Cyclic and linear peptides containing the Asn-Gly-Arg (NGR) motif have proven useful for delivering various anti-tumor compounds and viral particles to tumor vessels. We have investigated the role of cyclic constraints on the structure and tumor-homing properties of NGR peptides using tumor necrosis factor-␣ (TNF) derivatives containing disulfide-bridged (CNGRC-TNF) and linear (GNGRG-TNF) NGR domains. Experiments carried out in animal models showed that both GNGRG and CNGRC can target TNF to tumors. However, the antitumor activity of CNGRC-TNF was >10-fold higher than that of GNGRG-TNF. Molecular dynamic simulation of cyclic CNGRC showed the presence of a bend geometry involving residues Gly 3 -Arg 4 . Molecular dynamic simulation of the same peptide without disulfide constraints showed that the most populated and thermodynamically favored configuration is characterized by the presence of a ␤-turn involving residues Gly 3 -Arg 4 and hydrogen bonding interactions between the backbone atoms of Asn 2 and Cys 5 . These results suggest that the NGR motif has a strong propensity to form ␤-turn in linear peptides and may explain the finding that GNGRG peptide can target TNF to tumors, albeit to a lower extent than CNGRC. The disulfide bridge constraint is critical for stabilizing the bent conformation and for increasing the tumor targeting efficiency.Phage display peptide libraries are commonly used to obtain peptide sequences interacting with proteins differentially expressed in normal and pathological tissues (1, 2). For instance, in vivo panning of phage libraries in tumor-bearing animals have proven useful for selecting peptides able to interact with proteins expressed within tumor-associated vessels and to home to neoplastic tissues (3). Among the various tumor targeting ligands identified so far, the CNGRC peptide have proven useful for delivering various anti-tumor compounds, like chemotherapeutic drugs, apoptotic peptides and cytokines, to tumor vessels (3-5). For example, we have recently shown that targeted delivery of tumor necrosis factor-␣ (TNF) 1 to tumor vasculature can be obtained by coupling its N terminus to the C terminus of the CNGRC peptide, by genetic engineering technology (5). This approach markedly improved the therapeutic index of TNF in animal models, either when used alone (5) or in combination with chemotherapeutic agents (6). Studies on the mechanism of action showed that the targeting domain of this TNF derivative (called NGR-TNF) binds an aminopeptidase (CD13) isoform expressed in tumor vessels, and not other isoforms expressed in normal epithelia or myeloid cells (7). Besides CNGRC, other tumor vasculature targeting peptides containing the NGR motif have been identified, such as linear NGRAHA and cyclic CVLNGRMEC (3). These and other linear and cyclic NGR peptides have been used for targeting viral particles to endothelial cells (8, 9). Although these findings may suggest that peptide cyclization is not necessary for the targeting properties of NGR peptides, the role of cysteines and th...

The results of minimal model calculations indicate that the stability and the kinetic accessibility of the native state of small globular proteins are controlled by few "hot" sites. By means of molecular dynamics simulations around the native conformation, which describe the protein and the surrounding solvent at the all-atom level, an accurate and compact energetic map of the native state of the protein is generated. This map is further simplified by means of an eigenvalue decomposition. The components of the eigenvector associated with the lowest eigenvalue indicate which hot sites are likely to be responsible for the stability and for the rapid folding of the protein. The comparison of the results of the model with the findings of mutagenesis experiments performed for four small proteins show that the eigenvalue decomposition method is able to identify between 60% and 80% of these (hot) sites.Keywords: protein folding; protein stability; molecular dynamics; local elementary structures The study of how the structure and stability of a protein is connected with its amino acid sequence has been, during recent years, a major focus of research. In particular, the problem of protein folding and its relation to stability has been studied through a variety of experimental and theoretical techniques. These studies have highlighted the central role the free energy landscape plays in determining the properties displayed by proteins. The discussion has then been turned to the problem of determining this landscape for specific proteins (Shea and Brooks 2001).Experimental techniques have yielded detailed information on macroscopic features of protein dynamical behavior, such as folding times and stability, and on some specific issues at the level of amino acids, such as the sensitivity to mutations (cf. Fersht 1999). However, there is still no experimental procedure capable of providing insight at the amino acid level into either the folding process in its completeness or the stabilization determinants of proteins. To gain insight into these questions, one must turn to theoretical and computational methods.Because of the high computational costs involved, realistic models that provide an exhaustive description of the free energy landscape have been used in the study of only short peptides. Ferrara and Caflish (2000), for instance, could reconstruct the whole free energy landscape of a small designed ␤-sheet peptide with an all-atom representation of the solute and an implicit model of the solvent. Daura et al. (1998) were able to demonstrate the reversible folding of a small (seven residues) helix forming ␤-peptide in methanol solvent using long molecular dynamics (MD) simulations.On the other hand, minimal models (Chan and Dill 1991;Sali et al. 1994;Mirny and Shakhnovich 2001;Micheletti 2003) have provided important results about the general features of the free energy landscape of proteins. They give an approximate description of both the interaction energy among amino acids (usually through a contact potential en-

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