RFCRYS: Sequence-based protein crystallization propensity prediction by means of random forest

Jahandideh, Samad; Mahdavi, Abbas

doi:10.1016/j.jtbi.2012.04.028

Cited by 25 publications

(22 citation statements)

References 57 publications

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“…SCMCRYS [22], as a simple voting method, was developed based on the P-collocated amino acid pairs. To further improve performance, some methods, including XtalPred [23], Pxs [15], SVMCRYS [24], PPCPred [13], XANNPred [25], RFCRYS [26] and CRYSpred [27], have incorporated other informative features, such as predicted secondary structure, disorder and solvent accessibility. More recently, Jahandideh et al .…”

Section: Introductionmentioning

confidence: 99%

PredPPCrys: Accurate Prediction of Sequence Cloning, Protein Production, Purification and Crystallization Propensity from Protein Sequences Using Multi-Step Heterogeneous Feature Fusion and Selection

Wang

Tan

et al. 2014

PLoS ONE

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X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed ‘PredPPCrys’ using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of currently non-crystallizable proteins were provided as compendium data, which are anticipated to facilitate target selection and design for the worldwide structural genomics consortium. PredPPCrys is freely available at http://www.structbioinfor.org/PredPPCrys.

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Section: Introductionmentioning

confidence: 99%

PredPPCrys: Accurate Prediction of Sequence Cloning, Protein Production, Purification and Crystallization Propensity from Protein Sequences Using Multi-Step Heterogeneous Feature Fusion and Selection

Wang

Tan

et al. 2014

PLoS ONE

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“…To remedy this shortcoming of AAC, Chou (Chou 2001a, b;Shen and Chou 2008) proposed a PseAAC feature by incorporating protein sequential information into the traditional AAC feature. PseAAC has been widely used in protein attribute prediction problems (Huang et al 2009;Roy et al 2009;Jahandideh and Mahdavi 2012;Huang and Yuan 2013;Zou 2014) including AFP prediction (Kandaswamy et al 2011).…”

Section: Pseudo Amino Acid Composition Featurementioning

confidence: 99%

TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition

Han

et al. 2015

J Membrane Biol

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Antifreeze proteins (AFPs) are indispensable for living organisms to survive in an extremely cold environment and have a variety of potential biotechnological applications. The accurate prediction of antifreeze proteins has become an important issue and is urgently needed. Although considerable progress has been made, AFP prediction is still a challenging problem due to the diversity of species. In this study, we proposed a new sequence-based AFP predictor, called TargetFreeze. TargetFreeze utilizes an enhanced feature representation method that weightedly combines multiple protein features and takes the powerful support vector machine as the prediction engine. Computer experiments on benchmark datasets demonstrate the superiority of the proposed TargetFreeze over most recently released AFP predictors. We also implemented a user-friendly web server, which is openly accessible for academic use and is available at http://csbio.njust.edu.cn/bioinf/TargetFreeze. TargetFreeze supplements existing AFP predictors and will have potential applications in AFP-related biotechnology fields.

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“…Because of the different design aims and benchmarks used, it is not easy to assess which method and features are the most effective. From the study in [14] and Table 1, we can see that the SVM_POLY method (see the work [13]) using SVM has the highest accuracy among the non-ensemble methods. This method is one of the four SVM predictors that are integrated into PPCpred [13].…”

Section: Introductionmentioning

confidence: 96%

“…Many sequence-based computational methods, including OB-Score [6], SECRET [7], CRYSTALP [8], XtalPred [9], ParCrys [10], CRYSTALP2 [11], SVMCRYS [12], PPCpred [13] and RFCRYS [14], predict protein crystallization, as shown in Table 1. Both support vector machine (SVM) [7], [12], [13] and the ensemble mechanism [13], [14] are well-known techniques to enhance prediction accuracy. Because of the different design aims and benchmarks used, it is not easy to assess which method and features are the most effective.…”

Section: Introductionmentioning

confidence: 99%

“…PPCpred utilizes a comprehensive set of inputs that are based on energy and hydrophobicity indices, the composition of certain amino acid types, predicted disorder, secondary structure, solvent accessibility, and the content of certain buried and exposed residues [13]. RFCRYS predicts the protein crystallization by utilizing the mono-, di- and tri-peptide compositions; the frequencies of amino acids in different physicochemical groups; the isoelectric point; the molecular weight; and the length of the protein sequences [14]. However, the mechanism of these two ensemble classifiers suffers from low interpretability for biologists.…”

Section: Introductionmentioning

confidence: 99%

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SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs

et al. 2013

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Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p = 0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.

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RFCRYS: Sequence-based protein crystallization propensity prediction by means of random forest

Cited by 25 publications

References 57 publications

PredPPCrys: Accurate Prediction of Sequence Cloning, Protein Production, Purification and Crystallization Propensity from Protein Sequences Using Multi-Step Heterogeneous Feature Fusion and Selection

PredPPCrys: Accurate Prediction of Sequence Cloning, Protein Production, Purification and Crystallization Propensity from Protein Sequences Using Multi-Step Heterogeneous Feature Fusion and Selection

TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition

SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs

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