Highly Flexible Protein-Peptide Docking Using CABS-Dock

Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions

Design of peptide binders is an attractive strategy for targeting "undruggable" protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.

Yeast Protein Asf1 Possesses Modulating Activity towards Protein Kinase CK2

Protein kinase CK2 plays an important role in cell survival and protects regulatory proteins from caspase-mediated degradation during apoptosis. The consensus sequence of proteins phosphorylated by CK2 contains a cluster of acidic amino acids around the phosphorylation site. The poly-acidic sequence in yeast protein Asf1 is similar to the acidic loop in CK2β, which possesses a regulatory function. We observed that the overexpression of Asf1 in yeast cells influences cell growth. Experiments performed in vitro and in vivo indicate that yeast protein Asf1 inhibits protein kinase CK2. Our data suggest that each CK2 isoform might be regulated in a different way. Deletion of the amino or carboxyl end of Asf1 reveals that the acidic cluster close to the C-terminus is responsible for the activation or inhibition of CK2 activity.

Improving peptide-protein docking with AlphaFold-Multimer using forced sampling

Protein interactions are key in vital biological processes. In many cases, particularly in regulation, this interaction is between a protein and a shorter peptide fragment. Such peptides are often part of larger disordered regions in other proteins. The flexible nature of peptides enables the rapid yet specific regulation of important functions in cells, such as their life cycle. Consequently, knowledge of the molecular details of peptide-protein interactions is crucial for understanding and altering their function, and many specialized computational methods have been developed to study them. The recent release of AlphaFold and AlphaFold-Multimer has led to a leap in accuracy for the computational modeling of proteins. In this study, the ability of AlphaFold to predict which peptides and proteins interact, as well as its accuracy in modeling the resulting interaction complexes, are benchmarked against established methods. We find that AlphaFold-Multimer predicts the structure of peptide-protein complexes with acceptable or better quality (DockQ ≥0.23) for 66 of the 112 complexes investigated-25 of which were high quality (DockQ ≥0.8). This is a massive improvement on previous methods with 23 or 47 acceptable models and only four or eight high quality models, when using energy-based docking or interaction templates, respectively. In addition, AlphaFold-Multimer can be used to predict whether a peptide and a protein will interact. At 1% false positives, AlphaFold-Multimer found 26% of the possible interactions with a precision of 85%, the best among the methods benchmarked. However, the most interesting result is the possibility of improving AlphaFold by randomly perturbing the neural network weights to force the network to sample more of the conformational space. This increases the number of acceptable models from 66 to 75 and improves the median DockQ from 0.47 to 0.55 (17%) for first ranked models. The best possible DockQ improves from 0.58 to 0.72 (24%), indicating that selecting the best possible model is still a challenge. This scheme of generating more structures with AlphaFold should be generally useful for many applications involving multiple states, flexible regions, and disorder.

A plausible contributor to multiple sclerosis; presentation of antigenic myelin protein epitopes by major histocompatibility complexes

BACKGROUND

Multiple sclerosis (MS) can be induced upon successful presentation of myelin antigens by MHC I/II. Antigenic similarity between the myelin and viral proteins may worsen the immunological responses.

METHODOLOGY

Antigenic regions within myelin proteins; PLP1, MBP, MOG, and MAG were analyzed using SVMTrip and EMBOSS. Homology search identified sequence similarity between the predicted host epitopes and viral proteins. NetMHCpan predicted MHC I/II binding followed by peptide-protein docking through the HPEPDOCK server. Thereafter we analyzed conformational flexibility and stability of 15 protein-peptide complexes based on high docking scores. The binding free energy was calculated using conventional (MD) and Gaussian accelerated molecular dynamics simulation.

RESULTS

PLP1, MBP, MAG and MOG contained numerous antigenic epitopes. MBP and MOG epitopes had sequence similarity to HHV-6 BALF5; EBNA1 and CMV glycoprotein M (gM), and EBV LMP2B, gp350/220; HHV-8 ORFs respectively. Many herpes virus proteins like tegument, envelope glycoproteins, and ORFs of EBV, CMV, HHV-6, and HHV-8 demonstrated sequence similarity with MAG and PLP1. Some antigenic peptides were also linear B-cell epitopes and influenced cytokine production by T-cell. MHC I allele HLA-B*57:01 bound to PLP1 peptide and HLA-A*68:02 bound to a MAG peptide strongly. MHC II alleles HLA-DRB1*04:05 and HLA-DR1*01:01 associated with MAG- and MOG-derived peptides, respectively, demonstrating high HPEPDOCK scores. MD simulations established stable binding of certain peptides with the MHC namely HLA-B*51:01-MBP(DYKSAHKGFKGVDAQGTLSKIFKL), HLA-B*57:01-PLP1(PDKFVGITYALTVVWLLVFACSAVPVYIYF), HLA-DR1*01:01-MOG(VEDPFYWVSPGVLVLLAVLPVLLLQITVGLVFLCLQYR) and HLA-DRB1*04:05-MAG(TWVQVSLLHFVPTREA).

CONCLUSIONS

Cross-reactivity between self-antigens and pathogen derived immunodominant epitopes may induce MS. Our study supported the role of specific MHC alleles as a contributing MS risk factor.

Theoretical study of the interactions between peptide tyrosine tyrosine [PYY (1-36)], a newly identified modulator in type 2 diabetes pathophysiology, with receptors NPY1R and NPY4R

PURPOSE

Diabetes mellitus is a common condition in the clinically obese. Bariatric surgery is one of the ways to put type 2 diabetes in remission. Recent findings propose the appetite-regulator peptide tyrosine tyrosine (PYY) as a therapeutic option for patients with type 2 diabetes. This novel gut hormone restores impaired insulin and glucagon secretion in pancreatic islets and is implicated in type 2 diabetes reversal after bariatric surgery. The current study elucidates the interactions between PYY and the NPY1R and NPY4R receptors using computational methods.

METHODS

Protein structure prediction, molecular docking simulation, and molecular dynamics (MD) simulation were performed to elucidate the interactions of PYY with NPY1R and NPY4R.

RESULTS

The predicted binding models of PYY-NPY receptors are in agreement with those described in the literature, although different interaction partners are presented for the C-terminal tail of PYY. Non-polar interactions are predicted to drive the formation of the protein complex. The calculated binding energies show that PYY has higher affinity for NPY4R (ΔG_GBSA = -65.08 and ΔG_PBSA = -87.62 kcal/mol) than for NPY1R (ΔG_GBSA = -23.11 and ΔG_PBSA = -50.56 kcal/mol).

CONCLUSIONS

Based on the constructed models, the binding conformations obtained from docking and MD simulation for both the PYY-NPY1R and PYY-NPY4R complexes provide a detailed map of possible interactions. The calculated binding energies show a higher affinity of PYY for NPY4R. These findings may help to understand the mechanisms behind the improvement of diabetes following bariatric surgery.

Protein-Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.

Protocols for Fast Simulations of Protein Structure Flexibility Using CABS-Flex and SURPASS

Conformational flexibility of protein structures can play an important role in protein function. The flexibility is often studied using computational methods since experimental characterization can be difficult. Depending on protein system size, computational tools may require large computational resources or significant simplifications in the modeled systems to speed up calculations. In this work, we present the protocols for efficient simulations of flexibility of folded protein structures that use coarse-grained simulation tools of different resolutions: medium, represented by CABS-flex, and low, represented by SUPRASS. We test the protocols using a set of 140 globular proteins and compare the results with structure fluctuations observed in MD simulations, ENM modeling, and NMR ensembles. As demonstrated, CABS-flex predictions show high correlation to experimental and MD simulation data, while SURPASS is less accurate but promising in terms of future developments.

CABS-dock standalone: a toolbox for flexible protein-peptide docking

Summary

CABS-dock standalone is a multiplatform Python package for protein–peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein–peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation—it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results.

Availability and implementation

CABS-dock standalone is distributed under the MIT licence, which is free for academic and non-profit users. It is implemented in Python and Fortran. The CABS-dock standalone source code, wiki with documentation and examples of use and installation instructions for Linux, macOS and Windows are available in the CABS-dock standalone repository at https://bitbucket.org/lcbio/cabsdock.

Docking interactions determine early cleavage events in insulin proteolysis by pepsin: Experiment and simulation

In silico modelling of cascade enzymatic proteolysis is an exceedingly complex and challenging task. Here, we study partial proteolysis of insulin by pepsin: a process leading to the release of a highly amyloidogenic two chain 'H-fragment'. The H-fragment retains several cleavage sites for pepsin. However, under favorable conditions H-monomers rapidly self-assemble into proteolysis-resistant amyloid fibrils whose composition provides snapshots of early and intermediate stages of the proteolysis. In this work, we report a remarkable agreement of experimentally determined and simulation-predicted cleavage sites on different stages of the proteolysis. Prediction of cleavage sites was based on the comprehensive analysis of the docking interactions from direct simulation of coupled folding and binding of insulin (or its cleaved derivatives) to pepsin. The most frequent interactions were found to be between the pepsin's active site, or its direct vicinity, and the experimentally determined insulin cleavage sites, which suggest that the docking interactions govern the proteolytic process.

Flexible docking of peptides to proteins using CABS-dock

Molecular docking of peptides to proteins can be a useful tool in the exploration of the possible peptide binding sites and poses. CABS-dock is a method for protein-peptide docking that features significant conformational flexibility of both the peptide and the protein molecules during the peptide search for a binding site. The CABS-dock has been made available as a web server and a standalone package. The web server is an easy to use tool with a simple web interface. The standalone package is a command-line program dedicated to professional users. It offers a number of advanced features, analysis tools and support for large-sized systems. In this article, we outline the current status of the CABS-dock method, its recent developments, applications, and challenges ahead.

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.

Protein-peptide docking using CABS-dock and contact information

CABS-dock is a computational method for protein-peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein-peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein-peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/).

SHORT ABSTRACT

CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking.

Probing the local conformational flexibility in receptor recognition: mechanistic insight from an atomic-scale investigation

Inherent protein conformational flexibility is important for biomolecular recognition, but this critical property is often neglected in several studies. This event can lead to large deviations in the research results. In the current contribution, we disclose the effects of the local conformational flexibility on receptor recognition by using an atomic-scale computational method. The results indicated that both static and dynamic reaction modes have noticeable differences, and these originated from the structural features of the protein molecules. Dynamic interaction results displayed that the structural stability and conformational flexibility of the proteins had a significant influence on the recognition processes. This point related closely to the characteristics of the flexible loop regions where bixin located within the protein structures. The energy decomposition analyses and circular dichroism results validated the rationality of the recognition studies. More importantly, the conformational and energy changes of some residues around the bixin binding domain were found to be vital to biological reactions. These microscopic findings clarified the nature of the phenomenon that the local conformational flexibility could intervene in receptor recognition. Obviously, this report may provide biophysical evidence for the exploration of the structure-function relationships of the biological receptors in the human body.

In Silico and 3D QSAR Studies of Natural Based Derivatives as Xanthine Oxidase Inhibitors

BACKGROUND

A large number of disorders and their symptoms emerge from deficiency or overproduction of specific metabolites has drawn the attention for the discovery of new therapeutic agents for the treatment of disorders. Various approaches such as computational drug design have provided the new methodology for the selection and evaluation of target protein and the lead compound mechanistically. For instance, the overproduction of xanthine oxidase causes the accumulation of uric acid which can prompt gout.

OBJECTIVE

In the present study we critically discussed the various techniques such as 3-D QSAR and molecular docking for the study of the natural based xanthine oxidase inhibitors with their mechanistic insight into the interaction of xanthine oxidase and various natural leads.

CONCLUSION

The computational studies of deferent natural compounds were discussed as a result the flavonoids, anthraquinones, xanthones shown the remarkable inhibitory potential for xanthine oxidase inhibition moreover the flavonoids such as hesperidin and rutin were found as promising candidates for further exploration.

Rational Identification of a Colorectal Cancer Targeting Peptide through Phage Display

Colorectal cancer is frequently diagnosed at an advanced stage due to the absence of early clinical indicators. Hence, the identification of new targeting molecules is crucial for an early detection and development of targeted therapies. This study aimed to identify and characterize novel peptides specific for the colorectal cancer cell line RKO using a phage-displayed peptide library. After four rounds of selection plus a negative step with normal colorectal cells, CCD-841-CoN, there was an obvious phage enrichment that specifically bound to RKO cells. Cell-based enzyme-linked immunosorbent assay (ELISA) was performed to assess the most specific peptides leading to the selection of the peptide sequence CPKSNNGVC. Through fluorescence microscopy and cytometry, the synthetic peptide RKOpep was shown to specifically bind to RKO cells, as well as to other human colorectal cancer cells including Caco-2, HCT 116 and HCT-15, but not to the normal non-cancer cells. Moreover, it was shown that RKOpep specifically targeted human colorectal cancer cell tissues. A bioinformatics analysis suggested that the RKOpep targets the monocarboxylate transporter 1, which has been implicated in colorectal cancer progression and prognosis, proven through gene knockdown approaches and shown by immunocytochemistry co-localization studies. The peptide herein identified can be a potential candidate for targeted therapies for colorectal cancer.

Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields

The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein⁻peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein⁻peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

Structural distortions due to missense mutations in human formylglycine-generating enzyme leading to multiple sulfatase deficiency

The major candidate for multiple sulfatase deficiency is a defective formylglycine-generating enzyme (FGE). Though adequately produced, mutations in FGE stall the activation of sulfatases and prevent their activity. Missense mutations, viz. E130D, S155P, A177P, W179S, C218Y, R224W, N259I, P266L, A279V, C336R, R345C, A348P, R349Q and R349W associated with multiple sulfatase deficiency are yet to be computationally studied. Aforementioned mutants were initially screened through ws-SNPs&GO^3D program. Mutant R345C acquired the highest score, and hence was studied in detail. Discrete molecular dynamics explored structural distortions due to amino acid substitution. Therein, comparative analyses of wild type and mutant were carried out. Changes in structural contours were observed between wild type and mutant. Mutant had low conformational fluctuation, high atomic mobility and more compactness than wild type. Moreover, free energy landscape showed mutant to vary in terms of its conformational space as compared to wild type. Subsequently, wild type and mutant were subjected to single-model analyses. Mutant had lesser intra molecular interactions than wild type suggesting variations pertaining to its secondary structure. Furthermore, simulated thermal denaturation showed dissimilar pattern of hydrogen bond dilution. Effects of these variations were observed as changes in elements of secondary structure. Docking studies of mutant revealed less favourable binding energy towards its substrate as compared to wild type. Therefore, theoretical explanations for structural distortions of mutant R345C leading to multiple sulfatase deficiency were revealed. The protocol of the study could be useful to examine the effectiveness of pharmacological chaperones prior to experimental studies.

Improved scFv Anti-LOX-1 Binding Activity by Fusion with LOX-1-Binding Peptides

The oxidized low-density lipoprotein receptor-1 (LOX-1) targeted single-chain variable fragment (scFvs) is a promising molecule for the targeted delivery of imaging and therapeutic molecules of atherosclerotic diseases; however, its applications are limited by the inherent low antigen affinity. In this study, the three-dimensional (3D) model of the anti-LOX-1 scFv was constructed and its docking with the LOX-1 protein was developed. To improve the LOX-1-binding activity, the anti-LOX-1 scFv was designed to fuse with one of three LOX-1-binding heptapeptides, LTPATAI, FQTPPQL, and LSIPPKA, at its N-terminus and C-terminus and in the linker region, which have different LOX-1-binding interfaces with the anti-LOX-1 scFv analyzed by an array of computational approaches. These scFv/peptide fusions were constructed, successfully expressed in Brevibacillus choshinensis hosts, and purified by a two-step column purification process. The antigen binding activity, structural characteristics, thermal stability, and stability in serum of these fusion proteins were examined. Results showed that the scFv with N-terminal fusing peptides proteins demonstrated increased LOX-1-binding activity without decrease in stability. These findings will help increase the application efficacy of LOX-1 targeting scFv in LOX-1-based therapy.

Phage-Displayed Peptides Selected to Bind Envelope Glycoprotein Show Antiviral Activity against Dengue Virus Serotype 2

Dengue virus is a growing public health threat that affects hundreds of million peoples every year and leave huge economic and social damage. The virus is transmitted by mosquitoes and the incidence of the disease is increasing, among other causes, due to the geographical expansion of the vector's range and the lack of effectiveness in public health interventions in most prevalent countries. So far, no highly effective vaccine or antiviral has been developed for this virus. Here we employed phage display technology to identify peptides able to block the DENV2. A random peptide library presented in M13 phages was screened with recombinant dengue envelope and its fragment domain III. After four rounds of panning, several binding peptides were identified, synthesized, and tested against the virus. Three peptides were able to block the infectivity of the virus while not being toxic to the target cells. Blind docking simulations were done to investigate the possible mode of binding, showing that all peptides appear to bind domain III of the protein and may be mostly stabilized by hydrophobic interactions. These results are relevant to the development of novel therapeutics against this important virus.

A protocol for CABS-dock protein-peptide docking driven by side-chain contact information

Background

The characterization of protein–peptide interactions is a challenge for computational molecular docking. Protein–peptide docking tools face at least two major difficulties: (1) efficient sampling of large-scale conformational changes induced by binding and (2) selection of the best models from a large set of predicted structures. In this paper, we merge an efficient sampling technique with external information about side-chain contacts to sample and select the best possible models.

Methods

In this paper we test a new protocol that uses information about side-chain contacts in CABS-dock protein–peptide docking. As shown in our recent studies, CABS-dock enables efficient modeling of large-scale conformational changes without knowledge about the binding site. However, the resulting set of binding sites and poses is in many cases highly diverse and difficult to score.

Results

As we demonstrate here, information about a single side-chain contact can significantly improve the prediction accuracy. Importantly, the imposed constraints for side-chain contacts are quite soft. Therefore, the developed protocol does not require precise contact information and ensures large-scale peptide flexibility in the broad contact area.

Conclusions

The demonstrated protocol provides the extension of the CABS-dock method that can be practically used in the structure prediction of protein–peptide complexes guided by the knowledge of the binding interface.

Modeling EphB4-EphrinB2 protein-protein interaction using flexible docking of a short linear motif

Background

Many protein–protein interactions are mediated by a short linear motif. Usually, amino acid sequences of those motifs are known or can be predicted. It is much harder to experimentally characterize or predict their structure in the bound form. In this work, we test a possibility of using flexible docking of a short linear motif to predict the interaction interface of the EphB4-EphrinB2 complex (a system extensively studied for its significance in tumor progression).

Methods

In the modeling, we only use knowledge about the motif sequence and experimental structures of EphB4-EphrinB2 complex partners. The proposed protocol enables efficient modeling of significant conformational changes in the short linear motif fragment during molecular docking simulation. For the docking simulations, we use the CABS-dock method for docking fully flexible peptides to flexible protein receptors (available as a server at http://biocomp.chem.uw.edu.pl/CABSdock/). Based on the docking result, the protein–protein complex is reconstructed and refined.

Results

Using this novel protocol, we obtained an accurate EphB4-EphrinB2 interaction model.

Conclusions

The results show that the CABS-dock method may be useful as the primary docking tool in specific protein–protein docking cases similar to EphB4-EphrinB2 complex—that is, where a short linear motif fragment can be identified.

Protein-peptide molecular docking with large-scale conformational changes: the p53-MDM2 interaction

Protein-peptide interactions are often associated with large-scale conformational changes that are difficult to study either by classical molecular modeling or by experiment. Recently, we have developed the CABS-dock method for flexible protein-peptide docking that enables large-scale rearrangements of the protein chain. In this study, we use CABS-dock to investigate the binding of the p53-MDM2 complex, an element of the cell cycle regulation system crucial for anti-cancer drug design. Experimental data suggest that p53-MDM2 binding is affected by significant rearrangements of a lid region - the N-terminal highly flexible MDM2 fragment; however, the details are not clear. The large size of the highly flexible MDM2 fragments makes p53-MDM2 intractable for exhaustive binding dynamics studies using atomistic models. We performed extensive dynamics simulations using the CABS-dock method, including large-scale structural rearrangements of MDM2 flexible regions. Without a priori knowledge of the p53 peptide structure or its binding site, we obtained near-native models of the p53-MDM2 complex. The simulation results match well the experimental data and provide new insights into the possible role of the lid fragment in p53 binding. The presented case study demonstrates that CABS-dock methodology opens up new opportunities for protein-peptide docking with large-scale changes of the protein receptor structure.