SiteEngines: recognition and comparison of binding sites and protein–protein interfaces

Protein surface regions with similar physicochemical properties and shapes may perform similar functions and bind similar binding partners. Here we present two web servers and software packages for recognition of the similarity of binding sites and interfaces. Both methods recognize local geometrical and physicochemical similarity, which can be present even in the absence of overall sequence or fold similarity. The first method, SiteEngine (), receives as an input two protein structures and searches the complete surface of one protein for regions similar to the binding site of the other. The second, Interface-to-Interface (I2I)-SiteEngine (), compares protein–protein interfaces, which are regions of interaction between two protein molecules. It receives as an input two structures of protein–protein complexes, extracts the interfaces and finds the three-dimensional transformation that maximizes the similarity between two pairs of interacting binding sites. The output of both servers consists of a superimposition in PDB file format and a list of physicochemical properties shared by the compared entities. The methods are highly efficient and the freely available software packages are suitable for large-scale database searches of the entire PDB

RsiteDB: a database of protein binding pockets that interact with RNA nucleotide bases

We present a new database and an on-line search engine, which store and query the protein binding pockets that interact with single-stranded RNA nucleotide bases. The database consists of a classification of binding sites derived from protein–RNA complexes. Each binding site is assigned to a cluster of similar binding sites in other protein–RNA complexes. Cluster members share similar spatial arrangements of physico–chemical properties, thus can reveal novel similarity between proteins and RNAs with different sequences and folds. The clusters provide 3D consensus binding patterns important for protein–nucleotide recognition. The database search engine allows two types of useful queries: first, given a PDB code of a protein–RNA complex, RsiteDB can detail and classify the properties of the protein binding pockets accommodating extruded RNA nucleotides not involved in local RNA base pairing. Second, given an unbound protein structure, RsiteDB can perform an on-line structural search against the constructed database of 3D consensus binding patterns. Regions similar to known patterns are predicted to serve as binding sites. Alignment of the query to these patterns with their corresponding RNA nucleotides allows making unique predictions of the protein–RNA interactions at the atomic level of detail. This database is accessable at http://bioinfo3d.cs.tau.ac.il/RsiteDB

Proofs of ownership in remote storage systems

Cloud storage systems are increasingly popular nowadays, and a promising technology to keep their cost down is *deduplication*, namely removing unnecessary copies of repeating data. Moreover, *client-side deduplication* attempts to identify deduplication opportunities already at the client and save the bandwidth in uploading another copy of an existing file to the server. In this work we identify attacks that exploit client-side deduplication, allowing an attacker to gain access to potentially huge files of other users based on a very small amount of side information. For example, an attacker who knows the hash signature of a file can convince the storage service that it owns that file, hence the server later lets the attacker download the entire file. To overcome such attacks, we introduce proofs-of-ownership (PoWs), where a client proves to the server that it actually holds the data of the file and not just some short information about it. We formalize proof-of-ownership, present solutions based on Merkle trees and specific encodings, and analyze their security. We implemented one variant of the scheme, our performance measurements indicate that our protocol incurs only a small overhead (compared to naive client-side deduplication that is vulnerable to the attack)

Securing the infrastructure and the workloads of linux containers

One of the central building blocks of cloud platforms are linux containers which simplify the deployment and management of applications for scalability. However, they introduce new risks by allowing attacks on shared resources such as the file system, network and kernel. Existing security hardening mechanisms protect specific applications and are not designed to protect entire environments as those inside the containers. To address these, we present a LiCShield framework for securing of linux containers and their workloads via automatic construction of rules describing the expected activities of containers spawned from a given image. Specifically, given an image of interest LiCShield traces its execution and generates profiles of kernel security modules restricting the containers' capabilities. We distinguish between the operations on the linux host and the ones inside the container to provide the following protection mechanisms: (1) Increased host protection, by restricting the operations done by containers and container management daemon only to those observed in a testing environment; (2) Narrow container operations, by tightening the internal dynamic and noisy environments, without paying the high performance overhead of their on-line monitoring. Our experimental results show that this approach is efficient to prevent known attacks, while having almost no overhead on the production environment. We present our methodology and its technological insights and provide recommendations regarding its efficient deployment with intrusion detection tools to achieve both optimized performance and increased protection. The code of the LiCShield framework as well as the presented experimental results are freely available for use at https://github.com/LinuxContainerSecurity/LiCShield.git

Structural similarity of genetically interacting proteins

<p>Abstract</p> <p>Background</p> <p>The study of gene mutants and their interactions is fundamental to understanding gene function and backup mechanisms within the cell. The recent availability of large scale genetic interaction networks in yeast and worm allows the investigation of the biological mechanisms underlying these interactions at a global scale. To date, less than 2% of the known genetic interactions in yeast or worm can be accounted for by sequence similarity.</p> <p>Results</p> <p>Here, we perform a genome-scale structural comparison among protein pairs in the two species. We show that significant fractions of genetic interactions involve structurally similar proteins, spanning 7–10% and 14% of all known interactions in yeast and worm, respectively. We identify several structural features that are predictive of genetic interactions and show their superiority over sequence-based features.</p> <p>Conclusion</p> <p>Structural similarity is an important property that can explain and predict genetic interactions. According to the available data, the most abundant mechanism for genetic interactions among structurally similar proteins is a common interacting partner shared by two genetically interacting proteins.</p

Spatial chemical conservation of hot spot interactions in protein-protein complexes-1

<p><b>Copyright information:</b></p><p>Taken from "Spatial chemical conservation of hot spot interactions in protein-protein complexes"</p><p>http://www.biomedcentral.com/1741-7007/5/43</p><p>BMC Biology 2007;5():43-43.</p><p>Published online 9 Oct 2007</p><p>PMCID:PMC2231411.</p><p></p> dots [51]) and the pseudocenters (balls). Only surface exposed pseudocenters are considered. Hydrogen bond donors are blue, acceptors – red, donors/acceptors – green, and aromatic – white. The right figure illustrates the definition of pseudocenters and the bar at the bottom illustrates the complementarity of the pseudocenter properties. () Alignment of 6 PPIs of serine proteases with inhibitors. The trypsins (1cbwHG, 1tawA, 1ca0HG) are gray and the subtilisins (1cseE, 2sicE, 1oyvB) are blue. The corresponding inhibitors (1cbwI, 1tawI, 1ca0I, 1cseI, 2sicI, 1oyvI) are colored ranging from yellow to purple respectively. The right figure presents the 9 spatially conserved interactions (purple arrows). The catalytic residues of the serine proteases (gray sticks) were recognized to form 5 similar interactions (3 hydrogen bonds, 1 hydrophobic aliphatic and 1 aromatic) with the corresponding hot spots of the inhibitors K15(1cbw), R15(1taw,1ca0), K45(1cse), M73(2sic) and R5(1oyv). These residues, which have different amino acid identities and backbone locations are represented as black sticks

Spatial chemical conservation of hot spot interactions in protein-protein complexes-0

<p><b>Copyright information:</b></p><p>Taken from "Spatial chemical conservation of hot spot interactions in protein-protein complexes"</p><p>http://www.biomedcentral.com/1741-7007/5/43</p><p>BMC Biology 2007;5():43-43.</p><p>Published online 9 Oct 2007</p><p>PMCID:PMC2231411.</p><p></p> dots [51]) and the pseudocenters (balls). Only surface exposed pseudocenters are considered. Hydrogen bond donors are blue, acceptors – red, donors/acceptors – green, and aromatic – white. The right figure illustrates the definition of pseudocenters and the bar at the bottom illustrates the complementarity of the pseudocenter properties. () Alignment of 6 PPIs of serine proteases with inhibitors. The trypsins (1cbwHG, 1tawA, 1ca0HG) are gray and the subtilisins (1cseE, 2sicE, 1oyvB) are blue. The corresponding inhibitors (1cbwI, 1tawI, 1ca0I, 1cseI, 2sicI, 1oyvI) are colored ranging from yellow to purple respectively. The right figure presents the 9 spatially conserved interactions (purple arrows). The catalytic residues of the serine proteases (gray sticks) were recognized to form 5 similar interactions (3 hydrogen bonds, 1 hydrophobic aliphatic and 1 aromatic) with the corresponding hot spots of the inhibitors K15(1cbw), R15(1taw,1ca0), K45(1cse), M73(2sic) and R5(1oyv). These residues, which have different amino acid identities and backbone locations are represented as black sticks