Pharmacophore modeling, in silico screening, molecular docking and molecular dynamics approaches for potential alpha-delta bungarotoxin-4 inhibitors discovery.

BACKGROUND: The alpha-delta bungartoxin-4 (α-δ-Bgt-4) is a potent neurotoxin produced by highly venomous snake species, Bungarus caeruleus, mainly targeting neuronal acetylcholine receptors (nAchRs) and producing adverse biological malfunctions leading to respiratory paralysis and mortality. OBJECTIVE: In this study, we predicted the three-dimensional structure of α-δ-Bgt-4 using homology modeling and investigated the conformational changes and the key residues responsible for nAchRs inhibiting activity. MATERIALS AND METHODS: From the selected plants, which are traditionally used for snake bites, the active compounds are taken and performed molecular interaction studies and also used for modern techniques like pharmacophore modeling and mapping and absorption, distribution, metabolism, elimination and toxicity analysis which may increase the possibility of success. RESULTS: Moreover, 100's of drug-like compounds were retrieved and analyzed through computational virtual screening and allowed for pharmacokinetic profiling, molecular docking and dynamics simulation. CONCLUSION: Finally the top five drug-like compounds having competing level of inhibition toward α-δ-Bgt-4 toxin were suggested based on their interaction with α-δ-Bgt-4 toxin.

In silico analysis of potential inhibitors of Ca(2+) activated K(+) channel blocker, Charybdotoxin-C from Leiurus quinquestriatus hebraeus through molecular docking and dynamics studies.

OBJECTIVE: Charybdotoxin-C (ChTx-C), from the scorpion Leiurus, quinquestriatus hebraeus blocks the calcium-activated potassium channels and causes hyper excitability of the nervous system. Detailed understanding the structure of ChTx-C, conformational stability, and intermolecular interactions are required to select the potential inhibitors of the toxin. MATERIALS AND METHODS: The structure of ChTx-C was modeled using Modeller 9v7. The amino acid residues lining the binding site were predicted and used for toxin-ligand docking studies, further, selected toxin-inhibitor complexes were studied using molecular dynamics (MD) simulations. RESULTS: The predicted structure has 91.7% of amino acids in the core and allowed regions of Ramachandran plot. A total of 133 analog compounds of existing drugs for scorpion bites were used for docking. As a result of docking, a list of compounds was shown good inhibiting properties with target protein. By analyzing the interactions, Ser 15, Lys 32 had significant interactions with selected ligand molecules and Val5, which may have hydrophobic interaction with the cyclic group of the ligand. MD simulation studies revealed that the conformation and intermolecular interactions of all selected toxin-inhibitor complexes were stable. CONCLUSION: The interactions of the ligand and active site amino acids were found out for the best-docked poses in turn helpful in designing potential antitoxins which may further be exploited in toxin based therapies.

Development of a machine learning method to predict membrane protein-ligand binding residues using basic sequence information.

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.

Pharmacophore mapping based inhibitor selection and molecular interaction studies for identification of potential drugs on calcium activated potassium channel blockers, tamulotoxin.

BACKGROUND: Tamulotoxin (TmTx) from Buthus tamulus was found to be a highly venomous toxin which accelerates the neurotransmitter release that directly affects the cardiovascular tissues and the respiratory system leading to death. TmTx from red Indian scorpion is a crucial inhibitor for Ca(2+) activated K(+) channel in humans. OBJECTIVE: The study is aimed at the identification of potential inhibitors of TmTx through pharmacophore based inhibitor screening and understanding the molecular level interactions. MATERIALS AND METHOD: The potential inhibitors for TmTx were identified using pharmacophore model based descriptor information present in existing drugs with the analysis of pharmacokinetic properties. The compounds with good ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) descriptors were subjected to molecular interaction studies. The stability of bound toxin-inhibitor complex was studied using molecular dynamics simulation over a period of one nanosecond. RESULTS: From a dataset of 3406 compounds, few compounds were selected as potential inhibitors based on the generated best pharmacophore models, pharmacokinetic analysis, molecular docking and molecular dynamics studies. CONCLUSION: In conclusion, two compounds containing better inhibition properties against TmTx are suggested to be better lead molecules for drug development in future and this study will help us to explore more inhibitors from natural origin against tamulotoxin.

A computational perspective of molecular interactions through virtual screening, pharmacokinetic and dynamic prediction on ribosome toxin A chain and inhibitors of Ricinus communis.

BACKGROUND: Ricin is considered to be one of the most deadly toxins and gained its favor as a bioweapon that has a serious social and biological impact, due to its widespread nature and abundant availability. The hazardous effects of this toxin in human being are seen in almost all parts of the organ system. The severe consequences of the toxin necessitate the need for developing potential inhibitors that can effectively block its interaction with the host system. MATERIALS AND METHODS: In order to identify potential inhibitors that can effectively block ricin, we employed various computational approaches. In this work, we computationally screened and analyzed 66 analogs and further tested their ADME/T profiles. From the kinetic and toxicity studies we selected six analogs that possessed appropriate pharmacokinetic and dynamic property. We have also performed a computational docking of these analogs with the target. RESULTS: On the basis of the dock scores and hydrogen bond interactions we have identified analog 64 to be the best interacting molecule. Molecule 64 seems to have stable interaction with the residues Tyr80, Arg180, and Val81. The pharmacophore feature that describes the key functional features of a molecule was also studied and presented. CONCLUSION: The pharmacophore features of the drugs provided suggests the key functional groups that can aid in the design and synthesis of more potential inhibitors.

TMFunction: database for functional residues in membrane proteins.

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), V(max) (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/.

Discrimination of mesophilic and thermophilic proteins using machine learning algorithms.

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.

Conformations of terminal sialyloligosaccharide fragments--a molecular dynamics study.

Molecular dynamics simulations have been performed to understand the conformational features of the terminal sialyloligosaccharide fragments NeuNAc alpha(2-3)Gal, NeuNAc alpha(2-6)Gal, NeuNAc alpha(2-8)NeuNAc and NeuNAc alpha(2-9)NeuNAc. The conformational regions A(i), B(i) and C(i) were identified in the Ramachandran plot. Analysis of the 1000 ps trajectories collected through simulation (2000 ps in the case of NeuNAc alpha (2-9)NeuNAc) revealed that these molecules have conformational propensity in region B(i). The occurrence of these molecules in the common conformational space leads to a structural similarity between them. This structural similarity may be an essential requirement for the neuraminidase activity towards sialyloligosaccharides. The local change in the conformation of the active site residues of neuraminidases may contribute for the specificity differences between different linkages of sialyloligosaccharides. A highly conserved water-mediated hydrogen bond observed in these structures between the sugar residues, acts as an additional stabilizing force.