Protein-protein docking for interactomic studies and its aplication to personalized medicine

[eng] Proteins are the embodiment of the message encoded in the genes and they act as the building blocks and effector part of the cell. From gene regulation to cell signalling, as well as cell recognition and movement, protein-protein interactions (PPIs) drive many important cellular events by forming intricate interaction networks. The number of all non-redundant human binary interactions, forming the so-called interactome, ranges from 130,000 to 650,000 interactions as estimated by different studies. In some diseases, like cancer, these PPIs are altered by the presence of mutations in individual proteins, which can change the interaction networks of the cell resulting in a pathological state. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. To understand how these mutations can alter the PPIs, we need to look at the three-dimensional structure of the protein complexes at the atomic level. However, there are available structures for less than 10% of the estimated human interactome. Computational approaches such as protein-protein docking can help to extend the structural coverage of known PPIs. In the protein-protein docking field, rigid-body docking is a widely used docking approach, since es fast, computationally cheap and is often capable of generating a pool of models within which a near-native structure can be found. These models need to be scored in order to select the acceptable ones from the set of poses. In the present thesis, we have characterized the synergy between combination of protein-protein docking methods and several scoring functions. Our findings provide guides for the use of the most efficient scoring function for each docking method, as well as instruct future scoring functions development efforts Then we used docking calculations to predict interaction hotspots, i.e. residues that contribute the most to the binding energy, and interface patches by including neighbour residues to the predictions. We developed and validated a method, based in the Normalize Interface Propensity (NIP) score. The work of this thesis have extended the original NIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This methodology was also applied to predict the location of 14,551 nsSNPs in 4,254 proteins, for more than 12,000 interactions without 3D structure. We found that 34% of the disease-associated nsSNPs were located at a protein-protein interface. This opens future opportunities for the high-throughput characterization of pathological mutations at the atomic level resolution, and can help to design novel therapeutic strategies to re-stabilize the affected PPIs by disease-associated nsSNPs

Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations

Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.This work was funded by grants number BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness, and grant number EFA086/15 from Interreg POCTEFA. D. Barradas-Bautista was supported by a CONACyT predoctoral fellowship from the Mexican Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer ReviewedPostprint (published version

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Docking through democracy re-ranking protein-protein decoys with a voting system

We have develop a machine learning framework to enhance protein-protein docking results, using Schulze voting method applied to several models from Support Vector machines

IRaPPA: information retrieval based integration of biophysical models for protein assembly selection

Motivation: In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Results: Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. Availability and Implementation: IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/ approximately SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC00100

Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations

Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.This work was funded by grants number BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness, and grant number EFA086/15 from Interreg POCTEFA. D. Barradas-Bautista was supported by a CONACyT predoctoral fellowship from the Mexican Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer Reviewe

Prediction of interface residues and nsSNPs.

<p>(A) Prediction success (sensitivity and precision) of interface residues using pyDockNIP (alone or extended with neighbor residues) on the proteins of the protein-protein docking benchmark 4.0, according to NIP cutoff value. (B) Interface and nsSNPs predictions using the extended pyDockNIP predictions on the proteins of the structural interaction networks from the six selected diseases. The nsSNPs predictions are detailed for interface disease-related, polymorphism and unclassified nsSNPs.</p

Structurally unexplained pathological mutations of the RAS/MAPK pathway that are predicted to be involved at protein-protein interfaces.

<p>Proteins of the RAS/MAPK pathway are represented as circles, showing pathological mutations that were not previously characterized due to the lack of structural data, but that have been predicted here to be binding hot-spots when docking with specific protein partners from this pathway (circles) or from the first-degree interaction network (in cyan squares). These docking partners thus represent proteins whose interaction is predicted to be affected by the mutation to which they are linked. Thus, all edges here correspond to interface predictions from docking.</p