1
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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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2
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Görmez Y, Sabzekar M, Aydın Z. IGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction. Proteins 2021; 89:1277-1288. [PMID: 33993559 DOI: 10.1002/prot.26149] [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: 01/07/2021] [Revised: 04/21/2021] [Accepted: 05/11/2021] [Indexed: 11/10/2022]
Abstract
There is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, predicting secondary structure with higher accuracy will give valuable information about the tertiary structure. Recently, deep learning techniques have obtained promising improvements in several machine learning applications including PSSP. In this article, a novel deep learning model, based on convolutional neural network and graph convolutional network is proposed. PSIBLAST PSSM, HHMAKE PSSM, physico-chemical properties of amino acids are combined with structural profiles to generate a rich feature set. Furthermore, the hyper-parameters of the proposed network are optimized using Bayesian optimization. The proposed model IGPRED obtained 89.19%, 86.34%, 87.87%, 85.76%, and 86.54% Q3 accuracies for CullPDB, EVAset, CASP10, CASP11, and CASP12 datasets, respectively.
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Affiliation(s)
- Yasin Görmez
- Faculty of Economics and Administrative Sciences, Management Information Systems, Sivas Cumhuriyet University, Sivas, Turkey
| | - Mostafa Sabzekar
- Department of Computer Engineering, Birjand University of Technology, Birjand, Iran
| | - Zafer Aydın
- Engineering Faculty, Computer Engineering Department, Abdullah Gül University, Kayseri, Turkey
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3
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Ronel T, Harries M, Wicks K, Oakes T, Singleton H, Dearman R, Maxwell G, Chain B. The clonal structure and dynamics of the human T cell response to an organic chemical hapten. eLife 2021; 10:54747. [PMID: 33432924 PMCID: PMC7880692 DOI: 10.7554/elife.54747] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 01/12/2021] [Indexed: 12/27/2022] Open
Abstract
Diphenylcyclopropenone (DPC) is an organic chemical hapten which induces allergic contact dermatitis and is used in the treatment of warts, melanoma, and alopecia areata. This therapeutic setting therefore provided an opportunity to study T cell receptor (TCR) repertoire changes in response to hapten sensitization in humans. Repeated exposure to DPC induced highly dynamic transient expansions of a polyclonal diverse T cell population. The number of TCRs expanded early after sensitization varies between individuals and predicts the magnitude of the allergic reaction. The expanded TCRs show preferential TCR V and J gene usage and consist of clusters of TCRs with similar sequences, two characteristic features of antigen-driven responses. The expanded TCRs share subtle sequence motifs that can be captured using a dynamic Bayesian network. These observations suggest the response to DPC is mediated by a polyclonal population of T cells recognizing a small number of dominant antigens.
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Affiliation(s)
- Tahel Ronel
- Division of Infection and Immunity, University College London, London, United Kingdom.,Cancer Institute, University College London, London, United Kingdom
| | - Matthew Harries
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Salford Royal NHS Foundation Trust (Dermatology Centre), Salford, United Kingdom
| | - Kate Wicks
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Theres Oakes
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Helen Singleton
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Rebecca Dearman
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Gavin Maxwell
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Bedford, United Kingdom
| | - Benny Chain
- Division of Infection and Immunity, University College London, London, United Kingdom.,Department of Computer Science, University College London, London, United Kingdom
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4
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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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5
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An enhanced protein secondary structure prediction using deep learning framework on hybrid profile based features. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.105926] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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6
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Chen Y, Yuan X, Cang X. Population-based incremental learning for the prediction of Homo sapiens’ protein secondary structure. INT J BIOMATH 2019. [DOI: 10.1142/s1793524519500177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Protein structure prediction is the prediction of the 3D structure of a protein based on its amino acid sequence. It is a key component in disciplines such as medicine, biology, and biochemistry. The prediction of the protein secondary structure of Homo sapiens is one of the more important domains. Many methods have been used to feed forward neural networks or SVMs combined with a sliding window. This method’s mechanisms are too complex to be able to extract clear and straightforward physical meanings from it. This paper explores population-based incremental learning (PBIL), which is a method that combines the mechanisms of a generational genetic algorithm with simple competitive learning. The result shows that its accuracies are particularly associated with the Homo species. This new perspective reveals a number of different possibilities for the purposes of performance improvements.
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Affiliation(s)
- Ye Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221008, P. R. China
| | - Xiaoping Yuan
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221008, P. R. China
| | - Xiaohui Cang
- Institute of Genetics, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, P. R. China
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7
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Yang Y, Gao J, Wang J, Heffernan R, Hanson J, Paliwal K, Zhou Y. Sixty-five years of the long march in protein secondary structure prediction: the final stretch? Brief Bioinform 2018; 19:482-494. [PMID: 28040746 PMCID: PMC5952956 DOI: 10.1093/bib/bbw129] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 11/15/2016] [Indexed: 11/13/2022] Open
Abstract
Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82-84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88-90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.
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Affiliation(s)
- Yuedong Yang
- Insitute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
| | - Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China
| | - Jihua Wang
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
| | - Rhys Heffernan
- Signal Processing Laboratory, Griffith University, Brisbane, Australia
| | - Jack Hanson
- Signal Processing Laboratory, Griffith University, Brisbane, Australia
| | - Kuldip Paliwal
- Signal Processing Laboratory, Griffith University, Brisbane, Australia
| | - Yaoqi Zhou
- Insitute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, QLD, Australia
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
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8
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Polynomial-Time Algorithm for Learning Optimal BFS-Consistent Dynamic Bayesian Networks. ENTROPY 2018; 20:e20040274. [PMID: 33265365 PMCID: PMC7512791 DOI: 10.3390/e20040274] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 04/05/2018] [Accepted: 04/10/2018] [Indexed: 11/17/2022]
Abstract
Dynamic Bayesian networks (DBN) are powerful probabilistic representations that model stochastic processes. They consist of a prior network, representing the distribution over the initial variables, and a set of transition networks, representing the transition distribution between variables over time. It was shown that learning complex transition networks, considering both intra- and inter-slice connections, is NP-hard. Therefore, the community has searched for the largest subclass of DBNs for which there is an efficient learning algorithm. We introduce a new polynomial-time algorithm for learning optimal DBNs consistent with a breadth-first search (BFS) order, named bcDBN. The proposed algorithm considers the set of networks such that each transition network has a bounded in-degree, allowing for p edges from past time slices (inter-slice connections) and k edges from the current time slice (intra-slice connections) consistent with the BFS order induced by the optimal tree-augmented network (tDBN). This approach increases exponentially, in the number of variables, the search space of the state-of-the-art tDBN algorithm. Concerning worst-case time complexity, given a Markov lag m, a set of n random variables ranging over r values, and a set of observations of N individuals over T time steps, the bcDBN algorithm is linear in N, T and m; polynomial in n and r; and exponential in p and k. We assess the bcDBN algorithm on simulated data against tDBN, revealing that it performs well throughout different experiments.
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9
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Protein secondary structure prediction: A survey of the state of the art. J Mol Graph Model 2017; 76:379-402. [DOI: 10.1016/j.jmgm.2017.07.015] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 07/14/2017] [Accepted: 07/17/2017] [Indexed: 11/21/2022]
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10
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Constantinou AC, Fenton N. The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks. PLoS One 2017; 12:e0179297. [PMID: 28654698 PMCID: PMC5487021 DOI: 10.1371/journal.pone.0179297] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 05/26/2017] [Indexed: 11/29/2022] Open
Abstract
In 2015 the British government announced a number of major tax reforms for individual landlords. To give landlords time to adjust, some of these tax measures are being introduced gradually from April 2017, with full effect in tax year 2020/21. The changes in taxation have received much media attention since there has been widespread belief that the new measures were sufficiently skewed against landlords that they could signal the end of the Buy-To-Let (BTL) investment era in the UK. This paper assesses the prospective performance of BTL investments in London from the investor’s perspective, and examines the impact of incoming tax reforms using a novel Temporal Bayesian Network model. The model captures uncertainties of interest by simulating the impact of changing circumstances and the interventions available to an investor at various time-steps of a BTL investment portfolio. The simulation results suggest that the new tax reforms are likely to have a detrimental effect on net profits from rental income, and this hits risk-seeking investors who favour leverage much harder than risk-averse investors who do not seek to expand their property portfolio. The impact on net profits also poses substantial risks for lossmaking returns excluding capital gains, especially in the case of rising interest rates. While this makes it less desirable or even non-viable for some to continue being a landlord, based on the current status of all factors taken into consideration for simulation, investment prospects are still likely to remain good within a reasonable range of interest rate and capital growth rate variations. The results also suggest that the recent trend of property prices in London increasing faster than rents will not continue for much longer; either capital growth rates will have to decrease, rental growth rates will have to increase, or we shall observe a combination of the two events.
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Affiliation(s)
- Anthony C. Constantinou
- Risk and Information Management (RIM) Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
- * E-mail:
| | - Norman Fenton
- Risk and Information Management (RIM) Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
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11
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Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images. Comput Biol Chem 2017; 68:231-244. [DOI: 10.1016/j.compbiolchem.2017.04.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2017] [Revised: 03/11/2017] [Accepted: 04/11/2017] [Indexed: 11/20/2022]
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12
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Onisko A, Druzdzel MJ, Austin RM. How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling. J Pathol Inform 2016; 7:50. [PMID: 28163973 PMCID: PMC5248402 DOI: 10.4103/2153-3539.197191] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 11/17/2016] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. AIM The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. MATERIALS AND METHODS This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. RESULTS The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. CONCLUSION Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.
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Affiliation(s)
- Agnieszka Onisko
- Department of Pathology, University of Pittsburgh Medical Center, Magee-Womens Hospital, Pittsburgh, PA 15213, USA
- Faculty of Computer Science, Bialystok University of Technology, 15-351 Bialystok, Poland
| | - Marek J. Druzdzel
- Faculty of Computer Science, Bialystok University of Technology, 15-351 Bialystok, Poland
- Decision Systems Laboratory, School of Information Sciences and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - R. Marshall Austin
- Department of Pathology, University of Pittsburgh Medical Center, Magee-Womens Hospital, Pittsburgh, PA 15213, USA
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13
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Shaghaghi H, Ebrahimi HP, Fathi F, Bahrami Panah N, Jalali-Heravi M, Tafazzoli M. A simple graphical approach to predict local residue conformation using NMR chemical shifts and density functional theory. J Comput Chem 2016; 37:1296-305. [DOI: 10.1002/jcc.24323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 11/25/2015] [Accepted: 01/17/2016] [Indexed: 11/08/2022]
Affiliation(s)
- Hoora Shaghaghi
- Department of Radiology; University of Pennsylvania; Philadelphia Pennsylvania 19104
| | - Hossein Pasha Ebrahimi
- Department of Biochemistry and National Magnetic Resonance Facility at Madison; University of Wisconsin-Madison; Wisconsin
| | - Fariba Fathi
- Department of Chemistry; Sharif University of Technology; Tehran Iran
| | | | - Mehdi Jalali-Heravi
- Department of Chemistry and Biochemistry; California State University; Los Angeles California
| | - Mohsen Tafazzoli
- Department of Chemistry; Sharif University of Technology; Tehran Iran
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14
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Spencer M, Eickholt J, Cheng J. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:103-12. [PMID: 25750595 PMCID: PMC4348072 DOI: 10.1109/tcbb.2014.2343960] [Citation(s) in RCA: 138] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.
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Affiliation(s)
- Matt Spencer
- Informatics Institute, University of Missouri, Columbia, MO 65211.
| | - Jesse Eickholt
- Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859.
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, MO 65211.
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15
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Li Q, Dahl DB, Vannucci M, Hyun Joo, Tsai JW. Bayesian model of protein primary sequence for secondary structure prediction. PLoS One 2014; 9:e109832. [PMID: 25314659 PMCID: PMC4196994 DOI: 10.1371/journal.pone.0109832] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Accepted: 09/02/2014] [Indexed: 01/26/2023] Open
Abstract
Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein's function in the cell. Understanding a protein's secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.
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Affiliation(s)
- Qiwei Li
- Department of Statistics, Rice University, Houston, Texas, United States of America
| | - David B. Dahl
- Department of Statistics, Brigham Young University, Provo, Utah, United States of America
| | - Marina Vannucci
- Department of Statistics, Rice University, Houston, Texas, United States of America
| | - Hyun Joo
- Department of Chemistry, University of the Pacific, Stockton, California, United States of America
| | - Jerry W. Tsai
- Department of Chemistry, University of the Pacific, Stockton, California, United States of America
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16
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Seguritan V, Alves N, Arnoult M, Raymond A, Lorimer D, Burgin AB, Salamon P, Segall AM. Artificial neural networks trained to detect viral and phage structural proteins. PLoS Comput Biol 2012; 8:e1002657. [PMID: 22927809 PMCID: PMC3426561 DOI: 10.1371/journal.pcbi.1002657] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2011] [Accepted: 06/29/2012] [Indexed: 01/03/2023] Open
Abstract
Phages play critical roles in the survival and pathogenicity of their hosts, via lysogenic conversion factors, and in nutrient redistribution, via cell lysis. Analyses of phage- and viral-encoded genes in environmental samples provide insights into the physiological impact of viruses on microbial communities and human health. However, phage ORFs are extremely diverse of which over 70% of them are dissimilar to any genes with annotated functions in GenBank. Better identification of viruses would also aid in better detection and diagnosis of disease, in vaccine development, and generally in better understanding the physiological potential of any environment. In contrast to enzymes, viral structural protein function can be much more challenging to detect from sequence data because of low sequence conservation, few known conserved catalytic sites or sequence domains, and relatively limited experimental data. We have designed a method of predicting phage structural protein sequences that uses Artificial Neural Networks (ANNs). First, we trained ANNs to classify viral structural proteins using amino acid frequency; these correctly classify a large fraction of test cases with a high degree of specificity and sensitivity. Subsequently, we added estimates of protein isoelectric points as a feature to ANNs that classify specialized families of proteins, namely major capsid and tail proteins. As expected, these more specialized ANNs are more accurate than the structural ANNs. To experimentally validate the ANN predictions, several ORFs with no significant similarities to known sequences that are ANN-predicted structural proteins were examined by transmission electron microscopy. Some of these self-assembled into structures strongly resembling virion structures. Thus, our ANNs are new tools for identifying phage and potential prophage structural proteins that are difficult or impossible to detect by other bioinformatic analysis. The networks will be valuable when sequence is available but in vitro propagation of the phage may not be practical or possible. Bacteriophages are extremely abundant and diverse biological entities. All phage particles are comprised of nucleic acids and structural proteins, with few other packaged proteins. Despite their simplicity and abundance, more than 70% of phage sequences in the viral Reference Sequence database encode proteins with unknown function based on FASTA annotations. As a result, the use of sequence similarity is often insufficient for detecting virus structural proteins among unknown viral sequences. Viral structural protein function is challenging to detect from sequence data because structural proteins possess few known conserved catalytic motifs and sequence domains. To address these issues we investigated the use of Artificial Neural Networks as an alternative means of predicting function. Here, we trained thousands of networks using the amino acid frequency of structural protein sequences and identified the optimal architectures with the highest accuracies. Some hypothetical protein sequences detected by our networks were expressed and visualized by TEM, and produced images that strongly resemble virion structures. Our results support the utility of our neural networks in predicting the functions of unknown viral sequences.
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Affiliation(s)
- Victor Seguritan
- Program of Computational Science, San Diego State University, San Diego, California, United States of America
| | - Nelson Alves
- Department of Genetics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Michael Arnoult
- Department of Biology, San Diego State University, San Diego, California, United States of America
| | - Amy Raymond
- Emerald BioStructures, Seattle, Washington, United States of America
| | - Don Lorimer
- Emerald BioStructures, Seattle, Washington, United States of America
| | - Alex B. Burgin
- Emerald BioStructures, Seattle, Washington, United States of America
| | - Peter Salamon
- Department of Mathematics and Statistics, San Diego State University, San Diego, California, United States of America
| | - Anca M. Segall
- Program of Computational Science, San Diego State University, San Diego, California, United States of America
- Department of Biology, San Diego State University, San Diego, California, United States of America
- * E-mail:
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17
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Armano G, Ledda F. Exploiting intrastructure information for secondary structure prediction with multifaceted pipelines. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:799-808. [PMID: 22201070 DOI: 10.1109/tcbb.2011.159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Predicting the secondary structure of proteins is still a typical step in several bioinformatic tasks, in particular, for tertiary structure prediction. Notwithstanding the impressive results obtained so far, mostly due to the advent of sequence encoding schemes based on multiple alignment, in our view the problem should be studied from a novel perspective, in which understanding how available information sources are dealt with plays a central role. After revisiting a well-known secondary structure predictor viewed from this perspective (with the goal of identifying which sources of information have been considered and which have not), we propose a generic software architecture designed to account for all relevant information sources. To demonstrate the validity of the approach, a predictor compliant with the proposed generic architecture has been implemented and compared with several state-of-the-art secondary structure predictors. Experiments have been carried out on standard data sets, and the corresponding results confirm the validity of the approach. The predictor is available at http://iasc.diee.unica.it/ssp2/ through the corresponding web application or as downloadable stand-alone portable unpack-and-run bundle.
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Affiliation(s)
- Giuliano Armano
- Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, Cagliari 09123, Italy.
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Qi Y, Oja M, Weston J, Noble WS. A unified multitask architecture for predicting local protein properties. PLoS One 2012; 7:e32235. [PMID: 22461885 PMCID: PMC3312883 DOI: 10.1371/journal.pone.0032235] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2011] [Accepted: 01/25/2012] [Indexed: 01/27/2023] Open
Abstract
A variety of functionally important protein properties, such as secondary structure, transmembrane topology and solvent accessibility, can be encoded as a labeling of amino acids. Indeed, the prediction of such properties from the primary amino acid sequence is one of the core projects of computational biology. Accordingly, a panoply of approaches have been developed for predicting such properties; however, most such approaches focus on solving a single task at a time. Motivated by recent, successful work in natural language processing, we propose to use multitask learning to train a single, joint model that exploits the dependencies among these various labeling tasks. We describe a deep neural network architecture that, given a protein sequence, outputs a host of predicted local properties, including secondary structure, solvent accessibility, transmembrane topology, signal peptides and DNA-binding residues. The network is trained jointly on all these tasks in a supervised fashion, augmented with a novel form of semi-supervised learning in which the model is trained to distinguish between local patterns from natural and synthetic protein sequences. The task-independent architecture of the network obviates the need for task-specific feature engineering. We demonstrate that, for all of the tasks that we considered, our approach leads to statistically significant improvements in performance, relative to a single task neural network approach, and that the resulting model achieves state-of-the-art performance.
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Affiliation(s)
- Yanjun Qi
- Machine Learning Department, NEC Labs America, Princeton, New Jersey, United States of America
| | - Merja Oja
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Jason Weston
- Google, New York, New York, United States of America
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- * E-mail:
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19
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Bettella F, Rasinski D, Knapp EW. Protein Secondary Structure Prediction with SPARROW. J Chem Inf Model 2012; 52:545-56. [DOI: 10.1021/ci200321u] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Francesco Bettella
- Freie Universität
Berlin,
Institut für Chemie, Fabeckstr. 36a, D-14195 Berlin, Germany
- deCODE genetics, Sturlugata
8, 101 Reykjavik, Iceland
| | - Dawid Rasinski
- Freie Universität
Berlin,
Institut für Chemie, Fabeckstr. 36a, D-14195 Berlin, Germany
| | - Ernst Walter Knapp
- Freie Universität
Berlin,
Institut für Chemie, Fabeckstr. 36a, D-14195 Berlin, Germany
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20
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Wei Y, Thompson J, Floudas CA. CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization. Proc Math Phys Eng Sci 2011. [DOI: 10.1098/rspa.2011.0514] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Most of the protein structure prediction methods use a multi-step process, which often includes secondary structure prediction, contact prediction, fragment generation, clustering, etc. For many years, secondary structure prediction has been the workhorse for numerous methods aimed at predicting protein structure and function. This paper presents a new mixed integer linear optimization (MILP)-based consensus method: a Consensus scheme based On a mixed integer liNear optimization method for seCOndary stRucture preDiction (CONCORD). Based on seven secondary structure prediction methods, SSpro, DSC, PROF, PROFphd, PSIPRED, Predator and GorIV, the MILP-based consensus method combines the strengths of different methods, maximizes the number of correctly predicted amino acids and achieves a better prediction accuracy. The method is shown to perform well compared with the seven individual methods when tested on the PDBselect25 training protein set using sixfold cross validation. It also performs well compared with another set of 10 online secondary structure prediction servers (including several recent ones) when tested on the CASP9 targets (
http://predictioncenter.org/casp9/
). The average Q3 prediction accuracy is 83.04 per cent for the sixfold cross validation of the PDBselect25 set and 82.3 per cent for the CASP9 targets. We have developed a MILP-based consensus method for protein secondary structure prediction. A web server, CONCORD, is available to the scientific community at
http://helios.princeton.edu/CONCORD
.
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Affiliation(s)
- Y. Wei
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | - J. Thompson
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | - C. A. Floudas
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
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21
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Aydin Z, Singh A, Bilmes J, Noble WS. Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure. BMC Bioinformatics 2011; 12:154. [PMID: 21569525 PMCID: PMC3118164 DOI: 10.1186/1471-2105-12-154] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2010] [Accepted: 05/13/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic Bayesian networks (DBNs) and support vector machines (SVMs) have been shown to provide state-of-the-art performance in secondary structure prediction. As the size of the protein database grows, it becomes feasible to use a richer model in an effort to capture subtle correlations among the amino acids and the predicted labels. In this context, it is beneficial to derive sparse models that discourage over-fitting and provide biological insight. RESULTS In this paper, we first show that we are able to obtain accurate secondary structure predictions. Our per-residue accuracy on a well established and difficult benchmark (CB513) is 80.3%, which is comparable to the state-of-the-art evaluated on this dataset. We then introduce an algorithm for sparsifying the parameters of a DBN. Using this algorithm, we can automatically remove up to 70-95% of the parameters of a DBN while maintaining the same level of predictive accuracy on the SD576 set. At 90% sparsity, we are able to compute predictions three times faster than a fully dense model evaluated on the SD576 set. We also demonstrate, using simulated data, that the algorithm is able to recover true sparse structures with high accuracy, and using real data, that the sparse model identifies known correlation structure (local and non-local) related to different classes of secondary structure elements. CONCLUSIONS We present a secondary structure prediction method that employs dynamic Bayesian networks and support vector machines. We also introduce an algorithm for sparsifying the parameters of the dynamic Bayesian network. The sparsification approach yields a significant speed-up in generating predictions, and we demonstrate that the amino acid correlations identified by the algorithm correspond to several known features of protein secondary structure. Datasets and source code used in this study are available at http://noble.gs.washington.edu/proj/pssp.
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Affiliation(s)
- Zafer Aydin
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
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22
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Chen X, Hoffman MM, Bilmes JA, Hesselberth JR, Noble WS. A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data. ACTA ACUST UNITED AC 2010; 26:i334-42. [PMID: 20529925 PMCID: PMC2881360 DOI: 10.1093/bioinformatics/btq175] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Motivation: A global map of transcription factor binding sites (TFBSs) is critical to understanding gene regulation and genome function. DNaseI digestion of chromatin coupled with massively parallel sequencing (digital genomic footprinting) enables the identification of protein-binding footprints with high resolution on a genome-wide scale. However, accurately inferring the locations of these footprints remains a challenging computational problem. Results: We present a dynamic Bayesian network-based approach for the identification and assignment of statistical confidence estimates to protein-binding footprints from digital genomic footprinting data. The method, DBFP, allows footprints to be identified in a probabilistic framework and outperforms our previously described algorithm in terms of precision at a fixed recall. Applied to a digital footprinting data set from Saccharomyces cerevisiae, DBFP identifies 4679 statistically significant footprints within intergenic regions. These footprints are mainly located near transcription start sites and are strongly enriched for known TFBSs. Footprints containing no known motif are preferentially located proximal to other footprints, consistent with cooperative binding of these footprints. DBFP also identifies a set of statistically significant footprints in the yeast coding regions. Many of these footprints coincide with the boundaries of antisense transcripts, and the most significant footprints are enriched for binding sites of the chromatin-associated factors Abf1 and Rap1. Contact:jay.hesselberth@ucdenver.edu; william-noble@u.washington.edu Supplementary information:Supplementary material is available at Bioinformatics online.
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Affiliation(s)
- Xiaoyu Chen
- Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA
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Wang D, Wang Q, Shan F, Liu B, Lu C. Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers. BMC Infect Dis 2010; 10:251. [PMID: 20735842 PMCID: PMC2939639 DOI: 10.1186/1471-2334-10-251] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2009] [Accepted: 08/24/2010] [Indexed: 12/14/2022] Open
Abstract
Background Liver fibrosis progression is commonly found in patients with CHB. Liver biopsy is a gold standard for identifying the extent of liver fibrosis, but has many draw-backs. It is essential to construct a noninvasive model to predict the levels of risk for liver fibrosis. It would provide very useful information to help reduce the number of liver biopsies of CHB patients. Methods 339 chronic hepatitis B patients with HBsAg-positive were investigated retrospectively, and divided at random into 2 subsets with twice as many patients in the training set as in the validation set; 116 additional patients were consequently enrolled in the study as the testing set. A three-layer artificial neural network was developed using a Bayesian learning algorithm. Sensitivity and ROC analysis were performed to explain the importance of input variables and the performance of the neural network. Results There were 329 patients without significant fibrosis and 126 with significant fibrosis in the study. All markers except gender, HB, ALP and TP were found to be statistically significant factors associated with significant fibrosis. The sensitivity analysis showed that the most important factors in the predictive model were age, AST, platelet, and GGT, and the influence on the output variable among coal miners were 22.3-24.6%. The AUROC in 3 sets was 0.883, 0.884, and 0.920. In the testing set, for a decision threshold of 0.33, sensitivity and negative predictive values were 100% and all CHB patients with significant fibrosis would be identified. Conclusions The artificial neural network model based on routine and serum markers would predict the risk for liver fibrosis with a high accuracy. 47.4% of CHB patients at a decision threshold of 0.33 would be free of liver biopsy and wouldn't be missed.
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Affiliation(s)
- Danan Wang
- Institute of Immunology, China Medical University, Shenyang, Liaoning, China
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Austin RM, Onisko A, Druzdzel MJ. The Pittsburgh Cervical Cancer Screening Model: a risk assessment tool. Arch Pathol Lab Med 2010; 134:744-50. [PMID: 20441506 DOI: 10.5858/134.5.744] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
CONTEXT Evaluation of cervical cancer screening has grown increasingly complex with the introduction of human papillomavirus (HPV) vaccination and newer screening technologies approved by the US Food and Drug Administration. OBJECTIVE To create a unique Pittsburgh Cervical Cancer Screening Model (PCCSM) that quantifies risk for histopathologic cervical precancer (cervical intraepithelial neoplasia [CIN] 2, CIN3, and adenocarcinoma in situ) and cervical cancer in an environment predominantly using newer screening technologies. DESIGN The PCCSM is a dynamic Bayesian network consisting of 19 variables available in the laboratory information system, including patient history data (most recent HPV vaccination data), Papanicolaou test results, high-risk HPV results, procedure data, and histopathologic results. The model's graphic structure was based on the published literature. Results from 375 441 patient records from 2005 through 2008 were used to build and train the model. Additional data from 45 930 patients were used to test the model. RESULTS The PCCSM compares risk quantitatively over time for histopathologically verifiable CIN2, CIN3, adenocarcinoma in situ, and cervical cancer in screened patients for each current cytology result category and for each HPV result. For each current cytology result, HPV test results affect risk; however, the degree of cytologic abnormality remains the largest positive predictor of risk. Prior history also alters the CIN2, CIN3, adenocarcinoma in situ, and cervical cancer risk for patients with common current cytology and HPV test results. The PCCSM can also generate negative risk projections, estimating the likelihood of the absence of histopathologic CIN2, CIN3, adenocarcinoma in situ, and cervical cancer in screened patients. CONCLUSIONS The PCCSM is a dynamic Bayesian network that computes quantitative cervical disease risk estimates for patients undergoing cervical screening. Continuously updatable with current system data, the PCCSM provides a new tool to monitor cervical disease risk in the evolving postvaccination era.
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Affiliation(s)
- R Marshall Austin
- Department of Pathology, Magee-Womens Hospital, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213-3180, USA.
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Liu H, Tang Z, Yang Y, Weng D, Sun G, Duan Z, Chen J. Identification and classification of high risk groups for Coal Workers' Pneumoconiosis using an artificial neural network based on occupational histories: a retrospective cohort study. BMC Public Health 2009; 9:366. [PMID: 19785771 PMCID: PMC2760532 DOI: 10.1186/1471-2458-9-366] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2009] [Accepted: 09/29/2009] [Indexed: 11/23/2022] Open
Abstract
Background Coal workers' pneumoconiosis (CWP) is a preventable, but not fully curable occupational lung disease. More and more coal miners are likely to be at risk of developing CWP owing to an increase in coal production and utilization, especially in developing countries. Coal miners with different occupational categories and durations of dust exposure may be at different levels of risk for CWP. It is necessary to identify and classify different levels of risk for CWP in coal miners with different work histories. In this way, we can recommend different intervals for medical examinations according to different levels of risk for CWP. Our findings may provide a basis for further emending the measures of CWP prevention and control. Methods The study was performed using longitudinal retrospective data in the Tiefa Colliery in China. A three-layer artificial neural network with 6 input variables, 15 neurons in the hidden layer, and 1 output neuron was developed in conjunction with coal miners' occupational exposure data. Sensitivity and ROC analyses were adapted to explain the importance of input variables and the performance of the neural network. The occupational characteristics and the probability values predicted were used to categorize coal miners for their levels of risk for CWP. Results The sensitivity analysis showed that influence of the duration of dust exposure and occupational category on CWP was 65% and 67%, respectively. The area under the ROC in 3 sets was 0.981, 0.969, and 0.992. There were 7959 coal miners with a probability value < 0.001. The average duration of dust exposure was 15.35 years. The average duration of ex-dust exposure was 0.69 years. Of the coal miners, 79.27% worked in helping and mining. Most of the coal miners were born after 1950 and were first exposed to dust after 1970. One hundred forty-four coal miners had a probability value ≥0.1. The average durations of dust exposure and ex-dust exposure were 25.70 and 16.30 years, respectively. Most of the coal miners were born before 1950 and began to be exposed to dust before 1980. Of the coal miners, 90.28% worked in tunneling. Conclusion The duration of dust exposure and occupational category were the two most important factors for CWP. Coal miners at different levels of risk for CWP could be classified by the three-layer neural network analysis based on occupational history.
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Affiliation(s)
- Hongbo Liu
- Division of Pneumoconiosis, School of Public Health, China Medical University, Shenyang, PR China.
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Reynolds SM, Käll L, Riffle ME, Bilmes JA, Noble WS. Transmembrane topology and signal peptide prediction using dynamic bayesian networks. PLoS Comput Biol 2008; 4:e1000213. [PMID: 18989393 PMCID: PMC2570248 DOI: 10.1371/journal.pcbi.1000213] [Citation(s) in RCA: 174] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2008] [Accepted: 09/23/2008] [Indexed: 11/19/2022] Open
Abstract
Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane submodel. We introduce a two-stage DBN decoder that combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions. We report a relative improvement of 13% over Phobius in full-topology prediction accuracy on transmembrane proteins, and a sensitivity and specificity of 0.96 in detecting signal peptides. We also show that our confidence metrics correlate well with the observed precision. In addition, we have made predictions on all 6.3 million proteins in the Yeast Resource Center (YRC) database. This large-scale study provides an overall picture of the relative numbers of proteins that include a signal-peptide and/or one or more transmembrane segments as well as a valuable resource for the scientific community. All DBNs are implemented using the Graphical Models Toolkit. Source code for the models described here is available at http://noble.gs.washington.edu/proj/philius. A Philius Web server is available at http://www.yeastrc.org/philius, and the predictions on the YRC database are available at http://www.yeastrc.org/pdr.
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Affiliation(s)
- Sheila M. Reynolds
- Department of Electrical Engineering, University of Washington, Seattle, Washington, United States of America
| | - Lukas Käll
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Michael E. Riffle
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Jeff A. Bilmes
- Department of Electrical Engineering, University of Washington, Seattle, Washington, United States of America
- Department of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Department of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
- * E-mail:
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Gupta K, Sehgal V, Levchenko A. A method for probabilistic mapping between protein structure and function taxonomies through cross training. BMC STRUCTURAL BIOLOGY 2008; 8:40. [PMID: 18834528 PMCID: PMC2573881 DOI: 10.1186/1472-6807-8-40] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2008] [Accepted: 10/03/2008] [Indexed: 02/06/2023]
Abstract
BACKGROUND Prediction of function of proteins on the basis of structure and vice versa is a partially solved problem, largely in the domain of biophysics and biochemistry. This underlies the need of computational and bioinformatics approach to solve the problem. Large and organized latent knowledge on protein classification exists in the form of independently created protein classification databases. By creating probabilistic maps between classes of structural classification databases (e.g. SCOP) and classes of functional classification databases (e.g. PROSITE), structure and function of proteins could be probabilistically related. RESULTS We demonstrate that PROSITE and SCOP have significant semantic overlap, in spite of independent classification schemes. By training classifiers of SCOP using classes of PROSITE as attributes and vice versa, accuracy of Support Vector Machine classifiers for both SCOP and PROSITE was improved. Novel attributes, 2-D elastic profiles and Blocks were used to improve time complexity and accuracy. Many relationships were extracted between classes of SCOP and PROSITE using decision trees. CONCLUSION We demonstrate that presented approach can discover new probabilistic relationships between classes of different taxonomies and render a more accurate classification. Extensive mappings between existing protein classification databases can be created to link the large amount of organized data. Probabilistic maps were created between classes of SCOP and PROSITE allowing predictions of structure using function, and vice versa. In our experiments, we also found that functions are indeed more strongly related to structure than are structure to functions.
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Affiliation(s)
- Kshitiz Gupta
- The Whitaker Institute for Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India
| | - Vivek Sehgal
- Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India
- Department of Computer Science, University of Maryland, College ParkCollege Park, MD, USA
- Yahoo! Inc., 701 First Avenue, Sunnyvale, CA, USA
| | - Andre Levchenko
- The Whitaker Institute for Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
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