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作者： Karen Reue

In recent years, cDNA and genomic DNA clones have been isolated for many of the mammalian lipases and phospholipases described in this volume. These DNA clones have provided essential tools for the analysis of lipase expression, structure, and function. In addition, DNA sequence analysis has generated a wealth of information about lipase protein and gene structure, including nucleotide and amino acid sequence, exon/intron organization, chromosome localization, phylogenetic relationships among lipases, and the elucidation of lipase mutations that underlie lipase deficiencies and dyslipidemias. Much of the information derived from the molecular and genetic characterization of genes and proteins has been deposited into electronic databases. With the advent of widespread access to the Internet, researchers throughout the world can contribute to and retrieve information from a wide array of databases that are continuously updated to include the most recent findings. less

关键词： protein Analysis Genetic Analysis DNA Sequence Analysis Physical Mapping PCR Genetic Mapping structure Analysis Sequence Similarity Search PROSITE protein secondary structure prediction protein structure prediction Homology Modeling 2D PAGE Peptide Mass Fingerprinting SDS-PAGE Molecular Modeling GenBank Swiss-Prot Positional cloning Expasy Homo sapiens Mus musculus

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The prediction of protein secondary structure with a cascade correlation learning architecture of neural networks

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NEURAL COMPUTING & APPLICATIONS 1997年第1期6卷 57-62页

作者： Vivarelli, F Fariselli, P Casadio, R UNIV BOLOGNA DEPT BIOL BIOPHYS LAB I-40126 BOLOGNA ITALY

A Cascade Correlation Learning Architecture (CCLA) of neural networks is tested on the task of predicting the secondary structure of proteins. The results are compared with those obtained with Neural Networks (NN) trained with the back-propagation algorithm (BPNN) and generated with genetic algorithms. CCLA proceeds towards rite global minimum of the error function more efficiently than BPNN. However;only a slight improvement in the average efficiency value is noticeable (61.82% as compared with 61.61% obtained with BPNN). The values of the three correlation coefficients for the discriminated secondary structures are also rather similar (C-alpha, and C-beta and C-coil are 0.36, 0.29 and 0.36 with CCLA, and 0.36, 0.31 and 0.35 with BPNN). This indicates that the efficiency of the prediction does not depend upon the training algorithm, and confirms our previous observation that when single sequences are used as input code to the network system.

关键词： cascade correlation learning algorithm neural networks pattern recognition predictive methods protein secondary structure prediction

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Predicting protein secondary structure using stochastic tree grammars

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MACHINE LEARNING 1997年第2-3期29卷 275-301页

作者： Abe, N Mamitsuka, H NEC Corp Ltd Real World Comp Partnership C&C Media Res Labs Theory NEC Lab Miyamae Ku Kawasaki Kanagawa 216 Japan

We propose a new method for predicting protein secondary structure of a given amino acid sequence, based on a training algorithm for the probability parameters of a stochastic tree grammar. In particular, we concentrate on the problem of predicting beta-sheet regions, which has previously been considered difficult because of the unbounded dependencies exhibited by sequences corresponding to beta-sheets. To cope with this difficulty, we use a new family of stochastic tree grammars, which we call Stochastic Ranked Node Rewriting Grammars, which are powerful enough to capture the type of dependencies exhibited by the sequences of P-sheet regions, such as the 'parallel' and 'anti-parallel' dependencies and their combinations. The training algorithm we use is an extension of the 'inside-outside' algorithm for stochastic context-bee grammars, but with a number of significant modifications. We applied our method on real data obtained from the HSSP database !Homology-derived secondary structure of proteins Ver 1.0) and the results were encouraging: Our method was able to predict roughly 75 percent of the beta-strands correctly in a systematic evaluation experiment, in which the test sequences not only have less than 25 percent identity to the training sequences, but are totally unrelated to them. This figure compares favorably to the predictive accuracy of the state-of-the-art prediction methods in the field, even though our experiment was on a restricted type of beta-sheet structures and the test was done on a relatively small data size. We also stress that our method can predict the structure as well as the location of beta-sheet regions, which was not possible by conventional methods for secondary structure prediction. Extended abstracts of parts of the work presented in this paper have appeared in (Abe & Mamitsuka, 1994) and (Mamitsuka & Abe, 1993).

关键词： stochastic tree grammars protein secondary structure prediction beta-sheets maximum likelihood estimation minimum description length principle unsupervised learning

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prediction OF protein secondary structure BY COMBINING NEAREST-NEIGHBOR ALGORITHMS AND MULTIPLE SEQUENCE ALIGNMENTS

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JOURNAL OF MOLECULAR BIOLOGY 1995年第1期247卷 11-15页

作者： SALAMOV, AA SOLOVYEV, VV BAYLOR COLL MED DEPT CELL BIOLHOUSTONTX 77030

Recently Yi & Lander used a neural network and nearest-neighbor method with a scoring system that combined a sequence-similarity matrix with the local structural environment scoring scheme described by Bowie and co-workers for predicting protein secondary structure. We have improved their scoring system by taking into consideration N and C-terminal positions of alpha-helices and beta-strands and also beta-turns as distinctive types of secondary structure. Another improvement, which also decreases the time of computation, is performed by restricting a data base with a smaller subset of proteins that are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision procedure our method reaches a sustained overall three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural-network approach, tested on the same data set of 126 non-homologous protein chains.

关键词： protein secondary structure prediction NEAREST-NEIGHBOR ALGORITHM LOCAL STRUCTURAL ENVIRONMENT SCORING MULTIPLE SEQUENCE ALIGNMENT EVOLUTIONARY INFORMATION

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protein secondary structure prediction with partially recurrent neural networks

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SAR and QSAR in Environmental Research 1993年第2-3期1卷 153-159页

作者： M Reczko Department of Molecular Biophysics German Cancer Research Center Heidelberg Germany.

Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures a-helix, sheet, and “coil” for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13x3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corresponding feedforward net has a performance of 64.3%. © 1993, Taylor & Francis Group, LLC. All rights reserved.

关键词： hierarchical architectures incremental training protein secondary structure prediction recurrent neural networks

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HYBRID SYSTEM FOR protein secondary structure prediction

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JOURNAL OF MOLECULAR BIOLOGY 1992年第4期225卷 1049-1063页

作者： ZHANG, X MESIROV, JP WALTZ, DL Thinking Machines Corporation Cambridge MA 02142.

We have developed a hybrid system to predict the secondary structures (alpha-helix, beta-sheet and coil) of proteins and achieved 66.4% accuracy, with correlation coefficients of C(coil) = 0.429, C alpha = 0.470 and C beta = 0.387. This system contains three subsystems ("experts"): a neural network module, a statistical module and a memory-based reasoning module. First, the three experts independently learn the mapping between amino acid sequences and secondary structures from the known protein structures, then a Combiner learns to combine automatically the outputs of the experts to make final predictions. The hybrid system was tested with 107 protein structures through k-way cross-validation. Its performance was better than each expert and all previously reported methods with greater than 0.99 statistical significance. It was observed that for 20% of the residues, all three experts produced the same but wrong predictions. This may suggest an upper bound on the accuracy of secondary structure predictions based on local information from the currently available protein structures, and indicate places where non-local interactions may play a dominant role in conformation. For 64% of the residues, at least two experts were the same and correct, which shows that the Combiner performed better than majority vote. For 77% of the residues, at least one expert was correct, thus there may still be room for improvement in this hybrid approach. Rigorous evaluation procedures were used in testing the hybrid system, and statistical significance measures were developed in analyzing the differences among different methods. When measured in terms of the number of secondary structures (rather than the number of residues) that were predicted correctly, the prediction produced by the hybrid system was also better than those of individual experts.

关键词： protein secondary structure prediction HYBRID SYSTEM NEURAL NETWORKS MEMORY-BASED REASONING STATISTICAL METHODS

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WHEN AWAITING BIO-CHAMPOLLION - DYNAMIC-PROGRAMMING REGULARIZATION OF THE protein secondary structure predictionS

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protein ENGINEERING 1994年第10期7卷 1197-1202页

作者： ZIMMERMANN, K INRA UNITE INGN PROTF-78352 JOUY EN JOSASFRANCE

predictions of protein secondary structure using current methods are often unrealistic, i.e. the predicted alpha-helices or beta-strands are too short. To improve the realism, various heuristic 'filtering' or 'smoothing' methods are used. They are more or less intuitive and are based on ad hoc corrections. We present a regularization method to obtain a realistic secondary structure from predicted propensities. It is based on the known dynamic programming algorithm and is quite objective. It can be used with any prediction method which yields propensities. The regularized predictions conserve well the overall prediction accuracy and improve the 'protein-likeness' of the prediction.

关键词： DYNAMIC PROGRAMMING FILTERING MORPHOLOGICAL CONSTRAINTS protein secondary structure prediction REALISM REGULARIZATION

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A prediction OF THE secondary structure OF THE PLECKSTRIN HOMOLOGY DOMAIN

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proteinS-structure FUNCTION AND BIOINFORMATICS 1994年第1期20卷 1-3页

作者： JENNY, TF BENNER, SA SWISS FED INST TECHNOL DEPT CHEM CH-8092 ZURICH SWITZERLAND

A consensus prediction for the secondary structure of the pleckstrin homology (PH) domain is presented. The prediction is based on an analysis of patterns of conservation and variation of homologous protein sequences. The structure is predicted to be formed largely from beta strands with a single alpha helix. (C) 1994 Wiley-Liss, Inc.

关键词： protein secondary structure prediction PLECKSTRIN HOMOLOGY DOMAIN

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prediction OF protein secondary structure AT BETTER THAN 70-PERCENT ACCURACY

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JOURNAL OF MOLECULAR BIOLOGY 1993年第2期232卷 584-599页

作者： ROST, B SANDER, C European Molecular Biology Laboratory Meyerhofstra&szlig e 1 D-6900 Heidelberg Germany

We have trained a two-layered feed-forward neural network on a non-redundant data base of 130 protein chains to predict the secondary structure of water-soluble proteins. A new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences. The inclusion of protein family information in this form increases the prediction accuracy by six to eight percentage points. A combination of three levels of networks results in an overall three-state accuracy of 70·8% for globular proteins (sustained performance). If four membrane protein chains are included in the evaluation, the overall accuracy drops to 70·2%. The prediction is well balanced between α-helix, β-strand and loop: 65% of the observed strand residues are predicted correctly. The accuray in predicting the content of three secondary structure types is comparable to that of circular dichroism spectroscopy. The performance accuracy is verified by a sevenfold cross-validation test, and an additional test on 26 recently solved proteins. Of particular practical importance is the definition of a position-specific reliability index. For half of the residues predicted with a high level of reliability the overall accuracy increases to better than 82%. A further strength of the method is the more realistic prediction of segment length. The protein family prediction method is available for testing by academic researchers via an electronic mail server.

关键词： protein secondary structure prediction MULTIPLE SEQUENCE ALIGNMENTS secondary structure CONTENT NEURAL NETWORK

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BETA-TURNS AND THEIR DISTORTIONS - A PROPOSED NEW NOMENCLATURE

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protein ENGINEERING 1990年第6期3卷 479-493页

作者： WILMOT, CM THORNTON, JM UNIV LONDON BIRKBECK COLL DEPT CRYSTALLOG MOLEC BIOL LAB LONDON WC1E 7HX ENGLAND

Using the new version of a protein Structural Database, BIPED, .beta.-turns have been extracted from 58 non-identical proteins (resolution .ltoreq. 2 .ANG.) using the standard criteria that the distance between ***. and Ci+***. is < 7 .ANG. and that the central residues are not helical. It has been shown that 42% of these do not fit into the eight conventional turn types (I, I'', II, II'', VIa, VIb, and VIII) as defined by .vphi.,.psi. limits for residues i + 1 and i + 2. Most of the unclassified turns are ''distortions'' of the standard turn types, lying just outside the specific limits. Eleven per cent of the turns are not related to the standard types, and have completely different .vphi.,.psi. combinations. Therefore, the conformational space available to a trans tetrapeptide has been explored to find all two-residue .vphi.,.psi. combinations which satisfy the criteria for .beta.-turn formation. The conformations generated by the search are the conformations observed in the data. On the basis of these observations a new nomenclature is suggested for .beta.-turns based on a shorthand descriptor of populated regions of the .vphi.,.psi. plot. For example, it is proposed that a type I turn should now be described as an .alpha..alpha. turn, the descriptor indicating the .vphi.,.psi. regions of the i + 1 and i + 2 positions of the turn. The use of descriptors, which convey information about the turn type conformation, should aid protein structural workers in turn classification and visualization. The sequence preferences for the .alpha..beta. turn have been elucidated: Pro, Asp and Ser at i + 1;Asn and His at i + 2;Pro at i + 3. These preferences have been explained in terms of specific interactions involving the side-chains. The .beta.-turn prediction program, BTURN 1.0, has been further developed by alterations to the calculation of parameters, the removal of incompatible multiple turn predictions and the inclusion of the distorted types .alpha..alpha. and

关键词： β-turns protein secondary structure prediction

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