Deconstructing the DGAT1 enzyme: membrane interactions at substrate binding sites

Diacylglycerol acyltransferase 1 (DGAT1) is a key enzyme in the triacylglyceride synthesis pathway. Bovine DGAT1 is an endoplasmic reticulum membrane-bound protein associated with the regulation of fat content in milk and meat. The aim of this study was to evaluate the interaction of DGAT1 peptides corresponding to putative substrate binding sites with different types of model membranes. Whilst these peptides are predicted to be located in an extramembranous loop of the membrane-bound protein, their hydrophobic substrates are membrane-bound molecules. In this study, peptides corresponding to the binding sites of the two substrates involved in the reaction were examined in the presence of model membranes in order to probe potential interactions between them that might influence the subsequent binding of the substrates. Whilst the conformation of one of the peptides changed upon binding several types of micelles regardless of their surface charge, suggesting binding to hydrophobic domains, the other peptide bound strongly to negatively-charged model membranes. This binding was accompanied by a change in conformation, and produced leakage of the liposome-entrapped dye calcein. The different hydrophobic and electrostatic interactions observed suggest the peptides may be involved in the interactions of the enzyme with membrane surfaces, facilitating access of the catalytic histidine to the triacylglycerol substrates

Structural Dynamic of a Self-Assembling Peptide d-EAK16 Made of Only D-Amino Acids

We here report systematic study of structural dynamics of a 16-residue self-assembling peptide d-EAK16 made of only D-amino acids. We compare these results with its chiral counterpart L-form, l-EAK16. Circular dichroism was used to follow the structural dynamics under various temperature and pH conditions. At 25°C the d-EAK16 peptide displayed a typical beta-sheet spectrum. Upon increasing the temperature above 70°C, there was a spectrum shift as the 218 nm valley widens toward 210 nm. Above 80°C, the d-EAK16 peptide transformed into a typical alpha-helix CD spectrum without going through a detectable random-coil intermediate. When increasing the temperature from 4°C to 110°C then cooling back from 110°C to 4°C, there was a hysteresis: the secondary structure from beta-sheet to alpha-helix and then from alpha-helix to beta-sheet occurred. d-EAK16 formed an alpha-helical conformation at pH0.76 and pH12 but formed a beta-sheet at neutral pH. The effects of various pH conditions, ionic strength and denaturing agents were also noted. Since D-form peptides are resistant to natural enzyme degradation, such drastic structural changes may be exploited for fabricating molecular sensors to detect minute environmental changes. This provides insight into the behaviors of self-assembling peptides made of D-amino acids and points the way to designing new peptide materials for biomedical engineering and nanobiotechnology

Biocatalytic Synthesis of Polymers of Precisely Defined Structures

The fabrication of functional nanoscale devices requires the construction of complex architectures at length scales characteristic of atoms and molecules. Currently microlithography and micro-machining of macroscopic objects are the preferred methods for construction of small devices, but these methods are limited to the micron scale. An intriguing approach to nanoscale fabrication involves the association of individual molecular components into the desired architectures by supramolecular assembly. This process requires the precise specification of intermolecular interactions, which in turn requires precise control of molecular structure

Analysis and prediction of cancerlectins using evolutionary and domain information

<p>Abstract</p> <p>Background</p> <p>Predicting the function of a protein is one of the major challenges in the post-genomic era where a large number of protein sequences of unknown function are accumulating rapidly. Lectins are the proteins that specifically recognize and bind to carbohydrate moieties present on either proteins or lipids. Cancerlectins are those lectins that play various important roles in tumor cell differentiation and metastasis. Although the two types of proteins are linked, still there is no computational method available that can distinguish cancerlectins from the large pool of non-cancerlectins. Hence, it is imperative to develop a method that can distinguish between cancer and non-cancerlectins.</p> <p>Results</p> <p>All the models developed in this study are based on a non-redundant dataset containing 178 cancerlectins and 226 non-cancerlectins in which no two sequences have more than 50% sequence similarity. We have applied the similarity search based technique, i.e. BLAST, and achieved a maximum accuracy of 43.25%. The amino acids compositional analysis have shown that certain residues (e.g. Leucine, Proline) were preferred in cancerlectins whereas some other (e.g. Asparatic acid, Asparagine) were preferred in non-cancerlectins. It has been found that the PROSITE domain "Crystalline beta gamma" was abundant in cancerlectins whereas domains like "SUEL-type lectin domain" were found mainly in non-cancerlectins. An SVM-based model has been developed to differentiate between the cancer and non-cancerlectins which achieved a maximum Matthew's correlation coefficient (MCC) value of 0.32 with an accuracy of 64.84%, using amino acid compositions. We have developed a model based on dipeptide compositions which achieved an MCC value of 0.30 with an accuracy of 64.84%. Thereafter, we have developed models based on split compositions (2 and 4 parts) and achieved an MCC value of 0.31, 0.32 with accuracies of 65.10% and 66.09%, respectively. An SVM model based on Position Specific Scoring Matrix (PSSM), generated by PSI-BLAST, was developed and achieved an MCC value of 0.36 with an accuracy of 68.34%. Finally, we have integrated the PROSITE domain information with PSSM and developed an SVM model that has achieved an MCC value of 0.38 with 69.09% accuracy.</p> <p>Conclusion</p> <p>BLAST has been found inefficient to distinguish between cancer and non-cancerlectins. We analyzed the protein sequences of cancer and non-cancerlectins and identified interesting patterns. We have been able to identify PROSITE domains that are preferred in cancer and non-cancerlectins and thus provided interesting insights into the two types of proteins. The method developed in this study will be useful for researchers studying cancerlectins, lectins and cancer biology. The web-server based on the above study, is available at <url>http://www.imtech.res.in/raghava/cancer_pred/</url></p

SNOSite: Exploiting Maximal Dependence Decomposition to Identify Cysteine S-Nitrosylation with Substrate Site Specificity

S-nitrosylation, the covalent attachment of a nitric oxide to (NO) the sulfur atom of cysteine, is a selective and reversible protein post-translational modification (PTM) that regulates protein activity, localization, and stability. Despite its implication in the regulation of protein functions and cell signaling, the substrate specificity of cysteine S-nitrosylation remains unknown. Based on a total of 586 experimentally identified S-nitrosylation sites from SNAP/L-cysteine-stimulated mouse endothelial cells, this work presents an informatics investigation on S-nitrosylation sites including structural factors such as the flanking amino acids composition, the accessible surface area (ASA) and physicochemical properties, i.e. positive charge and side chain interaction parameter. Due to the difficulty to obtain the conserved motifs by conventional motif analysis, maximal dependence decomposition (MDD) has been applied to obtain statistically significant conserved motifs. Support vector machine (SVM) is applied to generate predictive model for each MDD-clustered motif. According to five-fold cross-validation, the MDD-clustered SVMs could achieve an accuracy of 0.902, and provides a promising performance in an independent test set. The effectiveness of the model was demonstrated on the correct identification of previously reported S-nitrosylation sites of Bos taurus dimethylarginine dimethylaminohydrolase 1 (DDAH1) and human hemoglobin subunit beta (HBB). Finally, the MDD-clustered model was adopted to construct an effective web-based tool, named SNOSite (http://csb.cse.yzu.edu.tw/SNOSite/), for identifying S-nitrosylation sites on the uncharacterized protein sequences

Unprocessed Viral DNA Could Be the Primary Target of the HIV-1 Integrase Inhibitor Raltegravir

Integration of HIV DNA into host chromosome requires a 3′-processing (3′-P) and a strand transfer (ST) reactions catalyzed by virus integrase (IN). Raltegravir (RAL), commonly used in AIDS therapy, belongs to the family of IN ST inhibitors (INSTIs) acting on IN-viral DNA complexes (intasomes). However, studies show that RAL fails to bind IN alone, but nothing has been reported on the behaviour of RAL toward free viral DNA. Here, we assessed whether free viral DNA could be a primary target for RAL, assuming that the DNA molecule is a receptor for a huge number of pharmacological agents. Optical spectroscopy, molecular dynamics and free energy calculations, showed that RAL is a tight binder of both processed and unprocessed LTR (long terminal repeat) ends. Complex formation involved mainly van der Waals forces and was enthalpy driven. Dissociation constants (Kds) revealed that RAL affinity for unbound LTRs was stronger than for bound LTRs. Moreover, Kd value for binding of RAL to LTRs and IC50 value (half concentration for inhibition) were in same range, suggesting that RAL binding to DNA and ST inhibition are correlated events. Accommodation of RAL into terminal base-pairs of unprocessed LTR is facilitated by an extensive end fraying that lowers the RAL binding energy barrier. The RAL binding entails a weak damping of fraying and correlatively of 3′-P inhibition. Noteworthy, present calculated RAL structures bound to free viral DNA resemble those found in RAL-intasome crystals, especially concerning the contacts between the fluorobenzyl group and the conserved 5′C4pA33′ step. We propose that RAL inhibits IN, in binding first unprocessed DNA. Similarly to anticancer drug poisons acting on topoisomerases, its interaction with DNA does not alter the cut, but blocks the subsequent joining reaction. We also speculate that INSTIs having viral DNA rather IN as main target could induce less resistance

Synthesis and Self-Assembly of Well-Defined Block Copolypeptides via Controlled NCA Polymerization

This article summarizes advances in the synthesis of well-defined polypeptides and block copolypeptides. Traditional methods used to polymerize α-amino acid-N-carboxyanhydrides (NCAs) are described, and limitations in the utility of these systems for the preparation of polypeptides are discussed. Improved initiators and methods that allow polypeptide synthesis with good control over chain length, chain length distribution, and chain-end functionality are also discussed. Using these methods, block and random copolypeptides of controlled dimensions (including molecular weight, sequence, composition, and molecular weight distribution) can now be prepared. The ability of well-defined block copolypeptides to assemble into supramolecular copolypeptide micelles, copolypeptide vesicles, and copolypeptide hydrogels is described. Many of these assemblies have been found to possess unique properties that are derived from the amino acid building blocks and ordered conformations of the polypeptide segments. © Springer-Verlag Berlin Heidelberg 2013