共查询到20条相似文献，搜索用时 0 毫秒
1.
有关蛋白质功能的研究是解析生命奥秘的基础,机器学习技术在该领域已有广泛应用。利用支持向量机(support vectormachine,SVM)方法,构建一个预测蛋白质功能位点的通用平台。该平台先提取非同源蛋白质序列,再对这些序列进行特征编码(包括序列的基本信息、物化特征、结构信息及序列保守性特征等),以编码好的样本作为训练数据,利用SVM进行训练,得到敏感性、特异性、Matthew相关系数、准确率及ROC曲线等评价指标,反复测试,得到评价指标最优的SVM模型后,便可以用来预测蛋白质序列上的功能位点。该平台除了应用在预测蛋白质功能位点之外,还可以应用于疾病相关单核苷酸多态性(SNP)预测分析、预测蛋白质结构域分析、生物分子间的相互作用等。 相似文献
2.
Knowing the quality of a protein structure model is important for its appropriate usage. We developed a model evaluation method to assess the absolute quality of a single protein model using only structural features with support vector machine regression. The method assigns an absolute quantitative score (i.e. GDT‐TS) to a model by comparing its secondary structure, relative solvent accessibility, contact map, and beta sheet structure with their counterparts predicted from its primary sequence. We trained and tested the method on the CASP6 dataset using cross‐validation. The correlation between predicted and true scores is 0.82. On the independent CASP7 dataset, the correlation averaged over 95 protein targets is 0.76; the average correlation for template‐based and ab initio targets is 0.82 and 0.50, respectively. Furthermore, the predicted absolute quality scores can be used to rank models effectively. The average difference (or loss) between the scores of the top‐ranked models and the best models is 5.70 on the CASP7 targets. This method performs favorably when compared with the other methods used on the same dataset. Moreover, the predicted absolute quality scores are comparable across models for different proteins. These features make the method a valuable tool for model quality assurance and ranking. Proteins 2009. © 2008 Wiley‐Liss, Inc. 相似文献
3.
Protein structure prediction is an important problem of both intellectual and practical interest. Most protein structure prediction approaches generate multiple candidate models first, and then use a scoring function to select the best model among these candidates. In this work, we develop a scoring function using support vector regression (SVR). Both consensus-based features and features from individual structures are extracted from a training data set containing native protein structures and predicted structural models submitted to CASP5 and CASP6. The SVR learns a scoring function that is a linear combination of these features. We test this scoring function on two data sets. First, when used to rank server models submitted to CASP7, the SVR score selects predictions that are comparable to the best performing server in CASP7, Zhang-Server, and significantly better than all the other servers. Even if the SVR score is not allowed to select Zhang-Server models, the SVR score still selects predictions that are significantly better than all the other servers. In addition, the SVR is able to select significantly better models and yield significantly better Pearson correlation coefficients than the two best Quality Assessment groups in CASP7, QA556 (LEE), and QA634 (Pcons). Second, this work aims to improve the ability of the Robetta server to select best models, and hence we evaluate the performance of the SVR score on ranking the Robetta server template-based models for the CASP7 targets. The SVR selects significantly better models than the Robetta K*Sync consensus alignment score. 相似文献
4.
5.
Zellner H Staudigel M Trenner T Bittkowski M Wolowski V Icking C Merkl R 《Proteins》2012,80(1):154-168
An important task of computational biology is to identify those parts of a polypeptide chain, which are involved in interactions with other proteins. For this purpose, we have developed the program PresCont, which predicts in a robust manner amino acids that constitute protein-protein interfaces (PPIs). PresCont reaches state-of-the-art classification quality on the basis of only four residue properties that can be readily deduced from the 3D structure of an individual protein and a multiple sequence alignment (MSA) composed of homologs. The core of PresCont is a support vector machine, which assesses solvent-accessible surface area, hydrophobicity, conservation, and the local environment of each amino acid on the protein surface. For training and performance testing, we compiled three nonoverlapping datasets consisting of permanently formed or transient complexes, respectively. A comparison with SPPIDER, ProMate, and meta-PPISP showed that PresCont compares favorably with these highly sophisticated programs, and that its prediction quality is less dependent on the type of protein complex being considered. This balance is due to a mutual compensation of classification weaknesses observed for individual properties: For PPIs of permanent complexes, solvent-accessible surface and hydrophobicity contribute most to classification quality, for PPIs of transient complexes, the assessment of the local environment is most significant. Moreover, we show that for permanent complexes a segmentation of PPIs into core and rim residues has only a moderate influence on prediction quality. PresCont is available as a web service at http://www-bioinf.uni-regensburg.de/. 相似文献
6.
Fabian Dey Qiangfeng Cliff Zhang Donald Petrey Barry Honig 《Protein science : a publication of the Protein Society》2013,22(4):359-366
We outline a set of strategies to infer protein function from structure. The overall approach depends on extensive use of homology modeling, the exploitation of a wide range of global and local geometric relationships between protein structures and the use of machine learning techniques. The combination of modeling with broad searches of protein structure space defines a “structural BLAST” approach to infer function with high genomic coverage. Applications are described to the prediction of protein–protein and protein–ligand interactions. In the context of protein–protein interactions, our structure‐based prediction algorithm, PrePPI, has comparable accuracy to high‐throughput experiments. An essential feature of PrePPI involves the use of Bayesian methods to combine structure‐derived information with non‐structural evidence (e.g. co‐expression) to assign a likelihood for each predicted interaction. This, combined with a structural BLAST approach significantly expands the range of applications of protein structure in the annotation of protein function, including systems level biological applications where it has previously played little role. 相似文献
7.
Protein–protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time‐consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modeling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein–protein interaction models. We show that InterPred represents a major improvement in protein–protein interaction detection with a performance comparable or better than experimental high‐throughput techniques. We also show that our full‐atom protein–protein complex modeling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment. InterPred source code can be downloaded from http://wallnerlab.org/InterPred Proteins 2017; 85:1159–1170. © 2017 Wiley Periodicals, Inc. 相似文献
8.
A number of methods for predicting levels of solvent accessibility or accessible surface area (ASA) of amino acid residues in proteins have been developed. These methods either predict regularly spaced states of relative solvent accessibility or an analogue real value indicating relative solvent accessibility. While discrete states of exposure can be easily obtained by post prediction assignment of thresholds to the predicted or computed real values of ASA, the reverse, that is, obtaining a real value from quantized states of predicted ASA, is not straightforward as a two-state prediction in such cases would give a large real valued errors. However, prediction of ASA into larger number of ASA states and then finding a corresponding scheme for real value prediction may be helpful in integrating the two approaches of ASA prediction. We report a novel method of obtaining numerical real values of solvent accessibility, using accumulation cutoff set and support vector machine. This so-called SVM-Cabins method first predicts discrete states of ASA of amino acid residues from their evolutionary profile and then maps the predicted states onto a real valued linear space by simple algebraic methods. Resulting performance of such a rigorous approach using 13-state ASA prediction is at least comparable with the best methods of ASA prediction reported so far. The mean absolute error in this method reaches the best performance of 15.1% on the tested data set of 502 proteins with a coefficient of correlation equal to 0.66. Since, the method starts with the prediction of discrete states of ASA and leads to real value predictions, performance of prediction in binary states and real values are simultaneously optimized. 相似文献
9.
A novel support vector regression (SVR) approach is proposed to predict protein accessible surface areas (ASAs) from their primary structures. In this work, we predict the real values of ASA in squared angstroms for residues instead of relative solvent accessibility. Based on protein residues, the mean and median absolute errors are 26.0 A(2) and 18.87 A(2), respectively. The correlation coefficient between the predicted and observed ASAs is 0.66. Cysteine is the best predicted amino acid (mean absolute error is 13.8 A(2) and median absolute error is 8.37 A(2)), while arginine is the least predicted amino acid (mean absolute error is 42.7 A(2) and median absolute error is 36.31 A(2)). Our work suggests that the SVR approach can be directly applied to the ASA prediction where data preclassification has been used. 相似文献
10.
A high-performance method was developed for protein secondary structure prediction based on the dual-layer support vector machine (SVM) and position-specific scoring matrices (PSSMs). SVM is a new machine learning technology that has been successfully applied in solving problems in the field of bioinformatics. The SVM's performance is usually better than that of traditional machine learning approaches. The performance was further improved by combining PSSM profiles with the SVM analysis. The PSSMs were generated from PSI-BLAST profiles, which contain important evolution information. The final prediction results were generated from the second SVM layer output. On the CB513 data set, the three-state overall per-residue accuracy, Q3, reached 75.2%, while segment overlap (SOV) accuracy increased to 80.0%. On the CB396 data set, the Q3 of our method reached 74.0% and the SOV reached 78.1%. A web server utilizing the method has been constructed and is available at http://www.bioinfo.tsinghua.edu.cn/pmsvm. 相似文献
11.
A previous computational analysis of missense mutations linked to monogenic disease found a high proportion of missense mutations affect protein stability, rather than other aspects of protein structure and function. The purpose of this study is to relate the presence of such stability damaging missense mutations to the levels of a particular protein present under \"in vivo\" like conditions, and to test the reliability of the computational methods. Experimental data on a set of missense mutations of the enzyme phenylalanine hydroxylase (PAH) associated with the monogenic disease phenylketonuria (PKU) have been compared with the expected in vivo impact on protein function, obtained using SNPs3D, an in silico analysis package. A high proportion of the PAH mutations are predicted to be destabilizing. The overall agreement between predicted stability impact and experimental evidence for lower protein levels is in accordance with the estimated error rates of the methods. For these mutations, destabilization of protein three-dimensional structure is the major molecular mechanism leading to PKU, and results in a substantial reduction of in vivo PAH protein concentration. Although of limited scale, the results support the view that destabilization is the most common mechanism by which missense mutations cause monogenic disease. In turn, this conclusion suggests the general therapeutic strategy of developing drugs targeted at restoring wild type stability. 相似文献
12.
With the development of many computational methods that predict the structural models of protein-protein complexes, there is a pressing need to benchmark their performance. As was the case for protein monomers, assessing the quality of models of protein complexes is not straightforward. An effective scoring scheme should be able to detect substructure similarity and estimate its statistical significance. Here, we focus on characterizing the similarity of the interfaces of the complex and introduce two scoring functions. The first, the interfacial Template Modeling score (iTM-score), measures the geometric distance between the interfaces, while the second, the Interface Similarity score (IS-score), evaluates their residue-residue contact similarity in addition to their geometric similarity. We first demonstrate that the IS-score is more suitable for assessing docking models than the iTM-score. The IS-score is then validated in a large-scale benchmark test on 1562 dimeric complexes. Finally, the scoring function is applied to evaluate docking models submitted to the Critical Assessment of Prediction of Interactions (CAPRI) experiments. While the results according to the new scoring scheme are generally consistent with the original CAPRI assessment, the IS-score identifies models whose significance was previously underestimated. 相似文献
13.
PPⅡ二级结构是一种稀有的蛋白质结构类型。目前使用机器学习方法预测此二级结构的工作还比较少见。引入一种新的方法———支持向量机 (SVM)来预测PPII二级结构 ,并与神经网络方法进行了比较 ,结果表明 ,SVM方法在预测PPII结构上表现良好 ,预测精度达到 76 .5 2 %。 相似文献
14.
Conformational switches observed in the protein backbone play a key role in a variety of fundamental biological activities. This paper describes a web-server that implements a pattern recognition algorithm trained on the examples from the Database of Macromolecular Movements to predict residue positions involved in conformational switches. Prediction can be performed at an adjustable false positive rate using a user-supplied protein sequence in FASTA format or a structure in a Protein Data Bank (PDB) file. If a protein sequence is submitted, then the web-server uses sequence-derived information only (such as evolutionary conservation of residue positions). If a PDB file is submitted, then the web-server uses sequence-derived information and residue solvent accessibility calculated from this file. 相似文献
15.
G protein-coupled receptors (GPCRs) are part of multi-protein networks called ‘receptosomes’. These GPCR interacting proteins
(GIPs) in the receptosomes control the targeting, trafficking and signaling of GPCRs. PDZ domain proteins constitute the largest
protein family among the GIPs, and the predominant function of the PDZ domain proteins is to assemble signaling pathway components
into close proximity by recognition of the last four C-terminal amino acids of GPCRs. We present here a machine learning based
approach for the identification of GPCR-binding PDZ domain proteins. In order to characterize the network of interactions
between amino acid residues that contribute to the stability of the PDZ domain-ligand complex and to encode the complex into
a feature vector, amino acid contact matrices and physicochemical distance matrix were constructed and adopted. This novel
machine learning based method displayed high performance for the identification of PDZ domain-ligand interactions and allowed
the identification of novel GPCR-PDZ domain protein interactions. 相似文献
16.
One approach for facilitating protein function prediction is to classify proteins into functional families. Recent studies on the classification of G-protein coupled receptors and other proteins suggest that a statistical learning method, Support vector machines (SVM), may be potentially useful for protein classification into functional families. In this work, SVM is applied and tested on the classification of enzymes into functional families defined by the Enzyme Nomenclature Committee of IUBMB. SVM classification system for each family is trained from representative enzymes of that family and seed proteins of Pfam curated protein families. The classification accuracy for enzymes from 46 families and for non-enzymes is in the range of 50.0% to 95.7% and 79.0% to 100% respectively. The corresponding Matthews correlation coefficient is in the range of 54.1% to 96.1%. Moreover, 80.3% of the 8,291 correctly classified enzymes are uniquely classified into a specific enzyme family by using a scoring function, indicating that SVM may have certain level of unique prediction capability. Testing results also suggest that SVM in some cases is capable of classification of distantly related enzymes and homologous enzymes of different functions. Effort is being made to use a more comprehensive set of enzymes as training sets and to incorporate multi-class SVM classification systems to further enhance the unique prediction accuracy. Our results suggest the potential of SVM for enzyme family classification and for facilitating protein function prediction. Our software is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi. 相似文献
17.
WHISCY: What information does surface conservation yield? Application to data‐driven docking 总被引：1，自引：0，他引：1
Protein-protein interactions play a key role in biological processes. Identifying the interacting residues is a first step toward understanding these interactions at a structural level. In this study, the interface prediction program WHISCY is presented. It combines surface conservation and structural information to predict protein-protein interfaces. The accuracy of the predictions is more than three times higher than a random prediction. These predictions have been combined with another interface prediction program, ProMate [Neuvirth et al. J Mol Biol 2004;338:181-199], resulting in an even more accurate predictor. The usefulness of the predictions was tested using the data-driven docking program HADDOCK [Dominguez et al. J Am Chem Soc 2003;125:1731-1737] in an unbound docking experiment, with the goal of generating as many near-native structures as possible. Unrefined rigid body docking solutions within 10 A ligand RMSD from the true structure were generated for 22 out of 25 docked complexes. For 18 complexes, more than 100 of the 8000 generated models were correct. Our results demonstrates the potential of using interface predictions to drive protein-protein docking. 相似文献
18.
19.
20.
Fast Fourier transform-based support vector machine for subcellular localization prediction using different substitution models 总被引：2，自引：0，他引：2
There are approximately 109 proteins in a cell. A hotspot in bioinformatics is how to identify a protein＇s subcellular localization, if its sequence is known. In this paper, a method using fast Fourier transform-based support vector machine is developed to predict the subcellular localization of proteins from their physicochemical properties and structural parameters. The prediction accuracies reached 83% in prokaryotic organisms and 84% in eukaryotic organisms with the substitution model of the c-p-v matrix （c, composition; p, polarity; and v, molecular volume）. The overall prediction accuracy was also evaluated using the ＂leave-one-out＂ jackknife procedure. The influence of the substitution model on prediction accuracy has also been discussed in the work. The source code of the new program is available on request from the authors. 相似文献