OMPcontact: An Outer Membrane Protein Inter-Barrel Residue Contact Prediction Method

Zhang, Li; Wang, Han; Yan, Lei; Su, Lingtao; Xu, Dong

doi:10.1089/cmb.2015.0236

Cited by 8 publications

(8 citation statements)

References 51 publications

(52 reference statements)

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“…In addition to α-helical membrane proteins, sequence coevolution analysis has been adapted to predict the structure of β-barrel outer membrane proteins [23,48] (Figure 2C). For these predictions, at first only coevolutionary constraints between adjacent β strands were used to ensure correct registry and orientation of the β strands in the barrel, then other coevolutionary constraints were later included in the in silico folding procedure.…”

Section: De Novo Protein Structure Predictionmentioning

confidence: 99%

Applications of sequence coevolution in membrane protein biochemistry

Nicoludis¹,

Gaudet²

2018

Biochimica et Biophysica Acta (BBA) - Biomembranes

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Recently, protein sequence coevolution analysis has matured into a predictive powerhouse for protein structure and function. Direct methods, which use global statistical models of sequence coevolution, have enabled the prediction of membrane and disordered protein structures, protein complex architectures, and the functional effects of mutations in proteins. The field of membrane protein biochemistry and structural biology has embraced these computational techniques, which provide functional and structural information in an otherwise experimentally-challenging field. Here we review recent applications of protein sequence coevolution analysis to membrane protein structure and function and highlight the promising directions and future obstacles in these fields. We provide insights and guidelines for membrane protein biochemists who wish to apply sequence coevolution analysis to a given experimental system.

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Section: De Novo Protein Structure Predictionmentioning

confidence: 99%

Applications of sequence coevolution in membrane protein biochemistry

Nicoludis¹,

Gaudet²

2018

Biochimica et Biophysica Acta (BBA) - Biomembranes

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“…It is challenging to evaluate MP-specific tools because of the following reasons. First, some tools such as OMPcontact (Zhang et al, 2016b) are not available. Second, some tools need extra input information, such as [24].…”

Section: Star Methodsmentioning

confidence: 99%

“…There are some contact prediction methods specifically developed for MPs. They employ some MP-specific features and are trained from a limited number of MPs, such as TMHcon (Fuchs et al, 2009), MEMPACK (Nugent and Jones, 2010), TMhit (Lo et al, 2009), TMhhcp (Wang et al, 2011), MemBrain (Yang et al, 2013), COMSAT (Zhang et al, 2016a) and OMPcontact (Zhang et al, 2016b). McAllister and Floudas (McAllister and Floudas, 2008) proposed a mixed integer programming method for MP contact prediction by optimizing an energy function subject to a set of physical constraints.…”

Section: Introductionmentioning

confidence: 99%

Folding Membrane Proteins by Deep Transfer Learning

Wang

et al. 2017

Cell Systems

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Summary Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling. Here we describe a high-throughput deep transfer learning method that first predicts MP contacts by learning from non-membrane proteins (non-MPs) and then predicting three-dimensional structure models using the predicted contacts as distance restraints. Tested on 510 non-redundant MPs, our method has contact prediction accuracy at least 0.18 better than existing methods, predicts correct folds for 218 MPs, and generates three-dimensional models with RMSD less than 4Å and 5Å for 57 and 108 MPs, respectively. A rigorous blind test in the continuous automated model evaluation (CAMEO) project shows that our method predicted high-resolution three-dimensional models for two recent test MPs of 210 residues with RMSD ~2Å. We estimated that our method could predict correct folds for 1,345–1,871 reviewed human multi-pass MPs including a few hundred new folds, which shall facilitate the discovery of drugs targeting at membrane proteins.

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“…Multiple sequence alignment discovered the evolution conservation against the large-scale protein sequence database; it had already been wildly applied in various biological sequence researches [18,25]. More than 30% of the homologous superfamilies described in CATH are composed mainly or entirely of α-helixes [26].…”

Section: Evolution Conservation Featurementioning

confidence: 99%

“…These methods had already tried the best to maximize the coevolution and machine learning algorithms, but it is still a challenging problem to improve the prediction accuracy to an acceptable level, where the key factor is how to find more IHRC-specific structural features for a proper machine learning algorithm. Our previous work on interbarrel residue contact prediction for outer membrane proteins accessed a high performance method by using MP-specific features [18]. It is possible to further improve the IHRC prediction accuracy when the IHRC-specific features meet the deep learning networks, as many successful cases did in the field of bioinformatics [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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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OMPcontact: An Outer Membrane Protein Inter-Barrel Residue Contact Prediction Method

Cited by 8 publications

References 51 publications

Applications of sequence coevolution in membrane protein biochemistry

Applications of sequence coevolution in membrane protein biochemistry

Folding Membrane Proteins by Deep Transfer Learning

IMPContact: An Interhelical Residue Contact Prediction Method

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