Structure Prediction of Membrane Proteins

Zhou, Chunlong; Zheng, Yao; Zhou, Yan

doi:10.1016/s1672-0229(04)02001-7

Cited by 12 publications

(10 citation statements)

References 30 publications

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“…As such, IMPs are in a unique position to mediate a host of cellular processes, including intercellular communication, vesicle trafficking, ion transport, protein translocation/integration, and propagation of signaling cascades. [1][2][3] Thus, it is not coincidence that some of the largest classes of drug targetssG-protein coupled receptors (GPCRs), ion channels, transporters, cytochrome P450sare IMPs. 4,5 The hydrophobic core of the phospholipid bilayer is the driving force behind IMP structure.…”

Section: Introductionmentioning

confidence: 99%

Proteomics of Integral Membrane ProteinsTheory and Application

2007

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Section: Introductionmentioning

confidence: 99%

Proteomics of Integral Membrane ProteinsTheory and Application

2007

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“…Even though class E has got such a high accuracy it has to be treated with care, since there has not been enough data for this protein class. The relatively poor accuracy for class F is most likely due to the fact that membrane proteins propose some difficulties in structure elucidation [34,42,64] and this results in low resolutions [36]. A poor resolution in turn may lead to inaccurate atom coordinates.…”

Section: Data Preprocessing and Machine Learning Settingsmentioning

confidence: 99%

Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

Villmann

Abel

Bohnsack

et al. 2020

Preprint

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In this contribution the discrimination between native and mirror models of proteins according to their chirality is tackled based on the structural protein information. This information is contained in the Ramachandran plots of the protein models. We provide an approach to classify those plots by means of an interpretable machine learning classifier - the Generalized Matrix Learning Vector Quantizer. Applying this tool, we are able to distinguish with high accuracy between mirror and native structures just evaluating the Ramachandran plots. The classifier model provides additional information regarding the importance of regions, e.g. α-helices and β-strands, to discriminate the structures precisely. This importance weighting differs for several considered protein classes.

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“…Because of their biological and pharmaceutical importance, identification of transmembrane helices in membrane proteins is a priority. Although promising methods in X-ray crystallography and nuclear magnetic resonance (NMR) have begun to open avenues to the determination of these structures 18)- 20) , the number of known three-dimensional structures remains small 6),21), 22) . Therefore, reliable algorithms to predict transmembrane protein structures would be very useful.…”

Section: Background Of This Studymentioning

confidence: 99%

A Generalized Hidden Markov Model Approach to Transmembrane Region Prediction with Poisson Distribution as State Duration Probabilities

Kaburagi

Matsumoto

2008

ipsjdc

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We present a novel algorithm to predict transmembrane regions from a primary amino acid sequence. Previous studies have shown that the Hidden Markov Model (HMM) is one of the powerful tools known to predict transmembrane regions; however, one of the conceptual drawbacks of the standard HMM is the fact that the state duration, i.e., the duration for which the hidden dynamics remains in a particular state follows the geometric distribution. Real data, however, does not always indicate such a geometric distribution. The proposed algorithm utilizes a Generalized Hidden Markov Model (GHMM), an extension of the HMM, to cope with this problem. In the GHMM, the state duration probability can be any discrete distribution, including a geometric distribution. The proposed algorithm employs a state duration probability based on a Poisson distribution. We consider the two-dimensional vector trajectory consisting of hydropathy index and charge associated with amino acids, instead of the 20 letter symbol sequences. Also a Monte Carlo method (Forward/Backward Sampling method) is adopted for the transmembrane region prediction step. Prediction accuracies using publicly available data sets show that the proposed algorithm yields reasonably good results when compared against some existing algorithms. 1 Since the state duration probability is a discrete probability defined in the range d ≥ 1, although the probability in Fig. 1 may look like a Gaussian distribution, it cannot be one.

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Structure Prediction of Membrane Proteins

Cited by 12 publications

References 30 publications

Proteomics of Integral Membrane ProteinsTheory and Application

Proteomics of Integral Membrane ProteinsTheory and Application

Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

A Generalized Hidden Markov Model Approach to Transmembrane Region Prediction with Poisson Distribution as State Duration Probabilities

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