontoyianni
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
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26
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Jamasb AR, Day B, Cangea C, Liò P, Blundell TL. Deep Learning for Protein-Protein Interaction Site Prediction. Methods Mol Biol 2021; 2361:263-288. [PMID: 34236667 DOI: 10.1007/978-1-0716-1641-3_16] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Protein-protein interactions (PPIs) are central to cellular functions. Experimental methods for predicting PPIs are well developed but are time and resource expensive and suffer from high false-positive error rates at scale. Computational prediction of PPIs is highly desirable for a mechanistic understanding of cellular processes and offers the potential to identify highly selective drug targets. In this chapter, details of developing a deep learning approach to predicting which residues in a protein are involved in forming a PPI-a task known as PPI site prediction-are outlined. The key decisions to be made in defining a supervised machine learning project in this domain are here highlighted. Alternative training regimes for deep learning models to address shortcomings in existing approaches and provide starting points for further research are discussed. This chapter is written to serve as a companion to developing deep learning approaches to protein-protein interaction site prediction, and an introduction to developing geometric deep learning projects operating on protein structure graphs.
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Affiliation(s)
- Arian R Jamasb
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Ben Day
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Cătălina Cangea
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, UK.
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27
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Antitoxin autoregulation of M. tuberculosis toxin-antitoxin expression through negative cooperativity arising from multiple inverted repeat sequences. Biochem J 2020; 477:2401-2419. [PMID: 32519742 PMCID: PMC7319586 DOI: 10.1042/bcj20200368] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/06/2020] [Accepted: 06/10/2020] [Indexed: 12/20/2022]
Abstract
Toxin-antitoxin systems play key roles in bacterial adaptation, including protection from antibiotic assault and infection by bacteriophages. The type IV toxin-antitoxin system AbiE encodes a DUF1814 nucleotidyltransferase-like toxin, and a two-domain antitoxin. In Streptococcus agalactiae, the antitoxin AbiEi negatively autoregulates abiE expression through positively co-operative binding to inverted repeats within the promoter. The human pathogen Mycobacterium tuberculosis encodes four DUF1814 putative toxins, two of which have antitoxins homologous to AbiEi. One such M. tuberculosis antitoxin, named Rv2827c, is required for growth and whilst the structure has previously been solved, the mode of regulation is unknown. To complete the gaps in our understanding, we first solved the structure of S. agalactiae AbiEi to 1.83 Å resolution for comparison with M. tuberculosis Rv2827c. AbiEi contains an N-terminal DNA binding domain and C-terminal antitoxicity domain, with bilateral faces of opposing charge. The overall AbiEi fold is similar to Rv2827c, though smaller, and with a 65° difference in C-terminal domain orientation. We further demonstrate that, like AbiEi, Rv2827c can autoregulate toxin-antitoxin operon expression. In contrast with AbiEi, the Prv2827c promoter contains two sets of inverted repeats, which bind Rv2827c with differing affinities depending on the sequence consensus. Surprisingly, Rv2827c bound with negative co-operativity to the full Prv2827c promoter, demonstrating an unexpectedly complex form of transcriptional regulation.
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28
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Ahamad S, Gupta D, Kumar V. Targeting SARS-CoV-2 nucleocapsid oligomerization: Insights from molecular docking and molecular dynamics simulations. J Biomol Struct Dyn 2020; 40:2430-2443. [PMID: 33140703 PMCID: PMC7663461 DOI: 10.1080/07391102.2020.1839563] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The outbreak of COVID-19 caused by SARS-CoV-2 virus continually led to infect a large population worldwide. Currently, there is no specific viral protein-targeted therapeutics. The Nucleocapsid (N) protein of the SARS-CoV-2 virus is necessary for viral RNA replication and transcription. The C-terminal domain of N protein (CTD) involves in the self-assembly of N protein into a filament that is packaged into new virions. In this study, the CTD (PDB ID: 6WJI) was targeted for the identification of possible inhibitors of oligomerization of N protein. Herein, multiple computational approaches were employed to explore the potential mechanisms of binding and inhibitor activity of five antiviral drugs toward CTD. The five anti-N drugs studied in this work are 4E1RCat, Silmitasertib, TMCB, Sapanisertib, and Rapamycin. Among the five drugs, 4E1RCat displayed highest binding affinity (-10.95 kcal/mol), followed by rapamycin (-8.91 kcal/mol), silmitasertib (-7.89 kcal/mol), TMCB (-7.05 kcal/mol), and sapanisertib (-6.14 kcal/mol). Subsequently, stability and dynamics of the protein-drug complex were examined with molecular dynamics (MD) simulations. Overall, drug binding increases the stability of the complex with maximum stability observed in the case of 4E1RCat. The CTD-drug complex systems behave differently in terms of the free energy landscape and showed differences in population distribution. Overall, the MD simulation parameters like RMSD, RMSF, Rg, hydrogen bonds analysis, PCA, FEL, and DCCM analysis indicated that 4E1RCat and TMCB complexes were more stable as compared to silmitasertib and sapanisertib and thus could act as effective drug compounds against CTD.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shahzaib Ahamad
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, India
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29
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Chang HJ, Choi H, Na S. Predicting the self-assembly film structure of class II hydrophobin NC2 and estimating its structural characteristics. Colloids Surf B Biointerfaces 2020; 195:111269. [DOI: 10.1016/j.colsurfb.2020.111269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 07/15/2020] [Accepted: 07/21/2020] [Indexed: 11/24/2022]
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30
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Sahoo BR. Structure of fish Toll-like receptors (TLR) and NOD-like receptors (NLR). Int J Biol Macromol 2020; 161:1602-1617. [PMID: 32755705 PMCID: PMC7396143 DOI: 10.1016/j.ijbiomac.2020.07.293] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/26/2020] [Accepted: 07/27/2020] [Indexed: 12/23/2022]
Abstract
Innate immunity driven by pattern recognition receptor (PRR) protects the host from invading pathogens. Aquatic animals like fish where the adaptive immunity is poorly developed majorly rely on their innate immunity modulated by PRRs like toll-like receptors (TLR) and NOD-like receptors (NLR). However, current development to improve the fish immunity via TLR/NLR signaling is affected by a poor understanding of its mechanistic and structural features. This review discusses the structure of fish TLRs/NLRs and its interaction with pathogen associated molecular patterns (PAMPs) and downstream signaling molecules. Over the past one decade, significant progress has been done in studying the structure of TLRs/NLRs in higher eukaryotes; however, structural studies on fish innate immune receptors are undermined. Several novel TLR genes are identified in fish that are absent in higher eukaryotes, but the function is still poorly understood. Unlike the fundamental progress achieved in developing antagonist/agonist to modulate human innate immunity, analogous studies in fish are nearly lacking due to structural inadequacy. This underlies the importance of exploring the structural and mechanistic details of fish TLRs/NLRs at an atomic and molecular level. This review outlined the mechanistic and structural basis of fish TLR and NLR activation.
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31
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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: 58] [Impact Index Per Article: 14.5] [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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32
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Cloutier TK, Sudrik C, Mody N, Sathish HA, Trout BL. Machine Learning Models of Antibody–Excipient Preferential Interactions for Use in Computational Formulation Design. Mol Pharm 2020; 17:3589-3599. [DOI: 10.1021/acs.molpharmaceut.0c00629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Theresa K. Cloutier
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Chaitanya Sudrik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Neil Mody
- Dosage Form Design and Development, AstraZeneca, Gaithersburg, Maryland 20878, United States
| | - Hasige A. Sathish
- Dosage Form Design and Development, AstraZeneca, Gaithersburg, Maryland 20878, United States
| | - Bernhardt L. Trout
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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33
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Savojardo C, Martelli PL, Casadio R. Protein–Protein Interaction Methods and Protein Phase Separation. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-011720-104428] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In the last decade, newly developed experimental methods have made it possible to highlight that macromolecules in the cell milieu physically interact to support physiology. This has shifted the problem of protein–protein interaction from a microscopic, electron-density scale to a mesoscopic one. Further, nowadays there is increasing evidence that proteins in the nucleus and in the cytoplasm can aggregate in membraneless organelles for different physiological reasons. In this scenario, it is urgent to face the problem of biomolecule functional annotation with efficient computational methods, suited to extract knowledge from reliable data and transfer information across different domains of investigation. Here, we revise the present state of the art of our knowledge of protein–protein interaction and the computational methods that differently implement it. Furthermore, we explore experimental and computational features of a set of proteins involved in phase separation.
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Affiliation(s)
- Castrense Savojardo
- Biocomputing Group, Department of Pharmacy and Biotechnology and Interdepartmental Center “Luigi Galvani” for Integrated Studies of Bioinformatics, Biophysics, and Biocomplexity, University of Bologna, 40126 Bologna, Italy
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology and Interdepartmental Center “Luigi Galvani” for Integrated Studies of Bioinformatics, Biophysics, and Biocomplexity, University of Bologna, 40126 Bologna, Italy
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology and Interdepartmental Center “Luigi Galvani” for Integrated Studies of Bioinformatics, Biophysics, and Biocomplexity, University of Bologna, 40126 Bologna, Italy
- Institute of Biomembranes, Bioenergetics, and Molecular Biotechnologies (IBIOM), Italian National Research Council (CNR), 70126 Bari, Italy
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34
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Paul A, Srinivasan N. Genome-wide and structural analyses of pseudokinases encoded in the genome of Arabidopsis thaliana provide functional insights. Proteins 2020; 88:1620-1638. [PMID: 32667690 DOI: 10.1002/prot.25981] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/26/2020] [Accepted: 07/12/2020] [Indexed: 12/31/2022]
Abstract
Protein Kinase-Like Non-Kinases (PKLNKs), commonly known as "pseudokinases", are homologous to eukaryotic Ser/Thr/Tyr protein kinases (PKs) but lack the crucial aspartate residue in the catalytic loop, indispensable for phosphotransferase activity. Therefore, they are predicted to be "catalytically inactive" enzyme homologs. Analysis of protein-kinase like sequences from Arabidopsis thaliana led to the identification of more than 120 pseudokinases lacking catalytic aspartate, majority of which are closely related to the plant-specific receptor-like kinase family. These pseudokinases engage in different biological processes, enabled by their diverse domain architectures and specific subcellular localizations. Structural comparison of pseudokinases with active and inactive conformations of canonical PKs, belonging to both plant and animal origin, revealed unique structural differences. The currently available crystal structures of pseudokinases show that the loop topologically equivalent to activation segment of PKs adopts a distinct-folded conformation, packing against the pseudoenzyme core, in contrast to the extended and inhibitory geometries observed for active and inactive states, respectively, of catalytic PKs. Salt-bridge between ATP-binding Lys and DFG-Asp as well as hydrophobic interactions between the conserved nonpolar residue C-terminal to the equivalent DFG motif and nonpolar residues in C-helix mediate such a conformation in pseudokinases. This results in enhanced solvent accessibility of the pseudocatalytic loop in pseudokinases that can possibly serve as an interacting surface while associating with other proteins. Specifically, our analysis identified several residues that may be involved in pseudokinase regulation and hints at the repurposing of pseudocatalytic residues to achieve mechanistic control over noncatalytic functions of pseudoenzymes.
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Affiliation(s)
- Anindita Paul
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
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35
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Ganakammal SR, Koirala M, Wu B, Alexov E. In-silico analysis to identify the role of MEN1 missense mutations in breast cancer. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2020. [DOI: 10.1142/s0219633620410023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background: The multiple endocrine neoplasia type 1 (MEN1) gene located on chromosome 11q13 encodes menin protein. Previously reported mutations were thought to result in loss of function of menin protein and that they are associated with multiple endocrine neoplasia 1 disorder. However, recently menin has also been characterized as an oncosuppressor protein and it was suggested that mutations in it are associated with various other tumors. Studies indicate that the menin protein stimulates the estrogen receptor (ER) that in turn increases the predisposition for inherited breast cancer. Methods: Here, we used our supervised in-house combinatory in-silico predictor method to investigate the impact of unclassified missense mutations in MEN1 gene found in breast cancer tissue. We also examined the biophysical and biochemical properties to predict the effects of these missense variants on the menin protein stability and interactions. The results are compared with the effects of known pathogenic mutations in menin causing neoplasia. Results: Our analysis indicates that some of the variants found in breast cancer tissue show similar pattern of destabilizing the menin protein and its interactions as the pathogenic variants associated with neoplasia. Taking together with the results of our in-silico consensus predictor, we classify missense mutations in menin protein found in breast cancer tissue into pathogenic and benign, and thus, suggesting as an indicator for early detection of elevated breast cancer risk.
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Affiliation(s)
| | - Mahesh Koirala
- Department of Physics, Clemson University, Clemson SC, USA
| | - Bohua Wu
- Department of Physics, Clemson University, Clemson SC, USA
| | - Emil Alexov
- Department of Healthcare Genetics, School of Nursing, Clemson University, Clemson SC, USA
- Department of Physics, Clemson University, Clemson SC, USA
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36
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Chopra K, Burdak B, Sharma K, Kembhavi A, Mande SC, Chauhan R. CoRNeA: A Pipeline to Decrypt the Inter-Protein Interfaces from Amino Acid Sequence Information. Biomolecules 2020; 10:biom10060938. [PMID: 32580303 PMCID: PMC7356028 DOI: 10.3390/biom10060938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 05/26/2020] [Accepted: 05/27/2020] [Indexed: 12/27/2022] Open
Abstract
Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information, but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here, we report a new hybrid pipeline for predicting the protein-protein interaction interfaces in a pairwise manner from the amino acid sequence information of the interacting proteins. It is based on the framework of Co-evolution, machine learning (Random Forest), and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use Co-evolution, physicochemical properties, and contact potential as major group of features to train the Random Forest classifier. We also incorporate the intra-contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence of a protein also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly.
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Affiliation(s)
- Kriti Chopra
- National Centre for Cell Science, Pune 411007, Maharashtra, India; (K.C.); (B.B.)
| | - Bhawna Burdak
- National Centre for Cell Science, Pune 411007, Maharashtra, India; (K.C.); (B.B.)
| | - Kaushal Sharma
- Inter-University Centre for Astronomy and Astrophysics, Pune 411007, Maharashtra, India; (K.S.); (A.K.)
| | - Ajit Kembhavi
- Inter-University Centre for Astronomy and Astrophysics, Pune 411007, Maharashtra, India; (K.S.); (A.K.)
| | - Shekhar C. Mande
- Council of Scientific and Industrial Research (CSIR), New Delhi 110001, India;
| | - Radha Chauhan
- National Centre for Cell Science, Pune 411007, Maharashtra, India; (K.C.); (B.B.)
- Correspondence: ; Tel.: +91-20-25708255
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Zhu H, Du X, Yao Y. ConvsPPIS: Identifying Protein-protein Interaction Sites by an Ensemble Convolutional Neural Network with Feature Graph. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191105155713] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background/Objective:
Protein-protein interactions are essentials for most cellular
processes and thus, unveiling how proteins interact with is a crucial question that can be better
understood by recognizing which residues participate in the interaction. Although many
computational approaches have been proposed to predict interface residues, their feature
perspective and model learning ability are not enough to achieve ideal results. So, our objective is
to improve the predictive performance under considering feature perspective and new learning
algorithm.
Method:
In this study, we proposed an ensemble deep convolutional neural network, which
explores the context and positional context of consecutive residues within a protein sub-sequence.
Specifically, unlike the feature view of previous methods, ConvsPPIS uses evolutionary,
physicochemical, and structural protein characteristics to construct their own feature graph
respectively. After that, three independent deep convolutional neural networks are trained on each
type of feature graph for learning the underlying pattern in sub-sequence. Lastly, we integrated
those three deep networks into an ensemble predictor with leveraging complementary information
of those features to predict potential interface residues.
Results:
Some comparative experiments have conducted through 10-fold cross-validation. The
results indicated that ConvsPPIS achieved superior performance on DBv5-Sel dataset with an
accuracy of 88%. Additional experiments on CAPRI-Alone dataset demonstrated ConvsPPIS has
also better prediction performance.
Conclusion:
The ConvsPPIS method provided a new perspective to capture protein feature
expression for identifying protein-protein interaction sites. The results proved the superiority of
this method.
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Affiliation(s)
- Huaixu Zhu
- School of Computer Science and Technology, Anhui University, Hefei, China
| | - Xiuquan Du
- School of Computer Science and Technology, Anhui University, Hefei, China
| | - Yu Yao
- School of Computer Science and Technology, Anhui University, Hefei, China
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38
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Lyu Y, Huang H, Gong X. A Novel Index of Contact Frequency from Noise Protein-Protein Interaction Data Help for Accurate Interface Residue Pair Prediction. Interdiscip Sci 2020; 12:204-216. [PMID: 32185690 DOI: 10.1007/s12539-020-00364-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 01/23/2020] [Accepted: 02/24/2020] [Indexed: 11/24/2022]
Abstract
Protein-protein interactions are important for most biological processes and have been studied for decades. However, the detailed formation mechanism of protein-protein interaction interface is still ambiguous, which makes it difficult to accurately predict the protein-protein interaction interface residue pairs. Here, we extract the interface residue-residue contacts from the decoys in the ZDOCK protein-protein complex decoy set with RMSD mostly larger than 3 Å. To accurately compute the interface residue-residue contacts, we define a new constant called interface residue pairs frequency, which counts the atom contact numbers between two interface residues. We normalize interface residue pairs frequency to pick out the top residue-residue pairs from all the possible pairs preferential to be on correct protein-protein interaction interface. When tested on 37 protein dimers from the decoy set where most decoys are incorrect, our method successfully predicts 30 protein dimers with a success rate of up to 81.1%. Higher accuracy than some other state-of-the-art methods confirmed the performance of our method.
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Affiliation(s)
- Yanfen Lyu
- Mathematical Intelligence Application Lab, Institute for Mathematical Sciences, School of Math, Renmin University of China, Beijing, 100872, China
| | - He Huang
- Mathematical Intelligence Application Lab, Institute for Mathematical Sciences, School of Math, Renmin University of China, Beijing, 100872, China
| | - Xinqi Gong
- Mathematical Intelligence Application Lab, Institute for Mathematical Sciences, School of Math, Renmin University of China, Beijing, 100872, China.
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39
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Sanyanga TA, Tastan Bishop Ö. Structural Characterization of Carbonic Anhydrase VIII and Effects of Missense Single Nucleotide Variations to Protein Structure and Function. Int J Mol Sci 2020; 21:E2764. [PMID: 32316137 PMCID: PMC7215520 DOI: 10.3390/ijms21082764] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/11/2020] [Accepted: 04/13/2020] [Indexed: 12/13/2022] Open
Abstract
Human carbonic anhydrase 8 (CA-VIII) is an acatalytic isoform of the α -CA family. Though the protein cannot hydrate CO2, CA-VIII is essential for calcium (Ca2+) homeostasis within the body, and achieves this by allosterically inhibiting the binding of inositol 1,4,5-triphosphate (IP3) to the IP3 receptor type 1 (ITPR1) protein. However, the mechanism of interaction of CA-VIII to ITPR1 is not well understood. In addition, functional defects to CA-VIII due to non-synonymous single nucleotide polymorphisms (nsSNVs) result in Ca2+ dysregulation and the development of the phenotypes such as cerebellar ataxia, mental retardation and disequilibrium syndrome 3 (CAMRQ3). The pathogenesis of CAMRQ3 is also not well understood. The structure and function of CA-VIII was characterised, and pathogenesis of CAMRQ3 investigated. Structural and functional characterisation of CA-VIII was conducted through SiteMap and CPORT to identify potential binding site residues. The effects of four pathogenic nsSNVs, S100A, S100P, G162R and R237Q, and two benign S100L and E109D variants on CA-VIII structure and function was then investigated using molecular dynamics (MD) simulations, dynamic cross correlation (DCC) and dynamic residue network (DRN) analysis. SiteMap and CPORT analyses identified 38 unique CA-VIII residues that could potentially bind to ITPR1. MD analysis revealed less conformational sampling within the variant proteins and highlighted potential increases to variant protein rigidity. Dynamic cross correlation (DCC) showed that wild-type (WT) protein residue motion is predominately anti-correlated, with variant proteins showing no correlation to greater residue correlation. DRN revealed variant-associated increases to the accessibility of the N-terminal binding site residues, which could have implications for associations with ITPR1, and further highlighted differences to the mechanism of benign and pathogenic variants. SNV presence is associated with a reduction to the usage of Trp37 in all variants, which has implications for CA-VIII stability. The differences to variant mechanisms can be further investigated to understand pathogenesis of CAMRQ3, enhancing precision medicine-related studies into CA-VIII.
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MESH Headings
- Binding Sites
- Biomarkers, Tumor/chemistry
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Cerebellar Ataxia/genetics
- Cerebellar Ataxia/pathology
- Databases, Genetic
- Humans
- Inositol 1,4,5-Trisphosphate Receptors/chemistry
- Inositol 1,4,5-Trisphosphate Receptors/metabolism
- Intellectual Disability/genetics
- Intellectual Disability/pathology
- Molecular Dynamics Simulation
- Mutation, Missense
- Polymorphism, Single Nucleotide
- Protein Binding
- Protein Interaction Maps
- Protein Stability
- Protein Structure, Tertiary
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Affiliation(s)
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa;
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40
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Gyulkhandanyan A, Rezaie AR, Roumenina L, Lagarde N, Fremeaux-Bacchi V, Miteva MA, Villoutreix BO. Analysis of protein missense alterations by combining sequence- and structure-based methods. Mol Genet Genomic Med 2020; 8:e1166. [PMID: 32096919 PMCID: PMC7196459 DOI: 10.1002/mgg3.1166] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/20/2020] [Accepted: 01/27/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Different types of in silico approaches can be used to predict the phenotypic consequence of missense variants. Such algorithms are often categorized as sequence based or structure based, when they necessitate 3D structural information. In addition, many other in silico tools, not dedicated to the analysis of variants, can be used to gain additional insights about the possible mechanisms at play. METHODS Here we applied different computational approaches to a set of 20 known missense variants present on different proteins (CYP, complement factor B, antithrombin and blood coagulation factor VIII). The tools that were used include fast computational approaches and web servers such as PolyPhen-2, PopMusic, DUET, MaestroWeb, SAAFEC, Missense3D, VarSite, FlexPred, PredyFlexy, Clustal Omega, meta-PPISP, FTMap, ClusPro, pyDock, PPM, RING, Cytoscape, and ChannelsDB. RESULTS We observe some conflicting results among the methods but, most of the time, the combination of several engines helped to clarify the potential impacts of the amino acid substitutions. CONCLUSION Combining different computational approaches including some that were not developed to investigate missense variants help to predict the possible impact of the amino acid substitutions. Yet, when the modified residues are involved in a salt-bridge, the tools tend to fail, even when the analysis is performed in 3D. Thus, interactive structural analysis with molecular graphics packages such as Chimera or PyMol or others are still needed to clarify automatic prediction.
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Affiliation(s)
- Aram Gyulkhandanyan
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- Laboratory SABNP, University of Evry, INSERM U1204, Université Paris-Saclay, Evry, France
| | - Alireza R Rezaie
- Cardiovascular Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Lubka Roumenina
- INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France
- Sorbonne Universités, Paris, France
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Nathalie Lagarde
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- Laboratoire GBCM, EA7528, Conservatoire national des arts et métiers, Hesam Université, Paris, France
| | - Veronique Fremeaux-Bacchi
- INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France
- Sorbonne Universités, Paris, France
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
- Assistance Publique-Hôpitaux de Paris, Service d'Immunologie Biologique, Hôpital Européen Georges Pompidou, Paris, France
| | - Maria A Miteva
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- Inserm U1268 MCTR, CNRS UMR 8038 CiTCoM, Faculté de Pharmacie de Paris, Univ. De Paris, Paris, France
| | - Bruno O Villoutreix
- INSERM U973, Laboratory MTi, University Paris Diderot, Paris, France
- INSERM, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, Université de Lille, Lille, France
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41
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Xie Z, Deng X, Shu K. Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets. Int J Mol Sci 2020; 21:E467. [PMID: 31940793 PMCID: PMC7013409 DOI: 10.3390/ijms21020467] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 12/23/2019] [Accepted: 01/08/2020] [Indexed: 12/20/2022] Open
Abstract
Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.
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Affiliation(s)
- Zengyan Xie
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
| | | | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
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42
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Wang W, Shin WJ, Zhang B, Choi Y, Yoo JS, Zimmerman MI, Frederick TE, Bowman GR, Gross ML, Leung DW, Jung JU, Amarasinghe GK. The Cap-Snatching SFTSV Endonuclease Domain Is an Antiviral Target. Cell Rep 2020; 30:153-163.e5. [PMID: 31914382 PMCID: PMC7214099 DOI: 10.1016/j.celrep.2019.12.020] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 10/31/2019] [Accepted: 12/06/2019] [Indexed: 01/08/2023] Open
Abstract
Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne virus with 12%-30% case mortality rates and is related to the Heartland virus (HRTV) identified in the United States. Together, SFTSV and HRTV are emerging segmented, negative-sense RNA viral (sNSV) pathogens with potential global health impact. Here, we characterize the amino-terminal cap-snatching endonuclease domain of SFTSV polymerase (L) and solve a 2.4-Å X-ray crystal structure. While the overall structure is similar to those of other cap-snatching sNSV endonucleases, differences near the C terminus of the SFTSV endonuclease suggest divergence in regulation. Influenza virus endonuclease inhibitors, including the US Food and Drug Administration (FDA) approved Baloxavir (BXA), inhibit the endonuclease activity in in vitro enzymatic assays and in cell-based studies. BXA displays potent activity with a half maximal inhibitory concentration (IC50) of ∼100 nM in enzyme inhibition and an EC50 value of ∼250 nM against SFTSV and HRTV in plaque assays. Together, our data support sNSV endonucleases as an antiviral target.
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Affiliation(s)
- Wenjie Wang
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Woo-Jin Shin
- Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Bojie Zhang
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Younho Choi
- Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Ji-Seung Yoo
- Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Maxwell I Zimmerman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Thomas E Frederick
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Michael L Gross
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Daisy W Leung
- Division of Infectious Diseases, John T. Milliken Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jae U Jung
- Department of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
| | - Gaya K Amarasinghe
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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43
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Barreto CAV, Baptista SJ, Preto AJ, Matos-Filipe P, Mourão J, Melo R, Moreira I. Prediction and targeting of GPCR oligomer interfaces. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 169:105-149. [PMID: 31952684 DOI: 10.1016/bs.pmbts.2019.11.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.
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Affiliation(s)
- Carlos A V Barreto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Salete J Baptista
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - António José Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Rita Melo
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - Irina Moreira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Science and Technology Faculty, University of Coimbra, Coimbra, Portugal.
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44
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Zhang B, Li J, Quan L, Chen Y, Lü Q. Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.05.013] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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45
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Reille S, Garnier M, Robert X, Gouet P, Martin J, Launay G. Identification and visualization of protein binding regions with the ArDock server. Nucleic Acids Res 2019; 46:W417-W422. [PMID: 29905873 PMCID: PMC6031020 DOI: 10.1093/nar/gky472] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/28/2018] [Indexed: 12/21/2022] Open
Abstract
ArDock (ardock.ibcp.fr) is a structural bioinformatics web server for the prediction and the visualization of potential interaction regions at protein surfaces. ArDock ranks the surface residues of a protein according to their tendency to form interfaces in a set of predefined docking experiments between the query protein and a set of arbitrary protein probes. The ArDock methodology is derived from large scale cross-docking studies where it was observed that randomly chosen proteins tend to dock in a non-random way at protein surfaces. The method predicts interaction site of the protein, or alternate interfaces in the case of proteins with multiple interaction modes. The server takes a protein structure as input and computes a score for each surface residue. Its output focuses on the interactive visualization of results and on interoperability with other services.
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Affiliation(s)
- Sébastien Reille
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Mélanie Garnier
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Xavier Robert
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Patrice Gouet
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Juliette Martin
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
| | - Guillaume Launay
- Molecular Microbiology and Structural Biochemistry, Unité Mixte de Recherche, Université Claude Bernard Lyon 1, Centre National de la Recherche Scientifique, 69367 Lyon Cedex 07, France
- To whom correspondence should be addressed. Tel: +33 437 652 936; Fax: +33 472 722 601;
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46
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Wang X, Yu B, Ma A, Chen C, Liu B, Ma Q. Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique. Bioinformatics 2019; 35:2395-2402. [PMID: 30520961 PMCID: PMC6612859 DOI: 10.1093/bioinformatics/bty995] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Revised: 11/19/2018] [Accepted: 12/03/2018] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The prediction of protein-protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples. RESULTS A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2-15.7% and 6.1-18.9% higher than the other existing tools, respectively. AVAILABILITY AND IMPLEMENTATION The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaoying Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China
- School of Mathematics, Shandong University, Jinan, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, China
| | - Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, China
- School of Life Sciences, University of Science and Technology of China, Hefei, China
| | - Anjun Ma
- Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
- Department Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Cheng Chen
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China
- Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, China
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, China
| | - Qin Ma
- Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
- Department Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA
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47
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Tanwar G, Purohit R. Gain of native conformation of Aurora A S155R mutant by small molecules. J Cell Biochem 2019; 120:11104-11114. [PMID: 30746758 DOI: 10.1002/jcb.28387] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 11/28/2018] [Accepted: 12/06/2018] [Indexed: 01/24/2023]
Abstract
Aurora A is a mitotic serine/threonine kinase protein that is a proposed target of the first-line anticancer drug design. It has been found to be overexpressed in many human cancer cells, including hematological, breast, and colorectal. Here, we focus on a particular somatic mutant S155R of Aurora kinase A protein, whose activity decreases because of loss of interaction with a TPX2 protein that results in ectopic expression of the Aurora kinase A protein, which contributes chromosome instability, centrosome amplification, and oncogenic transformation. The primary target of this study is to select a drug molecule whose binding results in gaining S155R mutant interaction with TPX2. The computational methodology applied in this study involves mapping of hotspots (for uncompetitive binding), virtual screening, protein-ligand docking, postdocking optimization, and protein-protein docking approach. In this study, we screen and validate ZINC968264, which acts as a potential molecule that can improve the loss of function occurred because of mutation (S155R) in Aurora A. Our approaches pave a suitable path to design a potential drug against physiological condition manifested because of S155R mutant in Aurora A.
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Affiliation(s)
- Garima Tanwar
- Structural Bioinformatics Lab, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh, India.,Biotechnology Division, CSIR-IHBT, Palampur, Himachal Pradesh, India
| | - Rituraj Purohit
- Structural Bioinformatics Lab, CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur, Himachal Pradesh, India.,Biotechnology Division, CSIR-IHBT, Palampur, Himachal Pradesh, India.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IHBT Campus, Palampur, Himachal Pradesh, India
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48
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Wong ETC, Gsponer J. Predicting Protein-Protein Interfaces that Bind Intrinsically Disordered Protein Regions. J Mol Biol 2019; 431:3157-3178. [PMID: 31207240 DOI: 10.1016/j.jmb.2019.06.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 06/01/2019] [Accepted: 06/04/2019] [Indexed: 12/18/2022]
Abstract
A long-standing goal in biology is the complete annotation of function and structure on all protein-protein interactions, a large fraction of which is mediated by intrinsically disordered protein regions (IDRs). However, knowledge derived from experimental structures of such protein complexes is disproportionately small due, in part, to challenges in studying interactions of IDRs. Here, we introduce IDRBind, a computational method that by combining gradient boosted trees and conditional random field models predicts binding sites of IDRs with performance approaching state-of-the-art globular interface predictions, making it suitable for proteome-wide applications. Although designed and trained with a focus on molecular recognition features, which are long interaction-mediating-elements in IDRs, IDRBind also predicts the binding sites of short peptides more accurately than existing specialized predictors. Consistent with IDRBind's specificity, a comparison of protein interface categories uncovered uniform trends in multiple physicochemical properties, positioning molecular recognition feature interfaces between peptide and globular interfaces.
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Affiliation(s)
- Eric T C Wong
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada; Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada; Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.
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49
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The Ferredoxin-Like Protein FerR Regulates PrbP Activity in Liberibacter asiaticus. Appl Environ Microbiol 2019; 85:AEM.02605-18. [PMID: 30552192 DOI: 10.1128/aem.02605-18] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Accepted: 12/08/2018] [Indexed: 12/26/2022] Open
Abstract
In Liberibacter asiaticus, PrbP is an important transcriptional accessory protein that regulates gene expression through interactions with the RNA polymerase β-subunit and a specific sequence on the promoter region. The constitutive expression of prbP observed upon chemical inactivation of PrbP-DNA interactions in vivo indicated that the expression of prbP was not autoregulated at the level of transcription. This observation suggested that a modulatory mechanism via protein-protein interactions may be involved. In silico genome association analysis identified FerR (CLIBASIA_01505), a putative ferredoxin-like protein, as a PrbP-interacting protein. Using a bacterial two-hybrid system and immunoprecipitation assays, interactions between PrbP and FerR were confirmed. In vitro transcription assays were used to show that FerR can increase the activity of PrbP by 16-fold when present in the PrbP-RNA polymerase reaction mixture. The FerR protein-protein interaction surface was predicted by structural modeling and followed by site-directed mutagenesis. Amino acids V20, V23, and C40 were identified as the most important residues in FerR involved in the modulation of PrbP activity in vitro The regulatory mechanism of FerR abundance was examined at the transcription level. In contrast to prbP of L. asiaticus (prbP Las), mRNA levels of ferR of L. asiaticus (ferR Las) are induced by an increase in osmotic pressure. The results of this study revealed that the activity of the transcriptional activator PrbPLas is modulated via interactions with FerRLas The induction of ferR Las expression by osmolarity provides insight into the mechanisms of adjusting gene expression in response to host environmental signals in L. asiaticus IMPORTANCE The rapid spread and aggressive progression of huanglongbing (HLB) in the major citrus-producing areas have raised global recognition of and vigilance to this disease. As a result, the causative agent, Liberibacter asiaticus, has been investigated from various perspectives. However, gene expression regulatory mechanisms that are important for the survival and persistence of this intracellular pathogen remain largely unexplored. PrbP is a transcriptional accessory protein important for L. asiaticus survival in the plant host. In this study, we investigated the interactions between PrbP in L. asiaticus (PrbPLas) and a ferredoxin-like protein (FerR) in L. asiaticus, FerRLas We show that the presence of FerR stabilizes and augments the activity of PrbPLas In addition, we demonstrate that the expression of ferR is induced by increases in osmolarity in Liberibacter crescens Altogether, these results suggest that FerRLas and PrbPLas may play important roles in the regulation of gene expression in response to changing environmental signals during L. asiaticus infection in the citrus host.
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50
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Macalino SJY, Basith S, Clavio NAB, Chang H, Kang S, Choi S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules 2018; 23:E1963. [PMID: 30082644 PMCID: PMC6222862 DOI: 10.3390/molecules23081963] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/03/2018] [Accepted: 08/04/2018] [Indexed: 12/14/2022] Open
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
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Affiliation(s)
- Stephani Joy Y Macalino
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Shaherin Basith
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Nina Abigail B Clavio
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Hyerim Chang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
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