A combination of dynamic Monte Carlo simulation techniques with a hydropathy scale method for the prediction of the location of transmembrane fragments in membrane proteins is described. The new hydropathy scale proposed here is based on experimental data for the interactions of tripeptides with phospholipid membranes (Jacobs, R.E., White, S.H. Biochemistry 26:6127–6134, 1987) and the self‐solvation effect in protein systems (Roseman, M.A., J. Mol. Biol. 200:513–522, 1988). The simulations give good predictions both for the state of association and the orientation of the peptide relative to the membrane surface of a number of peptides including Magainin2, M2δ, and melittin. Furthermore, for Pf1 bacterio‐phage coat protein, in accord with experiment, the simulations predict that the C‐terminus forms a transmembrane helix and the N‐terminus forms a helix which is adsorbed on the surface of the bilayer. Finally, the present series of simulations provide a number of insights into the mechanism of insertion of peptides into cell membranes. © 1993 Wiley‐Liss, Inc.

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.

We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.

MAP kinase-interacting kinases (MNK1 and MNK2) are often activated downstream of ERK and p38 MAPK in the MAP kinase family. The role of MNKs in the development and progression of solid tumors and hematological malignancies has been widely discussed, particularly in the context of cap dependent translation, regulated by phosphorylation of eIF4E. MNK/eIF4E axis is involved in the expression of pro angiogenic, antiapoptotic, cell cycle, and motility proteins, such as MCL1, VEGF, MMP3, SNAIL, SMAD2, β-catenin or cyclin D1, and is essential during Ras and c Myc-induced transformation. MNK1/2 emerged as eligible targets for drug discovery in oncology, based on the antitumor effects observed in genetic knockout and RNA interference experiments and at the same time lack of adverse effects in dual knockout animals. There is a high interest in the development of pharmacological inhibitors of MNK1/2 as not only tools for further basic research studies but also potential drugs in diseases characterized by deregulated translation. Unfortunately, the role of MNK1/2 in cancer still remains elusive due to the absence of potent and selective probes. Moreover, in many instances, hypotheses have been built reliant upon unspecific MNK1/2 inhibitors such as CGP57380 or cercosporamide. Lately, the first two clinical programs targeting MNKs in oncology have been revealed (eFT508 and BAY 1143269), although several other MNK programs are currently running at the preclinical stage. This review aims to provide an overview of recent progress in the development of MNK inhibitors.

A method for generating a full backbone protein structure from the coordinates of ␣-carbons, is presented. The method extracts information from known protein structures to generate statistical positions for the reconstructed atoms. Tests on a set of proteins structures show the algorithm to be of comparable accuracy to existing procedures. However, the basic advantage of the presented method is its simplicity and speed. In a test run, the present program is shown to be much faster than existing database searching algorithms, and reconstructs about 8000 residues per second. Thus, it may be included as an independent procedure in protein folding algorithms to rapidly generate approximate coordinates of backbone atoms.

Articles you may be interested inDeterminants of secondary structure of polypeptide chains: Interplay between short range and burial interactions A Monte Carlo method with different treatments for short and longrange interactions in conformational statistics of polypeptide chains J. Chem. Phys. 88, 3385 (1988); 10.1063/1.453934Effects of long range interactions on the conformational statistics of short polypeptide chains generated by a Monte Carlo method A simple model of short range interactions is proposed for a reduced lattice representation of polypeptide conformation. The potential is derived on the basis of statistical regularities seen in the known crystal structures of globular proteins. This potential accounts for the generic stiffness of polypeptides, the correlation between peptide bond plates, and the sequence dependent correlations between consecutive segments of the C␣-trace. This model is used for simulation of the equilibrium and dynamic properties of polypeptides in the denatured state. It is shown that the proposed factorization of the local conformational propensities reproduces secondary structure tendencies encoded in the protein sequence. Possible applications for modeling of protein folding are briefly discussed.

Computational methods were used to predict the sequences of peptides that bind to the MHC class I molecule, K(b). The rules for predicting binding sequences, which are limited, are based on preferences for certain amino acids in certain positions of the peptide. It is apparent though, that binding can be influenced by the amino acids in all of the positions of the peptide. An artificial neural network (ANN) has the ability to simultaneously analyze the influence of all of the amino acids of the peptide and thus may improve binding predictions. ANNs were compared to statistically analyzed peptides for their abilities to predict the sequences of K(b) binding peptides. ANN systems were trained on a library of binding and nonbinding peptide sequences from a phage display library. Statistical and ANN methods identified strong binding peptides with preferred amino acids. ANNs detected more subtle binding preferences, enabling them to predict medium binding peptides. The ability to predict class I MHC molecule binding peptides is useful for immunolological therapies involving cytotoxic-T cells.

Proposed is a method for locating functionally relevant atoms in protein structures and a representation of spatial arrangements of these atoms allowing for a flexible description of active sites in proteins. The search method is based on comparison of local structure features of proteins that share a common biochemical function. The method does not depend on overall similarity of structures and sequences of compared proteins or on previous knowledge about functionally relevant residues. The compared protein structures are condensed to a graph representation, with atoms as nodes and distances as edge labels. Protein graphs are then compared to extract all possible Common Structural Cliques. These cliques are merged to create Structural Templates: graphs that describe structural analogies between compared proteins. Structures of serine endopeptidases were compared in pairs using the presented algorithm with different geometrical parameters. Additionally, a Structural Template was extracted from the structures of aminotransferases, two different proteins that catalyze the same type of chemical reaction. The results presented show that the method works efficiently even in the case of large protein systems and allows for extraction of common structural features from proteins catalyzing a particular chemical reaction, but that evolved from different ancestors by convergent evolution.