Discriminating thermophilic proteins from their mesophilic counterparts is a challenging task and it would help to design stable proteins. In this work, we have systematically analyzed the amino acid compositions of 3075 mesophilic and 1609 thermophilic proteins belonging to 9 and 15 families, respectively. We found that the charged residues Lys, Arg, and Glu as well as the hydrophobic residues, Val and Ile have higher occurrence in thermophiles than mesophiles. Further, we have analyzed the performance of different methods, based on Bayes rules, logistic functions, neural networks, support vector machines, decision trees and so forth for discriminating mesophilic and thermophilic proteins. We found that most of the machine learning techniques discriminate these classes of proteins with similar accuracy. The neural network-based method could discriminate the thermophiles from mesophiles at the five-fold cross-validation accuracy of 89% in a dataset of 4684 proteins. Moreover, this method is tested with 325 mesophiles in Xylella fastidosa and 382 thermophiles in Aquifex aeolicus and it could successfully discriminate them with the accuracy of 91%. These accuracy levels are better than other methods in the literature and we suggest that this method could be effectively used to discriminate mesophilic and thermophilic proteins.
Venomous reptiles especially serpents are well known for their adverse effects after accidental conflicts with humans. Upon biting humans these serpents transmit arrays of detrimental toxins with diverse physiological activities that may either lead to minor symptoms such as dermatitis and allergic response or highly severe symptoms such as blood coagulation, disseminated intravascular coagulation, tissue injury, and hemorrhage. Other complications like respiratory arrest and necrosis may also occur. Bungarotoxins are a group of closely related neurotoxic proteins derived from the venom of kraits (Bungarus caeruleus) one of the six most poisonous snakes in India whose bite causes respiratory paralysis and mortality without showing any local symptoms. In the current study, by employing various pharmacoinformatic approaches, we have explored the antidote properties of 849 bioactive phytochemicals from 82 medicinal plants which have already shown antidote properties against various venomous toxins. These herbal compounds were taken and pharmacoinformatic approaches such as ADMET, docking and molecular dynamics were employed. The three-dimensional modelling approach provides structural insights on the interaction between bungarotoxin and phytochemicals. In silico simulations proved to be an effective analytical tools to investigate the toxin–ligand interaction, correlating with the affinity of binding. By analyzing the results from the present study, we proposed nine bioactive phytochemical compounds which are, 2-dodecanol, 7-hydroxycadalene, indole-3-(4'-oxo)butyric acid, nerolidol-2, trans-nerolidol, eugenol, benzene propanoic acid, 2-methyl-1-undecanol, germacren-4-ol can be used as antidotes for bungarotoxin.
We have developed the database TMFunction, which is a collection of more than 2900 experimentally observed functional residues in membrane proteins. Each entry includes the numerical values for the parameters IC50 (measure of the effectiveness of a compound in inhibiting biological function), Vmax (maximal velocity of transport), relative activity of mutants with respect to wild-type protein, binding affinity, dissociation constant, etc., which are important for understanding the sequence–structure–function relationship of membrane proteins. In addition, we have provided information about name and source of the protein, Uniprot and Protein Data Bank codes, mutational and literature information. Furthermore, TMFunction is linked to related databases and other resources. We have set up a web interface with different search and display options so that users have the ability to get the data in several ways. TMFunction is freely available at http://tmbeta-genome.cbrc.jp/TMFunction/.
Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment.
The prevalence of allergic disease is increasing dramatically in the developed world. Studies of allergic diseases have clearly demonstrated that histamine plays an important role in the pathogenesis of the early-phase allergic response. Histamine effects are mediated by H1, H2, H3, and H4 receptors. The presence of the histamine H4 receptors on leukocytes and mast cells suggests that the new histamine receptor H4 plays an important role in the modulation of the immune system. Thus, histamine H4 receptor is an attractive target for anti-allergic therapy. In our present study, we have generated a histamine H4 receptor model using I-TASSER based on human B2-adrenergic G-protein-coupled receptor. Structurally similar compounds of the three known antagonists JNJ777120, thioperamide, and Vuf6002 were retrieved from PubChem, and database was prepared. Virtual screening of those databases was performed, and six compounds with high docking score were identified. Also the binding mode revealed that all the six compounds had interaction with Asp94 of the receptor. Our results serve as a starting point in the development of novel lead compounds in anti-allergic therapy.
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