Are Protein Folds Atypical?

Protein structures are a very special class among all possible structures. It was suggested that a ``designability principle'' plays a crucial role in nature's selection of protein sequences and structures. Here we provide a theoretical base for such a selection principle, using a novel formulation of the protein folding problem based on hydrophobic interactions. A structure is reduced to a string of 0's and 1's which represent the surface and core sites, respectively, as the backbone is traced. Each structure is therefore associated with one point in a high dimensional space. Sequences are represented by strings of their hydrophobicities and thus can be mapped into the same space. A sequence which lies closer to a particular structure in this space than to any other structures will have that structure as its ground state. Atypical structures, namely those far away from other structures in the high dimensional space, have more sequences which fold into them, and are thermodynamically more stable. We argue that the most common folds of proteins are the most atypical in the space of possible structures.Comment: 15 pages, 5 figure

Timed Automata Semantics for Analyzing Creol

We give a real-time semantics for the concurrent, object-oriented modeling language Creol, by mapping Creol processes to a network of timed automata. We can use our semantics to verify real time properties of Creol objects, in particular to see whether processes can be scheduled correctly and meet their end-to-end deadlines. Real-time Creol can be useful for analyzing, for instance, abstract models of multi-core embedded systems. We show how analysis can be done in Uppaal.Comment: In Proceedings FOCLASA 2010, arXiv:1007.499

Types for Location and Data Security in Cloud Environments

Cloud service providers are often trusted to be genuine, the damage caused by being discovered to be attacking their own customers outweighs any benefits such attacks could reap. On the other hand, it is expected that some cloud service users may be actively malicious. In such an open system, each location may run code which has been developed independently of other locations (and which may be secret). In this paper, we present a typed language which ensures that the access restrictions put on data on a particular device will be observed by all other devices running typed code. Untyped, compromised devices can still interact with typed devices without being able to violate the policies, except in the case when a policy directly places trust in untyped locations. Importantly, our type system does not need a middleware layer or all users to register with a preexisting PKI, and it allows for devices to dynamically create new identities. The confidentiality property guaranteed by the language is defined for any kind of intruder: we consider labeled bisimilarity i.e. an attacker cannot distinguish two scenarios that differ by the change of a protected value. This shows our main result that, for a device that runs well typed code and only places trust in other well typed devices, programming errors cannot cause a data leakage.Comment: Short version to appear in Computer Security Foundations Symposium (CSF'17), August 201

Exploiting Homology Information in Nontemplate Based Prediction of Protein Structures

In this paper we describe a novel strategy for exploring the conformational space of proteins and show that this leads to better models for proteins the structure of which is not amenable to template based methods. Our strategy is based on the assumption that the energy global minimum of homologous proteins must correspond to similar conformations, while the precise profiles of their energy landscape, and consequently the positions of the local minima, are likely to be different. In line with this hypothesis, we apply a replica exchange Monte Carlo simulation protocol that, rather than using different parameters for each parallel simulation, uses the sequences of homologous proteins. We show that our results are competitive with respect to alternative methods, including those producing the best model for each of the analyzed targets in the CASP10 (10th Critical Assessment of techniques for protein Structure Prediction) experiment free modeling category

Visible Volume: a Robust Measure for Protein Structure Characterization

We propose a new characterization of protein structure based on the natural tetrahedral geometry of the β carbon and a new geometric measure of structural similarity, called visible volume. In our model, the side-chains are replaced by an ideal tetrahedron, the orientation of which is fixed with respect to the backbone and corresponds to the preferred rotamer directions. Visible volume is a measure of the non-occluded empty space surrounding each residue position after the side-chains have been removed. It is a robust, parameter-free, locally-computed quantity that accounts for many of the spatial constraints that are of relevance to the corresponding position in the native structure. When computing visible volume, we ignore the nature of both the residue observed at each site and the ones surrounding it. We focus instead on the space that, together, these residues could occupy. By doing so, we are able to quantify a new kind of invariance beyond the apparent variations in protein families, namely, the conservation of the physical space available at structurally equivalent positions for side-chain packing. Corresponding positions in native structures are likely to be of interest in protein structure prediction, protein design, and homology modeling. Visible volume is related to the degree of exposure of a residue position and to the actual rotamers in native proteins. In this article, we discuss the properties of this new measure, namely, its robustness with respect to both crystallographic uncertainties and naturally occurring variations in atomic coordinates, and the remarkable fact that it is essentially independent of the choice of the parameters used in calculating it. We also show how visible volume can be used to align protein structures, to identify structurally equivalent positions that are conserved in a family of proteins, and to single out positions in a protein that are likely to be of biological interest. These properties qualify visible volume as a powerful tool in a variety of applications, from the detailed analysis of protein structure to homology modeling, protein structural alignment, and the definition of better scoring functions for threading purposes.National Library of Medicine (LM05205-13

Geometry and symmetry presculpt the free-energy landscape of proteins

We present a simple physical model which demonstrates that the native state folds of proteins can emerge on the basis of considerations of geometry and symmetry. We show that the inherent anisotropy of a chain molecule, the geometrical and energetic constraints placed by the hydrogen bonds and sterics, and hydrophobicity are sufficient to yield a free energy landscape with broad minima even for a homopolymer. These minima correspond to marginally compact structures comprising the menu of folds that proteins choose from to house their native-states in. Our results provide a general framework for understanding the common characteristics of globular proteins.Comment: 23 pages, 5 figure

A complete, multi-level conformational clustering of antibody complementarity-determining regions

Classification of antibody complementarity-determining region (CDR) conformations is an important step that drives antibody modelling and engineering, prediction from sequence, directed mutagenesis and induced-fit studies, and allows inferences on sequence-to-structure relations. Most of the previous work performed conformational clustering on a reduced set of structures or after application of various structure pre-filtering criteria. In this study, it was judged that a clustering of every available CDR conformation would produce a complete and redundant repertoire, increase the number of sequence examples and allow better decisions on structure validity in the future. In order to cope with the potential increase in data noise, a first-level statistical clustering was performed using structure superposition Root-Mean-Square Deviation (RMSD) as a distance-criterion, coupled with second- and third-level clustering that employed Ramachandran regions for a deeper qualitative classification. The classification of a total of 12,712 CDR conformations is thus presented, along with rich annotation and cluster descriptions, and the results are compared to previous major studies. The present repertoire has procured an improved image of our current CDR Knowledge-Base, with a novel nesting of conformational sensitivity and specificity that can serve as a systematic framework for improved prediction from sequence as well as a number of future studies that would aid in knowledge-based antibody engineering such as humanisation

Encapsulating peritoneal sclerosis presenting with haemorrhagic ascites after transfer from peritoneal dialysis to haemodialysis

A patient with end-stage kidney disease due to chronic glomerulonephritis was initiated on continuous ambulatoryperitoneal dialysis. After three years he was transferred to haemodialysis following recurrent episodes of peritonitis.After the commencement of haemodialysis the patient developed progressive abdominal distension; paracentesisrevealed bloody ascites. Radiographic imaging revealed features of small bowel obstruction with bowel loops matted tothe posterior abdominal wall. A diagnosis of encapsulating peritoneal sclerosis was made. Treatment with prednisonewas initiated but the patients condition steadily worsened and he demised a year later due to severe malnutrition andsepsis

Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence

The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93 accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak