Abstract: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 pr… Show more
“…Signal peptide was predicted using Signal-3L 2.0 ( 79 ). Protein topology (inner, transmembrane, and outer regions) were predicted with MemBrain v3.1 ( 80 ). N-Glycosylation and O-Glycosylation sites were predicted using N-Glycosite ( 81 ) and NetOGlyc v4 ( 82 ) (score ≥ 0.5), respectively.…”
tests. Additionally, the updated HLA allelic frequencies provide a better representation of South America and may impact different immunogenetic studies.
“…Signal peptide was predicted using Signal-3L 2.0 ( 79 ). Protein topology (inner, transmembrane, and outer regions) were predicted with MemBrain v3.1 ( 80 ). N-Glycosylation and O-Glycosylation sites were predicted using N-Glycosite ( 81 ) and NetOGlyc v4 ( 82 ) (score ≥ 0.5), respectively.…”
tests. Additionally, the updated HLA allelic frequencies provide a better representation of South America and may impact different immunogenetic studies.
“…In Fig. 2 b, a representative diagram of ACE2 protein is shown, after analyzing with MemBrain 3.1, web server 11 . Figure 2 Topology of membrane protein ACE2 ( a ) from PDBTM (PDB ID 6M18) and ( b ) from MemBrain 3.1.…”
Angiotensin converting enzyme 2 (ACE2) (EC:3.4.17.23) is a transmembrane protein which is considered as a receptor for spike protein binding of novel coronavirus (SARS-CoV2). Since no specific medication is available to treat COVID-19, designing of new drug is important and essential. In this regard, in silico method plays an important role, as it is rapid and cost effective compared to the trial and error methods using experimental studies. Natural products are safe and easily available to treat coronavirus affected patients, in the present alarming situation. In this paper five phytochemicals, which belong to flavonoid and anthraquinone subclass, have been selected as small molecules in molecular docking study of spike protein of SARS-CoV2 with its human receptor ACE2 molecule. Their molecular binding sites on spike protein bound structure with its receptor have been analyzed. From this analysis, hesperidin, emodin and chrysin are selected as competent natural products from both Indian and Chinese medicinal plants, to treat COVID-19. Among them, the phytochemical hesperidin can bind with ACE2 protein and bound structure of ACE2 protein and spike protein of SARS-CoV2 noncompetitively. The binding sites of ACE2 protein for spike protein and hesperidin, are located in different parts of ACE2 protein. Ligand spike protein causes conformational change in three-dimensional structure of protein ACE2, which is confirmed by molecular docking and molecular dynamics studies. This compound modulates the binding energy of bound structure of ACE2 and spike protein. This result indicates that due to presence of hesperidin, the bound structure of ACE2 and spike protein fragment becomes unstable. As a result, this natural product can impart antiviral activity in SARS CoV2 infection. The antiviral activity of these five natural compounds are further experimentally validated with QSAR study.
“…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.…”
Transmembrane proteins (TMPs) play vital and diverse roles in many biological processes, such as molecular transportation and immune response. Like other proteins, many major interactions with other molecules happen in TMPs' surface area, which is important for function annotation and drug discovery. Under the condition that the structure of TMP is hard to derive from experiment and prediction, it is a practical way to predict the TMP residues' surface area, measured by the relative accessible surface area (rASA), based on computational methods. In this study, we presented a novel deep learning-based predictor TMP-SSurface for both alpha-helical and beta-barrel transmembrane proteins (α-TMP and β-TMP), where convolutional neural network (CNN), inception blocks, and CapsuleNet were combined to construct a network framework, simply accepting one-hot code and position-specific score matrix (PSSM) of protein fragment as inputs. TMP-SSurface was tested against an independent dataset achieving appreciable performance with 0.584 Pearson correlation coefficients (CC) value. As the first TMP's rASA predictor utilizing the deep neural network, our method provided a referenceable sample for the community, as well as a practical step to discover the interaction sites of TMPs based on their sequence.
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