On the development of computational tools for the study of protein-protein interactions and protein-protein binding

Proteins are involved in almost all cell processes, with physical interaction between them being key to their function and dictated by its 3D structure. Hence, the study of protein-protein interactions and protein-protein binding is crucial to fully understand biological systems. In this thesis, we present V-D2OCK, a fast and accurate data-driven docking tool for high throughput prediction of the structure of protein complexes. We have also studied the conformational space of potential encounter complexes by means of non-specific decoys obtained by docking in order to develop BADock, an accurate binding affinity predictor from the unbound individual structures. Finally, we have published online an integrated and centralized resource (InteractoMIX) that allows to the research community an easy access to a compendium of bioinformatic web applications to study protein-protein interactions.Les proteïnes estan implicades en gairebé tots els processos cel·lulars, amb la interacció física entre elles clau per la seva funció i dictada per la seva estructura 3D. Per tant, l’estudi de la unió i les interaccions proteïna-proteïna és crucial per entendre completament els sistemes biològics. En aquesta tesi, es presenta V-D2OCK, una eina de “docking” dirigit ràpida i precisa per predir l’estructura de complexes de proteïnes a gran escala. També hem estudiat l’espai conformacional de possibles complexes transitoris per mitjà de resultats de “docking” no específics per tal de desenvolupar BADock, un predictor d’energia d’unió a partir de les estructures individuals per separat. Finalment, hem publicat online un recurs integrat i centralitzat (InteractoMIX) que permet a la comunitat investigadora l’accés fàcil a un conjunt de aplicacions web de bioinformàtica per l’estudi de interaccions proteïna-proteïna

On the development of computational tools for the study of protein-protein interactions and protein-protein binding

Proteins are involved in almost all cell processes, with physical interaction between them being key to their function and dictated by its 3D structure. Hence, the study of protein-protein interactions and protein-protein binding is crucial to fully understand biological systems. In this thesis, we present V-D2OCK, a fast and accurate data-driven docking tool for high throughput prediction of the structure of protein complexes. We have also studied the conformational space of potential encounter complexes by means of non-specific decoys obtained by docking in order to develop BADock, an accurate binding affinity predictor from the unbound individual structures. Finally, we have published online an integrated and centralized resource (InteractoMIX) that allows to the research community an easy access to a compendium of bioinformatic web applications to study protein-protein interactions.Les proteïnes estan implicades en gairebé tots els processos cel·lulars, amb la interacció física entre elles clau per la seva funció i dictada per la seva estructura 3D. Per tant, l’estudi de la unió i les interaccions proteïna-proteïna és crucial per entendre completament els sistemes biològics. En aquesta tesi, es presenta V-D2OCK, una eina de “docking” dirigit ràpida i precisa per predir l’estructura de complexes de proteïnes a gran escala. També hem estudiat l’espai conformacional de possibles complexes transitoris per mitjà de resultats de “docking” no específics per tal de desenvolupar BADock, un predictor d’energia d’unió a partir de les estructures individuals per separat. Finalment, hem publicat online un recurs integrat i centralitzat (InteractoMIX) que permet a la comunitat investigadora l’accés fàcil a un conjunt de aplicacions web de bioinformàtica per l’estudi de interaccions proteïna-proteïna

VORFFIP-driven dock: V-D2OCK, a fast and accurate protein docking strategy.

The experimental determination of the structure of protein complexes cannot keep pace with the generation of interactomic data, hence resulting in an ever-expanding gap. As the structural details of protein complexes are central to a full understanding of the function and dynamics of the cell machinery, alternative strategies are needed to circumvent the bottleneck in structure determination. Computational protein docking is a valid and valuable approach to model the structure of protein complexes. In this work, we describe a novel computational strategy to predict the structure of protein complexes based on data-driven docking: VORFFIP-driven dock (V-D2OCK). This new approach makes use of our newly described method to predict functional sites in protein structures, VORFFIP, to define the region to be sampled during docking and structural clustering to reduce the number of models to be examined by users. V-D2OCK has been benchmarked using a validated and diverse set of protein complexes and compared to a state-of-art docking method. The speed and accuracy compared to contemporary tools justifies the potential use of VD2OCK for high-throughput, genome-wide, protein docking. Finally, we have developed a web interface that allows users to browser and visualize V-D2OCK predictions from the convenience of their web-browsers.This work was supported by Research Councils UK (RCUK) under the RCUK Academic Fellowship program (NFF) and a PhD scholarship awarded by the University of Leeds (JS). BO acknowledges support from the Spanish Ministry ofEconomy and Competitiveness; grant number BIO2011-22568 and MAML a PhD scholarship awarded by the Generalitat of Catalonia (FI-DGR2012)

ArchDB 2014: structural classification of loops in proteins

The function of a protein is determined by its three-dimensional structure, which is formed by regular (i.e. β-strands and α-helices) and non-periodic structural units such as loops. Compared to regular structural elements, non-periodic, non-repetitive conformational units enclose a much higher degree of variability--raising difficulties in the identification of regularities, and yet represent an important part of the structure of a protein. Indeed, loops often play a pivotal role in the function of a protein and different aspects of protein folding and dynamics. Therefore, the structural classification of protein loops is an important subject with clear applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design and catalytic loops) and function prediction. ArchDB, the database presented here (freely available at http://sbi.imim.es/archdb), represents such a resource and has been an important asset for the scientific community throughout the years. In this article, we present a completely reworked and updated version of ArchDB. The new version of ArchDB features a novel, fast and user-friendly web-based interface, and a novel graph-based, computationally efficient, clustering algorithm. The current version of ArchDB classifies 149,134 loops in 5739 classes and 9608 subclasses.This work has been funded by the Spanish Ministry of Science and Innovation (MICINN) [FEDER BIO2008-0205, FEDER BIO2011-22568, EUI2009-04018]; FI-DGR 2012 fellowship from/n‘Generalitat de Catalunya’ (to M.A.M.L.). Funding for open access charge: Spanish Ministry of Science and Innovatio

On the mechanisms of protein interactions: Predicting their affinity from unbound tertiary structures

MOTIVATION: The characterization of the protein-protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein-protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures. RESULTS: We present a new approach that relies on the unbound protein structures and protein docking to predict protein-protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol. AVAILABILITY AND IMPLEMENTATION: The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.The work has been supported by grants BIO2014-57518-R and BIO2011-22568 of the Spanish Ministry of Economy (MINECO), INB 2015-2017 of ISCIII, and 2014SGR1161 of Generalitat de Catalunya