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1.
A significant number of protein sequences in a given proteome have no obvious evolutionarily related protein in the database of solved protein structures, the PDB. Under these conditions, ab initio or template-free modeling methods are the sole means of predicting protein structure. To assess its expected performance on proteomes, the TASSER structure prediction algorithm is benchmarked in the ab initio limit on a representative set of 1129 nonhomologous sequences ranging from 40 to 200 residues that cover the PDB at 30% sequence identity and which adopt alpha, alpha + beta, and beta secondary structures. For sequences in the 40-100 (100-200) residue range, as assessed by their root mean square deviation from native, RMSD, the best of the top five ranked models of TASSER has a global fold that is significantly close to the native structure for 25% (16%) of the sequences, and with a correct identification of the structure of the protein core for 59% (36%). In the absence of a native structure, the structural similarity among the top five ranked models is a moderately reliable predictor of folding accuracy. If we classify the sequences according to their secondary structure content, then 64% (36%) of alpha, 43% (24%) of alpha + beta, and 20% (12%) of beta sequences in the 40-100 (100-200) residue range have a significant TM-score (TM-score > or = 0.4). TASSER performs best on helical proteins because there are less secondary structural elements to arrange in a helical protein than in a beta protein of equal length, since the average length of a helix is longer than that of a strand. In addition, helical proteins have shorter loops and dangling tails. If we exclude these flexible fragments, then TASSER has similar accuracy for sequences containing the same number of secondary structural elements, irrespective of whether they are helices and/or strands. Thus, it is the effective configurational entropy of the protein that dictates the average likelihood of correctly arranging the secondary structure elements. 相似文献
2.
This study presents different procedures for ab initio modeling of peptide loops of different sizes in proteins. Small loops (up to 8–12 residues) were generated by a straightforward procedure with subsequent “averaging” over all the low‐energy conformers obtained. The averaged conformer fairly represents the entire set of low‐energy conformers, root mean square deviation (RMSD) values being from 1.01 Å for a 4‐residue loop to 1.94 Å for an 8‐residue loop. Three‐dimensional (3D) structures for several medium loops (20–30 residues) and for two large loops (54 and 61 residues) were predicted using residue–residue contact matrices divided into variable parts corresponding to the loops, and into a constant part corresponding to the known core of the protein. For each medium loop, a very limited number of sterically reasonable Cα traces (from 1 to 3) was found; RMSD values ranged from 2.4 to 5.9 Å. Single Cα traces predicted for each of the large loops possessed RMSD values of 4.5 Å. Generally, ab initio loop modeling presented in this work combines elements of computational procedures developed both for protein folding and for peptide conformational analysis. © 2001 John Wiley & Sons, Inc. Biopolymers (Pept Sci) 60: 153–168, 2001 相似文献
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4.
We present a simulated annealing-based method for the prediction of the tertiary structures of proteins given knowledge of the secondary structure associated with each amino acid in the sequence. The backbone is represented in a detailed fashion whereas the sidechains and pairwise interactions are modeled in a simplified way, following the LINUS model of Srinivasan and Rose. A perceptron-based technique is used to optimize the interaction potentials for a training set of three proteins. For these proteins, the procedure is able to reproduce the tertiary structures to below 3 A in root mean square deviation (rmsd) from the PDB targets. We present the results of tests on twelve other proteins. For half of these, the lowest energy decoy has a rmsd from the native state below 6 A and, in 9 out of 12 cases, we obtain decoys whose rmsd from the native states are also well below 5 A. 相似文献
5.
Huiling Zhang Qingsheng Huang Zhendong Bei Yanjie Wei Christodoulos A. Floudas 《Proteins》2016,84(3):332-348
In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position–specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα‐Cα atoms. First, using a rigorous leave‐one‐protein‐out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state‐of‐the‐art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/ . Proteins 2016; 84:332–348. © 2016 Wiley Periodicals, Inc. 相似文献
6.
Fragment assembly using structural motifs excised from other solved proteins has shown to be an efficient method for ab initio protein‐structure prediction. However, how to construct accurate fragments, how to derive optimal restraints from fragments, and what the best fragment length is are the basic issues yet to be systematically examined. In this work, we developed a gapless‐threading method to generate position‐specific structure fragments. Distance profiles and torsion angle pairs are then derived from the fragments by statistical consistency analysis, which achieved comparable accuracy with the machine‐learning‐based methods although the fragments were taken from unrelated proteins. When measured by both accuracies of the derived distance profiles and torsion angle pairs, we come to a consistent conclusion that the optimal fragment length for structural assembly is around 10, and at least 100 fragments at each location are needed to achieve optimal structure assembly. The distant profiles and torsion angle pairs as derived by the fragments have been successfully used in QUARK for ab initio protein structure assembly and are provided by the QUARK online server at http://zhanglab.ccmb. med.umich.edu/QUARK/ . Proteins 2013. © 2012 Wiley Periodicals, Inc. 相似文献
7.
E. S. Huang R. Samudrala J. W. Ponder 《Protein science : a publication of the Protein Society》1998,7(9):1998-2003
For successful ab initio protein structure prediction, a method is needed to identify native-like structures from a set containing both native and non-native protein-like conformations. In this regard, the use of distance geometry has shown promise when accurate inter-residue distances are available. We describe a method by which distance geometry restraints are culled from sets of 500 protein-like conformations for four small helical proteins generated by the method of Simons et al. (1997). A consensus-based approach was applied in which every inter-Calpha distance was measured, and the most frequently occurring distances were used as input restraints for distance geometry. For each protein, a structure with lower coordinate root-mean-square (RMS) error than the mean of the original set was constructed; in three cases the topology of the fold resembled that of the native protein. When the fold sets were filtered for the best scoring conformations with respect to an all-atom knowledge-based scoring function, the remaining subset of 50 structures yielded restraints of higher accuracy. A second round of distance geometry using these restraints resulted in an average coordinate RMS error of 4.38 A. 相似文献
8.
Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of experimental limitations), reliable prediction of protein function through computational means has become crucial. This paper reviews the machine learning techniques used in the literature, following their evolution from simple algorithms such as logistic regression to more advanced methods like support vector machines and modern deep neural networks. Hyperparameter optimization methods adopted to boost prediction performance are presented. In parallel, the metamorphosis in the features used by these algorithms from classical physicochemical properties and amino acid composition, up to text-derived features from biomedical literature and learned feature representations using autoencoders, together with feature selection and dimensionality reduction techniques, are also reviewed. The success stories in the application of these techniques to both general and specific protein function prediction are discussed. 相似文献
9.
Lena Scheubert Rainer Schmidt Dirk Repsilber Mitja Lu?trek Georg Fuellen 《DNA research》2011,18(4):233-251
Pluripotent stem cells are able to self-renew, and to differentiate into all adult cell types. Many studies report data describing these cells, and characterize them in molecular terms. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus. 相似文献
10.
Adam J. Simpkin Jens M. H. Thomas Felix Simkovic Ronan M. Keegan Daniel J. Rigden 《Acta Crystallographica. Section D, Structural Biology》2019,75(12):1051-1062
Molecular replacement (MR) is the predominant route to solution of the phase problem in macromolecular crystallography. Where the lack of a suitable homologue precludes conventional MR, one option is to predict the target structure using bioinformatics. Such modelling, in the absence of homologous templates, is called ab initio or de novo modelling. Recently, the accuracy of such models has improved significantly as a result of the availability, in many cases, of residue‐contact predictions derived from evolutionary covariance analysis. Covariance‐assisted ab initio models representing structurally uncharacterized Pfam families are now available on a large scale in databases, potentially representing a valuable and easily accessible supplement to the PDB as a source of search models. Here, the unconventional MR pipeline AMPLE is employed to explore the value of structure predictions in the GREMLIN and PconsFam databases. It was tested whether these deposited predictions, processed in various ways, could solve the structures of PDB entries that were subsequently deposited. The results were encouraging: nine of 27 GREMLIN cases were solved, covering target lengths of 109–355 residues and a resolution range of 1.4–2.9 Å, and with target–model shared sequence identity as low as 20%. The cluster‐and‐truncate approach in AMPLE proved to be essential for most successes. For the overall lower quality structure predictions in the PconsFam database, remodelling with Rosetta within the AMPLE pipeline proved to be the best approach, generating ensemble search models from single‐structure deposits. Finally, it is shown that the AMPLE‐obtained search models deriving from GREMLIN deposits are of sufficiently high quality to be selected by the sequence‐independent MR pipeline SIMBAD. Overall, the results help to point the way towards the optimal use of the expanding databases of ab initio structure predictions. 相似文献
11.
A well-behaved physics-based all-atom scoring function for protein structure prediction is analyzed with several widely used all-atom decoy sets. The scoring function, termed AMBER/Poisson-Boltzmann (PB), is based on a refined AMBER force field for intramolecular interactions and an efficient PB model for solvation interactions. Testing on the chosen decoy sets shows that the scoring function, which is designed to consider detailed chemical environments, is able to consistently discriminate all 62 native crystal structures after considering the heteroatom groups, disulfide bonds, and crystal packing effects that are not included in the decoy structures. When NMR structures are considered in the testing, the scoring function is able to discriminate 8 out of 10 targets. In the more challenging test of selecting near-native structures, the scoring function also performs very well: for the majority of the targets studied, the scoring function is able to select decoys that are close to the corresponding native structures as evaluated by ranking numbers and backbone Calpha root mean square deviations. Various important components of the scoring function are also studied to understand their discriminative contributions toward the rankings of native and near-native structures. It is found that neither the nonpolar solvation energy as modeled by the surface area model nor a higher protein dielectric constant improves its discriminative power. The terms remaining to be improved are related to 1-4 interactions. The most troublesome term is found to be the large and highly fluctuating 1-4 electrostatics term, not the dihedral-angle term. These data support ongoing efforts in the community to develop protein structure prediction methods with physics-based potentials that are competitive with knowledge-based potentials. 相似文献
12.
Rojan Shrestha Francois Berenger Kam Y. J. Zhang 《Acta Crystallographica. Section D, Structural Biology》2011,67(9):804-812
Ab initio phasing is one of the remaining challenges in protein crystallography. Recent progress in computational structure prediction has enabled the generation of de novo models with high enough accuracy to solve the phase problem ab initio. This `ab initio phasing with de novo models' method first generates a huge number of de novo models and then selects some lowest energy models to solve the phase problem using molecular replacement. The amount of CPU time required is huge even for small proteins and this has limited the utility of this method. Here, an approach is described that significantly reduces the computing time required to perform ab initio phasing with de novo models. Instead of performing molecular replacement after the completion of all models, molecular replacement is initiated during the course of each simulation. The approach principally focuses on avoiding the refinement of the best and the worst models and terminating the entire simulation early once suitable models for phasing have been obtained. In a benchmark data set of 20 proteins, this method is over two orders of magnitude faster than the conventional approach. It was observed that in most cases molecular‐replacement solutions were determined soon after the coarse‐grained models were turned into full‐atom representations. It was also found that all‐atom refinement was hardly able to change the models sufficiently to enable successful molecular replacement if the coarse‐grained models were not very close to the native structure. Therefore, it remains critical to generate good‐quality coarse‐grained models to enable subsequent all‐atom refinement for successful ab initio phasing by molecular replacement. 相似文献
13.
随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据\"高维度\"及\"稀疏性\"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。 相似文献
14.
A novel method for ab initio prediction of protein tertiary structures, PROFESY (PROFile Enumerating SYstem), is proposed. This method utilizes the secondary structure prediction information of a query sequence and the fragment assembly procedure based on global optimization. Fifteen-residue-long fragment libraries are constructed using the secondary structure prediction method PREDICT, and fragments in these libraries are assembled to generate full-length chains of a query protein. Tertiary structures of 50 to 100 conformations are obtained by minimizing an energy function for proteins, using the conformational space annealing method that enables one to sample diverse low-lying local minima of the energy. We apply PROFESY for benchmark tests to proteins with known structures to demonstrate its feasibility. In addition, we participated in CASP5 and applied PROFESY to four new-fold targets for blind prediction. The results are quite promising, despite the fact that PROFESY was in its early stages of development. In particular, PROFESY successfully provided us the best model-one structure for the target T0161. 相似文献
15.
Lauren L Perskie George D Rose 《Protein science : a publication of the Protein Society》2010,19(6):1127-1136
We present a method with the potential to generate a library of coil segments from first principles. Proteins are built from α‐helices and/or β‐strands interconnected by these coil segments. Here, we investigate the conformational determinants of short coil segments, with particular emphasis on chain turns. Toward this goal, we extracted a comprehensive set of two‐, three‐, and four‐residue turns from X‐ray–elucidated proteins and classified them by conformation. A remarkably small number of unique conformers account for most of this experimentally determined set, whereas remaining members span a large number of rare conformers, many occurring only once in the entire protein database. Factors determining conformation were identified via Metropolis Monte Carlo simulations devised to test the effectiveness of various energy terms. Simulated structures were validated by comparison to experimental counterparts. After filtering rare conformers, we found that 98% of the remaining experimentally determined turn population could be reproduced by applying a hydrogen bond energy term to an exhaustively generated ensemble of clash‐free conformers in which no backbone polar group lacks a hydrogen‐bond partner. Further, at least 90% of longer coil segments, ranging from 5‐ to 20 residues, were found to be structural composites of these shorter primitives. These results are pertinent to protein structure prediction, where approaches can be divided into either empirical or ab initio methods. Empirical methods use database‐derived information; ab initio methods rely on physical–chemical principles exclusively. Replacing the database‐derived coil library with one generated from first principles would transform any empirically based method into its corresponding ab initio homologue. 相似文献
16.
The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a “wisdom of the crowd” approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression. 相似文献
17.
Jeremy R. H. Tame 《Acta Crystallographica. Section D, Structural Biology》2000,56(12):1554-1559
The phase problem remains a key rate‐limiting step in the determination of macromolecular X‐ray structures. Direct methods, applying probability theory to the native data set, can routinely solve structures of up to about 200 non‐H atoms, although much larger structures have been solved given sufficiently high resolution data and the presence of heavy atoms. Here it is shown that maximum‐likelihood refinement of free‐atom models with ARP/wARP can solve ab initio a much larger metalloprotein structure than the largest so far solved by conventional direct methods. The protein, OppA, is not naturally associated with metal ions but was co‐crystallized with uranium. 相似文献
18.
《Journal of molecular biology》2021,433(20):167088
I outline how over my career as a protein scientist Machine Learning has impacted my area of science and one of my pastimes, chess, where there are some interesting parallels. In 1968, modelling of three-dimensional structures was initiated based on a known structure as a template, the problem of the pathway of protein folding was posed and bets were taken in the emerging field of Machine Learning on whether computers could outplay humans at chess. Half a century later, Machine Learning has progressed from using computational power combined with human knowledge in solving problems to playing chess without human knowledge being used, where it has produced novel strategies. Protein structures are being solved by Machine Learning based on human-derived knowledge but without templates. There is much promise that programs like AlphaFold based on Machine Learning will be powerful tools for designing entirely novel protein folds and new activities. But, will they produce novel ideas on protein folding pathways and provide new insights into the principles that govern folds? 相似文献
19.
两种过滤特征基因选择算法的有效性研究 总被引：2，自引：0，他引：2
对基因表达谱进行特征基因选择不仅能改善疾病分类方法的效能,而且为寻找与疾病相关的特征基因提供新的途径．通过比较用调整p值的t检验、非参数评分两种特征基因选择算法后和未进行选择时支持向量机(SVM)分类器的分类性能、支持向量(SV)的吻合度、错分样本ID的吻合度和对样本均匀翻倍后的稳定性．结果发现：特征选择后线性、核函数为二阶多项式和径向基的SVM分类性能明显提高;特征选择前后的SV及错分样本ID的吻合度均较高;SVM的稳定性较好．由此得出结论：这两种特征选择算法具有一定的有效性． 相似文献
20.
Manuel Galli Italo Zoppis Andrew Smith Fulvio Magni Giancarlo Mauri 《Expert review of proteomics》2016,13(7):685-696
Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.
Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing.
Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data. 相似文献