Protein-peptide Complexes Research Articles

Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition.

We present the sixth report evaluating the performance of methods for predicting the atomic resolution structures of protein complexes offered as targets to the community-wide initiative on the Critical Assessment of Predicted Interactions (CAPRI). The evaluation is based on a total of 20,670 predicted models for 8 protein-peptide complexes, a novel category of targets in CAPRI, and 12 protein-protein targets in CAPRI prediction Rounds held during the years 2013-2016. For two of the protein-protein targets, the focus was on the prediction of side-chain conformation and positions of interfacial water molecules. Seven of the protein-protein targets were particularly challenging owing to their multicomponent nature, to conformational changes at the binding site, or to a combination of both. Encouragingly, the very large multiprotein complex with the nucleosome was correctly predicted, and correct models were submitted for the protein-peptide targets, but not for some of the challenging protein-protein targets. Models of acceptable quality or better were obtained for 14 of the 20 targets, including medium quality models for 13 targets and high quality models for 8 targets, indicating tangible progress of present-day computational methods in modeling protein complexes with increased accuracy. Our evaluation suggests that the progress stems from better integration of different modeling tools with docking procedures, as well as the use of more sophisticated evolutionary information to score models. Nonetheless, adequate modeling of conformational flexibility in interacting proteins remains an important area with a crucial need for improvement. Proteins 2017; 85:359-377. © 2016 Wiley Periodicals, Inc.

Protein-protein and peptide-protein docking and refinement using ATTRACT in CAPRI.

The ATTRACT coarse-grained docking approach in combination with various types of atomistic, flexible refinement methods has been applied to predict protein-protein and peptide-protein complexes in CAPRI rounds 28-36. For a large fraction of CAPRI targets (12 out of 18), at least one model of acceptable or better quality was generated, corresponding to a success rate of 67%. In particular, for several peptide-protein complexes excellent predictions were achieved. In several cases, a combination of template-based modeling and extensive molecular dynamics-based refinement yielded medium and even high quality solutions. In one particularly challenging case, the structure of an ubiquitylation enzyme bound to the nucleosome was correctly predicted as a set of acceptable quality solutions. Based on the experience with the CAPRI targets, new interface refinement approaches and methods for ab-initio peptide-protein docking have been developed. Failures and possible improvements of the docking method with respect to scoring and protein flexibility will also be discussed. Proteins 2017; 85:391-398. © 2016 Wiley Periodicals, Inc.

One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model.

Despite the significant increase in computational power, molecular modeling of protein structure using classical all-atom approaches remains inefficient, at least for most of the protein targets in the focus of biomedical research. Perhaps the most successful strategy to overcome the inefficiency problem is multiscale modeling to merge all-atom and coarse-grained models. This chapter describes a well-established CABS coarse-grained protein model. The CABS (C-Alpha, C-Beta, and Side chains) model assumes a 2-4 united-atom representation of amino acids, knowledge-based force field (derived from the statistical regularities seen in known protein sequences and structures) and efficient Monte Carlo sampling schemes (MC dynamics, MC replica-exchange, and combinations). A particular emphasis is given to the unique design of the CABS force-field, which is largely defined using one-dimensional structural properties of proteins, including protein secondary structure. This chapter also presents CABS-based modeling methods, including multiscale tools for de novo structure prediction, modeling of protein dynamics and prediction of protein-peptide complexes. CABS-based tools are freely available at http://biocomp.chem.uw.edu.pl/tools.

PyDock scoring for the new modeling challenges in docking: Protein-peptide, homo-multimers, and domain-domain interactions.

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.

Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction

Protein-peptide interactions play an important role in many cellular processes. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. However, predicting all-atom structures of protein-peptide complexes without any knowledge about the peptide binding site and the bound peptide conformation remains a big challenge. Here, we present a docking-based method for predicting protein-peptide complex structures, referred to as MDockPeP, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. MDockPeP was tested on the peptiDB benchmarking database using both bound and unbound protein structures. The results show that MDockPeP successfully generated near-native peptide binding modes in 95.0% of the bound docking cases and in 92.2% of the unbound docking cases. The performance is significantly better than other existing docking methods. MDockPeP is computationally efficient and suitable for large-scale applications.

A Dynamic Picture of the Early Events in Nociceptin Binding to the NOP Receptor by Metadynamics

Nociceptin (NCC, also known as FQ (N/OFQ)) is the 17-amino acid neuropeptide, endogenous ligand for the G-protein-coupled receptor (NOP, also known as ORL-1). In this study, starting from the recently reported x-ray structure at pH 7 of NOP in complex with an antagonist, new insights, to our knowledge, on the binding geometry of NCC to NOP have been provided in silico. After a rigid docking of NCC in an α-helix conformation, molecular dynamics (MD) and metadynamics (METAD), a method for the analysis of free-energy surfaces (FES), were performed on the protein-peptide complex. Free-energy profiles were obtained as a function of the α-helix content of different segments of the 17-mer ligand, and a structural ensemble of conformations of NCC, corresponding to the minimum of the FES, was extracted, thus representing the NCC bound to the inactive form of NOP. The structural features were compared with many known experimental data. The pose of the “message” domain (residues 1–4) of NCC differs from that of the known NOP antagonists, as being slightly slipped deeper inside the protein core. A residual α-helix content in the central part of the peptide (residues 4–9) is maintained, whereas the C-terminal segment (residues 13–17) is unstructured and highly flexible. An important stabilization due to interactions with residues D130 and D110 of the receptor has been found, in agreement with the large decrease in agonist potency reported for the D130A and D110A mutants. The importance of the extracellular domain 2 (ECL2) in the selectivity toward the endogenous ligand has been confirmed. A pivotal role for the conserved residue N133 is suggested and further supported by a study of the N133A in silico mutant. Accordingly, N133 can work as a molecular microswitch driving the change between the inactive and active NOP conformations, in the framework of an extended H-bond and water network rearrangement in the deep binding site.

DMM-PBSA: A New HADDOCK Scoring Function for Protein-Peptide Docking

Molecular-docking programs coupled with suitable scoring functions are now established and very useful tools enabling computational chemists to rapidly screen large chemical databases and thereby to identify promising candidate compounds for further experimental processing. In a broader scenario, predicting binding affinity is one of the most critical and challenging components of computer-aided structure-based drug design. The development of a molecular docking scoring function which in principle could combine both features, namely ranking putative poses and predicting complex affinity, would be of paramount importance. Here, we systematically investigated the performance of the MM-PBSA approach, using two different Poisson–Boltzmann solvers (APBS and DelPhi), in the currently rising field of protein-peptide interactions (PPIs), identifying the correct binding conformations of 19 different protein-peptide complexes and predicting their binding free energies. First, we scored the decoy structures from HADDOCK calculation via the MM-PBSA approach in order to assess the capability of retrieving near-native poses in the best-scoring clusters and of evaluating the corresponding free energies of binding. MM-PBSA behaves well in finding the poses corresponding to the lowest binding free energy, however the built-in HADDOCK score shows a better performance. In order to improve the MM-PBSA-based scoring function, we dampened the MM-PBSA solvation and coulombic terms by 0.2, as proposed in the HADDOCK score and LIE approaches. The new dampened MM-PBSA (dMM-PBSA) outperforms the original MM-PBSA and ranks the decoys structures as the HADDOCK score does. Second, we found a good correlation between the dMM-PBSA and HADDOCK scores for the near-native clusters of each system and the experimental binding energies, respectively. Therefore, we propose a new scoring function, dMM-PBSA, to be used together with the built-in HADDOCK score in the context of protein-peptide docking simulations.

Molecular basis and quantitative assessment of TRF1 and TRF2 protein interactions with TIN2 and Apollo peptides.

Shelterin is a six-protein complex (TRF1, TRF2, POT1, RAP1, TIN2, and TPP1) that also functions in smaller subsets in regulation and protection of human telomeres. Two closely related proteins, TRF1 and TRF2, make high-affinity contact directly with double-stranded telomeric DNA and serve as a molecular platform. Protein TIN2 binds to TRF1 and TRF2 dimer-forming domains, whereas Apollo makes interaction only with TRF2. To elucidate the molecular basis of these interactions, we employed molecular dynamics (MD) simulations of TRF1TRFH-TIN2TBM and TRF2TRFH-TIN2TBM/ApolloTBM complexes and of the isolated proteins. MD enabled a structural and dynamical comparison of protein-peptide complexes including H-bond interactions and interfacial residues that may regulate TRF protein binding to the given peptides, especially focusing on interactions described in crystallographic data. Residues with a selective function in both TRF1TRFH and TRF2TRFH and forming a stable hydrogen bond network with TIN2TBM or ApolloTBM peptides were traced. Our study revealed that TIN2TBM forms a well-defined binding mode with TRF1TRFH as compared to TRF2TRFH, and that the binding pocket of TIN2TBM is deeper for TRF2TRFH protein than ApolloTBM. The MD data provide a basis for the reinterpretation of mutational data obtained in crystallographic work for the TRF proteins. Together, the previously determined X-ray structure and our MD provide a detailed view of the TRF-peptide binding mode and the structure of TRF1/2 binding pockets. Particular TRF-peptide interactions are very specific for the formation of each protein-peptide complex, identifying TRF proteins as potential targets for the design of inhibitors/drugs modulating telomere machinery for anticancer therapy.

BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP.

Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc.

Bioregulator from rat liver tissue

It has been shown that the membranotropic homeostatic tissue-specific bioregulator isolated from rat liver tissue contains a nanosized peptide-protein complex consisting of low-molecular peptides (1–6.5 kDa) and a protein from the serum albumin family. This bioregulator modulated the peptide biological activity and determined the tissue specificity.

LEADS-PEP: A Benchmark Data Set for Assessment of Peptide Docking Performance.

With increasing interest in peptide-based therapeutics also the application of computational approaches such as peptide docking has gained more and more attention. In order to assess the suitability of docking programs for peptide placement and to support the development of peptide-specific docking tools, an independently constructed benchmark data set is urgently needed. Here we present the LEADS-PEP benchmark data set for assessing peptide docking performance. Using a rational and unbiased workflow, 53 protein-peptide complexes with peptide lengths ranging from 3 to 12 residues were selected. The data set is publicly accessible at www.leads-x.org . In a second step we evaluated several small molecule docking programs for their potential to reproduce peptide conformations as present in LEADS-PEP. While most tested programs were capable to generate native-like binding modes of small peptides, only Surflex-Dock and AutoDock Vina performed reasonably well for peptides consisting of more than five residues. Rescoring of docking poses with scoring functions ChemPLP, ChemScore, and ASP further increased the number of top-ranked near-native conformations. Our results suggest that small molecule docking programs are a good and fast alternative to specialized peptide docking programs.

Rational Design of an Orthogonal Molecular Interaction System at the Complex Interface of Lung Cancer-Related MDM2 Protein with p53 Peptide

The oncogenic protein MDM2 is an important negative regulator of p53 tumour suppressor. Overexpression of this protein is closely related to the pathological progression and metastasis of lung cancer and other tumours. Previously, a 12-mer peptide segment 17ETFSDLWKLLPE28 (p5317–28) corresponding to residues 17–28 of the human p53 transactivation domain was identified to interact moderately with MDM2. Here, we successfully created an orthogonal molecular interaction system between a native hydrogen bond (H-bond) and a designed halogen bond (X-bond) across the protein–peptide complex interface, where the X-bond was introduced by substituting the 3-hydrogen atom of the benzene ring of the p5317–28 Phe19 residue with a halogen atom X, resulting in a series of 3X-peptides (X = F, Cl, Br or I). Theoretical analysis found that chlorine is a good compromise between X-bonding strength and steric hindrance due to introducing a bulkier halogen atom to the tightly packed complex interface. Consequently, the 3Cl-peptide (Kd = 105 nM) was determined to exhibit ~5-fold affinity improvement relative to p5317–28 (Kd = 570 nM). In contrast, the binding affinity of the 2Cl-peptide (Kd = 492 nM), a negative control that cannot form the X-bond according to computational analysis, did not change considerably on the halogenation.

Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.

We introduce two GALAXY web servers called GalaxySite and GalaxyPepDock that predict protein complex structures with small organic compounds and peptides, respectively. GalaxySite predicts ligands that may bind the input protein and generates complex structures of the protein with the predicted ligands from the protein structure given as input or predicted from the input sequence. GalaxyPepDock takes a protein structure and a peptide sequence as input and predicts structures for the protein-peptide complex. Both GalaxySite and GalaxyPepDock rely on available experimentally resolved structures of protein-ligand complexes evolutionarily related to the target. With the continuously increasing size of the protein structure database, the probability of finding related proteins in the database is increasing. The servers further relax the complex structures to refine the structural aspects that are missing in the available structures or that are not compatible with the given protein by optimizing physicochemical interactions. GalaxyPepDock allows conformational change of the protein receptor induced by peptide binding. The atomistic interactions with ligands predicted by the GALAXY servers may offer important clues for designing new molecules or proteins with desired binding properties.

Development and Application of a Fully Blind Flexible Peptide-protein Docking Protocol, pepATTRACT

Peptide-mediated interactions are involved in many signaling and regulatory pathways as well as the DNA replication machinery and are linked to many pathological disorders. Many research groups are currently working towards a more detailed understanding of these important interactions by characterizing the 3D complex structures with experimental methods like X-ray crystallography and NMR. However, for a large number of peptide-protein complexes such atomistic structural information is lacking to date. Computational peptide docking methods can yield information complementary to experimental information by predicting the protein-peptide complex structure from the 3D structure of the protein and the peptide sequence. This approach can also be used to study interactions between folded and disordered proteins/protein regions (e.g., the interactions of the disordered regions in tumor suppressor p53 with its different partners). Here, we describe the development and usage of the fully blind, flexible peptide-protein docking protocol pepATTRACT. The ATTRACT docking engine is implemented as a suite of command line tools and options that can be combined at will. Therefore, ATTRACT protocols like pepATTRACT are typically invoked via a custom, hand-written shell script. Although this approach is very flexible, it limits the accessibility of ATTRACT to expert users only. To make pepATTRACT easily accessible to non-expert users, we created a web-interface which helps the user set up a peptide docking protocol by editing parameters in a web browser (www.attract.ph.tum.de/peptide.html). pepATTRACT docking scripts can then executed on the user's local machine, once the ATTRACT software has been installed. Here, we describe all the steps necessary for setting up a pepATTRACT docking run via the web-interface including installation of the ATTRACT software.

Identification and Characterization of the Interaction Site between cFLIPL and Calmodulin.

Overexpression of the cellular FLICE-like inhibitory protein (cFLIP) has been reported in a number of tumor types. As an inactive procaspase-8 homologue, cFLIP is recruited to the intracellular assembly known as the Death Inducing Signaling Complex (DISC) where it inhibits apoptosis, leading to cancer cell proliferation. Here we characterize the molecular details of the interaction between cFLIPL and calmodulin, a ubiquitous calcium sensing protein. By expressing the individual domains of cFLIPL, we demonstrate that the interaction with calmodulin is mediated by the N-terminal death effector domain (DED1) of cFLIPL. Additionally, we mapped the interaction to a specific region of the C-terminus of DED1, referred to as DED1 R4. By designing DED1/DED2 chimeric constructs in which the homologous R4 regions of the two domains were swapped, calmodulin binding properties were transferred to DED2 and removed from DED1. Furthermore, we show that the isolated DED1 R4 peptide binds to calmodulin and solve the structure of the peptide-protein complex using NMR and computational refinement. Finally, we demonstrate an interaction between cFLIPL and calmodulin in cancer cell lysates. In summary, our data implicate calmodulin as a potential player in DISC-mediated apoptosis and provide evidence for a specific interaction with the DED1 of cFLIPL.

Conformational thermodynamics of biomolecular complexes: The histogram-based method

Conformational changes in biomacromolecules govern majority of biological processes. Complete characterization of conformational contributions to thermodynamics of complexation of biomacromolecules has been challenging. Although, advances in NMR relaxation experiments and several computational studies have revealed important aspects of conformational entropy changes, efficient and large-scale estimations still remain an intriguing facet. Recent histogram-based method (HBM) offers a simple yet rigorous route to estimate both conformational entropy and free energy changes from same set of histograms in an efficient manner. The HBM utilizes the power of histograms which can be generated as accurately as desired from an arbitrarily large sample space from atomistic simulation trajectories. Here we discuss some recent applications of the HBM, using dihedral angles of amino acid residues as conformational variables, which provide good measure of conformational thermodynamics of several protein-peptide complexes, obtained from NMR, metal-ion binding to an important metalloprotein, interfacial changes in protein-protein complex and insight to protein function, coupled with conformational changes. We conclude the paper with a few future directions worth pursuing.

Fully Blind Peptide-Protein Docking with pepATTRACT

Peptide-protein interactions are ubiquitous in the celland form an important part of the interactome.Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. Here, we present a new fullyblindflexible peptide-protein docking protocol, pepATTRACT, which combines a rapid coarse-grained global peptide docking search of the entireprotein surface with a two-stage atomistic flexible refinement. Global unbound-unbound docking yielded near-native models for 70% of the docking cases when testing the protocol on the largest benchmark of peptide-protein complexes availableto date. This performance is similar tothat of state-of-the-art local docking protocols that rely on information about the binding site. Upon restricting the search to the peptide binding region, the resulting pepATTRACT-local approach outperformed existing methods. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html.

Mechanistic Basis Of Peptide-Protein Interaction In AtPep1-PEPR1 Complex In Arabidopsis thaliana.

Peptide-mediated immunity against pathogens in plants can provide information on protein-peptide interactions and drug discovery in general. The molecular structure of AtPep1, a 23-amino acid signaling peptide isolated from Arabidopsis thaliana leaves and implicated in innate immunity, has evaded structural determination by biophysical methods. The details of molecular interaction of AtPep1 peptide with its receptor (PEPR1), a 170 kDa leucine-rich repeat (LRR) kinase is also unknown. We report a computational approach to the modeling AtPep1 by conformational sampling and its interaction with the receptor PEPR1. Molecular dynamics simulations were employed to sample and cluster energetically favorable conformations of AtPep1 and modeling of PEPR1 through homology. Docking of AtPep1 to PEPR1 and filtering of the biologically relevant poses were facilitated by the computational Ala-scanning mutations and binding energy analysis of the peptide-protein complex. This study provides the first independent in silico validation of the Structure-Activity- Relationship studies carried out on the AtPep1 and provides a molecular mechanism of the peptide-protein complex system.

Trifluoroacetic acid as excipient destabilizes melittin causing the selective aggregation of melittin within the centrin-melittin-trifluoroacetic acid complex.

Trifluoroacetic acid (TFA) may be the cause of the bottleneck in high resolution structure determination for protein-peptide complexes. Fragment based drug design often involves the use of synthetic peptides which contain TFA (excipient). Our goal was to explore the effects of this excipient on a model complex: centrin-melittin-TFA. We performed Fourier transform infrared, two-dimensional infrared correlation spectroscopies and spectral simulations to analyze the amide I'/I'* band for the components and the ternary complex. Melittin (MLT) was observed to have increased helicity upon its interaction with centrin, followed by the thermally induced aggregation of MLT within the ternary complex in the TFA presence.

GalaxyPepDock: a protein-peptide docking tool based on interaction similarity and energy optimization.

Protein–peptide interactions are involved in a wide range of biological processes and are attractive targets for therapeutic purposes because of their small interfaces. Therefore, effective protein–peptide docking techniques can provide the basis for potential therapeutic applications by enabling an atomic-level understanding of protein interactions. With the increasing number of protein–peptide structures deposited in the protein data bank, the prediction accuracy of protein-peptide docking can be enhanced by utilizing the information provided by the database. The GalaxyPepDock web server, which is freely accessible at http://galaxy.seoklab.org/pepdock, performs similarity-based docking by finding templates from the database of experimentally determined structures and building models using energy-based optimization that allows for structural flexibility. The server can therefore effectively model the structural differences between the template and target protein–peptide complexes. The performance of GalaxyPepDock is superior to those of the other currently available web servers when tested on the PeptiDB set and on recently released complex structures. When tested on the CAPRI target 67, GalaxyPepDock generates models that are more accurate than the best server models submitted during the CAPRI blind prediction experiment.