Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes

The ranked feature list given by the Relief algorithm. Within the list, a feature with a smaller index indicates that it is more important for aptamer-protein interacting pair prediction. Such a list of ranked features are used to establish the optimal feature set in the IFS procedure. (XLS 56.5 kb

Disordered Proteins: Connecting Sequences to Emergent Properties

Many IDPs participate in coupled folding and binding reactions and form alpha helical structures in their bound complexes. Alanine, glycine, or proline scanning mutagenesis approaches are often used to dissect the contributions of intrinsic helicities to coupled folding and binding. These experiments can yield confounding results because the mutagenesis strategy changes the amino acid compositions of IDPs. Therefore, an important next step in mutagenesis-based approaches to mechanistic studies of coupled folding and binding is the design of sequences that satisfy three major constraints. These are (i) achieving a target intrinsic alpha helicity profile; (ii) fixing the positions of residues corresponding to the binding interface; and (iii) maintaining the native amino acid composition. Here, we report the development of a Genetic Algorithm for Design of Intrinsic secondary Structure (GADIS) for designing sequences that satisfy the specified constraints. We describe the algorithm and present results to demonstrate the applicability of GADIS by designing sequence variants of the intrinsically disordered PUMA system that undergoes coupled folding and binding to Mcl-1. Our sequence designs span a range of intrinsic helicity profiles. The predicted variations in sequence-encoded mean helicities are tested against experimental measurements.There is a significant collection of proteins with repeating blocks of oppositely charged residues where the consensus sequence is a block of four Glu residues followed by a block of four Lys or Arg residues, (Glu4(Lys/Arg)4)n. These proteins have been experimentally shown to form long single alpha helices (SAHs) under biologically relevant conditions. However, these results are confounding to disorder predictors and to certain atomistic simulations in that both predict these sequences to be strongly disordered. The current working hypothesis is that SAHs are stabilized by i:i+4 salt bridges between opposite charges in consecutive helical turns. We test the merits of this hypothesis to understand the sequence-encoded preference for SAHs and the logic behind the failure of certain atomistic simulations in anticipating the preference for stable SAHs.In simulations with fixed charges the favorable free energy of solvation of charged residues and the associated loss of sidechain entropy hinders the formation of SAHs. We proposed that alterations to charge states induced by sequence context might play an important role in stabilizing SAHs. We tested this hypothesis using a (Glu4Lys4)n repeat protein and a simulation strategy that permits the substitution of charged residues with neutralized protonated or deprotonated variants of Glu / Lys. Our results predict that stable SAH structures derive from the neutralization of approximately half the Glu residues. These findings explain experimental observations and also provide a coherent rationale for the failure of simulations based on fixed charge models. Large-scale sequence analysis reveals that naturally occurring sequences often include defects in charge patterns such as Gln or Ala substitutions. This sequence-encoded incorporation of uncharged residues combined with neutralization of charged residues might tilt the balance toward alpha helical conformations.Micron-sized, non-membrane bound cellular bodies can form as the result of collective interactions between modules of distinct multidomain proteins. Li et al. have examined the phase diagrams that result for polymers of SH3 domains and proline-rich modules (PRMs) while varying the number of interacting domains. It is noteworthy that flexible, intrinsically disordered linkers connect the interacting units within each polymer. Conventional wisdom holds that linkers play a passive role in determining the phase behavior of multidomain proteins that undergo phase separations. Here, we ask if this view is accurate. The motivation for our work comes from recent studies that have uncovered a rich diversity of composition-to-conformation and sequence-to-conformation relationships for intrinsically disordered proteins. The central finding is that disordered regions of proteins have distinct sequence-encoded conformational preferences. Accordingly, we reasoned that the conformational properties of linkers might be a contributing factor, in addition to polyvalency, to the phase behavior of multidomain proteins.We have developed and deployed a three-dimensional lattice model to arrive at a predictive framework to query the effects of linkers on the phase diagrams of polyvalent systems. We find that the critical concentration for phase transition can be influenced by the conformational properties of linkers. Specifically, our results show that linkers modulate the cooperative binding between domains of polymers that are already bound together. Depending on their conformational properties, linkers can also block access to the binding domains via excluded volume effects. Additionally, we find that the properties of the linkers can lead to controls over the mixing of proteins in these bodies. Specifically, we find that there are large ranges of parameters for three protein systems where the bodies isolate specific proteins to different regions of the bodies instead of uniformly mixing them. This result is validated by recent findings of organization inside some observed bodies

Prediction of Drugs Target Groups Based on ChEBI Ontology

Most drugs have beneficial as well as adverse effects and exert their biological functions by adjusting and altering the functions of their target proteins. Thus, knowledge of drugs target proteins is essential for the improvement of therapeutic effects and mitigation of undesirable side effects. In the study, we proposed a novel prediction method based on drug/compound ontology information extracted from ChEBI to identify drugs target groups from which the kind of functions of a drug may be deduced. By collecting data in KEGG, a benchmark dataset consisting of 876 drugs, categorized into four target groups, was constructed. To evaluate the method more thoroughly, the benchmark dataset was divided into a training dataset and an independent test dataset. It is observed by jackknife test that the overall prediction accuracy on the training dataset was 83.12%, while it was 87.50% on the test dataset-the predictor exhibited an excellent generalization. The good performance of the method indicates that the ontology information of the drugs contains rich information about their target groups, and the study may become an inspiration to solve the problems of this sort and bridge the gap between ChEBI ontology and drugs target groups

Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs

Storing and analysing biomolecular contacts

Biomolecular contacts play a crucial role in all areas of life. In particular, protein-protein (PP) interactions are essential for most processes in biological cells. Antigen-antibody recognition, enzyme substrate binding, hormone receptor binding, RNA splicing, DNA replication and signal transduction are just some examples for the rich variety of PP interactions. In the last years modern proteomic methods have helped to get a better understanding of the complexity within living cell and organism. More and more sequences of unknown proteins are deciphered, their function is revealed, structural details are detected and the interaction in the complex network of biological processes is uncovered. Contacts between proteins and small molecules (PL) describe the second important group for biomolecular contacts and play an essential role for drug design. The vast increase of such information necessitates the application of databases for easy handling and analysis of data. We created a database covering PP as well as PL interactions for which structural data are available. Using the database, we performed a number of analyses concerning features of protein-protein complexes, in particular the group of obligate and non-obligate interactions. Combining information from PP and PL complexes, we generated a prediction method for binding sites of small molecules on PP interface sites. Finally, we tested the applicability of features of PP interactions for the prediction of their kinetic parameters.Kontakte zwischen Biomolekülen spielen eine wichtige Rolle in allen Bereichen des lebenden Organismus. Insbesondere Protein-Protein Interaktionen (PP sind für die meisten Prozesse innerhalb der Zelle erforderlich. Die Antigen-Antikörper Erkennung, Enzym-Substrat Bindung, Bindung eines Hormons an seinen Rezeptor, Spleißen von RNA, DNA Replikation und Signaltransduktion sind nur einige Beispiele für die große Vielfalt an PP Interaktionen. In den vergangenen Jahren haben moderne Methoden aus dem Bereich der Proteomik dazu beigetragen, das Verständnis der Komplexität innerhalb der lebenden Zelle und des Organismus zu verbessern. Die Zahl an entschlüsselten Proteinsequenzen steigt beständig an, ihre Funktion wird erfaßt, strukturelle Details werden aufgedeckt und ihr Beitrag im Netzwerk der biologischen Prozesse wird durchleuchtet. Kontakte zwischen Proteinen und kleinen Molekülen bzw. Liganden (PL) stellen die zweite wichtige Gruppe an biomolekularen Kontakten dar und spielen eine wesentliche Rolle für die Entwicklung neuer Arzneistoffe. Der immense Anstieg derartiger Informationen erfordert den Einsatz von Datenbanken zur einfachen Handhabung und Auswertung der Daten. Wir erstellten eine Datenbank, die sowohl PP als auch PL Interaktionen umfasst und für die strukturelle Informationen vorhanden sind. Unter Zuhilfenahme dieser Datenbank führten wir Analysen zu Eigenschaften von Protein-Protein Komplexen, insbesondere zur Gruppe der obligaten und nicht-obliagate Interaktionen, durch. Indem wir Informationen aus PP und PL Komplexen miteinander kombinierten, schufen wir eine Vorhersagemethode für Bindungsstellen von kleinen Molekülen auf PP Oberflächen. Schließlich untersuchten wir physiko-chemische Merkmale von PP Interaktionen zur Vorhersage ihrer kinetischen Parameter

Defining the African green monkey (Chlorocebus Aethiops): expression behaviour of selected lipid metabolism genes in response to niacin

Philosophiae Doctor - PhDIn this century most major medical advances have resulted in part from research on animals and non-human primates such as the African green monkey and therefore often serve as a critical link between basic research and human clinical application. Due to its close evolutionary relationship to humans, the African green monkey is known to be an excellent and most sought after models for studies of human cardiovascular disease (CVD). While the human genome project and some others related to model organisms are very well advanced or even complete, little sequence information has been acquired for the African green monkey. Given the importance of this species in biomedical research generally and CVD specifically, and the fundamental significance of sequence data, it is critical that this paucity of genome information concerning this specific animal model be addressed in order to better define the molecular basis and to further understand the mechanism of cholesterol metabolism in this species which will also contribute immensely to primatology. There is a growing interest in the role of genetic polymorphisms in predicting susceptibility to disease and responsiveness to drug interventions. Since plasma lipid abnormalities are risk factors for coronary atherosclerosis, determination of these plasma lipid concentrations, especially for genes involved in lipid transport and metabolism may be influenced by genetic variations. In this study, the African green monkey was used as a model to evaluate the effect of niacin on plasma lipids and reverse cholesterol transport by examine gene expression and the influence of several polymorphisms found in genes that are involved in cholesterol metabolism in humans. A survey of genetic variation spanning ten prioritised “candidate” genes was conducted, all of which are known to produce proteins that play key roles in the reverse cholesterol pathway (RCT), and in the homeostatic regulation of blood lipid profiles related to cardiovascular health and disease. everse transcription polymerase chain reaction (RT-PCR) was used to evaluate mRNA expression of those “candidate” genes. Twenty two coincident singlenucleotide polymorphisms (cSNPs), reported to play a vital role in RCT, were genotyped within these genes. This study’s findings implicate a subset of six of the twenty two genetic variants, spanning five “candidate” genes. To assess possible involvement of these prioritised “candidate” genes and their polymorphisms, biochemical analyses of known risk factors of coronary artery disease such as HDL-C and LDL-C were conducted. Eight healthy African green monkeys were entered in this study of which four were treated with niacin at an escalating dosage. Their mean lipid-lowering response following drug therapy was analysed, compared to those with the same genotype in a control group. Niacin treatment was associated with a considerable reduction in LDL-Cholesterol, up-regulation of HDL synthesis, and increase of apo A-1 levels. Gene expression had minimal effect on niacin treatment, except CYP7A1 which was down-regulated at the same time when considerable change in HDL-C, LDL-C and apoA-1 levels was observed. The presence of CYP7A1:Asn233Ser polymorphism may have played a critical role in metabolising niacin and influencing the up-regulation of HDL-C synthesis in the African green monkey. Although cholesterol lowering alone may explain the anti-atherosclerotic effect of niacin on HDL-C, in this study, gene expression data also shed some light in supporting the hypothesis that genetic variants may influence the expression of genes involved in RCT, which may also have played a role in the anti-atherosclerotic effect of the drug