Improving protein secondary structure prediction using a simple<i>k</i>-mer model

Madera, Martin; Calmus, Ryan; Thiltgen, Grant; Karplus, Kevin; Gough, Julian

doi:10.1093/bioinformatics/btq020

Cited by 33 publications

(20 citation statements)

References 34 publications

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“…Green et al (2009) introduced parallel cascade identification (PCI), a tool from the field of nonlinear system identification, for secondary structure prediction (Green et al 2009). Madera et al (2010) developed a framework for secondary structure prediction that considers longrange interactions and it is described as a k-mer order model (Madera et al 2010). This model can be used on top of any secondary structure methods.…”

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confidence: 99%

CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization

Wei,

Thompson,

Floudas

2011

Proc. R. Soc. A.

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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 MILPbased 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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confidence: 99%

CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization

Wei,

Thompson,

Floudas

2011

Proc. R. Soc. A.

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“…TFs can be used to identify certain regions of biomolecules such as DNA or proteins (Zhang et al, 2011). Furthermore, it has been determined that TFs are species-or taxon-specific Karlin and Mrázek, 1997;Madera et al, 2010).…”

Section: Feature Extractionmentioning

confidence: 99%

Detection of Piwi-interacting RNAs based on sequence features

Liu

Zhang

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs. Distinguishing piRNAs from other non-coding RNAs is important because of their important role in the physiological regulation of spermatogenesis, genome protection from transposons, and regulation of mRNAs and long non-coding RNAs. Few computational studies have addressed piRNAs detection, and both effectiveness and efficiency of piRNA detection tools require improvement. In this study, a piRNA detection method based on sequence features and a support vector machine was developed. Four types of features are proposed: weighted k-mer, weighted k-mer with wildcards, position-specific base, and piRNA length. The piRNA sequences from human, mouse, rat, and drosophila were respectively used in this experiment. Compared to existing algorithms, the proposed method provides a better balance between precision and sensitivity (both are approximately 90%), and although these values were slightly slower than those obtained using the piRNA annotation approach, the proposed method was four-fold faster than piRPred and 229-fold faster than piRNA predictor.

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“…A 10 % improvement in prediction accuracy was achieved when evolutionary information from multiple sequence alignments (MSA) was included [24,25,26,8,27,28,29,30,31,32,33]. Some accuracy improvements were obtained by including long-range interactions [34,35]. A compound pyramid model (CPM) that used a two-level mixed-modal SVM (MMS) [32] for secondary structure predictions has the highest reported accuracy of 85.6 % [36].…”

Section: Introductionmentioning

confidence: 99%

Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure

et al. 2013

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Exponential growth in the number of available protein sequences is unmatched by the slower growth in the number of structures. As a result, the development of efficient and fast protein secondary structure prediction methods is essential for the broad comprehension of protein structures. Computational methods that can efficiently determine secondary structure can in turn facilitate protein tertiary structure prediction, since most methods rely initially on secondary structure predictions. Recently, we have developed a fast learning optimized prediction methodology (FLOPRED) for predicting protein secondary structure (S. Saraswathi, et al., [1]). Data are generated by using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and advanced particle swarm optimization (PSO) are used with this data to obtain better and faster convergence to more accurate secondary structure predicted results. A five-fold cross-validated testing accuracy of 83.8 % and a segment overlap (SOV) score of 78.3 % are obtained in this study. Secondary structure predictions and their accuracy are usually presented for three secondary structure elements: α-helix, β-strand and coil but rarely have the results been analyzed with respect to their constituent amino acids. In this paper, we use the results obtained with FLOPRED to provide detailed behaviors for different amino acid types in the secondary structure prediction. We investigate the influence of the composition, physico-chemical properties and position specific occurrence preferences of amino acids within secondary structure elements. In addition, we identify the correlation between these properties and prediction accuracy. The present detailed results suggest several important ways that secondary structure predictions can be improved in the future that might lead to improved protein design and engineering.

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Improving protein secondary structure prediction using a simplek-mer model

Cited by 33 publications

References 34 publications

CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization

CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization

Detection of Piwi-interacting RNAs based on sequence features

Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure

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