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Oncogenic RAS proteins are important for driving tumour formation, and for maintenance of the transformed phenotype, and thus their relevance as a cancer therapeutic target is undeniable. We focused here on obtaining peptidomimetics, which have good pharmacological properties, to block Ras–effector interaction. Computational analysis was used to identify hot spots of RAS relevant for these interactions and to screen a library of peptidomimetics. Nine compounds were synthesized and assayed for their activity as RAS inhibitors in cultured cells. Most of them induced a reduction in ERK and AKT activation by EGF, a marker of RAS activity. The most potent inhibitor disrupted Raf and PI3K interaction with oncogenic KRAS, corroborating its mechanism of action as an inhibitor of protein–protein interactions, and thus validating our computational methodology. Most interestingly, improvement of one of the compounds allowed us to obtain a peptidomimetic that decreased the survival of pancreatic cancer cell lines harbouring oncogenic KRAS.

The 6th CAPRI edition included new modelling challenges, such as the prediction of protein-peptide complexes, and the modelling of homo-oligomers and domain-domain interactions as part of the first joint CASP-CAPRI experiment. Other non-standard targets included the prediction of interfacial water positions and the modelling of the interactions between proteins and nucleic acids. We have participated in all proposed targets of this CAPRI edition both as predictors and as scorers, with new protocols to efficiently use our docking and scoring scheme pyDock in a large variety of scenarios. In addition, we have participated for the first time in the server section, with our recently developed webserver, pyDockWeb. Excluding the CASP-CAPRI cases, we submitted acceptable models (or better) for 7 out of the 18 evaluated targets as predictors, 4 out of the 11 targets as scorers, and 6 out of the 18 targets as servers. The overall success rates were below those in past CAPRI editions. This shows the challenging nature of this last edition, with many difficult targets for which no participant submitted a single acceptable model. Interestingly, we submitted acceptable models for 83% of the evaluated protein-peptide targets. As for the 25 cases of the CASP-CAPRI experiment, in which we used a larger variety of modelling techniques (template-based, symmetry restraints, literature information, etc.), we submitted acceptable models for 56% of the targets. In summary, this CAPRI edition showed that pyDock scheme can be efficiently adapted to the increasing variety of problems that the protein interactions field is currently facing. This article is protected by copyright. All rights reserved.

Cardiovascular disease (CVD) is a leading cause of death worldwide. Approximately 60% of patients treated with low‐density lipoprotein (LDL)‐lowering drug treatments, with on‐target plasma cholesterol levels, are still suffering clinical acute ischemic events. Mechanisms, such as LDL aggregation, underlie extracellular and intracellular cholesterol accumulation in the vasculature. A peptide sequence (P3) of the low‐density lipoprotein receptor‐related protein 1 (LRP1) efficiently protects LDL from sphingomyelinase (SMase‐) and phospholipase A2 (PLA2)‐induced LDL aggregation. The aim is to design families of peptide derivatives from P3 with enhanced potency and proteolytic stability. New peptides are designed through in silico conformational sampling and ApoB‐100 molecular docking, and are tested in dual (biochemical‐cellular) screening assays. A total of 46 new peptides including linear, fragment, cyclic, and alanine scanning derivatives are generated through two consecutive optimization rounds. Structurally and functionally optimized peptides contain hotspot residues that are replaced by alanine. This strategy confers an increased capacity to form prone alpha‐helix conformations crucial for the electrostatic interaction with ApoB‐100. These new compounds are highly efficient at inhibiting LDL aggregation and human coronary vascular smooth muscle cell‐cholesteryl ester loading and should be studied in preclinical models of atherosclerosis., A.B.-A. and C.P. contributed equally to this work. The authors thank Dr. Ignasi Gich, Professor of Clinical Pharmacology and Therapeutics and Researcher of Sant Pau Biomedical Research Institute (IIB-SantPau) and CIBER Epidemiología y Salud Pública (CIBERESP), for this help in statistical analysis and graph representations. The Ministry of Science and Innovation of Spain, in the framework of the State Plan of Scientific and Technical Innovation Investigation 2013–2016, awarded funding to the project “DEVELOPMENT OF AN INNOVATIVE THERAPY FOR THE TREATMENT OF THE ATHEROSCLEROSIS THROUGH INHIBITION OF CHOLESTEROL VASCULAR ACCUMULATION” led by IPROTEOS SL with File No. RTC-2016-5078-1. Support was also received from the Fundació MARATÓ TV3 Project 201521-10 (to VLl-C), and FIS PI18/01584 (to VLl-C) from the Instituto de Salud Carlos III (ISCIII) and cofinanced with ERDFs. Support was also received from Ministerio de Economía y Competitividad to DdG-C (IJCI-2016-29393). CIBER Enfermedades Cardiovasculares (CIBERCV; CB16/1100403 (DdG-C, VLl-C) are projects run by the Instituto de Salud Carlos III (ISCIII). The authors also acknowledge the support from “Secretaria d’Universitats i Recerca del Departament d’Economia I Coneixement de la Generalitat de Catalunya” (2017SGR946 to VLl-C).

Aggregated LDL is the first ligand reported to interact with the cluster II CR9 domain of low-density lipoprotein receptor-related protein 1 (LRP1). In particular, the C-terminal half of domain CR9, comprising the region Gly1127-Cys1140 exclusively recognizes aggregated LDL and it is crucial for aggregated LDL binding. Our aim was to study the effect of the sequence Gly1127-Cys1140 (named peptide LP3 and its retro-enantio version, named peptide DP3) on the structural characteristics of sphingomyelinase- (SMase) and phospholipase 2 (PLA2)-modified LDL particles. Turbidimetry, gel filtration chromatography (GFC) and transmission electronic microscopy (TEM) analysis showed that LP3 and DP3 peptides strongly inhibited SMase- and PLA2-induced LDL aggregation. Nondenaturing polyacrylamide gradient gel electrophoresis (GGE), agarose gel electrophoresis and high-performance thin-layer chromatography (HPTLC) indicated that LP3 and DP3 prevented SMase-induced alterations in LDL particle size, electric charge and phospholipid content, respectively, but not those induced by PLA2. Western blot analysis showed that LP3 and DP3 counteracted changes in ApoB-100 conformation induced by the two enzymes. LDL proteomics (LDL trypsin digestion followed by mass spectroscopy) and computational modeling methods evidenced that peptides preserve ApoB-100 conformation due to their electrostatic interactions with a basic region of ApoB-100. These results demonstrate that LRP1-derived peptides are protective against LDL aggregation, even in conditions of extreme lipolysis, through their capacity to bind to ApoB-100 regions critical for ApoB-100 conformational preservation. These results suggests that these LRP1(CR9) derived peptides could be promising tools to prevent LDL aggregation induced by the main proteolytic enzymes acting in the arterial intima., The Ministry of Science and Innovation of Spain, in the framework of the State Plan of Scientific and Technical Innovation Investigation 2013–2016, awarded funding to the project “DEVELOPMENT OF AN INNOVATIVE THERAPY FOR THE TREATMENT OF THE ATHEROSCLEROSIS THROUGH INHIBITION OF CHOLESTEROL VASCULAR ACCUMULATION”, led by IPROTEOS SL with file number RTC-2016-5078-1. This project was also financed by the Ministry of Economy, Industry and Competitiveness (MINECO) in the framework of the Subprogram RETOS-COLABORACIÓN, 2016 call. The project is also co-financed by the European Union with the objective to promoting the technological development, innovation and quality research. Support was also received from SAF2017-89613R (to SV), co-financed by the European Regional Development Fund (ERDF), the Fundació la Marató de TV3 Project 201521-10 (to VLlC), FIS PI13/00364 and PI16/00471 (to JSQ) and FIS PI18/01584 (to VLlC) from the Instituto de Salud Carlos III (ISCIII) and co-financed with ERDF. Support was also received from Ministerio de Economía y Competitividad to DdG-C (IJCI-2016-29393). CIBER Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM; CB07/08/0016 (JSQ) and CIBER de Enfermedades Cardiovasculares (CIBERCV; CB16/1100403 (DdG-C, VLlC) are projects run by the Instituto de Salud Carlos III (ISCIII). The CRG/UPF Proteomics Unit is member of ProteoRed PRB3 consortium which is supported by grant PT17/0019 of the PE I+D+i 2013–2016 from the Instituto de Salud Carlos III (ISCIII) and ERDF. We also acknowledge support from the Spanish Ministry of Economy and Competitiveness, “Centro de Excelencia Severo Ochoa 2013–2017”, SEV-2012-0208, and “Secretaria d'Universitats i Recerca del Departament d'Economia I Coneixement de la Generalitat de Catalunya” (2017SGR595 to ES and 2017SGR946 to VLl-C). SB, JLS-Q, AR-U, DdG-C and VL-C are members of the Group of Vascular Biology of the Spanish Society of Atherosclerosis (SEA).

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.

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.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 A, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

An important aspect of protein functionality is the formation of specific complexes with other proteins, which are involved in the majority of biological processes. The functional characterization of such interactions at molecular level is necessary, not only to understand biological and pathological phenomena but also to design improved, or even new interfaces, or to develop new therapeutic approaches. X-ray crystallography and NMR spectroscopy have increased the number of 3D protein complex structures deposited in the Protein Data Bank (PDB). However, one of the more challenging objectives in biological research is to functionally characterize protein interactions and thus identify residues that significantly contribute to the binding. Considering that the experimental characterization of protein interfaces remains expensive, time-consuming, and labor-intensive, computational approaches represent a significant breakthrough in proteomics, assisting or even replacing experimental efforts. Thanks to the technological advances in computing and data processing, these techniques now cover a vast range of protocols, from the estimation of the evolutionary conservation of amino acid positions in a protein, to the energetic contribution of each residue to the binding affinity. In this chapter, we review several existing computational protocols to model the phylogenetic, structural, and energetic properties of residues within protein-protein interfaces.

An important aspect of protein functionality is the formation of specific complexes with other proteins, which are involved in the majority of biological processes. The functional characterization of such interactions at molecular level is necessary, not only to understand biological and pathological phenomena but also to design improved, or even new interfaces, or to develop new therapeutic approaches. X-ray crystallography and NMR spectroscopy have increased the number of 3D protein complex structures deposited in the Protein Data Bank (PDB). However, one of the more challenging objectives in biological research is to functionally characterize protein interactions and thus identify residues that significantly contribute to the binding. Considering that the experimental characterization of protein interfaces remains expensive, timeconsuming, and labor-intensive, computational approaches represent a significant breakthrough in proteomics, assisting or even replacing experimental efforts. Thanks to the technological advances in computing and data processing, these techniques now cover a vast range of protocols, from the estimation of the evolutionary conservation of amino acid positions in a protein, to the energetic contribution of each residue to the binding affinity. In this chapter, we review several existing computational protocols to model the phylogenetic, structural, and energetic properties of residues within protein–protein interfaces.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein–protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. We are most grateful to the PDBe at the European Bioinformatics Institute in Hinxton, UK, for hosting the CAPRI website. Our deepest thanks go to all the structural biologists and to the following structural genomics initiatives: Northeast Structural Genomics Consortium, Joint Center for Structural Genomics, NatPro PSI:Biology, New York Structural Genomics Research Center, Midwest Center for Structural Genomics, Structural Genomics Consortium, for contributing the targets for this joint CASP-CAPRI experiment. MFL acknowledges support from the FRABio FR3688 Research Federation “Structural & Functional Biochemistry of Biomolecular Assemblies.”

The sixth CAPRI edition included new modeling challenges, such as the prediction of protein-peptide complexes, and the modeling of homo-oligomers and domain-domain interactions as part of the first joint CASP-CAPRI experiment. Other non-standard targets included the prediction of interfacial water positions and the modeling of the interactions between proteins and nucleic acids. We have participated in all proposed targets of this CAPRI edition both as predictors and as scorers, with new protocols to efficiently use our docking and scoring scheme pyDock in a large variety of scenarios. In addition, we have participated for the first time in the servers section, with our recently developed webserver, pyDockWeb. Excluding the CASP-CAPRI cases, we submitted acceptable models (or better) for 7 out of the 18 evaluated targets as predictors, 4 out of the 11 targets as scorers, and 6 out of the 18 targets as servers. The overall success rates were below those in past CAPRI editions. This shows the challenging nature of this last edition, with many difficult targets for which no participant submitted a single acceptable model. Interestingly, we submitted acceptable models for 83% of the evaluated protein-peptide targets. As for the 25 cases of the CASP-CAPRI experiment, in which we used a larger variety of modeling techniques (template-based, symmetry restraints, literature information, etc.), we submitted acceptable models for 56% of the targets. In summary, this CAPRI edition showed that pyDock scheme can be efficiently adapted to the increasing variety of problems that the protein interactions field is currently facing. Proteins 2017; 85:487-496. © 2016 Wiley Periodicals, Inc.

A challenge for microbial pathogens is to assure that their translocated effector proteins target only the correct host cell compartment during infection. The Legionella pneumophila effector vacuolar protein sorting inhibitor protein D (VipD) localizes to early endosomal membranes and alters their lipid and protein composition, thereby protecting the pathogen from endosomal fusion. This process requires the phospholipase A1 (PLA1) activity of VipD that is triggered specifically on VipD binding to the host cell GTPase Rab5, a key regulator of endosomes. Here, we present the crystal structure of VipD in complex with constitutively active Rab5 and reveal the molecular mechanism underlying PLA1 activation. An active site-obstructing loop that originates from the C-terminal domain of VipD is repositioned on Rab5 binding, thereby exposing the catalytic pocket within the N-terminal PLA1 domain. Substitution of amino acid residues located within the VipD–Rab5 interface prevented Rab5 binding and PLA1 activation and caused a failure of VipD mutant proteins to target to Rab5-enriched endosomal structures within cells. Experimental and computational analyses confirmed an extended VipD-binding interface on Rab5, explaining why this L. pneumophila effector can compete with cellular ligands for Rab5 binding. Together, our data explain how the catalytic activity of a microbial effector can be precisely linked to its subcellular localization.

The tumor suppressor p53 regulates the expression of genes involved in cell cycle progression, senescence and apoptosis. Here, we investigated the effect of single point mutations in the oligomerization domain (OD) on tetramerization, transcription, ubiquitylation and stability of p53. As predicted by docking and molecular dynamics simulations, p53 OD mutants show functional defects on transcription, Mdm2-dependent ubiquitylation and 26S proteasome-mediated degradation. However, mutants unable to form tetramers are well degraded by the 20S proteasome. Unexpectedly, despite the lower structural stability compared to WT p53, p53 OD mutants form heterotetramers with WT p53 when expressed transiently or stably in cells wild type or null for p53. In consequence, p53 OD mutants interfere with the capacity of WT p53 tetramers to be properly ubiquitylated and result in changes of p53-dependent protein expression patterns, including the pro-apoptotic proteins Bax and PUMA under basal and adriamycin-induced conditions. Importantly, the patient derived p53 OD mutant L330R (OD1) showed the more severe changes in p53-dependent gene expression. Thus, in addition to the well-known effects on p53 stability, ubiquitylation defects promote changes in p53-dependent gene expression with implications on some of its functions.

Two errors were made in this article. First, a reference was omitted from the second sentence of the fourth paragraph in the Introduction section. The sentence should read: SidD lacks any obvious sequence homology with the AT-N or other known proteins, although fold recognition analysis of the N-terminal portion of SidD predicted limited resemblance with members of the metal-dependent protein phosphatase (PPM) family (Rigden 2011). The reference is: Rigden DJ. (2011) Identification and modelling of a PPM protein phosphatase fold in the Legionella pneumophila deAMPylase SidD. FEBS Lett. 585:2749-54. doi: 10.1016/j.febslet.2011.08.006. Second, there were some omissions from the acknowledgements section, which should instead read as follows: We thank K. B. Decker, Y. Lin, and A.H. Gaspar for comments on the manuscript, and A. Vidaurrazaga for assistance with biochemical experiments. This study made use of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) under the Block Allocation Group (BAG) MX1420, the Diamond Light Source (Oxfordshire, UK) under the rapid access mode MX7512 and BAG MX8302, the beamline PROXIMA1 at the SOLEIL synchrotron (Saint-Aubin, France), the X-ray crystallography platform and proteomics platform (member of CIBERehd and ProteoRed-ISCIII) at CIC bioGUNE (Derio, Spain), and the SGIker analytical facility at UPV/EHU (Leioa, Spain). We thank all the staff from these facilities for technical and human support. Access to synchrotron facilities was part funded by the the BioStruct-X project (proposal number 2460).

In addition to protein-protein docking, this CAPRI edition included new challenges, like protein-water and protein-sugar interactions, or the prediction of binding affinities and ΔΔG changes upon mutation. Regarding the standard protein-protein docking cases, our approach, mostly based on the pyDock scheme, submitted correct models as predictors and as scorers for 67% and 57% of the evaluated targets, respectively. In this edition, available information on known interface residues hardly made any difference for our predictions. In one of the targets, the inclusion of available experimental small-angle X-ray scattering (SAXS) data using our pyDockSAXS approach slightly improved the predictions. In addition to the standard protein-protein docking assessment, new challenges were proposed. One of the new problems was predicting the position of the interface water molecules, for which we submitted models with 20% and 43% of the water-mediated native contacts predicted as predictors and scorers, respectively. Another new problem was the prediction of protein-carbohydrate binding, where our submitted model was very close to being acceptable. A set of targets were related to the prediction of binding affinities, in which our pyDock scheme was able to discriminate between natural and designed complexes with area under the curve = 83%. It was also proposed to estimate the effect of point mutations on binding affinity. Our approach, based on machine learning methods, showed high rates of correctly classified mutations for all cases. The overall results were highly rewarding, and show that the field is ready to move forward and face new interesting challenges in interactomics. Proteins 2013; 81:2192-2200. © 2013 Wiley Periodicals, Inc.

The covalent attachment of adenosine monophosphate (AMP) to proteins, a process called AMPylation (adenylylation), has recently emerged as a novel theme in microbial pathogenesis. Although several AMPylating enzymes have been characterized, the only known virulence protein with de-AMPylation activity is SidD from the human pathogen Legionella pneumophila. SidD de-AMPylates mammalian Rab1, a small GTPase involved in secretory vesicle transport, thereby targeting the host protein for inactivation. The molecular mechanisms underlying Rab1 recognition and de-AMPylation by SidD are unclear. Here, we report the crystal structure of the catalytic region of SidD at 1.6 Å resolution. The structure reveals a phosphatase-like fold with additional structural elements not present in generic PP2C-type phosphatases. The catalytic pocket contains a binuclear metal-binding site characteristic of hydrolytic metalloenzymes, with strong dependency on magnesium ions. Subsequent docking and molecular dynamics simulations between SidD and Rab1 revealed the interface contacts and the energetic contribution of key residues to the interaction. In conjunction with an extensive structure-based mutational analysis, we provide in vivo and in vitro evidence for a remarkable adaptation of SidD to its host cell target Rab1 which explains how this effector confers specificity to the reaction it catalyses., Author Summary The covalent attachment of adenosine monophosphate (AMP) to proteins, a process called AMPylation (adenylylation), has recently emerged as a novel theme in microbial pathogenesis. While AMPylases from various pathogenic microorganisms have recently been characterized, the only virulence protein with de-AMPylation activity known to date is the Legionella pneumophila effector SidD which catalyzes AMP removal from the host GTPase Rab1. Thus, both AMPylation and de-AMPylation constitute a novel catalytic mechanism to precisely control the function and membrane dynamics of a host Rab GTPase. In spite of this pivotal role, the molecular mechanism of AMP removal and the structural determinants for Rab1 recognition by SidD have remained largely unexplored. Here, we present the crystal structure of the de-adenylylation domain of SidD and reveal the catalytic mechanism of Rab1 de-adenylylation. Surprisingly, the structure of SidD is not related to the other known enzyme with de-AMPylation activity, the Escherichia coli GS-ATase. Instead, the catalitic domain of SidD is remarkably similar to that of the metal-dependent protein phosphatases (PPMs), however with distinctive structural features to distinguish AMPylated Rab1 from similarly modified substrates. Importantly, we provide a model for the SidD-Rab1 complex which sheds light into the specific details of substrate recognition and catalysis by this virulence factor.

To understand cellular processes at the molecular level we need to improve our knowledge of protein−protein interactions, from a structural, mechanistic, and energetic point of view. Current theoretical studies and computational docking simulations show that protein dynamics plays a key role in protein association and support the need for including protein flexibility in modeling protein interactions. Assuming the conformational selection binding mechanism, in which the unbound state can sample bound conformers, one possible strategy to include flexibility in docking predictions would be the use of conformational ensembles originated from unbound protein structures. Here we present an exhaustive computational study about the use of precomputed unbound ensembles in the context of protein docking, performed on a set of 124 cases of the Protein−Protein Docking Benchmark 3.0. Conformational ensembles were generated by conformational optimization and refinement with MODELLER and by short molecular dynamics trajectories with AMBER. We identified those conformers providing optimal binding and investigated the role of protein conformational heterogeneity in protein−protein recognition. Our results show that a restricted conformational refinement can generate conformers with better binding properties and improve docking encounters in medium-flexible cases. For more flexible cases, a more extended conformational sampling based on Normal Mode Analysis was proven helpful. We found that successful conformers provide better energetic complementarity to the docking partners, which is compatible with recent views of binding association. In addition to the mechanistic considerations, these findings could be exploited for practical docking predictions of improved efficiency.