Facile detection of peptide-protein interactions using an electrophoretic crosslinking shift assay.

Protein-protein interactions with high specificity and low affinity are functionally important but are not comprehensively understood because they are difficult to identify. Particularly intriguing are the dynamic and specific interactions between folded protein domains and short unstructured peptides known as short linear motifs. Such domain-motif interactions (DMIs) are often difficult to identify and study because affinities are modest to weak. Here we describe "electrophoretic crosslinking shift assay" (ECSA), a simple in vitro approach that detects transient, low affinity interactions by covalently crosslinking a prey protein and a fluorescently labeled bait. We demonstrate this technique on the well characterized DMI between MAP kinases and unstructured D-motif peptide ligands. We show that ECSA detects sequence-specific micromolar interactions using less than a microgram of input prey protein per reaction, making it ideal for verifying candidate low-affinity DMIs of components that purify with low yield. We propose ECSA as an intermediate step in SLiM characterization that bridges the gap between high throughput techniques such as phage display and more resource-intensive biophysical and structural analysis.

Matching protein surface structural patches for high-resolution blind peptide docking.

Peptide docking can be perceived as a subproblem of protein­protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein­peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide­protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide­protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.

A computational approach to identifying peptide inhibitors againstWhite Spot Syndrome Virus: Targeting the virus envelope protein.

The white spot syndrome virus (WSSV), a rapidly replicating and highly lethal pathogen that targets Penaeid shrimp, has emerged as one of the most widespread viruses globally due to its high virulence. With effective chemotherapeutics still unavailable, the pursuit of novel and viable strategies against WSSV remains a crucial focus in the field of shrimp farming. The envelope proteins of WSSV are essential for virus entry, serving as excellent targets for the development of antiviral therapeutics. Novel strategies in the design of inhibitory peptides, especially those targeting envelope protein (VP28) located on the surface of the virus particle, play a critical role as a significant virulence factor during the early stages of inherent WSSV infection in shrimp. In this direction, the current computational study focused on identifying self-inhibitory peptides from the hydrophobic membrane regions of the VP28 protein, employing peptide docking and molecular dynamics simulation (MDS) approaches. Such inhibitory peptides could be useful building blocks for the rational engineering of inhibitory therapeutics since they imitate the mechanism of binding to homologous partners used by their origin domain to interact with other molecules. The N-terminal sequence of VP28 has been reported as the potential site for membrane interactions during the virus entry. Moreover, drug delivery systems mediated by chitosan and gold nanoparticles are being developed to enhance the therapeutic efficacy of anti-viral peptides. These systems can increase the solubility, stability, and selectivity of peptides, possessing better qualities than conventional delivery methods. This computational study on self-inhibitory peptides could be a valuable resource for further in vitro and in vivo studies on anti-viral therapeutics in the aquaculture industry.

Structural insights into the role of the WW2 domain on tandem WW-PPxY motif interactions of oxidoreductase WWOX.

Class I WW domains are present in many proteins of various functions and mediate protein interactions by binding to short linear PPxY motifs. Tandem WW domains often bind peptides with multiple PPxY motifs, but the interplay of WW-peptide interactions is not always intuitive. The WW domain-containing oxidoreductase (WWOX) harbors two WW domains: an unstable WW1 capable of PPxY binding and stable WW2 that cannot bind PPxY. The WW2 domain has been suggested to act as a WW1 domain chaperone, but the underlying mechanism of its chaperone activity remains to be revealed. Here, we combined NMR, isothermal calorimetry, and structural modeling to elucidate the roles of both WW domains in WWOX binding to its PPxY-containing substrate ErbB4. Using NMR, we identified an interaction surface between these two domains that supports a WWOX conformation compatible with peptide substrate binding. Isothermal calorimetry and NMR measurements also indicated that while binding affinity to a single PPxY motif is marginally increased in the presence of WW2, affinity to a dual-motif peptide increases 10-fold. Furthermore, we found WW2 can directly bind double-motif peptides using its canonical binding site. Finally, differential binding of peptides in mutagenesis experiments was consistent with a parallel N- to C-terminal PPxY tandem motif orientation in binding to the WW1-WW2 tandem domain, validating structural models of the interaction. Taken together, our results reveal the complex nature of tandem WW-domain organization and substrate binding, highlighting the contribution of WWOX WW2 to both protein stability and target binding.

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.

Breaking the 'don't eat me' signal: in silico design of CD47-directed peptides for cancer immunotherapy.

The leading cause of death worldwide is cancer. Although there are various therapies available to treat cancer, finding a successful one can be like searching for a needle in a haystack. Immunotherapy appears to be one of those needles in the haystack of cancer treatment. Immunotherapeutic agents enhance the immune response of the patient's body to tumor cells. One of the immunotherapeutic targets, Cluster of Differentiation 47 (CD47), releases the "don't eat me" signal when it binds to its receptor, Signal Regulatory Protein (SIRPα). Tumor cells use this signal to circumvent the immune system, rendering it ineffective. To stop tumor cells from releasing the "don't eat me" signal, the CD47-SIRPα interaction is specifically targeted in this study. To do so, in silico peptides were designed based on the structural analysis of the interaction between two proteins using point mutations on the interacting residues with the other amino acids. The peptide library was designed and docked on SIRPα using computational tools. Later on, after analyzing the docked complex, the best of them was selected for MD simulation studies of 100 ns. Further analysis after MD studies was carried out to determine the possible potential anti-SIRPα peptides.

Pep-Whisperer: Inhibitory peptide design.

Designing peptides for protein-protein interaction inhibition is of significant interest in computer-aided drug design. Such inhibitory peptides could mimic and compete with the binding of the partner protein to the inhibition target. Experimental peptide design is a laborious, time consuming, and expensive multi-step process. Therefore, in silico peptide design can be beneficial for achieving this task. We present a novel algorithm, Pep-Whisperer, which aims to design inhibitory peptides for protein-protein interaction. The desirable peptides would have a relatively high predicted binding affinity to the target protein in a given protein-protein complex. The algorithm outputs linear peptides which are based on an initial template. The template could either be a peptide which is retrieved from the interaction site, or a patch of nonconsecutive amino acids from the protein-protein interface which is completed to a linear peptide by short polyalanine linkers. In addition, the algorithm takes into consideration the conservation of the amino acids in the ligand-protein binding site by using evolutionary information for choosing the preferred amino acids in each position of the designed peptide. Our algorithm was able to design peptides with high predicted binding affinity to the target protein. The method is fully automated and available as a web server at http://bioinfo3d.cs.tau.ac.il/PepWhisperer/.

Des3PI: a fragment-based approach to design cyclic peptides targeting protein-protein interactions.

Protein-protein interactions (PPIs) play crucial roles in many cellular processes and their deregulation often leads to cellular dysfunctions. One promising way to modulate PPIs is to use peptide derivatives that bind their protein target with high affinity and high specificity. Peptide modulators are often designed using secondary structure mimics. However, fragment-based design is an alternative emergent approach in the PPI field. Most of the reported computational fragment-based libraries targeting PPIs are composed of small molecules or already approved drugs, but, according to our knowledge, no amino acid based library has been reported yet. In this context, we developed a novel fragment-based approach called Des3PI (design of peptides targeting protein-protein interactions) with a library composed of natural amino acids. All the amino acids are docked into the target surface using Autodock Vina. The resulting binding modes are geometrically clustered, and, in each cluster, the most recurrent amino acids are identified and form the hotspots that will compose the designed peptide. This approach was applied on Ras and Mcl-1 proteins, as well as on A[Formula: see text] protofibril. For each target, at least five peptides generated by Des3PI were tested in silico: the peptides were first blindly docked on their target, and then, the stability of the successfully docked complexes was verified using 200 ns MD simulations. Des3PI shows very encouraging results by yielding at least 3 peptides for each protein target that succeeded in passing the two-step assessment.

Computer- and NMR-Aided Design of Small-Molecule Inhibitors of the Hub1 Protein.

By binding to the spliceosomal protein Snu66, the human ubiquitin-like protein Hub1 is a modulator of the spliceosome performance and facilitates alternative splicing. Small molecules that bind to Hub1 would be of interest to study the protein-protein interaction of Hub1/Snu66, which is linked to several human pathologies, such as hypercholesterolemia, premature aging, neurodegenerative diseases, and cancer. To identify small molecule ligands for Hub1, we used the interface analysis, peptide modeling of the Hub1/Snu66 interaction and the fragment-based NMR screening. Fragment-based NMR screening has not proven sufficient to unambiguously search for fragments that bind to the Hub1 protein. This was because the Snu66 binding pocket of Hub1 is occupied by pH-sensitive residues, making it difficult to distinguish between pH-induced NMR shifts and actual binding events. The NMR analyses were therefore verified experimentally by microscale thermophoresis and by NMR pH titration experiments. Our study found two small peptides that showed binding to Hub1. These peptides are the first small-molecule ligands reported to interact with the Hub1 protein.

Central residues of FSHß (89-97) peptide are not critical for FSHR binding: Implications for peptidomimetic design.

In our previous study, we had identified a 9-mer peptide (FSHß (89-97)) derived from seat belt loop of human FSHß and demonstrated its ability to function as FSHR antagonist in vivo. Structure analysis revealed that the four central residues 91STDC94 within this peptide may not be critical for receptor binding. In the present study, 91STDC94 residues were substituted with alanine to generate ΔFSHß 89-97(91STDC94/AAAA) peptide. Analogous to the parent peptide, ΔFSHß 89-97(91STDC94/AAAA) peptide inhibited binding of iodinated FSH to rat FSHR and reduced FSH-induced cAMP production. The peptide could impede granulosa cell proliferation leading to reduction in FSH-mediated ovarian weight gain in immature female rats. In these rats, peptide administration further downregulated androgen receptor and estrogen receptor-alpha expression and upregulated estrogen receptor-beta expression. The results indicate that substitution of 91STDC94 with alanine did not significantly alter FSHR antagonist activity of FSHß (89-97) peptide implying that these residues are not critical for FSH-FSHR interaction and can be replaced with non-peptidic moieties for development of more potent peptidomimetics.

Conformational Insights into the Control of CNF1 Toxin Activity by Peptidyl-Prolyl Isomerization: A Molecular Dynamics Perspective.

The cytotoxic necrotizing factor 1 (CNF1) toxin from uropathogenic Escherichia coli constitutively activates Rho GTPases by catalyzing the deamidation of a critical glutamine residue located in the switch II (SWII). In crystallographic structures of the CNF1 catalytic domain (CNF1CD), surface-exposed P768 and P968 peptidyl-prolyl imide bonds (X-Pro) adopt an unusual cis conformation. Here, we show that mutation of each proline residue into glycine abrogates CNF1CD in vitro deamidase activity, while mutant forms of CNF1 remain functional on RhoA in cells. Using molecular dynamics simulations coupled to protein-peptide docking, we highlight the long-distance impact of peptidyl-prolyl cis-trans isomerization on the network of interactions between the loops bordering the entrance of the catalytic cleft. The energetically favorable isomerization of P768 compared with P968, induces an enlargement of loop L1 that fosters the invasion of CNF1CD catalytic cleft by a peptide encompassing SWII of RhoA. The connection of the P968 cis isomer to the catalytic cysteine C866 via a ladder of stacking interactions is alleviated along the cis-trans isomerization. Finally, the cis-trans conversion of P768 favors a switch of the thiol side chain of C866 from a resting to an active orientation. The long-distance impact of peptidyl-prolyl cis-trans isomerizations is expected to have implications for target modification.

Multiscale Modeling of Amyloid Fibrils Formed by Aggregating Peptides Derived from the Amyloidogenic Fragment of the A-Chain of Insulin.

Computational prediction of molecular structures of amyloid fibrils remains an exceedingly challenging task. In this work, we propose a multi-scale modeling procedure for the structure prediction of amyloid fibrils formed by the association of ACC1-13 aggregation-prone peptides derived from the N-terminal region of insulin's A-chain. First, a large number of protofilament models composed of five copies of interacting ACC1-13 peptides were predicted by application of CABS-dock coarse-grained (CG) docking simulations. Next, the models were reconstructed to all-atom (AA) representations and refined during molecular dynamics (MD) simulations in explicit solvent. The top-scored protofilament models, selected using symmetry criteria, were used for the assembly of long fibril structures. Finally, the amyloid fibril models resulting from the AA MD simulations were compared with atomic force microscopy (AFM) imaging experimental data. The obtained results indicate that the proposed multi-scale modeling procedure is capable of predicting protofilaments with high accuracy and may be applied for structure prediction and analysis of other amyloid fibrils.

Exploring Molecular Contacts of MUC1 at CIN85 Binding Interface to Address Future Drug Design Efforts.

The modulation of protein-protein interactions (PPIs) by small molecules represents a valuable strategy for pharmacological intervention in several human diseases. In this context, computer-aided drug discovery techniques offer useful resources to predict the network of interactions governing the recognition process between protein partners, thus furnishing relevant information for the design of novel PPI modulators. In this work, we focused our attention on the MUC1-CIN85 complex as a crucial PPI controlling cancer progression and metastasis. MUC1 is a transmembrane glycoprotein whose extracellular domain contains a variable number of tandem repeats (VNTRs) regions that are highly glycosylated in normal cells and under-glycosylated in cancer. The hypo-glycosylation fosters the exposure of the backbone to new interactions with other proteins, such as CIN85, that alter the intracellular signalling in tumour cells. Herein, different computational approaches were combined to investigate the molecular recognition pattern of MUC1-CIN85 PPI thus unveiling new structural information useful for the design of MUC1-CIN85 PPI inhibitors as potential anti-metastatic agents.

The Structural Basis of Peptide Binding at Class A G Protein-Coupled Receptors.

G protein-coupled receptors (GPCRs) represent the largest membrane protein family and a significant target class for therapeutics. Receptors from GPCRs' largest class, class A, influence virtually every aspect of human physiology. About 45% of the members of this family endogenously bind flexible peptides or peptides segments within larger protein ligands. While many of these peptides have been structurally characterized in their solution state, the few studies of peptides in their receptor-bound state suggest that these peptides interact with a shared set of residues and undergo significant conformational changes. For the purpose of understanding binding dynamics and the development of peptidomimetic drug compounds, further studies should investigate the peptide ligands that are complexed to their cognate receptor.

Molecular Dynamics Scoring of Protein-Peptide Models Derived from Coarse-Grained Docking.

One of the major challenges in the computational prediction of protein-peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein-peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor-ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein-peptide complexes show that the proposed MD-based scoring approach can be used to identify protein-peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.

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.

Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45.

Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy models for a particularly challenging target, T121. In contrast to the classical peptide-mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta-sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide-protein interactions, we extracted PeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta-sheet complementation, and tested our protocol for global peptide-docking PIPER-FlexPepDock on this dataset. We find that the beta-strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.

Docking proteins and peptides under evolutionary constraints in Critical Assessment of PRediction of Interactions rounds 38 to 45.

Computational structural prediction of macromolecular interactions is a fundamental tool toward the global understanding of cellular processes. The Critical Assessment of PRediction of Interactions (CAPRI) community-wide experiment provides excellent opportunities for blind testing computational docking methods and includes original targets, thus widening the range of docking applications. Our participation in CAPRI rounds 38 to 45 enabled us to expand the way we include evolutionary information in structural predictions beyond our standard free docking InterEvDock pipeline. InterEvDock integrates a coarse-grained potential that accounts for interface coevolution based on joint multiple sequence alignments of two protein partners (co-alignments). However, even though such co-alignments could be built for none of the CAPRI targets in rounds 38 to 45, including host-pathogen and protein-oligosaccharide complexes and a redesigned interface, we identified multiple strategies that can be used to incorporate evolutionary constraints, which helped us to identify the most likely macromolecular binding modes. These strategies include template-based modeling where only local adjustments should be applied when query-template sequence identity is above 30% and larger perturbations are needed below this threshold; covariation-based structure prediction for individual protein partners; and the identification of evolutionarily conserved and structurally recurrent anchoring interface motifs. Overall, we submitted correct predictions among the top 5 models for 12 out of 19 interface challenges, including four High- and five Medium-quality predictions. Our top 20 models included correct predictions for three out of the five targets we missed in the top 5, including two targets for which misleading biological data led us to downgrade correct free docking models.

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 ß-sheet, ß-sheet formed through binding, ß-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.

Drug Discovery Targeting the Disorder-To-Order Transition Regions through the Conformational Diversity Mimicking and Statistical Analysis.

Intrinsically disordered proteins exist as highly dynamic conformational ensembles of diverse forms. However, the majority of virtual screening only focuses on proteins with defined structures. This means that computer-aided drug discovery is restricted. As a breakthrough, understanding the structural characteristics of intrinsically disordered proteins and its application can open the gate for unrestricted drug discovery. First, we segmented the target disorder-to-order transition region into a series of overlapping 20-amino-acid-long peptides. Folding prediction generated diverse conformations of these peptides. Next, we applied molecular docking, new evaluation score function, and statistical analysis. This approach successfully distinguished known compounds and their corresponding binding regions. Especially, Myc proto-oncogene protein (MYC) inhibitor 10058F4 was well distinguished from others of the chemical compound library. We also studied differences between the two Methyl-CpG-binding domain protein 2 (MBD2) inhibitors (ABA (2-amino-N-[[(3S)-2,3-dihydro-1,4-benzodioxin-3-yl]methyl]-acetamide) and APC ((R)-(3-(2-Amino-acetylamino)-pyrrolidine-1-carboxylic acid tert-butyl ester))). Both compounds bind MBD2 through electrostatic interaction behind its p66α-binding site. ABA is also able to bind p66α through electrostatic interaction behind its MBD2-binding site while APC-p66α binding was nonspecific. Therefore, structural heterogeneity mimicking of the disorder-to-order transition region at the peptide level and utilization of the new docking score function represent a useful approach that can efficiently discriminate compounds for expanded virtual screening toward intrinsically disordered proteins.