ModLink+: improving fold recognition by using protein-protein interactions

InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research

Virtually all the biological processes that occur inside or outside cells are mediated by protein–protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com).

Structural and functional insights into CARDs of zebrafish (Danio rerio) NOD1 and NOD2, and their interaction with adaptor protein RIP2

Nucleotide-binding and oligomerization domain-containing protein 1 (NOD1) and NOD2 are cytosolic pattern-recognition receptors (PRRs) composed of an N-terminal caspase activation and recruitment domain (CARD), a central NACHT domain and C-terminal leucine-rich repeats (LRRs). They play a vital role in innate immune signaling by activating the NF-κB pathway via recognition of peptidoglycans by LRRs, and ATP-dependent self-oligomerization of NACHT followed by downstream signaling. After oligomerization, CARD/s play a crucial role in activating downstream signaling via the adaptor molecule, RIP2. Due to the inadequacy of experimental 3D structures of CARD/s of NOD2 and RIP2, and results from differential experimental setups, the RIP2-mediated CARD-CARD interaction has remained as a contradictory statement. We employed a combinatorial approach involving protein modeling, docking, molecular dynamics simulation, and binding free energy calculation to illuminate the molecular mechanism that shows the possible involvement of either the acidic or basic patch of zebrafish NOD1/2-CARD/a and RIP2-CARD in CARD-CARD interaction. Herein, we have hypothesized 'type-I' mode of CARD-CARD interaction in NOD1 and NOD2, where NOD1/2-CARD/a involve their acidic surfaces to interact with RIP2. Asp37 and Glu51 (of NOD1) and Arg477, Arg521 and Arg529 (of RIP2) were identified to be crucial for NOD1-RIP2 interaction. However, in NOD2-RIP2, Asp32 (of NOD2) and Arg477 and Arg521 (of RIP2) were anticipated to be significant for downstream signaling. Furthermore, we found that strong electrostatic contacts and salt bridges are crucial for protein-protein interactions. Altogether, our study has provided novel insights into the RIP2-mediated CARD-CARD interaction in zebrafish NOD1 and NOD2, which will be helpful to understand the molecular basis of the NOD1/2 signaling mechanism.

Structural and functional analysis of a novel psychrophilic β-mannanase from Glaciozyma antarctica PI12

The structure of a novel psychrophilic β-mannanase enzyme from Glaciozyma antarctica PI12 yeast has been modelled and analysed in detail. To our knowledge, this is the first attempt to model a psychrophilic β-mannanase from yeast. To this end, a 3D structure of the enzyme was first predicted using a threading method because of the low sequence identity (<30%) using MODELLER9v12 and simulated using GROMACS at varying low temperatures for structure refinement. Comparisons with mesophilic and thermophilic mannanases revealed that the psychrophilic mannanase contains longer loops and shorter helices, increases in the number of aromatic and hydrophobic residues, reductions in the number of hydrogen bonds and salt bridges and numerous amino acid substitutions on the surface that increased the flexibility and its efficiency for catalytic reactions at low temperatures.

Sequence and structural investigation of a novel psychrophilic α-amylase from Glaciozyma antarctica PI12 for cold-adaptation analysis

A novel α-amylase was isolated successfully from Glaciozyma antarctica PI12 using DNA walking and reverse transcription-polymerase chain reaction (RT-PCR) methods. The structure of this psychrophilic α-amylase (AmyPI12) from G. antarctica PI12 has yet to be studied in detail. A 3D model of AmyPI12 was built using a homology modelling approach to search for a suitable template and to generate an optimum target-template alignment, followed by model building using MODELLER9.9. Analysis of the AmyPI12 model revealed the presence of binding sites for a conserved calcium ion (CaI), non-conserved calcium ions (CaII and CaIII) and a sodium ion (Na). Compared with its template-the thermostable α-amylase from Bacillus stearothermophilus (BSTA)-the binding of CaII, CaIII and Na ions in AmyPI12 was observed to be looser, which suggests that the low stability of AmyPI12 allows the protein to work at different temperature scales. The AmyPI12 amino acid sequence and model were compared with thermophilic α-amylases from Bacillus species that provided the highest structural similarities with AmyPI12. These comparative studies will enable identification of possible determinants of cold adaptation.

Networks of ProteinProtein Interactions: From Uncertainty to Molecular Details

Proteins are the bricks and mortar of cells. The work of proteins is structural and functional, as they are the principal element of the organization of the cell architecture, but they also play a relevant role in its metabolism and regulation. To perform all these functions, proteins need to interact with each other and with other bio-molecules, either to form complexes or to recognize precise targets of their action. For instance, a particular transcription factor may activate one gene or another depending on its interactions with other proteins and not only with DNA. Hence, the ability of a protein to interact with other bio-molecules, and the partners they have at each particular time and location can be crucial to characterize the role of a protein. Proteins rarely act alone; they rather constitute a mingled network of physical interactions or other types of relationships (such as metabolic and regulatory) or signaling cascades. In this context, understanding the function of a protein implies to recognize the members of its neighborhood and to grasp how they associate, both at the systemic and atomic level. The network of physical interactions between the proteins of a system, cell or organism, is defined as the interactome. The purpose of this review is to deepen the description of interactomes at different levels of detail: from the molecular structure of complexes to the global topology of the network of interactions. The approaches and techniques applied experimentally and computationally to attain each level are depicted. The limits of each technique and its integration into a model network, the challenges and actual problems of completeness of an interactome, and the reliability of the interactions are reviewed and summarized. Finally, the application of the current knowledge of protein-protein interactions on modern network medicine and protein function annotation is also explored.

On the analysis of protein-protein interactions via knowledge-based potentials for the prediction of protein-protein docking

Development of effective methods to screen binary interactions obtained by rigid-body protein-protein docking is key for structure prediction of complexes and for elucidating physicochemical principles of protein-protein binding. We have derived empirical knowledge-based potential functions for selecting rigid-body docking poses. These potentials include the energetic component that provides the residues with a particular secondary structure and surface accessibility. These scoring functions have been tested on a state-of-art benchmark dataset and on a decoy dataset of permanent interactions. Our results were compared with a residue-pair potential scoring function (RPScore) and an atomic-detailed scoring function (Zrank). We have combined knowledge-based potentials to score protein-protein poses of decoys of complexes classified either as transient or as permanent protein-protein interactions. Being defined from residue-pair statistical potentials and not requiring of an atomic level description, our method surpassed Zrank for scoring rigid-docking decoys where the unbound partners of an interaction have to endure conformational changes upon binding. However, when only moderate conformational changes are required (in rigid docking) or when the right conformational changes are ensured (in flexible docking), Zrank is the most successful scoring function. Finally, our study suggests that the physicochemical properties necessary for the binding are allocated on the proteins previous to its binding and with independence of the partner. This information is encoded at the residue level and could be easily incorporated in the initial grid scoring for Fast Fourier Transform rigid-body docking methods.

Protein sequence comparison and fold recognition: progress and good-practice benchmarking

Protein sequence comparison methods have grown increasingly sensitive during the last decade and can often identify distantly related proteins sharing a common ancestor some 3 billion years ago. Although cellular function is not conserved so long, molecular functions and structures of protein domains often are. In combination with a domain-centered approach to function and structure prediction, modern remote homology detection methods have a great and largely underexploited potential for elucidating protein functions and evolution. Advances during the last few years include nonlinear scoring functions combining various sequence features, the use of sequence context information, and powerful new software packages. Since progress depends on realistically assessing new and existing methods and published benchmarks are often hard to compare, we propose 10 rules of good-practice benchmarking.

Biana: a software framework for compiling biological interactions and analyzing networks

Background

The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.

Results

We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php.

Conclusions

BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.