1
|
Hong L, Hu Z, Sun S, Tang X, Wang J, Tan Q, Zheng L, Wang S, Xu S, King I, Gerstein M, Li Y. Fast, sensitive detection of protein homologs using deep dense retrieval. Nat Biotechnol 2024:10.1038/s41587-024-02353-6. [PMID: 39123049 DOI: 10.1038/s41587-024-02353-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 07/12/2024] [Indexed: 08/12/2024]
Abstract
The identification of protein homologs in large databases using conventional methods, such as protein sequence comparison, often misses remote homologs. Here, we offer an ultrafast, highly sensitive method, dense homolog retriever (DHR), for detecting homologs on the basis of a protein language model and dense retrieval techniques. Its dual-encoder architecture generates different embeddings for the same protein sequence and easily locates homologs by comparing these representations. Its alignment-free nature improves speed and the protein language model incorporates rich evolutionary and structural information within DHR embeddings. DHR achieves a >10% increase in sensitivity compared to previous methods and a >56% increase in sensitivity at the superfamily level for samples that are challenging to identify using alignment-based approaches. It is up to 22 times faster than traditional methods such as PSI-BLAST and DIAMOND and up to 28,700 times faster than HMMER. The new remote homologs exclusively found by DHR are useful for revealing connections between well-characterized proteins and improving our knowledge of protein evolution, structure and function.
Collapse
Affiliation(s)
- Liang Hong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Zhihang Hu
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Siqi Sun
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China.
- Shanghai AI Laboratory, Shanghai, China.
| | - Xiangru Tang
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Jiuming Wang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
- OneAIM Ltd., Hong Kong SAR, China
| | - Qingxiong Tan
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Liangzhen Zheng
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shanghai Zelixir Biotech Company Ltd., Shanghai, China
| | - Sheng Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shanghai Zelixir Biotech Company Ltd., Shanghai, China
| | - Sheng Xu
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, China
- Shanghai AI Laboratory, Shanghai, China
| | - Irwin King
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Mark Gerstein
- Department of Computer Science, Yale University, New Haven, CT, USA.
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA.
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA.
| | - Yu Li
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
- Shanghai AI Laboratory, Shanghai, China.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Chinese University of Hong Kong Shenzhen Research Institute, Shenzhen, China.
| |
Collapse
|
2
|
Roestel JA, Wiersema JH, Jansen RK, Borsch T, Gruenstaeudl M. On the importance of sequence alignment inspections in plastid phylogenomics - an example from revisiting the relationships of the water-lilies. Cladistics 2024. [PMID: 38761095 DOI: 10.1111/cla.12584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/27/2024] [Accepted: 04/29/2024] [Indexed: 05/20/2024] Open
Abstract
The water-lily clade represents the second earliest-diverging branch of angiosperms. Most of its species belong to Nymphaeaceae, of which the "core Nymphaeaceae"-comprising the genera Euryale, Nymphaea and Victoria-is the most diverse clade. Despite previous molecular phylogenetic studies on the core Nymphaeaceae, various aspects of their evolutionary relationships have remained unresolved. The length-variable introns and intergenic spacers are known to contain most of the sequence variability within the water-lily plastomes. Despite the challenges with multiple sequence alignment, any new molecular phylogenetic investigation on the core Nymphaeaceae should focus on these noncoding plastome regions. For example, a new plastid phylogenomic study on the core Nymphaeaceae should generate DNA sequence alignments of all plastid introns and intergenic spacers based on the principle of conserved sequence motifs. In this investigation, we revisit the phylogenetic history of the core Nymphaeaceae by employing such an approach. Specifically, we use a plastid phylogenomic analysis strategy in which all coding and noncoding partitions are separated and then undergo software-driven DNA sequence alignment, followed by a motif-based alignment inspection and adjustment. This approach allows us to increase the reliability of the character base compared to the default practice of aligning complete plastomes through software algorithms alone. Our approach produces significantly different phylogenetic tree reconstructions for several of the plastome regions under study. The results of these reconstructions underscore that Nymphaea is paraphyletic in its current circumscription, that each of the five subgenera of Nymphaea is monophyletic, and that the subgenus Nymphaea is sister to all other subgenera of Nymphaea. Our results also clarify many evolutionary relationships within the Nymphaea subgenera Brachyceras, Hydrocallis and Nymphaea. In closing, we discuss whether the phylogenetic reconstructions obtained through our motif-based alignment adjustments are in line with morphological evidence on water-lily evolution.
Collapse
Affiliation(s)
- Jessica A Roestel
- Institut für Biologie, Systematische Botanik und Pflanzengeographie, Freie Universität Berlin, Berlin, 14195, Germany
| | - John H Wiersema
- Department of Botany, National Museum of Natural History - Smithsonian Institution, Washington, DC, 37012, USA
| | - Robert K Jansen
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, 78712, USA
| | - Thomas Borsch
- Institut für Biologie, Systematische Botanik und Pflanzengeographie, Freie Universität Berlin, Berlin, 14195, Germany
- Botanischer Garten und Botanisches Museum Berlin, Freie Universität Berlin, 14195, Berlin, Germany
| | - Michael Gruenstaeudl
- Institut für Biologie, Systematische Botanik und Pflanzengeographie, Freie Universität Berlin, Berlin, 14195, Germany
- Department of Biological Sciences, Fort Hays State University, Hays, KS, 67601, USA
| |
Collapse
|
3
|
Crauwels C, Heidig SL, Díaz A, Vranken WF. Large-scale structure-informed multiple sequence alignment of proteins with SIMSApiper. Bioinformatics 2024; 40:btae276. [PMID: 38648741 PMCID: PMC11099654 DOI: 10.1093/bioinformatics/btae276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/20/2024] [Accepted: 04/18/2024] [Indexed: 04/25/2024] Open
Abstract
SUMMARY SIMSApiper is a Nextflow pipeline that creates reliable, structure-informed MSAs of thousands of protein sequences faster than standard structure-based alignment methods. Structural information can be provided by the user or collected by the pipeline from online resources. Parallelization with sequence identity-based subsets can be activated to significantly speed up the alignment process. Finally, the number of gaps in the final alignment can be reduced by leveraging the position of conserved secondary structure elements. AVAILABILITY AND IMPLEMENTATION The pipeline is implemented using Nextflow, Python3, and Bash. It is publicly available on github.com/Bio2Byte/simsapiper.
Collapse
Affiliation(s)
- Charlotte Crauwels
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, 1050, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, 1050, Belgium
- AI Lab, Vrije Universiteit Brussel, Brussels, 1050, Belgium
| | - Sophie-Luise Heidig
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, 1050, Belgium
- AI Lab, Vrije Universiteit Brussel, Brussels, 1050, Belgium
- Evolutionary Biology & Ecology, Université libre de Bruxelles, Brussels, 1050, Belgium
| | - Adrián Díaz
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, 1050, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, 1050, Belgium
- AI Lab, Vrije Universiteit Brussel, Brussels, 1050, Belgium
| | - Wim F Vranken
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, 1050, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, 1050, Belgium
- AI Lab, Vrije Universiteit Brussel, Brussels, 1050, Belgium
| |
Collapse
|
4
|
Upadhyay A, Pal D, Kumar A. Molecular drilling to combat salmonella typhi biofilm using L-Asparaginase via multiple targeting process. Expert Opin Ther Targets 2024; 28:323-334. [PMID: 38639582 DOI: 10.1080/14728222.2024.2344699] [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: 03/17/2023] [Accepted: 04/15/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES Salmonella Typhibiofilm condition is showing as a major public health problem due to the development of antibiotic resistance and less available druggable target proteins. Therefore, we aimed to identify some more druggable targets of S. Typhibiofilm using computational drilling at the genome/proteome level so that the target shortage problem could be overcome and more antibiofilm agents could be designed in the future against the disease. METHODS We performed protein-protein docking and interaction analysis between the homological identified target proteins of S.Typhi biofilm and a therapeutic protein L-Asparaginase. RESULTS We have identified some druggable targets CsgD, BcsA, OmpR, CsgG, CsgE, and CsgF in S.Typhi. These targets showed high-binding affinity BcsA (-219.8 Kcal/mol) >csgF (-146.52 Kcal/mol) >ompR (-135.68 Kcal/mol) >CsgE (-134.66 Kcal/mol) >CsgG (-113.81 Kcal/mol) >CsgD(-95.39 Kcal/mol) with therapeutic enzyme L-Asparaginase through various hydrogen-bonds and salt-bridge. We found six proteins of S. Typhi biofilm from the Csg family as druggable multiple targets. CONCLUSION This study provides insight into the idea of identification of new druggable targets and their multiple targeting with L-Asparaginase to overcome target shortage in S. Typhibiofilm-mediated infections. Results further indicated that L-Asparaginase could potentially be utilized as an antibiofilm biotherapeutic agent against S.Typhi.
Collapse
Affiliation(s)
- Aditya Upadhyay
- Department of Biotechnology, National Institute of Technology, Raipur, India
| | - Dharm Pal
- Department of Chemical Engineering, National Institute of Technology, Raipur, India
| | - Awanish Kumar
- Department of Biotechnology, National Institute of Technology, Raipur, India
| |
Collapse
|
5
|
Drogaris C, Butyaev A, Nazarova E, Sarrazin-Gendron R, Patel H, Singh A, Kadota B, Waldispühl J. When online citizen science meets teaching: Storyfication of a science discovery game to teach, learn, and contribute to genomic research. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2024; 52:145-155. [PMID: 37929794 DOI: 10.1002/bmb.21796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/22/2023] [Indexed: 11/07/2023]
Abstract
In the last decade, video games became a common vehicle for citizen science initiatives in life science, allowing participants to contribute to real scientific data analysis while learning about it. Since 2010, our scientific discovery game (SDG) Phylo enlists participants in comparative genomic data analysis. It is frequently used as a learning tool, but the activities were difficult to aggregate to build a coherent teaching activity. Here, we describe a strategy and series of recipes to facilitate the integration of SDGs in courses and implement this approach in Phylo. We developed new roles and functionalities enabling instructors to create assignments and monitor the progress of students. A story mode progressively introduces comparative genomics concepts, allowing users to learn and contribute to the analysis of real genomic sequences. Preliminary results from a user study suggest this framework may help to boost user motivation and clarify pedagogical objectives.
Collapse
Affiliation(s)
| | | | - Elena Nazarova
- School of Computer Science, McGill University, Montréal, QC, Canada
| | | | - Harsh Patel
- School of Computer Science, McGill University, Montréal, QC, Canada
| | - Akash Singh
- School of Computer Science, McGill University, Montréal, QC, Canada
| | - Brenden Kadota
- School of Computer Science, McGill University, Montréal, QC, Canada
| | | |
Collapse
|
6
|
Farooq M, Ahmad R, Shahzad M, Rehman SU, Sajjad Y, Hassan A, Shah MM, Afroz A, Khan SA. Real-time expression and in silico characterization of pea genes involved in salt and water-deficit stress. Mol Biol Rep 2023; 51:18. [PMID: 38099977 DOI: 10.1007/s11033-023-09064-2] [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: 06/13/2023] [Accepted: 10/26/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND To tolerate salt and water-deficit stress, the plant adapts to the adverse environment by regulating its metabolism and expressing certain stress-induced metabolic pathways. This research analyzed the relative expression of four pea genes (P5CR, PAL1, SOD, and POX) in three pea varieties (Climax, Green grass, and Meteor) under different levels of salt and water-deficit stress. METHODS AND RESULTS The experiments on salt stress and water-deficit stress were carried out within greenhouse settings under controlled environment. The saturation percentage was employed to create artificial salinity conditions: Control without NaCl treatment, Treatment 1: 50 mM NaCl treatment, Treatment 2: 75 mM NaCl treatment, and Treatment 3: 100 mM NaCl treatment. Field capacity (FC) was used for the development of artificial water-deficit treatments in the pots, i.e., Treatment 1 (Control; water application 100% of FC), Treatment 2 (water application 75% of FC), and Treatment 3 (water application 50% of FC). Pea genes involved in biosynthetic pathways of proline, flavonoids, and enzymatic antioxidant enzymes including P5CR, PAL1, SOD, and POX were selected based on literature. Quantitative real-time PCR using cDNA as a template was used to analyze the gene expression. Pea genes were analyzed for phylogenetic analysis in closely related crops having similarity percent identity 80% and above. In silico characterization of selected proteins including the family classification was done by the NCBI CDD and INTERPRO online servers. Results from RT-qPCR analysis showed increased expression of P5CR, PAL1, and POX genes, while SOD gene expression decreased under both stresses. Climax exhibited superior stress tolerance with elevated expression of P5CR and PAL1, while Meteor showed better tolerance through increased POX expression. Phylogenetic analysis revealed common ancestry with other species like chickpea, red clover, mung bean, and barrel clover, suggesting the cross relationship among these plant species. Conserved domain analysis of respective proteins revealed that these proteins contain PLNO 2688, PLN02457, Cu-Zn Superoxide dismutase, and secretory peroxidase conserved domains. Furthermore, protein family classification indicated that the oxidation-reduction process is the most common chemical process involved in these stresses given to pea plant which validates the relationship of these proteins. CONCLUSIONS Salt and water-deficit stresses trigger distinct metabolic pathways, leading to the up-regulation of specific genes and the synthesis of corresponding proteins. These findings further emphasize the conservation of stress-tolerance-related genes and proteins across various plant species. This knowledge enhances our understanding of plant adaptation to stress and offers opportunities for developing strategies to improve stress resilience in crops, thereby addressing global food security challenges.
Collapse
Affiliation(s)
- Muhammad Farooq
- Department of Biotechnology, COMSATS University Islamabad, Abbottabad Campus, University Road, Abbottabad, 22060, Pakistan
| | - Rafiq Ahmad
- Department of Biotechnology, COMSATS University Islamabad, Abbottabad Campus, University Road, Abbottabad, 22060, Pakistan
| | - Muhammad Shahzad
- Department of Environmental Sciences, COMSATS University Islamabad, Abbottabad Campus, University Road, Abbottabad, 22060, Pakistan
| | - Saad Ur Rehman
- Department of Bioinformatics, Government Postgraduate College, Mandian, Abbottabad, Pakistan
| | - Yasar Sajjad
- Department of Biotechnology, COMSATS University Islamabad, Abbottabad Campus, University Road, Abbottabad, 22060, Pakistan
| | - Amjad Hassan
- Department of Biotechnology, COMSATS University Islamabad, Abbottabad Campus, University Road, Abbottabad, 22060, Pakistan
| | - Mohammad Maroof Shah
- Department of Biotechnology, COMSATS University Islamabad, Abbottabad Campus, University Road, Abbottabad, 22060, Pakistan
| | - Amber Afroz
- Department of Biochemistry and Biotechnology, University of Gujrat, Gujrat, Punjab, Pakistan
| | - Sabaz Ali Khan
- Department of Biotechnology, COMSATS University Islamabad, Abbottabad Campus, University Road, Abbottabad, 22060, Pakistan.
| |
Collapse
|
7
|
Ren H, Li Y, Huang T. Anomaly Detection Models for SARS-CoV-2 Surveillance Based on Genome k-mers. Microorganisms 2023; 11:2773. [PMID: 38004784 PMCID: PMC10673111 DOI: 10.3390/microorganisms11112773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/06/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
Since COVID-19 has brought great challenges to global public health governance, developing methods that track the evolution of the virus over the course of an epidemic or pandemic is useful for public health. This paper uses anomaly detection models to analyze SARS-CoV-2 virus genome k-mers to predict possible new critical variants in the collected samples. We used the sample data from Argentina, China and Portugal obtained from the Global Initiative on Sharing All Influenza Data (GISAID) to conduct multiple rounds of evaluation on several anomaly detection models, to verify the feasibility of this virus early warning and surveillance idea and find appropriate anomaly detection models for actual epidemic surveillance. Through multiple rounds of model testing, we found that the LUNAR (learnable unified neighborhood-based anomaly ranking) and LUNAR+LUNAR stacking model performed well in new critical variants detection. The results of simulated dynamic detection validate the feasibility of this approach, which can help efficiently monitor samples in local areas.
Collapse
Affiliation(s)
- Haotian Ren
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
| | - Yixue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Guangzhou Laboratory, Guangzhou 510005, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
| |
Collapse
|
8
|
Riaz Z, Hussain M, Parveen S, Sultana M, Saeed S, Ishaque U, Faiz Z, Tayyab M. In Silico Analysis: Genome-Wide Identification, Characterization and Evolutionary Adaptations of Bone Morphogenetic Protein (BMP) Gene Family in Homo sapiens. Mol Biotechnol 2023:10.1007/s12033-023-00944-3. [PMID: 37914865 DOI: 10.1007/s12033-023-00944-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/09/2023] [Indexed: 11/03/2023]
Abstract
We systematically analyzed BMP gene family in H. sapiens to elucidate genetic structure, phylogenetic relationships, adaptive evolution and tissue-specific expression pattern. Total of 13 BMPs genes were identified in the H. sapiens genome. Bone morphogenetic proteins (BMPs) are composed of a variable number of exons ranging from 2 to 21. They exhibit a molecular weight ranging from 31,081.81 to 82,899.61 Da. These proteins possess hydrophilic characteristics, display thermostability, and exhibit a pH range from acidic to basic. We identified four segmental and two tandem duplication events in BMP gene family of H. sapiens. All of the vertebrate species that were studied show the presence of BMPs 1, 2, 3, 4, 5, 6, 7, 8A, and 15, however only Homo sapiens demonstrated the presence of BMP9 and BMP11. The pathway and process enrichment analysis of BMPs genes showed that these were considerably enriched in positive regulation of pathway-restricted SMAD protein phosphorylation (92%) and cartilage development (77%) biological processes. These genes exhibited positive selection signals that were shown to be conserved across vertebrate lineages. The results showed that BMP2/3/5/6/8a/15 proteins underwent adaptive selection at many amino acid locations and increased positive selection was detected in TGF-β propeptide and TGF-β super family domains which were involved in dorso-ventral patterning, limb bud development. More over the expression pattern of BMP genes revealed that BMP1 and BMP5; BMP4 and BMP6 exhibited substantially identical expression patterns in all tissues while BMP10, BMP15, and BMP3 showed tissue-specific expression.
Collapse
Affiliation(s)
- Zainab Riaz
- Department of Zoology, The Government Sadiq College Women University, Bahawalpur, 63100, Punjab, Pakistan
| | - Muhammad Hussain
- Department of Veterinary and Animal Sciences, University of Veterinary and Animal Sciences, Lahore, Punjab, Pakistan
| | - Shakeela Parveen
- Department of Zoology, The Government Sadiq College Women University, Bahawalpur, 63100, Punjab, Pakistan.
- Department of Zoology, Wildlife and Fisheries, University of Agriculture, Faisalabad, Punjab, Pakistan.
| | - Mehwish Sultana
- Department of Zoology, The Government Sadiq College Women University, Bahawalpur, 63100, Punjab, Pakistan
| | - Saba Saeed
- Department of Zoology, The Government Sadiq College Women University, Bahawalpur, 63100, Punjab, Pakistan
- Institue of Zoology, University of the Punjab, Lahore, Punjab, Pakistan
| | - Urwah Ishaque
- Department of Zoology, The Government Sadiq College Women University, Bahawalpur, 63100, Punjab, Pakistan
| | - Zunaira Faiz
- Department of Zoology, The Government Sadiq College Women University, Bahawalpur, 63100, Punjab, Pakistan
| | - Muhammad Tayyab
- Department of Zoology, Government College University Faisalabad, Faisalabad, Punjab, Pakistan
| |
Collapse
|
9
|
Liu Y, Yuan H, Zhang Q, Wang Z, Xiong S, Wen N, Zhang Y. Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding. Bioinformatics 2023; 39:btad636. [PMID: 37856335 PMCID: PMC10628385 DOI: 10.1093/bioinformatics/btad636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 07/24/2023] [Accepted: 10/17/2023] [Indexed: 10/21/2023] Open
Abstract
MOTIVATION Multiple sequence alignment (MSA) is one of the hotspots of current research and is commonly used in sequence analysis scenarios. However, there is no lasting solution for MSA because it is a Nondeterministic Polynomially complete problem, and the existing methods still have room to improve the accuracy. RESULTS We propose Deep reinforcement learning with Positional encoding and self-Attention for MSA, based on deep reinforcement learning, to enhance the accuracy of the alignment Specifically, inspired by the translation technique in natural language processing, we introduce self-attention and positional encoding to improve accuracy and reliability. Firstly, positional encoding encodes the position of the sequence to prevent the loss of nucleotide position information. Secondly, the self-attention model is used to extract the key features of the sequence. Then input the features into a multi-layer perceptron, which can calculate the insertion position of the gap according to the features. In addition, a novel reinforcement learning environment is designed to convert the classic progressive alignment into progressive column alignment, gradually generating each column's sub-alignment. Finally, merge the sub-alignment into the complete alignment. Extensive experiments based on several datasets validate our method's effectiveness for MSA, outperforming some state-of-the-art methods in terms of the Sum-of-pairs and Column scores. AVAILABILITY AND IMPLEMENTATION The process is implemented in Python and available as open-source software from https://github.com/ZhangLab312/DPAMSA.
Collapse
Affiliation(s)
- Yuhang Liu
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Hao Yuan
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Qiang Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Zixuan Wang
- College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
| | - Shuwen Xiong
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Naifeng Wen
- School of Mechanical and Electrical Engineering, Dalian Minzu University, Dalian 116600, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| |
Collapse
|
10
|
Larrea-Sebal A, Jebari-Benslaiman S, Galicia-Garcia U, Jose-Urteaga AS, Uribe KB, Benito-Vicente A, Martín C. Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies. Curr Atheroscler Rep 2023; 25:839-859. [PMID: 37847331 PMCID: PMC10618353 DOI: 10.1007/s11883-023-01154-7] [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] [Accepted: 09/15/2023] [Indexed: 10/18/2023]
Abstract
PURPOSE OF REVIEW Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH. RECENT FINDINGS In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.
Collapse
Affiliation(s)
- Asier Larrea-Sebal
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
- Fundación Biofisika Bizkaia, 48940, Leioa, Spain
| | - Shifa Jebari-Benslaiman
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - Unai Galicia-Garcia
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - Ane San Jose-Urteaga
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
| | - Kepa B Uribe
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
| | - Asier Benito-Vicente
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain
| | - César Martín
- Department of Biochemistry and Molecular Biology, Universidad del País Vasco UPV/EHU, 48080, Bilbao, Spain.
- Department of Molecular Biophysics, Biofisika Institute, University of Basque Country and Consejo Superior de Investigaciones Científicas (UPV/EHU, CSIC), 48940, Leioa, Spain.
| |
Collapse
|
11
|
Fruzangohar M, Moolhuijzen P, Bakaj N, Taylor J. CoreDetector: a flexible and efficient program for core-genome alignment of evolutionary diverse genomes. Bioinformatics 2023; 39:btad628. [PMID: 37878789 PMCID: PMC10663985 DOI: 10.1093/bioinformatics/btad628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 09/20/2023] [Accepted: 10/23/2023] [Indexed: 10/27/2023] Open
Abstract
MOTIVATION Whole genome alignment of eukaryote species remains an important method for the determination of sequence and structural variations and can also be used to ascertain the representative non-redundant core-genome sequence of a population. Many whole genome alignment tools were first developed for the more mature analysis of prokaryote species with few current tools containing the functionality to process larger genomes of eukaryotes as well as genomes of more divergent species. In addition, the functionality of these tools becomes computationally prohibitive due to the significant compute resources needed to handle larger genomes. RESULTS In this research, we present CoreDetector, an easy-to-use general-purpose program that can align the core-genome sequences for a range of genome sizes and divergence levels. To illustrate the flexibility of CoreDetector, we conducted alignments of a large set of closely related fungal pathogen and hexaploid wheat cultivar genomes as well as more divergent fly and rodent species genomes. In all cases, compared to existing multiple genome alignment tools, CoreDetector exhibited improved flexibility, efficiency, and competitive accuracy in tested cases. AVAILABILITY AND IMPLEMENTATION CoreDetector was developed in the cross platform, and easily deployable, Java language. A packaged pipeline is readily executable in a bash terminal without any external need for Perl or Python environments. Installation, example data, and usage instructions for CoreDetector are freely available from https://github.com/mfruzan/CoreDetector.
Collapse
Affiliation(s)
- Mario Fruzangohar
- The Biometry Hub, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, South Australia 5064, Australia
| | - Paula Moolhuijzen
- Centre for Crop Disease Management, School of Molecular and Life Sciences, Curtin University, Bentley, Western Australia 6102, Australia
| | - Nicolette Bakaj
- The Biometry Hub, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, South Australia 5064, Australia
| | - Julian Taylor
- The Biometry Hub, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, South Australia 5064, Australia
| |
Collapse
|
12
|
Grigorjew A, Gynter A, Dias FHC, Buchfink B, Drost HG, Tomescu AI. Sensitive inference of alignment-safe intervals from biodiverse protein sequence clusters using EMERALD. Genome Biol 2023; 24:168. [PMID: 37461051 DOI: 10.1186/s13059-023-03008-6] [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: 01/11/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023] Open
Abstract
Sequence alignments are the foundations of life science research, but most innovation so far focuses on optimal alignments, while information derived from suboptimal solutions is ignored. We argue that one optimal alignment per pairwise sequence comparison is a reasonable approximation when dealing with very similar sequences but is insufficient when exploring the biodiversity of the protein universe at tree-of-life scale. To overcome this limitation, we introduce pairwise alignment-safety to uncover the amino acid positions robustly shared across all suboptimal solutions. We implement EMERALD, a software library for alignment-safety inference, and apply it to 400k sequences from the SwissProt database.
Collapse
Affiliation(s)
- Andreas Grigorjew
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Artur Gynter
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Fernando H C Dias
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Benjamin Buchfink
- Computational Biology Group, Max Planck Institute for Biology, Tübingen, Germany
| | - Hajk-Georg Drost
- Computational Biology Group, Max Planck Institute for Biology, Tübingen, Germany.
| | | |
Collapse
|
13
|
Santus L, Garriga E, Deorowicz S, Gudyś A, Notredame C. Towards the accurate alignment of over a million protein sequences: Current state of the art. Curr Opin Struct Biol 2023; 80:102577. [PMID: 37012200 DOI: 10.1016/j.sbi.2023.102577] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/21/2023] [Accepted: 02/27/2023] [Indexed: 04/04/2023]
Abstract
Large-scale genomics requires highly scalable and accurate multiple sequence alignment methods. Results collected over this last decade suggest accuracy loss when scaling up over a few thousand sequences. This issue has been actively addressed with a number of innovative algorithmic solutions that combine low-level hardware optimization with novel higher-level heuristics. This review provides an extensive critical overview of these recent methods. Using established reference datasets we conclude that albeit significant progress has been achieved, a unified framework able to consistently and efficiently produce high-accuracy large-scale multiple alignments is still lacking.
Collapse
|
14
|
Zong F, Long C, Hu W, Chen S, Dai W, Xiao ZX, Cao Y. Abalign: a comprehensive multiple sequence alignment platform for B-cell receptor immune repertoires. Nucleic Acids Res 2023:7173809. [PMID: 37207341 DOI: 10.1093/nar/gkad400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
The utilization of high-throughput sequencing (HTS) for B-cell receptor (BCR) immune repertoire analysis has become widespread in the fields of adaptive immunity and antibody drug development. However, the sheer volume of sequences generated by these experiments presents a challenge in data processing. Specifically, multiple sequence alignment (MSA), a critical aspect of BCR analysis, remains inadequate for handling massive BCR sequencing data and lacks the ability to provide immunoglobulin-specific information. To address this gap, we introduce Abalign, a standalone program specifically designed for ultrafast MSA of BCR/antibody sequences. Benchmark tests demonstrate that Abalign achieves comparable or even better accuracy than state-of-the-art MSA tools, and shows remarkable advantages in terms of speed and memory consumption, reducing the time required for high-throughput analysis from weeks to hours. In addition to its alignment capabilities, Abalign offers a broad range of BCR analysis features, including extracting BCRs, constructing lineage trees, assigning VJ genes, analyzing clonotypes, profiling mutations, and comparing BCR immune repertoires. With its user-friendly graphic interface, Abalign can be easily run on personal computers instead of computing clusters. Overall, Abalign is an easy-to-use and effective tool that enables researchers to analyze massive BCR/antibody sequences, leading to new discoveries in the field of immunoinformatics. The software is freely available at http://cao.labshare.cn/abalign/.
Collapse
Affiliation(s)
- Fanjie Zong
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
- Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, Microbiology and Metabolic Engineering Key Laboratory of Sichuan Province, Chengdu, China
| | - Chenyu Long
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
- Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, Microbiology and Metabolic Engineering Key Laboratory of Sichuan Province, Chengdu, China
| | - Wanxin Hu
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
- Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, Microbiology and Metabolic Engineering Key Laboratory of Sichuan Province, Chengdu, China
| | - Shuang Chen
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Wentao Dai
- NHC Key Laboratory of Reproduction Regulation & Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Zhi-Xiong Xiao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
- Animal Disease Prevention and Food Safety Key Laboratory of Sichuan Province, Microbiology and Metabolic Engineering Key Laboratory of Sichuan Province, Chengdu, China
| |
Collapse
|
15
|
Rayhan M, Siddiquee MF, Shahriar A, Ahmed H, Mahmud AR, Alam MS, Uddin MR, Acharjee M, Shimu MSS, Shamsir MS, Emran TB. Structural characterization of a novel luciferase-like-monooxygenase from Pseudomonas meliae– an in-silico approach.. [DOI: 10.1101/2023.03.27.534437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
AbstractBackgroundLuciferase is a well-known oxidative enzyme that produces bioluminescence. ThePseudomonas meliaeis a plant pathogen that causes wood rot on nectarine and peach and possesses a luciferase-like monooxygenase. After activation, it produces bioluminescence, and the pathogen’s bioluminescence is a visual indicator of diseased plants.MethodsThe present study aims to model and characterize the luciferase-like monooxygenase protein inP. meliaefor its similarity to well-established luciferase. In this study, the luciferase-like monooxygenase fromP. meliaeinfects chinaberry plants has been modeled first and then studied by comparing it with existing known luciferase. Also, the similarities between uncharacterized luciferase fromP. meliaeand template fromGeobacillus thermodenitrificanswere analyzed to find the novelty ofP. meliae.ResultsThe results suggest that the absence of bioluminescence inP. meliaecould be due to the evolutionary mutation in positions 138 and 311. The active site remains identical except for two amino acids;P. meliaeTyr138 instead of His138 and Leu311 instead of His311. Therefore, theP. meliaewill have a potential future application, and mutation of the residues 138 and 311 can be restored luciferase light-emitting ability.ConclusionsThis study will help further improve, activate, and repurpose the luciferase fromP. meliaeas a reporter for gene expression.
Collapse
|
16
|
Khodji H, Collet P, Thompson JD, Jeannin-Girardon A. De-MISTED: Image-based classification of erroneous multiple sequence alignments using convolutional neural networks. APPL INTELL 2023. [DOI: 10.1007/s10489-022-04390-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
|
17
|
Alkilani S, Sevimoglu T. In silico analysis of substitution mutations in the β-globin gene in Turkish population of β-thalassemia. J Biomol Struct Dyn 2023; 41:14028-14035. [PMID: 36752381 DOI: 10.1080/07391102.2023.2176924] [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: 07/04/2022] [Accepted: 01/30/2023] [Indexed: 02/09/2023]
Abstract
Beta-thalassemia is a genetic blood disorder represented by anomalies in hemoglobin's beta chain production. Most hemoglobin defects are a result of mutations of the structural β-globin gene. Many diseases, including β-thalassemia, benefit from computational studies that aid researchers in investigating the association of genotype and phenotype. In this study, the alanine substitution mutations of the β-globin protein sub-units in the Turkish population (Hb Ankara, Hb Siirt and Hb Izmir) and the effects of those mutations on the β-globin protein structure and performance are examined using molecular dynamics simulation. While Hb Ankara variant showed a non-conservative mutation, Hb Siirt and Hb Izmir showed a semi-conservative mutation. RMSF values of Hb Siirt, between residues 95 and 99, were higher than wild-type and the other mutant proteins. The residues of Hb Ankara showed lower fluctuation compared to the other structures. The mean ROG values were 1.47 nm, 1.46 nm, 1.49 nm and 1.48 and the average number of the hydrogen bonds were 92, 100, 99, and 89 for Hb Ankara, Hb Siirt and Hb Izmir, respectively. Moreover, a significant increase in overall motion in Hb Siirt was observed based on PCA analysis. Hb Siirt substitution mutation might cause an effect in β-globin proteins which could impact the protein function. This indicates a major role on beta globin subunit's stability for alanine on 27th position. However, Hb Ankara and Hb Izmir variants may act as a silent mutation, since these two mutations did not show a large change in the dynamics of the protein.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Sima Alkilani
- Department of Bioengineering, Uskudar University, Uskudar, Istanbul, Türkiye
| | - Tuba Sevimoglu
- Department of Bioengineering, University of Health Sciences, Uskudar, Istanbul, Türkiye
| |
Collapse
|
18
|
Rosignoli S, Paiardini A. Boosting the Full Potential of PyMOL with Structural Biology Plugins. Biomolecules 2022; 12:biom12121764. [PMID: 36551192 PMCID: PMC9775141 DOI: 10.3390/biom12121764] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses.
Collapse
|
19
|
Cordier BA, Sawaya NPD, Guerreschi GG, McWeeney SK. Biology and medicine in the landscape of quantum advantages. J R Soc Interface 2022; 19:20220541. [PMID: 36448288 PMCID: PMC9709576 DOI: 10.1098/rsif.2022.0541] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.
Collapse
Affiliation(s)
- Benjamin A. Cordier
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA
| | | | | | - Shannon K. McWeeney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97202, USA,Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97202, USA,Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR 97202, USA
| |
Collapse
|
20
|
Evaluation of the Effectiveness of Derived Features of AlphaFold2 on Single-Sequence Protein Binding Site Prediction. BIOLOGY 2022; 11:biology11101454. [PMID: 36290358 PMCID: PMC9598995 DOI: 10.3390/biology11101454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 09/30/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022]
Abstract
Simple Summary With the development of artificial intelligence, researchers can roughly predict the crystal structure of a protein by computer without the need for biological experiments, which provides new ideas and solutions to problems, such as protein-protein interaction and drug-target predictions. In this study, we proposed strategies to combine predicted protein structures with deep learning networks and evaluated them on different protein binding site prediction tasks. Our computational experiment results showed that all proposed strategies could effectively encode structural information for deep learning models. Abstract Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations.
Collapse
|
21
|
Newport RA, Russo C, Liu S, Suman AA, Di Ieva A. SoftMatch: Comparing Scanpaths Using Combinatorial Spatio-Temporal Sequences with Fractal Curves. SENSORS (BASEL, SWITZERLAND) 2022; 22:7438. [PMID: 36236535 PMCID: PMC9570610 DOI: 10.3390/s22197438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/24/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
Recent studies matching eye gaze patterns with those of others contain research that is heavily reliant on string editing methods borrowed from early work in bioinformatics. Previous studies have shown string editing methods to be susceptible to false negative results when matching mutated genes or unordered regions of interest in scanpaths. Even as new methods have emerged for matching amino acids using novel combinatorial techniques, scanpath matching is still limited by a traditional collinear approach. This approach reduces the ability to discriminate between free viewing scanpaths of two people looking at the same stimulus due to the heavy weight placed on linearity. To overcome this limitation, we here introduce a new method called SoftMatch to compare pairs of scanpaths. SoftMatch diverges from traditional scanpath matching in two different ways: firstly, by preserving locality using fractal curves to reduce dimensionality from 2D Cartesian (x,y) coordinates into 1D (h) Hilbert distances, and secondly by taking a combinatorial approach to fixation matching using discrete Fréchet distance measurements between segments of scanpath fixation sequences. These matching "sequences of fixations over time" are a loose acronym for SoftMatch. Results indicate high degrees of statistical and substantive significance when scoring matches between scanpaths made during free-form viewing of unfamiliar stimuli. Applications of this method can be used to better understand bottom up perceptual processes extending to scanpath outlier detection, expertise analysis, pathological screening, and salience prediction.
Collapse
Affiliation(s)
- Robert Ahadizad Newport
- Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, Australia
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, Australia
| | - Carlo Russo
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, Australia
| | - Sidong Liu
- Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, Australia
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, Australia
| | - Abdulla Al Suman
- Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, Australia
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, Australia
| | - Antonio Di Ieva
- Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, Australia
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Macquarie University, Balaclava Road, Sydney, NSW 2109, Australia
| |
Collapse
|
22
|
Bai Z, Zhang YZ, Miyano S, Yamaguchi R, Fujimoto K, Uematsu S, Imoto S. Identification of bacteriophage genome sequences with representation learning. Bioinformatics 2022; 38:4264-4270. [PMID: 35920769 PMCID: PMC9477532 DOI: 10.1093/bioinformatics/btac509] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 05/20/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Bacteriophages/phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models. RESULTS We propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions. AVAILABILITY AND IMPLEMENTATION The codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Zeheng Bai
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
| | | | - Satoru Miyano
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan,M&D Data Science Center, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Rui Yamaguchi
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan,Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya 464-8681, Japan,Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya 466-8560, Japan
| | - Kosuke Fujimoto
- Division of Metagenome Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan,Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Satoshi Uematsu
- Division of Metagenome Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan,Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Seiya Imoto
- To whom correspondence should be addressed. or
| |
Collapse
|
23
|
Nakshathram S, Duraisamy R. Protein remote homology recognition using local and global structural sequence alignment. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Protein Remote Homology and fold Recognition (PRHR) is the most crucial task to predict the protein patterns. To achieve this task, Sequence-Order Frequency Matrix-Sampling and Deep learning with Smith-Waterman (SOFM-SDSW) were designed using large-scale Protein Sequences (PSs), which take more time to determine the high-dimensional attributes. Also, it was ineffective since the SW was only applied for local alignment, which cannot find the most matches between the PSs. Hence, in this manuscript, a rapid semi-global alignment algorithm called SOFM-SD-GlobalSW (SOFM-SDGSW) is proposed that facilitates the affine-gap scoring and uses sequence similarity to align the PSs. The major aim of this paper is to enhance the alignment of SW algorithm in both locally and globally for PRHR. In this algorithm, the Maximal Exact Matches (MEMs) are initially obtained by the bit-level parallelism rather than to align the individual characters. After that, a subgroup of MEMs is obtained to determine the global Alignment Score (AS) using the new adaptive programming scheme. Also, the SW local alignment scheme is used to determine the local AS. Then, both local and global ASs are combined to produce a final AS. Further, this resultant AS is considered to train the Support Vector Machine (SVM) classifier to recognize the PRH and folds. Finally, the test results reveal the SOFM-SDGSW algorithm on SCOP 1.53, SCOP 1.67 and Superfamily databases attains an ROC of 0.97, 0.941 and 0.938, respectively, as well as, an ROC50 of 0.819, 0.846 and 0.86, respectively compared to the conventional PRHR algorithms.
Collapse
|
24
|
Žarković M, Hufsky F, Markert UR, Marz M. The Role of Non-Coding RNAs in the Human Placenta. Cells 2022; 11:cells11091588. [PMID: 35563893 PMCID: PMC9104507 DOI: 10.3390/cells11091588] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/01/2022] [Accepted: 05/03/2022] [Indexed: 12/11/2022] Open
Abstract
Non-coding RNAs (ncRNAs) play a central and regulatory role in almost all cells, organs, and species, which has been broadly recognized since the human ENCODE project and several other genome projects. Nevertheless, a small fraction of ncRNAs have been identified, and in the placenta they have been investigated very marginally. To date, most examples of ncRNAs which have been identified to be specific for fetal tissues, including placenta, are members of the group of microRNAs (miRNAs). Due to their quantity, it can be expected that the fairly larger group of other ncRNAs exerts far stronger effects than miRNAs. The syncytiotrophoblast of fetal origin forms the interface between fetus and mother, and releases permanently extracellular vesicles (EVs) into the maternal circulation which contain fetal proteins and RNA, including ncRNA, for communication with neighboring and distant maternal cells. Disorders of ncRNA in placental tissue, especially in trophoblast cells, and in EVs seem to be involved in pregnancy disorders, potentially as a cause or consequence. This review summarizes the current knowledge on placental ncRNA, their transport in EVs, and their involvement and pregnancy pathologies, as well as their potential for novel diagnostic tools.
Collapse
Affiliation(s)
- Milena Žarković
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany; (M.Ž.); (F.H.)
- European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany;
| | - Franziska Hufsky
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany; (M.Ž.); (F.H.)
- European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany
| | - Udo R. Markert
- Placenta Lab, Department of Obstetrics, University Hospital Jena, Am Klinikum 1, 07747 Jena, Germany;
| | - Manja Marz
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, Leutragraben 1, 07743 Jena, Germany; (M.Ž.); (F.H.)
- European Virus Bioinformatics Center, Leutragraben 1, 07743 Jena, Germany
- FLI Leibniz Institute for Age Research, Beutenbergstraße 11, 07745 Jena, Germany
- Aging Research Center (ARC), 07745 Jena, Germany
- Correspondence:
| |
Collapse
|
25
|
Young C, Meng S, Moshiri N. An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology. Viruses 2022; 14:v14040774. [PMID: 35458504 PMCID: PMC9032411 DOI: 10.3390/v14040774] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/04/2022] [Accepted: 04/06/2022] [Indexed: 01/25/2023] Open
Abstract
The use of viral sequence data to inform public health intervention has become increasingly common in the realm of epidemiology. Such methods typically utilize multiple sequence alignments and phylogenies estimated from the sequence data. Like all estimation techniques, they are error prone, yet the impacts of such imperfections on downstream epidemiological inferences are poorly understood. To address this, we executed multiple commonly used viral phylogenetic analysis workflows on simulated viral sequence data, modeling Human Immunodeficiency Virus (HIV), Hepatitis C Virus (HCV), and Ebolavirus, and we computed multiple methods of accuracy, motivated by transmission-clustering techniques. For multiple sequence alignment, MAFFT consistently outperformed MUSCLE and Clustal Omega, in both accuracy and runtime. For phylogenetic inference, FastTree 2, IQ-TREE, RAxML-NG, and PhyML had similar topological accuracies, but branch lengths and pairwise distances were consistently most accurate in phylogenies inferred by RAxML-NG. However, FastTree 2 was the fastest, by orders of magnitude, and when the other tools were used to optimize branch lengths along a fixed FastTree 2 topology, the resulting phylogenies had accuracies that were indistinguishable from their original counterparts, but with a fraction of the runtime.
Collapse
|
26
|
Chao J, Tang F, Xu L. Developments in Algorithms for Sequence Alignment: A Review. Biomolecules 2022; 12:biom12040546. [PMID: 35454135 PMCID: PMC9024764 DOI: 10.3390/biom12040546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 01/27/2023] Open
Abstract
The continuous development of sequencing technologies has enabled researchers to obtain large amounts of biological sequence data, and this has resulted in increasing demands for software that can perform sequence alignment fast and accurately. A number of algorithms and tools for sequence alignment have been designed to meet the various needs of biologists. Here, the ideas that prevail in the research of sequence alignment and some quality estimation methods for multiple sequence alignment tools are summarized.
Collapse
Affiliation(s)
- Jiannan Chao
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China;
| | - Furong Tang
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China;
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518055, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518055, China
- Correspondence:
| |
Collapse
|
27
|
Zhang Y, Zhang Q, Zhou J, Zou Q. A survey on the algorithm and development of multiple sequence alignment. Brief Bioinform 2022; 23:6546258. [PMID: 35272347 DOI: 10.1093/bib/bbac069] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/30/2022] [Accepted: 02/09/2022] [Indexed: 12/21/2022] Open
Abstract
Multiple sequence alignment (MSA) is an essential cornerstone in bioinformatics, which can reveal the potential information in biological sequences, such as function, evolution and structure. MSA is widely used in many bioinformatics scenarios, such as phylogenetic analysis, protein analysis and genomic analysis. However, MSA faces new challenges with the gradual increase in sequence scale and the increasing demand for alignment accuracy. Therefore, developing an efficient and accurate strategy for MSA has become one of the research hotspots in bioinformatics. In this work, we mainly summarize the algorithms for MSA and its applications in bioinformatics. To provide a structured and clear perspective, we systematically introduce MSA's knowledge, including background, database, metric and benchmark. Besides, we list the most common applications of MSA in the field of bioinformatics, including database searching, phylogenetic analysis, genomic analysis, metagenomic analysis and protein analysis. Furthermore, we categorize and analyze classical and state-of-the-art algorithms, divided into progressive alignment, iterative algorithm, heuristics, machine learning and divide-and-conquer. Moreover, we also discuss the challenges and opportunities of MSA in bioinformatics. Our work provides a comprehensive survey of MSA applications and their relevant algorithms. It could bring valuable insights for researchers to contribute their knowledge to MSA and relevant studies.
Collapse
Affiliation(s)
- Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China.,School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, China
| | - Qiang Zhang
- School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China
| | - Jiliu Zhou
- School of Computer Science, Chengdu University of Information Technology, 610225, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, 610054, Chengdu, China
| |
Collapse
|
28
|
Yang P, Ning K. How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives. IMETA 2022; 1:e9. [PMID: 38867727 PMCID: PMC10989767 DOI: 10.1002/imt2.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/23/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2024]
Abstract
It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.
Collapse
Affiliation(s)
- Pengshuo Yang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐Imaging, Department of Bioinformatics and Systems BiologyCenter of AI Biology, College of Life Science and Technology, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐Imaging, Department of Bioinformatics and Systems BiologyCenter of AI Biology, College of Life Science and Technology, Huazhong University of Science and TechnologyWuhanHubeiChina
| |
Collapse
|
29
|
Molecular signature characters complement taxonomic diagnoses: a bioinformatic approach exemplified by ciliated protists (Ciliophora, Oligotrichea). Mol Phylogenet Evol 2022; 170:107433. [DOI: 10.1016/j.ympev.2022.107433] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/13/2022] [Accepted: 01/20/2022] [Indexed: 11/21/2022]
|
30
|
Raghavan V, Kraft L, Mesny F, Rigerte L. A simple guide to de novo transcriptome assembly and annotation. Brief Bioinform 2022; 23:6514404. [PMID: 35076693 PMCID: PMC8921630 DOI: 10.1093/bib/bbab563] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/03/2021] [Accepted: 12/09/2021] [Indexed: 12/13/2022] Open
Abstract
A transcriptome constructed from short-read RNA sequencing (RNA-seq) is an easily attainable proxy catalog of protein-coding genes when genome assembly is unnecessary, expensive or difficult. In the absence of a sequenced genome to guide the reconstruction process, the transcriptome must be assembled de novo using only the information available in the RNA-seq reads. Subsequently, the sequences must be annotated in order to identify sequence-intrinsic and evolutionary features in them (for example, protein-coding regions). Although straightforward at first glance, de novo transcriptome assembly and annotation can quickly prove to be challenging undertakings. In addition to familiarizing themselves with the conceptual and technical intricacies of the tasks at hand and the numerous pre- and post-processing steps involved, those interested must also grapple with an overwhelmingly large choice of tools. The lack of standardized workflows, fast pace of development of new tools and techniques and paucity of authoritative literature have served to exacerbate the difficulty of the task even further. Here, we present a comprehensive overview of de novo transcriptome assembly and annotation. We discuss the procedures involved, including pre- and post-processing steps, and present a compendium of corresponding tools.
Collapse
Affiliation(s)
- Venket Raghavan
- Corresponding authors: Venket Raghavan, Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany. E-mail: ; Louis Kraft, Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany. E-mail:
| | - Louis Kraft
- Corresponding authors: Venket Raghavan, Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany. E-mail: ; Louis Kraft, Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany. E-mail:
| | | | | |
Collapse
|
31
|
Identification of HIV Rapid Mutations Using Differences in Nucleotide Distribution over Time. Genes (Basel) 2022; 13:genes13020170. [PMID: 35205215 PMCID: PMC8872422 DOI: 10.3390/genes13020170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/08/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023] Open
Abstract
Mutation is the driving force of species evolution, which may change the genetic information of organisms and obtain selective competitive advantages to adapt to environmental changes. It may change the structure or function of translated proteins, and cause abnormal cell operation, a variety of diseases and even cancer. Therefore, it is particularly important to identify gene regions with high mutations. Mutations will cause changes in nucleotide distribution, which can be characterized by natural vectors globally. Based on natural vectors, we propose a mathematical formula for measuring the difference in nucleotide distribution over time to investigate the mutations of human immunodeficiency virus. The studied dataset is from public databases and includes gene sequences from twenty HIV-infected patients. The results show that the mutation rate of the nine major genes or gene segment regions in the genome exhibits discrepancy during the infected period, and the Env gene has the fastest mutation rate. We deduce that the peak of virus mutation has a close temporal relationship with viral divergence and diversity. The mutation study of HIV is of great significance to clinical diagnosis and drug design.
Collapse
|
32
|
O’Boyle B, Shrestha S, Kochut K, Eyers PA, Kannan N. Computational tools and resources for pseudokinase research. Methods Enzymol 2022; 667:403-426. [PMID: 35525549 PMCID: PMC9733567 DOI: 10.1016/bs.mie.2022.03.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Pseudokinases regulate diverse cellular processes associated with normal cellular functions and disease. They are defined bioinformatically based on the absence of one or more catalytic residues that are required for canonical protein kinase functions. The ability to define pseudokinases based on primary sequence comparison has enabled the systematic mapping and cataloging of pseudokinase orthologs across the tree of life. While these sequences contain critical information regarding pseudokinase evolution and functional specialization, extracting this information and generating testable hypotheses based on integrative mining of sequence and structural data requires specialized computational tools and resources. In this chapter, we review recent advances in the development and application of open-source tools and resources for pseudokinase research. Specifically, we describe the application of an interactive data analytics framework, KinView, for visualizing the patterns of conservation and variation in the catalytic domain motifs of pseudokinases and evolutionarily related canonical kinases using a consistent set of curated alignments organized based on the widely used kinome evolutionary hierarchy. We also demonstrate the application of an integrated Protein Kinase Ontology (ProKinO) and an interactive viewer, ProtVista, for mapping and analyzing primary sequence motifs and annotations in the context of 3D structures and AlphaFold2 models. We provide examples and protocols for generating testable hypotheses on pseudokinase functions both for bench biologists and advanced users.
Collapse
Affiliation(s)
- Brady O’Boyle
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602, USA
| | - Safal Shrestha
- Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
| | - Krzysztof Kochut
- Department of Computer Science, University of Georgia, Athens, GA 30602, USA
| | - Patrick A Eyers
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | - Natarajan Kannan
- Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 30602, USA,Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA,Corresponding author:
| |
Collapse
|
33
|
Lameh F, Baseer AQ, Ashiru AG. Comparative molecular docking and molecular-dynamic simulation of wild-type- and mutant carboxylesterase with BTA-hydrolase for enhanced binding to plastic. Eng Life Sci 2022; 22:13-29. [PMID: 35024024 PMCID: PMC8727734 DOI: 10.1002/elsc.202100083] [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: 07/10/2021] [Revised: 09/25/2021] [Accepted: 10/10/2021] [Indexed: 01/09/2023] Open
Abstract
According to the literature review, microbial degradation of polyethylene terephthalate by PETases has been detected effective and eco-friendly. However, the number of microorganisms capable of such feats is limited with some undesirable bioprospecting results. BTA-hydrolase has been already reported capable of degrading polyethylene terephthalate. Therefore, mutation by in silico site-directed mutagenesis means to introduce current isomer of PETase for polyethylene terephthalate degradative capability as a better approach to resolve this issue. This study aimed to use in silico site-directed mutagenesis to convert a carboxylesterase from Archaeoglobus fulgidus to BTA-hydrolase from Thermobifida fusca by replacing six amino acids in specific locations. This work was followed by molecular docking analysis with polyethylene terephthalate and polypropylene to compare their interactions. The best-docked enzyme-substrate complex was further subjected to molecular dynamics simulation to gauge the binding quality of the BTA-hydrolase, wild-type and mutant-carboxylesterase with only polyethylene terephthalate as a substrate. Results of molecular docking revealed lowest binding energy for the wild-type carboxylesterase-polypropylene complex (-7.5 kcal/mol). The root-mean-square deviation value was observed stable for BTA-hydrolase. Meanwhile, root-mean-square fluctuation was assessed with higher fluctuation for the mutated residue Lys178. Consequently, the Rg value for BTA-hydrolase-ligand complex (∼1.68 nm) was the lowest compared to the mutant and wild-type carboxylesterase. The collective data conveyed that mutations imparted a minimal change in the ability of the mutant carboxylesterase to bind to polyethylene terephthalate.
Collapse
Affiliation(s)
- Fatana Lameh
- Department of BotanyFaculty of BiologyKabul UniversityKabulAfghanistan
- Department of BiosciencesFaculty of ScienceUniversiti Teknologi MalaysiaJohor BahruMalaysia
| | - Abdul Qadeer Baseer
- Department of BiosciencesFaculty of ScienceUniversiti Teknologi MalaysiaJohor BahruMalaysia
- Department of BiologyFaculty of EducationKandahar UniversityKandaharAfghanistan
| | - Abubakar Garba Ashiru
- Department of ChemistryZamfara State College of EducationMaruNigeria
- Green Chemistry Research GroupDepartment of Chemistry, Faculty of ScienceUniversiti Teknologi MalaysiaJohor BahruMalaysia
| |
Collapse
|
34
|
Liu H, Zou Q, Xu Y. A novel fast multiple nucleotide sequence alignment method based on FM-index. Brief Bioinform 2021; 23:6458932. [PMID: 34893794 DOI: 10.1093/bib/bbab519] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/19/2021] [Accepted: 11/14/2021] [Indexed: 11/13/2022] Open
Abstract
Multiple sequence alignment (MSA) is fundamental to many biological applications. But most classical MSA algorithms are difficult to handle large-scale multiple sequences, especially long sequences. Therefore, some recent aligners adopt an efficient divide-and-conquer strategy to divide long sequences into several short sub-sequences. Selecting the common segments (i.e. anchors) for division of sequences is very critical as it directly affects the accuracy and time cost. So, we proposed a novel algorithm, FMAlign, to improve the performance of multiple nucleotide sequence alignment. We use FM-index to extract long common segments at a low cost rather than using a space-consuming hash table. Moreover, after finding the longer optimal common segments, the sequences are divided by the longer common segments. FMAlign has been tested on virus and bacteria genome and human mitochondrial genome datasets, and compared with existing MSA methods such as MAFFT, HAlign and FAME. The experiments show that our method outperforms the existing methods in terms of running time, and has a high accuracy on long sequence sets. All the results demonstrate that our method is applicable to the large-scale nucleotide sequences in terms of sequence length and sequence number. The source code and related data are accessible in https://github.com/iliuh/FMAlign.
Collapse
Affiliation(s)
- Huan Liu
- School of Computer Science, University of Science and Technology of China and Key Laboratory on High Performance Computing of Anhui, China
| | - Quan Zou
- Institute of basic and Frontier Sciences, University of Electronic Science and Technology of China and Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Yun Xu
- School of Computer Science, University of Science and Technology of China and Key Laboratory on High Performance Computing of Anhui, China
| |
Collapse
|
35
|
Fotakis G, Trajanoski Z, Rieder D. Computational cancer neoantigen prediction: current status and recent advances. IMMUNO-ONCOLOGY TECHNOLOGY 2021; 12:100052. [PMID: 35755950 PMCID: PMC9216660 DOI: 10.1016/j.iotech.2021.100052] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Over the last few decades, immunotherapy has shown significant therapeutic efficacy in a broad range of cancer types. Antitumor immune responses are contingent on the recognition of tumor-specific antigens, which are termed neoantigens. Tumor neoantigens are ideal targets for immunotherapy since they can be recognized as non-self antigens by the host immune system and thus are able to elicit an antitumor T-cell response. There are an increasing number of studies that highlight the importance of tumor neoantigens in immunoediting and in the sensitivity to immune checkpoint blockade. Therefore, one of the most fundamental tasks in the field of immuno-oncology research is the identification of patient-specific neoantigens. To this end, a plethora of computational approaches have been developed in order to predict tumor-specific aberrant peptides and quantify their likelihood of binding to patients' human leukocyte antigen molecules in order to be recognized by T cells. In this review, we systematically summarize and present the most recent advances in computational neoantigen prediction, and discuss the challenges and novel methods that are being developed to resolve them. Tumors have the ability to acquire immune escape mechanisms. Tumor-specific aberrant peptides (neoantigens) can elicit an immune response by the host immune system. The identification of neoantigens is one of the most fundamental tasks in the field of immuno-oncology research. A plethora of computational approaches have been developed in order to predict patient-specificneoantigens.
Collapse
Affiliation(s)
- G Fotakis
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Z Trajanoski
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - D Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| |
Collapse
|
36
|
Generator based approach to analyze mutations in genomic datasets. Sci Rep 2021; 11:21084. [PMID: 34702945 PMCID: PMC8548350 DOI: 10.1038/s41598-021-00609-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 10/13/2021] [Indexed: 11/09/2022] Open
Abstract
In contrast to the conventional approach of directly comparing genomic sequences using sequence alignment tools, we propose a computational approach that performs comparisons between sequence generators. These sequence generators are learned via a data-driven approach that empirically computes the state machine generating the genomic sequence of interest. As the state machine based generator of the sequence is independent of the sequence length, it provides us with an efficient method to compute the statistical distance between large sets of genomic sequences. Moreover, our technique provides a fast and efficient method to cluster large datasets of genomic sequences, characterize their temporal and spatial evolution in a continuous manner, get insights into the locality sensitive information about the sequences without any need for alignment. Furthermore, we show that the technique can be used to detect local regions with mutation activity, which can then be applied to aid alignment techniques for the fast discovery of mutations. To demonstrate the efficacy of our technique on real genomic data, we cluster different strains of SARS-CoV-2 viral sequences, characterize their evolution and identify regions of the viral sequence with mutations.
Collapse
|
37
|
Wang Y, Zhao Y, Pan Q. Advances, challenges and opportunities of phylogenetic and social network analysis using COVID-19 data. Brief Bioinform 2021; 23:6380452. [PMID: 34601563 DOI: 10.1093/bib/bbab406] [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: 06/30/2021] [Revised: 08/04/2021] [Accepted: 09/03/2021] [Indexed: 11/15/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) has attracted research interests from all fields. Phylogenetic and social network analyses based on connectivity between either COVID-19 patients or geographic regions and similarity between syndrome coronavirus 2 (SARS-CoV-2) sequences provide unique angles to answer public health and pharmaco-biological questions such as relationships between various SARS-CoV-2 mutants, the transmission pathways in a community and the effectiveness of prevention policies. This paper serves as a systematic review of current phylogenetic and social network analyses with applications in COVID-19 research. Challenges in current phylogenetic network analysis on SARS-CoV-2 such as unreliable inferences, sampling bias and batch effects are discussed as well as potential solutions. Social network analysis combined with epidemiology models helps to identify key transmission characteristics and measure the effectiveness of prevention and control strategies. Finally, future new directions of network analysis motivated by COVID-19 data are summarized.
Collapse
Affiliation(s)
- Yue Wang
- School of Mathematical and Natural Science, Arizona State University, 4701 W Thunderbird Rd, 85306, Arizona, USA
| | - Yunpeng Zhao
- School of Mathematical and Natural Science, Arizona State University, 4701 W Thunderbird Rd, 85306, Arizona, USA
| | - Qing Pan
- Department of Statistics, George Washington University, 801 22nd St. NW, 20052, Washington DC, USA
| |
Collapse
|
38
|
Metaheuristics for multiple sequence alignment: A systematic review. Comput Biol Chem 2021; 94:107563. [PMID: 34425495 DOI: 10.1016/j.compbiolchem.2021.107563] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 08/04/2021] [Accepted: 08/09/2021] [Indexed: 11/21/2022]
Abstract
The Multiple Sequence Alignment (MSA) is a key task in bioinformatics, because it is used in different important biological analysis, such as function and structure prediction of unknown proteins. There are several approaches to perform MSA and the use of metaheuristics stands out because of the search ability of these methods, which generally leads to good results in a reasonable amount of time. This paper presents a Systematic Literature Review (SLR) on metaheuristics for MSA, compiling relevant works published between 2014 and 2019. The results of our SLR show the constant interest in this subject, due to the several recent publications that use different metaheuristics to obtain more accurate alignments. Moreover, the final results of our SLR show a multi-objective and hybrid approaches trends, which generally leads these methods to achieve even better results. Thus, we show in this work how the use of metaheuristics to perform MSA still remains an important and promising open research field.
Collapse
|
39
|
Schaller D, Geiß M, Hellmuth M, Stadler PF. Heuristic algorithms for best match graph editing. Algorithms Mol Biol 2021; 16:19. [PMID: 34404422 PMCID: PMC8369769 DOI: 10.1186/s13015-021-00196-3] [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: 03/17/2021] [Accepted: 06/26/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Best match graphs (BMGs) are a class of colored digraphs that naturally appear in mathematical phylogenetics as a representation of the pairwise most closely related genes among multiple species. An arc connects a gene x with a gene y from another species (vertex color) Y whenever it is one of the phylogenetically closest relatives of x. BMGs can be approximated with the help of similarity measures between gene sequences, albeit not without errors. Empirical estimates thus will usually violate the theoretical properties of BMGs. The corresponding graph editing problem can be used to guide error correction for best match data. Since the arc set modification problems for BMGs are NP-complete, efficient heuristics are needed if BMGs are to be used for the practical analysis of biological sequence data. RESULTS Since BMGs have a characterization in terms of consistency of a certain set of rooted triples (binary trees on three vertices) defined on the set of genes, we consider heuristics that operate on triple sets. As an alternative, we show that there is a close connection to a set partitioning problem that leads to a class of top-down recursive algorithms that are similar to Aho's supertree algorithm and give rise to BMG editing algorithms that are consistent in the sense that they leave BMGs invariant. Extensive benchmarking shows that community detection algorithms for the partitioning steps perform best for BMG editing. CONCLUSION Noisy BMG data can be corrected with sufficient accuracy and efficiency to make BMGs an attractive alternative to classical phylogenetic methods.
Collapse
|
40
|
The Natterin Proteins Diversity: A Review on Phylogeny, Structure, and Immune Function. Toxins (Basel) 2021; 13:toxins13080538. [PMID: 34437409 PMCID: PMC8402412 DOI: 10.3390/toxins13080538] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/12/2021] [Accepted: 07/21/2021] [Indexed: 12/14/2022] Open
Abstract
Since the first record of the five founder members of the group of Natterin proteins in the venom of the medically significant fish Thalassophryne nattereri, new sequences have been identified in other species. In this work, we performed a detailed screening using available genome databases across a wide range of species to identify sequence members of the Natterin group, sequence similarities, conserved domains, and evolutionary relationships. The high-throughput tools have enabled us to dramatically expand the number of members within this group of proteins, which has a remote origin (around 400 million years ago) and is spread across Eukarya organisms, even in plants and primitive Agnathans jawless fish. Overall, the survey resulted in 331 species presenting Natterin-like proteins, mainly fish, and 859 putative genes. Besides fish, the groups with more species included in our analysis were insects and birds. The number and variety of annotations increased the knowledge of the obtained sequences in detail, such as the conserved motif AGIP in the pore-forming loop involved in the transmembrane barrel insertion, allowing us to classify them as important constituents of the innate immune defense system as effector molecules activating immune cells by interacting with conserved intracellular signaling mechanisms in the hosts.
Collapse
|
41
|
Lemoine F, Blassel L, Voznica J, Gascuel O. COVID-Align: accurate online alignment of hCoV-19 genomes using a profile HMM. Bioinformatics 2021; 37:1761-1762. [PMID: 33045068 PMCID: PMC7745650 DOI: 10.1093/bioinformatics/btaa871] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 05/23/2020] [Accepted: 09/24/2020] [Indexed: 11/12/2022] Open
Abstract
Motivation The first cases of the COVID-19 pandemic emerged in December 2019. Until the end of February 2020, the number of available genomes was below 1000 and their multiple alignment was easily achieved using standard approaches. Subsequently, the availability of genomes has grown dramatically. Moreover, some genomes are of low quality with sequencing/assembly errors, making accurate re-alignment of all genomes nearly impossible on a daily basis. A more efficient, yet accurate approach was clearly required to pursue all subsequent bioinformatics analyses of this crucial data. Results hCoV-19 genomes are highly conserved, with very few indels and no recombination. This makes the profile HMM approach particularly well suited to align new genomes, add them to an existing alignment and filter problematic ones. Using a core of ∼2500 high quality genomes, we estimated a profile using HMMER, and implemented this profile in COVID-Align, a user-friendly interface to be used online or as standalone via Docker. The alignment of 1000 genomes requires ∼50 minutes on our cluster. Moreover, COVID-Align provides summary statistics, which can be used to determine the sequencing quality and evolutionary novelty of input genomes (e.g. number of new mutations and indels). Availability and implementation https://covalign.pasteur.cloud, hub.docker.com/r/evolbioinfo/covid-align. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Frédéric Lemoine
- Unité de Bioinformatique Evolutive, USR 3756 (DBC/C3BI), Institut Pasteur & CNRS, 75015 - Paris, France.,Hub de Bioinformatique et Biostatistique, USR 3756 (DBC/C3BI), Institut Pasteur & CNRS, 75015 - Paris, France
| | - Luc Blassel
- Unité de Bioinformatique Evolutive, USR 3756 (DBC/C3BI), Institut Pasteur & CNRS, 75015 - Paris, France.,ED515, Sorbonne Université, Collège Doctoral, 75006 - Paris, France
| | - Jakub Voznica
- Unité de Bioinformatique Evolutive, USR 3756 (DBC/C3BI), Institut Pasteur & CNRS, 75015 - Paris, France.,Université de Paris, 75006 Paris, France
| | - Olivier Gascuel
- Unité de Bioinformatique Evolutive, USR 3756 (DBC/C3BI), Institut Pasteur & CNRS, 75015 - Paris, France.,Académie des Sciences, USR 3756, CNRS, 75015 - Paris, France
| |
Collapse
|
42
|
Aadland K, Kolaczkowski B. Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy. Genome Biol Evol 2021; 12:1549-1565. [PMID: 32785673 PMCID: PMC7523730 DOI: 10.1093/gbe/evaa164] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2020] [Indexed: 12/31/2022] Open
Abstract
Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, whereas phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein sequence alignment can more strongly affect inferred ancestral sequences. Here, we show that errors in sequence alignment can produce errors in ASR across a range of realistic and simplified evolutionary scenarios. Importantly, sequence reconstruction errors can lead to errors in estimates of structural and functional properties of ancestral proteins, potentially undermining the reliability of analyses relying on ASR. We introduce an alignment-integrated ASR approach that combines information from many different sequence alignments. We show that integrating alignment uncertainty improves ASR accuracy and the accuracy of downstream structural and functional inferences, often performing as well as highly accurate structure-guided alignment. Given the growing evidence that sequence alignment errors can impact the reliability of ASR studies, we recommend that future studies incorporate approaches to mitigate the impact of alignment uncertainty. Probabilistic modeling of insertion and deletion events has the potential to radically improve ASR accuracy when the model reflects the true underlying evolutionary history, but further studies are required to thoroughly evaluate the reliability of these approaches under realistic conditions.
Collapse
Affiliation(s)
- Kelsey Aadland
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida
| | - Bryan Kolaczkowski
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida
| |
Collapse
|
43
|
Deigin Y, Segreto R. SARS-CoV-2's claimed natural origin is undermined by issues with genome sequences of its relative strains: Coronavirus sequences RaTG13, MP789 and RmYN02 raise multiple questions to be critically addressed by the scientific community. Bioessays 2021; 43:e2100015. [PMID: 34046923 PMCID: PMC8209872 DOI: 10.1002/bies.202100015] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/05/2021] [Accepted: 04/21/2021] [Indexed: 12/15/2022]
Abstract
RaTG13, MP789, and RmYN02 are the strains closest to SARS‐CoV‐2, and their existence came to light only after the start of the pandemic. Their genomes have been used to support a natural origin of SARS‐CoV‐2 but after a close examination all of them exhibit several issues. We specifically address the presence in RmYN02 and closely related RacCSxxx strains of a claimed natural PAA/PVA amino acid insertion at the S1/S2 junction of their spike protein at the same position where the PRRA insertion in SARS‐CoV‐2 has created a polybasic furin cleavage site. We show that RmYN02/RacCSxxx instead of the claimed insertion carry a 6‐nucleotide deletion in the region and that the 12‐nucleotide insertion in SARS‐CoV‐2 remains unique among Sarbecoviruses. Also, our analysis of RaTG13 and RmYN02's metagenomic datasets found unexpected reads which could indicate possible contamination. Because of their importance to inferring SARS‐CoV‐2′s origin, we call for a careful reevaluation of RaTG13, MP789 and RmYN02 sequencing records and assembly methods.
Collapse
Affiliation(s)
- Yuri Deigin
- Youthereum Genetics Inc., Toronto, Ontario, Canada
| | - Rossana Segreto
- Department of Microbiology, University of Innsbruck, Innsbruck, Austria
| |
Collapse
|
44
|
Abstract
Multiple sequence alignment is a core first step in many bioinformatics analyses, and errors in these alignments can have negative consequences for scientific studies. In this article, we review some of the recent literature evaluating multiple sequence alignment methods and identify specific challenges that arise when performing these evaluations. In particular, we discuss the different trends observed in simulation studies and when using biological benchmarks. Overall, we find that multiple sequence alignment, far from being a "solved problem," would benefit from new attention.
Collapse
Affiliation(s)
- Tandy Warnow
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| |
Collapse
|
45
|
Saha I, Ghosh N, Pradhan A, Sharma N, Maity D, Mitra K. Whole genome analysis of more than 10 000 SARS-CoV-2 virus unveils global genetic diversity and target region of NSP6. Brief Bioinform 2021; 22:1106-1121. [PMID: 33725111 PMCID: PMC7989641 DOI: 10.1093/bib/bbab025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/24/2020] [Accepted: 01/19/2021] [Indexed: 01/21/2023] Open
Abstract
Whole genome analysis of SARS-CoV-2 is important to identify its genetic diversity. Moreover, accurate detection of SARS-CoV-2 is required for its correct diagnosis. To address these, first we have analysed publicly available 10 664 complete or near-complete SARS-CoV-2 genomes of 73 countries globally to find mutation points in the coding regions as substitution, deletion, insertion and single nucleotide polymorphism (SNP) globally and country wise. In this regard, multiple sequence alignment is performed in the presence of reference sequence from NCBI. Once the alignment is done, a consensus sequence is build to analyse each genomic sequence to identify the unique mutation points as substitutions, deletions, insertions and SNPs globally, thereby resulting in 7209, 11700, 119 and 53 such mutation points respectively. Second, in such categories, unique mutations for individual countries are determined with respect to other 72 countries. In case of India, unique 385, 867, 1 and 11 substitutions, deletions, insertions and SNPs are present in 566 SARS-CoV-2 genomes while 458, 1343, 8 and 52 mutation points in such categories are common with other countries. In majority (above 10%) of virus population, the most frequent and common mutation points between global excluding India and India are L37F, P323L, F506L, S507G, D614G and Q57H in NSP6, RdRp, Exon, Spike and ORF3a respectively. While for India, the other most frequent mutation points are T1198K, A97V, T315N and P13L in NSP3, RdRp, Spike and ORF8 respectively. These mutations are further visualised in protein structures and phylogenetic analysis has been done to show the diversity in virus genomes. Third, a web application is provided for searching mutation points globally and country wise. Finally, we have identified the potential conserved region as target that belongs to the coding region of ORF1ab, specifically to the NSP6 gene. Subsequently, we have provided the primers and probes using that conserved region so that it can be used for detecting SARS-CoV-2. Contact: indrajit@nitttrkol.ac.in Supplementary information: Supplementary data are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-Mutation-10K
Collapse
Affiliation(s)
- Indrajit Saha
- Department of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata, West Bengal, India
| | - Nimisha Ghosh
- Department of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Ayan Pradhan
- Department of Computer Science and Engineering, Techno India University, West Bengal, India
| | - Nikhil Sharma
- Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
| | - Debasree Maity
- Department of Electronics and Communication Engineering, MCKV Institute of Engineering, Howrah, West Bengal, India
| | - Kaushik Mitra
- Department of Community Medicine, Burdwan Medical College, Barddhaman, West Bengal, India
| |
Collapse
|
46
|
Downard KM. Protein phylogenetics with mass spectrometry. A comparison of methods. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:1442-1454. [PMID: 33725067 DOI: 10.1039/d1ay00153a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Advances in protein mass spectrometry have provided the ability to identify and sequence proteins with unprecedented speed, sensitivity and accuracy. These benefits now offer advantages for studies of protein evolution and phylogeny avoiding the need to generate and align DNA sequences which can prove time consuming, costly and difficult in the case of large genomes and for highly diverse organisms. The methods of phylogenetic analysis using protein mass spectrometry can be classified into three categories: (1) de novo protein sequencing followed by multiple sequence alignment for classical phylogenetic reconstruction, (2) direct phylogenetic reconstruction using expressed protein mass profiles exploited in microbial biotyping applications, and (3) the construction of trees using proteolytic peptide mass map or fingerprint data. This review describes the three approaches together with the relevant tools and algorithms required to implement them. It also compares each of these alternative protein based methods alongside conventional gene sequence based phylogenetics.
Collapse
Affiliation(s)
- Kevin M Downard
- Infectious Disease Responses Laboratory, Prince of Wales Clinical Research Sciences, Sydney, NSW 2031, Australia.
| |
Collapse
|
47
|
Naglah AM, Moustafa GO, Elhenawy AA, Mounier MM, El-Sayed H, Al-Omar MA, Almehizia AA, Bhat MA. N α-1, 3-Benzenedicarbonyl-Bis-(Amino Acid) and Dipeptide Candidates: Synthesis, Cytotoxic, Antimicrobial and Molecular Docking Investigation. DRUG DESIGN DEVELOPMENT AND THERAPY 2021; 15:1315-1332. [PMID: 33790542 PMCID: PMC8006965 DOI: 10.2147/dddt.s276504] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 01/23/2021] [Indexed: 12/14/2022]
Abstract
Purpose The objective of our work was to prepare a potent and safe antimicrobial and anticancer agents, through synthesis of several peptides and examine their biological activities, namely as, cytotoxically potent and antimicrobial and antifungal agents. Introduction Multidrug-resistant microbial strains have arisen against all antibiotics in clinical use. Infections caused by these bacteria threaten global public health and are associated with high mortality rates. Methods The main backbone structure for the novel synthesized linear peptide is Nα-1, 3-benzenedicarbonyl-bis-(Amino acids)-X, (3–11). A computational docking study against DNA gyrase was performed to formulate a mode of action of the small compounds as antimicrobial agents. Results The peptide-bearing methionine-ester (4) exhibited potent antimicrobial activity compared to the other synthesized compounds, while, peptide (8), which had methionine-hydrazide fragment was the most potent as antifungal agent against Aspergillus niger with 100% inhibition percent. Compounds (6 and 7) showed the highest potency against breast human tumor cell line “MCF-7” with 95.1% and 79.8% of cell inhibition, respectively. The nine compounds possessed weak to moderate antiproliferative effect over colon tumor cell line. The docking results suggest good fitting through different hydrogen bond interactions with the protein residues. In silico ADMET study also evaluated and suggested that these compounds had promising oral bioavailability features. Conclusion The tested compounds need further modification to have significant antimicrobial and antitumor efficacy compared to the reference drugs.
Collapse
Affiliation(s)
- Ahmed M Naglah
- Department of Pharmaceutical Chemistry, Drug Exploration & Development Chair (DEDC), College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia.,Peptide Chemistry Department, Chemical Industries Research Division, National Research Centre, Cairo, Egypt
| | - Gaber O Moustafa
- Peptide Chemistry Department, Chemical Industries Research Division, National Research Centre, Cairo, Egypt
| | - Ahmed A Elhenawy
- Chemistry Department, Faculty of Science, Al-Azhar University (Boys'Branch), Cairo, Egypt.,Chemistry Department, Faculty of Science, Albaha University, Al Baha, Saudi Arabia
| | - Marwa M Mounier
- Pharmacognosy Department, Pharmaceutical and Drug Industries Research Division, National Research Centre, Giza, Egypt
| | - Heba El-Sayed
- Botany and Microbiology Department, Faculty of Science, Helwan University, Helwan, Egypt
| | - Mohamed A Al-Omar
- Department of Pharmaceutical Chemistry, Drug Exploration & Development Chair (DEDC), College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Abdulrahman A Almehizia
- Department of Pharmaceutical Chemistry, Drug Exploration & Development Chair (DEDC), College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia.,Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Mashooq A Bhat
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia
| |
Collapse
|
48
|
Korotkov EV, Suvorova YM, Kostenko DO, Korotkova MA. Multiple Alignment of Promoter Sequences from the Arabidopsis thaliana L. Genome. Genes (Basel) 2021; 12:135. [PMID: 33494278 PMCID: PMC7909805 DOI: 10.3390/genes12020135] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/15/2021] [Accepted: 01/18/2021] [Indexed: 11/16/2022] Open
Abstract
In this study, we developed a new mathematical method for performing multiple alignment of highly divergent sequences (MAHDS), i.e., sequences that have on average more than 2.5 substitutions per position (x). We generated sets of artificial DNA sequences with x ranging from 0 to 4.4 and applied MAHDS as well as currently used multiple sequence alignment algorithms, including ClustalW, MAFFT, T-Coffee, Kalign, and Muscle to these sets. The results indicated that most of the existing methods could produce statistically significant alignments only for the sets with x < 2.5, whereas MAHDS could operate on sequences with x = 4.4. We also used MAHDS to analyze a set of promoter sequences from the Arabidopsis thaliana genome and discovered many conserved regions upstream of the transcription initiation site (from -499 to +1 bp); a part of the downstream region (from +1 to +70 bp) also significantly contributed to the obtained alignments. The possibilities of applying the newly developed method for the identification of promoter sequences in any genome are discussed. A server for multiple alignment of nucleotide sequences has been created.
Collapse
Affiliation(s)
- Eugene V. Korotkov
- Institute of Bioengineering, Research Center of Biotechnology of the Russian Academy of Sciences, Bld.2, 33 Leninsky Ave., 119071 Moscow, Russia;
- National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 31 Kashirskoye Shosse, 115409 Moscow, Russia; (D.O.K.); (M.A.K.)
| | - Yulia M. Suvorova
- Institute of Bioengineering, Research Center of Biotechnology of the Russian Academy of Sciences, Bld.2, 33 Leninsky Ave., 119071 Moscow, Russia;
| | - Dmitrii O. Kostenko
- National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 31 Kashirskoye Shosse, 115409 Moscow, Russia; (D.O.K.); (M.A.K.)
| | - Maria A. Korotkova
- National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 31 Kashirskoye Shosse, 115409 Moscow, Russia; (D.O.K.); (M.A.K.)
| |
Collapse
|
49
|
Winklemann I, Matsuoka R, Meier PF, Shutin D, Zhang C, Orellana L, Sexton R, Landreh M, Robinson CV, Beckstein O, Drew D. Structure and elevator mechanism of the mammalian sodium/proton exchanger NHE9. EMBO J 2020; 39:e105908. [PMID: 33118634 PMCID: PMC7737618 DOI: 10.15252/embj.2020105908] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 09/09/2020] [Accepted: 09/10/2020] [Indexed: 12/21/2022] Open
Abstract
Na+ /H+ exchangers (NHEs) are ancient membrane-bound nanomachines that work to regulate intracellular pH, sodium levels and cell volume. NHE activities contribute to the control of the cell cycle, cell proliferation, cell migration and vesicle trafficking. NHE dysfunction has been linked to many diseases, and they are targets of pharmaceutical drugs. Despite their fundamental importance to cell homeostasis and human physiology, structural information for the mammalian NHE was lacking. Here, we report the cryogenic electron microscopy structure of NHE isoform 9 (SLC9A9) from Equus caballus at 3.2 Å resolution, an endosomal isoform highly expressed in the brain and associated with autism spectrum (ASD) and attention deficit hyperactivity (ADHD) disorders. Despite low sequence identity, the NHE9 architecture and ion-binding site are remarkably similar to distantly related bacterial Na+ /H+ antiporters with 13 transmembrane segments. Collectively, we reveal the conserved architecture of the NHE ion-binding site, their elevator-like structural transitions, the functional implications of autism disease mutations and the role of phosphoinositide lipids to promote homodimerization that, together, have important physiological ramifications.
Collapse
Affiliation(s)
- Iven Winklemann
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
| | - Rei Matsuoka
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
| | - Pascal F Meier
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
| | - Denis Shutin
- Department of ChemistryUniversity of OxfordOxfordUK
| | - Chenou Zhang
- Department of PhysicsCenter for Biological PhysicsArizona State UniversityTempeAZUSA
| | - Laura Orellana
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
| | - Ricky Sexton
- Department of PhysicsCenter for Biological PhysicsArizona State UniversityTempeAZUSA
| | - Michael Landreh
- Department of Microbiology, Tumor and Cell BiologyKarolinska InstituteStockholmSweden
| | | | - Oliver Beckstein
- Department of PhysicsCenter for Biological PhysicsArizona State UniversityTempeAZUSA
| | - David Drew
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
| |
Collapse
|
50
|
Singh N, Rai SN, Singh V, Singh MP. Molecular characterization, pathogen-host interaction pathway and in silico approaches for vaccine design against COVID-19. J Chem Neuroanat 2020; 110:101874. [PMID: 33091590 PMCID: PMC7571424 DOI: 10.1016/j.jchemneu.2020.101874] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 10/14/2020] [Accepted: 10/15/2020] [Indexed: 12/12/2022]
Abstract
COVID-19 has forsaken the world because of extremely high infection rates and high mortality rates. At present we have neither medicine nor vaccine to prevent this pandemic. Lockdowns, curfews, isolations, quarantines, and social distancing are the only ways to mitigate their infection. This is badly affecting the mental health of people. Hence, there is an urgent need to address this issue. Coronavirus disease 2019 (COVID-19) is caused by a novel Betacorona virus named SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) which has emerged in the city of Wuhan in China and declared a pandemic by WHO since it affected almost all the countries the world, infected 24,182,030 people and caused 825,798 death as per data are compiled from John Hopkins University (JHU). The genome of SARS-CoV-2 has a single-stranded positive (+) sense RNA of ∼30 kb nucleotides. Phylogenetic analysis reveals that SARS-CoV-2 shares the highest nucleotide sequence similarity (∼79 %) with SARS-CoV. Envelope and nucleocapsids are two evolutionary conserved regions of SARS-CoV-2 having a sequence identity of about 96 % and 89.6 %, respectively as compared to SARS-CoV. The characterization of SARS-CoV-2 is based on polymerase chain reaction (PCR) and metagenomic next-generation sequencing. Transmission of this virus in the human occurs through the respiratory tract and decreases the respiration efficiency of lungs. Humans are generally susceptible to SARS-CoV-2 with an incubation period of 2-14 days. The virus first infects the lower airway and bind with angiotensin-converting enzyme 2 (ACE2) of alveolar epithelial cells. Due to the unavailability of drugs or vaccines, it is very urgent to design potential vaccines or drugs for COVID-19. Reverse vaccinology and immunoinformatic play an important role in designing potential vaccines against SARS-CoV-2. The suitable vaccine selects for SARS-CoV-2 based on binding energy between the target protein and the designed vaccine. The stability and activity of the designed vaccine can be estimated by using molecular docking and dynamic simulation approaches. This review mainly focused on the brief up to date information about COVID-19, molecular characterization, pathogen-host interaction pathways involved during COVID-19 infection. It also covers potential vaccine design against COVID-19 by using various computational approaches. SARS-CoV-2 enters brain tissue through the different pathway and harm human's brain and causes severe neurological disruption.
Collapse
Affiliation(s)
- Nidhi Singh
- Centre of Bioinformatics, University of Allahabad, Prayagraj, 211002, India
| | - Sachchida Nand Rai
- Centre of Biotechnology, University of Allahabad, Prayagraj, 211002, India
| | - Veer Singh
- School of Biochemical Engineering, IIT (BHU) Varanasi, 221005, India
| | - Mohan P Singh
- Centre of Biotechnology, University of Allahabad, Prayagraj, 211002, India.
| |
Collapse
|