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Li X, Pu Q, Xu Y, Yang H, Wu Y, Wang W, Li Y. The masking phenomenon of microplastics additives on oxidative stress responses in freshwater food chains. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172156. [PMID: 38588742 DOI: 10.1016/j.scitotenv.2024.172156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/31/2024] [Accepted: 03/31/2024] [Indexed: 04/10/2024]
Abstract
The variability and intrinsic mechanisms of oxidative stress induced by microplastics at different trophic levels in freshwater food chains are not well understood. To comprehensively assess the oxidative stress induced by polystyrene microplastics (PS-MPs) in freshwater food chains, the present study first quantified the oxidative stress induced by PS-MPs in organisms at different trophic levels using factorial experimental design and molecular dynamics methods. Then focuses on analyzing the variability of these responses across different trophic levels using mathematical statistical analysis. Notably, higher trophic level organisms exhibit diminished responses under PS-MPs exposure. Furthermore, the coexistence of multiple additives was found to mask these responses, with antioxidant plastic additives significantly influencing oxidative stress responses. Mechanism analysis using computational chemistry simulation determines that protein structure and amino acid characteristics are key factors driving PS-MPs induced oxidative stress variation in freshwater organisms at different nutrient levels. Increased hydrophobic additives induce protein helicalization and amino acid residue aggregation. This study systematically reveals the variability of biological oxidative stress response under different nutrient levels, emphasizing the pivotal role of chemical additives. Overall, this study offers crucial insights into PS-MPs' impact on oxidative stress responses in freshwater ecosystems, informing future environmental risk assessment.
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Affiliation(s)
- Xinao Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Qikun Pu
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Yingjie Xu
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Hao Yang
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Yang Wu
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China
| | - Wenwen Wang
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China.
| | - Yu Li
- College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China; MOE Key Laboratory of Resources and Environmental System Optimization, North China Electric Power University, Beijing 102206, China.
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2
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Basu S, Zhao B, Biró B, Faraggi E, Gsponer J, Hu G, Kloczkowski A, Malhis N, Mirdita M, Söding J, Steinegger M, Wang D, Wang K, Xu D, Zhang J, Kurgan L. DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options. Nucleic Acids Res 2024; 52:D426-D433. [PMID: 37933852 PMCID: PMC10767971 DOI: 10.1093/nar/gkad985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/12/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.
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Affiliation(s)
- Sushmita Basu
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Bi Zhao
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Bálint Biró
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
- Department of Animal Biotechnology, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| | - Eshel Faraggi
- Physics Department, Indiana University, Indianapolis, IN, USA
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, P.R. China
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Milot Mirdita
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
- Institute of Molecular Biology & Genetics, Seoul National University, Seoul, Republic of Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Duolin Wang
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, USA
| | - Kui Wang
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, P.R. China
| | - Dong Xu
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, USA
| | - Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang, P.R. China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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3
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Song J, Kurgan L. Availability of web servers significantly boosts citations rates of bioinformatics methods for protein function and disorder prediction. BIOINFORMATICS ADVANCES 2023; 3:vbad184. [PMID: 38146538 PMCID: PMC10749743 DOI: 10.1093/bioadv/vbad184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/08/2023] [Accepted: 12/15/2023] [Indexed: 12/27/2023]
Abstract
Motivation Development of bioinformatics methods is a long, complex and resource-hungry process. Hundreds of these tools were released. While some methods are highly cited and used, many suffer relatively low citation rates. We empirically analyze a large collection of recently released methods in three diverse protein function and disorder prediction areas to identify key factors that contribute to increased citations. Results We show that provision of a working web server significantly boosts citation rates. On average, methods with working web servers generate three times as many citations compared to tools that are available as only source code, have no code and no server, or are no longer available. This observation holds consistently across different research areas and publication years. We also find that differences in predictive performance are unlikely to impact citation rates. Overall, our empirical results suggest that a relatively low-cost investment into the provision and long-term support of web servers would substantially increase the impact of bioinformatics tools.
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Affiliation(s)
- Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Clayton, VIC 3800, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, United States
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4
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Atif HB, Alvi H, Naveed H. Masked Language Modeling for Resource Constrained Biological Natural Language Processing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083556 DOI: 10.1109/embc40787.2023.10340499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Recent advances in Natural Language Processing (NLP) have produced state of the art results on several sequence to sequence (seq2seq) tasks. Enhancements in embedders and their training methodologies have shown significant improvement on downstream tasks. Word vector models like Word2Vec, FastText & Glove were widely used over one-hot encoded vectors for years until the advent of deep contextualized embedders. Protein sequences consist of 20 naturally occurring amino acids that can be treated as the language of nature. These amino acids in combinations with each other makeup the biological functions. The choice of vector representation and architecture design for a biological task is highly dependent upon the nature of the task. We utilize unlabelled protein sequences to train a Convolution and Gated Recurrent Network (CGRN) embedder using Masked Language Modeling (MLM) technique that shows significant performance boost under resource constraint setting on two downstream tasks i.e., F1-score(Q8) of 73.1% on Secondary Structure Prediction (SSP) & F1-score of 84% on Intrinsically Disordered Region Prediction (IDRP). We also compare different architectures on downstream tasks to show the impact of the nature of biological task on the performance of the model.
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5
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S. G, E.R. V. Protein secondary structure prediction using Cascaded Feature Learning Model. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
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6
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Gogoi CR, Rahman A, Saikia B, Baruah A. Protein Dihedral Angle Prediction: The State of the Art. ChemistrySelect 2023. [DOI: 10.1002/slct.202203427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
| | - Aziza Rahman
- Department of Chemistry Dibrugarh University Dibrugarh Assam India
| | - Bondeepa Saikia
- Department of Chemistry Dibrugarh University Dibrugarh Assam India
| | - Anupaul Baruah
- Department of Chemistry Dibrugarh University Dibrugarh Assam India
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7
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Chandra A, Tünnermann L, Löfstedt T, Gratz R. Transformer-based deep learning for predicting protein properties in the life sciences. eLife 2023; 12:82819. [PMID: 36651724 PMCID: PMC9848389 DOI: 10.7554/elife.82819] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/06/2023] [Indexed: 01/19/2023] Open
Abstract
Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model-the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.
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Affiliation(s)
- Abel Chandra
- Department of Computing Science, Umeå UniversityUmeåSweden
| | - Laura Tünnermann
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural SciencesUmeåSweden
| | - Tommy Löfstedt
- Department of Computing Science, Umeå UniversityUmeåSweden
| | - Regina Gratz
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural SciencesUmeåSweden
- Department of Forest Ecology and Management, Swedish University of Agricultural SciencesUmeåSweden
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8
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Mufassirin MMM, Newton MAH, Sattar A. Artificial intelligence for template-free protein structure prediction: a comprehensive review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10350-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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9
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Ismi DP, Pulungan R, Afiahayati. Deep learning for protein secondary structure prediction: Pre and post-AlphaFold. Comput Struct Biotechnol J 2022; 20:6271-6286. [PMID: 36420164 PMCID: PMC9678802 DOI: 10.1016/j.csbj.2022.11.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/05/2022] [Accepted: 11/05/2022] [Indexed: 11/13/2022] Open
Abstract
This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure prediction. Previous studies showed that deep neural networks had uplifted the accuracy of three-state secondary structure prediction to more than 80%. Favored deep learning methods, such as convolutional neural networks, recurrent neural networks, inception networks, and graph neural networks, have been implemented in protein secondary structure prediction. Methods adapted from natural language processing (NLP) and computer vision are also employed, including attention mechanism, ResNet, and U-shape networks. In the post-AlphaFold era, PSSP studies focus on different objectives, such as enhancing the quality of evolutionary information and exploiting protein language models as the PSSP input. The recent trend to utilize pre-trained language models as input features for secondary structure prediction provides a new direction for PSSP studies. Moreover, the state-of-the-art accuracy achieved by previous PSSP models is still below its theoretical limit. There are still rooms for improvement to be made in the field.
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Affiliation(s)
- Dewi Pramudi Ismi
- Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Department of Infomatics, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
| | - Reza Pulungan
- Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Afiahayati
- Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
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10
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Bongirwar V, Mokhade AS. Different methods, techniques and their limitations in protein structure prediction: A review. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 173:72-82. [PMID: 35588858 DOI: 10.1016/j.pbiomolbio.2022.05.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 04/16/2022] [Accepted: 05/11/2022] [Indexed: 11/17/2022]
Abstract
Because of the increase in different types of diseases in human habitats, demands for designing various types of drugs are also increasing. Protein and its structure play a very important role in drug design. Therefore researchers from different areas like mathematics, medicines, and computer science are teaming up for getting better solutions in the said field. In this paper, we have discussed different methods of secondary and tertiary protein structure prediction (PSP), along with the limitations of different approaches. Different types of datasets used in PSP are also discussed here. This paper also tells about different performance measures to evaluate the prediction accuracy of PSP methods. Different software's/servers are available for download, which are used to find the protein structures for the input protein sequence. These softwares will also help to compare the performance of any new algorithm with other available methods. Details of those softwares are also mentioned in this paper.
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Affiliation(s)
| | - A S Mokhade
- Visvesvaraya National Institute of Technology, Nagpur, India
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11
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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.
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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,
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12
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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.
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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:
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13
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Biró B, Zhao B, Kurgan L. Complementarity of the residue-level protein function and structure predictions in human proteins. Comput Struct Biotechnol J 2022; 20:2223-2234. [PMID: 35615015 PMCID: PMC9118482 DOI: 10.1016/j.csbj.2022.05.003] [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: 02/21/2022] [Revised: 05/02/2022] [Accepted: 05/02/2022] [Indexed: 11/24/2022] Open
Abstract
Sequence-based predictors of the residue-level protein function and structure cover a broad spectrum of characteristics including intrinsic disorder, secondary structure, solvent accessibility and binding to nucleic acids. They were catalogued and evaluated in numerous surveys and assessments. However, methods focusing on a given characteristic are studied separately from predictors of other characteristics, while they are typically used on the same proteins. We fill this void by studying complementarity of a representative collection of methods that target different predictions using a large, taxonomically consistent, and low similarity dataset of human proteins. First, we bridge the gap between the communities that develop structure-trained vs. disorder-trained predictors of binding residues. Motivated by a recent study of the protein-binding residue predictions, we empirically find that combining the structure-trained and disorder-trained predictors of the DNA-binding and RNA-binding residues leads to substantial improvements in predictive quality. Second, we investigate whether diverse predictors generate results that accurately reproduce relations between secondary structure, solvent accessibility, interaction sites, and intrinsic disorder that are present in the experimental data. Our empirical analysis concludes that predictions accurately reflect all combinations of these relations. Altogether, this study provides unique insights that support combining results produced by diverse residue-level predictors of protein function and structure.
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Affiliation(s)
- Bálint Biró
- Institute of Genetics and Biotechnology, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States
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14
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Yang W, Liu Y, Xiao C. Deep metric learning for accurate protein secondary structure prediction. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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15
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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.
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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
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16
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A. Tawfeek M, Alanazi S, A. Abd El-Aziz A. Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics. COMPUTERS, MATERIALS & CONTINUA 2022; 72:6091-6106. [DOI: 10.32604/cmc.2022.028391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/18/2022] [Indexed: 09/01/2023]
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17
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Modee R, Laghuvarapu S, Priyakumar UD. Benchmark study on deep neural network potentials for small organic molecules. J Comput Chem 2021; 43:308-318. [PMID: 34870332 DOI: 10.1002/jcc.26790] [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: 09/16/2021] [Revised: 11/13/2021] [Accepted: 11/15/2021] [Indexed: 11/06/2022]
Abstract
There has been tremendous advancement in machine learning (ML) applications in computational chemistry, particularly in neural network potentials (NNP). NNPs can approximate potential energy surface (PES) as a high dimensional function by learning from existing reference data, thereby circumventing the need to solve the electronic Schrödinger equation explicitly. As a result, ML accelerates chemical space exploration and property prediction compared to quantum mechanical methods. Novel ML methods have the potential to provide efficient means for predicting the properties of molecules. However, this potential has been limited by the lack of standard comparative evaluations. In this work, we compare four selected models, that is, ANI, PhysNet, SchNet, and BAND-NN, developed to represent the PES of small organic molecules. We evaluate these models for their accuracy and transferability on two different test sets (i) Small organic molecules of up to eight-heavy atoms on which ANI and SchNet achieve root mean square error (RMSE) of 0.55 and 0.60 kcal/mol, respectively. (ii) On random selection of molecules from the GDB-11 database with 10-heavy atoms, ANI achieves RMSE of 1.17 kcal/mol and SchNet achieves RMSE of 1.89 kcal/mol. We examine their ability to produce smooth meaningful surface by performing PES scans for bond stretch, angle bend, and dihedral rotations on relatively large molecules to assess their possible application in molecular dynamics simulations. We also evaluate their performance for yielding minimum energy structures via geometry optimization using various minimization algorithms. All these models were also able to accurately differentiate different isomers of the same empirical formula C 10 H 20 . ANI and PhysNet achieve an RMSE of 0.29 and 0.52 kcal/mol, respectively, on C 10 H 20 isomers.
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Affiliation(s)
- Rohit Modee
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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18
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Miao Z, Wang Q, Xiao X, Kamal GM, Song L, Zhang X, Li C, Zhou X, Jiang B, Liu M. CSI-LSTM: a web server to predict protein secondary structure using bidirectional long short term memory and NMR chemical shifts. JOURNAL OF BIOMOLECULAR NMR 2021; 75:393-400. [PMID: 34510297 DOI: 10.1007/s10858-021-00383-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/06/2021] [Indexed: 06/13/2023]
Abstract
Protein secondary structure provides rich structural information, hence the description and understanding of protein structure relies heavily on it. Identification or prediction of secondary structures therefore plays an important role in protein research. In protein NMR studies, it is more convenient to predict secondary structures from chemical shifts as compared to the traditional determination methods based on inter-nuclear distances provided by NOESY experiment. In recent years, there was a significant improvement observed in deep neural networks, which had been applied in many research fields. Here we proposed a deep neural network based on bidirectional long short term memory (biLSTM) to predict protein 3-state secondary structure using NMR chemical shifts of backbone nuclei. While comparing with the existing methods the proposed method showed better prediction accuracy. Based on the proposed method, a web server has been built to provide protein secondary structure prediction service.
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Affiliation(s)
- Zhiwei Miao
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
| | - Qianqian Wang
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
| | - Xiongjie Xiao
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
| | - Ghulam Mustafa Kamal
- Department of Chemistry, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan, Punjab, 64200, Pakistan
| | - Linhong Song
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, 10049, China
| | - Xu Zhang
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, 10049, China
| | - Conggang Li
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, 10049, China
| | - Xin Zhou
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, 10049, China
| | - Bin Jiang
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China.
- University of Chinese Academy of Sciences, Beijing, 10049, China.
| | - Maili Liu
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China.
- University of Chinese Academy of Sciences, Beijing, 10049, China.
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Robson B. Testing machine learning techniques for general application by using protein secondary structure prediction. A brief survey with studies of pitfalls and benefits using a simple progressive learning approach. Comput Biol Med 2021; 138:104883. [PMID: 34598067 DOI: 10.1016/j.compbiomed.2021.104883] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/05/2021] [Accepted: 09/17/2021] [Indexed: 01/05/2023]
Abstract
Many researchers have recently used the prediction of protein secondary structure (local conformational states of amino acid residues) to test advances in predictive and machine learning technology such as Neural Net Deep Learning. Protein secondary structure prediction continues to be a helpful tool in research in biomedicine and the life sciences, but it is also extremely enticing for testing predictive methods such as neural nets that are intended for different or more general purposes. A complication is highlighted here for researchers testing their methods for other applications. Modern protein databases inevitably contain important clues to the answer, so-called "strong buried clues", though often obscurely; they are hard to avoid. This is because most proteins or parts of proteins in a modern protein data base are related to others by biological evolution. For researchers developing machine learning and predictive methods, this can overstate and so confuse understanding of the true quality of a predictive method. However, for researchers using the algorithms as tools, understanding strong buried clues is of great value, because they need to make maximum use of all information available. A simple method related to the GOR methods but with some features of neural nets in the sense of progressive learning of large numbers of weights, is used to explore this. It can acquire tens of millions and hence gigabytes of weights, but they are learned stably by exhaustive sampling. The significance of the findings is discussed in the light of promising recent results from AlphaFold using Google's DeepMind.
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Affiliation(s)
- Barry Robson
- Ingine Inc. Ohio, USA and the Dirac Foundation Oxfordshire, UK.
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20
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digIS: towards detecting distant and putative novel insertion sequence elements in prokaryotic genomes. BMC Bioinformatics 2021; 22:258. [PMID: 34016050 PMCID: PMC8147514 DOI: 10.1186/s12859-021-04177-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 05/09/2021] [Indexed: 12/02/2022] Open
Abstract
Background The insertion sequence elements (IS elements) represent the smallest and the most abundant mobile elements in prokaryotic genomes. It has been shown that they play a significant role in genome organization and evolution. To better understand their function in the host genome, it is desirable to have an effective detection and annotation tool. This need becomes even more crucial when considering rapid-growing genomic and metagenomic data. The existing tools for IS elements detection and annotation are usually based on comparing sequence similarity with a database of known IS families. Thus, they have limited ability to discover distant and putative novel IS elements. Results In this paper, we present digIS, a software tool based on profile hidden Markov models assembled from catalytic domains of transposases. It shows a very good performance in detecting known IS elements when tested on datasets with manually curated annotation. The main contribution of digIS is in its ability to detect distant and putative novel IS elements while maintaining a moderate level of false positives. In this category it outperforms existing tools, especially when tested on large datasets of archaeal and bacterial genomes. Conclusion We provide digIS, a software tool using a novel approach based on manually curated profile hidden Markov models, which is able to detect distant and putative novel IS elements. Although digIS can find known IS elements as well, we expect it to be used primarily by scientists interested in finding novel IS elements. The tool is available at https://github.com/janka2012/digIS. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04177-6.
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21
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Uddin MR, Mahbub S, Rahman MS, Bayzid MS. SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction. Bioinformatics 2021; 36:4599-4608. [PMID: 32437517 DOI: 10.1093/bioinformatics/btaa531] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 05/10/2020] [Accepted: 05/16/2020] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction. RESULTS We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins. AVAILABILITY AND IMPLEMENTATION SAINT is freely available as an open-source project at https://github.com/SAINTProtein/SAINT.
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Affiliation(s)
- Mostofa Rafid Uddin
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh.,Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
| | - Sazan Mahbub
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - M Saifur Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Md Shamsuzzoha Bayzid
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
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22
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Zhao B, Katuwawala A, Oldfield CJ, Dunker AK, Faraggi E, Gsponer J, Kloczkowski A, Malhis N, Mirdita M, Obradovic Z, Söding J, Steinegger M, Zhou Y, Kurgan L. DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Res 2021; 49:D298-D308. [PMID: 33119734 PMCID: PMC7778963 DOI: 10.1093/nar/gkaa931] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/11/2020] [Accepted: 10/05/2020] [Indexed: 12/30/2022] Open
Abstract
We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.
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Affiliation(s)
- Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | | | - A Keith Dunker
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Eshel Faraggi
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Andrzej Kloczkowski
- Battelle Center for Mathematical Medicine at the Nationwide Children's Hospital, and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
| | - Milot Mirdita
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Zoran Obradovic
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Martin Steinegger
- School of Biological Sciences and Institute of Molecular Biology & Genetics, Seoul National University, Seoul, Republic of Korea
| | - Yaoqi Zhou
- Institute for Glycomics, Griffith University, Gold Coast, Queensland, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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23
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Winklemann I, Matsuoka R, Meier PF, Shutin D, Zhang C, Orellana L, Sexton R, Landreh M, Robinson CV, Beckstein O, Drew D. Structure and elevator mechanism of the mammalian sodium/proton exchanger NHE9. EMBO J 2020; 39:e105908. [PMID: 33118634 PMCID: PMC7737618 DOI: 10.15252/embj.2020105908] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 09/09/2020] [Accepted: 09/10/2020] [Indexed: 12/21/2022] Open
Abstract
Na+ /H+ exchangers (NHEs) are ancient membrane-bound nanomachines that work to regulate intracellular pH, sodium levels and cell volume. NHE activities contribute to the control of the cell cycle, cell proliferation, cell migration and vesicle trafficking. NHE dysfunction has been linked to many diseases, and they are targets of pharmaceutical drugs. Despite their fundamental importance to cell homeostasis and human physiology, structural information for the mammalian NHE was lacking. Here, we report the cryogenic electron microscopy structure of NHE isoform 9 (SLC9A9) from Equus caballus at 3.2 Å resolution, an endosomal isoform highly expressed in the brain and associated with autism spectrum (ASD) and attention deficit hyperactivity (ADHD) disorders. Despite low sequence identity, the NHE9 architecture and ion-binding site are remarkably similar to distantly related bacterial Na+ /H+ antiporters with 13 transmembrane segments. Collectively, we reveal the conserved architecture of the NHE ion-binding site, their elevator-like structural transitions, the functional implications of autism disease mutations and the role of phosphoinositide lipids to promote homodimerization that, together, have important physiological ramifications.
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Affiliation(s)
- Iven Winklemann
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
| | - Rei Matsuoka
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
| | - Pascal F Meier
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
| | - Denis Shutin
- Department of ChemistryUniversity of OxfordOxfordUK
| | - Chenou Zhang
- Department of PhysicsCenter for Biological PhysicsArizona State UniversityTempeAZUSA
| | - Laura Orellana
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
| | - Ricky Sexton
- Department of PhysicsCenter for Biological PhysicsArizona State UniversityTempeAZUSA
| | - Michael Landreh
- Department of Microbiology, Tumor and Cell BiologyKarolinska InstituteStockholmSweden
| | | | - Oliver Beckstein
- Department of PhysicsCenter for Biological PhysicsArizona State UniversityTempeAZUSA
| | - David Drew
- Department of Biochemistry and BiophysicsStockholm UniversityStockholmSweden
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24
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Guo L, Jiang Q, Jin X, Liu L, Zhou W, Yao S, Wu M, Wang Y. A Deep Convolutional Neural Network to Improve the Prediction of Protein Secondary Structure. Curr Bioinform 2020. [DOI: 10.2174/1574893615666200120103050] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Protein secondary structure prediction (PSSP) is a fundamental task in
bioinformatics that is helpful for understanding the three-dimensional structure and biological
function of proteins. Many neural network-based prediction methods have been developed for
protein secondary structures. Deep learning and multiple features are two obvious means to improve
prediction accuracy.
Objective:
To promote the development of PSSP, a deep convolutional neural network-based
method is proposed to predict both the eight-state and three-state of protein secondary structure.
Methods:
In this model, sequence and evolutionary information of proteins are combined as multiple
input features after preprocessing. A deep convolutional neural network with no pooling layer and
connection layer is then constructed to predict the secondary structure of proteins. L2 regularization,
batch normalization, and dropout techniques are employed to avoid over-fitting and obtain better
prediction performance, and an improved cross-entropy is used as the loss function.
Results:
Our proposed model can obtain Q3 prediction results of 86.2%, 84.5%, 87.8%, and 84.7%,
respectively, on CullPDB, CB513, CASP10 and CASP11 datasets, with corresponding Q8
prediction results of 74.1%, 70.5%, 74.9%, and 71.3%.
Conclusion:
We have proposed the DCNN-SS deep convolutional-network-based PSSP method,
and experimental results show that DCNN-SS performs competitively with other methods.
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Affiliation(s)
- Lin Guo
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Qian Jiang
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Xin Jin
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Lin Liu
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Wei Zhou
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Shaowen Yao
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Min Wu
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Yun Wang
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
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25
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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.
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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
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26
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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.
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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
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27
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Predicting Secondary Structure Propensities in IDPs Using Simple Statistics from Three-Residue Fragments. J Mol Biol 2020; 432:5447-5459. [DOI: 10.1016/j.jmb.2020.07.026] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/30/2020] [Accepted: 07/31/2020] [Indexed: 01/21/2023]
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28
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Hadarovich AY, Kalinouski AA, Tuzikov AV. Protein homodimers structure prediction based on deep neural network. INFORMATICS 2020. [DOI: 10.37661/1816-0301-2020-17-2-44-53] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Structural prediction of protein-protein complexes has important application in such domains as modeling of biological processes and drug design. Homodimers (complexes which consist of two identical proteins) are the most common type of protein complexes in nature but there is still no universal algorithm to predict their 3D structures. Experimental techniques to identify the structure of protein complex require enormous amount of time and resources, and each method has its own limitations. Recently Deep Neural Networks allowed to predict structures of individual proteins greatly prevailing in accuracy over other algorithmic approaches. Building on the idea of this approach, we developed an algorithm to model the 3D structure of homodimer based on deep learning. It consists of two major steps: at the first step a protein complex contact map is predicted with the deep convolutional neural network, and the second stage is used to predict 3D structure of homodimer based on obtained contact map and optimization procedure. The use of the neural network in combination with optimization procedure based on gradient descent method allowed to predict structures for protein homodimers. The suggested approach was tested and validated on a dataset of protein homodimers from Protein Data Bank (PDB). The developed procedure could be also used for evaluating protein homodimer models as one of the stages in drug compounds developing.
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Affiliation(s)
- A. Y. Hadarovich
- The United Institute of Informatics Problems of the National Academy of Sciences of Belarus; Belarusian State University
| | - A. A. Kalinouski
- The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
| | - A. V. Tuzikov
- The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
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29
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Gao J, Miao Z, Zhang Z, Wei H, Kurgan L. Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment. Curr Drug Targets 2020; 20:579-592. [PMID: 30360734 DOI: 10.2174/1389450119666181022153942] [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: 08/25/2018] [Revised: 10/03/2018] [Accepted: 10/04/2018] [Indexed: 12/20/2022]
Abstract
BACKGROUND Ion channels are a large and growing protein family. Many of them are associated with diseases, and consequently, they are targets for over 700 drugs. Discovery of new ion channels is facilitated with computational methods that predict ion channels and their types from protein sequences. However, these methods were never comprehensively compared and evaluated. OBJECTIVE We offer first-of-its-kind comprehensive survey of the sequence-based predictors of ion channels. We describe eight predictors that include five methods that predict ion channels, their types, and four classes of the voltage-gated channels. We also develop and use a new benchmark dataset to perform comparative empirical analysis of the three currently available predictors. RESULTS While several methods that rely on different designs were published, only a few of them are currently available and offer a broad scope of predictions. Support and availability after publication should be required when new methods are considered for publication. Empirical analysis shows strong performance for the prediction of ion channels and modest performance for the prediction of ion channel types and voltage-gated channel classes. We identify a substantial weakness of current methods that cannot accurately predict ion channels that are categorized into multiple classes/types. CONCLUSION Several predictors of ion channels are available to the end users. They offer practical levels of predictive quality. Methods that rely on a larger and more diverse set of predictive inputs (such as PSIONplus) are more accurate. New tools that address multi-label prediction of ion channels should be developed.
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Affiliation(s)
- Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Zhen Miao
- College of Life Sciences, Nankai University, Tianjin, China
| | - Zhaopeng Zhang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Hong Wei
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, United States
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30
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Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning. J Comput Biol 2020; 27:796-814. [DOI: 10.1089/cmb.2019.0193] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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31
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Abbass J, Nebel JC. Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure. BMC Bioinformatics 2020; 21:170. [PMID: 32357827 PMCID: PMC7195757 DOI: 10.1186/s12859-020-3491-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 04/13/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Whenever suitable template structures are not available, usage of fragment-based protein structure prediction becomes the only practical alternative as pure ab initio techniques require massive computational resources even for very small proteins. However, inaccuracy of their energy functions and their stochastic nature imposes generation of a large number of decoys to explore adequately the solution space, limiting their usage to small proteins. Taking advantage of the uneven complexity of the sequence-structure relationship of short fragments, we adjusted the fragment insertion process by customising the number of available fragment templates according to the expected complexity of the predicted local secondary structure. Whereas the number of fragments is kept to its default value for coil regions, important and dramatic reductions are proposed for beta sheet and alpha helical regions, respectively. RESULTS The evaluation of our fragment selection approach was conducted using an enhanced version of the popular Rosetta fragment-based protein structure prediction tool. It was modified so that the number of fragment candidates used in Rosetta could be adjusted based on the local secondary structure. Compared to Rosetta's standard predictions, our strategy delivered improved first models, + 24% and + 6% in terms of GDT, when using 2000 and 20,000 decoys, respectively, while reducing significantly the number of fragment candidates. Furthermore, our enhanced version of Rosetta is able to deliver with 2000 decoys a performance equivalent to that produced by standard Rosetta while using 20,000 decoys. We hypothesise that, as the fragment insertion process focuses on the most challenging regions, such as coils, fewer decoys are needed to explore satisfactorily conformation spaces. CONCLUSIONS Taking advantage of the high accuracy of sequence-based secondary structure predictions, we showed the value of that information to customise the number of candidates used during the fragment insertion process of fragment-based protein structure prediction. Experimentations conducted using standard Rosetta showed that, when using the recommended number of decoys, i.e. 20,000, our strategy produces better results. Alternatively, similar results can be achieved using only 2000 decoys. Consequently, we recommend the adoption of this strategy to either improve significantly model quality or reduce processing times by a factor 10.
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Affiliation(s)
- Jad Abbass
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE UK
- Department of Computer Science, Lebanese International University, Bekaa, Lebanon
| | - Jean-Christophe Nebel
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE UK
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Smolarczyk T, Roterman-Konieczna I, Stapor K. Protein Secondary Structure Prediction: A Review of Progress and Directions. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191017104639] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Over the last few decades, a search for the theory of protein folding has
grown into a full-fledged research field at the intersection of biology, chemistry and informatics.
Despite enormous effort, there are still open questions and challenges, like understanding the rules
by which amino acid sequence determines protein secondary structure.
Objective:
In this review, we depict the progress of the prediction methods over the years and
identify sources of improvement.
Methods:
The protein secondary structure prediction problem is described followed by the discussion
on theoretical limitations, description of the commonly used data sets, features and a review
of three generations of methods with the focus on the most recent advances. Additionally, methods
with available online servers are assessed on the independent data set.
Results:
The state-of-the-art methods are currently reaching almost 88% for 3-class prediction and
76.5% for an 8-class prediction.
Conclusion:
This review summarizes recent advances and outlines further research directions.
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Affiliation(s)
- Tomasz Smolarczyk
- Institute of Informatics, Silesian University of Technology, Gliwice, Poland
| | - Irena Roterman-Konieczna
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Krakow, Poland
| | - Katarzyna Stapor
- Institute of Informatics, Silesian University of Technology, Gliwice, Poland
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Torrisi M, Pollastri G, Le Q. Deep learning methods in protein structure prediction. Comput Struct Biotechnol J 2020; 18:1301-1310. [PMID: 32612753 PMCID: PMC7305407 DOI: 10.1016/j.csbj.2019.12.011] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/19/2019] [Accepted: 12/20/2019] [Indexed: 01/01/2023] Open
Abstract
Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the '60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels of detail. In this review, we briefly introduce the problem of protein structure prediction and essential elements of Deep Learning (such as Convolutional Neural Networks, Recurrent Neural Networks and basic feed-forward Neural Networks they are founded on), after which we discuss the evolution of predictive methods for one-dimensional and two-dimensional Protein Structure Annotations, from the simple statistical methods of the early days, to the computationally intensive highly-sophisticated Deep Learning algorithms of the last decade. In the process, we review the growth of the databases these algorithms are based on, and how this has impacted our ability to leverage knowledge about evolution and co-evolution to achieve improved predictions. We conclude this review outlining the current role of Deep Learning techniques within the wider pipelines to predict protein structures and trying to anticipate what challenges and opportunities may arise next.
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Affiliation(s)
- Mirko Torrisi
- School of Computer Science, University College Dublin, Ireland
| | | | - Quan Le
- Centre for Applied Data Analytics Research, University College Dublin, Ireland
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34
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Shi Q, Chen W, Huang S, Wang Y, Xue Z. Deep learning for mining protein data. Brief Bioinform 2019; 22:194-218. [PMID: 31867611 DOI: 10.1093/bib/bbz156] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 10/21/2019] [Accepted: 11/07/2019] [Indexed: 01/16/2023] Open
Abstract
The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.
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Affiliation(s)
- Qiang Shi
- School of Software Engineering, Huazhong University of Science and Technology. His main interests cover machine learning especially deep learning, protein data analysis, and big data mining
| | - Weiya Chen
- School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. His research interests cover bioinformatics, virtual reality, and data visualization
| | - Siqi Huang
- Software Engineering at Huazhong University of science and technology, focusing on Machine learning and data mining
| | - Yan Wang
- School of life, University of Science & Technology; her main interests cover protein structure and function prediction and big data mining
| | - Zhidong Xue
- School of Software Engineering, Huazhong University of Science & Technology, Wuhan, China. His research interests cover bioinformatics, machine learning, and image processing
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35
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Laghuvarapu S, Pathak Y, Priyakumar UD. BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules. J Comput Chem 2019; 41:790-799. [DOI: 10.1002/jcc.26128] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 11/13/2019] [Accepted: 11/21/2019] [Indexed: 12/26/2022]
Affiliation(s)
- Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and BioinformaticsInternational Institute of Information Technology Hyderabad 500 032 India
| | - Yashaswi Pathak
- Center for Computational Natural Sciences and BioinformaticsInternational Institute of Information Technology Hyderabad 500 032 India
| | - U. Deva Priyakumar
- Center for Computational Natural Sciences and BioinformaticsInternational Institute of Information Technology Hyderabad 500 032 India
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36
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FRSite: Protein drug binding site prediction based on faster R–CNN. J Mol Graph Model 2019; 93:107454. [DOI: 10.1016/j.jmgm.2019.107454] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 09/17/2019] [Accepted: 09/17/2019] [Indexed: 11/16/2022]
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37
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Smolarczyk T, Stapor K, Roterman-Konieczna I. Backbone dihedral angles prediction servers for protein early-stage structure prediction. BIO-ALGORITHMS AND MED-SYSTEMS 2019. [DOI: 10.1515/bams-2019-0034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractThree-dimensional protein structure prediction is an important task in science at the intersection of biology, chemistry, and informatics, and it is crucial for determining the protein function. In the two-stage protein folding model, based on an early- and late-stage intermediates, we propose to use state-of-the-art secondary structure prediction servers for backbone dihedral angles prediction and devise an early-stage structure. Early-stage structures are used as a starting point for protein folding simulations, and any errors in this stage affect the final predictions. We have shown that modern secondary structure prediction servers could increase the accuracy of early-stage predictions compared to previously reported models.
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Affiliation(s)
- Tomasz Smolarczyk
- Institute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, Poland
| | - Katarzyna Stapor
- Institute of Informatics, Silesian University of Technology, Akademicka 16, Gliwice, Poland
| | - Irena Roterman-Konieczna
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Kraków, Poland
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38
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Guo Y, Wang B, Li W, Yang B. Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks. J Bioinform Comput Biol 2019; 16:1850021. [PMID: 30419785 DOI: 10.1142/s021972001850021x] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature vector dimension. Common approaches to predict secondary structures only focus on the amino-acid residue dimension. However, the feature vector dimension may also contain useful information for PSSP. To integrate the information on both dimensions of the matrix, we propose a hybrid deep learning framework, two-dimensional convolutional bidirectional recurrent neural network (2C-BRNN), for improving the accuracy of 8-class secondary structure prediction. The proposed hybrid framework is to extract the discriminative local interactions between amino-acid residues by two-dimensional convolutional neural networks (2DCNNs), and then further capture long-range interactions between amino-acid residues by bidirectional gated recurrent units (BGRUs) or bidirectional long short-term memory (BLSTM). Specifically, our proposed 2C-BRNNs framework consists of four models: 2DConv-BGRUs, 2DCNN-BGRUs, 2DConv-BLSTM and 2DCNN-BLSTM. Among these four models, the 2DConv- models only contain two-dimensional (2D) convolution operations. Moreover, the 2DCNN- models contain 2D convolutional and pooling operations. Experiments are conducted on four public datasets. The experimental results show that our proposed 2DConv-BLSTM model performs significantly better than the benchmark models. Furthermore, the experiments also demonstrate that the proposed models can extract more meaningful features from the matrix of proteins, and the feature vector dimension is also useful for PSSP. The codes and datasets of our proposed methods are available at https://github.com/guoyanb/JBCB2018/ .
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Affiliation(s)
- Yanbu Guo
- * School of Information Science and Engineering, Yunnan University, No. 2 North Cuihu Road, Kunming 650091, P. R. China
| | - Bingyi Wang
- † The Research Institute of Resource Insects, Chinese Academy of Forestry, Bailongsi, Kunming 650224, P. R. China
| | - Weihua Li
- * School of Information Science and Engineering, Yunnan University, No. 2 North Cuihu Road, Kunming 650091, P. R. China
| | - Bei Yang
- ‡ MD. Cardiology Department, The Second People's Hospital of Yunnan Province, No. 176 Qingnian Road, Kunming 650021, P. R. China
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39
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Shen C, Ding J, Wang Z, Cao D, Ding X, Hou T. From machine learning to deep learning: Advances in scoring functions for protein–ligand docking. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1429] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
| | - Junjie Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing P. R. China
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University Changsha P. R. China
| | - Xiaoqin Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing P. R. China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University Hangzhou P. R. China
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40
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Ludwiczak J, Winski A, da Silva Neto AM, Szczepaniak K, Alva V, Dunin-Horkawicz S. PiPred - a deep-learning method for prediction of π-helices in protein sequences. Sci Rep 2019; 9:6888. [PMID: 31053765 PMCID: PMC6499831 DOI: 10.1038/s41598-019-43189-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 04/16/2019] [Indexed: 11/17/2022] Open
Abstract
Canonical π-helices are short, relatively unstable secondary structure elements found in proteins. They comprise seven or more residues and are present in 15% of all known protein structures, often in functionally important regions such as ligand- and ion-binding sites. Given their similarity to α-helices, the prediction of π-helices is a challenging task and none of the currently available secondary structure prediction methods tackle it. Here, we present PiPred, a neural network-based tool for predicting π-helices in protein sequences. By performing a rigorous benchmark we show that PiPred can detect π-helices with a per-residue precision of 48% and sensitivity of 46%. Interestingly, some of the α-helices mispredicted by PiPred as π-helices exhibit a geometry characteristic of π-helices. Also, despite being trained only with canonical π-helices, PiPred can identify 6-residue-long α/π-bulges. These observations suggest an even higher effective precision of the method and demonstrate that π-helices, α/π-bulges, and other helical deformations may impose similar constraints on sequences. PiPred is freely accessible at: https://toolkit.tuebingen.mpg.de/#/tools/quick2d. A standalone version is available for download at: https://github.com/labstructbioinf/PiPred, where we also provide the CB6133, CB513, CASP10, and CASP11 datasets, commonly used for training and validation of secondary structure prediction methods, with correctly annotated π-helices.
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Affiliation(s)
- Jan Ludwiczak
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097, Warsaw, Poland.,Laboratory of Bioinformatics, Nencki Institute of Experimental Biology, Pasteura 3, 02-093, Warsaw, Poland
| | - Aleksander Winski
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097, Warsaw, Poland
| | - Antonio Marinho da Silva Neto
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097, Warsaw, Poland
| | - Krzysztof Szczepaniak
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097, Warsaw, Poland
| | - Vikram Alva
- Department of Protein Evolution, Max-Planck-Institute for Developmental Biology, Max-Planck-Ring 5, 72076, Tübingen, Germany
| | - Stanislaw Dunin-Horkawicz
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097, Warsaw, Poland.
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41
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O’Brien KT, Golla K, Kranjc T, O’Donovan D, Allen S, Maguire P, Simpson JC, O’Connell D, Moran N, Shields DC. Computational and experimental analysis of bioactive peptide linear motifs in the integrin adhesome. PLoS One 2019; 14:e0210337. [PMID: 30689642 PMCID: PMC6349357 DOI: 10.1371/journal.pone.0210337] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 12/20/2018] [Indexed: 12/15/2022] Open
Abstract
Therapeutic modulation of protein interactions is challenging, but short linear motifs (SLiMs) represent potential targets. Focal adhesions play a central role in adhesion by linking cells to the extracellular matrix. Integrins are central to this process, and many other intracellular proteins are components of the integrin adhesome. We applied a peptide network targeting approach to explore the intracellular modulation of integrin function in platelets. Firstly, we computed a platelet-relevant integrin adhesome, inferred via homology of known platelet proteins to adhesome components. We then computationally selected peptides from the set of platelet integrin adhesome cytoplasmic and membrane adjacent protein-protein interfaces. Motifs of interest in the intracellular component of the platelet integrin adhesome were identified using a predictor of SLiMs based on analysis of protein primary amino acid sequences (SLiMPred), a predictor of strongly conserved motifs within disordered protein regions (SLiMPrints), and information from the literature regarding protein interactions in the complex. We then synthesized peptides incorporating these motifs combined with cell penetrating factors (tat peptide and palmitylation for cytoplasmic and membrane proteins respectively). We tested for the platelet activating effects of the peptides, as well as their abilities to inhibit activation. Bioactivity testing revealed a number of peptides that modulated platelet function, including those derived from α-actinin (ACTN1) and syndecan (SDC4), binding to vinculin and syntenin respectively. Both chimeric peptide experiments and peptide combination experiments failed to identify strong effects, perhaps characterizing the adhesome as relatively robust against within-adhesome synergistic perturbation. We investigated in more detail peptides targeting vinculin. Combined experimental and computational evidence suggested a model in which the positively charged tat-derived cell penetrating part of the peptide contributes to bioactivity via stabilizing charge interactions with a region of the ACTN1 negatively charged surface. We conclude that some interactions in the integrin adhesome appear to be capable of modulation by short peptides, and may aid in the identification and characterization of target sites within the complex that may be useful for therapeutic modulation.
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Affiliation(s)
- Kevin T. O’Brien
- School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Kalyan Golla
- Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Tilen Kranjc
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
- School of Biology and Environment Science, University College Dublin, Dublin, Ireland
| | - Darragh O’Donovan
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
- School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | - Seamus Allen
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
- School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | - Patricia Maguire
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
- School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | - Jeremy C. Simpson
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
- School of Biology and Environment Science, University College Dublin, Dublin, Ireland
| | - David O’Connell
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
- School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland
| | - Niamh Moran
- Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Denis C. Shields
- School of Medicine, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
- * E-mail:
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42
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Mason TO, Shimanovich U. Fibrous Protein Self-Assembly in Biomimetic Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2018; 30:e1706462. [PMID: 29883013 DOI: 10.1002/adma.201706462] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 03/28/2018] [Indexed: 05/22/2023]
Abstract
Protein self-assembly processes, by which polypeptides interact and independently form multimeric structures, lead to a wide array of different endpoints. Structures formed range from highly ordered molecular crystals to amorphous aggregates. Order arises in the system from a balance between many low-energy processes occurring due to a set of interactions between residues in a chain, between residues in different chains, and between solute and solvent. In Nature, self-assembling protein systems have evolved over millions of years to organize into supramolecular structures, optimized for specific functions, with this propensity determined by the sequence of their constituent amino acids, of which only 20 are encoded in DNA. The structural materials that arise from biological self-assembly can display remarkable mechanical properties, often as a result of hierarchical structure on the nano- and microscales, and much research has been devoted to mimicking and exploiting these properties for a variety of end uses. This work presents a review of a range of studies in which biological functions are effectively reproduced through the design of self-assembling fibrous protein systems.
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Affiliation(s)
- Thomas O Mason
- Department of Materials and Interfaces, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Ulyana Shimanovich
- Department of Materials and Interfaces, Weizmann Institute of Science, Rehovot, 7610001, Israel
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Delarue M, Koehl P. Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)? F1000Res 2018; 7. [PMID: 30079234 PMCID: PMC6058471 DOI: 10.12688/f1000research.14870.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/19/2018] [Indexed: 11/20/2022] Open
Abstract
Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences.
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Affiliation(s)
- Marc Delarue
- Unité Dynamique Structurale des Macromolécules, Institut Pasteur, and UMR 3528 du CNRS, Paris, France
| | - Patrice Koehl
- Department of Computer Science, Genome Center, University of California, Davis, Davis, California, USA
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