DDESC: Dragon database for exploration of sodium channels in human

<p>Abstract</p> <p>Background</p> <p>Sodium channels are heteromultimeric, integral membrane proteins that belong to a superfamily of ion channels. The mutations in genes encoding for sodium channel proteins have been linked with several inherited genetic disorders such as febrile epilepsy, Brugada syndrome, ventricular fibrillation, long QT syndrome, or channelopathy associated insensitivity to pain. In spite of these significant effects that sodium channel proteins/genes could have on human health, there is no publicly available resource focused on sodium channels that would support exploration of the sodium channel related information.</p> <p>Results</p> <p>We report here Dragon Database for Exploration of Sodium Channels in Human (DDESC), which provides comprehensive information related to sodium channels regarding different entities, such as "genes and proteins", "metabolites and enzymes", "toxins", "chemicals with pharmacological effects", "disease concepts", "human anatomy", "pathways and pathway reactions" and their potential links. DDESC is compiled based on text- and data-mining. It allows users to explore potential associations between different entities related to sodium channels in human, as well as to automatically generate novel hypotheses.</p> <p>Conclusion</p> <p>DDESC is first publicly available resource where the information related to sodium channels in human can be explored at different levels. This database is freely accessible for academic and non-profit users via the worldwide web <url>http://apps.sanbi.ac.za/ddesc</url>.</p

Protein Domain Boundary Predictions: A Structural Biology Perspective

One of the important fields to apply computational tools for domain boundaries prediction is structural biology. They can be used to design protein constructs that must be expressed in a stable and functional form and must produce diffraction-quality crystals. However, prediction of protein domain boundaries on the basis of amino acid sequences is still very problematical. In present study the performance of several computational approaches are compared. It is observed that the statistical significance of most of the predictions is rather poor. Nevertheless, when the right number of domains is correctly predicted, domain boundaries are predicted within very few residues from their real location. It can be concluded that prediction methods cannot be used yet as routine tools in structural biology, though some of them are rather promising

3D QSAR Pharmacophore Modeling, in Silico Screening, and Density Functional Theory (DFT) Approaches for Identification of Human Chymase Inhibitors

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating “Hypo1”, it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC50) data thus successfully validating “Hypo1” by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors

Likelihood of protein structure determination

Strukturelle Genomanalyse (SG) beinhaltet die, mit hohem datendurchsatz verbundene bestimmung der dreidimensionalen struktur von makromolekülen durch experimentelle Methoden wie röntgenstrahlen-kristallographie und NMR spektroskopie. Eines der ziele von SG ist es, zeit und kosten der bestimmung von dreidimensionalen proteinstrukturen zu reduzieren, für die homologe strukturen noch nicht gelöst worden sind. Mehrere faktoren wie unregelmäßige conformationen, unzulässige selektion von domängrenzen und löslichkeit können die produktion von proteinkonstrukten für die strukturbiologie erschweren. Zuverlässige, auf aminosäuresequenz basierende prädiktoren zur berechnung von proteinkristallisation sind folglich von nöten. Die vorhersage von unregelmäßigen konformationen ist essentiell, da diese schwierigkeiten in der kristallisation verursachen können. In dieser arbeit wird eine neue methode präsentiert, die es erlaubt, ungeordnete residuen auf basis der aminosäuresequenz mit hoher genauigkeit vorherzusagen, indem verschiedene, auf einer konsensusmethode basierende vorhersagemittel verwendet werden. Die Leistung dieser neuen methode ist signifikant besser als von jedem einzelnen, bisher erwähnten Prädiktor. Zusätzlich ist es wichtig, die voraussetzungen für den quartärstatus eines proteins auf basis seiner sequenz vorherzusagen. Eine Proteinkette kann aus einem monomeren protein bestehen, oder kann, zusammen mit anderen ketten, oligomere komplexe formen, die entweder aus homo-oligomeren oder hetero-oligomeren bestehen können. Im letzten fall muss vermieden werden, die dreidimensionale struktur eines einzelnen protomers zu bestimmen, weil es nicht funktionell ist und auch extrem schwer in löslicher form zu exprimieren ist. Es ist daher erstrebenswert, ein berechnungsmittel zu nützen, das vorherzusagen erlaubt, ob ein potentielles genprodukt teil eines permanenten und obligaten hetero-oligomeren komplexes ist. Hier wird eine neue, auf der aminosäuresequenz basierende methode präsentiert, um hetero-oligomere von monomer und homo-oligomeren proteinen und auch um monomere von homo-oligomeren mit hoher genauigkeit zu unterscheiden. Das erfordernis von metallionen ist im design von strukturbiologischen experimenten ebenso wichtig. Metalloproteine bilden etwa ein drittel der proteoms. Die vorhersage von metalloproteinen hilft kristallographen, geeignetes wachstumsmedium für überexpressionsstudien auszuwählen und auch die wahrscheinlichkeit zu erhöhen, ein korrekt gefaltetes und funktionelles molekül zu erhalten. Hier wird gezeigt, dass die aufnahme von metallionen von proteinen auf basis der aminosäurenzusammensetzung und durch verwenden von lernfähigen analyseprogrammen mit hoher genauigkeit vorhergesagt werden kann. Die ergebnisse in der vorliegenden Doktorarbeit stellen die basis für das sorgfältige design von Proteinkonstrukten dar. Diese computer basierenden selektionsmethoden sind hilfreich, um die auswahl von unmöglichen Zielen zu vermeiden – ein Muss in Strukturbiologie und Proteomics.Structural Genomics (SG) involves the high-throughput determination of threedimensional structures of macromolecules by experimental methods such as X-ray crystallography and NMR spectroscopy. One of the aims of SG is to reduce the time and cost in the determination of three-dimensional protein structures for which a homologous structure had not yet been solved. Several factors such as conformational disorder, improper selection of domain boundaries and solubility can hamper the production of protein constructs for structural biology. Reliable computational protein crystallization propensity predictors, based on amino acid sequences, are consequently required. Prediction of protein conformational disorder is important since it can cause difficulty in crystallization. In this work, a new procedure is presented that allows one to predict disordered residues with high accuracy on the basis of amino acid sequences, by using a consensus method based on various prediction tools. The performance of this new procedure is significantly better than that of each individual predictor previously reported. Furthermore, it is important to be able to predict the quaternary status requirements of a protein on the basis of its sequence. A protein chain can be a monomeric protein or it can form, together with other chains, oligomeric assemblies, which can be either homooligomers or hetero-oligomers. In the later case, it must be avoided to determine the three-dimensional structure of a single protomer, since it will not be functional and it will also be extremely difficult to express in a soluble form. It is thus desirable to have a computational tool that allows one to predict if a potential gene product is a part of permanent and obligate hetero-oligomeric assembly. A new method is presented for discriminating hetero-oligomers from monomeric and homo-oligomeric proteins and also between monomers and homo-oliogmers with high accuracy on the basis of amino acid sequences. Metal ion requirements are also important in designing structural biology experiments. Metalloproteins constitute about one-third of the proteome. Prediction of metalloprotein helps crystallographers to select the proper growth medium for over-expression studies and also to increase the probability of obtaining a properly folded and functional molecule. Here it is shown that the uptake of metal ions by proteins can be predicted with high accuracy on the basis of the amino acid composition and by using machine learning methods. The results described in the present Thesis provide a basis for the careful design of protein constructs. These computational screening methods are helpful to avoid the selection of 'impossible' targets- a must in structural biology and proteomics