In silico study of protein-protein interactions

2011 - 2012Protein-protein interactions are at the basis of many of the most important molecular processes in the cell, which explains the constantly growing interest within the scientific community for the structural characterization of protein complexes.1 However, experimental knowledge of the 3D structure of the great majority of such complexes is missing, and this spurred their accurate prediction through molecular docking simulations, one of the major challenges in the field of structural computational biology and bioinformatics.2,3 My PhD work aims to contribute to the field, by providing novel computational instruments and giving useful insight on specific case studies in the field. In particular, in the first part of my PhD thesis, I present novel methods I developed: i) for analysing and comparing the 3D structure of protein complexes, to immediately extract useful information on the interaction based on a contact map visualization (COCOMAPS4 web tool, Chapter 2), and ii) for analysing a set of multiple docking solutions, to single out the key inter-residue contacts and to distinguish native-like solutions from the incorrect ones (CONS-COCOMAPS5 web tool and CONS-RANK program, Chapter 3 and 4, respectively). In the second part of the thesis, these methods have been applied, in combination with classical state-of-art computational biology techniques, to predict and analyse the binding mode in real biological systems, related to particular diseases. This part of the work has been afforded in collaboration with experimental groups, to take advantage of specific biological information on the systems under study. In particular, the interaction between proteins involved in the autoimmune response in celiac disease6,7 (Chapters 5 and 6) has been studied in collaboration with the group directed by Prof. Sblattero, University of Piemonte Orientale (Italy) and the group directed by Prof. Esposito, University of Salerno (Italy). In addition, recognition properties of 3 the FXa enzymatic system8 has been studied through dynamic characterization of a FXa pathogenic mutant that causes problems in the blood coagulation cascade (Chapter 7). This study has been performed in collaboration with the group directed by Prof. De Cristofaro, Catholic University School of Medicine, Rome (Italy) and the group directed by Prof. Peyvandi, Ospedale Maggiore Policlinico and Università degli Studi di Milano (Italy)... [edited by author]XI n.s

Structural Basis for the Recognition in an Idiotype-Anti-Idiotype Antibody Complex Related to Celiac Disease

Anti-idiotype antibodies have potential therapeutic applications in many fields, including autoimmune diseases. Herein we report the isolation and characterization of AIM2, an anti-idiotype antibody elicited in a mouse model upon expression of the celiac disease-specific autoantibody MB2.8 (directed against the main disease autoantigen type 2 transglutaminase, TG2). To characterize the interaction between the two antibodies, a 3D model of the MB2.8-AIM2 complex has been obtained by molecular docking. Analysis and selection of the different obtained docking solutions was based on the conservation within them of the inter-residue contacts. The selected model is very well representative of the different solutions found and its stability is confirmed by molecular dynamics simulations. Furthermore, the binding mode it adopts is very similar to that observed in most of the experimental structures available for idiotype-anti-idiotype antibody complexes. In the obtained model, AIM2 is directed against the MB2.8 CDR region, especially on its variable light chain. This makes the concurrent formation of the MB2.8-AIM2 complex and of the MB2.8-TG2 complex incompatible, thus explaining the experimentally observed inhibitory effect on the MB2.8 binding to TG2

Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

We present an updated and integrated version of our widely used protein–protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein–protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody–antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r = 0.52 overall and r = 0.72 for the rigid complexes.Peer ReviewedPostprint (author's final draft

An overview of data‐driven HADDOCK strategies in CAPRI rounds 38-45

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models

Mycobacterium tuberculosis SIT42 Infection in an Abused Dog in Southern Italy

A case of Mycobacterium tuberculosis infection is described in a dead adult male dog in Southern Italy. The carcass was found by the Health Authority in a gypsy encampment. It was admitted to our forensic veterinary medicine unit, with a suspicion of cruelty to the animal. Necropsy showed beating and traumatism signs, and mistreating was confirmed. Gross lesions included multiple nodular hepatic lesions, hemorrhagic enteritis with enlarged mesenteric lymph nodes, body cavity effusions, and an adrenal neoplasm. Bacteriological and molecular analyses were carried out on the liver lesions that enabled to identify M. tuberculosis SIT42 (LAM9). Drug-resistance patterns were evaluated by screening mutations on the rpoB and katG genes that showed susceptibility to both rifampin and isoniazid, respectively. Very few studies report canine tuberculosis, and little is known about the disease in Italy. To the authors' knowledge, this is the first report of Mycobacterium tuberculosis SIT42 infection in a dog in Italy

CONS-COCOMAPS: a novel tool to measure and visualize the conservation of inter-residue contacts in multiple docking solutions

Background: The development of accurate protein-protein docking programs is making this kind of simulations an effective tool to predict the 3D structure and the surface of interaction between the molecular partners in macromolecular complexes. However, correctly scoring multiple docking solutions is still an open problem. As a consequence, the accurate and tedious screening of many docking models is usually required in the analysis step. Methods: All the programs under CONS-COCOMAPS have been written in python, taking advantage of python libraries such as SciPy and Matplotlib. CONS-COCOMAPS is freely available as a web tool at the URL: http://www.molnac.unisa.it/BioTools/conscocomaps/. Results: Here we presented CONS-COCOMAPS, a novel tool to easily measure and visualize the consensus in multiple docking solutions. CONS-COCOMAPS uses the conservation of inter-residue contacts as an estimate of the similarity between different docking solutions. To visualize the conservation, CONS-COCOMAPS uses intermolecular contact maps. Conclusions: The application of CONS-COCOMAPS to test-cases taken from recent CAPRI rounds has shown that it is very efficient in highlighting even a very weak consensus that often is biologically meaningful

Contacts-based prediction of binding affinity in protein–protein complexes

Almost all critical functions in cells rely on specific protein–protein interactions.Understanding these is therefore crucial in the investigation of biological systems.Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins.Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts.We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy.Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods

Biological vs. Crystallographic protein interfaces : An overview of computational approaches for their classification

Complexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structural characterization of protein-protein complexes. Correctly identifying the biologically relevant interface from the crystallographic ones is, however, not trivial and can be prone to errors. Over the past two decades, computational methodologies have been developed to study the differences of those interfaces and automatically classify them as biological or crystallographic. Overall, protein-protein interfaces show differences in terms of composition, energetics and evolutionary conservation between biological and crystallographic ones. Based on those observations, a number of computational methods have been developed for this classification problem, which can be grouped into three main categories: Energy-, empirical knowledge-and machine learning-based approaches. In this review, we give a comprehensive overview of the training datasets and methods so far implemented, providing useful links and a brief description of each method