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Amino Acid Encoding Methods for protein Sequences: A Comprehensive Review and Assessment

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第6期17卷 1918-1931页

作者： Jing, Xiaoyang Dong, Qiwen Hong, Daocheng Lu, Ruqian Fudan Univ Shanghai Key Lab Intelligent Informat Proc Sch Comp Sci 220 Handan Rd Shanghai 200433 Peoples R China East China Normal Univ Sch Data Sci & Engn 3663 Zhongshan Rd Shanghai 200062 Peoples R China

As the first step of machine-learning based protein structure and function prediction, the amino acid encoding play a fundamental role in the final success of those methods. Different from the protein sequence encoding, the amino acid encoding can be used in both residue-level and sequence-level prediction of protein properties by combining them with different algorithms. However, it has not attracted enough attention in the past decades, and there are no comprehensive reviews and assessments about encoding methods so far. In this article, we make a systematic classification and propose a comprehensive review and assessment for various amino acid encoding methods. Those methods are grouped into five categories according to their information sources and information extraction methodologies, including binary encoding, physicochemical properties encoding, evolution-based encoding, structure-based encoding, and machine-learning encoding. Then, 16 representative methods from five categories are selected and compared on protein secondary structure prediction and protein fold recognition tasks by using large-scale benchmark datasets. The results show that the evolution-based position-dependent encoding method PSSM achieved the best performance, and the structure-based and machine-learning encoding methods also show some potential for further application, the neural network based distributed representation of amino acids in particular may bring new light to this area. We hope that the review and assessment are useful for future studies in amino acid encoding.

关键词： Amino acids Encoding protein sequence Standards Machine learning Task analysis Amino acid encoding feature extraction residue encoding protein structure and function prediction protein secondary structure prediction protein fold recognition

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Learning protein secondary structure from sequential and relational data

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NEURAL NETWORKS 2005年第8期18卷 1029-1039页

作者： Ceroni, A Frasconi, P Pollastri, G Univ Florence Dipartimento Sistemi & Informat Florence Italy Natl Univ Ireland Univ Coll Dublin Dept Comp Sci Dublin 4 Ireland

We propose a method for sequential supervised learning that exploits explicit knowledge of short- and long-range dependencies. The architecture consists of a recursive and bi-directional neural network that takes as input a sequence along with an associated interaction graph. The interaction graph models (partial) knowledge about long-range dependency relations. We tested the method on the prediction of protein secondary structure, a task in which relations due to beta-strand pairings and other spatial proximities are known to have a significant effect on the prediction accuracy. In this particular task, interactions can be derived from knowledge of protein contact maps at the residue level. Our results show that prediction accuracy can be significantly boosted by the integration of interaction graphs. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： recursive neural networks relational learning protein secondary structure prediction protein contact maps

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Predicting protein secondary structure using a mixed-modal SVM method in a compound pyramid model

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KNOWLEDGE-BASED SYSTEMS 2011年第2期24卷 304-313页

作者： Yang, Bingru Wu, Qu Ying, Zhou Sui, Haifeng Univ Sci & Technol Beijing Sch Informat Engn Beijing 100083 Peoples R China

Accurate protein secondary structure prediction plays an important role in direct tertiary structure modeling, and can also significantly improve sequence analysis and sequence-structure threading for structure and function determination. Hence improving the accuracy of secondary structure prediction is essential for future developments throughout the field of protein research. In this article, we propose a mixed-modal support vector machine (SVM) method for predicting protein secondary structure. Using the evolutionary information contained in the physicochemical properties of each amino acid and a position-specific scoring matrix generated by a PSI-BLAST multiple sequence alignment as input for a mixed-modal SVM, secondary structure can be predicted at significantly increased accuracy. Using a Knowledge Discovery Theory based on the Inner Cognitive Mechanism (KDTICM) method, we have proposed a compound pyramid model, which is composed of three layers of intelligent interface that integrate a mixed-modal SVM (MMS) module, a modified Knowledge Discovery in Databases (KDD*) process, a mixed-modal back propagation neural network (MMBP) module and so on. Testing against data sets of non-redundant protein sequences returned values for the Q(3) accuracy measure that ranged from 84.0% to 85.6%,while values for the SOV99 segment overlap measure ranged from 79.8% to 80.6%. When compared using a blind test dataset from the CASP8 meeting against currently available secondary structure prediction methods, our new approach shows superior accuracy. Availability: http://***/protein_Web/. (C) 2010 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction Physicochemical properties Mixed-modal SVM Compound pyramid model

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prediction of Beta-Turn in protein Using E-SSpred and Support Vector Machine

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protein JOURNAL 2009年第3-4期28卷 175-181页

作者： Liu, Lirong Fang, Yaping Li, Menglong Wang, Cuicui Sichuan Univ Coll Chem Key Lab Green Chem & Technol Minist Educ Chengdu 610064 Peoples R China

beta-Turn is a secondary protein structure type that plays an important role in protein configuration and function. Here, we introduced an approach of beta-turn prediction that used the support vector machine (SVM) algorithm combined with predicted secondary structure information. The secondary structure information was obtained by using E-SSpred, a new secondary protein structure prediction method. A 7-fold cross validation based on the benchmark dataset of 426 non-homologous protein chains was used to evaluate the performance of our method. The prediction results broke the 80% Q (total) barrier and achieved Q (total) = 80.9%, MCC = 0.44, and Q (predicted) higher 0.9% when compared with the best method. The results in our research are coincident with the conclusion that beta-turn prediction accuracy can be improved by inclusion of secondary structure information.

关键词： beta-Turn prediction protein secondary structure prediction Support vector machines

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CSI-LSTM: a web server to predict protein secondary structure using bidirectional long short term memory and NMR chemical shifts

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JOURNAL OF BIOMOLECULAR NMR 2021年第10-12期75卷 393-400页

作者： Miao, Zhiwei Wang, Qianqian Xiao, Xiongjie Kamal, Ghulam Mustafa Song, Linhong Zhang, Xu Li, Conggang Zhou, Xin Jiang, Bin Liu, Maili Chinese Acad Sci Key Lab Magnet Resonance Biol Syst State Key Lab Magnet Resonance & Atom & Mol Phys Natl Ctr Magnet Resonance WuhanWuhan Natl Lab Op Wuhan 430071 Peoples R China Univ Chinese Acad Sci Beijing 10049 Peoples R China Khwaja Fareed Univ Engn & Informat Technol Dept Chem Rahim Yar Khan 64200 Punjab Pakistan

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.

关键词： Nuclear magnetic resonance Chemical shifts protein secondary structure prediction Deep learning Long short term memory

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Method to Generate Complex Predictive Features for Machine Learning-Based prediction of the Local structure and Functions of proteins

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MOLECULAR BIOLOGY 2023年第1期57卷 136-145页

作者： Milchevskiy, Yu. V. Milchevskaya, V. Yu. Kravatsky, Yu. V. Russian Acad Sci Engelhardt Inst Mol Biol Moscow 119991 Russia Univ Cologne Inst Med Stat & Bioinformat Fac Med D-50931 Cologne Germany Russian Acad Sci Engelhardt Inst Mol Biol Ctr Precis Genome Editing & Genet Technol Biomed Moscow 119991 Russia

Recently, prediction of the structure and function of a protein from its sequence underwent a rapid increase in performance. It is primarily due to the application of machine learning methods, many of which rely on the predictive features supplied to them. It is thus crucial to retrieve the information encoded in the amino acid sequence of a protein. Here we propose a method to generate a set of complex yet interpretable predictors, which aids in revealing factors that influence protein conformation. The method makes it possible to generate predictive features and test them for significance both in the context of a general description of the protein structures and functions and in the context of highly specific predictive tasks. Having generated an exhaustive set of predictors, we narrow it down to a smaller curated set of informative features using feature selection methods, which increases the performance of subsequent predictive modelling. We illustrate the efficiency of our methodology by applying it to local protein structure prediction, where the rate of correct prediction for DSSP Q3 (three-class classification) is 81.3%. The method is implemented in C++ for command line use and can be run on any operating system. The source code is released on GitHub at .

关键词： local structure prediction protein secondary structure prediction protein function protein sequence encoding protein conformation stepwise regression stepwise discriminant analysis

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STRUCTURAL MODEL FOR INTERFERONS

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FEBS LETTERS 1985年第2期186卷 143-148页

作者： PTITSYN, OB FINKELSTEIN, AV MURZIN, AG Institute of Protein Research. Academy of Sciences of the USSR 142292 Pushchino Moscow Region USSR

secondary structures of leucocyte alpha 1- and alpha 2-interferons and of fibroblast beta-interferon are calculated using the molecular theory of protein secondary structures. The common secondary structure calculated for alpha- and beta-interferons is used to predict the three-dimensional structures of fragments 1-110 and 111-166 of the chains (which are supposed to be quasi-independent domains). The predicted structure of the active domain I (1-110) is an 'up-and-down' tetrahelical complex (in which the second helix is shorter than the others and can be absent in alpha 1-interferon) similar to the mirror image of myohaemoerythrin. The predicted structure of domain II (111-166) is either a three-stranded beta-sheet screened from one side by two alpha-helices or a three-helical complex (similar to that in the N-domain of papain), the first structure being more consistent with the circular dichroism data of alpha-interferon and its C-end fragment.

关键词： Interferon protein secondary structure prediction protein tertiary structure prediction

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Functional structures and folding dynamics of two peptides

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CHINESE PHYSICS 2005年第11期14卷 2365-2369页

作者： Sheng, YB Li, J Ma, BL Wang, W Nanjing Univ Natl Lab Solid State Microstruct Inst Biophys Nanjing 210093 Peoples R China Nanjing Univ Dept Phys Nanjing 210093 Peoples R China Chinese Acad Sci Interdisciplinary Ctr Theoret Studies Beijing 100080 Peoples R China

The folding dynamics and structural characteristics of peptides RTKAWNRQLYPEW (PI) and RTKQLYPEW (P2) are investigated by using all-atomic simulation procedure CHARMM in this work. The results show that P1, a segment of an antigen, has a folding motif of a-helix, whereas P2, which is derived by deleting four residues AWNR from peptide P1, prevents the formation of helix and presents a beta-strand. And peptide P1 experiences a more rugged energy landscape than peptide P2. From our results, it is inferred that the antibody CD8 cytolytic T lymphocyte prefers an antigen with a beta-folding structure to that with an a-helical one.

关键词： peptide folding molecular dynamics protein secondary structure prediction

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PSSP with dynamic weighted kernel fusion based on SVM-PHGS

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KNOWLEDGE-BASED SYSTEMS 2012年 27卷 424-442页

作者： Zangooei, Mohammad Hossein Jalili, Saeed Tarbiat Modares Univ SCS Lab Elect & Comp Engn Fac Tehran Iran

Since 1960s, researchers have proposed several prediction methods, for protein secondary structure prediction (PSSP), whereas the accuracy of them is no more than 80%. In this case, there is an urgent need to introduce a high accuracy prediction method. One learning method called support vector machines (SVMs) has shown comparable or better results than neural networks on bioinformatics applications. This research proposes a method based on SVM which has been improved by a new parallel multi class (PMC) method, parallel hierarchical grid search (PHGS), cross validation (CV) technique and weighted kernel fusion (WKF) method. The presented PHGS has been applied to regularize parameters of SVM's kernel function which have an important impact on the accuracy. Using a suitable input data and kernel function for a particular problem can improve the prediction results remarkably. Also to improve our method, Position Scoring Matrix (PSSM) profiles are used as the input information to it. The goals of this study are to calibrate kernel function parameters and fusion of different kernel functions' result in order to determine protein secondary structure classes accurately. The right choice of a fusion method is an important issue in creating a supreme performance so we propose a dynamic weight allocation method based on a non-linear analysis system. The obtained classification accuracies of our method are 84.65% and 83.94% on RS126 and CB513 datasets respectively and they are very promising with regard to other classification methods in the literature for this problem. Also for evaluating our method behavior in comparison to other state of arts methods, an independent dataset is used. The results show that the comprehensibility of WKF based on SVM-PHGS is much better than other methods. (C) 2011 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction Machine learning approach Support vector machines Parallel hierarchical grid search Weighted kernel fusion

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Combining discriminant models with new multi-class SVMs

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PATTERN ANALYSIS AND APPLICATIONS 2002年第2期5卷 168-179页

作者： Guermeur, Y LORIA F-54506 Vandoeuvre Les Nancy France

The idea of performing model combination, instead of model selection, has a long theoretical background in statistics. However, making use of theoretical results is ordinarily subject to the satisfaction of strong hypotheses (weak error correlation, availability of large training sets, possibility to rerun the training procedure an arbitrary number of times, etc.). In contrast, the practitioner is frequently faced with the problem of combining a given set of pre-trained classifiers, with highly correlated errors, using only a small training sample. Overfitting is then the main risk, which cannot be overcome but with a strict complexity control of the combiner selected. This suggests that SVMs should be well suited for these difficult situations. Investigating this idea, we introduce a family of multi-class SVMs and assess them as ensemble methods on a real-world problem. This task, protein secondary structure prediction, is an open problem in biocomputing for which model combination appears to be an issue of central importance. Experimental evidence highlights the gain in quality resulting from combining some of the most widely used prediction methods with our SVMs rather than with the ensemble methods traditionally used in the field. The gain increases when the outputs of the combiners are post-processed with a DP algorithm.

关键词： classifier fusion generalisation performance hierarchical sequence processing systems protein secondary structure prediction statistical learning theory Support Vector Machines

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