Elongation of the C-terminal domain of an anti-amyloid β single-chain variable fragment increases its thermodynamic stability and decreases its aggregation tendency

This work was supported by Instituto de Salud Carlos III (FIS-PI10-00975), Generalitat de Catalunya (SGR 2009-00761), FEDER (BIO2011-22568) and MICIN (BES-2009-024653). G.R-H is supported by a MAEC-AECI fellowship and M.M-A by a PIF (UAB) fellowship.Amyloid β (Aβ) immunotherapy is considered a promising approach to Alzheimer disease treatment. In contrast to the use of complete antibodies, administration of single-chain variable fragments (scFv) has not been associated with either eningoencephalitis or cerebral hemorrhage. ScFv-h3D6 is known to preclude cytotoxicity of the Aβ 1-42 peptide by removing its oligomers from the amyloid pathway. As is the case for other scFv molecules, the recombinant production of scFv-h3D6 is limited by its folding and stability properties. Here, we show that its urea-induced unfolding pathway is characterized by the presence of an intermediate state composed of the unfolded VL domain and the folded VH domain, which suggests the VL domain as a target for thermodynamic stability redesign. The modeling of the 3D structure revealed that the VL domain, located at the C-terminal of the molecule, was ending before its latest β-strand was completed. Three elongation mutants, beyond VL-K107, showed increased thermodynamic stability and lower aggregation tendency, as determined from urea denaturation experiments and Fourier-transform infrared spectroscopy, respectively. Because the mutants maintained the capability of removing Aβ-oligomers from the amyloid pathway, we expect these traits to increase the half-life of scFv-h3D6 in vivo and, consequently, to decrease the effective doses. Our results led to the improvement of a potential Alzheimer disease treatment and may be extrapolated to other class-I scFv molecules of therapeutic interest

Schlafen 12 restricts HIV-1 latency reversal by a codon-usage dependent post-transcriptional block in CD4+ T cells

Latency is a major barrier towards virus elimination in HIV-1-infected individuals. Yet, the mechanisms that contribute to the maintenance of HIV-1 latency are incompletely understood. Here we describe the Schlafen 12 protein (SLFN12) as an HIV-1 restriction factor that establishes a post-transcriptional block in HIV-1-infected cells and thereby inhibits HIV-1 replication and virus reactivation from latently infected cells. The inhibitory activity is dependent on the HIV-1 codon usage and on the SLFN12 RNase active sites. Within HIV-1-infected individuals, SLFN12 expression in PBMCs correlated with HIV-1 plasma viral loads and proviral loads suggesting a link with the general activation of the immune system. Using an RNA FISH-Flow HIV-1 reactivation assay, we demonstrate that SLFN12 expression is enriched in infected cells positive for HIV-1 transcripts but negative for HIV-1 proteins. Thus, codon-usage dependent translation inhibition of HIV-1 proteins participates in HIV-1 latency and can restrict the amount of virus release after latency reversal. In cell lines and HIV-1 patient PBMCs, the Schlafen 12 protein (SLFN12) is shown to be an HIV-1 restriction factor that inhibits HIV-1 replication and virus reactivatio

Human Albumin Impairs Amyloid β-peptide Fibrillation Through its C-terminus: From docking Modeling to Protection Against Neurotoxicity in Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative process characterized by the accumulation of extracellular deposits of amyloid β-peptide (Aβ), which induces neuronal death. Monomeric Aβ is not toxic but tends to aggregate into β-sheets that are neurotoxic. Therefore to prevent or delay AD onset and progression one of the main therapeutic approaches would be to impair Aβ assembly into oligomers and fibrils and to promote disaggregation of the preformed aggregate. Albumin is the most abundant protein in the cerebrospinal fluid and it was reported to bind Aβ impeding its aggregation. In a previous work we identified a 35-residue sequence of clusterin, a well-known protein that binds Aβ, that is highly similar to the C-terminus (CTerm) of albumin. In this work, the docking experiments show that the average binding free energy of the CTerm-Aβ1–42 simulations was significantly lower than that of the clusterin-Aβ1–42 binding, highlighting the possibility that the CTerm retains albumin's binding properties. To validate this observation, we performed in vitro structural analysis of soluble and aggregated 1 μM Aβ1–42 incubated with 5 μM CTerm, equimolar to the albumin concentration in the CSF. Reversed-phase chromatography and electron microscopy analysis demonstrated a reduction of Aβ1–42 aggregates when the CTerm was present. Furthermore, we treated a human neuroblastoma cell line with soluble and aggregated Aβ1–42 incubated with CTerm obtaining a significant protection against Aβ-induced neurotoxicity. These in silico and in vitro data suggest that the albumin CTerm is able to impair Aβ aggregation and to promote disassemble of Aβ aggregates protecting neurons

Splitting statistical potentials into meaningful scoring functions: testing the prediction of near-native structures from decoy conformations

Background: Recent advances on high-throughput technologies have produced a vast amount of protein sequences, while the number of high-resolution structures has seen a limited increase. This has impelled the production of many strategies to built protein structures from its sequence, generating a considerable amount of alternative models. The selection of the closest model to the native conformation has thus become crucial for structure prediction. Several methods have been developed to score protein models by energies, knowledge-based potentials and combination of both./nResults: Here, we present and demonstrate a theory to split the knowledge-based potentials in scoring terms biologically meaningful and to combine them in new scores to predict near-native structures. Our strategy allows circumventing the problem of defining the reference state. In this approach we give the proof for a simple and linear application that can be further improved by optimizing the combination of Zscores. Using the simplest composite score () we obtained predictions similar to state-of-the-art methods. Besides, our approach has the advantage of identifying the most relevant terms involved in the stability of the protein structure. Finally, we also use the composite Zscores to assess the conformation of models and to detect local errors./nConclusion: We have introduced a method to split knowledge-based potentials and to solve the problem of defining a reference state. The new scores have detected near-native structures as accurately as state-of-art methods and have been successful to identify wrongly modeled regions of many near-native conformations.This work was supported by the Spanish Ministry of Science and Innovation (MICINN) with PROFIT grants PSE-0100000-2007 and PSE-0100000-2009. BO acknowledges support received from MICINN grant BIO2008-0205. PA acknowledges support received from MICINN grant BIO2007-62426 and the European Comission under FP7 Grant Agreement 223101 (AntiPathoGN

Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization

Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimer's disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.Departament d’Educació i Universitats de la Generalitat de Catalunya i del Fons Social Europeu (Department of Education and Universities of the Generalitat of Catalonia and the European Social Fons). Spanish Ministry of Science and Innovation (MICINN), FEDER (Fonds Européen de Développement Régional) BIO2008-0205, BIO2011-22568, PSE-0100000-2007, and PSE-0100000-2009; and by EU grant EraSysbio+(SHIPREC) Euroinvestigación (EUI2009-04018

Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes

Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.Spanish Ministry of Science and Innovation (MICINN) FEDER BIO2011-22568 (http://www.mineco.gob.es/portal/site/mineco/); and by EU grantEraSysbio+(SHIPREC http://www.erasysbio.net/index.php?index=279) Euroinvestigación (EUI2009-04018

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

Mapping the mutual information network of enzymatic families in the protein structure to unveil functional features

Amino acids committed to a particular function correlate tightly along evolution and tend to form clusters in the 3D structure of the protein. Consequently, a protein can be seen as a network of co-evolving clusters of residues. The goal of this work is two-fold: first, we have combined mutual information and structural data to describe the amino acid networks within a protein and their interactions. Second, we have investigated how this information can be used to improve methods of prediction of functional residues by reducing the search space. As a main result, we found that clusters of co-evolving residues related to the catalytic site of an enzyme have distinguishable topological properties in the network. We also observed that these clusters usually evolve independently, which could be related to a fail-safe mechanism. Finally, we discovered a significant enrichment of functional residues (e.g. metal binding, susceptibility to detrimental mutations) in the clusters, which could be the foundation of new prediction toolsThis work was supported by grants from the Spanish Ministry of Science and Innovation and the Argentine Ministry of Science AR2009-0015. BO and DA acknowledge support from the European Regional Development Fund BIO2008-0205. CMB acknowledges support from Consejo Nacional de Investigaciones Científicas y Técnica