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

Large–scale data–driven network analysis of human–plasmodium falciparum interactome: extracting essential targets and processes for malaria drug discovery

Background: Plasmodium falciparum malaria is an infectious disease considered to have great impact on public health due to its associated high mortality rates especially in sub Saharan Africa. Falciparum drugresistant strains, notably, to chloroquine and sulfadoxine-pyrimethamine in Africa is traced mainly to Southeast Asia where artemisinin resistance rate is increasing. Although careful surveillance to monitor the emergence and spread of artemisinin-resistant parasite strains in Africa is on-going, research into new drugs, particularly, for African populations, is critical since there is no replaceable drug for artemisinin combination therapies (ACTs) yet. Objective: The overall objective of this study is to identify potential protein targets through host–pathogen protein–protein functional interaction network analysis to understand the underlying mechanisms of drug failure and identify those essential targets that can play their role in predicting potential drug candidates specific to the African populations through a protein-based approach of both host and Plasmodium falciparum genomic analysis. Methods: We leveraged malaria-specific genome wide association study summary statistics data obtained from Gambia, Kenya and Malawi populations, Plasmodium falciparum selective pressure variants and functional datasets (protein sequences, interologs, host-pathogen intra-organism and host-pathogen inter-organism protein-protein interactions (PPIs)) from various sources (STRING, Reactome, HPID, Uniprot, IntAct and literature) to construct overlapping functional network for both host and pathogen. Developed algorithms and a large-scale data-driven computational framework were used in this study to analyze the datasets and the constructed networks to identify densely connected subnetworks or hubs essential for network stability and integrity. The host-pathogen network was analyzed to elucidate the influence of parasite candidate key proteins within the network and predict possible resistant pathways due to host-pathogen candidate key protein interactions. We performed biological and pathway enrichment analysis on critical proteins identified to elucidate their functions. In order to leverage disease-target-drug relationships to identify potential repurposable already approved drug candidates that could be used to treat malaria, pharmaceutical datasets from drug bank were explored using semantic similarity approach based of target–associated biological processes Results: About 600,000 significant SNPs (p-value< 0.05) from the summary statistics data were mapped to their associated genes, and we identified 79 human-associated malaria genes. The assembled parasite network comprised of 8 clusters containing 799 functional interactions between 155 reviewed proteins of which 5 clusters contained 43 key proteins (selective variants) and 2 clusters contained 2 candidate key proteins(key proteins characterized by high centrality measure), C6KTB7 and C6KTD2. The human network comprised of 32 clusters containing 4,133,136 interactions between 20,329 unique reviewed proteins of which 7 clusters contained 760 key proteins and 2 clusters contained 6 significant human malaria-associated candidate key proteins or genes P22301 (IL10), P05362 (ICAM1), P01375 (TNF), P30480 (HLA-B), P16284 (PECAM1), O00206 (TLR4). The generated host-pathogen network comprised of 31,512 functional interactions between 8,023 host and pathogen proteins. We also explored the association of pfk13 gene within the host-pathogen. We observed that pfk13 cluster with host kelch–like proteins and other regulatory genes but no direct association with our identified host candidate key malaria targets. We implemented semantic similarity based approach complemented by Kappa and Jaccard statistical measure to identify 115 malaria–similar diseases and 26 potential repurposable drug hits that can be 3 appropriated experimentally for malaria treatment. Conclusion: In this study, we reviewed existing antimalarial drugs and resistance–associated variants contributing to the diminished sensitivity of antimalarials, especially chloroquine, sulfadoxine-pyrimethamine and artemisinin combination therapy within the African population. We also described various computational techniques implemented in predicting drug targets and leads in drug research. In our data analysis, we showed that possible mechanisms of resistance to artemisinin in Africa may arise from the combinatorial effects of many resistant genes to chloroquine and sulfadoxine–pyrimethamine. We investigated the role of pfk13 within the host–pathogen network. We predicted key targets that have been proposed to be essential for malaria drug and vaccine development through structural and functional analysis of host and pathogen function networks. Based on our analysis, we propose these targets as essential co-targets for combinatorial malaria drug discovery

Cavity-based negative images in molecular docking

In drug development, computer-based methods are constantly evolving as a result of increasing computing power and cumulative costs of generating new pharmaceuticals. With virtual screening (VS), it is possible to screen even hundreds of millions of compounds and select the best molecule candidates for in vitro testing instead of investing time and resources in analysing all molecules systematically in laboratories. However, there is a constant need to generate more reliable and effective software for VS. For example, molecular docking, one of the most central methods in structure-based VS, can be a very successful approach for certain targets while failing completely with others. However, it is not necessarily the docking sampling but the scoring of the docking poses that is the bottleneck. In this thesis, a novel rescoring method, negative image-based rescoring (R-NiB), is introduced, which generates a negative image of the ligand binding cavity and compares the shape and electrostatic similarity between the generated model and the docked molecule pose. The performance of the method is tested comprehensively using several different protein targets, benchmarking sets and docking software. Additionally, it is compared to other rescoring methods. R-NiB is shown to be a fast and effective method to rescore the docking poses producing notable improvement in active molecule recognition. Furthermore, the NIB model optimization method based on a greedy algorithm is introduced that uses a set of known active and inactive molecules as a training set. This approach, brute force negative image-based optimization (BR-NiB), is shown to work remarkably well producing impressive in silico results even with very limited active molecule training sets. Importantly, the results suggest that the in silico hit rates of the optimized models in docking rescoring are on a level needed in real-world VS and drug discovery projects.Tietokoneiden laskentatehojen ja lääketutkimuksen tuotekehityskulujen kasvaessa tietokonepohjaiset menetelmät kehittyvät jatkuvasti lääkekehityksessä. Virtuaaliseulonnalla voidaan seuloa jopa satoja miljoonia molekyylejä ja valita vain parhaat molekyyliehdokkaat laboratoriotestaukseen sen sijaan, että tuhlattaisiin aikaa ja resursseja analysoimalla järjestelmällisesti kaikki molekyylit laboratoriossa. Tästä huolimatta on koko ajan jatkuva tarve kehittää luotettavampia ja tehokkaampia menetelmiä virtuaaliseulontaan. Esimerkiksi telakointi, yksi keskeisimmistä työkaluista rakennepohjaisessa lääkeainekehityksessä, saattaa toimia erinomaisesti yhdellä kohteella ja epäonnistua täysin toisella. Ongelma ei välttämättä ole telakoitujen molekyylien luonnissa vaan niiden pisteytyksessä. Tässä väitöskirjassa tähän ongelmaan esitellään ratkaisuksi uudenlainen pisteytysmenetelmä R-NiB, jossa verrataan ligandinsitomisalueen negatiivikuvan muodon ja sähköstaattisen potentiaalin samankaltaisuutta telakoituihin molekyyleihin. Menetelmän suorituskykyä testataan usealla eri molekyylisarjalla, lääkeainekohteella, telakointiohjelmalla ja vertaamalla tuloksia muihin pisteytysmenetelmiin. R-NiB:n näytetään olevan nopea ja tehokas menetelmä telakointiasentojen pisteytykseen tuottaen huomattavan parannuksen aktiivisten molekyylien tunnistukseen. Tämän lisäksi esitellään ns. ahneeseen algoritmiin perustuva negatiivikuvan optimointimenetelmä, joka käyttää sarjaa tunnettuja aktiivisia ja inaktiivisia molekyylejä harjoitusjoukkona. Tämän BR-NiB-menetelmän näytetään toimivan ainakin tietokonemallinnuksessa todella hyvin tuottaen vaikuttavia tuloksia jopa silloin, kun harjoitusjoukko koostuu vain muutamista aktiivisista molekyyleistä. Mikä tärkeintä, in silico -tulokset viittaavat optimointimenetelmän osumaprosentin telakoinnin uudelleenpisteytyksessä olevan riittävän korkea myös oikeisiin virtuaaliseulontaprojekteihin

Classification and Scoring of Protein Complexes

Proteins interactions mediate all biological systems in a cell; understanding their interactions means understanding the processes responsible for human life. Their structure can be obtained experimentally, but such processes frequently fail at determining structures of protein complexes. To address the issue, computational methods have been developed that attempt to predict the structure of a protein complex, using information of its constituents. These methods, known as docking, generate thousands of possible poses for each complex, and require effective and reliable ways to quickly discriminate the correct pose among the set of incorrect ones. In this thesis, a new scoring function was developed that uses machine learning techniques and features extracted from the structure of the interacting proteins, to correctly classify and rank the putative poses. The developed function has shown to be competitive with current state-of-the-art solutions