SBILib: a handle for protein modeling and engineering.

SUMMARY: The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library. AVAILABILITY AND IMPLEMENTATION: SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.

The N-Terminal Phosphorylation of RB by p38 Bypasses Its Inactivation by CDKs and Prevents Proliferation in Cancer Cells.

Control of the G1/S phase transition by the Retinoblastoma (RB) tumor suppressor is critical for the proliferation of normal cells in tissues, and its inactivation is one of the most fundamental events leading to cancer. Cyclin-dependent kinase (CDK) phosphorylation inactivates RB to promote cell cycle-regulated gene expression. Here we show that, upon stress, the p38 stress-activated protein kinase (SAPK) maximizes cell survival by downregulating E2F gene expression through the targeting of RB. RB undergoes selective phosphorylation by p38 in its N terminus; these phosphorylations render RB insensitive to the inactivation by CDKs. p38 phosphorylation of RB increases its affinity toward the E2F transcription factor, represses gene expression, and delays cell-cycle progression. Remarkably, introduction of a RB phosphomimetic mutant in cancer cells reduces colony formation and decreases their proliferative and tumorigenic potential in mice.

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions.

BACKGROUND: Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. RESULTS: Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. CONCLUSIONS: While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. SERVER ADDRESS: https://sbi.upf.edu/spserver/ .

Transcription Factor NRF2 as a Therapeutic Target for Chronic Diseases: A Systems Medicine Approach.

Systems medicine has a mechanism-based rather than a symptom- or organ-based approach to disease and identifies therapeutic targets in a nonhypothesis-driven manner. In this work, we apply this to transcription factor nuclear factor (erythroid-derived 2)-like 2 (NRF2) by cross-validating its position in a protein-protein interaction network (the NRF2 interactome) functionally linked to cytoprotection in low-grade stress, chronic inflammation, metabolic alterations, and reactive oxygen species formation. Multiscale network analysis of these molecular profiles suggests alterations of NRF2 expression and activity as a common mechanism in a subnetwork of diseases (the NRF2 diseasome). This network joins apparently heterogeneous phenotypes such as autoimmune, respiratory, digestive, cardiovascular, metabolic, and neurodegenerative diseases, along with cancer. Importantly, this approach matches and confirms in silico several applications for NRF2-modulating drugs validated in vivo at different phases of clinical development. Pharmacologically, their profile is as diverse as electrophilic dimethyl fumarate, synthetic triterpenoids like bardoxolone methyl and sulforaphane, protein-protein or DNA-protein interaction inhibitors, and even registered drugs such as metformin and statins, which activate NRF2 and may be repurposed for indications within the NRF2 cluster of disease phenotypes. Thus, NRF2 represents one of the first targets fully embraced by classic and systems medicine approaches to facilitate both drug development and drug repurposing by focusing on a set of disease phenotypes that appear to be mechanistically linked. The resulting NRF2 drugome may therefore rapidly advance several surprising clinical options for this subset of chronic diseases.

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.

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.

CACNA1A Mutations Causing Early Onset Ataxia: Profiling Clinical, Dysmorphic and Structural-Functional Findings.

The CACNA1A gene encodes the pore-forming α1A subunit of the voltage-gated CaV2.1 Ca2+ channel, essential in neurotransmission, especially in Purkinje cells. Mutations in CACNA1A result in great clinical heterogeneity with progressive symptoms, paroxysmal events or both. During infancy, clinical and neuroimaging findings may be unspecific, and no dysmorphic features have been reported. We present the clinical, radiological and evolutionary features of three patients with congenital ataxia, one of them carrying a new variant. We report the structural localization of variants and their expected functional consequences. There was an improvement in cerebellar syndrome over time despite a cerebellar atrophy progression, inconsistent response to acetazolamide and positive response to methylphenidate. The patients shared distinctive facial gestalt: oval face, prominent forehead, hypertelorism, downslanting palpebral fissures and narrow nasal bridge. The two α1A affected residues are fully conserved throughout evolution and among the whole human CaV channel family. They contribute to the channel pore and the voltage sensor segment. According to structural data analysis and available functional characterization, they are expected to exert gain- (F1394L) and loss-of-function (R1664Q/R1669Q) effect, respectively. Among the CACNA1A-related phenotypes, our results suggest that non-progressive congenital ataxia is associated with developmental delay and dysmorphic features, constituting a recognizable syndromic neurodevelopmental disorder.

CDK2-dependent activation of PARP-1 is required for hormonal gene regulation in breast cancer cells.

Eukaryotic gene regulation implies that transcription factors gain access to genomic information via poorly understood processes involving activation and targeting of kinases, histone-modifying enzymes, and chromatin remodelers to chromatin. Here we report that progestin gene regulation in breast cancer cells requires a rapid and transient increase in poly-(ADP)-ribose (PAR), accompanied by a dramatic decrease of cellular NAD that could have broad implications in cell physiology. This rapid increase in nuclear PARylation is mediated by activation of PAR polymerase PARP-1 as a result of phosphorylation by cyclin-dependent kinase CDK2. Hormone-dependent phosphorylation of PARP-1 by CDK2, within the catalytic domain, enhances its enzymatic capabilities. Activated PARP-1 contributes to the displacement of histone H1 and is essential for regulation of the majority of hormone-responsive genes and for the effect of progestins on cell cycle progression. Both global chromatin immunoprecipitation (ChIP) coupled with deep sequencing (ChIP-seq) and gene expression analysis show a strong overlap between PARP-1 and CDK2. Thus, progestin gene regulation involves a novel signaling pathway that connects CDK2-dependent activation of PARP-1 with histone H1 displacement. Given the multiplicity of PARP targets, this new pathway could be used for the pharmacological management of breast cancer.

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: j.planas-iglesias@warwick.ac.uk or baldo.oliva@upf.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Deciphering the role of the signal- and Sty1 kinase-dependent phosphorylation of the stress-responsive transcription factor Atf1 on gene activation.

Adaptation to stress triggers the most dramatic shift in gene expression in fission yeast (Schizosaccharomyces pombe), and this response is driven by signaling via the MAPK Sty1. Upon activation, Sty1 accumulates in the nucleus and stimulates expression of hundreds of genes via the nuclear transcription factor Atf1, including expression of atf1 itself. However, the role of stress-induced, Sty1-mediated Atf1 phosphorylation in transcriptional activation is unclear. To this end, we expressed Atf1 phosphorylation mutants from a constitutive promoter to uncouple Atf1 activity from endogenous, stress-activated Atf1 expression. We found that cells expressing a nonphosphorylatable Atf1 variant are sensitive to oxidative stress because of impaired transcription of a subset of stress genes whose expression is also controlled by another transcription factor, Pap1. Furthermore, cells expressing a phospho-mimicking Atf1 mutant display enhanced stress resistance, and although expression of the Pap1-dependent genes still relied on stress induction, another subset of stress-responsive genes was constitutively expressed in these cells. We also observed that, in cells expressing the phospho-mimicking Atf1 mutant, the presence of Sty1 was completely dispensable, with all stress defects of Sty1-deficient cells being suppressed by expression of the Atf1 mutant. We further demonstrated that Sty1-mediated Atf1 phosphorylation does not stimulate binding of Atf1 to DNA but, rather, establishes a platform of interactions with the basal transcriptional machinery to facilitate transcription initiation. In summary, our results provide evidence that Atf1 phosphorylation by the MAPK Sty1 is required for oxidative stress responses in fission yeast cells by promoting transcription initiation.

Phospho-mimicking Atf1 mutants bypass the transcription activating function of the MAP kinase Sty1 of fission yeast.

Stress-dependent activation of signaling cascades is often mediated by phosphorylation events, but the exact nature and role of these phosphorelays are frequently poorly understood. Here, we review which are the consequences of the stress-dependent phosphorylation of a transcription factor on gene activation. In fission yeast, the MAP kinase Sty1 is activated upon several environmental hazards and promotes cell adaptation and survival, greatly through activation of a gene program mediated by the transcription factor Atf1. Although described decades ago, the role of the phosphorylation of Atf1 by Sty1 is still a matter of debate. We present here a brief review of recent data, obtained through the characterization of several phosphorylation mutant derivatives of Atf1, demonstrating that Atf1 phosphorylation does not stabilize the factor nor stimulates its binding to DNA. Rather, it provides a structural platform of interaction with the transcriptional machinery. Based on these findings, future work will establish how this phosphorylated trans-activation domain promotes the massive gene expression shift allowing cellular adaptation to stress.

Targeting Importin-α7 as a Therapeutic Approach against Pandemic Influenza Viruses.

Viral drug resistance is believed to be less likely to occur if compounds are directed against cellular rather than viral proteins. In this study, we analyzed the feasibility of a crucial viral replication factor, namely, importin-α7, as a cellular drug target to combat pandemic influenza viruses. Surprisingly, only five viral lung-to-lung passages were required to achieve 100% lethality in importin-α7⁻/⁻ mice that otherwise are resistant. Viral escape from importin-α7 requirement was mediated by five mutations in the viral ribonucleoprotein complex and the surface glycoproteins. Moreover, the importin-α7⁻/⁻ mouse-adapted strain became even more virulent for wild-type mice than the parental strain. These studies show that targeting host proteins may still result in viral escape by alternative pathways, eventually giving rise to even more virulent virus strains. Thus, therapeutic intervention strategies should consider a multitarget approach to reduce viral drug resistance. IMPORTANCE Here, we investigated the long-standing hypothesis based on in vitro studies that viral drug resistance occurrence is less likely if compounds are directed against cellular rather than viral proteins. Here, we challenged this hypothesis by analyzing, in an in vivo animal model, the feasibility of targeting the cellular factor importin-α7, which is crucial for human influenza virus replication and pathogenesis, as an efficient antiviral strategy against pandemic influenza viruses. In summary, our studies suggest that resistance against cellular factors is possible in vivo, and the emergence of even more virulent viral escape variants calls for particular caution. Thus, therapeutic intervention strategies should consider a multitarget approach using compounds against viral as well as cellular factors to reduce the risk of viral drug resistance and potentially increased virulence.

Knowledge-based modeling of peptides at protein interfaces: PiPreD.

MOTIVATION: Protein-protein interactions (PPIs) underpin virtually all cellular processes both in health and disease. Modulating the interaction between proteins by means of small (chemical) agents is therefore a promising route for future novel therapeutic interventions. In this context, peptides are gaining momentum as emerging agents for the modulation of PPIs. RESULTS: We reported a novel computational, structure and knowledge-based approach to model orthosteric peptides to target PPIs: PiPreD. PiPreD relies on a precompiled and bespoken library of structural motifs, iMotifs, extracted from protein complexes and a fast structural modeling algorithm driven by the location of native chemical groups on the interface of the protein target named anchor residues. PiPreD comprehensive and systematically samples the entire interface deriving peptide conformations best suited for the given region on the protein interface. PiPreD complements the existing technologies and provides new solutions for the disruption of selected interactions. AVAILABILITY AND IMPLEMENTATION: Database and accessory scripts and programs are available upon request to the authors or at http://www.bioinsilico.org/PIPRED. CONTACT: narcis.fernandez@gmail.com.

InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research.

Virtually all the biological processes that occur inside or outside cells are mediated by protein-protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com).

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. ß-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.

The crystal structure of a cardiovirus RNA-dependent RNA polymerase reveals an unusual conformation of the polymerase active site.

UNLABELLED: Encephalomyocarditis virus (EMCV) is a member of the Cardiovirus genus within the large Picornaviridae family, which includes a number of important human and animal pathogens. The RNA-dependent RNA polymerase (RdRp) 3Dpol is a key enzyme for viral genome replication. In this study, we report the X-ray structures of two different crystal forms of the EMCV RdRp determined at 2.8- and 2.15-Å resolution. The in vitro elongation and VPg uridylylation activities of the purified enzyme have also been demonstrated. Although the overall structure of EMCV 3Dpol is shown to be similar to that of the known RdRps of other members of the Picornaviridae family, structural comparisons show a large reorganization of the active-site cavity in one of the crystal forms. The rearrangement affects mainly motif A, where the conserved residue Asp240, involved in ribonucleoside triphosphate (rNTP) selection, and its neighbor residue, Phe239, move about 10 Å from their expected positions within the ribose binding pocket toward the entrance of the rNTP tunnel. This altered conformation of motif A is stabilized by a cation-π interaction established between the aromatic ring of Phe239 and the side chain of Lys56 within the finger domain. Other contacts, involving Phe239 and different residues of motif F, are also observed. The movement of motif A is connected with important conformational changes in the finger region flanked by residues 54 to 63, harboring Lys56, and in the polymerase N terminus. The structures determined in this work provide essential information for studies on the cardiovirus RNA replication process and may have important implications for the development of new antivirals targeting the altered conformation of motif A. IMPORTANCE: The Picornaviridae family is one of the largest virus families known, including many important human and animal pathogens. The RNA-dependent RNA polymerase (RdRp) 3Dpol is a key enzyme for picornavirus genome replication and a validated target for the development of antiviral therapies. Solving the X-ray structure of the first cardiovirus RdRp, EMCV 3Dpol, we captured an altered conformation of a conserved motif in the polymerase active site (motif A) containing the aspartic acid residue involved in rNTP selection and binding. This altered conformation of motif A, which interferes with the correct positioning of the rNTP substrate in the active site, is stabilized by a number of residues strictly conserved among picornaviruses. The rearrangements observed suggest that this motif A segment is a dynamic element that can be modulated by external effectors, either activating or inhibiting enzyme activity, and this type of modulation appears to be general to all picornaviruses.

GUILDify: a web server for phenotypic characterization of genes through biological data integration and network-based prioritization algorithms.

SUMMARY: Determining genetic factors underlying various phenotypes is hindered by the involvement of multiple genes acting cooperatively. Over the past years, disease-gene prioritization has been central to identify genes implicated in human disorders. Special attention has been paid on using physical interactions between the proteins encoded by the genes to link them with diseases. Such methods exploit the guilt-by-association principle in the protein interaction network to uncover novel disease-gene associations. These methods rely on the proximity of a gene in the network to the genes associated with a phenotype and require a set of initial associations. Here, we present GUILDify, an easy-to-use web server for the phenotypic characterization of genes. GUILDify offers a prioritization approach based on the protein-protein interaction network where the initial phenotype-gene associations are retrieved via free text search on biological databases. GUILDify web server does not restrict the prioritization to any predefined phenotype, supports multiple species and accepts user-specified genes. It also prioritizes drugs based on the ranking of their targets, unleashing opportunities for repurposing drugs for novel therapies. AVAILABILITY AND IMPLEMENTATION: Available online at http://sbi.imim.es/GUILDify.php

A transcriptome-proteome integrated network identifies endoplasmic reticulum thiol oxidoreductase (ERp57) as a hub that mediates bone metastasis.

Bone metastasis is the most common distant relapse in breast cancer. The identification of key proteins involved in the osteotropic phenotype would represent a major step toward the development of new prognostic markers and therapeutic improvements. The aim of this study was to characterize functional phenotypes that favor bone metastasis in human breast cancer. We used the human breast cancer cell line MDA-MB-231 and its osteotropic BO2 subclone to identify crucial proteins in bone metastatic growth. We identified 31 proteins, 15 underexpressed and 16 overexpressed, in BO2 cells compared with parental cells. We employed a network-modeling approach in which these 31 candidate proteins were prioritized with respect to their potential in metastasis formation, based on the topology of the protein-protein interaction network and differential expression. The protein-protein interaction network provided a framework to study the functional relationships between biological molecules by attributing functions to genes whose functions had not been characterized. The combination of expression profiles and protein interactions revealed an endoplasmic reticulum-thiol oxidoreductase, ERp57, functioning as a hub that retained four down-regulated nodes involved in antigen presentation associated with the human major histocompatibility complex class I molecules, including HLA-A, HLA-B, HLA-E, and HLA-F. Further analysis of the interaction network revealed an inverse correlation between ERp57 and vimentin, which influences cytoskeleton reorganization. Moreover, knockdown of ERp57 in BO2 cells confirmed its bone organ-specific prometastatic role. Altogether, ERp57 appears as a multifunctional chaperone that can regulate diverse biological processes to maintain the homeostasis of breast cancer cells and promote the development of bone metastasis.

Protein kinase CK2-dependent phosphorylation of the human Regulators of Calcineurin reveals a novel mechanism regulating the calcineurin-NFATc signaling pathway.

Cyclosporine A and FK506 produce immunosuppression by blocking calcineurin phosphatase activity and consequently activation of cytosolic Nuclear Factor of Activated T-cell (NFATc) transcription factor. Due to the chronic toxicity associated with their administration, the development of more specific immunosuppressants is currently an important unmet medical need. In this context, an immunosuppressant peptide derived from the CIC motif of the human Regulators of Calcineurin (RCAN) proteins has been shown to inhibit NFATc signaling without affecting general phosphatase activity of calcineurin. Here we show that protein kinase CK2 phosphorylates a conserved serine residue within the CIC motif of vertebrate RCANs, which increases its affinity for calcineurin and consequently its inhibition of NFATc-dependent gene expression in activated T-cells. Molecular modeling studies have led us to identify a positively charged interaction site on the surface of calcineurin where the phosphorylated serine residue of the CIC motif would normally locate. Finally, we have also identified RCAN3 as a new phosphoprotein with multiple phosphorylation sites. Therefore, our findings reveal for the first time a novel molecular mechanism underlying the regulation of calcineurin-NFATc signaling by means of phosphorylation of the CIC motif of RCAN proteins. The knowledge of how RCAN proteins modulate the calcineurin-NFATc pathway paves the way for the development of potent novel selective immunosuppressant drugs.

iLoops: a protein-protein interaction prediction server based on structural features.

SUMMARY: Protein-protein interactions play a critical role in many biological processes. Despite that, the number of servers that provide an easy and comprehensive method to predict them is still limited. Here, we present iLoops, a web server that predicts whether a pair of proteins can interact using local structural features. The inputs of the server are as follows: (i) the sequences of the query proteins and (ii) the pairs to be tested. Structural features are assigned to the query proteins by sequence similarity. Pairs of structural features (formed by loops or domains) are classified according to their likelihood to favor or disfavor a protein-protein interaction, depending on their observation in known interacting and non-interacting pairs. The server evaluates the putative interaction using a random forest classifier. AVAILABILITY: iLoops is available at http://sbi.imim.es/iLoops.php CONTACT: baldo.oliva@upf.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.