R2C: improving <i>ab initio</i> residue contact map prediction using dynamic fusion strategy and Gaussian noise filter

Yang, Jing; Jin, Qiyu; Zhang, Biao; Shen, Hong‐Bin

doi:10.1093/bioinformatics/btw181

Cited by 28 publications

(13 citation statements)

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“…Based on the determined TMHs, the prediction of TMH–TMH residue contacts can provide crucial spatial constraints for accurately modeling tertiary structures of membrane proteins [ 15 , 20 ]. The MemBrain-Contact prediction module is constructed by combining statistical machine learning algorithms and biological residue coevolution analysis from multiple sequence alignments as shown in Fig.…”

Section: Membrain Prediction Functionsmentioning

confidence: 99%

“…Although the TMH structure predictions can help figuring out the general structure topology of α-helical membrane protein, it is not enough to build the 3D structure of a membrane protein. The residues contact map provides spatial constraints for constructing tertiary structure models of TMH proteins, which has recently been a hot topic in protein structure prediction [ 12 – 15 ]. The existing methods for predicting residue–residue contacts of α-helix proteins and TMH–TMH interactions from the primary sequences can be generally divided into two categories: (1) machine learning-based methods, (2) statistical-based coevolution mining methods.…”

Section: Introductionmentioning

confidence: 99%

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MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

et al. 2017

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Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein’s structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of A TMH, 87.1% of A P, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/.

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Section: Membrain Prediction Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

et al. 2017

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“…ML-based methods, such as CMAPpro [7], identify the contacts by abstracting various structural features of a protein sequence to a classifier. It is apparent that the coevolutionary residues can be used in an ML-based predictor to improve the prediction accuracy, which was proved by R2C [8].…”

Section: Introductionmentioning

confidence: 93%

IMPContact: An Interhelical Residue Contact Prediction Method

Fang

Jia

et al. 2020

BioMed Research International

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As an important category of proteins, alpha-helix transmembrane proteins (αTMPs) play an important role in various biological activities. Because the solved αTMP structures are inadequate, predicting the residue contacts among the transmembrane segments of an αTMP exhibits the basis of protein fold, which can be used to further discover more protein functions. A few efforts have been devoted to predict the interhelical residue contact using machine learning methods based on the prior knowledge of transmembrane protein structure. However, it is still a challenge to improve the prediction accuracy, while the deep learning method provides an opportunity to utilize the structural knowledge in a different insight. For this purpose, we proposed a novel αTMP residue-residue contact prediction method IMPContact, in which a convolutional neural network (CNN) was applied to recognize those interhelical contacts in a TMP using its specific structural features. There were four sequence-based TMP-specific features selected to descript a pair of residues, namely, evolutionary covariation, predicted topology structure, residue relative position, and evolutionary conservation. An up-to-date dataset was used to train and test the IMPContact; our method achieved better performance compared to peer methods. In the case studies, IHRCs in the regular transmembrane helixes were better predicted than in the irregular ones.

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“…As we have shown, predicted contacts deriving from evolutionary covariance already offer exciting possibilities to the experimental structural biologist as much as to the bioinformatician. The area remains highly active and new approaches (see, for example, Yang et al, 2016) can confidently be expected to continue to improve performance in the near future. These include approaches where additional information can be exploited to improve the precision of contact predictions (Zhang et al, 2016;Hopf et al, 2012;Wang & Barth, 2015;Hö nigschmid & Frishman, 2016).…”

Section: Topical Reviews 8 Conclusionmentioning

confidence: 99%

Applications of contact predictions to structural biology

Šimkovic

Овчинников

Baker

et al. 2017

IUCrJ

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Evolutionary pressure on residue interactions, intramolecular or intermolecular, that are important for protein structure or function can lead to covariance between the two positions. Recent methodological advances allow much more accurate contact predictions to be derived from this evolutionary covariance signal. The practical application of contact predictions has largely been confined to structural bioinformatics, yet, as this work seeks to demonstrate, the data can be of enormous value to the structural biologist working in X-ray crystallography, cryo-EM or NMR. Integrative structural bioinformatics packages such as Rosetta can already exploit contact predictions in a variety of ways. The contribution of contact predictions begins at construct design, where structural domains may need to be expressed separately and contact predictions can help to predict domain limits. Structure solution by molecular replacement (MR) benefits from contact predictions in diverse ways: in difficult cases, more accurate search models can be constructed using ab initio modelling when predictions are available, while intermolecular contact predictions can allow the construction of larger, oligomeric search models. Furthermore, MR using supersecondary motifs or large-scale screens against the PDB can exploit information, such as the parallel or antiparallel nature of any -strand pairing in the target, that can be inferred from contact predictions. Contact information will be particularly valuable in the determination of lower resolution structures by helping to assign sequence register. In large complexes, contact information may allow the identity of a protein responsible for a certain region of density to be determined and then assist in the orientation of an available model within that density. In NMR, predicted contacts can provide long-range information to extend the upper size limit of the technique in a manner analogous but complementary to experimental methods. Finally, predicted contacts can distinguish between biologically relevant interfaces and mere lattice contacts in a final crystal structure, and have potential in the identification of functionally important regions and in foreseeing the consequences of mutations.

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R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 28 publications

References 40 publications

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

IMPContact: An Interhelical Residue Contact Prediction Method

Applications of contact predictions to structural biology

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