1
|
Sun J, Kulandaisamy A, Liu J, Hu K, Gromiha MM, Zhang Y. Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications. Comput Struct Biotechnol J 2023; 21:1205-1226. [PMID: 36817959 PMCID: PMC9932300 DOI: 10.1016/j.csbj.2023.01.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/16/2023] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.
Collapse
Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Headington, Oxford OX3 7LD, UK
| | - Arulsamy Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - Jacklyn Liu
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - Kai Hu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India,Corresponding authors.
| | - Yuan Zhang
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China,Corresponding authors.
| |
Collapse
|
2
|
Kurgan L. Resources for computational prediction of intrinsic disorder in proteins. Methods 2022; 204:132-141. [DOI: 10.1016/j.ymeth.2022.03.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 12/26/2022] Open
|
3
|
Lee I, Nam H. Sequence-based prediction of protein binding regions and drug-target interactions. J Cheminform 2022; 14:5. [PMID: 35135622 PMCID: PMC8822694 DOI: 10.1186/s13321-022-00584-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/20/2022] [Indexed: 12/19/2022] Open
Abstract
Identifying drug-target interactions (DTIs) is important for drug discovery. However, searching all drug-target spaces poses a major bottleneck. Therefore, recently many deep learning models have been proposed to address this problem. However, the developers of these deep learning models have neglected interpretability in model construction, which is closely related to a model's performance. We hypothesized that training a model to predict important regions on a protein sequence would increase DTI prediction performance and provide a more interpretable model. Consequently, we constructed a deep learning model, named Highlights on Target Sequences (HoTS), which predicts binding regions (BRs) between a protein sequence and a drug ligand, as well as DTIs between them. To train the model, we collected complexes of protein-ligand interactions and protein sequences of binding sites and pretrained the model to predict BRs for a given protein sequence-ligand pair via object detection employing transformers. After pretraining the BR prediction, we trained the model to predict DTIs from a compound token designed to assign attention to BRs. We confirmed that training the BRs prediction model indeed improved the DTI prediction performance. The proposed HoTS model showed good performance in BR prediction on independent test datasets even though it does not use 3D structure information in its prediction. Furthermore, the HoTS model achieved the best performance in DTI prediction on test datasets. Additional analysis confirmed the appropriate attention for BRs and the importance of transformers in BR and DTI prediction. The source code is available on GitHub ( https://github.com/GIST-CSBL/HoTS ).
Collapse
Affiliation(s)
- Ingoo Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-ku, Gwangju, 61005 Republic of Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-ku, Gwangju, 61005 Republic of Korea
| |
Collapse
|
4
|
Singh J, Litfin T, Singh J, Paliwal K, Zhou Y. SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model. Bioinformatics 2022; 38:1888-1894. [PMID: 35104320 PMCID: PMC9113311 DOI: 10.1093/bioinformatics/btac053] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/21/2021] [Accepted: 01/26/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Accurate prediction of protein contact-map is essential for accurate protein structure and function prediction. As a result, many methods have been developed for protein contact map prediction. However, most methods rely on protein-sequence-evolutionary information, which may not exist for many proteins due to lack of naturally occurring homologous sequences. Moreover, generating evolutionary profiles is computationally intensive. Here, we developed a contact-map predictor utilizing the output of a pre-trained language model ESM-1b as an input along with a large training set and an ensemble of residual neural networks. RESULTS We showed that the proposed method makes a significant improvement over a single-sequence-based predictor SSCpred with 15% improvement in the F1-score for the independent CASP14-FM test set. It also outperforms evolutionary-profile-based methods trRosetta and SPOT-Contact with 48.7% and 48.5% respective improvement in the F1-score on the proteins without homologs (Neff = 1) in the independent SPOT-2018 set. The new method provides a much faster and reasonably accurate alternative to evolution-based methods, useful for large-scale prediction. AVAILABILITY AND IMPLEMENTATION Stand-alone-version of SPOT-Contact-LM is available at https://github.com/jas-preet/SPOT-Contact-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-contact-single. The datasets used in this research can also be downloaded from the GitHub. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | - Thomas Litfin
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Jaswinder Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | | | - Yaoqi Zhou
- To whom correspondence should be addressed. or or
| |
Collapse
|
5
|
Zhao L, Zhang J, Zhang Y, Ye S, Zhang G, Chen X, Jiang B, Jiang J. Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors. JACS AU 2021; 1:2377-2384. [PMID: 34977905 PMCID: PMC8715543 DOI: 10.1021/jacsau.1c00449] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Indexed: 05/08/2023]
Abstract
A data-driven approach to simulate circular dichroism (CD) spectra is appealing for fast protein secondary structure determination, yet the challenge of predicting electric and magnetic transition dipole moments poses a substantial barrier for the goal. To address this problem, we designed a new machine learning (ML) protocol in which ordinary pure geometry-based descriptors are replaced with alternative embedded density descriptors and electric and magnetic transition dipole moments are successfully predicted with an accuracy comparable to first-principle calculation. The ML model is able to not only simulate protein CD spectra nearly 4 orders of magnitude faster than conventional first-principle simulation but also obtain CD spectra in good agreement with experiments. Finally, we predicted a series of CD spectra of the Trp-cage protein associated with continuous changes of protein configuration along its folding path, showing the potential of our ML model for supporting real-time CD spectroscopy study of protein dynamics.
Collapse
Affiliation(s)
- Luyuan Zhao
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
| | - Jinxiao Zhang
- Guangxi
Key Laboratory of Electrochemical and Magneto-chemical Functional
Materials, College of Chemistry and Bioengineering, Guilin University of Technology, Guilin 541006, P. R. China
| | - Yaolong Zhang
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
| | - Sheng Ye
- School
of Artificial Intelligence, Anhui University, Hefei, Anhui 230601, P. R. China
| | - Guozhen Zhang
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
| | - Xin Chen
- Gusu
Laboratory of Materials, Suzhou, Jiangsu 215123, P. R. China
| | - Bin Jiang
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
| | - Jun Jiang
- Hefei
National Laboratory for Physical Sciences at the Microscale, Collaborative
Innovation Center of Chemistry for Energy Materials, School of Chemistry
and Materials Science, University of Science
and Technology of China, Hefei, Anhui 230026, P. R. China
| |
Collapse
|
6
|
Geethu S, Vimina ER. Improved 3-D Protein Structure Predictions using Deep ResNet Model. Protein J 2021; 40:669-681. [PMID: 34510309 DOI: 10.1007/s10930-021-10016-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/09/2021] [Indexed: 10/20/2022]
Abstract
Protein Structure Prediction (PSP) is considered to be a complicated problem in computational biology. In spite of, the remarkable progress made by the co-evolution-based method in PSP, it is still a challenging and unresolved problem. Recently, along with co-evolutionary relationships, deep learning approaches have been introduced in PSP that lead to significant progress. In this paper a novel methodology using deep ResNet architecture for predicting inter-residue distance and dihedral angles is proposed, that aims to generate 125 homologous sequences in an average from a set of customized sequence database. These sequences are used to generate input features. As an outcome of neural networks, a pool of structures is generated from which the lowest potential structure is chosen as the final predicted 3-D protein structure. The proposed method is trained using 6521 protein sequences extracted from Protein Data Bank (PDB). For testing 48 protein sequences whose residue length is less than 400 residues are chosen from the 13th Critical Assessment of protein Structure Prediction (CASP 13) dataset are used. The model is compared with Alphafold, Zhang, and RaptorX. The template modeling (TM) score is used to evaluate the accuracy of the estimated structure. The proposed method produces better performances for 52% of the target sequences while that of Alphafold, Zhang, RaptorX were 10%, 22.9%, and 6% respectively. Additionally, for 37.5% target sequences, the proposed method was able to achieve accuracy greater than or equal to 0.80. The TM score obtained for the sequences under consideration were 0.69, 0.67, 0.65, and 0.58 respectively for the proposed method, Alphafold, Zhang, and RaptorX.
Collapse
Affiliation(s)
- S Geethu
- Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, Ernakulam, India.
| | - E R Vimina
- Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, Ernakulam, India
| |
Collapse
|
7
|
Hanumanthappa AK, Singh J, Paliwal K, Singh J, Zhou Y. Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network. Bioinformatics 2021; 36:5169-5176. [PMID: 33106872 DOI: 10.1093/bioinformatics/btaa652] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 06/30/2020] [Accepted: 07/14/2020] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition. RESULTS Using the same training set from the recent predictor RNAsol, RNAsnap2 provides an 11% improvement in median Pearson Correlation Coefficient (PCC) and 9% improvement in mean absolute errors for the same test set of 45 RNA chains. A larger improvement (22% in median PCC) is observed for 31 newly deposited RNA chains that are non-redundant and independent from the training and the test sets. A single-sequence version of RNAsnap2 (i.e. without using sequence profiles generated from homology search by Infernal) has achieved comparable performance to the profile-based RNAsol. In addition, RNAsnap2 has achieved comparable performance for protein-bound and protein-free RNAs. Both RNAsnap2 and RNAsnap2 (SingleSeq) are expected to be useful for searching structural signatures and locating functional regions of non-coding RNAs. AVAILABILITY AND IMPLEMENTATION Standalone-versions of RNAsnap2 and RNAsnap2 (SingleSeq) are available at https://github.com/jaswindersingh2/RNAsnap2. Direct prediction can also be made at https://sparks-lab.org/server/rnasnap2. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Anil Kumar Hanumanthappa
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Jaswinder Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Jaspreet Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
| |
Collapse
|
8
|
Zhang T, Singh J, Litfin T, Zhan J, Paliwal K, Zhou Y. RNAcmap: A Fully Automatic Pipeline for Predicting Contact Maps of RNAs by Evolutionary Coupling Analysis. Bioinformatics 2021; 37:3494-3500. [PMID: 34021744 DOI: 10.1093/bioinformatics/btab391] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/27/2021] [Accepted: 05/18/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary coupling or direct coupling analysis. Currently, these coupling analyses relied on manually curated multiple sequence alignments collected in the Rfam database, which contains 3016 families. By comparison, millions of non-coding RNA sequences are known. Here, we established RNAcmap, a fully automatic pipeline that enables evolutionary coupling analysis for any RNA sequences. The homology search was based on the covariance model built by INFERNAL according to two secondary structure predictors: a folding-based algorithm RNAfold and the latest deep-learning method SPOT-RNA. RESULTS We showed that the performance of RNAcmap is less dependent on the specific evolutionary coupling tool but is more dependent on the accuracy of secondary structure predictor with the best performance given by RNAcmap (SPOT-RNA). The performance of RNAcmap (SPOT-RNA) is comparable to that based on Rfam-supplied alignment and consistent for those sequences that are not in Rfam collections. Further improvement can be made with a simple meta predictor RNAcmap (SPOT-RNA/RNAfold) depending on which secondary structure predictor can find more homologous sequences. Reliable base-pairing information generated from RNAcmap, for RNAs with high effective homologous sequences, in particular, will be useful for aiding RNA structure prediction. AVAILABILITY RNAcmap is available as a web server at https://sparks-lab.org/server/rnacmap/ and as a standalone application along with the datasets at https://github.com/sparks-lab-org/RNAcmap_standalone. A platform independent and fully configured docker image of RNAcmap is also provided at https://hub.docker.com/r/jaswindersingh2/rnacmap.
Collapse
Affiliation(s)
- Tongchuan Zhang
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Jaswinder Singh
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Thomas Litfin
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Jian Zhan
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD 4111, Australia
| | - Yaoqi Zhou
- Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr. Southport, QLD 4222, Australia.,Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| |
Collapse
|
9
|
Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. BIOTECHNOL BIOPROC E 2021; 25:895-930. [PMID: 33437151 PMCID: PMC7790479 DOI: 10.1007/s12257-020-0049-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/27/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.
Collapse
Affiliation(s)
- Hyunho Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Eunyoung Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Ingoo Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Bongsung Bae
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Minsu Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| |
Collapse
|
10
|
Kotowski K, Smolarczyk T, Roterman-Konieczna I, Stapor K. ProteinUnet-An efficient alternative to SPIDER3-single for sequence-based prediction of protein secondary structures. J Comput Chem 2020; 42:50-59. [PMID: 33058261 PMCID: PMC7756333 DOI: 10.1002/jcc.26432] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/21/2020] [Accepted: 09/23/2020] [Indexed: 12/16/2022]
Abstract
Predicting protein function and structure from sequence remains an unsolved problem in bioinformatics. The best performing methods rely heavily on evolutionary information from multiple sequence alignments, which means their accuracy deteriorates for sequences with a few homologs, and given the increasing sequence database sizes requires long computation times. Here, a single‐sequence‐based prediction method is presented, called ProteinUnet, leveraging an U‐Net convolutional network architecture. It is compared to SPIDER3‐Single model, based on long short‐term memory‐bidirectional recurrent neural networks architecture. Both methods achieve similar results for prediction of secondary structures (both three‐ and eight‐state), half‐sphere exposure, and contact number, but ProteinUnet has two times fewer parameters, 17 times shorter inference time, and can be trained 11 times faster. Moreover, ProteinUnet tends to be better for short sequences and residues with a low number of local contacts. Additionally, the method of loss weighting is presented as an effective way of increasing accuracy for rare secondary structures.
Collapse
Affiliation(s)
- Krzysztof Kotowski
- Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
| | - Tomasz Smolarczyk
- Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
| | - Irena Roterman-Konieczna
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Kraków, Poland
| | - Katarzyna Stapor
- Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
| |
Collapse
|
11
|
Hanson J, Litfin T, Paliwal K, Zhou Y. Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning. Bioinformatics 2020; 36:1107-1113. [PMID: 31504193 DOI: 10.1093/bioinformatics/btz691] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 07/24/2019] [Accepted: 08/31/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Protein intrinsic disorder describes the tendency of sequence residues to not fold into a rigid three-dimensional shape by themselves. However, some of these disordered regions can transition from disorder to order when interacting with another molecule in segments known as molecular recognition features (MoRFs). Previous analysis has shown that these MoRF regions are indirectly encoded within the prediction of residue disorder as low-confidence predictions [i.e. in a semi-disordered state P(D)≈0.5]. Thus, what has been learned for disorder prediction may be transferable to MoRF prediction. Transferring the internal characterization of protein disorder for the prediction of MoRF residues would allow us to take advantage of the large training set available for disorder prediction, enabling the training of larger analytical models than is currently feasible on the small number of currently available annotated MoRF proteins. In this paper, we propose a new method for MoRF prediction by transfer learning from the SPOT-Disorder2 ensemble models built for disorder prediction. RESULTS We confirm that directly training on the MoRF set with a randomly initialized model yields substantially poorer performance on independent test sets than by using the transfer-learning-based method SPOT-MoRF, for both deep and simple networks. Its comparison to current state-of-the-art techniques reveals its superior performance in identifying MoRF binding regions in proteins across two independent testing sets, including our new dataset of >800 protein chains. These test chains share <30% sequence similarity to all training and validation proteins used in SPOT-Disorder2 and SPOT-MoRF, and provide a much-needed large-scale update on the performance of current MoRF predictors. The method is expected to be useful in locating functional disordered regions in proteins. AVAILABILITY AND IMPLEMENTATION SPOT-MoRF and its data are available as a web server and as a standalone program at: http://sparks-lab.org/jack/server/SPOT-MoRF/index.php. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Jack Hanson
- Signal Processing Laboratory, Griffith University, Brisbane, QLD 4122, Australia
| | - Thomas Litfin
- Institute for Glycomics, School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, Griffith University, Brisbane, QLD 4122, Australia
| | - Yaoqi Zhou
- Institute for Glycomics, School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
| |
Collapse
|