SCOPPI: a structural classification of protein–protein interfaces

SCOPPI, the structural classification of protein–protein interfaces, is a comprehensive database that classifies and annotates domain interactions derived from all known protein structures. SCOPPI applies SCOP domain definitions and a distance criterion to determine inter-domain interfaces. Using a novel method based on multiple sequence and structural alignments of SCOP families, SCOPPI presents a comprehensive geometrical classification of domain interfaces. Various interface characteristics such as number, type and position of interacting amino acids, conservation, interface size, and permanent or transient nature of the interaction are further provided. Proteins in SCOPPI are annotated with Gene Ontology terms, and the ontology can be used to quickly browse SCOPPI. Screenshots are available for every interface and its participating domains. Here, we describe contents and features of the web-based user interface as well as the underlying methods used to generate SCOPPI's data. In addition, we present a number of examples where SCOPPI becomes a useful tool to analyze viral mimicry of human interface binding sites, gene fusion events, conservation of interface residues and diversity of interface localizations. SCOPPI is available at

SNAPPI-DB: a database and API of Structures, iNterfaces and Alignments for Protein–Protein Interactions

SNAPPI-DB, a high performance database of Structures, iNterfaces and Alignments of Protein–Protein Interactions, and its associated Java Application Programming Interface (API) is described. SNAPPI-DB contains structural data, down to the level of atom co-ordinates, for each structure in the Protein Data Bank (PDB) together with associated data including SCOP, CATH, Pfam, SWISSPROT, InterPro, GO terms, Protein Quaternary Structures (PQS) and secondary structure information. Domain–domain interactions are stored for multiple domain definitions and are classified by their Superfamily/Family pair and interaction interface. Each set of classified domain–domain interactions has an associated multiple structure alignment for each partner. The API facilitates data access via PDB entries, domains and domain–domain interactions. Rapid development, fast database access and the ability to perform advanced queries without the requirement for complex SQL statements are provided via an object oriented database and the Java Data Objects (JDO) API. SNAPPI-DB contains many features which are not available in other databases of structural protein–protein interactions. It has been applied in three studies on the properties of protein–protein interactions and is currently being employed to train a protein–protein interaction predictor and a functional residue predictor. The database, API and manual are available for download at:

The Many Faces of Protein–Protein Interactions: A Compendium of Interface Geometry

A systematic classification of protein–protein interfaces is a valuable resource for understanding the principles of molecular recognition and for modelling protein complexes. Here, we present a classification of domain interfaces according to their geometry. Our new algorithm uses a hybrid approach of both sequential and structural features. The accuracy is evaluated on a hand-curated dataset of 416 interfaces. Our hybrid procedure achieves 83% precision and 95% recall, which improves the earlier sequence-based method by 5% on both terms. We classify virtually all domain interfaces of known structure, which results in nearly 6,000 distinct types of interfaces. In 40% of the cases, the interacting domain families associate in multiple orientations, suggesting that all the possible binding orientations need to be explored for modelling multidomain proteins and protein complexes. In general, hub proteins are shown to use distinct surface regions (multiple faces) for interactions with different partners. Our classification provides a convenient framework to query genuine gene fusion, which conserves binding orientation in both fused and separate forms. The result suggests that the binding orientations are not conserved in at least one-third of the gene fusion cases detected by a conventional sequence similarity search. We show that any evolutionary analysis on interfaces can be skewed by multiple binding orientations and multiple interaction partners. The taxonomic distribution of interface types suggests that ancient interfaces common to the three major kingdoms of life are enriched by symmetric homodimers. The classification results are online at http://www.scoppi.org

Using convex hulls to extract interaction interfaces from known structures.

Motivation: Protein interactions provide an important context for the understanding of function. Experimental approaches have been complemented with computational ones, such as PSIMAP, which computes domain-domain interactions for all multi-domain and multi-chain proteins in the Protein Data Bank (PDB). PSIMAP has been used to determine that superfamilies occurring in many species have many interaction partners, to show examples of convergent evolution through shared interaction partners and to uncover complexes in the interaction map. To determine an interaction, the original PSIMAP algorithm checks all residue pairs of any domain pair defined by classification systems such as SCOP. The computation takes several days for the PDB. The computation of PSIMAP has two shortcomings: first, the original PSIMAP algorithm considers only interactions of residue pairs rather than atom pairs losing information for detailed analysis of contact patterns. At the atomic level the original algorithm would take months. Second, with the superlinear growth of PDB, PSIMAP is not sustainable. Results: We address these two shortcomings by developing a family of new algorithms for the computation of domain-domain interactions based on the idea of bounding shapes, which are used to prune the search space. The best of the algorithms improves on the old PSIMAP algorithm by a factor of 60 on the PDB. Additionally, the algorithms allow a distributed computation, which we carry out on a farm of 80 Linux PCs. Overall, the new algorithms reduce the computation at atomic level from months to 20 min. The combination of pruning and distribution makes the new algorithm scalable and sustainable even with the superlinear growth in PDBclose161

Development of computational approaches for structural classification, analysis and prediction of molecular recognition regions in proteins

The vast and growing volume of 3D protein structural data stored in the PDB contains abundant information about macromolecular complexes, and hence, data about protein interfaces. Non-covalent contacts between amino acids are the basis of protein interactions, and they are responsible for binding afinity and specificity in biological processes. In addition, water networks in protein interfaces can also complement direct interactions contributing significantly to molecular recognition, although their exact role is still not well understood. It is estimated that protein complexes in the PDB are substantially underrepresented due to their crystallization dificulties. Methods for automatic classifification and description of the protein complexes are essential to study protein interfaces, and to propose putative binding regions. Due to this strong need, several protein-protein interaction databases have been developed. However, most of them do not take into account either protein-peptide complexes, solvent information or a proper classification of the binding regions, which are fundamental components to provide an accurate description of protein interfaces. In the firest stage of my thesis, I developed the SCOWLP platform, a database and web application that structurally classifies protein binding regions at family level and defines accurately protein interfaces at atomic detail. The analysis of the results showed that protein-peptide complexes are substantially represented in the PDB, and are the only source of interacting information for several families. By clustering the family binding regions, I could identify 9,334 binding regions and 79,803 protein interfaces in the PDB. Interestingly, I observed that 65% of protein families interact to other molecules through more than one region and in 22% of the cases the same region recognizes different protein families. The database and web application are open to the research community (www.scowlp.org) and can tremendously facilitate high-throughput comparative analysis of protein binding regions, as well as, individual analysis of protein interfaces. SCOWLP and the other databases collect and classify the protein binding regions at family level, where sequence and structure homology exist. Interestingly, it has been observed that many protein families also present structural resemblances within each other, mostly across folds. Likewise, structurally similar interacting motifs (binding regions) have been identified among proteins with different folds and functions. For these reasons, I decided to explore the possibility to infer protein binding regions independently of their fold classification. Thus, I performed the firest systematic analysis of binding region conservation within all protein families that are structurally similar, calculated using non-sequential structural alignment methods. My results indicate there is a substantial molecular recognition information that could be potentially inferred among proteins beyond family level. I obtained a 6 to 8 fold enrichment of binding regions, and identified putative binding regions for 728 protein families that lack binding information. Within the results, I found out protein complexes from different folds that present similar interfaces, confirming the predictive usage of the methodology. The data obtained with my approach may complement the SCOWLP family binding regions suggesting alternative binding regions, and can be used to assist protein-protein docking experiments and facilitate rational ligand design. In the last part of my thesis, I used the interacting information contained in the SCOWLP database to help understand the role that water plays in protein interactions in terms of affinity and specificity. I carried out one of the firest high-throughput analysis of solvent in protein interfaces for a curated dataset of transient and obligate protein complexes. Surprisingly, the results highlight the abundance of water-bridged residues in protein interfaces (40.1% of the interfacial residues) that reinforces the importance of including solvent in protein interaction studies (14.5% extra residues interacting only water- mediated). Interestingly, I also observed that obligate and transient interfaces present a comparable amount of solvent, which contrasts the old thoughts saying that obligate protein complexes are expected to exhibit similarities to protein cores having a dry and hydrophobic interfaces. I characterized novel features of water-bridged residues in terms of secondary structure, temperature factors, residue composition, and pairing preferences that differed from direct residue-residue interactions. The results also showed relevant aspects in the mobility and energetics of water-bridged interfacial residues. Collectively, my doctoral thesis work can be summarized in the following points: 1. I developed SCOWLP, an improved framework that identiffies protein interfaces and classifies protein binding regions at family level. 2. I developed a novel methodology to predict alternative binding regions among structurally similar protein families independently of the fold they belong to. 3. I performed a high-throughput analysis of water-bridged interactions contained in SCOWLP to study the role of solvent in protein interfaces. These three components of my thesis represent novel methods for exploiting existing structural information to gain insights into protein- protein interactions, key mechanisms to understand biological processes

A structural classification of protein-protein interactions for detection of convergently evolved motifs and for prediction of protein binding sites on sequence level

BACKGROUND: A long-standing challenge in the post-genomic era of Bioinformatics is the prediction of protein-protein interactions, and ultimately the prediction of protein functions. The problem is intrinsically harder, when only amino acid sequences are available, but a solution is more universally applicable. So far, the problem of uncovering protein-protein interactions has been addressed in a variety of ways, both experimentally and computationally. MOTIVATION: The central problem is: How can protein complexes with solved threedimensional structure be utilized to identify and classify protein binding sites and how can knowledge be inferred from this classification such that protein interactions can be predicted for proteins without solved structure? The underlying hypothesis is that protein binding sites are often restricted to a small number of residues, which additionally often are well-conserved in order to maintain an interaction. Therefore, the signal-to-noise ratio in binding sites is expected to be higher than in other parts of the surface. This enables binding site detection in unknown proteins, when homology based annotation transfer fails. APPROACH: The problem is addressed by first investigating how geometrical aspects of domain-domain associations can lead to a rigorous structural classification of the multitude of protein interface types. The interface types are explored with respect to two aspects: First, how do interface types with one-sided homology reveal convergently evolved motifs? Second, how can sequential descriptors for local structural features be derived from the interface type classification? Then, the use of sequential representations for binding sites in order to predict protein interactions is investigated. The underlying algorithms are based on machine learning techniques, in particular Hidden Markov Models. RESULTS: This work includes a novel approach to a comprehensive geometrical classification of domain interfaces. Alternative structural domain associations are found for 40% of all family-family interactions. Evaluation of the classification algorithm on a hand-curated set of interfaces yielded a precision of 83% and a recall of 95%. For the first time, a systematic screen of convergently evolved motifs in 102.000 protein-protein interactions with structural information is derived. With respect to this dataset, all cases related to viral mimicry of human interface bindings are identified. Finally, a library of 740 motif descriptors for binding site recognition - encoded as Hidden Markov Models - is generated and cross-validated. Tests for the significance of motifs are provided. The usefulness of descriptors for protein-ligand binding sites is demonstrated for the case of "ATP-binding", where a precision of 89% is achieved, thus outperforming comparable motifs from PROSITE. In particular, a novel descriptor for a P-loop variant has been used to identify ATP-binding sites in 60 protein sequences that have not been annotated before by existing motif databases

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Characterization, classification and alignment of protein-protein interfaces

Protein structural models provide essential information for the research on protein-protein interactions. In this dissertation, we describe two projects on the analysis of protein interactions using structural information. The focus of the first is to characterize and classify different types of interactions. We discriminate between biological obligate and biological non-obligate interactions, and crystal packing contacts. To this end, we defined six interface properties and used them to compare the three types of interactions in a hand-curated dataset. Based on the analysis, a classifier, named NOXclass, was constructed using a support vector machine algorithm in order to generate predictions of interaction types. NOXclass was tested on a non-redundant dataset of 243 protein-protein interactions and reaches an accuracy of 91.8%. The program is benecial for structural biologists for the interpretation of protein quaternary structures and to form hypotheses about the nature of proteinprotein interactions when experimental data are yet unavailable. In the second part of the dissertation, we present Galinter, a novel program for the geometrical comparison of protein-protein interfaces. The Galinter program aims at identifying similar patterns of different non-covalent interactions at interfaces. It is a graph-based approach optimized for aligning non-covalent interactions. A scoring scheme was developed for estimating the statistical signicance of the alignments. We tested the Galinter method on a published dataset of interfaces. Galinter alignments agree with those delivered by methods based on interface residue comparison and backbone structure comparison. In addition, we applied Galinter on four medically relevant examples of protein mimicry. Our results are consistent with previous human-curated analysis. The Galinter program provides an intuitive method of comparative analysis and visualization of binding modes and may assist in the prediction of interaction partners, and the design and engineering of protein interactions and interaction inhibitors