1
|
Hallee L, Rafailidis N, Horger C, Hong D, Gleghorn JP. Annotation Vocabulary (Might Be) All You Need. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.30.605924. [PMID: 39131267 PMCID: PMC11312613 DOI: 10.1101/2024.07.30.605924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Protein Language Models (pLMs) have revolutionized the computational modeling of protein systems, building numerical embeddings that are centered around structural features. To enhance the breadth of biochemically relevant properties available in protein embeddings, we engineered the Annotation Vocabulary, a transformer readable language of protein properties defined by structured ontologies. We trained Annotation Transformers (AT) from the ground up to recover masked protein property inputs without reference to amino acid sequences, building a new numerical feature space on protein descriptions alone. We leverage AT representations in various model architectures, for both protein representation and generation. To showcase the merit of Annotation Vocabulary integration, we performed 515 diverse downstream experiments. Using a novel loss function and only $3 in commercial compute, our premier representation model CAMP produces state-of-the-art embeddings for five out of 15 common datasets with competitive performance on the rest; highlighting the computational efficiency of latent space curation with Annotation Vocabulary. To standardize the comparison of de novo generated protein sequences, we suggest a new sequence alignment-based score that is more flexible and biologically relevant than traditional language modeling metrics. Our generative model, GSM, produces high alignment scores from annotation-only prompts with a BERT-like generation scheme. Of particular note, many GSM hallucinations return statistically significant BLAST hits, where enrichment analysis shows properties matching the annotation prompt - even when the ground truth has low sequence identity to the entire training set. Overall, the Annotation Vocabulary toolbox presents a promising pathway to replace traditional tokens with members of ontologies and knowledge graphs, enhancing transformer models in specific domains. The concise, accurate, and efficient descriptions of proteins by the Annotation Vocabulary offers a novel way to build numerical representations of proteins for protein annotation and design.
Collapse
Affiliation(s)
- Logan Hallee
- Center for Bioinformatics and Computational Biology, University of Delaware
| | - Niko Rafailidis
- Center for Bioinformatics and Computational Biology, University of Delaware
| | - Colin Horger
- Department of Biomedical Engineering, University of Delaware
| | - David Hong
- Department of Electrical and Computer Engineering, University of Delaware
| | - Jason P Gleghorn
- Center for Bioinformatics and Computational Biology, University of Delaware
- Department of Biomedical Engineering, University of Delaware
| |
Collapse
|
2
|
Sanjeevi M, Mohan A, Ramachandran D, Jeyaraman J, Sekar K. CSSP-2.0: A refined consensus method for accurate protein secondary structure prediction. Comput Biol Chem 2024; 112:108158. [PMID: 39053174 DOI: 10.1016/j.compbiolchem.2024.108158] [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: 12/23/2023] [Revised: 06/19/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024]
Abstract
Studying the relationship between sequences and their corresponding three-dimensional structure assists structural biologists in solving the protein-folding problem. Despite several experimental and in-silico approaches, still understanding or decoding the three-dimensional structures from the sequence remains a mystery. In such cases, the accuracy of the structure prediction plays an indispensable role. To address this issue, an updated web server (CSSP-2.0) has been created to improve the accuracy of our previous version of CSSP by deploying the existing algorithms. It uses input as probabilities and predicts the consensus for the secondary structure as a highly accurate three-state Q3 (helix, strand, and coil). This prediction is achieved using six recent top-performing methods: MUFOLD-SS, RaptorX, PSSpred v4, PSIPRED, JPred v4, and Porter 5.0. CSSP-2.0 validation includes datasets involving various protein classes from the PDB, CullPDB, and AlphaFold databases. Our results indicate a significant improvement in the accuracy of the consensus Q3 prediction. Using CSSP-2.0, crystallographers can sort out the stable regular secondary structures from the entire complex structure, which would aid in inferring the functional annotation of hypothetical proteins. The web server is freely available at https://bioserver3.physics.iisc.ac.in/cgi-bin/cssp-2/.
Collapse
Affiliation(s)
- Madhumathi Sanjeevi
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore 560012, India; Structural Biology and Bio-Computing Laboratory, Department of Bioinformatics, Alagappa University, Karaikudi 630004, India
| | - Ajitha Mohan
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore 560012, India
| | | | - Jeyakanthan Jeyaraman
- Structural Biology and Bio-Computing Laboratory, Department of Bioinformatics, Alagappa University, Karaikudi 630004, India.
| | - Kanagaraj Sekar
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore 560012, India.
| |
Collapse
|
3
|
Cheng P, Mao C, Tang J, Yang S, Cheng Y, Wang W, Gu Q, Han W, Chen H, Li S, Chen Y, Zhou J, Li W, Pan A, Zhao S, Huang X, Zhu S, Zhang J, Shu W, Wang S. Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering. Cell Res 2024:10.1038/s41422-024-00989-2. [PMID: 38969803 DOI: 10.1038/s41422-024-00989-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/03/2024] [Indexed: 07/07/2024] Open
Abstract
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we present Protein Mutational Effect Predictor (ProMEP), a general and multiple sequence alignment-free method that enables zero-shot prediction of mutation effects. A multimodal deep representation learning model embedded in ProMEP was developed to comprehensively learn both sequence and structure contexts from ~160 million proteins. ProMEP achieves state-of-the-art performance in mutational effect prediction and accomplishes a tremendous improvement in speed, enabling efficient and intelligent protein engineering. Specifically, ProMEP accurately forecasts mutational consequences on the gene-editing enzymes TnpB and TadA, and successfully guides the development of high-performance gene-editing tools with their engineered variants. The gene-editing efficiency of a 5-site mutant of TnpB reaches up to 74.04% (vs 24.66% for the wild type); and the base editing tool developed on the basis of a TadA 15-site mutant (in addition to the A106V/D108N double mutation that renders deoxyadenosine deaminase activity to TadA) exhibits an A-to-G conversion frequency of up to 77.27% (vs 69.80% for ABE8e, a previous TadA-based adenine base editor) with significantly reduced bystander and off-target effects compared to ABE8e. ProMEP not only showcases superior performance in predicting mutational effects on proteins but also demonstrates a great capability to guide protein engineering. Therefore, ProMEP enables efficient exploration of the gigantic protein space and facilitates practical design of proteins, thereby advancing studies in biomedicine and synthetic biology.
Collapse
Affiliation(s)
- Peng Cheng
- Bioinformatics Center of AMMS, Beijing, China
| | - Cong Mao
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jin Tang
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Sen Yang
- Bioinformatics Center of AMMS, Beijing, China
| | - Yu Cheng
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wuke Wang
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Qiuxi Gu
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wei Han
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Hao Chen
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sihan Li
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
| | | | | | - Wuju Li
- Bioinformatics Center of AMMS, Beijing, China
| | - Aimin Pan
- Zhejiang Lab, Hangzhou, Zhejiang, China
| | - Suwen Zhao
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xingxu Huang
- Zhejiang Lab, Hangzhou, Zhejiang, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | | | - Jun Zhang
- State Key Laboratory of Reproductive Medicine and Offspring Health, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Wenjie Shu
- Bioinformatics Center of AMMS, Beijing, China.
| | | |
Collapse
|
4
|
Xu S, Onoda A. Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning. J Chem Inf Model 2024; 64:2901-2911. [PMID: 37883249 DOI: 10.1021/acs.jcim.3c01202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the past few decades. Predicting IDPs from the primary structures of proteins offers a rapid and facile means of protein analysis without necessitating crystal structures. In particular, machine learning methods have demonstrated their potential in this field. Recently, protein language models (PLMs) are emerging as a promising approach to extracting essential information from protein sequences and have been employed in protein modeling to utilize their advantages of precision and efficiency. In this article, we developed a novel IDP prediction method named IDP-ELM to predict the intrinsically disordered regions (IDRs) as well as their functions including disordered flexible linkers and disordered protein binding. This method utilizes high-dimensional representations extracted from several state-of-the-art PLMs and predicts IDRs by ensemble learning based on bidirectional recurrent neural networks. The performance of the method was evaluated on two independent test data sets from CAID (critical assessment of protein intrinsic disorder prediction) and CAID2, indicating notable improvements in terms of area under the receiver operating characteristic (AUC), Matthew's correlation coefficient (MCC), and F1 score. Moreover, IDP-ELM requires solely protein sequences as inputs and does not entail a time-consuming process of protein profile generation, which is a prerequisite for most existing state-of-the-art methods, enabling an accurate, fast, and convenient tool for proteome-level analysis. The corresponding reproducible source code and model weights are available at https://github.com/xu-shi-jie/idp-elm.
Collapse
Affiliation(s)
- Shijie Xu
- Graduate School of Environmental Science, Hokkaido University, Sapporo 060-0810, Japan
| | - Akira Onoda
- Graduate School of Environmental Science, Hokkaido University, Sapporo 060-0810, Japan
- Faculty of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan
| |
Collapse
|
5
|
Ektefaie Y, Shen A, Bykova D, Marin M, Zitnik M, Farhat M. Evaluating generalizability of artificial intelligence models for molecular datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581982. [PMID: 38464295 PMCID: PMC10925170 DOI: 10.1101/2024.02.25.581982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Deep learning has made rapid advances in modeling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. Benchmarks traditionally interrogate model generalizability by generating metadata based (MB) or sequence-similarity based (SB) train and test splits of input data before assessing model performance. Here, we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap, i.e., similarity between train and test splits. We introduce Spectra, a spectral framework for comprehensive model evaluation. For a given model and input data, Spectra plots model performance as a function of decreasing cross-split overlap and reports the area under this curve as a measure of generalizability. We apply Spectra to 18 sequencing datasets with associated phenotypes ranging from antibiotic resistance in tuberculosis to protein-ligand binding to evaluate the generalizability of 19 state-of-the-art deep learning models, including large language models, graph neural networks, diffusion models, and convolutional neural networks. We show that SB and MB splits provide an incomplete assessment of model generalizability. With Spectra, we find as cross-split overlap decreases, deep learning models consistently exhibit a reduction in performance in a task- and model-dependent manner. Although no model consistently achieved the highest performance across all tasks, we show that deep learning models can generalize to previously unseen sequences on specific tasks. Spectra paves the way toward a better understanding of how foundation models generalize in biology.
Collapse
Affiliation(s)
- Yasha Ektefaie
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew Shen
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
| | - Daria Bykova
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Maximillian Marin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
| | - Maha Farhat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
6
|
Peng J, Zhao L. The origin and structural evolution of de novo genes in Drosophila. Nat Commun 2024; 15:810. [PMID: 38280868 PMCID: PMC10821953 DOI: 10.1038/s41467-024-45028-1] [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: 02/23/2023] [Accepted: 01/09/2024] [Indexed: 01/29/2024] Open
Abstract
Recent studies reveal that de novo gene origination from previously non-genic sequences is a common mechanism for gene innovation. These young genes provide an opportunity to study the structural and functional origins of proteins. Here, we combine high-quality base-level whole-genome alignments and computational structural modeling to study the origination, evolution, and protein structures of lineage-specific de novo genes. We identify 555 de novo gene candidates in D. melanogaster that originated within the Drosophilinae lineage. Sequence composition, evolutionary rates, and expression patterns indicate possible gradual functional or adaptive shifts with their gene ages. Surprisingly, we find little overall protein structural changes in candidates from the Drosophilinae lineage. We identify several candidates with potentially well-folded protein structures. Ancestral sequence reconstruction analysis reveals that most potentially well-folded candidates are often born well-folded. Single-cell RNA-seq analysis in testis shows that although most de novo gene candidates are enriched in spermatocytes, several young candidates are biased towards the early spermatogenesis stage, indicating potentially important but less emphasized roles of early germline cells in the de novo gene origination in testis. This study provides a systematic overview of the origin, evolution, and protein structural changes of Drosophilinae-specific de novo genes.
Collapse
Affiliation(s)
- Junhui Peng
- Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Li Zhao
- Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY, USA.
| |
Collapse
|
7
|
Zhou Y, Litfin T, Zhan J. 3 = 1 + 2: how the divide conquered de novo protein structure prediction and what is next? Natl Sci Rev 2023; 10:nwad259. [PMID: 38033736 PMCID: PMC10684263 DOI: 10.1093/nsr/nwad259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 09/18/2023] [Indexed: 12/02/2023] Open
Affiliation(s)
- Yaoqi Zhou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, China
- Institute for Glycomics, Griffith University, Australia
| | - Thomas Litfin
- Institute for Glycomics, Griffith University, Australia
| | - Jian Zhan
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, China
| |
Collapse
|
8
|
Wang YW, McKeon MC, Elmore H, Hess J, Golan J, Gage H, Mao W, Harrow L, Gonçalves SC, Hull CM, Pringle A. Invasive Californian death caps develop mushrooms unisexually and bisexually. Nat Commun 2023; 14:6560. [PMID: 37875491 PMCID: PMC10598064 DOI: 10.1038/s41467-023-42317-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 10/05/2023] [Indexed: 10/26/2023] Open
Abstract
Canonical sexual reproduction among basidiomycete fungi involves the fusion of two haploid individuals of different mating types, resulting in a heterokaryotic mycelial body made up of genetically different nuclei. Using population genomics data and experiments, we discover mushrooms of the invasive and deadly Amanita phalloides can also be homokaryotic; evidence of sexual reproduction by single, unmated individuals. In California, genotypes of homokaryotic mushrooms are also found in heterokaryotic mushrooms, implying nuclei of homokaryotic mycelia are also involved in outcrossing. We find death cap mating is controlled by a single mating type locus, but the development of homokaryotic mushrooms appears to bypass mating type gene control. Ultimately, sporulation is enabled by nuclei able to reproduce alone as well as with others, and nuclei competent for both unisexuality and bisexuality have persisted in invaded habitats for at least 17 but potentially as long as 30 years. The diverse reproductive strategies of invasive death caps are likely facilitating its rapid spread, suggesting a profound similarity between plant, animal and fungal invasions.
Collapse
Affiliation(s)
- Yen-Wen Wang
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA.
| | - Megan C McKeon
- Department of Genetics, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Jacob Golan
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA
| | - Hunter Gage
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - William Mao
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA
| | - Lynn Harrow
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Susana C Gonçalves
- Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, Coimbra, Portugal
| | - Christina M Hull
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medical Microbiology & Immunology, University of Wisconsin-Madison, Madison, WI, USA
| | - Anne Pringle
- Department of Botany, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| |
Collapse
|
9
|
Wang YW, McKeon MC, Elmore H, Hess J, Golan J, Gage H, Mao W, Harrow L, Gon ßalves SC, Hull CM, Pringle A. Invasive Californian death caps develop mushrooms unisexually and bisexually. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.525609. [PMID: 36778337 PMCID: PMC9915504 DOI: 10.1101/2023.01.30.525609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Canonical sexual reproduction among basidiomycete fungi involves the fusion of two haploid individuals of different sexes, resulting in a heterokaryotic mycelial body made up of genetically different nuclei 1 . Using population genomics data, we discovered mushrooms of the deadly invasive Amanita phalloides are also homokaryotic, evidence of sexual reproduction by single individuals. In California, genotypes of homokaryotic mushrooms are also found in heterokaryotic mushrooms, implying nuclei of homokaryotic mycelia also promote outcrossing. We discovered death cap mating is controlled by a single mating-type locus ( A. phalloides is bipolar), but the development of homokaryotic mushrooms appears to bypass mating-type gene control. Ultimately, sporulation is enabled by nuclei able to reproduce alone as well as with others, and nuclei competent for both unisexuality and bisexuality have persisted in invaded habitats for at least 17 but potentially as long as 30 years. The diverse reproductive strategies of invasive death caps are likely facilitating its rapid spread, revealing a profound similarity between plant, animal and fungal invasions 2,3 .
Collapse
|
10
|
Li S, Yuan L, Ma Y, Liu Y. WG-ICRN: Protein 8-state secondary structure prediction based on Wasserstein generative adversarial networks and residual networks with Inception modules. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:7721-7737. [PMID: 37161169 DOI: 10.3934/mbe.2023333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Protein secondary structure is the basis of studying the tertiary structure of proteins, drug design and development, and the 8-state protein secondary structure can provide more adequate protein information than the 3-state structure. Therefore, this paper proposes a novel method WG-ICRN for predicting protein 8-state secondary structures. First, we use the Wasserstein generative adversarial network (WGAN) to extract protein features in the position-specific scoring matrix (PSSM). The extracted features are combined with PSSM into a new feature set of WG-data, which contains richer feature information. Then, we use the residual network (ICRN) with Inception to further extract the features in WG-data and complete the prediction. Compared with the residual network, ICRN can reduce parameter calculations and increase the width of feature extraction to obtain more feature information. We evaluated the prediction performance of the model using six datasets. The experimental results show that the WGAN has excellent feature extraction capabilities, and ICRN can further improve network performance and improve prediction accuracy. Compared with four popular models, WG-ICRN achieves better prediction performance.
Collapse
Affiliation(s)
- Shun Li
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Lu Yuan
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Yuming Ma
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Yihui Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| |
Collapse
|
11
|
Yuan L, Ma Y, Liu Y. Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory. Front Bioeng Biotechnol 2023; 11:1051268. [PMID: 36860882 PMCID: PMC9968878 DOI: 10.3389/fbioe.2023.1051268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/03/2023] [Indexed: 02/16/2023] Open
Abstract
Protein secondary structure prediction (PSSP) is a challenging task in computational biology. However, existing models with deep architectures are not sufficient and comprehensive for deep long-range feature extraction of long sequences. This paper proposes a novel deep learning model to improve Protein secondary structure prediction. In the model, our proposed bidirectional temporal convolutional network (BTCN) can extract the bidirectional deep local dependencies in protein sequences segmented by the sliding window technique, the bidirectional long short-term memory (BLSTM) network can extract the global interactions between residues, and our proposed multi-scale bidirectional temporal convolutional network (MSBTCN) can further capture the bidirectional multi-scale long-range features of residues while preserving the hidden layer information more comprehensively. In particular, we also propose that fusing the features of 3-state and 8-state Protein secondary structure prediction can further improve the prediction accuracy. Moreover, we also propose and compare multiple novel deep models by combining bidirectional long short-term memory with temporal convolutional network (TCN), reverse temporal convolutional network (RTCN), multi-scale temporal convolutional network (multi-scale bidirectional temporal convolutional network), bidirectional temporal convolutional network and multi-scale bidirectional temporal convolutional network, respectively. Furthermore, we demonstrate that the reverse prediction of secondary structure outperforms the forward prediction, suggesting that amino acids at later positions have a greater impact on secondary structure recognition. Experimental results on benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14, and CB513 show that our methods achieve better prediction performance compared to five state-of-the-art methods.
Collapse
Affiliation(s)
- Lu Yuan
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yuming Ma
- *Correspondence: Yuming Ma, ; Yihui Liu,
| | - Yihui Liu
- *Correspondence: Yuming Ma, ; Yihui Liu,
| |
Collapse
|
12
|
Yuan L, Ma Y, Liu Y. Protein secondary structure prediction based on Wasserstein generative adversarial networks and temporal convolutional networks with convolutional block attention modules. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:2203-2218. [PMID: 36899529 DOI: 10.3934/mbe.2023102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
As an important task in bioinformatics, protein secondary structure prediction (PSSP) is not only beneficial to protein function research and tertiary structure prediction, but also to promote the design and development of new drugs. However, current PSSP methods cannot sufficiently extract effective features. In this study, we propose a novel deep learning model WGACSTCN, which combines Wasserstein generative adversarial network with gradient penalty (WGAN-GP), convolutional block attention module (CBAM) and temporal convolutional network (TCN) for 3-state and 8-state PSSP. In the proposed model, the mutual game of generator and discriminator in WGAN-GP module can effectively extract protein features, and our CBAM-TCN local extraction module can capture key deep local interactions in protein sequences segmented by sliding window technique, and the CBAM-TCN long-range extraction module can further capture the key deep long-range interactions in sequences. We evaluate the performance of the proposed model on seven benchmark datasets. Experimental results show that our model exhibits better prediction performance compared to the four state-of-the-art models. The proposed model has strong feature extraction ability, which can extract important information more comprehensively.
Collapse
Affiliation(s)
- Lu Yuan
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Yuming Ma
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Yihui Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| |
Collapse
|
13
|
Yuan L, Hu X, Ma Y, Liu Y. DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system. RSC Adv 2022; 12:33479-33487. [PMID: 36505696 PMCID: PMC9682407 DOI: 10.1039/d2ra06433b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Protein secondary structure prediction (PSSP) is not only beneficial to the study of protein structure and function but also to the development of drugs. As a challenging task in computational biology, experimental methods for PSSP are time-consuming and expensive. In this paper, we propose a novel PSSP model DLBLS_SS based on deep learning and broad learning system (BLS) to predict 3-state and 8-state secondary structure. We first use a bidirectional long short-term memory (BLSTM) network to extract global features in residue sequences. Then, our proposed SEBTCN based on temporal convolutional networks (TCN) and channel attention can capture bidirectional key long-range dependencies in sequences. We also use BLS to rapidly optimize fused features while further capturing local interactions between residues. We conduct extensive experiments on public test sets including CASP10, CASP11, CASP12, CASP13, CASP14 and CB513 to evaluate the performance of the model. Experimental results show that our model exhibits better 3-state and 8-state PSSP performance compared to five state-of-the-art models.
Collapse
Affiliation(s)
- Lu Yuan
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) Jinan 250353 China
| | - Xiaopei Hu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) Jinan 250353 China
| | - Yuming Ma
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) Jinan 250353 China
| | - Yihui Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences) Jinan 250353 China
| |
Collapse
|
14
|
Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Wang Y, Jones L, Gibbs T, Feher T, Angerer C, Steinegger M, Bhowmik D, Rost B. ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022. [PMID: 34232869 DOI: 10.1101/2020.07.12.199554] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life. All our models are available through https://github.com/agemagician/ProtTrans.
Collapse
|
15
|
Nacar C. Propensities of Some Amino Acid Pairings in α-Helices Vary with Length. Protein J 2022; 41:551-562. [PMID: 36169766 DOI: 10.1007/s10930-022-10076-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/15/2022] [Indexed: 11/29/2022]
Abstract
The results of secondary structure prediction methods are widely used in applications in biotechnology and bioinformatics. However, the accuracy limit of these methods could be improved up to 92%. One approach to achieve this goal is to harvest information from the primary structure of the peptide. This study aims to contribute to this goal by investigating the variations in propensity of amino acid pairings to α-helices in globular proteins depending on helix length. (n):(n + 4) residue pairings were determined using a comprehensive peptide data set according to backbone hydrogen bond criterion which states that backbone hydrogen bond is the dominant driving force of protein folding. Helix length is limited to 13 to 26 residues. Findings of this study show that propensities of ALA:GLY and GLY:GLU pairings to α-helix in globular protein increase with increasing helix length but of ALA:ALA and ALA:VAL decrease. While the frequencies of ILE:ALA, LEU:ALA, LEU:GLN, LEU:GLU, LEU:LEU, MET:ILE and VAL:LEU pairings remain roughly constant with length, the 25 residue pairings have varying propensities in narrow helix lengths. The remaining pairings have no prominent propensity to α-helices.
Collapse
Affiliation(s)
- Cevdet Nacar
- Department of Biophysics, School of Medicine, Marmara University, Istanbul, Turkey.
| |
Collapse
|
16
|
Capel H, Weiler R, Dijkstra M, Vleugels R, Bloem P, Feenstra KA. ProteinGLUE multi-task benchmark suite for self-supervised protein modeling. Sci Rep 2022; 12:16047. [PMID: 36163232 PMCID: PMC9512797 DOI: 10.1038/s41598-022-19608-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
Self-supervised language modeling is a rapidly developing approach for the analysis of protein sequence data. However, work in this area is heterogeneous and diverse, making comparison of models and methods difficult. Moreover, models are often evaluated only on one or two downstream tasks, making it unclear whether the models capture generally useful properties. We introduce the ProteinGLUE benchmark for the evaluation of protein representations: a set of seven per-amino-acid tasks for evaluating learned protein representations. We also offer reference code, and we provide two baseline models with hyperparameters specifically trained for these benchmarks. Pre-training was done on two tasks, masked symbol prediction and next sentence prediction. We show that pre-training yields higher performance on a variety of downstream tasks such as secondary structure and protein interaction interface prediction, compared to no pre-training. However, the larger base model does not outperform the smaller medium model. We expect the ProteinGLUE benchmark dataset introduced here, together with the two baseline pre-trained models and their performance evaluations, to be of great value to the field of protein sequence-based property prediction. Availability: code and datasets from https://github.com/ibivu/protein-glue .
Collapse
Affiliation(s)
- Henriette Capel
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Robin Weiler
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Maurits Dijkstra
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Reinier Vleugels
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - Peter Bloem
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands
| | - K Anton Feenstra
- Informatics Institute, Vrije Universiteit, 1081 HV, Amsterdam, The Netherlands.
| |
Collapse
|
17
|
Geffen Y, Ofran Y, Unger R. DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts. Bioinformatics 2022; 38:ii95-ii98. [PMID: 36124789 DOI: 10.1093/bioinformatics/btac474] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
SUMMARY Recently, deep learning models, initially developed in the field of natural language processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. Recently, 'distilled' models using the concept of student and teacher networks have been widely used in NLP. Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model. Implementing this approach, we reduced the size of the network and the running time by 50%, and the computational resources needed for pretraining by 98% relative to ProtBert model. Using two published tasks, we showed that the performance of the distilled model approaches that of the full model. We next tested the ability of DistilProtBert to distinguish between real and random protein sequences. The task is highly challenging if the composition is maintained on the level of singlet, doublet and triplet amino acids. Indeed, traditional machine-learning algorithms have difficulties with this task. Here, we show that DistilProtBert preforms very well on singlet, doublet and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91 and 0.87, respectively. Finally, we suggest that by examining the small number of false-positive classifications (i.e. shuffled sequences classified as proteins by DistilProtBert), we may be able to identify de novo potential natural-like proteins based on random shuffling of amino acid sequences. AVAILABILITY AND IMPLEMENTATION https://github.com/yarongef/DistilProtBert.
Collapse
Affiliation(s)
- Yaron Geffen
- The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 5290002, Israel
| | - Yanay Ofran
- The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 5290002, Israel
| | - Ron Unger
- The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 5290002, Israel
| |
Collapse
|
18
|
A multifaceted strategy to improve recombinant expression and structural characterisation of a Trypanosoma invariant surface protein. Sci Rep 2022; 12:12706. [PMID: 35882923 PMCID: PMC9325691 DOI: 10.1038/s41598-022-16958-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 07/19/2022] [Indexed: 11/16/2022] Open
Abstract
Identification of a protein minimal fragment amenable to crystallisation can be time- and labour intensive especially if large amounts are required and the protein has a complex fold and functionally important post-translational modifications. In addition, a lack of homologues and structural information can further complicate the design of a minimal expression construct. Recombinant expression in E. coli promises high yields, low costs and fast turnover times, but falls short for many extracellular, eukaryotic proteins. Eukaryotic expression systems provide an alternative but are costly, slow and require special handling and equipment. Using a member of a structurally uncharacterized, eukaryotic receptor family as an example we employ hydrogen–deuterium exchange mass spectrometry (HDX-MS) guided construct design in conjunction with truncation scanning and targeted expression host switching to identify a minimal expression construct that can be produced with high yields and moderate costs.
Collapse
|
19
|
Jin X, Guo L, Jiang Q, Wu N, Yao S. Prediction of protein secondary structure based on an improved channel attention and multiscale convolution module. Front Bioeng Biotechnol 2022; 10:901018. [PMID: 35935483 PMCID: PMC9355137 DOI: 10.3389/fbioe.2022.901018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Prediction of the protein secondary structure is a key issue in protein science. Protein secondary structure prediction (PSSP) aims to construct a function that can map the amino acid sequence into the secondary structure so that the protein secondary structure can be obtained according to the amino acid sequence. Driven by deep learning, the prediction accuracy of the protein secondary structure has been greatly improved in recent years. To explore a new technique of PSSP, this study introduces the concept of an adversarial game into the prediction of the secondary structure, and a conditional generative adversarial network (GAN)-based prediction model is proposed. We introduce a new multiscale convolution module and an improved channel attention (ICA) module into the generator to generate the secondary structure, and then a discriminator is designed to conflict with the generator to learn the complicated features of proteins. Then, we propose a PSSP method based on the proposed multiscale convolution module and ICA module. The experimental results indicate that the conditional GAN-based protein secondary structure prediction (CGAN-PSSP) model is workable and worthy of further study because of the strong feature-learning ability of adversarial learning.
Collapse
Affiliation(s)
- Xin Jin
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
| | - Lin Guo
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
| | - Qian Jiang
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
| | - Nan Wu
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
| | - Shaowen Yao
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
- *Correspondence: Shaowen Yao,
| |
Collapse
|
20
|
Rahbar MR, Mubarak SMH, Hessami A, Khalesi B, Pourzardosht N, Khalili S, Zanoos KA, Jahangiri A. A unique antigen against SARS-CoV-2, Acinetobacter baumannii, and Pseudomonas aeruginosa. Sci Rep 2022; 12:10852. [PMID: 35760825 PMCID: PMC9237110 DOI: 10.1038/s41598-022-14877-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/14/2022] [Indexed: 02/07/2023] Open
Abstract
The recent outbreak of COVID-19 has increased hospital admissions, which could elevate the risk of nosocomial infections, such as A. baumannii and P. aeruginosa infections. Although effective vaccines have been developed against SARS-CoV-2, no approved treatment option is still available against antimicrobial-resistant strains of A. baumannii and P. aeruginosa. In the current study, an all-in-one antigen was designed based on an innovative, state-of-the-art strategy. In this regard, experimentally validated linear epitopes of spike protein (SARS-CoV-2), OmpA (A. baumannii), and OprF (P. aeruginosa) were selected to be harbored by mature OmpA as a scaffold. The selected epitopes were used to replace the loops and turns of the barrel domain in OmpA; OprF311–341 replaced the most similar sequence within the OmpA, and three validated epitopes of OmpA were retained intact. The obtained antigen encompasses five antigenic peptides of spike protein, which are involved in SARS-CoV-2 pathogenicity. One of these epitopes, viz. QTQTNSPRRARSV could trigger antibodies preventing super-antigenic characteristics of spike and alleviating probable autoimmune responses. The designed antigen could raise antibodies neutralizing emerging variants of SARS-CoV-2 since at least two epitopes are consensus. In conclusion, the designed antigen is expected to raise protective antibodies against SARS-CoV-2, A. baumannii, and P. aeruginosa.
Collapse
Affiliation(s)
- Mohammad Reza Rahbar
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shaden M H Mubarak
- Department of Clinical Laboratory Science, Faculty of Pharmacy, University of Kufa, Najaf, Iraq
| | - Anahita Hessami
- School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Bahman Khalesi
- Department of Research and Production of Poultry Viral Vaccine, Razi Vaccine and Serum Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Navid Pourzardosht
- Biochemistry Department, Guilan University of Medical Sciences, Rasht, Iran
| | - Saeed Khalili
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
| | - Kobra Ahmadi Zanoos
- Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Abolfazl Jahangiri
- Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Vanak Sq. Molasadra St., P.O. Box 1435915371, Tehran, Iran.
| |
Collapse
|
21
|
Guo Y, Wu J, Ma H, Wang S, Huang J. Deep Ensemble Learning with Atrous Spatial Pyramid Networks for Protein Secondary Structure Prediction. Biomolecules 2022; 12:biom12060774. [PMID: 35740899 PMCID: PMC9221033 DOI: 10.3390/biom12060774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 02/04/2023] Open
Abstract
The secondary structure of proteins is significant for studying the three-dimensional structure and functions of proteins. Several models from image understanding and natural language modeling have been successfully adapted in the protein sequence study area, such as Long Short-term Memory (LSTM) network and Convolutional Neural Network (CNN). Recently, Gated Convolutional Neural Network (GCNN) has been proposed for natural language processing. It has achieved high levels of sentence scoring, as well as reduced the latency. Conditionally Parameterized Convolution (CondConv) is another novel study which has gained great success in the image processing area. Compared with vanilla CNN, CondConv uses extra sample-dependant modules to conditionally adjust the convolutional network. In this paper, we propose a novel Conditionally Parameterized Convolutional network (CondGCNN) which utilizes the power of both CondConv and GCNN. CondGCNN leverages an ensemble encoder to combine the capabilities of both LSTM and CondGCNN to encode protein sequences by better capturing protein sequential features. In addition, we explore the similarity between the secondary structure prediction problem and the image segmentation problem, and propose an ASP network (Atrous Spatial Pyramid Pooling (ASPP) based network) to capture fine boundary details in secondary structure. Extensive experiments show that the proposed method can achieve higher performance on protein secondary structure prediction task than existing methods on CB513, Casp11, CASP12, CASP13, and CASP14 datasets. We also conducted ablation studies over each component to verify the effectiveness. Our method is expected to be useful for any protein related prediction tasks, which is not limited to protein secondary structure prediction.
Collapse
Affiliation(s)
- Yuzhi Guo
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA; (Y.G.); (H.M.); (S.W.)
| | | | - Hehuan Ma
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA; (Y.G.); (H.M.); (S.W.)
| | - Sheng Wang
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA; (Y.G.); (H.M.); (S.W.)
| | - Junzhou Huang
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA; (Y.G.); (H.M.); (S.W.)
- Correspondence:
| |
Collapse
|
22
|
Chiu SH, Ho WL, Sun YC, Kuo JC, Huang JR. Phase separation driven by interchangeable properties in the intrinsically disordered regions of protein paralogs. Commun Biol 2022; 5:400. [PMID: 35487971 PMCID: PMC9054762 DOI: 10.1038/s42003-022-03354-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/10/2022] [Indexed: 11/09/2022] Open
Abstract
Paralogs, arising from gene duplications, increase the functional diversity of proteins. Protein functions in paralog families have been extensively studied, but little is known about the roles that intrinsically disordered regions (IDRs) play in their paralogs. Without a folded structure to restrain them, IDRs mutate more diversely along with evolution. However, how the diversity of IDRs in a paralog family affects their functions is unexplored. Using the RNA-binding protein Musashi family as an example, we applied multiple structural techniques and phylogenetic analysis to show how members in a paralog family have evolved their IDRs to different physicochemical properties but converge to the same function. In this example, the lower prion-like tendency of Musashi-1's IDRs, rather than Musashi-2's, is compensated by its higher α-helical propensity to assist their assembly. Our work suggests that, no matter how diverse they become, IDRs could evolve different traits to a converged function, such as liquid-liquid phase separation.
Collapse
Affiliation(s)
- Shih-Hui Chiu
- Institute of Biochemistry and Molecular Biology, National Yang Ming Chiao Tung University, No. 155 Section 2, Li-nong Street, Taipei, Taiwan
| | - Wen-Lin Ho
- Institute of Biochemistry and Molecular Biology, National Yang Ming Chiao Tung University, No. 155 Section 2, Li-nong Street, Taipei, Taiwan
| | - Yung-Chen Sun
- Institute of Biochemistry and Molecular Biology, National Yang Ming Chiao Tung University, No. 155 Section 2, Li-nong Street, Taipei, Taiwan
| | - Jean-Cheng Kuo
- Institute of Biochemistry and Molecular Biology, National Yang Ming Chiao Tung University, No. 155 Section 2, Li-nong Street, Taipei, Taiwan
| | - Jie-Rong Huang
- Institute of Biochemistry and Molecular Biology, National Yang Ming Chiao Tung University, No. 155 Section 2, Li-nong Street, Taipei, Taiwan. .,Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, No. 155 Section 2, Li-nong Street, Taipei, Taiwan. .,Department of Life Sciences and Institute of Genome Sciences, National Yang Ming Chiao Tung University, No. 155 Section 2, Li-nong Street, Taipei, Taiwan.
| |
Collapse
|
23
|
Stapor K, Kotowski K, Smolarczyk T, Roterman I. Lightweight ProteinUnet2 network for protein secondary structure prediction: a step towards proper evaluation. BMC Bioinformatics 2022; 23:100. [PMID: 35317722 PMCID: PMC8939211 DOI: 10.1186/s12859-022-04623-z] [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: 09/13/2021] [Accepted: 02/28/2022] [Indexed: 11/10/2022] Open
Abstract
Background The prediction of protein secondary structures is a crucial and significant step for ab initio tertiary structure prediction which delivers the information about proteins activity and functions. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. Currently, most of the top methods use evolutionary-based input features produced by PSSM and HHblits software, although quite recently the embeddings—the new description of protein sequences generated by language models (LM) have appeared that could be leveraged as input features. Apart from input features calculation, the top models usually need extensive computational resources for training and prediction and are barely possible to run on a regular PC. SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Moreover, as the benchmark datasets are not random samples, the classical statistical null hypothesis testing based on the Neyman–Pearson approach is not appropriate. Results We present a lightweight deep network ProteinUnet2 for SS prediction which is based on U-Net convolutional architecture and evolutionary-based input features (from PSSM and HHblits) as well as SPOT-Contact features. Through an extensive evaluation study, we report the performance of ProteinUnet2 in comparison with top SS prediction methods based on evolutionary information (SAINT and SPOT-1D). We also propose a new statistical methodology for prediction performance assessment based on the significance from Fisher–Pitman permutation tests accompanied by practical significance measured by Cohen’s effect size. Conclusions Our results suggest that ProteinUnet2 architecture has much shorter training and inference times while maintaining results similar to SAINT and SPOT-1D predictors. Taking into account the relatively long times of calculating evolutionary-based features (from PSSM in particular), it would be worth conducting the predictive ability tests on embeddings as input features in the future. We strongly believe that our proposed here statistical methodology for the evaluation of SS prediction results will be adopted and used (and even expanded) by the research community. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04623-z.
Collapse
Affiliation(s)
- Katarzyna Stapor
- Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland.
| | - Krzysztof Kotowski
- Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
| | - Tomasz Smolarczyk
- Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Medyczna 7, 30-688, Kraków, Poland
| |
Collapse
|
24
|
Abstract
Membrane proteins, particularly those that are α-helical, such as transporters and G-protein-coupled receptors (GPCRs), have significant biological relevance. However, their expression and purification pose difficulties because of their poor water solubilities, which impedes progress in this field. The QTY method, a code-based protein-engineering approach, was recently developed to produce soluble transmembrane proteins. Here, we describe a comprehensive Web server built for QTY design and its relevance for in silico analyses. Typically, the simple design model is expected to require only 2 to 4 min of computer time, and the library design model requires 2 to 5 h, depending on the target protein size and the number of transmembrane helices. Detailed protocols for using the server with both the simple design and library design modules are provided. Methods for experiments following the QTY design are also included to facilitate the implementation of this approach. The design pipeline was further evaluated using microbial transmembrane proteins and structural alignment between the designed proteins and their origins by employing AlphaFold2. The results reveal that mutants generated by the developed pipeline were highly identical to their origins in terms of three-dimensional (3D) structures. In summary, the utilization of our Web server and associated protocols will enable QTY-based protein engineering to be implemented in a convenient, fast, accurate, and rational manner. The Protein Solubilizing Server (PSS) is publicly available at http://pss.sjtu.edu.cn. IMPORTANCE Water-soluble expression and purification are of considerable importance for protein identification and characterization. However, there has been a lack of an effective method for water-soluble expression of membrane proteins, which has severely hampered their studies. Here, an enabling comprehensive Web server, PSS, was developed for designing water-soluble mutants of α-helical membrane proteins, based on QTY design, a code-based protein-engineering approach. With microbial transmembrane proteins and GPCRs as examples, we systematically evaluated the server and demonstrated its successful performance. PSS is readily available for worldwide users as a Web-based tool, rendering QTY-based protein engineering convenient, efficient, accurate, and rational.
Collapse
|
25
|
Maleki A, Russo G, Parasiliti Palumbo GA, Pappalardo F. In silico design of recombinant multi-epitope vaccine against influenza A virus. BMC Bioinformatics 2022; 22:617. [PMID: 35109785 PMCID: PMC8808469 DOI: 10.1186/s12859-022-04581-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 01/20/2022] [Indexed: 11/10/2022] Open
Abstract
Background Influenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine and has eradicated numerous infectious diseases. Recently, multi-epitope vaccines, which are based on the selection of epitopes, have been increasingly investigated.
Results This study utilized an immunoinformatic approach to design a recombinant multi-epitope vaccine based on a highly conserved epitope of hemagglutinin, neuraminidase, and membrane matrix proteins with fewer changes or mutate over time. The potential B cells, cytotoxic T lymphocytes (CTL), and CD4 T cell epitopes were identified. The recombinant multi-epitope vaccine was designed using specific linkers and a proper adjuvant. Moreover, some bioinformatics online servers and datasets were used to evaluate the immunogenicity and chemical properties of selected epitopes. In addition, Universal Immune System Simulator (UISS) in silico trial computational framework was run after influenza exposure and recombinant multi-epitope vaccine administration, showing a good immune response in terms of immunoglobulins of class G (IgG), T Helper 1 cells (TH1), epithelial cells (EP) and interferon gamma (IFN-g) levels. Furthermore, after a reverse translation (i.e., convertion of amino acid sequence to nucleotide one) and codon optimization phase, the optimized sequence was placed between the two EcoRV/MscI restriction sites in the PET32a+ vector. Conclusions The proposed “Recombinant multi-epitope vaccine” was predicted with unique and acceptable immunological properties. This recombinant multi-epitope vaccine can be successfully expressed in the prokaryotic system and accepted for immunogenicity studies against the influenza virus at the in silico level. The multi-epitope vaccine was then tested with the Universal Immune System Simulator (UISS) in silico trial platform. It revealed slight immune protection against the influenza virus, shedding the light that a multistep bioinformatics approach including molecular and cellular level is mandatory to avoid inappropriate vaccine efficacy predictions. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04581-6.
Collapse
Affiliation(s)
- Avisa Maleki
- Department of Mathematics and Computer Science, University of Catania, 95125, Catania, Italy
| | - Giulia Russo
- Department of Drug and Health Sciences, University of Catania, 95125, Catania, Italy
| | | | - Francesco Pappalardo
- Department of Drug and Health Sciences, University of Catania, 95125, Catania, Italy.
| |
Collapse
|
26
|
Abstract
Protein secondary structure prediction is an important topic in bioinformatics. This paper proposed a novel model named WS-BiLSTM, which combined the wavelet scattering convolutional network and the long-short-term memory network for the first time to predict protein secondary structure. This model captures nonlocal interactions between amino acid sequences and remembers long-range interactions between amino acids. In our WS-BiLSTM model, the wavelet scattering convolutional network is used to extract protein features from the PSSM sliding window; the extracted features are combined with the original PSSM data as the input features of the long-short-term memory network to predict protein secondary structure. It is worth noting that the wavelet scattering convolutional network is asymmetric as a member of the continuous wavelet family. The Q3 accuracy on the test set CASP9, CASP10, CASP11, CASP12, CB513, and PDB25 reached 85.26%, 85.84%, 84.91%, 85.13%, 86.10%, and 85.52%, which were higher 2.15%, 2.16%, 3.5%, 3.19%, 4.22%, and 2.75%, respectively, than using the long-short-term memory network alone. Comparing our results with the state-of-art methods shows that our proposed model achieved better results on the CB513 and CASP12 data sets. The experimental results show that the features extracted from the wavelet scattering convolutional network can effectively improve the accuracy of protein secondary structure prediction.
Collapse
|
27
|
Kondo R, Kasahara K, Takahashi T. Information quantity for secondary structure propensities of protein subsequences in the Protein Data Bank. Biophys Physicobiol 2022; 19:1-12. [PMID: 35532457 PMCID: PMC8926306 DOI: 10.2142/biophysico.bppb-v19.0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/02/2022] [Indexed: 12/05/2022] Open
Abstract
Elucidating the principles of sequence–structure relationships of proteins is a long-standing issue in biology. The nature of a short segment of a protein is determined by both the subsequence of the segment itself and its environment. For example, a type of subsequence, the so-called chameleon sequences, can form different secondary structures depending on its environments. Chameleon sequences are considered to have a weak tendency to form a specific structure. Although many chameleon sequences have been identified, they are only a small part of all possible subsequences in the proteome. The strength of the tendency to take a specific structure for each subsequence has not been fully quantified. In this study, we comprehensively analyzed subsequences consisting of four to nine amino acid residues, or N-gram (4≤N≤9), observed in non-redundant sequences in the Protein Data Bank (PDB). Tendencies to form a specific structure in terms of the secondary structure and accessible surface area are quantified as information quantities for each N-gram. Although the majority of observed subsequences have low information quantity due to lack of samples in the current PDB, thousands of N-grams with strong tendencies, including known structural motifs, were found. In addition, machine learning partially predicted the tendency of unknown N-grams, and thus, this technique helps to extract knowledge from the limited number of samples in the PDB.
Collapse
Affiliation(s)
- Ryohei Kondo
- Graduate School of Life Sciences, Ritsumeikan University
| | | | | |
Collapse
|
28
|
Ho CT, Huang YW, Chen TR, Lo CH, Lo WC. Discovering the Ultimate Limits of Protein Secondary Structure Prediction. Biomolecules 2021; 11:1627. [PMID: 34827624 PMCID: PMC8615938 DOI: 10.3390/biom11111627] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/25/2021] [Accepted: 10/28/2021] [Indexed: 12/29/2022] Open
Abstract
Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy. In the first 60 years, the accuracy of three-state SSP rose from ~56% to 81%; after that, it has long stayed at 81-86%. In the 1990s, the theoretical limit of three-state SSP accuracy had been estimated to be 88%. Thus, SSP is now generally considered not challenging or too challenging to improve. However, we found that the limit of three-state SSP might be underestimated. Besides, there is still much room for improving segment-based and eight-state SSPs, but the limits of these emerging topics have not been determined. This work performs large-scale sequence and structural analyses to estimate SSP accuracy limits and assess state-of-the-art SSP methods. The limit of three-state SSP is re-estimated to be ~92%, 4-5% higher than previously expected, indicating that SSP is still challenging. The estimated limit of eight-state SSP is 84-87%. Several proposals for improving future SSP algorithms are made based on our results. We hope that these findings will help move forward the development of SSP and all its applications.
Collapse
Affiliation(s)
- Chia-Tzu Ho
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (C.-T.H.); (Y.-W.H.); (T.-R.C.); (C.-H.L.)
| | - Yu-Wei Huang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (C.-T.H.); (Y.-W.H.); (T.-R.C.); (C.-H.L.)
| | - Teng-Ruei Chen
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (C.-T.H.); (Y.-W.H.); (T.-R.C.); (C.-H.L.)
| | - Chia-Hua Lo
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (C.-T.H.); (Y.-W.H.); (T.-R.C.); (C.-H.L.)
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Wei-Cheng Lo
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; (C.-T.H.); (Y.-W.H.); (T.-R.C.); (C.-H.L.)
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
- The Center for Bioinformatics Research, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| |
Collapse
|
29
|
Song M, Lim JM, Min S, Oh JS, Kim DY, Woo JS, Nishimasu H, Cho SR, Yoon S, Kim HH. Generation of a more efficient prime editor 2 by addition of the Rad51 DNA-binding domain. Nat Commun 2021; 12:5617. [PMID: 34556671 PMCID: PMC8460726 DOI: 10.1038/s41467-021-25928-2] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/07/2021] [Indexed: 12/26/2022] Open
Abstract
Although prime editing is a promising genome editing method, the efficiency of prime editor 2 (PE2) is often insufficient. Here we generate a more efficient variant of PE2, named hyPE2, by adding the Rad51 DNA-binding domain. When tested at endogenous sites, hyPE2 shows a median of 1.5- or 1.4- fold (range, 0.99- to 2.6-fold) higher efficiencies than PE2; furthermore, at sites where PE2-induced prime editing is very inefficient (efficiency < 1%), hyPE2 enables prime editing with efficiencies ranging from 1.1% to 2.9% at up to 34% of target sequences, potentially facilitating prime editing applications. While prime editing is a promising technology, PE2 systems often have low efficiency. Here the authors fuse a Rad51 DNA-binding domain to create hyPE2 with improved editing efficiency.
Collapse
Affiliation(s)
- Myungjae Song
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.,Graduate School of Medical Science, Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jung Min Lim
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seonwoo Min
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jeong-Seok Oh
- Department of Life Sciences, Korea University, Seoul, Republic of Korea
| | - Dong Young Kim
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.,Graduate School of Medical Science, Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae-Sung Woo
- Department of Life Sciences, Korea University, Seoul, Republic of Korea
| | - Hiroshi Nishimasu
- Structural Biology Division, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Sung-Rae Cho
- Graduate School of Medical Science, Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea.,Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.,Graduate Program of Nano Science and Technology, Yonsei University, Seoul, Republic of Korea.,Rehabilitation Institute of Neuromuscular Disease, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sungroh Yoon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.,Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea
| | - Hyongbum Henry Kim
- Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea. .,Graduate School of Medical Science, Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea. .,Graduate Program of Nano Science and Technology, Yonsei University, Seoul, Republic of Korea. .,Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea. .,Graduate Program of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Yonsei University, Seoul, Republic of Korea. .,Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
| |
Collapse
|
30
|
Chen TR, Juan SH, Huang YW, Lin YC, Lo WC. A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction. PLoS One 2021; 16:e0255076. [PMID: 34320027 PMCID: PMC8318245 DOI: 10.1371/journal.pone.0255076] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 07/11/2021] [Indexed: 11/18/2022] Open
Abstract
Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.
Collapse
Affiliation(s)
- Teng-Ruei Chen
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Sheng-Hung Juan
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Wei Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yen-Cheng Lin
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Wei-Cheng Lo
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- The Center for Bioinformatics Research, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- * E-mail:
| |
Collapse
|
31
|
Goodswen SJ, Kennedy PJ, Ellis JT. Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning. Front Genet 2021; 12:716132. [PMID: 34367264 PMCID: PMC8343536 DOI: 10.3389/fgene.2021.716132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 06/28/2021] [Indexed: 12/02/2022] Open
Abstract
Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina, and Babesia divergens. There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein’s local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome Babesia bovis proteins with an 86–92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for B. bovis, B. bigemina, and two other haemoprotozoan species, Babesia canis, and Plasmodium falciparum.
Collapse
Affiliation(s)
- Stephen J Goodswen
- School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia
| | - Paul J Kennedy
- School of Computer Science, Faculty of Engineering and Information Technology and the Australian Artificial Intelligence Institute, University of Technology Sydney, Ultimo, NSW, Australia
| | - John T Ellis
- School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia
| |
Collapse
|
32
|
Chen TR, Lo CH, Juan SH, Lo WC. The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction. PLoS One 2021; 16:e0254555. [PMID: 34260641 PMCID: PMC8279362 DOI: 10.1371/journal.pone.0254555] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 06/29/2021] [Indexed: 11/28/2022] Open
Abstract
The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the theoretical upper limit, SSP has been considered no longer challenging or too challenging to make advances. With the belief that the substantial improvement of SSP will move forward many fields depending on it, we conducted this study, which focused on three issues that have not been noticed or thoroughly examined yet but may have affected the reliability of the evaluation of previous SSP algorithms. These issues are all about the sequence homology between or within the developmental and evaluation datasets. We thus designed many different homology layouts of datasets to train and evaluate SSP prediction models. Multiple repeats were performed in each experiment by random sampling. The conclusions obtained with small experimental datasets were verified with large-scale datasets using state-of-the-art SSP algorithms. Very different from the long-established assumption, we discover that the sequence homology between query datasets for training, testing, and independent tests exerts little influence on SSP accuracy. Besides, the sequence homology redundancy between or within most datasets would make the accuracy of an SSP algorithm overestimated, while the redundancy within the reference dataset for extracting predictive features would make the accuracy underestimated. Since the overestimating effects are more significant than the underestimating effect, the accuracy of some SSP methods might have been overestimated. Based on the discoveries, we propose a rigorous procedure for developing SSP algorithms and making reliable evaluations, hoping to bring substantial improvements to future SSP methods and benefit all research and application fields relying on accurate prediction of protein secondary structures.
Collapse
Affiliation(s)
- Teng-Ruei Chen
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chia-Hua Lo
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Sheng-Hung Juan
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Wei-Cheng Lo
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- The Center for Bioinformatics Research, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| |
Collapse
|
33
|
Izert MA, Szybowska PE, Górna MW, Merski M. The Effect of Mutations in the TPR and Ankyrin Families of Alpha Solenoid Repeat Proteins. FRONTIERS IN BIOINFORMATICS 2021; 1:696368. [PMID: 36303725 PMCID: PMC9581033 DOI: 10.3389/fbinf.2021.696368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 06/22/2021] [Indexed: 11/20/2022] Open
Abstract
Protein repeats are short, highly similar peptide motifs that occur several times within a single protein, for example the TPR and Ankyrin repeats. Understanding the role of mutation in these proteins is complicated by the competing facts that 1) the repeats are much more restricted to a set sequence than non-repeat proteins, so mutations should be harmful much more often because there are more residues that are heavily restricted due to the need of the sequence to repeat and 2) the symmetry of the repeats in allows the distribution of functional contributions over a number of residues so that sometimes no specific site is singularly responsible for function (unlike enzymatic active site catalytic residues). To address this issue, we review the effects of mutations in a number of natural repeat proteins from the tetratricopeptide and Ankyrin repeat families. We find that mutations are context dependent. Some mutations are indeed highly disruptive to the function of the protein repeats while mutations in identical positions in other repeats in the same protein have little to no effect on structure or function.
Collapse
Affiliation(s)
| | | | | | - Matthew Merski
- *Correspondence: Maria Wiktoria Górna, ; Matthew Merski,
| |
Collapse
|
34
|
Kagami LP, Orlando G, Raimondi D, Ancien F, Dixit B, Gavaldá-García J, Ramasamy P, Roca-Martínez J, Tzavella K, Vranken W. b2bTools: online predictions for protein biophysical features and their conservation. Nucleic Acids Res 2021; 49:W52-W59. [PMID: 34057475 PMCID: PMC8262692 DOI: 10.1093/nar/gkab425] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/21/2021] [Accepted: 05/05/2021] [Indexed: 11/13/2022] Open
Abstract
We provide integrated protein sequence-based predictions via https://bio2byte.be/b2btools/. The aim of our predictions is to identify the biophysical behaviour or features of proteins that are not readily captured by structural biology and/or molecular dynamics approaches. Upload of a FASTA file or text input of a sequence provides integrated predictions from DynaMine backbone and side-chain dynamics, conformational propensities, and derived EFoldMine early folding, DisoMine disorder, and Agmata β-sheet aggregation. These predictions, several of which were previously not available online, capture 'emergent' properties of proteins, i.e. the inherent biophysical propensities encoded in their sequence, rather than context-dependent behaviour (e.g. final folded state). In addition, upload of a multiple sequence alignment (MSA) in a variety of formats enables exploration of the biophysical variation observed in homologous proteins. The associated plots indicate the biophysical limits of functionally relevant protein behaviour, with unusual residues flagged by a Gaussian mixture model analysis. The prediction results are available as JSON or CSV files and directly accessible via an API. Online visualisation is available as interactive plots, with brief explanations and tutorial pages included. The server and API employ an email-free token-based system that can be used to anonymously access previously generated results.
Collapse
Affiliation(s)
- Luciano Porto Kagami
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
| | - Gabriele Orlando
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
| | - Daniele Raimondi
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
| | - Francois Ancien
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
- 3Bio, Université Libre de Bruxelles, Brussels 1050, Belgium
| | - Bhawna Dixit
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels 1050, Belgium
- VIB Structural Biology Research Centre, Brussels, 1050, Belgium
| | - Jose Gavaldá-García
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels 1050, Belgium
- VIB Structural Biology Research Centre, Brussels, 1050, Belgium
| | - Pathmanaban Ramasamy
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
- VIB Structural Biology Research Centre, Brussels, 1050, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9000, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, 9000, Belgium
| | - Joel Roca-Martínez
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels 1050, Belgium
- VIB Structural Biology Research Centre, Brussels, 1050, Belgium
| | - Konstantina Tzavella
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels 1050, Belgium
- VIB Structural Biology Research Centre, Brussels, 1050, Belgium
| | - Wim Vranken
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels 1050, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels 1050, Belgium
- VIB Structural Biology Research Centre, Brussels, 1050, Belgium
| |
Collapse
|
35
|
Sharma AK, Srivastava R. Variable Length Character N-Gram Embedding of Protein Sequences for Secondary Structure Prediction. Protein Pept Lett 2021; 28:501-507. [PMID: 33143605 DOI: 10.2174/0929866527666201103145635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 09/23/2020] [Accepted: 09/26/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND The prediction of a protein's secondary structure from its amino acid sequence is an essential step towards predicting its 3-D structure. The prediction performance improves by incorporating homologous multiple sequence alignment information. Since homologous details not available for all proteins. Therefore, it is necessary to predict the protein secondary structure from single sequences. OBJECTIVE AND METHODS Protein secondary structure predicted from their primary sequences using n-gram word embedding and deep recurrent neural network. Protein secondary structure depends on local and long-range neighbor residues in primary sequences. In the proposed work, the local contextual information of amino acid residues captures variable-length character n-gram words. An embedding vector represents these variable-length character n-gram words. Further, the bidirectional long short-term memory (Bi-LSTM) model is used to capture the long-range contexts by extracting the past and future residues information in primary sequences. RESULTS The proposed model evaluates on three public datasets ss.txt, RS126, and CASP9. The model shows the Q3 accuracy of 92.57%, 86.48%, and 89.66% for ss.txt, RS126, and CASP9. CONCLUSION The proposed model performance compares with state-of-the-art methods available in the literature. After a comparative analysis, it observed that the proposed model performs better than state-of-the-art methods.
Collapse
Affiliation(s)
- Ashish Kumar Sharma
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India
| | - Rajeev Srivastava
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India
| |
Collapse
|
36
|
Suh D, Lee JW, Choi S, Lee Y. Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction. Int J Mol Sci 2021; 22:6032. [PMID: 34199677 PMCID: PMC8199773 DOI: 10.3390/ijms22116032] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 05/29/2021] [Accepted: 05/29/2021] [Indexed: 01/23/2023] Open
Abstract
The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.
Collapse
Affiliation(s)
- Donghyuk Suh
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Jai Woo Lee
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Sun Choi
- Global AI Drug Discovery Center, School of Pharmaceutical Sciences, College of Pharmacy and Graduate, Ewha Womans University, Seoul 03760, Korea; (D.S.); (J.W.L.); (S.C.)
| | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Korea
| |
Collapse
|
37
|
Covaceuszach S, Peche L, Konarev P, Lamba D. A combined evolutionary and structural approach to disclose the primary structural determinants essential for proneurotrophins biological functions. Comput Struct Biotechnol J 2021; 19:2891-2904. [PMID: 34094000 PMCID: PMC8144349 DOI: 10.1016/j.csbj.2021.05.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/03/2021] [Accepted: 05/03/2021] [Indexed: 12/24/2022] Open
Abstract
The neurotrophins, i.e., Nerve Growth Factor (NGF), Brain-Derived Neurotrophic Factor (BDNF), Neurotrophin 3 (NT3) and Neurotrophin 4 (NT4), are known to play a range of crucial functions in the developing and adult peripheral and central nervous systems. Initially synthesized as precursors, i.e., proneurotrophins (proNTs), that are cleaved to release C-terminal mature forms, they act through two types of receptors, the specific Trk receptors (Tropomyosin-related kinases) and the pan-neurotrophin receptor p75NTR, to initiate survival and differentiative responses. Recently, all the proNTs but proNT4 have been demonstrated to be not just inactive precursors, but signaling ligands that mediate opposing actions in fundamental aspects of the nervous system with respect to the mature counterparts through dual-receptor complexes formation with a member of the VPS10 family and p75NTR. Despite the functional relevance, the molecular determinants underpinning the interactions between the pro-domains and their receptors are still elusive probably due to their intrinsically disordered nature. Here we present an evolutionary approach coupled to an experimental study aiming to uncover the structural and dynamical basis of the biological function displayed by proNGF, proBDNF and proNT3 but missing in proNT4. A bioinformatic analysis allowed to elucidate the functional adaptability of the proNTs family in vertebrates, identifying conserved key structural features. The combined biochemical and SAXS experiments shed lights on the structure and dynamic behavior of the human proNTs in solution, giving insights on the evolutionary conserved structural motifs, essential for the multifaceted roles of proNTs in physiological as well as in pathological contexts.
Collapse
Affiliation(s)
- S. Covaceuszach
- Istituto di Cristallografia, Consiglio Nazionale delle Ricerche, Trieste, Italy
| | - L.Y. Peche
- Istituto di Cristallografia, Consiglio Nazionale delle Ricerche, Trieste, Italy
| | - P.V. Konarev
- A.V. Shubnikov Institute of Crystallography of Federal Scientific Research Centre “Crystallography and Photonics” of Russian Academy of Sciences, Moscow, Russia
| | - D. Lamba
- Istituto di Cristallografia, Consiglio Nazionale delle Ricerche, Trieste, Italy
- Interuniversity Consortium “Biostructures and Biosystems National Institute”, Roma, Italy
| |
Collapse
|
38
|
Sharma AK, Srivastava R. Protein Secondary Structure Prediction Using Character Bi-gram Embedding and Bi-LSTM. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200601122840] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Protein secondary structure is vital to predicting the tertiary structure,
which is essential in deciding protein function and drug designing. Therefore, there is a high
requirement of computational methods to predict secondary structure from their primary sequence.
Protein primary sequences represented as a linear combination of twenty amino acid characters and
contain the contextual information for secondary structure prediction.
Objective and Methods:
Protein secondary structure predicted from their primary sequences using a
deep recurrent neural network. Protein secondary structure depends on local and long-range residues
in primary sequences. In the proposed work, the local contextual information of amino acid residues
captures with character n-gram. A dense embedding vector represents this local contextual
information. Furthermore, the bidirectional long short-term memory (Bi-LSTM) model is used to
capture the long-range contexts by extracting the past and future residues information in primary
sequences.
Results:
The proposed deep recurrent architecture is evaluated for its efficacy for datasets, namely
ss.txt, RS126, and CASP9. The model shows the Q3 accuracies of 88.45%, 83.48%, and 86.69% for
ss.txt, RS126, and CASP9, respectively. The performance of the proposed model is also compared
with other state-of-the-art methods available in the literature.
Conclusion:
After a comparative analysis, it was observed that the proposed model is performing
better in comparison to state-of-art methods.
Collapse
Affiliation(s)
- Ashish Kumar Sharma
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India
| | - Rajeev Srivastava
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India
| |
Collapse
|
39
|
Krieger S, Kececioglu J. Boosting the accuracy of protein secondary structure prediction through nearest neighbor search and method hybridization. Bioinformatics 2021; 36:i317-i325. [PMID: 32657384 PMCID: PMC7355242 DOI: 10.1093/bioinformatics/btaa336] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Protein secondary structure prediction is a fundamental precursor to many bioinformatics tasks. Nearly all state-of-the-art tools when computing their secondary structure prediction do not explicitly leverage the vast number of proteins whose structure is known. Leveraging this additional information in a so-called template-based method has the potential to significantly boost prediction accuracy. METHOD We present a new hybrid approach to secondary structure prediction that gains the advantages of both template- and non-template-based methods. Our core template-based method is an algorithmic approach that uses metric-space nearest neighbor search over a template database of fixed-length amino acid words to determine estimated class-membership probabilities for each residue in the protein. These probabilities are then input to a dynamic programming algorithm that finds a physically valid maximum-likelihood prediction for the entire protein. Our hybrid approach exploits a novel accuracy estimator for our core method, which estimates the unknown true accuracy of its prediction, to discern when to switch between template- and non-template-based methods. RESULTS On challenging CASP benchmarks, the resulting hybrid approach boosts the state-of-the-art Q8 accuracy by more than 2-10%, and Q3 accuracy by more than 1-3%, yielding the most accurate method currently available for both 3- and 8-state secondary structure prediction. AVAILABILITY AND IMPLEMENTATION A preliminary implementation in a new tool we call Nnessy is available free for non-commercial use at http://nnessy.cs.arizona.edu.
Collapse
Affiliation(s)
- Spencer Krieger
- Department of Computer Science, The University of Arizona, Tucson, AZ 85721, USA
| | - John Kececioglu
- Department of Computer Science, The University of Arizona, Tucson, AZ 85721, USA
| |
Collapse
|
40
|
Kruglikov A, Rakesh M, Wei Y, Xia X. Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 Research. J Proteome Res 2021; 20:1457-1463. [PMID: 33617253 PMCID: PMC7927282 DOI: 10.1021/acs.jproteome.0c00734] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Indexed: 01/25/2023]
Abstract
Since the outset of COVID-19, the pandemic has prompted immediate global efforts to sequence SARS-CoV-2, and over 450 000 complete genomes have been publicly deposited over the course of 12 months. Despite this, comparative nucleotide and amino acid sequence analyses often fall short in answering key questions in vaccine design. For example, the binding affinity between different ACE2 receptors and SARS-COV-2 spike protein cannot be fully explained by amino acid similarity at ACE2 contact sites because protein structure similarities are not fully reflected by amino acid sequence similarities. To comprehensively compare protein homology, secondary structure (SS) analysis is required. While protein structure is slow and difficult to obtain, SS predictions can be made rapidly, and a well-predicted SS structure may serve as a viable proxy to gain biological insight. Here we review algorithms and information used in predicting protein SS to highlight its potential application in pandemics research. We also showed examples of how SS predictions can be used to compare ACE2 proteins and to evaluate the zoonotic origins of viruses. As computational tools are much faster than wet-lab experiments, these applications can be important for research especially in times when quickly obtained biological insights can help in speeding up response to pandemics.
Collapse
Affiliation(s)
- Alibek Kruglikov
- Department
of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Mohan Rakesh
- Department
of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Yulong Wei
- Department
of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Xuhua Xia
- Department
of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Ottawa
Institute of Systems Biology, University
of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| |
Collapse
|
41
|
Zhao Y, Liu Y. OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction. PLoS One 2021; 16:e0245982. [PMID: 33534819 PMCID: PMC7857624 DOI: 10.1371/journal.pone.0245982] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 01/12/2021] [Indexed: 11/19/2022] Open
Abstract
Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results.
Collapse
Affiliation(s)
- Yawu Zhao
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yihui Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- * E-mail:
| |
Collapse
|
42
|
Shey RA, Ghogomu SM, Shintouo CM, Nkemngo FN, Nebangwa DN, Esoh K, Yaah NE, Manka’aFri M, Nguve JE, Ngwese RA, Njume FN, Bertha FA, Ayong L, Njemini R, Vanhamme L, Souopgui J. Computational Design and Preliminary Serological Analysis of a Novel Multi-Epitope Vaccine Candidate against Onchocerciasis and Related Filarial Diseases. Pathogens 2021; 10:99. [PMID: 33494344 PMCID: PMC7912539 DOI: 10.3390/pathogens10020099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/14/2021] [Accepted: 01/18/2021] [Indexed: 11/16/2022] Open
Abstract
: Onchocerciasis is a skin and eye disease that exerts a heavy socio-economic burden, particularly in sub-Saharan Africa, a region which harbours greater than 96% of either infected or at-risk populations. The elimination plan for the disease is currently challenged by many factors including amongst others; the potential emergence of resistance to the main chemotherapeutic agent, ivermectin (IVM). Novel tools, including preventative and therapeutic vaccines, could provide additional impetus to the disease elimination tool portfolio. Several observations in both humans and animals have provided evidence for the development of both natural and artificial acquired immunity. In this study, immuno-informatics tools were applied to design a filarial-conserved multi-epitope subunit vaccine candidate, (designated Ov-DKR-2) consisting of B-and T-lymphocyte epitopes of eight immunogenic antigens previously assessed in pre-clinical studies. The high-percentage conservation of the selected proteins and epitopes predicted in related nematode parasitic species hints that the generated chimera may be instrumental for cross-protection. Bioinformatics analyses were employed for the prediction, refinement, and validation of the 3D structure of the Ov-DKR-2 chimera. In-silico immune simulation projected significantly high levels of IgG1, T-helper, T-cytotoxic cells, INF-γ, and IL-2 responses. Preliminary immunological analyses revealed that the multi-epitope vaccine candidate reacted with antibodies in sera from both onchocerciasis-infected individuals, endemic normals as well as loiasis-infected persons but not with the control sera from European individuals. These results support the premise for further characterisation of the engineered protein as a vaccine candidate for onchocerciasis.
Collapse
Affiliation(s)
- Robert Adamu Shey
- Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, Buea 99999, Cameroon; (R.A.S.); (S.M.G.); (C.M.S.); (D.N.N.); (N.E.Y.); (M.M.); (J.E.N.); (R.A.N.); (F.N.N.)
- Department of Molecular Biology, Institute of Biology and Molecular Medicine, IBMM, Université Libre de Bruxelles, Gosselies Campus, 6040 Gosselies, Belgium;
| | - Stephen Mbigha Ghogomu
- Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, Buea 99999, Cameroon; (R.A.S.); (S.M.G.); (C.M.S.); (D.N.N.); (N.E.Y.); (M.M.); (J.E.N.); (R.A.N.); (F.N.N.)
| | - Cabirou Mounchili Shintouo
- Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, Buea 99999, Cameroon; (R.A.S.); (S.M.G.); (C.M.S.); (D.N.N.); (N.E.Y.); (M.M.); (J.E.N.); (R.A.N.); (F.N.N.)
- Frailty in Ageing Research Group, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium;
- Department of Gerontology, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium
| | - Francis Nongley Nkemngo
- Department of Microbiology and Parasitology, Faculty of Science, University of Buea, Buea 99999, Cameroon;
- Centre for Research in Infectious Diseases (CRID), Department of Parasitology and Medical Entomology, Yaounde BP 13591, Cameroon
| | - Derrick Neba Nebangwa
- Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, Buea 99999, Cameroon; (R.A.S.); (S.M.G.); (C.M.S.); (D.N.N.); (N.E.Y.); (M.M.); (J.E.N.); (R.A.N.); (F.N.N.)
| | - Kevin Esoh
- Division of Human Genetics, Health Sciences Campus, Department of Pathology, University of Cape Town, Anzio Rd, Observatory, Cape Town 7925, South Africa;
| | - Ntang Emmaculate Yaah
- Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, Buea 99999, Cameroon; (R.A.S.); (S.M.G.); (C.M.S.); (D.N.N.); (N.E.Y.); (M.M.); (J.E.N.); (R.A.N.); (F.N.N.)
| | - Muyanui Manka’aFri
- Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, Buea 99999, Cameroon; (R.A.S.); (S.M.G.); (C.M.S.); (D.N.N.); (N.E.Y.); (M.M.); (J.E.N.); (R.A.N.); (F.N.N.)
| | - Joel Ebai Nguve
- Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, Buea 99999, Cameroon; (R.A.S.); (S.M.G.); (C.M.S.); (D.N.N.); (N.E.Y.); (M.M.); (J.E.N.); (R.A.N.); (F.N.N.)
| | - Roland Akwelle Ngwese
- Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, Buea 99999, Cameroon; (R.A.S.); (S.M.G.); (C.M.S.); (D.N.N.); (N.E.Y.); (M.M.); (J.E.N.); (R.A.N.); (F.N.N.)
| | - Ferdinand Ngale Njume
- Department of Biochemistry and Molecular Biology, Faculty of Science, University of Buea, Buea 99999, Cameroon; (R.A.S.); (S.M.G.); (C.M.S.); (D.N.N.); (N.E.Y.); (M.M.); (J.E.N.); (R.A.N.); (F.N.N.)
- Department of Molecular Biology, Institute of Biology and Molecular Medicine, IBMM, Université Libre de Bruxelles, Gosselies Campus, 6040 Gosselies, Belgium;
| | - Fru Asa Bertha
- Department of Public Health and Hygiene, Faculty of Health Science, University of Buea, Buea 99999, Cameroon;
| | - Lawrence Ayong
- Malaria Research Unit, Centre Pasteur Cameroon, Yaoundé Rue 2005, Cameroon;
| | - Rose Njemini
- Frailty in Ageing Research Group, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium;
- Department of Gerontology, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium
| | - Luc Vanhamme
- Department of Molecular Biology, Institute of Biology and Molecular Medicine, IBMM, Université Libre de Bruxelles, Gosselies Campus, 6040 Gosselies, Belgium;
| | - Jacob Souopgui
- Department of Molecular Biology, Institute of Biology and Molecular Medicine, IBMM, Université Libre de Bruxelles, Gosselies Campus, 6040 Gosselies, Belgium;
| |
Collapse
|
43
|
Thadani NN, Zhou Q, Reyes Gamas K, Butler S, Bueno C, Schafer NP, Morcos F, Wolynes PG, Suh J. Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus. Biophys J 2020; 120:489-503. [PMID: 33359833 DOI: 10.1016/j.bpj.2020.12.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/08/2020] [Accepted: 12/08/2020] [Indexed: 01/03/2023] Open
Abstract
Adeno-associated virus (AAV) is a promising gene therapy vector because of its efficient gene delivery and relatively mild immunogenicity. To improve delivery target specificity, researchers use combinatorial and rational library design strategies to generate novel AAV capsid variants. These approaches frequently propose high proportions of nonforming or noninfective capsid protein sequences that reduce the effective depth of synthesized vector DNA libraries, thereby raising the discovery cost of novel vectors. We evaluated two computational techniques for their ability to estimate the impact of residue mutations on AAV capsid protein-protein interactions and thus predict changes in vector fitness, reasoning that these approaches might inform the design of functionally enriched AAV libraries and accelerate therapeutic candidate identification. The Frustratometer computes an energy function derived from the energy landscape theory of protein folding. Direct-coupling analysis (DCA) is a statistical framework that captures residue coevolution within proteins. We applied the Frustratometer to select candidate protein residues predicted to favor assembled or disassembled capsid states, then predicted mutation effects at these sites using the Frustratometer and DCA. Capsid mutants were experimentally assessed for changes in virus formation, stability, and transduction ability. The Frustratometer-based metric showed a counterintuitive correlation with viral stability, whereas a DCA-derived metric was highly correlated with virus transduction ability in the small population of residues studied. Our results suggest that coevolutionary models may be able to elucidate complex capsid residue-residue interaction networks essential for viral function, but further study is needed to understand the relationship between protein energy simulations and viral capsid metastability.
Collapse
Affiliation(s)
| | - Qin Zhou
- Department of Biological Sciences, University of Texas at Dallas, Richardson, Texas
| | | | - Susan Butler
- Department of Bioengineering, Rice University, Houston, Texas
| | - Carlos Bueno
- Center for Theoretical Biological Physics, Rice University, Houston, Texas; Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas
| | - Nicholas P Schafer
- Center for Theoretical Biological Physics, Rice University, Houston, Texas; Department of Chemistry, Rice University, Houston, Texas
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, Texas; Center for Systems Biology, University of Texas at Dallas, Richardson, Texas; Department of Bioengineering, University of Texas at Dallas, Richardson, Texas
| | - Peter G Wolynes
- Center for Theoretical Biological Physics, Rice University, Houston, Texas; Department of Chemistry, Rice University, Houston, Texas; Department of Biosciences, Rice University, Houston, Texas; Department of Physics, Rice University, Houston, Texas
| | - Junghae Suh
- Department of Bioengineering, Rice University, Houston, Texas; Department of Biosciences, Rice University, Houston, Texas; Department of Chemical and Biomolecular Engineering, Rice University, Houston, Texas; Systems, Synthetic, and Physical Biology Program, Rice University, Houston, Texas.
| |
Collapse
|
44
|
Salmanian S, Pezeshk H, Sadeghi M. Inter-protein residue covariation information unravels physically interacting protein dimers. BMC Bioinformatics 2020; 21:584. [PMID: 33334319 PMCID: PMC7745481 DOI: 10.1186/s12859-020-03930-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 12/09/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Predicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown interacting counterparts. Most of co-evolutionary methods discover a combination of physical interplays and functional associations. However, there are only a handful of approaches which specifically infer physical interactions. Hybrid co-evolutionary methods exploit inter-protein residue coevolution to unravel specific physical interacting proteins. In this study, we introduce a hybrid co-evolutionary-based approach to predict physical interplays between pairs of protein families, starting from protein sequences only. RESULTS In the present analysis, pairs of multiple sequence alignments are constructed for each dimer and the covariation between residues in those pairs are calculated by CCMpred (Contacts from Correlated Mutations predicted) and three mutual information based approaches for ten accessible surface area threshold groups. Then, whole residue couplings between proteins of each dimer are unified into a single Frobenius norm value. Norms of residue contact matrices of all dimers in different accessible surface area thresholds are fed into support vector machine as single or multiple feature models. The results of training the classifiers by single features show no apparent different accuracies in distinct methods for different accessible surface area thresholds. Nevertheless, mutual information product and context likelihood of relatedness procedures may roughly have an overall higher and lower performances than other two methods for different accessible surface area cut-offs, respectively. The results also demonstrate that training support vector machine with multiple norm features for several accessible surface area thresholds leads to a considerable improvement of prediction performance. In this context, CCMpred roughly achieves an overall better performance than mutual information based approaches. The best accuracy, sensitivity, specificity, precision and negative predictive value for that method are 0.98, 1, 0.962, 0.96, and 0.962, respectively. CONCLUSIONS In this paper, by feeding norm values of protein dimers into support vector machines in different accessible surface area thresholds, we demonstrate that even small number of proteins in pairs of multiple alignments could allow one to accurately discriminate between positive and negative dimers.
Collapse
Affiliation(s)
- Sara Salmanian
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Hamid Pezeshk
- School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
- Present Address: Department of Mathematics and Statistics, Concordia University, Montreal, Canada
- School of Biological Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
| | - Mehdi Sadeghi
- National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| |
Collapse
|
45
|
Tian K, Zhao X, Wan X, Yau SST. Amino acid torsion angles enable prediction of protein fold classification. Sci Rep 2020; 10:21773. [PMID: 33303802 PMCID: PMC7729947 DOI: 10.1038/s41598-020-78465-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 11/23/2020] [Indexed: 11/29/2022] Open
Abstract
Protein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins are complex and require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction. Here we describe a new approach to predict of protein structures and structure classes from amino acid sequences. Our prediction model performs well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. The average accuracy is 92.5% for structure classification, which is higher than that of previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence. The results show that our method provides a new effective and reliable tool for protein structure prediction research.
Collapse
Affiliation(s)
- Kun Tian
- School of Mathematics, Renmin University of China, Beijing, 100872, People's Republic of China
| | - Xin Zhao
- Department of Cryptography and Technology, Beijing Electronic Science and Technology Institute, Beijing, 100070, People's Republic of China
| | - Xiaogeng Wan
- Department of Mathematical Sciences, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Stephen S-T Yau
- Department of Mathematical Sciences, Tsinghua University, Beijing, 100084, People's Republic of China.
| |
Collapse
|
46
|
Kotowski K, Smolarczyk T, Roterman-Konieczna I, Stapor K. ProteinUnet-An efficient alternative to SPIDER3-single for sequence-based prediction of protein secondary structures. J Comput Chem 2020; 42:50-59. [PMID: 33058261 PMCID: PMC7756333 DOI: 10.1002/jcc.26432] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/21/2020] [Accepted: 09/23/2020] [Indexed: 12/16/2022]
Abstract
Predicting protein function and structure from sequence remains an unsolved problem in bioinformatics. The best performing methods rely heavily on evolutionary information from multiple sequence alignments, which means their accuracy deteriorates for sequences with a few homologs, and given the increasing sequence database sizes requires long computation times. Here, a single‐sequence‐based prediction method is presented, called ProteinUnet, leveraging an U‐Net convolutional network architecture. It is compared to SPIDER3‐Single model, based on long short‐term memory‐bidirectional recurrent neural networks architecture. Both methods achieve similar results for prediction of secondary structures (both three‐ and eight‐state), half‐sphere exposure, and contact number, but ProteinUnet has two times fewer parameters, 17 times shorter inference time, and can be trained 11 times faster. Moreover, ProteinUnet tends to be better for short sequences and residues with a low number of local contacts. Additionally, the method of loss weighting is presented as an effective way of increasing accuracy for rare secondary structures.
Collapse
Affiliation(s)
- Krzysztof Kotowski
- Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
| | - Tomasz Smolarczyk
- Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
| | - Irena Roterman-Konieczna
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Kraków, Poland
| | - Katarzyna Stapor
- Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
| |
Collapse
|
47
|
Urban G, Torrisi M, Magnan CN, Pollastri G, Baldi P. Protein profiles: Biases and protocols. Comput Struct Biotechnol J 2020; 18:2281-2289. [PMID: 32994887 PMCID: PMC7486441 DOI: 10.1016/j.csbj.2020.08.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/14/2020] [Accepted: 08/15/2020] [Indexed: 11/13/2022] Open
Abstract
The use of evolutionary profiles to predict protein secondary structure, as well as other protein structural features, has been standard practice since the 1990s. Using profiles in the input of such predictors, in place or in addition to the sequence itself, leads to significantly more accurate predictions. While profiles can enhance structural signals, their role remains somewhat surprising as proteins do not use profiles when folding in vivo. Furthermore, the same sequence-based redundancy reduction protocols initially derived to train and evaluate sequence-based predictors, have been applied to train and evaluate profile-based predictors. This can lead to unfair comparisons since profiles may facilitate the bleeding of information between training and test sets. Here we use the extensively studied problem of secondary structure prediction to better evaluate the role of profiles and show that: (1) high levels of profile similarity between training and test proteins are observed when using standard sequence-based redundancy protocols; (2) the gain in accuracy for profile-based predictors, over sequence-based predictors, strongly relies on these high levels of profile similarity between training and test proteins; and (3) the overall accuracy of a profile-based predictor on a given protein dataset provides a biased measure when trying to estimate the actual accuracy of the predictor, or when comparing it to other predictors. We show, however, that this bias can be mitigated by implementing a new protocol (EVALpro) which evaluates the accuracy of profile-based predictors as a function of the profile similarity between training and test proteins. Such a protocol not only allows for a fair comparison of the predictors on equally hard or easy examples, but also reduces the impact of choosing a given similarity cutoff when selecting test proteins. The EVALpro program is available in the SCRATCH suite ( www.scratch.proteomics.ics.uci.edu) and can be downloaded at: www.download.igb.uci.edu/#evalpro.
Collapse
Affiliation(s)
- Gregor Urban
- Department of Computer Science & Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697, USA
| | - Mirko Torrisi
- UCD Institute for Discovery, University College Dublin, Dublin, 4, Ireland
| | - Christophe N Magnan
- Department of Computer Science & Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697, USA
| | - Gianluca Pollastri
- UCD Institute for Discovery, University College Dublin, Dublin, 4, Ireland
| | - Pierre Baldi
- Department of Computer Science & Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697, USA
| |
Collapse
|
48
|
Guo Z, Hou J, Cheng J. DNSS2: Improved ab initio protein secondary structure prediction using advanced deep learning architectures. Proteins 2020; 89:207-217. [PMID: 32893403 DOI: 10.1002/prot.26007] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 07/07/2020] [Accepted: 09/02/2020] [Indexed: 12/27/2022]
Abstract
Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction. The major improvements over the DNSS1 method include (a) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict 3-state and 8-state secondary structure, and (b) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on independent test data sets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the protein targets of 2018 CASP13 experiment and achieved the Q3 score of 81.62%, SOV score of 72.19%, and Q8 score of 73.28%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.
Collapse
Affiliation(s)
- Zhiye Guo
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jie Hou
- Department of Computer Science, Saint Louis University, St. Louis, Missouri, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| |
Collapse
|
49
|
Kashani-Amin E, Tabatabaei-Malazy O, Sakhteman A, Larijani B, Ebrahim-Habibi A. A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools. Curr Drug Discov Technol 2020; 16:159-172. [PMID: 29493456 DOI: 10.2174/1570163815666180227162157] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 02/15/2018] [Accepted: 02/22/2018] [Indexed: 01/22/2023]
Abstract
BACKGROUND Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. OBJECTIVE A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. METHODS Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. RESULTS Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. CONCLUSION This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.
Collapse
Affiliation(s)
- Elaheh Kashani-Amin
- Biosensor Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ozra Tabatabaei-Malazy
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amirhossein Sakhteman
- Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.,Medicinal Chemistry and Natural Products Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Azadeh Ebrahim-Habibi
- Biosensor Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
50
|
Banerjee S, Majumder K, Gutierrez GJ, Gupta D, Mittal B. Immuno-informatics approach for multi-epitope vaccine designing against SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020. [PMID: 32743567 PMCID: PMC7386484 DOI: 10.1101/2020.07.23.218529] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The novel Corona Virus Disease 2019 (COVID-19) pandemic has set the fatality rates ablaze across the world. So, to combat this disease, we have designed a multi-epitope vaccine from various proteins of Severe Acute Respiratory Syndrome Corona virus 2 (SARS-CoV-2) with an immuno-informatics approach, validated in silico to be stable, non-allergic and antigenic. Cytotoxic T-cell, helper T-cell, and B-cell epitopes were computationally predicted from six conserved protein sequences among four viral strains isolated across the world. The T-cell epitopes, overlapping with the B-cell epitopes, were included in the vaccine construct to assure the humoral and cell-mediated immune response. The beta-subunit of cholera toxin was added as an adjuvant at the N-terminal of the construct to increase immunogenicity. Interferon-gamma inducing epitopes were even predicted in the vaccine. Molecular docking and binding energetics studies revealed strong interactions of the vaccine with immune-stimulatory toll-like receptors (TLR) −2, 3, 4. Molecular dynamics simulation of the vaccine ensured in vivo stability in the biological system. The immune simulation of vaccine evinced elevated immune response. The efficient translation of the vaccine in an expression vector was assured utilizing in silico cloning approach. Certainly, such a vaccine construct could reliably be effective against COVID-19.
Collapse
Affiliation(s)
- Souvik Banerjee
- Department of Microbiology, St. Xavier's College (Autonomous), Kolkata
| | - Kaustav Majumder
- Department of Biosciences and Bioengineering, Indian Institute of Technology, Bombay
| | | | - Debkishore Gupta
- Department of Clinical Microbiology and Infection Control, The Calcutta Medical Research Institute and BM Birla Heart Research Centre, Kolkata
| | | |
Collapse
|