PREDICTING LOCAL QUALITY OF A SEQUENCE–STRUCTURE ALIGNMENT

2009 ◽  
Vol 07 (05) ◽  
pp. 789-810 ◽  
Author(s):  
XIN GAO ◽  
JINBO XU ◽  
SHUAI CHENG LI ◽  
MING LI
Keyword(s):  
Structure Prediction ◽  
Solvent Accessibility ◽  
Structure Alignment ◽  
Evolutionary Information ◽  
Support Vector ◽  
Sequence Structure ◽  
Protein Model ◽  
Local Sequence ◽  
Local Quality

Although protein structure prediction has made great progress in recent years, a protein model derived from automated prediction methods is subject to various errors. As methods for structure prediction develop, a continuing problem is how to evaluate the quality of a protein model, especially to identify some well-predicted regions of the model, so that the structural biology community can benefit from the automated structure prediction. It is also important to identify badly-predicted regions in a model so that some refinement measurements can be applied to it. We present two complementary techniques, FragQA and PosQA, to accurately predict local quality of a sequence–structure (i.e. sequence–template) alignment generated by comparative modeling (i.e. homology modeling and threading). FragQA and PosQA predict local quality from two different perspectives. Different from existing methods, FragQA directly predicts cRMSD between a continuously aligned fragment determined by an alignment and the corresponding fragment in the native structure, while PosQA predicts the quality of an individual aligned position. Both FragQA and PosQA use an SVM (Support Vector Machine) regression method to perform prediction using similar information extracted from a single given alignment. Experimental results demonstrate that FragQA performs well on predicting local fragment quality, and PosQA outperforms two top-notch methods, ProQres and ProQprof. Our results indicate that (1) local quality can be predicted well; (2) local sequence evolutionary information (i.e. sequence similarity) is the major factor in predicting local quality; and (3) structural information such as solvent accessibility and secondary structure helps to improve the prediction performance.

scholarly journals Improved protein model quality assessments by changing the target function

2018 ◽  
Author(s):  
Karolis Uziela ◽  
David Menéndez Hurtado ◽  
Nanjiang Shu ◽  
Björn Wallner ◽  
Arne Elofsson
Keyword(s):  
Structure Prediction ◽  
Target Function ◽  
Local Environment ◽  
Global Model ◽  
Model Quality ◽  
Quality Estimation ◽  
Protein Model ◽  
Local Quality ◽  
Quality Assessments

AbstractProtein modeling quality is an important part of protein structure prediction. We have for more than a decade developed a set of methods for this problem. We have used various types of description of the protein and different machine learning methodologies. However, common to all these methods has been the target function used for training. The target function in ProQ describes the local quality of a residue in a protein model. In all versions of ProQ the target function has been the S-score. However, other quality estimation functions also exist, which can be divided into superposition- and contact-based methods. The superposition-based methods, such as S-score, are based on a rigid body superposition of a protein model and the native structure, while the contact-based methods compare the local environment of each residue. Here, we examine the effects of retraining our latest predictor, ProQ3D, using identical inputs but different target functions. We find that the c ntact-based methods are easier to predict and that predictors trained on these measures provide some advantages when it comes to identifying the best model. One possible reason for this is that contact based methods are better at estimating the quality of multi-domain targets. However, training on the S-score gives the best correlation with the GDT_TS score, which is commonly used in CASP to score the global model quality. To take the advantage of both of these features we provide an updated version of ProQ3D that predicts local and global model quality estimates based on different quality estimates.

scholarly journals Zoomqa: Residue-Level Single-Model QA Support Vector Machine Utilizing Sequential and 3D Structural Features

2021 ◽  
Author(s):  
Kyle Hippe ◽  
Cade Lilley ◽  
William Berkenpas ◽  
Kiyomi Kishaba ◽  
Renzhi Cao
Keyword(s):  
Protein Complex ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
State Of The Art ◽  
Chemical Properties ◽  
Structural Features ◽  
Residue Level ◽  
Support Vector ◽  
Single Model

ABSTRACTMotivationThe Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. When predictions are made for proteins of which we do not know the native structure, we run into an issue to tell how good a tertiary structure prediction is, especially the protein binding regions, which are useful for drug discovery. Currently, most methods only evaluate the overall quality of a protein decoy, and few can work on residue level and protein complex. Here we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure / complex prediction at residue level. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius r of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grades their placement within the protein as a whole. Moreover, ZoomQA can evaluate the quality of protein complex, which is unique.ResultsWe benchmark ZoomQA on CASP14, it outperforms other state of the art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features, and shows our method is able to match the performance of other state-of-the-art methods without the use of homology searching against database or PSSM matrix.Availabilityhttp://[email protected]

LOCAL SEQUENCE-STRUCTURE MOTIFS IN RNA

2004 ◽  
Vol 02 (04) ◽  
pp. 681-698 ◽  
Author(s):  
ROLF BACKOFEN ◽  
SEBASTIAN WILL
Keyword(s):  
Information Structure ◽  
Structure Alignment ◽  
General Definition ◽  
Local Alignment ◽  
Sequence Information ◽  
Sequence Structure ◽  
Worst Case ◽  
Rna Molecules ◽  
Alignment Algorithms ◽  
Local Sequence

Ribonuclic acid (RNA) enjoys increasing interest in molecular biology; despite this interest fundamental algorithms are lacking, e.g. for identifying local motifs. As proteins, RNA molecules have a distinctive structure. Therefore, in addition to sequence information, structure plays an important part in assessing the similarity of RNAs. Furthermore, common sequence-structure features in two or several RNA molecules are often only spatially local, where possibly large parts of the molecules are dissimilar. Consequently, we address the problem of comparing RNA molecules by computing an optimal local alignment with respect to sequence and structure information. While local alignment is superior to global alignment for identifying local similarities, no general local sequence-structure alignment algorithms are currently known. We suggest a new general definition of locality for sequence-structure alignments that is biologically motivated and efficiently tractable. To show the former, we discuss locality of RNA and prove that the defined locality means connectivity by atomic and non-atomic bonds. To show the latter, we present an efficient algorithm for the newly defined pairwise local sequence-structure alignment (lssa) problem for RNA. For molecules of lengthes n and m, the algorithm has worst-case time complexity of O(n2·m2· max (n,m)) and a space complexity of only O(n·m). An implementation of our algorithm is available at . Its runtime is competitive with global sequence-structure alignment.

FragQA: predicting local fragment quality of a sequence-structure alignment

2007 ◽  
Author(s):  
Xin Gao ◽  
Dongbo Bu ◽  
Shuai Cheng Li ◽  
Jinbo Xu ◽  
Ming Li
Keyword(s):  
Structure Alignment ◽  
Sequence Structure

scholarly journals PSICA: a fast and accurate web service for protein model quality analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W443-W450 ◽  
Author(s):  
Wenbo Wang ◽  
Zhaoyu Li ◽  
Junlin Wang ◽  
Dong Xu ◽  
Yi Shang
Keyword(s):  
Web Service ◽  
Structural Information ◽  
Protein Structures ◽  
Quality Analysis ◽  
Consensus Method ◽  
Model Quality ◽  
Protein Model ◽  
Local Quality ◽  
The Difference

Abstract This paper presents a new fast and accurate web service for protein model quality analysis, called PSICA (Protein Structural Information Conformity Analysis). It is designed to evaluate how much a tertiary model of a given protein primary sequence conforms to the known protein structures of similar protein sequences, and to evaluate the quality of predicted protein models. PSICA implements the MUfoldQA_S method, an efficient state-of-the-art protein model quality assessment (QA) method. In CASP12, MUfoldQA_S ranked No. 1 in the protein model QA select-20 category in terms of the difference between the predicted and true GDT-TS value of each model. For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model. Additionally, PSICA implements MUfoldQA_C, a new consensus method based on MUfoldQA_S. In CASP12, MUfoldQA_C ranked No. 1 in top 1 model GDT-TS loss on the select-20 QA category and No. 2 in the average difference between the predicted and true GDT-TS value of each model for both select-20 and best-150 QA categories. The PSICA server is freely available at http://qas.wangwb.com/∼wwr34/mufoldqa/index.html.

scholarly journals Deep convolutional neural networks for predicting the quality of single protein structural models

2019 ◽  
Author(s):  
Jie Hou ◽  
Renzhi Cao ◽  
Jianlin Cheng
Keyword(s):  
Structure Prediction ◽  
Structural Model ◽  
Structural Models ◽  
Structural Features ◽  
Fixed Number ◽  
Deep Convolutional Neural Networks ◽  
Global Quality ◽  
Protein Model ◽  
Single Protein

AbstractPredicting the global quality and local (residual-specific) quality of a single protein structural model is important for protein structure prediction and application. In this work, we developed a deep one-dimensional convolutional neural network (1DCNN) that predicts the absolute local quality of a single protein model as well as two 1DCNNs to predict both local and global quality simultaneously through a novel multi-task learning framework. The networks accept sequential and structural features (i.e. amino acid sequence, agreement of secondary structure and solvent accessibilities, residual disorder properties and Rosetta energies) of a protein model of any size as input to predict its quality, which is different from existing methods using a fixed number of hand-crafted features as input. Our three methods (InteractQA-net, JointQA-net and LocalQA-net) were trained on the structural models of the single-domain protein targets of CASP8, 9, 10 and evaluated on the models of CASP11 and CASP12 targets. The results show that the performance of our deep learning methods is comparable to the state-of-the-art quality assessment methods. Our study also demonstrates that combining local and global quality predictions together improves the global quality prediction accuracy. The source code and executable of our methods are available at:https://github.com/multicom-toolbox/DeepCovQA

scholarly journals Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiao Chen ◽  
Jian Liu ◽  
Zhiye Guo ◽  
Tianqi Wu ◽  
Jie Hou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Structural Models ◽  
Single Model ◽  
Model Accuracy ◽  
Model Quality ◽  
Distance Information ◽  
Evaluation Of Performance ◽  
Protein Model

AbstractThe inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.

PROTEIN SECONDARY STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION

2006 ◽  
Vol 06 (04) ◽  
pp. 551-567 ◽  
Author(s):  
JAYAVARDHANA GUBBI ◽  
DANIEL T. H. LAI ◽  
MARIMUTHU PALANISWAMI ◽  
MICHAEL PARKER
Keyword(s):  
Support Vector Machines ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Feature Representation ◽  
Evolutionary Information ◽  
Support Vector ◽  
Vector Machines

Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou–Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q3 accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on .

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