A sequence‐based computational model for the prediction of the solvent accessible surface area for α‐helix and β‐barrel transmembrane residues

Abstract

Predicting the solvent accessible surface area (ASA) of transmembrane (TM) residues is of great importance for experimental researchers to elucidate diverse physiological processes. TM residues fall into two major structural classes (α-helix membrane protein and β-barrel membrane protein). The reported solvent ASA prediction models were developed for these two types of TM residues respectively. However, this prevents the general use of these methods because one cannot determine which model is suitable for a given TM residue without information of its type. To conquer this limitation, we developed a new computational model that can be used for predicting the ASA of both TM α-helix and β-barrel residues. The model was developed from 78 α-helix membrane protein chains and 24 β-barrel membrane protein. Its prediction ability was evaluated by cross validation method and its prediction result on an independent test set of 20 membrane protein chains. The results show that our model performs well for both types of TM residues and outperforms other prediction model which was developed for the specific type of TM residues. The prediction results also proved that the random forest model incorporating conservation score is an effective sequence-based computational approach for predicting the solvent ASA of TM residues.

Accurate single-sequence prediction of solvent accessible surface area using local and global features

“…A given residue along the protein chain can be surrounded by other residues in the chain or have a part of it accessible to the solvent housing the protein or to other interactions external to its own protein chain. The parameter associated with this quality of a residue is the accessible surface area (ASA) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. Specifically, the ASA is defined as the surface area of a protein that is accessible to a solvent and is given here in units of square Angstroms.…”

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

“…21 A random forest-based method recently reported by Wang et al achieved a PCC of 0.68. 61 Although these two methods reportedly have better performance than TMH-Expo on RSA prediction, it should be pointed out that the cross-validation scheme employed in these studies might have favorably biased the performance. In fact, using the same cross-validation scheme, Illergård et al trained a RSA predictor MPRAP which achieved the same PCC as TMH-Expo.…”

TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues

“…ProperTM [14] 2004 59 knowledge α-TMP TM region Burial state ASAP [18] 2006 73 SVR all TMP TM region ASA TMX [15] 2007 43 SVC α-TMP TM region Burial state MPRAP [19] 2010 80 SVR α-TMP full sequence rASA Yao et al (2011) [16] 2011 53 SVM α-TMP TM region Burial state Yao et al (2012) [20] 2012 122 RF all TMP TM region ASA TMexpoSVR [17] 2013 110 SVR α-TMP TM region rASA TMexpoSVC [17] 2013 110 SVC α-TMP TM region Burial state MenBrain-Rasa [21,22] 2015 80 SVR α-TMP full sequence rASA Although considerable achievements have been made in the field of TMP surface accessibility prediction, there are still several issues that deserved to be further improved. First of all, none of the mentioned methods could predict the rASA of the whole sequence of all kinds of TMPs.…”