Secondarystructurepredictions areincreasinglybecomingtheworkhorseforseveralmethodsaimingatpredictingproteinstructure andfunction.Hereweuseensemblesofbidirectionalrecurrentneuralnetworkarchitectures, PSIBLAST -derivedprofiles,andalargenonredundant trainingsettoderivetwonewpredictors:(a)the secondversionoftheSSproprogramforsecondary structureclassificationintothreecategoriesand(b) thefirstversionoftheSSpro8programforsecondarystructureclassificationintotheeightclasses producedbytheDSSPprogram.Wedescribethe resultsofthreedifferenttestsetsonwhichSSpro achievedasustainedperformanceofabout78% correctprediction.Wereportconfusionmatrices, comparePSI-BLASTtoBLAST-derivedprofiles,and assessthecorrespondingperformanceimprovements. SSproandSSpro8areimplementedasweb servers,availabletogetherwithotherstructural featurepredictorsat:http://promoter.ics.uci.edu/ BRNN-PRED/.Proteins2002;47:228--235.

We describe a general methodology for the design of large-scale recursive neural network architectures (DAG-RNNs) which comprises three fundamental steps: (1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables; (2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks; and (3) application of weight-sharing within appropriate subsets of DAG connections to capture stationarity and control model complexity. Here we use these principles to derive several specific classes of DAG-RNN architectures based on lattices, trees, and other structured graphs. These architectures can process a wide range of data structures with variable sizes and dimensions. While the overall resulting models remain probabilistic, the internal deterministic dynamics allows efficient propagation of information, as well as training by gradient descent, in order to tackle large-scale problems. These methods are used here to derive state-of-the-art predictors for protein structural features such as secondary structure (1D) and both fine- and coarse-grained contact maps (2D). Extensions, relationships to graphical models, and implications for the design of neural architectures are briefly discussed. The protein prediction servers are available over the

Motivation: Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction.

Motivation: Recognizing proteins that have similar tertiary structure is the key step of template-based protein structure prediction methods. Traditionally, a variety of alignment methods are used to identify similar folds, based on sequence similarity and sequencestructure compatibility. Although these methods are complementary, their integration has not been thoroughly exploited. Statistical machine learning methods provide tools for integrating multiple features, but so far these methods have been used primarily for protein and fold classification, rather than addressing the retrieval problem of fold recognition–finding a proper template for a given query protein. Results: Here we present a two-stage machine learning, information retrieval, approach to fold recognition. First, we use alignment methods to derive pairwise similarity features for query-template protein pairs. We also use global profile-profile alignments in combination with predicted secondary structure, relative solvent accessibility, contact map, and beta-strand pairing to extract pairwise structural compatibility features. Second, we apply support vector machines to these features to predict the structural relevance (i.e. in the same fold or not) of the query-template pairs. For each query, the continuous relevance scores are used to rank the templates. The FOLDpro approach is modular, scalable, and effective. Compared to 11 other fold recognition methods, FOLDpro yields the best results in almost all standard categories on a comprehensive benchmark dataset. Using predictions of the top-ranked template, the sensitivity is about 85%, 56%, and 27 % at the family, superfamily, and fold levels respectively. Using the 5 top-ranked templates, the sensitivity increases to 90%, 70%, and 48%. Availability: The FOLDpro server is available with the SCRATCH

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Abstract. Intrinsically disordered regions in proteins are relatively frequent and important for our understanding of molecular recognition and assembly, and protein structure and function. From an algorithmic standpoint, flagging large disordered regions is also important for ab initio protein structure prediction methods. Here we first extract a curated, non-redundant, data set of protein disordered regions from the Protein Data Bank and compute relevant statistics on the length and location of these regions. We then develop an ab initio predictor of disordered regions called DISpro which uses evolutionary information in the form of profiles, predicted secondary structure and relative solvent accessibility, and ensembles of 1D-recursive neural networks. DISpro is trained and cross validated using the curated data set. The experimental results show that DISpro achieves an accuracy of 92.8% with a false positive rate of 5%. DISpro is a member of the SCRATCH suite of protein data mining tools available through

Abstract. Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69 % of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76 % and specificity of 85 % with respect to the single-domain proteins and sensitivity of 59 % and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71 % and 71 % respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fischer et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at

We develop and test new machine learning methods for the prediction of topological representations of protein structures in the form of coarse- or fine-grained contact or distance maps that are translation and rotation invariant. The methods are based on generalized input-output hidden Markov models (GIOHMMs) and generalized recursive neural networks (GRNNs). The methods are used to predict topology directly in the fine-grained case and, in the coarsegrained case, indirectly by first learning how to score candidate graphs and then using the scoring function to search the space of possible configurations. Computer simulations show that the predictors achieve state-of-the-art performance.

Comparisons were made among four categories of protein flexibility: (1) low-B-factor ordered regions, (2) high-B-factor ordered regions, (3) short disordered regions, and (4) long disordered regions. Amino acid compositions of the four categories were found to be significantly different from each other, with high-Bfactor ordered and short disordered regions being the most similar pair. The high-B-factor (flexible) ordered regions are characterized by a higher average flexibility index, higher average hydrophilicity, higher average absolute net charge, and higher total charge than disordered regions. The low-B-factor regions are significantly enriched in hydrophobic residues and depleted in the total number of charged residues compared to the other three categories. We examined the predictability of the high-B-factor regions and developed a predictor that discriminates between regions of low and high B-factors. This predictor achieved an accuracy of 70 % and a correlation of 0.43 with experimental data, outperforming the 64 % accuracy and 0.32 correlation of predictors based solely on flexibility indices. To further clarify the differences between short disordered regions and ordered regions, a predictor of short disordered regions was developed. Its relatively high accuracy of 81 % indicates considerable differences between ordered and disordered regions. The distinctive amino acid biases of high-B-factor ordered regions, short disordered regions, and long disordered

While new high-throughput experimental techniques are being developed for proteomics applications (e.g. mass spectrometry, protein chips), it is clear that given the fundamental importance of proteins to biology, biotechnology, and medicine, computer methods that can rapidly sift through massive amounts of data and help determine the structure and function of a large number of proteins in a given genome remain important.