Exposure to ultraviolet (UV) radiation from sunlight accounts for 90% of the symptoms of premature skin aging and skin cancer. The tumor suppressor serine-threonine kinase LKB1 is mutated in Peutz-Jeghers syndrome and in a spectrum of epithelial cancers whose etiology suggests a cooperation with environmental insults. Here we analyzed the role of LKB1 in a UV-dependent mouse skin cancer model and show that LKB1 haploinsufficiency is enough to impede UVB-induced DNA damage repair, contributing to tumor development driven by aberrant growth factor signaling. We demonstrate that LKB1 and its downstream kinase NUAK1 bind to CDKN1A. In response to UVB irradiation, LKB1 together with NUAK1 phosphorylates CDKN1A regulating the DNA damage response. Upon UVB treatment, LKB1 or NUAK1 deficiency results in CDKN1A accumulation, impaired DNA repair and resistance to apoptosis. Importantly, analysis of human tumor samples suggests that LKB1 mutational status could be a prognostic risk factor for UV-induced skin cancer. Altogether, our results identify LKB1 as a DNA damage sensor protein regulating skin UV-induced DNA damage response.
The atomic structures of protein-protein interactions are central to understanding their role in biological systems, and a wide variety of biophysical functions and potentials have been developed for their characterization and the construction of predictive models. These tools are scattered across a multitude of stand-alone programs, and are often available only as model parameters requiring reimplementation. This acts as a significant barrier to their widespread adoption. CCharPPI integrates many of these tools into a single web server. It calculates up to 108 parameters, including models of electrostatics, desolvation and hydrogen bonding, as well as interface packing and complementarity scores, empirical potentials at various resolutions, docking potentials and composite scoring functions. Availability and implementation: The server does not require registration by the user and is freely available for non-commercial academic use at http://life.bsc.es/pid/ccharppi CONTACT: email@example.com.
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.
Variable practice has been shown to be an effective strategy to improve open motor skills. However, the usefulness of this procedure in closed motor skills remains controversial. The following study has the objective of analysing the effects of variability practice in the improvement of a closed skill. The skill studied has been the tennis serve. Thirty young tennis players (13 ± 1.52 years), divided in two groups, took part in this study. One group practiced in variable conditions and the other group in consistency conditions. Both groups performed 12 training sessions (60 serves/session). The variable practice group improved their accuracy significantly compared with the consistency group (F3.25 = 3.078; P = 0.035). The velocity of serve increased after training in both groups (F3.25 = 15.890; P = 0.001). The practice in variable conditions seems to be effective in improving the performance of the tennis serve.
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.
Heteromeric amino acid transporters (HATs) are the unique example, known in all kingdoms of life, of solute transporters composed of two subunits linked by a conserved disulfide bridge. In metazoans, the heavy subunit is responsible for the trafficking of the heterodimer to the plasma membrane, and the light subunit is the transporter. HATs are involved in human pathologies such as amino acidurias, tumor growth and invasion, viral infection and cocaine addiction. However structural information about interactions between the heavy and light subunits of HATs is scarce. In this work, transmission electron microscopy and single-particle analysis of purified human 4F2hc/L-type amino acid transporter 2 (LAT2) heterodimers overexpressed in the yeast Pichia pastoris, together with docking analysis and crosslinking experiments, reveal that the extracellular domain of 4F2hc interacts with LAT2, almost completely covering the extracellular face of the transporter. 4F2hc increases the stability of the light subunit LAT2 in detergent-solubilized Pichia membranes, allowing functional reconstitution of the heterodimer into proteoliposomes. Moreover, the extracellular domain of 4F2hc suffices to stabilize solubilized LAT2. The interaction of 4F2hc with LAT2 gives insights into the structural bases for light subunit recognition and the stabilizing role of the ancillary protein in HATs.
Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearsons Correlation Coefficient of 0.79 with experimental off-rates and a Matthews Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.
Translin is a highly conserved RNA- and DNA-binding protein that plays essential roles in eukaryotic cells. Human translin functions as an octamer, but in the octameric crystallographic structure, the residues responsible for nucleic acid binding are not accessible. Moreover, electron microscopy data reveal very different octameric configurations. Consequently, the functional assembly and the mechanism of nucleic acid binding by the protein remain unclear. Here, we present an integrative study combining small-angle X-ray scattering (SAXS), site-directed mutagenesis, biochemical analysis and computational techniques to address these questions. Our data indicate a significant conformational heterogeneity for translin in solution, formed by a lesser-populated compact octameric state resembling the previously solved X-ray structure, and a highly populated open octameric state that had not been previously identified. On the other hand, our SAXS data and computational analyses of translin in complex with the RNA oligonucleotide (GU)12 show that the internal cavity found in the octameric assemblies can accommodate different nucleic acid conformations. According to this model, the nucleic acid binding residues become accessible for binding, which facilitates the entrance of the nucleic acids into the cavity. Our data thus provide a structural basis for the functions that translin performs in RNA metabolism and transport.
Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.
The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning.
pyDockWEB is a web server for the rigid-body docking prediction of protein-protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, pyDockWEB returns the best docking orientations as scored mainly by electrostatics and desolvation energy. Availability and implementation: The server does not require registration by the user and is freely accessible for academics at http://life.bsc.es/servlet/pydock.
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.
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.
Protein-protein interactions are central to almost all biological functions, and the atomic details of such interactions can yield insights into the mechanisms that underlie these functions. We present a web server that wraps and extends the SwarmDock flexible protein-protein docking algorithm. After uploading PDB files of the binding partners, the server generates low energy conformations and returns a ranked list of clustered docking poses and their corresponding structures. The user can perform full global docking, or focus on particular residues that are implicated in binding. The server is validated in the CAPRI blind docking experiment, against the most current docking benchmark, and against the ClusPro docking server, the highest performing server currently available.
Aberrant activation of MAP kinase signaling pathway and loss of tumor suppressor LKB1 have been implicated in lung cancer development and progression. Although oncogenic KRAS mutations are frequent, BRAF mutations (BRAF(V600E)) are found in 3% of human non-small cell lung cancers. Contrary to KRAS mutant tumors, BRAF(V600E)-induced tumors are benign adenomas that fail to progess. Interestingly, loss of tumor supressor LKB1 coexists with KRAS oncogenic mutations and synergizes in tumor formation and progression, however, its cooperation with BRAF(V600E) oncogene is unknown. Our results describe a lung cell population in neonates mice where expression of BRAF(V600E) leads to lung adenoma development. Importantly, expression of BRAF(V600E) concomitant with the loss of only a single-copy of Lkb1, overcomes senencence-like features of BRAF(V600E)-mutant adenomas leading malignization to carcinomas. These results posit LKB1 haploinsufficiency as a risk factor for tumor progression of BRAF(V600E) mutated lung adenomas in human cancer patients.
The RAS to extracellular signal-regulated kinase (ERK) signal transduction cascade is crucial to cell proliferation, differentiation, and survival. Although numerous growth factors activate the RAS-ERK pathway, they can have different effects on the amplitude and duration of the ERK signal and, therefore, on the biological consequences. For instance, nerve growth factor, which elicits a larger and more sustained increase in ERK phosphorylation in PC12 cells than does epidermal growth factor (EGF), stimulates PC12 cell differentiation, whereas EGF stimulates PC12 cell proliferation. Here, we show that protein arginine methylation limits the ERK1/2 signal elicited by particular growth factors in different cell types from various species. We found that this restriction in ERK1/2 phosphorylation depended on methylation of RAF proteins by protein arginine methyltransferase 5 (PRMT5). PRMT5-dependent methylation enhanced the degradation of activated CRAF and BRAF, thereby reducing their catalytic activity. Inhibition of PRMT5 activity or expression of RAF mutants that could not be methylated not only affected the amplitude and duration of ERK phosphorylation in response to growth factors but also redirected the response of PC12 cells to EGF from proliferation to differentiation. This additional level of regulation within the RAS pathway may lead to the identification of new targets for therapeutic intervention.
The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.
Structural prediction of protein-protein complexes given the structures of the two interacting compounds in their unbound state is a key problem in biophysics. In addition to the problem of sampling of near-native orientations, one of the modeling main difficulties is to discriminate true from false positives. Here, we present a hierarchical protocol for docking refinement able to discriminate near native poses from a group of docking candidates. The main idea is to combine an efficient sampling of the full system hydrogen bond network and side chains, together with an all-atom force field and a surface generalized born implicit solvent. We tested our method on a set of twenty two complexes containing a near-native solution within the top 100 docking poses, obtaining a near native solution as the top pose in 70% of the cases. We show that all atom force fields optimized H-bond networks do improve significantly state of the art scoring functions.
Protein-protein interactions are fundamental for the majority of biological processes, so their structural, functional, and energetic characterization is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational docking approaches to the structural prediction of protein-protein complexes have been reported, with encouraging results. However, a major bottleneck is found in cases with conformational movements upon binding, for which docking algorithms have to be extended beyond the rigid-body framework by introducing flexibility. Given the high computational cost of flexible docking, coarse-grained models offer an efficient alternative to full-atom descriptions. This work describes pyDockCG, a new coarse-grained potential for protein-protein docking scoring and refinement, based on the known UNRES model for polypeptide chains. The main novelty is the inclusion of two new terms accounting for the Coulomb electrostatics and the solvation energy. The latter has been devised by adapting the EEF1 model to the coarse-grained approach, with optimal parameters for protein-protein docking. The coarse-grained potential yielded highly similar values to the full-atom scoring function pyDock when applied to the rigid body docking sets, but at much lower computational cost. This efficiency makes it suitable for the treatment of flexibility during docking.
Protein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces.
Computational prediction of protein functional sites can be a critical first step for analysis of large or complex proteins. Contemporary methods often require several homologous sequences and/or a known protein structure, but these resources are not available for many proteins. Leucine-rich repeats (LRRs) are ligand interaction domains found in numerous proteins across all taxonomic kingdoms, including immune system receptors in plants and animals. We devised Repeat Conservation Mapping (RCM), a computational method that predicts functional sites of LRR domains. RCM utilizes two or more homologous sequences and a generic representation of the LRR structure to identify conserved or diversified patches of amino acids on the predicted surface of the LRR. RCM was validated using solved LRR+ligand structures from multiple taxa, identifying ligand interaction sites. RCM was then used for de novo dissection of two plant microbe-associated molecular pattern (MAMP) receptors, EF-TU RECEPTOR (EFR) and FLAGELLIN-SENSING 2 (FLS2). In vivo testing of Arabidopsis thaliana EFR and FLS2 receptors mutagenized at sites identified by RCM demonstrated previously unknown functional sites. The RCM predictions for EFR, FLS2 and a third plant LRR protein, PGIP, compared favorably to predictions from ODA (optimal docking area), Consurf, and PAML (positive selection) analyses, but RCM also made valid functional site predictions not available from these other bioinformatic approaches. RCM analyses can be conducted with any LRR-containing proteins at www.plantpath.wisc.edu/RCM, and the approach should be modifiable for use with other types of repeat protein domains.
A molecular linkage between the MAPK and the LKB1-AMPK energy sensor pathways suggests that combined MAPK oncogene inhibition and metabolic modulation of AMPK would be more effective than either manipulation alone in melanoma cell lines.
A detailed and complete structural knowledge of the interactome is one of the grand challenges in Biology, and a variety of computational docking approaches have been developed to complement experimental efforts and help in the characterization of protein-protein interactions. Among the different docking scoring methods, those based on physicochemical considerations can give the maximum accuracy at the atomic level, but they are usually computationally demanding and necessarily noisy when implemented in rigid-body approaches. Coarser-grained knowledge-based potentials are less sensitive to details of atomic arrangements, thus providing an efficient alternative for scoring of rigid-body docking poses. In this study, we have extracted new statistical potentials from intermolecular pairs of exposed residues in known complex structures, which were then used to score protein-protein docking poses. The new method, called SIPPER (scoring by intermolecular pairwise propensities of exposed residues), combines the value of residue desolvation based on solvent-exposed area with the propensity-based contribution of intermolecular residue pairs. This new scoring function found a near-native orientation within the top 10 predictions in nearly one-third of the cases of a standard docking benchmark and proved to be also useful as a filtering step, drastically reducing the number of docking candidates needed by energy-based methods like pyDock.
Inflammation and fibrogenesis are directly related to chronic liver disease progression, including hepatocellular carcinoma (HCC) development. Currently there are few therapeutic options available to inhibit liver fibrosis. We have evaluated the hepatoprotective and anti-fibrotic potential of orally-administered 5-methylthioadenosine (MTA) in Mdr2(-/-) mice, a clinically relevant model of sclerosing cholangitis and spontaneous biliary fibrosis, followed at later stages by HCC development.
We describe here our results in the last CAPRI edition. We have participated in all targets, both as predictors and as scorers, using our pyDock docking methodology. The new challenges (homology-based modeling of the interacting subunits, domain-domain assembling, and protein-RNA interactions) have pushed our computer tools to the limits and have encouraged us to devise new docking approaches. Overall, the results have been quite successful, in line with previous editions, especially considering the high difficulty of some of the targets. Our docking approaches succeeded in five targets as predictors or as scorers (T29, T34, T35, T41, and T42). Moreover, with the inclusion of available information on the residues expected to be involved in the interaction, our protocol would have also succeeded in two additional cases (T32 and T40). In the remaining targets (except T37), results were equally poor for most of the groups. We submitted the best model (in ligand RMSD) among scorers for the unbound-bound target T29, the second best model among scorers for the protein-RNA target T34, and the only correct model among predictors for the domain assembly target T35. In summary, our excellent results for the new proposed challenges in this CAPRI edition showed the limitations and applicability of our approaches and encouraged us to continue developing methodologies for automated biomolecular docking.
Protein-protein interactions are fundamental for the majority of cellular processes and their study is of enormous biotechnological and therapeutic interest. In recent years, a variety of computational approaches to the protein-protein docking problem have been reported, with encouraging results. Most of the currently available protein-protein docking algorithms are composed of two clearly defined parts: the sampling of the rotational and translational space of the interacting molecules, and the scoring and clustering of the resulting orientations. Although this kind of strategy has shown some of the most successful results in the CAPRI blind test http://www.ebi.ac.uk/msd-srv/capri, more efforts need to be applied. Thus, the sampling protocol should generate a pool of conformations that include a sufficient number of near-native ones, while the scoring function should discriminate between near-native and non-near-native proposed conformations. On the other hand, protocols to efficiently include full flexibility on the protein structures are increasingly needed.
X-ray crystallography and NMR can provide detailed structural information of protein-protein complexes, but technical problems make their application challenging in the high-throughput regime. Other methods such as small-angle X-ray scattering (SAXS) are more promising for large-scale application, but at the cost of lower resolution, which is a problem that can be solved by complementing SAXS data with theoretical simulations. Here, we propose a novel strategy that combines SAXS data and accurate protein-protein docking simulations. The approach has been benchmarked on a large pool of known structures with synthetic SAXS data, and on three experimental examples. The combined approach (pyDockSAXS) provided a significantly better success rate (43% for the top 10 predictions) than either of the two methods alone. Further analysis of the influence of different docking parameters made it possible to increase the success rates for specific cases, and to define guidelines for improving the data-driven protein-protein docking protocols.
Melanoma is the most deadly form of skin cancer without effective treatment. Methylthioadenosine (MTA) is a naturally occurring nucleoside with differential effects on normal and transformed cells. MTA has been widely demonstrated to promote anti-proliferative and pro-apoptotic responses in different cell types. In this study we have assessed the therapeutic potential of MTA in melanoma treatment.
Translin is a single-stranded RNA- and DNA-binding protein, which has been highly conserved in eukaryotes, from man to Schizosaccharomyces pombe. TRAX is a Translin paralog associated with Translin, which has coevolved with it. We generated structural models of the S. pombe Translin (spTranslin), based on the solved 3D structure of the human ortholog. Using several bioinformatics computation tools, we identified in the equatorial part of the protein a putative nucleic acids interaction surface, which includes many polar and positively charged residues, mostly arginines, surrounding a shallow cavity. Experimental verification of the bioinformatics predictions was obtained by assays of nucleic acids binding to amino acid substitution variants made in this region. Bioinformatics combined with yeast two-hybrid assays and proteomic analyses of deletion variants, also identified at the top of the spTranslin structure a region required for interaction with spTRAX, and for spTranslin dimerization. In addition, bioinformatics predicted the presence of a second protein-protein interaction site at the bottom of the spTranslin structure. Similar nucleic acid and protein interaction sites were also predicted for the human Translin. Thus, our results appear to generally apply to the Translin family of proteins, and are expected to contribute to a further elucidation of their functions.
Alpha helices are useful scaffolds to build biologically active peptides. The intrinsic stability of an alpha-helix is a key feature that can be successfully designed, and it is governed by the constituting amino acid residues. Their individual contributions to helix stability are given, according to Lifson-Roig theory, by their w parameters, which are known for all proteinogenic amino acids, but not for non-natural ones. On the other hand, non-natural, conformationally-restricted amino acids can be used to impart biochemical stability to peptides intended for in vivo administration. Efficient design of peptides based on these amino acids requires the previous determination of their w parameters. We begin here this task by determining the w parameters of two restricted analogs of alanine: (alpha-methyl)alanine and 1-aminocyclopropanecarboxylic acid. According to their w values (alpha-methyl)alanine is almost as good a helix forming residue as alanine, while 1-aminocyclopropanecarboxylic acid is, similarly to proline, a helix breaker.
In recent years, protein-protein interactions are becoming the object of increasing attention in many different fields, such as structural biology, molecular biology, systems biology, and drug discovery. From a structural biology perspective, it would be desirable to integrate current efforts into the structural proteomics programs. Given that experimental determination of many protein-protein complex structures is highly challenging, and in the context of current high-performance computational capabilities, different computer tools are being developed to help in this task. Among them, computational docking aims to predict the structure of a protein-protein complex starting from the atomic coordinates of its individual components, and in recent years, a growing number of docking approaches are being reported with increased predictive capabilities. The improvement of speed and accuracy of these docking methods, together with the modeling of the interaction networks that regulate the most critical processes in a living organism, will be essential for computational proteomics. The ultimate goal is the rational design of drugs capable of specifically inhibiting or modifying protein-protein interactions of therapeutic significance. While rational design of protein-protein interaction inhibitors is at its very early stage, the first results are promising.
Prediction of protein-protein complexes from the coordinates of their unbound components usually starts by generating many potential predictions from a rigid-body 6D search followed by a second stage that aims to refine such predictions. Here, we present and evaluate a new method to effectively address the complexity and sampling requirements of the initial exhaustive search. In this approach we combine the projection of the interaction terms into 3D grid-based potentials with the efficiency of spherical harmonics approximations to accelerate the search. The binding energy upon complex formation is approximated as a correlation function composed of van der Waals, electrostatics and desolvation potential terms. The interaction-energy minima are identified by a novel, fast and exhaustive rotational docking search combined with a simple translational scanning. Results obtained on standard protein-protein benchmarks demonstrate its general applicability and robustness. The accuracy is comparable to that of existing state-of-the-art initial exhaustive rigid-body docking tools, but achieving superior efficiency. Moreover, a parallel version of the method performs the docking search in just a few minutes, opening new application opportunities in the current omics world.
Background: Computational approaches such as docking and scoring are becoming routine in drug discovery as a complement to other more traditional techniques. However, so far, computer drug design methods have been applied to inhibit the function of individual proteins, and there is little available data on the use of these computational techniques to target protein-protein interactions. Objective: To establish a strategy for the use of current computational tools in drug discovery targeting protein-protein interactions. Method: Individual techniques applied to specific cases could be studied to derive a general strategy for targeting protein-protein interactions. Conclusion: Protein docking, interface prediction and hot-spot identification can contribute to the discovery of small molecule inhibitors targeting protein interactions of therapeutic interest, especially when little structural information is available.
Desolvation property is used here to predict protein-protein binding sites exploiting the fact that lower-valued optimal docking area ODA (Fernandez-Recio et al., 2005) points form cluster at the interface. The proposed method involves two steps; clustering the ODA points and representing ODA points by average ODA values. On 51 nonredundant proteins, results show the success rate improved considerably. Considering only significant ODA, the previous ODA method has obtained a success rate of 65% with overall success rate of 39%. The proposed method improved the overall success rate to 61%. Further, comparable results were found for X-ray and NMR structures.
The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms. In particular, more that 10,000 high-confidence protein-protein interactions have been described between the roughly 6,000 proteins encoded in the budding yeast genome (Saccharomyces cerevisiae). However, unfortunately, high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs. Here, we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies, according to our benchmarks. To increase the coverage of the interaction space, we also explore the possibility of using homology models of varying quality in the docking experiments, instead of experimental structures, and assess how it would affect the global performance of the methods. In total, we have applied the docking procedure to 217 experimental structures and 1,023 homology models, providing putative structural models for over 3,000 protein-protein interactions in the yeast interactome. Finally, we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community. The results of our experiment will be integrated into the general 3D-Repertoire pipeline, a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution. All docking results are available at http://gatealoy.pcb.ub.es/HT_docking/.
Plant immune responses often depend on leucine-rich repeat receptors that recognize microbe-associated molecular patterns or pathogen-specific virulence proteins, either directly or indirectly. When the recognition is direct, a molecular arms race takes place where plant receptors continually and rapidly evolve in response to virulence factor evolution. A useful model system to study ligand-receptor coevolution dynamics at the protein level is represented by the interaction between pathogen-derived polygalacturonases (PGs) and plant polygalacturonase-inhibiting proteins (PGIPs). We have applied codon substitution models to PGIP sequences of different eudicotyledonous families to identify putative positively selected sites and then compared these sites with the propensity of protein surface residues to interact with protein partners, based on desolvation energy calculations. The 2 approaches remarkably correlated in pinpointing several residues in the concave face of the leucine-rich repeat domain. These residues were mutated into alanine and their effect on the recognition of several PGs was tested, leading to the identification of unique hotspots for the PGIP-PG interaction. The combined approach used in this work can be of general utility in cases where structural information about a pattern-recognition receptor or resistance-gene product is available.
Signal transducer and activator of transcription (STAT) proteins play a crucial role in the activation of gene transcription in response to extracellular stimuli. The regulation and activity of these proteins require a complex rearrangement of the domains. According to the established models, based on crystallographic data, STATs convert from a basal antiparallel inactive dimer into a parallel active one following phosphorylation. The simultaneous analysis of small-angle X-ray scattering data measured at different concentrations of unphosphorylated human STAT5a core domain unambiguously identifies the simultaneous presence of a monomer and a dimer. The dimer is the minor species but could be structurally characterized by SAXS in the presence of the monomer using appropriate computational tools and shown to correspond to the antiparallel assembly. The equilibrium is governed by a moderate dissociation constant of K(d) approximately 90 microM. Integration of these results with previous knowledge of the N-terminal domain structure and dissociation constants allows the modeling of the full-length protein. A complex network of intermolecular interactions of low or medium affinity is suggested. These contacts can be eventually formed or broken to trigger the dramatic modifications in the dimeric arrangement needed for STAT regulation and activity.
Understanding the biochemical mechanisms contributing to melanoma development and progression is critical for therapeutical intervention. LKB1 is a multi-task Ser/Thr kinase that phosphorylates AMPK controlling cell growth and apoptosis under metabolic stress conditions. Additionally, LKB1(Ser428) becomes phosphorylated in a RAS-Erk1/2-p90(RSK) pathway dependent manner. However, the connection between the RAS pathway and LKB1 is mostly unknown.
Hepatocellular carcinoma (HCC) is a chemoresistant tumor strongly associated with chronic hepatitis. Identification of molecular links connecting inflammation with cell growth/survival, and characterization of pro-tumorigenic intracellular pathways is therefore of therapeutic interest. The epidermal growth factor receptor (EGFR) signaling system stands at a crossroad between inflammatory signals and intracellular pathways associated with hepatocarcinogenesis. We investigated the regulation and activity of different components of the EGFR system, including the EGFR ligand amphiregulin (AR) and its sheddase ADAM17, and the modulation of intracellular EGFR signaling by a novel mechanism involving protein methylation.
Empirical models for the prediction of how changes in sequence alter protein-protein binding kinetics and thermodynamics can garner insights into many aspects of molecular biology. However, such models require empirical training data and proper validation before they can be widely applied. Previous databases contained few stabilizing mutations and no discussion of their inherent biases or how this impacts model construction or validation.
The application of docking to large-scale experiments or the explicit treatment of protein flexibility are part of the new challenges in structural bioinformatics that will require large computer resources and more efficient algorithms. Highly optimized fast Fourier transform (FFT) approaches are broadly used in docking programs but their optimal code implementation leaves hardware acceleration as the only option to significantly reduce the computational cost of these tools. In this work we present Cell-Dock, an FFT-based docking algorithm adapted to the Cell BE processor. We show that Cell-Dock runs faster than FTDock with maximum speedups of above 200×, while achieving results of similar quality.
Androgen receptor (AR) is a major therapeutic target that plays pivotal roles in prostate cancer (PCa) and androgen insensitivity syndromes. We previously proposed that compounds recruited to ligand-binding domain (LBD) surfaces could regulate AR activity in hormone-refractory PCa and discovered several surface modulators of AR function. Surprisingly, the most effective compounds bound preferentially to a surface of unknown function [binding function 3 (BF-3)] instead of the coactivator-binding site [activation function 2 (AF-2)]. Different BF-3 mutations have been identified in PCa or androgen insensitivity syndrome patients, and they can strongly affect AR activity. Further, comparison of AR x-ray structures with and without bound ligands at BF-3 and AF-2 showed structural coupling between both pockets. Here, we combine experimental evidence and molecular dynamic simulations to investigate whether BF-3 mutations affect AR LBD function and dynamics possibly via allosteric conversation between surface sites. Our data indicate that AF-2 conformation is indeed closely coupled to BF-3 and provide mechanistic proof of their structural interconnection. BF-3 mutations may function as allosteric elicitors, probably shifting the AR LBD conformational ensemble toward conformations that alter AF-2 propensity to reorganize into subpockets that accommodate N-terminal domain and coactivator peptides. The induced conformation may result in either increased or decreased AR activity. Activating BF-3 mutations also favor the formation of another pocket (BF-4) in the vicinity of AF-2 and BF-3, which we also previously identified as a hot spot for a small compound. We discuss the possibility that BF-3 may be a protein-docking site that binds to the N-terminal domain and corepressors. AR surface sites are attractive pharmacological targets to develop allosteric modulators that might be alternative lead compounds for drug design.
Most processes in living organisms occur through an intricate network of protein-protein interactions, in which any malfunctioning can lead to pathological situations. Therefore, current research in biomedicine is starting to focus on protein interaction networks. A detailed structural knowledge of these interactions at molecular level will be necessary for drug discovery targeting protein-protein interactions. The challenge from a structural biology point of view is determining the structure of the specific complex formed upon interaction of two or several proteins, and/or locating the surface residues involved in the interaction and identify which of them are the most important ones for binding (hot-spots). In this line, an increasing number of computer tools are available to complement experimental efforts. Docking algorithms can achieve successful predictive rates in many complexes, as shown in the community assessment experiment CAPRI, and have already been applied to a variety of cases of biomedical interest. On the other side, many methods for interface and hotspot prediction have been reported, based on a variety of evolutionary, geometrical and physico-chemical parameters. Computer predictions are reaching a significant level of maturity, and can be very useful to guide experiments and suggest mutations, or to provide a mechanistic framework to the experimental results on a given interaction. We will review here existing computer approaches for proteinprotein docking, interface prediction and hot-spot identification, with focus to drug discovery targeting protein-protein interactions.
We present here an extended protein-RNA docking benchmark composed of 71 test cases in which the coordinates of the interacting protein and RNA molecules are available from experimental structures, plus an additional set of 35 cases in which at least one of the interacting subunits is modeled by homology. All cases in the experimental set have available unbound protein structure, and include five cases with available unbound RNA structure, four cases with a pseudo-unbound RNA structure, and 62 cases with the bound RNA form. The additional set of modeling cases comprises five unbound-model, eight model-unbound, 19 model-bound, and three model-model protein-RNA cases. The benchmark covers all major functional categories and contains cases with different degrees of difficulty for docking, as far as protein and RNA flexibility is concerned. The main objective of this benchmark is to foster the development of protein-RNA docking algorithms and to contribute to the better understanding and prediction of protein-RNA interactions. The benchmark is freely available at http://life.bsc.es/pid/protein-rna-benchmark.
Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.
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