Exploring a Structural Data Mining Approach to Design Linkers for Head-to-Tail Peptide Cyclization.

Peptides have recently regained interest as therapeutic candidates, but their development remains confronted with several limitations including low bioavailability. Backbone head-to-tail cyclization, i.e., setting a covalent peptide bond linking the last amino acid with the first one, is one effective strategy of peptide-based drug design to stabilize the conformation of bioactive peptides while preserving peptide properties in terms of low toxicity, binding affinity, target selectivity, and preventing enzymatic degradation. Starting from an active peptide, it usually requires the design of a linker of a few amino acids to make it possible to cyclize the peptide, possibly preserving the conformation of the initial peptide and not affecting its activity. However, very little is known about the sequence-structure relationship requirements of designing linkers for peptide cyclization in a rational manner. Recently, we have shown that large-scale data-mining of available protein structures can lead to the precise identification of protein loop conformations, even from remote structural classes. Here, we transpose this approach to linkers, allowing head-to-tail peptide cyclization. First we show that given a linker sequence and the conformation of the linear peptide, it is possible to accurately predict the cyclized peptide conformation. Second, and more importantly, we show that it seems possible to elaborate on the information inferred from protein structures to propose effective candidate linker sequences constrained by length and amino acid composition, providing the first framework for the rational design of head-to-tail cyclization linkers. Finally, we illustrate this for two peptides using a limited set of amino-acids likely not to interfere with peptide function. For a linear peptide derived from Nrf2, the peptide cyclized starting from the experimental structure showed a 26-fold increase in the binding affinity. For urotensin II, a peptide already cyclized by a disulfide bond that exerts a broad array of biological activities, we were able, starting from models of the structure, to design a head-to-tail cyclized peptide, the first synthesized bicyclic 14-residue long urotensin II analogue, showing a retention of in vitro activity. Although preliminary, our results strongly suggest that such an approach has strong potential for cyclic peptide-based drug design.

Evolutionarily conserved protein motifs drive interactions between the plant nucleoskeleton and nuclear pores.

The nucleoskeleton forms a filamentous meshwork under the nuclear envelope and contributes to the regulation of nuclear shape and gene expression. To understand how the Arabidopsis (Arabidopsis thaliana) nucleoskeleton physically connects to the nuclear periphery in plants, we investigated the Arabidopsis nucleoskeleton protein KAKU4 and sought for functional regions responsible for its localization at the nuclear periphery. We identified 3 conserved peptide motifs within the N-terminal region of KAKU4, which are required for intermolecular interactions of KAKU4 with itself, interaction with the nucleoskeleton protein CROWDED NUCLEI (CRWN), localization at the nuclear periphery, and nuclear elongation in differentiated tissues. Unexpectedly, we find these motifs to be present also in NUP82 and NUP136, 2 plant-specific nucleoporins from the nuclear pore basket. We further show that NUP82, NUP136, and KAKU4 have a common evolutionary history predating nonvascular land plants with KAKU4 mainly localizing outside the nuclear pore suggesting its divergence from an ancient nucleoporin into a new nucleoskeleton component. Finally, we demonstrate that both NUP82 and NUP136, through their shared N-terminal motifs, interact with CRWN and KAKU4 proteins revealing the existence of a physical continuum between the nuclear pore and the nucleoskeleton in plants.

DockSurf: A Molecular Modeling Software for the Prediction of Protein/Surface Adhesion.

The elucidation of structural interfaces between proteins and inorganic surfaces is a crucial aspect of bionanotechnology development. Despite its significance, the interfacial structures between proteins and metallic surfaces are yet to be fully understood, and the lack of experimental investigation has impeded the development of many devices. To overcome this limitation, we suggest considering the generation of protein/surface structures as a molecular docking problem with a homogenous plan as the target. To this extent, we propose a new software, DockSurf, which aims to quickly propose reliable protein/surface structures. Our approach considers the conformational exploration with Euler's angles, which provide a cartography instead of a unique structure. Interaction energies were derived from quantum mechanics computations for a set of small molecules that describe protein atom types and implemented in a Derjaguin, Landau, Verwey, and Overbeek potential for the consideration of large systems such as proteins. The validation of DockSurf software was conducted with molecular dynamics for corona proteins with gold surfaces and provided enthusiastic results. This software is implemented in the RPBS platform to facilitate widespread access to the scientific community.

PEP-FOLD4: a pH-dependent force field for peptide structure prediction in aqueous solution.

Accurate and fast structure prediction of peptides of less 40 amino acids in aqueous solution has many biological applications, but their conformations are pH- and salt concentration-dependent. In this work, we present PEP-FOLD4 which goes one step beyond many machine-learning approaches, such as AlphaFold2, TrRosetta and RaptorX. Adding the Debye-Hueckel formalism for charged-charged side chain interactions to a Mie formalism for all intramolecular (backbone and side chain) interactions, PEP-FOLD4, based on a coarse-grained representation of the peptides, performs as well as machine-learning methods on well-structured peptides, but displays significant improvements for poly-charged peptides. PEP-FOLD4 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4. This server is free and there is no login requirement.

In Silico and In Vivo Studies of a Tumor-Penetrating and Interfering Peptide with Antitumoral Effect on Xenograft Models of Breast Cancer.

The combination of a tumor-penetrating peptide (TPP) with a peptide able to interfere with a given protein-protein interaction (IP) is a promising strategy with potential clinical application. Little is known about the impact of fusing a TPP with an IP, both in terms of internalization and functional effect. Here, we analyze these aspects in the context of breast cancer, targeting PP2A/SET interaction, using both in silico and in vivo approaches. Our results support the fact that state-of-the-art deep learning approaches developed for protein-peptide interaction modeling can reliably identify good candidate poses for the IP-TPP in interaction with the Neuropilin-1 receptor. The association of the IP with the TPP does not seem to affect the ability of the TPP to bind to Neuropilin-1. Molecular simulation results suggest that peptide IP-GG-LinTT1 in a cleaved form interacts with Neuropilin-1 in a more stable manner and has a more helical secondary structure than the cleaved IP-GG-iRGD. Surprisingly, in silico investigations also suggest that the non-cleaved TPPs can bind the Neuropilin-1 in a stable manner. The in vivo results using xenografts models show that both bifunctional peptides resulting from the combination of the IP and either LinTT1 or iRGD are effective against tumoral growth. The peptide iRGD-IP shows the highest stability to serum proteases degradation while having the same antitumoral effect as Lin TT1-IP, which is more sensitive to proteases degradation. Our results support the development of the TPP-IP strategy as therapeutic peptides against cancer.

A refined pH-dependent coarse-grained model for peptide structure prediction in aqueous solution.

Introduction: Peptides carry out diverse biological functions and the knowledge of the conformational ensemble of polypeptides in various experimental conditions is important for biological applications. All fast dedicated softwares perform well in aqueous solution at neutral pH. Methods: In this study, we go one step beyond by combining the Debye-Hückel formalism for charged-charged amino acid interactions and a coarse-grained potential of the amino acids to treat pH and salt variations. Results: Using the PEP-FOLD framework, we show that our approach performs as well as the machine-leaning AlphaFold2 and TrRosetta methods for 15 well-structured sequences, but shows significant improvement in structure prediction of six poly-charged amino acids and two sequences that have no homologous in the Protein Data Bank, expanding the range of possibilities for the understanding of peptide biological roles and the design of candidate therapeutic peptides.

Binding and Kinetic Analysis of Human Protein Phosphatase PP2A Interactions with Caspase 9 Protein and the Interfering Peptide C9h.

The serine/threonine phosphatase PP2A and the cysteine protease Caspase 9 are two proteins involved in physiological and pathological processes, including cancer and apoptosis. We previously demonstrated the interaction between Caspase 9 and PP2A and identified the C9h peptide, corresponding to the binding site of Caspase 9 to PP2A. This interfering peptide can modulate Caspase 9/PP2A interaction leading to a strong therapeutic effect in vitro and in vivo in mouse models of tumor progression. In this manuscript, we investigate (I) the peptide binding to PP2A combining docking with molecular dynamics and (II) the secondary structure of the peptide using CD spectroscopy. Additionally, we compare the binding affinity, using biolayer interferometry, of the wild-type protein PP2A with Caspase 9 and vice versa to that observed between the PP2A protein and the interfering peptide C9h. This result strongly encourages the use of peptides as new therapeutics against cancer, as shown for the C9h peptide already in clinical trial.

A Generalized Attraction-Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction.

Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for non-bonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small ß-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.

Dynamics of Amyloid Formation from Simplified Representation to Atomistic Simulations.

Amyloid fibril formation is an intrinsic property of short peptides, non-disease proteins, and proteins associated with neurodegenerative diseases. Aggregates of the Aß and tau proteins, the α-synuclein protein, and the prion protein are observed in the brain of Alzheimer's, Parkinson's, and prion disease patients, respectively. Due to the transient short-range and long-range interactions of all species and their high aggregation propensities, the conformational ensemble of these devastating proteins, the exception being for the monomeric prion protein, remains elusive by standard structural biology methods in bulk solution and in lipid membranes. To overcome these limitations, an increasing number of simulations using different sampling methods and protein models have been performed. In this chapter, we first review our main contributions to the field of amyloid protein simulations aimed at understanding the early aggregation steps of short linear amyloid peptides, the conformational ensemble of the Aß40/42 dimers in bulk solution, and the stability of Aß aggregates in lipid membrane models. Then we focus on our studies on the interactions of amyloid peptides/inhibitors to prevent aggregation, and long amyloid sequences, including new results on a monomeric tau construct.

PEPscan: A Broad Spectrum Approach for the Characterization of Protein-Binder Interactions?

In a previous study, we have shown that PEPscan can provide a cheap and rapid means to identify candidate interfering peptides (IPs), i.e., peptides able to disrupt a target protein-protein interaction. PEPscan was shown to be effective in identifying a limited number of candidate IPs specific to the target interaction. Here, we investigate the results of 14 new PEPscan experiments for protein complexes of known 3D structures. We show that for almost all complexes, PEPscan is able to identify candidate IPs that are located at the protein-protein interface. The information it provides about the binding site seems, however, too ambiguous to be exploited in a simple manner to assist the modeling of protein complexes. Moreover, these candidates are associated with false positives. For these, we suggest they could correspond to non-specific binders, which leaves room for further optimization of the PEPscan protocol. Another unexpected advance comes from the observation of the applicability of PEPscan for polysaccharides and labeled peptides, suggesting that PEPscan could become a large spectrum approach to investigate protein-binder interactions, the binder not necessarily being a protein.

Isolation of Primary Hepatocytes for Testing Tumor Penetrating Peptides.

Methods for isolation of human primary cells is an important tool to be able to test the effect of several drugs for the treatment of diseases. Protocols have been described for the isolation of healthy and tumoral hepatocytes; however, the quality and the amount of the isolated cells is not satisfactory. We describe here protocols for the isolation of healthy and tumoral hepatocytes as well as the use of tumor penetrating and interfering peptides as new therapeutics. Peptides are showed to be safe drugs and taking into account recent improvements in peptide administration, biodelivery, and bioavailability. Tumor penetrating peptides fused to an interfering peptide have a great potential to become therapeutic agents.

Bi-Functional Peptides as a New Therapeutic Tool for Hepatocellular Carcinoma.

BACKGROUND: The interfering peptides that block protein-protein interactions have been receiving increasing attention as potential therapeutic tools. METHODS: We measured the internalization and biological effect of four bi-functional tumor-penetrating and interfering peptides into primary hepatocytes isolated from three non-malignant and 11 hepatocellular carcinomas. RESULTS: These peptides are internalized in malignant hepatocytes but not in non-malignant cells. Furthermore, the degree of peptide internalization correlated with receptor expression level and tumor aggressiveness levels. Importantly, penetration of the peptides iRGD-IP, LinTT1-IP, TT1-IP, and RPARPAR-IP induced apoptosis of the malignant hepatocytes without effect on non-malignant cells. CONCLUSION: Receptor expression levels correlated with the level of peptide internalization and aggressiveness of the tumor. This study highlights the potential to exploit the expression of tumor-penetrating peptide receptors as a predictive marker of liver tumor aggressiveness. These bi-functional peptides could be developed for personalized tumor treatment.

SeamDock: An Interactive and Collaborative Online Docking Resource to Assist Small Compound Molecular Docking.

In silico assessment of protein receptor interactions with small ligands is now part of the standard pipeline for drug discovery, and numerous tools and protocols have been developed for this purpose. With the SeamDock web server, we propose a new approach to facilitate access to small molecule docking for nonspecialists, including students. The SeamDock online service integrates different docking tools in a common framework that allows ligand global and/or local docking and a hierarchical approach combining the two for easy interaction site identification. This service does not require advanced computer knowledge, and it works without the installation of any programs with the exception of a common web browser. The use of the Seamless framework linking the RPBS calculation server to the user's browser allows the user to navigate smoothly and interactively on the SeamDock web page. A major effort has been put into the 3D visualization of ligand, receptor, and docking poses and their interactions with the receptor. The advanced visualization features combined with the seamless library allow a user to share with an unlimited number of collaborators, a docking session, and its full visualization states. As a result, SeamDock can be seen as a free, simple, didactic, evolving online docking resource best suited for education and training.

Pepscan Approach for the Identification of Protein-Protein Interfaces: Lessons from Experiment.

PEPscan is an old approach that has recently gained renewed interest for the identification of interfering peptides (IPs), i.e., peptides able to interfere with protein-protein interactions (PPIs). Its principle is to slice a protein sequence as a series of short overlapping peptides that are synthesized on a peptide array and tested for their ability to bind a partner, with positive spots corresponding to candidate IPs. PEPscan has been applied with a rather large success in various contexts, but the structural determinants underlying this success remain obscure. Here, we analyze the results of 14 PEPscan experiments, and confront the in vitro results with the available structural information. PEPscan identifies candidate IPs in limited numbers that in all cases correspond to solvent-accessible regions of the structures, their location at the protein-protein interface remaining to be further demonstrated. A strong point of PEPscan seems to be its ability to identify specific IPs. IPs identified from the same protein differ depending on the target PPI, and correspond to patches not frequently involved in the interactions seen in the 3D structures available. Overall, PEPscan seems to provide a cheap and rapid manner to identify candidate IPs, that also comes with room for improvement.

Proteo3Dnet: a web server for the integration of structural information with interactomics data.

Proteo3Dnet is a web server dedicated to the analysis of mass spectrometry interactomics experiments. Given a flat list of proteins, its aim is to organize it in terms of structural interactions to provide a clearer overview of the data. This is achieved using three means: (i) the search for interologs with resolved structure available in the protein data bank, including cross-species remote homology search, (ii) the search for possibly weaker interactions mediated through Short Linear Motifs as predicted by ELM-a unique feature of Proteo3Dnet, (iii) the search for protein-protein interactions physically validated in the BioGRID database. The server then compiles this information and returns a graph of the identified interactions and details about the different searches. The graph can be interactively explored to understand the way the core complexes identified could interact. It can also suggest undetected partners to the experimentalists, or specific cases of conditionally exclusive binding. The interest of Proteo3Dnet, previously demonstrated for the difficult cases of the proteasome and pragmin complexes data is, here, illustrated in the context of yeast precursors to the small ribosomal subunits and the smaller interactome of 14-3-3zeta frequent interactors. The Proteo3Dnet web server is accessible at http://bioserv.rpbs.univ-paris-diderot.fr/services/Proteo3Dnet/.

InterEvDock3: a combined template-based and free docking server with increased performance through explicit modeling of complex homologs and integration of covariation-based contact maps.

The InterEvDock3 protein docking server exploits the constraints of evolution by multiple means to generate structural models of protein assemblies. The server takes as input either several sequences or 3D structures of proteins known to interact. It returns a set of 10 consensus candidate complexes, together with interface predictions to guide further experimental validation interactively. Three key novelties were implemented in InterEvDock3 to help obtain more reliable models: users can (i) generate template-based structural models of assemblies using close and remote homologs of known 3D structure, detected through an automated search protocol, (ii) select the assembly models most consistent with contact maps from external methods that implement covariation-based contact prediction with or without deep learning and (iii) exploit a novel coevolution-based scoring scheme at atomic level, which leads to significantly higher free docking success rates. The performance of the server was validated on two large free docking benchmark databases, containing respectively 230 unbound targets (Weng dataset) and 812 models of unbound targets (PPI4DOCK dataset). Its effectiveness has also been proven on a number of challenging examples. The InterEvDock3 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock3/.

The CH1α domain of mucosal gp41 IgA contributes to antibody specificity and antiviral functions in HIV-1 highly exposed Sero-Negative individuals.

The antibody molecule comprises a variable domain conferring antigen specificity and affinity distinct from the heavy chain constant (CH) domains dictating effector functions. We here interrogate this paradigm by evaluating the unique influence of the CH1α domain on epitope specificity and functions using two mucosal gp41-specific Fab-IgAs (FabA) derived from HIV-1 highly-exposed but persistently seronegative individuals (HESN). These HESN develop selectively affinity-matured HIV-1-specific mucosal IgA that target the gp41 viral envelope and might provide protection although by unclear mechanisms. Isotype-switching FabAs into Fab-IgGs (FabGs) results in a >10-fold loss in affinity for HIV-1 clade A, B, and C gp41, together with reduced neutralization of HIV-1 cross-clade. The FabA conformational epitopes map selectively on gp41 in 6-Helix bundle and pre-fusion conformations cross-clade, unlike FabGs. Finally, we designed in silico, a 12 amino-acid peptide recapitulating one FabA conformational epitope that inhibits the FabA binding to gp41 cross-clade and its neutralizing activity. Altogether, our results reveal that the CH1α domain shapes the antibody paratope through an allosteric effect, thereby strengthening the antibody specificity and functional activities. Further, they clarify the mechanisms by which these HESN IgAs might confer protection against HIV-1-sexual acquisition. The IgA-specific epitope we characterized by reverse vaccinology could help designing a mucosal HIV-1 vaccine.

Identification of peptides interfering with the LRRK2/PP1 interaction.

Serine/threonine phosphatases are responsible for modulating the activities of the protein kinases implicated in the development of several pathologies. Here we identified by a PEP-scan approach a peptide of LRRK2, a Parkinson's disease associated protein, interacting with the phosphatase PP1. In order to study its biological activity, the peptide was fused via its N-terminal to an optimized cell penetrating peptide. We synthesized from the original peptide five interfering peptides and identified two (Mut3DPT-LRRK2-Short and Mut3DPT-LRRK2-Long) able to disrupt the LRRK2/PP1 interaction by competition in anti-LRRK2 immunoprecipitates. Using FITC-labelled peptides, we confirmed their internalization into cell lines as well as into primary cells obtained from healthy or ill human donors. We confirmed by ELISA test the association of Mut3DPT-LRRK2-Long peptide to purified PP1 protein. The peptides Mut3DPT-LRRK2-5 to 8 with either N or C-terminal deletions were not able to disrupt the association LRRK2/PP1 nor to associate with purified PP1 protein. The interfering sequences blocking the PP1/LRRK2 interaction were also fused to a shuttle peptide able to cross the blood brain barrier and showed that the newly generated peptides BBB-LRRK2-Short and BBB-LRRK2-Long were highly resistant to protease degradation. Furthermore, they blocked PP1/LRRK2 interaction and they penetrated into cells. Hence, these newly generated peptides can be employed as new tools in the investigation of the role of the LRRK2/PP1 interaction in normal and pathological conditions.

An information gain-based approach for evaluating protein structure models.

For three decades now, knowledge-based scoring functions that operate through the "potential of mean force" (PMF) approach have continuously proven useful for studying protein structures. Although these statistical potentials are not to be confused with their physics-based counterparts of the same name-i.e. PMFs obtained by molecular dynamics simulations-their particular success in assessing the native-like character of protein structure predictions has lead authors to consider the computed scores as approximations of the free energy. However, this physical justification is a matter of controversy since the beginning. Alternative interpretations based on Bayes' theorem have been proposed, but the misleading formalism that invokes the inverse Boltzmann law remains recurrent in the literature. In this article, we present a conceptually new method for ranking protein structure models by quality, which is (i) independent of any physics-based explanation and (ii) relevant to statistics and to a general definition of information gain. The theoretical development described in this study provides new insights into how statistical PMFs work, in comparison with our approach. To prove the concept, we have built interatomic distance-dependent scoring functions, based on the former and new equations, and compared their performance on an independent benchmark of 60,000 protein structures. The results demonstrate that our new formalism outperforms statistical PMFs in evaluating the quality of protein structural decoys. Therefore, this original type of score offers a possibility to improve the success of statistical PMFs in the various fields of structural biology where they are applied. The open-source code is available for download at https://gitlab.rpbs.univ-paris-diderot.fr/src/ig-score.

The search of sequence variants using a constrained protein evolution simulation approach.

Protein engineering or candidate therapeutic peptide optimization are processes in which the identification of relevant sequence variants is critical. Starting from one amino-acid sequence, the choice of the substitutions must meet the objective of not disrupting the structure of the protein, not impacting the main functional properties of the starting entity, while also meeting the condition to enhance some expected property such as thermal stability, resistance to degradation, … Here, we introduce a new approach of sequence evolution that focuses on the objective of not disrupting the structure of the initial protein by embedding a point to point control on the preservation of the local structure at each position in the sequence. For 6 mini-proteins, we find that, starting from a single sequence, our simple approach intrinsically contains information about site-specific rate heterogeneity of substitution, and that it is able to reproduce sequence diversity as can be observed in the sequences available in the Uniref repository. We show that our approach is able to provide information about positions not to substitute and about substitutions not to perform at a given position to maintain structure integrity. Overall, our results demonstrate that point to point preservation of the local structure along a sequence is an important determinant of sequence evolution.