Computational design of selective peptides to discriminate between similar PDZ domains in an oncogenic pathway

DexDesign: A new OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan, which enable exponential reductions in the size of the peptide sequence search space. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a new framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead therapeutic candidates. We provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

Selective inhibitors targeting Fis1/Mid51 protein-protein interactions protect against hypoxia-induced damage in cardiomyocytes

Cardiovascular diseases (CVDs) are the most common non-communicable diseases globally. An estimated 17.9 million people died from CVDs in 2019, representing 32% of all global deaths. Mitochondria play critical roles in cellular metabolic homeostasis, cell survival, and cell death, as well as producing most of the cell's energy. Protein-protein interactions (PPIs) have a significant role in physiological and pathological processes, and aberrant PPIs are associated with various diseases, therefore they are potential drug targets for a broad range of therapeutic areas. Due to their ability to mimic natural interaction motifs and cover relatively larger interaction region, peptides are very promising as PPI inhibitors. To expedite drug discovery, computational approaches are widely used for screening potential lead compounds. Here, we developed peptides that inhibit mitochondrial fission 1 (Fis1)/mitochondrial dynamics 51 kDa (Mid51) PPI to reduce the cellular damage that can lead to various human pathologies, such as CVDs. Based on a rational design approach we developed peptide inhibitors of the Fis1/Mid51 PPI. In silico and in vitro studies were done to evaluate the biological activity and molecular interactions of the peptides. Two peptides, CVP-241 and CVP-242 were identified based on low binding energy and molecular dynamics simulations. These peptides inhibit Fis1/Mid51 PPI (-1324.9 kcal mol-1) in docking calculations (CVP-241, -741.3 kcal mol-1, and CVP-242, -747.4 kcal mol-1), as well as in vitro experimental studies Fis1/Mid51 PPI (KD 0.054 µM) Fis1/Mid51 PPI + CVP-241 (KD 3.43 µM), and Fis1/Mid51 PPI + CVP-242 (KD 44.58 µM). Finally, these peptides have no toxicity to H9c2 cells, and they increase cell viability in cardiomyocytes (H9c2 cells). Consequently, the identified inhibitor peptides could serve as potent molecules in basic research and as leads for therapeutic development.

Multiplex measurement of protein-peptide dissociation constants using dialysis and mass spectrometry

We propose a high-throughput method for quantitively measuring hundreds of protein-peptide binding affinities in parallel. In this assay a solution of protein is dialyzed into a buffer containing a pool of potential binding peptides, such that upon equilibration the relative abundance of a peptide species is mathematically related to that peptide's dissociation constant, K_d . We use isobaric multiplexed quantitative proteomics to simultaneously determine the relative abundance, and hence the K_d and its associated error, for an entire peptide library. We apply this technique, which we call PEDAL (Parallel Equilibrium Dialysis for Affinity Learning), to determine accurate K_d 's between a PDZ domain and hundreds of peptides, spanning an affinity range of multiple orders of magnitude in a single experiment. PEDAL is a convenient, fast, and low-cost method for measuring large numbers of protein-peptide affinities in parallel, providing a rare combination of true in-solution binding equilibria with the ability to multiplex. This article is protected by copyright. All rights reserved.

Computational Tools and Strategies to Develop Peptide-Based Inhibitors of Protein-Protein Interactions

Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.

Targeting the Wnt signaling pathway for breast cancer bone metastasis therapy

Osteolytic bone destruction is found in approximately 60% of advanced breast cancer patients. With the pathogenesis of bone metastasis being unclear, traditional antiresorptive therapeutic strategies might not be ideal for treatment. The Wnt pathway is a highly organized cascade involved in multiple stages of cancer bone metastasis, and Wnt-targeted therapeutic strategies have shown promise in achieving favorable outcomes. In this review, we summarize the current progress of pharmacological Wnt modulators against breast cancer bone metastasis, discuss emerging therapeutic strategies based on Wnt pathway-related targets for bone therapy, and highlight opportunities to better harness the Wnt pathway for bone metastasis therapeutics to further reveal the implications of the Wnt pathway in bone metastasis pathology and provide new ideas for the development of Wnt-based intervention strategies against breast cancer bone metastasis.

A New Story of the Three Magi: Scaffolding Proteins and lncRNA Suppressors of Cancer

Scaffolding molecules exert a critical role in orchestrating cellular response through the spatiotemporal assembly of effector proteins as signalosomes. By increasing the efficiency and selectivity of intracellular signaling, these molecules can exert (anti/pro)oncogenic activities. As an archetype of scaffolding proteins with tumor suppressor property, the present review focuses on MAGI1, 2, and 3 (membrane-associated guanylate kinase inverted), a subgroup of the MAGUK protein family, that mediate networks involving receptors, junctional complexes, signaling molecules, and the cytoskeleton. MAGI1, 2, and 3 are comprised of 6 PDZ domains, 2 WW domains, and 1 GUK domain. These 9 protein binding modules allow selective interactions with a wide range of effectors, including the PTEN tumor suppressor, the β-catenin and YAP1 proto-oncogenes, and the regulation of the PI3K/AKT, the Wnt, and the Hippo signaling pathways. The frequent downmodulation of MAGIs in various human malignancies makes these scaffolding molecules and their ligands putative therapeutic targets. Interestingly, MAGI1 and MAGI2 genetic loci generate a series of long non-coding RNAs that act as a tumor promoter or suppressor in a tissue-dependent manner, by selectively sponging some miRNAs or by regulating epigenetic processes. Here, we discuss the different paths followed by the three MAGIs to control carcinogenesis.

Structural basis for peptide substrate specificities of glycosyltransferase GalNAc-T2

The polypeptide N-acetylgalactosaminyl transferase (GalNAc-T) enzyme family initiates O-linked mucin-type glycosylation. The family constitutes 20 isoenzymes in humans. GalNAc-Ts exhibit both redundancy and finely tuned specificity for a wide range of peptide substrates. In this work, we deciphered the sequence and structural motifs that determine the peptide substrate preferences for the GalNAc-T2 isoform. Our approach involved sampling and characterization of peptide-enzyme conformations obtained from Rosetta Monte Carlo-minimization-based flexible docking. We computationally scanned 19 amino acid residues at positions -1 and +1 of an eight-residue peptide substrate, which comprised a dataset of 361 (19x19) peptides with previously characterized experimental GalNAc-T2 glycosylation efficiencies. The calculations recapitulated experimental specificity data, successfully discriminating between glycosylatable and non-glycosylatable peptides with a probability of 96.5% (ROC-AUC score), a balanced accuracy of 85.5% and a false positive rate of 7.3%. The glycosylatable peptide substrates viz. peptides with proline, serine, threonine, and alanine at the -1 position of the peptide preferentially exhibited cognate sequon-like conformations. The preference for specific residues at the -1 position of the peptide was regulated by enzyme residues R362, K363, Q364, H365 and W331, which modulate the pocket size and specific enzyme-peptide interactions. For the +1 position of the peptide, enzyme residues K281 and K363 formed gating interactions with aromatics and glutamines at the +1 position of the peptide, leading to modes of peptide-binding sub-optimal for catalysis. Overall, our work revealed enzyme features that lead to the finely tuned specificity observed for a broad range of peptide substrates for the GalNAc-T2 enzyme. We anticipate that the key sequence and structural motifs can be extended to analyze specificities of other isoforms of the GalNAc-T family and can be used to guide design of variants with tailored specificity.

Exploring targeting peptide-shell interactions in encapsulin nanocompartments

Encapsulins are recently discovered protein compartments able to specifically encapsulate cargo proteins in vivo. Encapsulation is dependent on C-terminal targeting peptides (TPs). Here, we characterize and engineer TP-shell interactions in the Thermotoga maritima and Myxococcus xanthus encapsulin systems. Using force-field modeling and particle fluorescence measurements we show that TPs vary in native specificity and binding strength, and that TP-shell interactions are determined by hydrophobic and ionic interactions as well as TP flexibility. We design a set of TPs with a variety of predicted binding strengths and experimentally characterize these designs. This yields a set of TPs with novel binding characteristics representing a potentially useful toolbox for future nanoreactor engineering aimed at controlling cargo loading efficiency and the relative stoichiometry of multiple concurrently loaded cargo proteins.

A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures

Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.

Exploring Energy Profiles of Protein-Protein Interactions (PPIs) Using DFT Method

Background:

Large-scale energy landscape characterization of protein-protein interactions (PPIs) is important to understand the interaction mechanism and protein-protein docking methods. The experimental methods for detecting energy landscapes are tedious and the existing computational methods require longer simulation time.

Objective:

The objective of the present work is to ascertain the energy profiles at the interface regions in a rapid manner to analyze the energy landscape of protein-protein interactions.

Methods:

The atomic coordinates obtained from the X-ray and NMR spectroscopy data are considered as inputs to compute cumulative energy profiles for experimentally validated protein-protein complexes. The energies computed by the program were comparable to the standard molecular dynamics simulations.

Results:

The PPI Profiler not only enables rapid generation of energy profiles but also facilitates the detection of hot spot residue atoms involved therein.

Conclusion:

The hotspot residues and their computed energies matched with the experimentally determined hot spot residues and their energies which correlated well by employing the MM/GBSA method. The proposed method can be employed to scan entire proteomes across species at an atomic level to study the key PPI interactions.

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1

Understanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%-38% sequence identity to any known native Bcl-2 family protein ligand. High-resolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering.

Simplifying the Design of Protein-Peptide Interaction Specificity with Sequence-Based Representations of Atomistic Models

Computationally designed peptides targeting protein-protein interaction interfaces are of great interest as reagents for biological research and potential therapeutics. In recent years, it has been shown that detailed structure-based calculations can, in favorable cases, describe relevant determinants of protein-peptide recognition. Yet, despite large increases in available computing power, such accurate modeling of the binding reaction is still largely outside the realm of protein design. The chief limitation is in the large sequence spaces generally involved in protein design problems, such that it is typically infeasible to apply expensive modeling techniques to score each sequence. Toward addressing this issue, we have previously shown that by explicitly evaluating the scores of a relatively small number of sequences, it is possible to synthesize a direct mapping between sequences and scores, such that the entire sequence space can be analyzed extremely rapidly. The associated method, called Cluster Expansion, has been used in a number of studies to design binding affinity and specificity. In this chapter, we provide instructions and guidance for applying this technique in the context of designing protein-peptide interactions to enable the use of more detailed and expensive scoring approaches than is typically possible.

Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design

Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.

Interfering peptides targeting protein-protein interactions: the next generation of drugs?

Protein-protein interactions (PPIs) are well recognized as promising therapeutic targets. Consequently, interfering peptides (IPs) - natural or synthetic peptides capable of interfering with PPIs - are receiving increasing attention. Given their physicochemical characteristics, IPs seem better suited than small molecules to interfere with the large surfaces implicated in PPIs. Progress on peptide administration, stability, biodelivery and safety are also encouraging the interest in peptide drug development. The concept of IPs has been validated for several PPIs, generating great expectations for their therapeutic potential. Here, we describe approaches and methods useful for IPs identification and in silico, physicochemical and biological-based strategies for their design and optimization. Selected promising in-vivo-validated examples are described and advantages, limitations and potential of IPs as therapeutic tools are discussed.

Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity

Therapeutic proteins of wide-ranging function hold great promise for treating disease, but immune surveillance of these macromolecules can drive an antidrug immune response that compromises efficacy and even undermines safety. To eliminate widespread T-cell epitopes in any biotherapeutic and thereby mitigate this key source of detrimental immune recognition, we developed a Pareto optimal deimmunization library design algorithm that optimizes protein libraries to account for the simultaneous effects of combinations of mutations on both molecular function and epitope content. Active variants identified by high-throughput screening are thus inherently likely to be deimmunized. Functional screening of an optimized 10-site library (1,536 variants) of P99 β-lactamase (P99βL), a component of ADEPT cancer therapies, revealed that the population possessed high overall fitness, and comprehensive analysis of peptide-MHC II immunoreactivity showed the population possessed lower average immunogenic potential than the wild-type enzyme. Although similar functional screening of an optimized 30-site library (2.15 × 10⁹ variants) revealed reduced population-wide fitness, numerous individual variants were found to have activity and stability better than the wild type despite bearing 13 or more deimmunizing mutations per enzyme. The immunogenic potential of one highly active and stable 14-mutation variant was assessed further using ex vivo cellular immunoassays, and the variant was found to silence T-cell activation in seven of the eight blood donors who responded strongly to wild-type P99βL. In summary, our multiobjective library-design process readily identified large and mutually compatible sets of epitope-deleting mutations and produced highly active but aggressively deimmunized constructs in only one round of library screening.

MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory

Multispecificity-the ability of a single receptor protein molecule to interact with multiple substrates-is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities.

Computational Design of the Tiam1 PDZ Domain and Its Ligand Binding

PDZ domains direct protein-protein interactions and serve as models for protein design. Here, we optimized a protein design energy function for the Tiam1 and Cask PDZ domains that combines a molecular mechanics energy, Generalized Born solvent, and an empirical unfolded state model. Designed sequences were recognized as PDZ domains by the Superfamily fold recognition tool and had similarity scores comparable to natural PDZ sequences. The optimized model was used to redesign the two PDZ domains, by gradually varying the chemical potential of hydrophobic amino acids; the tendency of each position to lose or gain a hydrophobic character represents a novel hydrophobicity index. We also redesigned four positions in the Tiam1 PDZ domain involved in peptide binding specificity. The calculated affinity differences between designed variants reproduced experimental data and suggest substitutions with altered specificities.

Deletion of Na+/H+ exchanger regulatory factor 2 represses colon cancer progress by suppression of Stat3 and CD24

The Na(+)/H(+) exchanger regulatory factor (NHERF) family of proteins is scaffolds that orchestrate interaction of receptors and cellular proteins. Previous studies have shown that NHERF1 functions as a tumor suppressor. The goal of this study is to determine whether the loss of NHERF2 alters colorectal cancer (CRC) progress. We found that NHERF2 expression is elevated in advanced-stage CRC. Knockdown of NHERF2 decreased cancer cell proliferation in vitro and in a mouse xenograft tumor model. In addition, deletion of NHERF2 in Apc(Min/+) mice resulted in decreased tumor growth in Apc(Min/+) mice and increased lifespan. Blocking NHERF2 interaction with a small peptide designed to bind the second PDZ domain of NHERF2 attenuated cancer cell proliferation. Although NHERF2 is known to facilitate the effects of lysophosphatidic acid receptor 2 (LPA2), transcriptome analysis of xenograft tumors revealed that NHERF2-dependent genes largely differ from LPA2-regulated genes. Activation of β-catenin and ERK1/2 was mitigated in Apc(Min/+);Nherf2(-/-) adenomas. Moreover, Stat3 phosphorylation and CD24 expression levels were suppressed in Apc(Min/+);Nherf2(-/-) adenomas. Consistently, NHERF2 knockdown attenuated Stat3 activation and CD24 expression in colon cancer cells. Interestingly, CD24 was important in the maintenance of Stat3 phosphorylation, whereas NHERF2-dependent increase in CD24 expression was blocked by inhibition of Stat3, suggesting that NHERF2 regulates Stat3 phosphorylation through a positive feedback mechanism between Stat3 and CD24. In summary, this study identifies NHERF2 as a novel oncogenic protein and a potential target for cancer treatment. NHERF2 potentiates the oncogenic effects in part by regulation of Stat3 and CD24.

Computationally Designed Bispecific Antibodies using Negative State Repertoires

A challenge in the structure-based design of specificity is modeling the negative states, i.e., the complexes that you do not want to form. This is a difficult problem because mutations predicted to destabilize the negative state might be accommodated by small conformational rearrangements. To overcome this challenge, we employ an iterative strategy that cycles between sequence design and protein docking in order to build up an ensemble of alternative negative state conformations for use in specificity prediction. We have applied our technique to the design of heterodimeric CH3 interfaces in the Fc region of antibodies. Combining computationally and rationally designed mutations produced unique designs with heterodimer purities greater than 90%. Asymmetric Fc crystallization was able to resolve the interface mutations; the heterodimer structures confirmed that the interfaces formed as designed. With these CH3 mutations, and those made at the heavy-/light-chain interface, we demonstrate one-step synthesis of four fully IgG-bispecific antibodies.

Design of Specific Peptide-Protein Recognition

Selective targeting of protein-protein interactions in the cell is of great interest in biological research. Computational structure-based design of peptides to bind protein interaction interfaces could provide a potential means of generating such reagents. However, to avoid perturbing off-target interactions, methods that explicitly account for interaction specificity are needed. Further, as peptides often retain considerable flexibility upon association, their binding reaction is computationally demanding to model-a stark limitation for structure-based design. Here we present a protocol for designing peptides that selectively target a given peptide-binding domain, relative to a pre-specified set of possibly related domains. We recently used the method to design peptides that discriminate with high selectivity between two closely related PDZ domains. The framework accounts for the flexibility of the peptide in the binding site, but is efficient enough to quickly analyze trade-offs between affinity and selectivity, enabling the identification of optimal peptides.

In Silico Generation of Peptides by Replica Exchange Monte Carlo: Docking-Based Optimization of Maltose-Binding-Protein Ligands

Short peptides can be designed in silico and synthesized through automated techniques, making them advantageous and versatile protein binders. A number of docking-based algorithms allow for a computational screening of peptides as binders. Here we developed ex-novo peptides targeting the maltose site of the Maltose Binding Protein, the prototypical system for the study of protein ligand recognition. We used a Monte Carlo based protocol, to computationally evolve a set of octapeptides starting from a polialanine sequence. We screened in silico the candidate peptides and characterized their binding abilities by surface plasmon resonance, fluorescence and electrospray ionization mass spectrometry assays. These experiments showed the designed binders to recognize their target with micromolar affinity. We finally discuss the obtained results in the light of further improvement in the ex-novo optimization of peptide based binders.