Pep-Whisperer: Inhibitory peptide design.

Designing peptides for protein-protein interaction inhibition is of significant interest in computer-aided drug design. Such inhibitory peptides could mimic and compete with the binding of the partner protein to the inhibition target. Experimental peptide design is a laborious, time consuming, and expensive multi-step process. Therefore, in silico peptide design can be beneficial for achieving this task. We present a novel algorithm, Pep-Whisperer, which aims to design inhibitory peptides for protein-protein interaction. The desirable peptides would have a relatively high predicted binding affinity to the target protein in a given protein-protein complex. The algorithm outputs linear peptides which are based on an initial template. The template could either be a peptide which is retrieved from the interaction site, or a patch of nonconsecutive amino acids from the protein-protein interface which is completed to a linear peptide by short polyalanine linkers. In addition, the algorithm takes into consideration the conservation of the amino acids in the ligand-protein binding site by using evolutionary information for choosing the preferred amino acids in each position of the designed peptide. Our algorithm was able to design peptides with high predicted binding affinity to the target protein. The method is fully automated and available as a web server at http://bioinfo3d.cs.tau.ac.il/PepWhisperer/.

Design of Disruptors of the Hsp90-Cdc37 Interface.

The molecular chaperone Hsp90 is a ubiquitous ATPase-directed protein responsible for the activation and structural stabilization of a large clientele of proteins. As such, Hsp90 has emerged as a suitable candidate for the treatment of a diverse set of diseases, such as cancer and neurodegeneration. The inhibition of the chaperone through ATP-competitive inhibitors, however, was shown to lead to undesirable side effects. One strategy to alleviate this problem is the development of molecules that are able to disrupt specific protein-protein interactions, thus modulating the activity of Hsp90 only in the particular cellular pathway that needs to be targeted. Here, we exploit novel computational and theoretical approaches to design a set of peptides that are able to bind Hsp90 and compete for its interaction with the co-chaperone Cdc37, which is found to be responsible for the promotion of cancer cell proliferation. In spite of their capability to disrupt the Hsp90-Cdc37 interaction, no important cytotoxicity was observed in human cancer cells exposed to designed compounds. These findings imply the need for further optimization of the compounds, which may lead to new ways of interfering with the Hsp90 mechanisms that are important for tumour growth.

Memdock: an α-helical membrane protein docking algorithm.

MOTIVATION: A wide range of fundamental biological processes are mediated by membrane proteins. Despite their large number and importance, less than 1% of all 3D protein structures deposited in the Protein Data Bank are of membrane proteins. This is mainly due to the challenges of crystallizing such proteins or performing NMR spectroscopy analyses. All the more so, there is only a small number of membrane protein-protein complexes with known structure. Therefore, developing computational tools for docking membrane proteins is crucial. Numerous methods for docking globular proteins exist, however few have been developed especially for membrane proteins and designed to address docking within the lipid bilayer environment. RESULTS: We present a novel algorithm, Memdock, for docking α-helical membrane proteins which takes into consideration the lipid bilayer environment for docking as well as for refining and ranking the docking candidates. We show that our algorithm improves both the docking accuracy and the candidates ranking compared to a standard protein-protein docking algorithm. AVAILABILITY AND IMPLEMENTATION: http://bioinfo3d.cs.tau.ac.il/Memdock/ CONTACTS: namih@tau.ac.il or wolfson@tau.ac.il SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Memdock: An α-Helical Membrane Protein Docking Algorithm.

Memdock is a tool for docking α-helical membrane proteins which takes into consideration the lipid bilayer environment. Given two α-helical membrane located protein molecules, the method outputs a list of potential complexes sorted by energy criteria. The program includes three steps: docking, refinement, and re-ranking of the results. All three docking steps have been customized to the membrane environment in order to improve performance and reduce program run-time. In this chapter, we describe the application of our web server, referred to as Memdock, for prediction of the docking complex for a pair of input membrane protein structures. Memdock is freely available for academic users without registration at http://bioinfo3d.cs.tau.ac.il/Memdock/index.html .

Towards genome-scale structure prediction for transmembrane proteins.

In this paper we briefly review some of the recent progress made by ourselves and others in developing methods for predicting the structures of transmembrane proteins from amino acid sequence. Transmembrane proteins are an important class of proteins involved in many diverse biological functions, many of which have great impact in terms of disease mechanism and drug discovery. Despite their biological importance, it has proven very difficult to solve the structures of these proteins by experimental techniques, and so there is a great deal of pressure to develop effective methods for predicting their structure. The methods we discuss range from methods for transmembrane topology prediction to new methods for low resolution folding simulations in a knowledge-based force field. This potential is designed to reproduce the properties of the lipid bilayer. Our eventual aim is to apply these methods in tandem so that useful three-dimensional models can be built for a large fraction of the transmembrane protein domains in whole proteomes.