Classification of loops in protein structures : applications on loop modeling and protein function /

Consultable des del TDXTítol obtingut de la portada digitalitzadaAquesta tesis esta estructurada en cinc capítols. Al capítol I, es fa una introducció als llaços, el subjecte d'estudi d'aquesta tesis. A més, es fa una petita introducció a les bases de dades biològiques de us corrent i de protocols bio-informàtics en comparacions de seqüències. Del capítol II al IV s'explora el paper que els llaços juguen a les proteïnes utilitzant un enfocament bio-informàtic, es realitza una classificació estructural de llaços (capítol II); es realitza un estudi per inferir relacions d'estructura i funció (capítol III) i es realitza un estudi de predicció d'estructura de llaços (capítol IV). Finalment al capítol V es presenten unes consideracions finals al treball realitzat i es proposen futures extensions al mateix. El treball realitza per el Dr. Oliva ha sigut el punt d'inici d'aquesta tesis. Al capítol II es presenta un procés totalment automatitzat de classificació estructural de llaços de proteïnes quinases. Diferent millores varen ser introduïdes al treball original del Dr. Oliva: (i) un nou procés de reagrupació que evita els solapament entre agrupacions de llaços, (ii) un servidor web que permet l'accés i recerca de dades sobre els llaços classificats a través de internet, (iii) referències creuades amb altre bases de dades important. El capítol III es centra en dues qüestions bàsiques: la conservació de la estructura dels llaços i la seva funció i la conservació de la estructura dels llaços i la seva relació amb l'evolució. Un extensiu estudi sobre una classificació estructural de llaços de proteïnes quinases va ser realitzat. El motiu pe el quan les quinases varen ser escollides com a subjecte d'estudi es degut a la seva importància biològica i perquè hi ha molta informació disponible a la literatura. Finalment al capítol IV s'estudia la aplicabilitat de les classificacions estructurals de llaços en el camp de la predicció d'estructura. Es va realitzat un test de validació (Jack-knife test) per provar la utilitat de la informació de la seqüència en forma de perfils de les agrupacions estructurals de llaços.This thesis is structured into five chapters. In chapter I, protein loops - the topics of this thesis work - are introduced. Also, a short description of biological databases and current protocols in sequence comparison are given. Chapters II to IV explore a major role that loop segments play in protein structures by using a structural bio-informatics approach: (i) the structural classification (ii) the relationship between the structure and function and (iii) the structure prediction of loops. The conclusive chapter V is devoted to several considerations that complement the conclusions given in previous chapters. Extensions of this thesis work are also suggested. The research project on structural classification of loops, which was carried out by Dr. Oliva (Oliva et al. 1997), has been the starting point for all the other subsequent projects. In chapter II, a fully automated process for the structural classification of loops of kinases is presented. Several methodological improvements were made on the basis of Oliva's original work: (i) a newly introduced re-clustering process allows to avoid overlaps in classified loop clusters, (ii) a new web server was established to provide access and/or to query data through the internet, and (iii) cross referencing links were introduced with other biological databases. Chapter III focuses on two questions: the conservation of loop structures and functions and the extent of conservation of loop structures during evolution. An extensive analysis of a structural loop database of protein kinases was carried out. There are two main reasons why kinases were selected for the subject of this study: first, their critical biological relevance, and second the vast amount of functional information available in the literature and biological databases. Finally, in chapter IV, we apply ArchDB(Espadaler et al. 2004) for loop structure prediction. A Jack-knife test is performed to assess the usefulness of sequence information, which is included in the form of profiles in our structural clusters

Preclinical development of a humanized chimeric antigen receptor against B-cell maturation antigen for multiple myeloma

Multiple myeloma is a prevalent and incurable disease, despite the development of new and effective drugs. The recent development of chimeric antigen receptor (CAR)T cells has shown impressive results in the treatment of patients with relapsed or refractory hematologic B-cell malignancies. In recent years, B-cell maturation antigen (BCMA) has appeared as a promising antigen to target using a variety of immunotherapy treatments, including CART cells, for patients with multiple myeloma. To this end, we generated clinical-grade murine CART cells directed against BCMA, named ARI2m cells. Having demonstrated its efficacy, and in an attempt to avoid the immune rejection of CART cells by the patient, the single chain variable fragment was humanized, creating ARI2h cells. ARI2h cells showed comparable in vitro and in vivo efficacy to that of ARI2m cells, and superiority in cases of high tumor burden disease. In terms of inflammatory response, ARI2h cells produced less tumor necrosis factor-αand were associated with a milder in vivo toxicity profile. Large-scale expansion of both ARI2m and ARI2h cells was efficiently conducted following Good Manufacturing Practice guidelines, obtaining the target CART-cell dose required for treatment of multiple myeloma patients. Moreover, we demonstrated that soluble BCMA and BCMA released in vesicles both affect CAR-BCMA activity. In summary, this study sets the bases for the implementation of a clinical trial (EudraCT code: 2019-001472-11) to study the efficacy of ARI2h-cell treatment for patients with multiple myeloma

Efficient elimination of primary B-ALL cells in vitro and in vivo using a novel 4-1BB-based CAR targeting a membrane-distal CD22 epitope

Altres ajuts: Funding This work was supported by the Obra Social La Caixa (LCF/PR/HR19/52160011), the Spanish Cancer Association and Leo Messi Foundation to PM.Background There are few therapeutic options available for patients with B-cell acute lymphoblastic leukemia (B-ALL) relapsing as CD19 - either after chemotherapy or CD19-targeted immunotherapies. CD22-chimeric antigen receptor (CAR) T cells represent an attractive addition to CD19-CAR T cell therapy because they will target both CD22 + CD19 - B-ALL relapses and CD19 - preleukemic cells. However, the immune escape mechanisms from CD22-CAR T cells, and the potential contribution of the epitope binding of the anti-CD22 single-chain variable fragment (scFv) remain understudied. Methods Here, we have developed and comprehensively characterized a novel CD22-CAR (clone hCD22.7) targeting a membrane-distal CD22 epitope and tested its cytotoxic effects against B-ALL cells both in in vitro and in vivo assays. Results Conformational epitope mapping, cross-blocking, and molecular docking assays revealed that the hCD22.7 scFv is a high-affinity binding antibody which specifically binds to the ESTKDGKVP sequence, located in the Ig-like V-type domain, the most distal domain of CD22. We observed efficient killing of B-ALL cells in vitro, although the kinetics were dependent on the level of CD22 expression. Importantly, we show an efficient in vivo control of patients with B-ALL derived xenografts with diverse aggressiveness, coupled to long-term hCD22.7-CAR T cell persistence. Remaining leukemic cells at sacrifice maintained full expression of CD22, ruling out CAR pressure-mediated antigen loss. Finally, the immunogenicity capacity of this hCD22.7-scFv was very similar to that of other CD22 scFv previously used in adoptive T cell therapy. Conclusions We report a novel, high-affinity hCD22.7 scFv which targets a membrane-distal epitope of CD22. 4-1BB-based hCD22.7-CAR T cells efficiently eliminate clinically relevant B- CD22 high and CD22 low ALL primary samples in vitro and in vivo. Our study supports the clinical translation of this hCD22.7-CAR as either single or tandem CD22-CD19-CAR for both naive and anti-CD19-resistant patients with B-ALL

Chromosomal instability in aneuploid acute lymphoblastic leukemia associates with disease progression

Chromosomal instability (CIN) lies at the core of cancer development leading to aneuploidy, chromosomal copy-number heterogeneity (chr-CNH) and ultimately, unfavorable clinical outcomes. Despite its ubiquity in cancer, the presence of CIN in childhood B-cell acute lymphoblastic leukemia (cB-ALL), the most frequent pediatric cancer showing high frequencies of aneuploidy, remains unknown. Here, we elucidate the presence of CIN in aneuploid cB-ALL subtypes using single-cell whole-genome sequencing of primary cB-ALL samples and by generating and functionally characterizing patient-derived xenograft models (cB-ALL-PDX). We report higher rates of CIN across aneuploid than in euploid cB-ALL that strongly correlate with intraclonal chr-CNH and overall survival in mice. This association was further supported by in silico mathematical modeling. Moreover, mass-spectrometry analyses of cB-ALL-PDX revealed a "CIN signature" enriched in mitotic-spindle regulatory pathways, which was confirmed by RNA-sequencing of a large cohort of cB-ALL samples. The link between the presence of CIN in aneuploid cB-ALL and disease progression opens new possibilities for patient stratification and offers a promising new avenue as a therapeutic target in cB-ALL treatment.</p

A collection of designed peptides to target SARS-CoV-2 spike RBD-ACE2 interaction

The angiotensin-converting enzyme 2 (ACE2) is the receptor used by SARS-CoV and SARS-CoV-2 coronaviruses to attach to cells via the receptor-binding domain (RBD) of their viral spike protein. Since the start of the COVID-19 pandemic, several structures of protein complexes involving ACE2 and RBD as well as monoclonal antibodies and nanobodies have become available. We have leveraged the structural data to design peptides to target the interaction between the RBD of SARS-CoV-2 and ACE2 and SARS-CoV and ACE2, as contrasting exemplar, as well as the dimerization surface of ACE2 monomers. The peptides were modelled using our original method: PiPreD that uses native elements of the interaction between the targeted protein and cognate partner(s) that are subsequently included in the designed peptides. These peptides recapitulate stretches of residues present in the native interface plus novel and highly diverse conformations surrogating key interactions at the interface. To facilitate the access to this information we have created a freely available and dedicated web-based repository, PepI-Covid19 database, providing convenient access to this wealth of information to the scientific community with the view of maximizing its potential impact in the development of novel therapeutic and diagnostic agents

CAPS-DB: A structural classification of helix-capping motifs

The regions of the polypeptide chain immediately preceding or following an α-helix are known as Nt- and Ct cappings, respectively. Cappings play a central role stabilizing α-helices due to lack of intrahelical hydrogen bonds in the first and last turn. Sequence patterns of amino acid type preferences have been derived for cappings but the structural motifs associated to them are still unclassified. CAPS-DB is a database of clusters of structural patterns of different capping types. The clustering algorithm is based in the geometry and the (ϕ–ψ)-space conformation of these regions. CAPS-DB is a relational database that allows the user to search, browse, inspect and retrieve structural data associated to cappings. The contents of CAPS-DB might be of interest to a wide range of scientist covering different areas such as protein design and engineering, structural biology and bioinformatics. The database is accessible at: http://www.bioinsilico.org/CAPSDB.Research Councils United Kingdom Academic Fellow scheme (to N.F.F.); internal scholarship awarded by the Leeds Institute of Molecular Medicine (to J.S.M.); MICINN and FEDER (BIO2011-22568) to B.O. Funding for open access charge: Research Councils United Kingdom (RCUK

Mining drug-target and drug-adverse drug reaction databases to identify target-adverse drug reaction relationships

The level of attrition on drug discovery, particularly at advanced stages, is very high due to unexpected adverse drug reactions (ADRs) caused by drug candidates, and thus, being able to predict undesirable responses when modulating certain protein targets would contribute to the development of safer drugs and have important economic implications. On the one hand, there are a number of databases that compile information of drug-target interactions. On the other hand, there are a number of public resources that compile information on drugs and ADR. It is therefore possible to link target and ADRs using drug entities as connecting elements. Here, we present T-ARDIS (Target-Adverse Reaction Database Integrated Search) database, a resource that provides comprehensive information on proteins and associated ADRs. By combining the information from drug-protein and drug-ADR databases, we statistically identify significant associations between proteins and ADRs. Besides describing the relationship between proteins and ADRs, T-ARDIS provides detailed description about proteins along with the drug and adverse reaction information. Currently T-ARDIS contains over 3000 ADR and 248 targets for a total of more 17 000 pairwise interactions. Each entry can be retrieved through multiple search terms including target Uniprot ID, gene name, adverse effect and drug name. Ultimately, the T-ARDIS database has been created in response to the increasing interest in identifying early in the drug development pipeline potentially problematic protein targets whose modulation could result in ADRs. Database URL: http://www.bioinsilico.org/T-ARDIS.Authors acknowledge support from MINECO grant numbers RYC2015-17519 and BIO2017-85329-R

On the prediction of DNA-binding preferences of C2H2-ZF domains using structural models: application on human CTCF

Cis2-His2 zinc finger (C2H2-ZF) proteins are the largest family of transcription factors in human and higher metazoans. To date, the DNA-binding preferences of many members of this family remain unknown. We have developed a computational method to predict their DNA-binding preferences. We have computed theoretical position weight matrices (PWMs) of proteins composed by C2H2-ZF domains, with the only requirement of an input structure. We have predicted more than two-third of a single zinc-finger domain binding site for about 70% variants of Zif268, a classical member of this family. We have successfully matched between 60 and 90% of the binding-site motif of examples of proteins composed by three C2H2-ZF domains in JASPAR, a standard database of PWMs. The tests are used as a proof of the capacity to scan a DNA fragment and find the potential binding sites of transcription-factors formed by C2H2-ZF domains. As an example, we have tested the approach to predict the DNA-binding preferences of the human chromatin binding factor CTCF. We offer a server to model the structure of a zinc-finger protein and predict its PWM.Spanish Ministry of Economy (MICINN) [BIO2017-85329-R, RYC2015-17519, MDM2014-0370] and European Regional Development Fund (FEDER) [BIO2017-85329-R, RYC-2015-17519, MDM-2014-0370]; Erasmus+ Fellowship 2019 by EU (to F.Å.); Research Formation of ‘Generalitat de Catalunya’ (FI) Fellowship (to A.M)

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)

Evaluation of computationally designed peptides against TWEAK, a cytokine of the tumour necrosis factor ligand family

The tumour necrosis factor-like weak inducer of apoptosis (TWEAK) is a member of the tumour necrosis factor ligand family and has been shown to be overexpressed in tumoral cells together with the fibroblast growth factor-inducible 14 (Fn14) receptor. TWEAK-Fn14 interaction triggers a set of intracellular pathways responsible for tumour cell invasion and migration, as well as proliferation and angiogenesis. Hence, modulation of the TWEAK-Fn14 interaction is an important therapeutic goal. The targeting of protein-protein interactions by external agents, e.g., drugs, remains a substantial challenge. Given their intrinsic features, as well as recent advances that improve their pharmacological profiles, peptides have arisen as promising agents in this regard. Here, we report, by in silico structural design validated by cell-based and in vitro assays, the discovery of four peptides able to target TWEAK. Our results show that, when added to TWEAK-dependent cellular cultures, peptides cause a down-regulation of genes that are part of TWEAK-Fn14 signalling pathway. The direct, physical interaction between the peptides and TWEAK was further elucidated in an in vitro assay which confirmed that the bioactivity shown in cell-based assays was due to the targeting of TWEAK. The results presented here are framed within early pre-clinical drug development and therefore these peptide hits represent a starting point for the development of novel therapeutic agents. Our approach exemplifies the powerful combination of in silico and experimental efforts to quickly identify peptides with desirable traits.The authors received support from the Spanish Ministry of Science (MINECO) [RYC2015-17519, BIO2017-83591-R, BIO2014-57518R, BIO2017-85329-R, AGL2013-48923-C2 and AGL2017-89097-C2-R2], the ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER) [FIS-PI14/00336 and FIS-PI18/00916], the EU Action NADIR FP7-228394 and the Maria de Maeztu Program for Center of Excellence program (AEI CEX2018-000792-M.)