1
|
Sengupta K, Saha S, Halder AK, Chatterjee P, Nasipuri M, Basu S, Plewczynski D. PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms. Front Genet 2022; 13:969915. [PMID: 36246645 PMCID: PMC9556876 DOI: 10.3389/fgene.2022.969915] [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: 06/15/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at https://sites.google.com/view/pfp-go/.
Collapse
Affiliation(s)
- Kaustav Sengupta
- Laboratory of Functional and Structural Genomics, Center of New Technologies, University of Warsaw, Warsaw, Poland
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Sovan Saha
- Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, West Bengal, India
| | - Anup Kumar Halder
- Laboratory of Functional and Structural Genomics, Center of New Technologies, University of Warsaw, Warsaw, Poland
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Piyali Chatterjee
- Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India
| | - Mita Nasipuri
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | - Subhadip Basu
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
- *Correspondence: Subhadip Basu, Dariusz Plewczynski,
| | - Dariusz Plewczynski
- Laboratory of Functional and Structural Genomics, Center of New Technologies, University of Warsaw, Warsaw, Poland
- Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
- *Correspondence: Subhadip Basu, Dariusz Plewczynski,
| |
Collapse
|
2
|
Mahmud S, Guo Z, Quadir F, Liu J, Cheng J. Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps. BMC Bioinformatics 2022; 23:283. [PMID: 35854211 PMCID: PMC9295499 DOI: 10.1186/s12859-022-04829-1] [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: 05/11/2022] [Accepted: 07/08/2022] [Indexed: 01/25/2023] Open
Abstract
The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction of protein domain boundaries (i.e., sequence regions separating two domains) from sequence remains a significant challenge. In this work, we develop a deep learning method based on multi-head U-Nets (called DistDom) to predict protein domain boundaries utilizing 1D sequence features and predicted 2D inter-residue distance map as input. The 1D features contain the evolutionary and physicochemical information of protein sequences, whereas the 2D distance map includes the structural information of proteins that was rarely used in domain boundary prediction before. The 1D and 2D features are processed by the 1D and 2D U-Nets respectively to generate hidden features. The hidden features are then used by the multi-head attention to predict the probability of each residue of a protein being in a domain boundary, leveraging both local and global information in the features. The residue-level domain boundary predictions can be used to classify proteins as single-domain or multi-domain proteins. It classifies the CASP14 single-domain and multi-domain targets at the accuracy of 75.9%, 13.28% more accurate than the state-of-the-art method. Tested on the CASP14 multi-domain protein targets with expert annotated domain boundaries, the average per-target F1 measure score of the domain boundary prediction by DistDom is 0.263, 29.56% higher than the state-of-the-art method.
Collapse
Affiliation(s)
- Sajid Mahmud
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Zhiye Guo
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Farhan Quadir
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Jian Liu
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Jianlin Cheng
- grid.134936.a0000 0001 2162 3504Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| |
Collapse
|
3
|
Wang Y, Zhang H, Zhong H, Xue Z. Protein domain identification methods and online resources. Comput Struct Biotechnol J 2021; 19:1145-1153. [PMID: 33680357 PMCID: PMC7895673 DOI: 10.1016/j.csbj.2021.01.041] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 01/03/2023] Open
Abstract
Protein domains are the basic units of proteins that can fold, function, and evolve independently. Knowledge of protein domains is critical for protein classification, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Thus, over the past two decades, a number of protein domain identification approaches have been developed, and a variety of protein domain databases have also been constructed. This review divides protein domain prediction methods into two categories, namely sequence-based and structure-based. These methods are introduced in detail, and their advantages and limitations are compared. Furthermore, this review also provides a comprehensive overview of popular online protein domain sequence and structure databases. Finally, we discuss potential improvements of these prediction methods.
Collapse
Affiliation(s)
- Yan Wang
- Institute of Medical Artificial Intelligence, Binzhou Medical College, Yantai, Shandong 264003, China
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Hang Zhang
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Haolin Zhong
- School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Zhidong Xue
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| |
Collapse
|
4
|
Saha S, Prasad A, Chatterjee P, Basu S, Nasipuri M. Protein function prediction from dynamic protein interaction network using gene expression data. J Bioinform Comput Biol 2020; 17:1950025. [PMID: 31617461 DOI: 10.1142/s0219720019500252] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Computational prediction of functional annotation of proteins is an uphill task. There is an ever increasing gap between functional characterization of protein sequences and deluge of protein sequences generated by large-scale sequencing projects. The dynamic nature of protein interactions is frequently observed which is mostly influenced by any new change of state or change in stimuli. Functional characterization of proteins can be inferred from their interactions with each other, which is dynamic in nature. In this work, we have used a dynamic protein-protein interaction network (PPIN), time course gene expression data and protein sequence information for prediction of functional annotation of proteins. During progression of a particular function, it has also been observed that not all the proteins are active at all time points. For unannotated active proteins, our proposed methodology explores the dynamic PPIN consisting of level-1 and level-2 neighboring proteins at different time points, filtered by Damerau-Levenshtein edit distance to estimate the similarity between two protein sequences and coefficient variation methods to assess the strength of an edge in a network. Finally, from the filtered dynamic PPIN, at each time point, functional annotations of the level-2 proteins are assigned to the unknown and unannotated active proteins through the level-1 neighbor, following a bottom-up strategy. Our proposed methodology achieves an average precision, recall and F-Score of 0.59, 0.76 and 0.61 respectively, which is significantly higher than the reported state-of-the-art methods.
Collapse
Affiliation(s)
- Sovan Saha
- Department of Computer Science & Engineering, Dr. Sudhir Chandra Sur Degree Engineering College, 540, Dum Dum Road, Near Dum Dum Jn. Station, Surermath, Kolkata 700074, India
| | - Abhimanyu Prasad
- Department of Computer Science & Engineering, Dr. Sudhir Chandra Sur Degree Engineering College, 540, Dum Dum Road, Near Dum Dum Jn. Station, Surermath, Kolkata 700074, India
| | - Piyali Chatterjee
- Department of Computer Science & Engineering, Netaji Subhash Engineering College, Techno City, Panchpota, Garia, Kolkata 700152, India
| | - Subhadip Basu
- Department of Computer Science & Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, India
| | - Mita Nasipuri
- Department of Computer Science & Engineering, Netaji Subhash Engineering College, Techno City, Panchpota, Garia, Kolkata 700152, India
| |
Collapse
|
5
|
Saha S, Prasad A, Chatterjee P, Basu S, Nasipuri M. Protein function prediction from protein-protein interaction network using gene ontology based neighborhood analysis and physico-chemical features. J Bioinform Comput Biol 2018; 16:1850025. [PMID: 30400756 DOI: 10.1142/s0219720018500257] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Protein Function Prediction from Protein-Protein Interaction Network (PPIN) and physico-chemical features using the Gene Ontology (GO) classification are indeed very useful for assigning biological or biochemical functions to a protein. They also lead to the identification of those significant proteins which are responsible for the generation of various diseases whose drugs are still yet to be discovered. So, the prediction of GO functional terms from PPIN and sequence is an important field of study. In this work, we have proposed a methodology, Multi Label Protein Function Prediction (ML_PFP) which is based on Neighborhood analysis empowered with physico-chemical features of constituent amino acids to predict the functional group of unannotated protein. A protein does not perform functions in isolation rather it performs functions in a group by interacting with others. So a protein is involved in many functions or, in other words, may be associated with multiple functional groups or labels or GO terms. Though functional group of other known interacting partner protein and its physico-chemical features provide useful information, assignment of multiple labels to unannotated protein is a very challenging task. Here, we have taken Homo sapiens or Human PPIN as well as Saccharomyces cerevisiae or yeast PPIN along with their GO terms to predict functional groups or GO terms of unannotated proteins. This work has become very challenging as both Human and Yeast protein dataset are voluminous and complex in nature and multi-label functional groups assignment has also added a new dimension to this challenge. Our algorithm has been observed to achieve a better performance in Cellular Function, Molecular Function and Biological Process of both yeast and human network when compared with the other existing state-of-the-art methodologies which will be discussed in detail in the results section.
Collapse
Affiliation(s)
- Sovan Saha
- * Department of Computer Science & Engineering, Dr. Sudhir Chandra Sur Degree Engineering College, 540, Dum Dum Road, Near Dum Dum Jn. Station, Surermath, Kolkata 700074, India
| | - Abhimanyu Prasad
- * Department of Computer Science & Engineering, Dr. Sudhir Chandra Sur Degree Engineering College, 540, Dum Dum Road, Near Dum Dum Jn. Station, Surermath, Kolkata 700074, India
| | - Piyali Chatterjee
- † Department of Computer Science & Engineering, Netaji Subhash Engineering College, Techno City, Panchpota, Garia, Kolkata 700152, India
| | - Subhadip Basu
- ‡ Department of Computer Science & Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, India
| | - Mita Nasipuri
- ‡ Department of Computer Science & Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, India
| |
Collapse
|
6
|
Asrani KH, Cheng L, Cheng CJ, Subramanian RR. Arginase I mRNA therapy - a novel approach to rescue arginase 1 enzyme deficiency. RNA Biol 2018; 15:914-922. [PMID: 29923457 DOI: 10.1080/15476286.2018.1475178] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Arginase I (ARG1) deficiency is an autosomal recessive urea cycle disorder, caused by deficiency of the enzyme Arginase I, resulting in accumulation of arginine in blood. Current Standard of Care (SOC) for ARG1 deficiency in patients or those having detrimental mutations of ARG1 gene is diet control. Despite diet and drug therapy with nitrogen scavengers, ~25% of patients suffer from severe mental deficits and loss of ambulation. 75% of patients whose symptoms can be managed through diet therapy continue to suffer neuro-cognitive deficits. In our research, we demonstrate in vitro and in vivo that administration of ARG1 mRNA increased ARG1 protein expression and specific activity in relevant cell types, including ARG1-deficient patient cell lines, as well as in wild type mice for up to 4 days. These studies demonstrate that ARG1 mRNA treatment led to increased functional protein expression of ARG1 and subsequently an increase in urea. Hence, ARG1 mRNA therapy could be a potential treatment option to develop for patients.
Collapse
Affiliation(s)
- Kirtika H Asrani
- a Discovery Research Cambridge , Alexion Pharmaceuticals, Inc ., Cambridge , MA , USA
| | - Lei Cheng
- a Discovery Research Cambridge , Alexion Pharmaceuticals, Inc ., Cambridge , MA , USA
| | - Christopher J Cheng
- b Nucleic Acid Technology , Alexion Pharmaceuticals, Inc ., New Haven , CT , USA
| | - Romesh R Subramanian
- a Discovery Research Cambridge , Alexion Pharmaceuticals, Inc ., Cambridge , MA , USA
| |
Collapse
|