Protein-peptide Complexes Research Articles

The methyl 13C-edited/13C-filtered transferred NOE for studying protein interactions with short linear motifs.

Many proteins interact with their ligand proteins by recognition of short linear motifs that are often intrinsically disordered. These interactions are usually weak and are characterized by fast exchange. NMR spectroscopy is a powerful tool to study weak interactions. The methods that have been commonly used are analysis of chemicals shift perturbations (CSP) upon ligand binding and saturation transfer difference spectroscopy. These two methods identify residues at the binding interface between the protein and its ligand. In the present study, we used a combination of transferred-NOE, specific methyl-labeling and an optimized isotope-edited/isotope-filtered NOESY experiment to study specific interactions between the 42kDa p38α mitogen-activated protein kinase and the kinase interaction motif (KIM) on the STEP phosphatase. These measurements distinguished between residues that both exhibit CSPs upon ligand binding and interact with the KIM peptide from residues that exhibit CSPs but do not interact with the peptide. In addition, these results provide information about pairwise interactions that is important for a more reliable docking of the KIM peptide into its interacting surface on p38α. This combination of techniques should be applicable for many protein-peptide complexes up to 80kDa for which methyl resonance assignment can be achieved.

A new soaking procedure for X-ray crystallographic structural determination of protein–peptide complexes

Interactions between a protein and a peptide motif of its protein partner are prevalent in nature. Often, a protein also has multiple interaction partners. X-ray protein crystallography is commonly used to examine these interactions in terms of bond distances and angles as well as to describe hotspots within protein complexes. However, the crystallization process presents a significant bottleneck in structure determination since it often requires notably time-consuming screening procedures, which involve testing a broad range of crystallization conditions via a trial-and-error approach. This difficulty is also increased as each protein-peptide complex does not necessarily crystallize under the same conditions. Here, a new co-crystallization/peptide-soaking method is presented which circumvents the need to return to the initial lengthy crystal screening and optimization processes for each consequent new complex. The 14-3-3σ protein, which has multiple interacting partners with specific peptidic motifs, was used as a case study. It was found that co-crystals of 14-3-3σ and a low-affinity peptide from one of its partners, c-Jun, could easily be soaked with another interacting peptide to quickly and easily generate new structures at high resolution. Not only does this significantly reduce the production time, but new 14-3-3-peptide structures that were previously not accessible with the 14-3-3σ isoform, despite screening hundreds of other different conditions, were now also able to be resolved. The findings achieved in this study may be considered as a supporting and practical guide to potentially enable the acceleration of the crystallization process of any protein-peptide system.

Stimulation of Structural and Functional Recovery of the Kidney in Rats with Postischemic Acute Renal Failure of Different Severity by Embryonic Protein-Peptide Complex Therapy.

In male rats, acute renal failure was simulated by clamping the vascular pedicle of the left kidney for 60 or 90 min and right-sided nephrectomy. In the control series, no therapy was performed. In the experimental series, the animals were daily injected subcutaneously with Cellex, a protein-peptide complex (PPC) chromatographically isolated from the brain tissue of pig embryos with a molecular weight of its components from 10 to 250 kDa. PPC was administered 5 times a week (10 injections) in a dose of 0.1 ml/kg (0.1 mg active substance per 1 kg body weight). Ischemia of a single kidney led to the development of acute renal failure, more severe after 90-min ischemia. PPC therapy reduced the severity of functional disorders mainly at the early stages (3 and 7 days) with normalization of blood concentrations of urea and creatinine, creatinine clearance, tubular reabsorption of sodium and calcium, including the cases with 90-min ischemia, which did not occur in the control series. PPC therapy also contributed to hypertrophy of many glomeruli, prevented the development of glomerulosclerosis, and reduced damage to the epithelium of the renal tubules. At the same time, neither pronounced lymphohistiocytic infiltration, nor focal nephrosclerosis typical of control series were observed.

962 - Prevention of acute postischemic renal failure with the use of a protein-peptide complex from embryonic tissue

962 - Prevention of acute postischemic renal failure with the use of a protein-peptide complex from embryonic tissue

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement.

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein–peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR–peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface root-mean-square-deviation measure.

Comprehensive Evaluation of Fourteen Docking Programs on Protein-Peptide Complexes.

A large number of protein-protein interactions (PPIs) are mediated by the interactions between proteins and peptide segments binding partners, and therefore determination of protein-peptide interactions (PpIs) is quite crucial to elucidate important biological processes and design peptides or peptidomimetic drugs that can modulate PPIs. Nowadays, as a powerful computation tool, molecular docking has been widely utilized to predict the binding structures of protein-peptide complexes. However, although a number of docking programs have been available, the systematic study on the assessment of their performance for PpIs has never been reported. In this study, a benchmark data set called PepSet consisting of 185 protein-peptide complexes with peptide length ranging from 5 to 20 residues was employed to evaluate the performance of 14 docking programs, including three protein-protein docking programs (ZDOCK, FRODOCK, and HawkDock), three small molecule docking programs (GOLD, Surflex-Dock, and AutoDock Vina), and eight protein-peptide docking programs (GalaxyPepDock, MDockPeP, HPEPDOCK, CABS-dock, pepATTRACT, DINC, AutoDock CrankPep (ADCP), and HADDOCK peptide docking). A new evaluation parameter, named IL_RMSD, was proposed to measure the docking accuracy with fnat (the fraction of native contacts). In global docking, HPEPDOCK performs the best for the entire data set and yields the success rates of 4.3%, 24.3%, and 55.7% at the top 1, 10, and 100 levels, respectively. In local docking, overall, ADCP achieves the best predictions and reaches the success rates of 11.9%, 37.3%, and 70.3% at the top 1, 10, and 100 levels, respectively. It is expected that our work can provide some helpful insights into the selection and development of improved docking programs for PpIs. The benchmark data set is freely available at http://cadd.zju.edu.cn/pepset/.

Biocatalytic cascades operating on macromolecular scaffolds and in confined environments

Biocatalytic cascades guide complex, efficient and selective intracellular transformations. These unique features originate from the spatial organization of the biocatalysts in confined cellular environments that allow the directional channelling of reaction intermediates across the cells. Here we address efforts directed towards the development of synthetic cell analogues and supramolecular ensembles acting as nano/microenvironments for operating biocatalytic cascades. Multienzyme systems are integrated within metal–organic frameworks, polymersomes, lipid-stabilized microdroplets and hydrogel microparticles acting as cell-like containments. Also, multienzyme systems are spatially positioned on one-dimensional DNA wires, two-dimensional DNA strips or origami tiles, and three-dimensional DNA origami bundles or cages, and specific protein–protein interactions or peptide–protein complexes provide versatile scaffolds for engineering enzyme assemblies. Biocatalytic cascades operating on these scaffolds or in confined nano/microenvironments reveal substantially enhanced reaction yields compared with the analogous diffusional mixtures of the biocomponents. Mechanistic pathways accounting for the enhanced biocatalytic activities and future challenges in developing and applying biocatalytic cascades are presented. Spatial organization of biocatalytic cascades can improve their performance. In this Review Article, Itamar Willner and colleagues discuss technologies to artificially confine and localize enzyme cascades, the origin of observed rate enhancements and potential applications of such designed systems.

Enhanced Sampling of Peptide Binding to Proteins through Gaussian Accelerated Molecular Dynamics Simulations

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to sufficiently sample peptide-protein interactions through conventional molecular dynamics simulations, due to long timescales and extremely high system flexibility. Here, all-atom simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method have been applied for enhanced sampling of peptide binding to proteins. The GaMD simulations have identified important low-energy conformational states for three model peptide-protein complexes.

Modeling protein‐protein, protein‐peptide, and protein‐oligosaccharide complexes: CAPRI 7th edition

We present the seventh report on 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. Performance was evaluated on the basis of 36 114 models of protein complexes submitted by 57 groups-including 13 automatic servers-in prediction rounds held during the years 2016 to 2019 for eight protein-protein, three protein-peptide, and five protein-oligosaccharide targets with different length ligands. Six of the protein-protein targets represented challenging hetero-complexes, due to factors such as availability of distantly related templates for the individual subunits, or for the full complex, inter-domain flexibility, conformational adjustments at the binding region, or the multi-component nature of the complex. The main challenge for the protein-peptide and protein-oligosaccharide complexes was to accurately model the ligand conformation and its interactions at the interface. Encouragingly, models of acceptable quality, or better, were obtained for a total of six protein-protein complexes, which included four of the challenging hetero-complexes and a homo-decamer. But fewer of these targets were predicted with medium or higher accuracy. High accuracy models were obtained for two of the three protein-peptide targets, and for one of the protein-oligosaccharide targets. The remaining protein-sugar targets were predicted with medium accuracy. Our analysis indicates that progress in predicting increasingly challenging and diverse types of targets is due to closer integration of template-based modeling techniques with docking, scoring, and model refinement procedures, and to significant incremental improvements in the underlying methodologies.

SAC3B is a target of CML19, the centrin 2 of Arabidopsis thaliana.

Arabidopsis centrin 2, also known as calmodulin-like protein 19 (CML19), is a member of the EF-hand superfamily of calcium (Ca2+)-binding proteins. In addition to the notion that CML19 interacts with the nucleotide excision repair protein RAD4, CML19 was suggested to be a component of the transcription export complex 2 (TREX-2) by interacting with SAC3B. However, the molecular determinants of this interaction have remained largely unknown. Herein, we identified a CML19-binding site within the C-terminus of SAC3B and characterized the binding properties of the corresponding 26-residue peptide (SAC3Bp), which exhibits the hydrophobic triad centrin-binding motif in a reversed orientation (I8W4W1). Using a combination of spectroscopic and calorimetric experiments, we shed light on the SAC3Bp-CML19 complex structure in solution. We demonstrated that the peptide interacts not only with Ca2+-saturated CML19, but also with apo-CML19 to form a protein-peptide complex with a 1 : 1 stoichiometry. Both interactions involve hydrophobic and electrostatic contributions and include the burial of Trp residues of SAC3Bp. However, the peptide likely assumes different conformations upon binding to apo-CML19 or Ca2+-CML19. Importantly, the peptide dramatically increases the affinity for Ca2+ of CML19, especially of the C-lobe, suggesting that in vivo the protein would be Ca2+-saturated and bound to SAC3B even at resting Ca2+-levels. Our results, providing direct evidence that Arabidopsis SAC3B is a CML19 target and proposing that CML19 can bind to SAC3B through its C-lobe independent of a Ca2+ stimulus, support a functional role for these proteins in TREX-2 complex and mRNA export.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set.

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .

InterPep2: global peptide-protein docking using interaction surface templates.

MotivationInteractions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming and difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability.ResultsInterPep2 is a freely available method for predicting the structure of peptide–protein interactions. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18).Availability and implementationThe program is available from: http://wallnerlab.org/InterPep2.Supplementary information Supplementary data are available at Bioinformatics online.

Understanding Docking Complexes of Macromolecules Using HADDOCK: The Synergy between Experimental Data and Computations.

This protocol illustrates the modelling of a protein-peptide complex using the synergic combination of in silico analysis and experimental results. To this end, we use the integrative modelling software HADDOCK, which possesses the powerful ability to incorporate experimental data, such as NMR Chemical Shift Perturbations and biochemical protein-peptide interaction data, as restraints to guide the docking process. Based on the modelling results, a rational mutagenesis approach is used to validate the generated models. The experimental results allow to select a final structural model best representing the bona fide protein-peptide complex. The described protocol can also be applied to model protein-protein complexes. There is no size limit for the macromolecular complexes that can be characterized by HADDOCK as long as the 3D structures of the individual components are available.

MDockPeP: A Web Server for Blind Prediction of Protein-Peptide Complex Structures.

Protein-peptide interactions mediate a wide range of important cellular tasks. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. Recently, we developed a docking-based method for predicting protein-peptide complex structures, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. The produced modes are then evaluated with a statistical potential-based scoring function. The method has been implemented into an online server, MDockPeP server, which is freely available at http://zougrouptoolkit.missouri.edu/mdockpep . The server can be used for protein-peptide complex structure prediction. The server can also be used for initial-stage sampling of the protein-peptide binding modes for computational-demanding simulation or docking methods.

Protocols for All-Atom Reconstruction and High-Resolution Refinement of Protein-Peptide Complex Structures.

Structural characterizations of protein-peptide complexes may require further improvements. These may include reconstruction of missing atoms and/or structure optimization leading to higher accuracy models. In this work, we describe a workflow that generates accurate structural models of peptide-protein complexes starting from protein-peptide models in C-alpha representation generated using CABS-dock molecular docking. First, protein-peptide models are reconstructed from their C-alpha traces to all-atom representation using MODELLER. Next, they are refined using Rosetta FlexPepDock. The described workflow allows for reliable all-atom reconstruction of CABS-dock models and their further improvement to high-resolution models.

GP96 Heat Shock Protein-Peptide Complex Vaccine in Treating Patients With Liver Cancer

GP96 Heat Shock Protein-Peptide Complex Vaccine in Treating Patients With Liver Cancer

Predicting and designing therapeutics against the Nipah virus.

Despite Nipah virus outbreaks having high mortality rates (>70% in Southeast Asia), there are no licensed drugs against it. In this study, we have considered all 9 Nipah proteins as potential therapeutic targets and computationally identified 4 putative peptide inhibitors (against G, F and M proteins) and 146 small molecule inhibitors (against F, G, M, N, and P proteins). The computations include extensive homology/ab initio modeling, peptide design and small molecule docking. An important contribution of this study is the increased structural characterization of Nipah proteins by approximately 90% of what is deposited in the PDB. In addition, we have carried out molecular dynamics simulations on all the designed protein-peptide complexes and on 13 of the top shortlisted small molecule ligands to check for stability and to estimate binding strengths. Details, including atomic coordinates of all the proteins and their ligand bound complexes, can be accessed at http://cospi.iiserpune.ac.in/Nipah. Our strategy was to tackle the development of therapeutics on a proteome wide scale and the lead compounds identified could be attractive starting points for drug development. To counter the threat of drug resistance, we have analysed the sequences of the viral strains from different outbreaks, to check whether they would be sensitive to the binding of the proposed inhibitors.

Structure prediction of biological assemblies using GALAXY in CAPRI rounds 38-45.

We participated in CARPI rounds 38-45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein-protein, protein-peptide, and protein-oligosaccharide interactions. Both template-based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo-oligomer protein, GalaxyPepDock for protein-peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock-ab-initio for protein-peptide complex, GalaxyDock2 and Galaxy7TM for protein-oligosaccharide complex) have been tested. Template-based methods depend heavily on the availability of proper templates and template-target similarity, and template-target difference is responsible for inaccuracy of template-based models. Inaccurate template-based models could be improved by our structure refinement and loop modeling methods based on physics-based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics-based methods for such problems is still to come.

PepPro: A Nonredundant Structure Data Set for Benchmarking Peptide-Protein Computational Docking.

We present a nonredundant benchmark, coined PepPro, for testing peptide-protein docking algorithms. Currently, PepPro contains 89 nonredundant experimentally determined peptide-protein complex structures, with peptide sequence lengths ranging from 5 to 30 amino acids. The benchmark covers peptides with distinct secondary structures, including helix, partial helix, a mixture of helix and β-sheet, β-sheet formed through binding, β-sheet formed through self-folding, and coil. In addition, unbound proteins' structures are provided for 58 complexes and can be used for testing the ability of a docking algorithm handling the conformational changes of proteins during the binding process. PepPro should benefit the docking community for the development and improvement of peptide docking algorithms. The benchmark is available at http://zoulab.dalton.missouri.edu/PepPro_benchmark. © 2019 Wiley Periodicals, Inc.

Профилактика развития острой постишемической почечной недостаточности с использованием белково-пептидного комплекса эмбриональной ткани

Профилактика развития острой постишемической почечной недостаточности с использованием белково-пептидного комплекса эмбриональной ткани