1
|
Sahoo BR, Bardwell JCA. SERF, a family of tiny highly conserved, highly charged proteins with enigmatic functions. FEBS J 2023; 290:4150-4162. [PMID: 35694898 DOI: 10.1111/febs.16555] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/07/2022] [Accepted: 06/10/2022] [Indexed: 11/27/2022]
Abstract
Amyloid formation is a misfolding process that has been linked to age-related diseases, including Alzheimer's and Huntington's. Understanding how cellular factors affect this process in vivo is vital in realizing the dream of controlling this insidious process that robs so many people of their humanity. SERF (small EDRK-rich factor) was initially isolated as a factor that accelerated polyglutamine amyloid formation in a C. elegans model. SERF knockouts inhibit amyloid formation of a number of proteins that include huntingtin, α-synuclein and β-amyloid which are associated with Huntington's, Parkinson's and Alzheimer's disease, respectively, and purified SERF protein speeds their amyloid formation in vitro. SERF proteins are highly conserved, highly charged and conformationally dynamic proteins that form a fuzzy complex with amyloid precursors. They appear to act by specifically accelerating the primary step of amyloid nucleation. Brain-specific SERF knockout mice, though viable, appear to be more prone to deposition of amyloids, and show modified fibril morphology. Whole-body knockouts are perinatally lethal due to an apparently unrelated developmental issue. Recently, it was found that SERF binds RNA and is localized to nucleic acid-rich membraneless compartments. SERF-related sequences are commonly found fused to zinc finger sequences. These results point towards a nucleic acid-binding function. How this function relates to their ability to accelerate amyloid formation is currently obscure. In this review, we discuss the possible biological functions of SERF family proteins in the context of their structural fuzziness, modulation of amyloid pathway, nucleic acid binding and their fusion to folded proteins.
Collapse
Affiliation(s)
- Bikash R Sahoo
- Department of Molecular, Cellular and Developmental Biology, Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI, USA
| | - James C A Bardwell
- Department of Molecular, Cellular and Developmental Biology, Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
2
|
Bandyopadhyay SS, Halder AK, Saha S, Chatterjee P, Nasipuri M, Basu S. Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human–Coronavirus Family Interactome. Vaccines (Basel) 2023; 11:vaccines11030549. [PMID: 36992133 DOI: 10.3390/vaccines11030549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/19/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host–pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host–pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host–pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host–pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.
Collapse
Affiliation(s)
- Soumyendu Sekhar Bandyopadhyay
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
- Department of Computer Science and Engineering, School of Engineering and Technology, Adamas University, Kolkata 700126, India
| | - Anup Kumar Halder
- Faculty of Mathematics and Information Sciences, Warsaw University of Technology, 00-662 Warsaw, Poland
| | - Sovan Saha
- Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Techno Main Salt Lake, Sector V, Kolkata 700091, India
| | - Piyali Chatterjee
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata 700152, India
| | - Mita Nasipuri
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| |
Collapse
|
3
|
Saha S, Halder AK, Bandyopadhyay SS, Chatterjee P, Nasipuri M, Basu S. Computational modeling of human-nCoV protein-protein interaction network. Methods 2022; 203:488-497. [PMID: 34902553 PMCID: PMC8662836 DOI: 10.1016/j.ymeth.2021.12.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 01/25/2023] Open
Abstract
Novel coronavirus(SARS-CoV2) replicates the host cell's genome by interacting with the host proteins. Due to this fact, the identification of virus and host protein-protein interactions could be beneficial in understanding the disease transmission behavior of the virus as well as in potential COVID-19 drug identification. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to the SARS-CoV epidemic in 2003 (∼89% similarity). With this hypothesis, the present work focuses on developing a computational model for the nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions. Initially, level-1 and level-2 human spreader proteins are identified in the SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered potential human targets for nCoV bait proteins. A gene-ontology-based fuzzy affinity function has been used to construct the nCoV-Human protein interaction network at a ∼99.98% specificity threshold. This also identifies 37 level-1 human spreaders for COVID-19 in the human protein-interaction network. 2474 level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using six potential FDA-listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins.
Collapse
Affiliation(s)
- Sovan Saha
- Department of Computer Science & Engineering, Institute of Engineering & Management, Salt Lake Electronics Complex, Kolkata 700091, West Bengal, India
| | - Anup Kumar Halder
- Department of Computer Science & Engineering, University of Engineering & Management, Kolkata 700156, West Bengal, India
| | - Soumyendu Sekhar Bandyopadhyay
- Department of Computer Science & Engineering, School of Engineering and Technology, Adamas University, Kolkata 700126, West Bengal, India; Department of Computer Science & Engineering, Jadavpur University, Jadavpur, Kolkata, West Bengal 700032, India
| | - Piyali Chatterjee
- Department of Computer Science & Engineering, Netaji Subhash Engineering College, Garia, Kolkata, West Bengal 700152, India
| | - Mita Nasipuri
- Department of Computer Science & Engineering, Jadavpur University, Jadavpur, Kolkata, West Bengal 700032, India
| | - Subhadip Basu
- Department of Computer Science & Engineering, Jadavpur University, Jadavpur, Kolkata, West Bengal 700032, India.
| |
Collapse
|
4
|
Noori S, Al‐A'araji N, Al‐Shamery E. Construction of dynamic protein interaction network based on gene expression data and quartile one principle. Proteins 2022; 90:1219-1228. [DOI: 10.1002/prot.26304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Soheir Noori
- Software Department University of Babylon Hillah Babylon Iraq
- Computer Science Department University of Kerbala Karbala Iraq
| | | | - Eman Al‐Shamery
- Software Department University of Babylon Hillah Babylon Iraq
| |
Collapse
|
5
|
Xu W, Zhao Z, Zhang H, Hu M, Yang N, Wang H, Wang C, Jiao J, Gu L. Deep neural learning based protein function prediction. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:2471-2488. [PMID: 35240793 DOI: 10.3934/mbe.2022114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
It is vital for the annotation of uncharacterized proteins by protein function prediction. At present, Deep Neural Network based protein function prediction is mainly carried out for dataset of small scale proteins or Gene Ontology, and usually explore the relationships between single protein feature and function tags. The practical methods for large-scale multi-features protein prediction still need to be studied in depth. This paper proposes a DNN based protein function prediction approach IGP-DNN. This method uses Grasshopper Optimization Algorithm (GOA) and Intuitionistic Fuzzy c-Means clustering (IFCM) based protein function modules extracting algorithm to extract the features of protein modules, utilizing Kernel Principal Component Analysis (KPCA) method to reduce the dimensionality of the protein attribute information, and integrating module features and attribute features. Inputting integrated data into DNN through multiple hidden layers to classify proteins and predict protein functions. In the experiments, the F-measure value of IGP-DNN on the DIP dataset reaches 0.4436, which shows better performance.
Collapse
Affiliation(s)
- Wenjun Xu
- Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture, Hefei 230036, China
- Institute of Intelligent Agriculture, Anhui Agricultural University, Hefei 230036, China
- School of Life Sciences, Anhui Agricultural University, Hefei 230036, China
| | - Zihao Zhao
- School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture, Hefei 230036, China
- Institute of Intelligent Agriculture, Anhui Agricultural University, Hefei 230036, China
| | - Hongwei Zhang
- School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture, Hefei 230036, China
- Institute of Intelligent Agriculture, Anhui Agricultural University, Hefei 230036, China
| | - Minglei Hu
- School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture, Hefei 230036, China
- Institute of Intelligent Agriculture, Anhui Agricultural University, Hefei 230036, China
| | - Ning Yang
- School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture, Hefei 230036, China
- Institute of Intelligent Agriculture, Anhui Agricultural University, Hefei 230036, China
| | - Hui Wang
- School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture, Hefei 230036, China
- Institute of Intelligent Agriculture, Anhui Agricultural University, Hefei 230036, China
| | - Chao Wang
- School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture, Hefei 230036, China
- Institute of Intelligent Agriculture, Anhui Agricultural University, Hefei 230036, China
| | - Jun Jiao
- School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture, Hefei 230036, China
- Institute of Intelligent Agriculture, Anhui Agricultural University, Hefei 230036, China
| | - Lichuan Gu
- School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
- Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture, Hefei 230036, China
- Institute of Intelligent Agriculture, Anhui Agricultural University, Hefei 230036, China
- School of Life Sciences, Anhui Agricultural University, Hefei 230036, China
| |
Collapse
|
6
|
Saha S, Chatterjee P, Nasipuri M, Basu S. Detection of spreader nodes in human-SARS-CoV protein-protein interaction network. PeerJ 2021; 9:e12117. [PMID: 34567845 PMCID: PMC8428263 DOI: 10.7717/peerj.12117] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/15/2021] [Indexed: 12/20/2022] Open
Abstract
The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein-protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein-protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art.
Collapse
Affiliation(s)
- Sovan Saha
- Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India
| | - Piyali Chatterjee
- Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, India
| | - Mita Nasipuri
- Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| | - Subhadip Basu
- Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
| |
Collapse
|
7
|
Abstract
In recent biomedical studies, multidimensional profiling, which collects proteomics as well as other types of omics data on the same subjects, is getting increasingly popular. Proteomics, transcriptomics, genomics, epigenomics, and other types of data contain overlapping as well as independent information, which suggests the possibility of integrating multiple types of data to generate more reliable findings/models with better classification/prediction performance. In this chapter, a selective review is conducted on recent data integration techniques for both unsupervised and supervised analysis. The main objective is to provide the "big picture" of data integration that involves proteomics data and discuss the "intuition" beneath the recently developed approaches without invoking too many mathematical details. Potential pitfalls and possible directions for future developments are also discussed.
Collapse
Affiliation(s)
- Mengyun Wu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Yu Jiang
- School of Public Health, University of Memphis, Memphis, TN, USA
| | - Shuangge Ma
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA.
| |
Collapse
|
8
|
Poverennaya E, Kiseleva O, Romanova A, Pyatnitskiy M. Predicting Functions of Uncharacterized Human Proteins: From Canonical to Proteoforms. Genes (Basel) 2020; 11:E677. [PMID: 32575886 PMCID: PMC7350264 DOI: 10.3390/genes11060677] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/09/2020] [Accepted: 06/19/2020] [Indexed: 01/22/2023] Open
Abstract
Despite tremendous efforts in genomics, transcriptomics, and proteomics communities, there is still no comprehensive data about the exact number of protein-coding genes, translated proteoforms, and their function. In addition, by now, we lack functional annotation for 1193 genes, where expression was confirmed at the proteomic level (uPE1 proteins). We re-analyzed results of AP-MS experiments from the BioPlex 2.0 database to predict functions of uPE1 proteins and their splice forms. By building a protein-protein interaction network for 12 ths. identified proteins encoded by 11 ths. genes, we were able to predict Gene Ontology categories for a total of 387 uPE1 genes. We predicted different functions for canonical and alternatively spliced forms for four uPE1 genes. In total, functional differences were revealed for 62 proteoforms encoded by 31 genes. Based on these results, it can be carefully concluded that the dynamics and versatility of the interactome is ensured by changing the dominant splice form. Overall, we propose that analysis of large-scale AP-MS experiments performed for various cell lines and under various conditions is a key to understanding the full potential of genes role in cellular processes.
Collapse
Affiliation(s)
- Ekaterina Poverennaya
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (O.K.); (A.R.); (M.P.)
- Institute of Environmental and Agricultural Biology (X-BIO),Tyumen State University, 625003 Tyumen, Russia
| | - Olga Kiseleva
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (O.K.); (A.R.); (M.P.)
| | - Anastasia Romanova
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (O.K.); (A.R.); (M.P.)
- Faculty of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny, 141701 Moscow, Russia
| | - Mikhail Pyatnitskiy
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (O.K.); (A.R.); (M.P.)
- Department of Molecular Biology and Genetics, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, 119435 Moscow, Russia
| |
Collapse
|