The amino acid composition is different between the cytoplasmic and extracellular sides in membrane proteins

Nakashima, Hiroshi; Nishikawa, Ken

doi:10.1016/0014-5793(92)80506-c

Cited by 72 publications

(26 citation statements)

References 63 publications

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“…More specifically, the extracellular regions are expected to be dominated by residues favoring the β-sheet structure, such as Val, Ile, Phe and Tyr. Interestingly, aromatic residues (Trp, Tyr and Phe) are preferred on the extracellular side of membranes, whereas charged residues, both basic (Arg, Lys) and acidic (Glu, Asp), are preferentially sited on the cytoplasmic side [31]. These results are in accord with previous studies that performed structural predictions and showed that the extracellular domains of BY-kinases from Proteobacteria tend to favor β-structures [6].…”

Section: Resultssupporting

confidence: 86%

“…The NAKH920103 property is the AA composition of EXT of single-spanning proteins and provides the average amino acid composition of the extracellular regions of single-spanning transmembrane proteins [31]. The SCM-generated amino acid scores, positively correlated ( p = 0.50) with the NAKH920103 scale.…”

Section: Resultsmentioning

confidence: 99%

“…The SCM-generated amino acid scores, positively correlated ( p = 0.50) with the NAKH920103 scale. This scale was derived by the results of Nakashima et al [31], who studied 73 peptides longer than 50 residues, from 45 single-spanning membrane proteins. The BY-kinases can be divided into two groups based on their architecture.…”

Section: Resultsmentioning

confidence: 99%

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SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides

et al. 2016

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BackgroundBacterial tyrosine-kinases (BY-kinases), which play an important role in numerous cellular processes, are characterized as a separate class of enzymes and share no structural similarity with their eukaryotic counterparts. However, in silico methods for predicting BY-kinases have not been developed yet. Since these enzymes are involved in key regulatory processes, and are promising targets for anti-bacterial drug design, it is desirable to develop a simple and easily interpretable predictor to gain new insights into bacterial tyrosine phosphorylation. This study proposes a novel SCMBYK method for predicting and characterizing BY-kinases.ResultsA dataset consisting of 797 BY-kinases and 783 non-BY-kinases was established to design the SCMBYK predictor, which achieved training and test accuracies of 97.55 and 96.73%, respectively. Furthermore, the leave-one-phylum-out method was used to predict specific bacterial phyla hosts of target sequences, gaining 97.39% average test accuracy. After analyzing SCMBYK-derived propensity scores, four characteristics of BY-kinases were determined: 1) BY-kinases tend to be composed of α-helices; 2) the amino-acid content of extracellular regions of BY-kinases is expected to be dominated by residues such as Val, Ile, Phe and Tyr; 3) BY-kinases structurally resemble nuclear proteins; 4) different domains play different roles in triggering BY-kinase activity.ConclusionsThe SCMBYK predictor is an effective method for identification of possible BY-kinases. Furthermore, it can be used as a part of a novel drug repurposing method, which recognizes putative BY-kinases and matches them to approved drugs. Among other results, our analysis revealed that azathioprine could suppress the virulence of M. tuberculosis, and thus be considered as a potential antibiotic for tuberculosis treatment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1371-4) contains supplementary material, which is available to authorized users.

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Section: Resultssupporting

confidence: 86%

Section: Resultsmentioning

confidence: 99%

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SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides

et al. 2016

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“…Note that acidic residues are rare even compared to positively charged residues, which are about three to four times more frequent. On a much smaller dataset of single-spanning transmembrane proteins, Nakashima and Nishikawa [51] made similar compositional studies. To compare, they found 0.94% glutamate and 0.94% aspartate within just the TMH region (these values are very similar to ours from TMHs with small flanks; apparently, they used more outwardly defined TMH boundaries), but the content of each glutamate and aspartate within the extracellular or cytoplasmic domains is larger by an order of magnitude, between 5.26% and 9.34%.…”

Section: Resultsmentioning

confidence: 84%

Charged residues next to transmembrane regions revisited: “Positive-inside rule” is complemented by the “negative inside depletion/outside enrichment rule”

et al. 2017

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BackgroundTransmembrane helices (TMHs) frequently occur amongst protein architectures as means for proteins to attach to or embed into biological membranes. Physical constraints such as the membrane’s hydrophobicity and electrostatic potential apply uniform requirements to TMHs and their flanking regions; consequently, they are mirrored in their sequence patterns (in addition to TMHs being a span of generally hydrophobic residues) on top of variations enforced by the specific protein’s biological functions.ResultsWith statistics derived from a large body of protein sequences, we demonstrate that, in addition to the positive charge preference at the cytoplasmic inside (positive-inside rule), negatively charged residues preferentially occur or are even enriched at the non-cytoplasmic flank or, at least, they are suppressed at the cytoplasmic flank (negative-not-inside/negative-outside (NNI/NO) rule). As negative residues are generally rare within or near TMHs, the statistical significance is sensitive with regard to details of TMH alignment and residue frequency normalisation and also to dataset size; therefore, this trend was obscured in previous work. We observe variations amongst taxa as well as for organelles along the secretory pathway. The effect is most pronounced for TMHs from single-pass transmembrane (bitopic) proteins compared to those with multiple TMHs (polytopic proteins) and especially for the class of simple TMHs that evolved for the sole role as membrane anchors.ConclusionsThe charged-residue flank bias is only one of the TMH sequence features with a role in the anchorage mechanisms, others apparently being the leucine intra-helix propensity skew towards the cytoplasmic side, tryptophan flanking as well as the cysteine and tyrosine inside preference. These observations will stimulate new prediction methods for TMHs and protein topology from a sequence as well as new engineering designs for artificial membrane proteins.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-017-0404-4) contains supplementary material, which is available to authorized users.

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“…The properties are propensity (BLAM930101) [9] (HQI1), information value for accessibility (BIOV880101) [10] (HQI2), normalized frequency of alpha-helix (MAXF760101) [11] (HQI3), volumes including the crystallographic waters (TSAJ990101) [12] (HQI4), amino acid composition of MEM of multi-spanning proteins (NAKH920108) [13] (HQI5), composition of amino acids in intracellular proteins (CEDJ970104) [14] (HQI6), conformational preference for all beta-strand (LIFS790101) [15] (HQI7), and optimized relative partition energies (MIYS990104) [16] (HQI8). HQI features are extracted by representing the amino acid residues surrounding the hydroxylation site by a property of a corresponding index.…”

Section: Methodsmentioning

confidence: 99%

RF-Hydroxysite: a random forest based predictor for hydroxylation sites

Ismail

Newman

2016

Mol. BioSyst.

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Protein hydroxylation is an emerging posttranslational modification involved in both normal cellular processes and a growing number of pathological states, including several cancers. Protein hydroxylation is mediated by members of the hydroxylase family of enzymes, which catalyze the conversion of an alkyne group at select lysine or proline residues on their target substrates to a hydroxyl. Traditionally, hydroxylation has been identified using expensive and time-consuming experimental methods, such as tandem mass spectrometry. Therefore, to facilitate identification of putative hydroxylation sites and to complement existing experimental approaches, computational methods designed to predict the hydroxylation sites in protein sequences have recently been developed. Building on these efforts, we have developed a new method, termed RF-Hydroxysite, that uses random forest to identify putative hydroxylysine and hydroxyproline residues in proteins using only the primary amino acid sequence as input. RF-Hydroxysite integrates features previously shown to contribute to hydroxylation site prediction with several new features that we found to augment the performance remarkably. These include features that capture physicochemical, structural, sequence-order and evolutionary information from the protein sequences. The features used in the final model were selected based on their contribution to the prediction. Physicochemical information was found to contribute the most to the model. The present study also sheds light on the contribution of evolutionary, sequence order, and protein disordered region information to hydroxylation site prediction. The web server for RF-Hydroxysite is available online at http://bcb.ncat.edu/RF_hydroxy/.

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The amino acid composition is different between the cytoplasmic and extracellular sides in membrane proteins

Cited by 72 publications

References 63 publications

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides

Charged residues next to transmembrane regions revisited: “Positive-inside rule” is complemented by the “negative inside depletion/outside enrichment rule”

RF-Hydroxysite: a random forest based predictor for hydroxylation sites

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