High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock

Competitive binding of actin and SH3 domains at proline-rich regions of Las17/WASP regulates actin polymerisation

Eukaryotic actin filaments bind factors that regulate their assembly and disassembly creating a self-organising system, the actin cytoskeleton. Despite extensive knowledge of signals that modulate actin organisation, significant gaps remain in our understanding of spatiotemporal regulation of de novo filament initiation. Yeast Las17/WASP is essential for actin polymerisation initiation supporting membrane invagination in Saccharomyces cerevisiae endocytosis and therefore its tight regulation is critical. The adaptor protein Sla1 inhibits Las17 but mechanisms underpinning Las17 activation remain elusive. Here we show that Las17 binding of tandem Sla1 SH3 domains is >100-fold stronger than single domains. Furthermore, SH3 domains directly compete with G-actin for binding in the Las17 polyproline region, thus rationalising how SH3 interactions can affect actin polymerisation despite their distance from C-terminal actin-binding and Arp2/3-interacting VCA domains. Our data and proposed model also highlight the likely importance of multiple weak interactions that together ensure spatial and temporal regulation of endocytosis.

Main methods and tools for peptide development based on protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) regulate essential physiological and pathological processes. Due to their large and shallow binding surfaces, PPIs are often considered challenging drug targets for small molecules. Peptides offer a viable alternative, as they can bind these targets, acting as regulators or mimicking interaction partners. This review focuses on competitive peptides, a class of orthosteric modulators that disrupt PPI formation. We provide a concise yet comprehensive overview of recent advancements in in-silico peptide design, highlighting computational strategies that have improved the efficiency and accuracy of PPI-targeting peptides. Additionally, we examine cutting-edge experimental methods for evaluating PPI-based peptides. By exploring the interplay between computational design and experimental validation, this review presents a structured framework for developing effective peptide therapeutics targeting PPIs in various diseases.

Gene delivery of SUMO1-derived peptide rescues neuronal degeneration and motor deficits in a mouse model of Parkinson's disease

Developing α-synuclein aggregation inhibitors is challenging because its aggregation process involves several microscopic steps and heterogeneous intermediates. Previously, we identified a SUMO1-derived peptide, SUMO1(15-55), that exhibits tight binding to monomeric α-synuclein via SUMO-SUMO-interacting motif (SIM) interactions, and effectively blocks the initiation of aggregation and formation of toxic aggregates in vitro. In cellular and Drosophila models, SUMO1(15-55) was efficacious in protecting neuronal cells from α-synuclein-induced neurotoxicity and neuronal degeneration. Given the demonstrated ability of SUMO1(15-55) to sequester α-synuclein monomers thereby blocking oligomer formation, we sought to evaluate whether it could be equally effective against the aggregation-prone familial mutant α-synuclein-A53T. Herein, we show that SUMO1(15-55) selectively binds to monomeric α-synuclein-A53T, inhibits primary nucleation, and prevents the formation of structured protofibrils in vitro, thereby protecting neuronal cells from protofibril-induced cell death. We further demonstrate that larval feeding of a designed His10-SUMO1(15-55) that exhibits enhanced sub-stoichiometric suppression of α-synuclein-A53T aggregation in vitro can ameliorate Parkinson's disease (PD)-related symptoms in α-synuclein-A53T transgenic Drosophila models, while its rAAV-mediated gene delivery can relieve the PD-related histological and behavioral deficiencies in an rAAV-α-synuclein-A53T mouse PD model. Our findings suggest that gene delivery of His10-SUMO1(15-55) may serve as a clinical therapy for a spectrum of α-synuclein-aggregation associated synucleinopathies.

Protein-Peptide Docking with ESMFold Language Model

Designing peptide therapeutics requires precise peptide docking, which remains a challenge. We assessed the ESMFold language model, originally designed for protein structure prediction, for its effectiveness in protein-peptide docking. Various docking strategies, including polyglycine linkers and sampling-enhancing modifications, were explored. The number of acceptable-quality models among top-ranking results is comparable to traditional methods and generally lower than AlphaFold-Multimer or Alphafold 3, though ESMFold surpasses it in some cases. The combination of result quality and computational efficiency underscores ESMFold's potential value as a component in a consensus approach for high-throughput peptide design.

AlphaFold and what is next: bridging functional, systems and structural biology

INTRODUCTION

The DeepMind's AlphaFold (AF) has revolutionized biomedical and biocience research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective for predicting structures of rigid and globular proteins, it is not able to fully capture the dynamics, conformational variability, and interactions of proteins with ligands and other biomacromolecules.

AREAS COVERED

In this review, we present a comprehensive overview of the latest advancements in 3D model predictions for biomacromolecules using AF. We also provide a detailed analysis its of strengths and limitations, and explore more recent iterations, modifications, and practical applications of this strategy. Moreover, we map the path forward for expanding the landscape of AF toward predicting structures of every protein and peptide, and their interactions in the proteome in the most physiologically relevant form. This discussion is based on an extensive literature search performed using PubMed and Google Scholar.

EXPERT OPINION

While significant progress has been made to enhance AF's modeling capabilities, we argue that a combined approach integrating both various in silico and in vitro methods will be most beneficial for the future of structural biology, bridging the gaps between static and dynamic features of proteins and their functions.

Artificial intelligence in peptide-based drug design

Protein-protein interactions (PPIs) are fundamental to a variety of biological processes, but targeting them with small molecules is challenging because of their large and complex interaction interfaces. However, peptides have emerged as highly promising modulators of PPIs, because they can bind to protein surfaces with high affinity and specificity. Nonetheless, computational peptide design remains difficult, hindered by the intrinsic flexibility of peptides and the substantial computational resources required. Recent advances in artificial intelligence (AI) are paving new paths for peptide-based drug design. In this review, we explore the advanced deep generative models for designing target-specific peptide binders, highlight key challenges, and offer insights into the future direction of this rapidly evolving field.

Target-Specific De Novo Peptide Binder Design with DiffPepBuilder

Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein-peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three-dimensional and binding site information.

Oligomerization of protein arginine methyltransferase 1 and its functional impact on substrate arginine methylation

Protein arginine methyltransferases (PRMTs) are important posttranslational modifying enzymes in eukaryotic proteins and regulate diverse pathways from gene transcription, RNA splicing, and signal transduction to metabolism. Increasing evidence supports that PRMTs exhibit the capacity to form higher-order oligomeric structures, but the structural basis of PRMT oligomerization and its functional consequence are elusive. Herein, we revealed for the first time different oligomeric structural forms of the predominant arginine methyltransferase PRMT1 using cryo-EM, which included tetramer (dimer of dimers), hexamer (trimer of dimers), octamer (tetramer of dimers), decamer (pentamer of dimers), and also helical filaments. Through a host of biochemical assays, we showed that PRMT1 methyltransferase activity was substantially enhanced as a result of the high-ordered oligomerization. High-ordered oligomerization increased the catalytic turnover and the multimethylation processivity of PRMT1. Presence of a catalytically dead PRMT1 mutant also enhanced the activity of WT PRMT1, pointing out a noncatalytic role of oligomerization. Structural modeling demonstrates that oligomerization enhances substrate retention at the PRMT1 surface through electrostatic force. Our studies offered key insights into PRMT1 oligomerization and established that oligomerization constitutes a novel molecular mechanism that positively regulates the enzymatic activity of PRMTs in biology.

PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations

Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering. PepBinding has been demonstrated on seven distinct model peptides. In peptide docking using HPEPDOCK, the peptide backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures ranged from 3.8 to 16.0 Å, corresponding to the medium to inaccurate quality models according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. The Pep-GaMD simulations performed for only 200 ns significantly improved the docking models, resulting in five medium and two acceptable quality models. Therefore, PepBinding is an efficient workflow for predicting peptide binding structures and is publicly available at https://github.com/MiaoLab20/PepBinding.

Peptidomimetics design and characterization: Bridging experimental and computer-based approaches

Peptidomimetics, designed to mimic peptide biological activity with more drug-like properties, are increasingly pivotal in medicinal chemistry. They offer enhanced systemic delivery, cell penetration, target specificity, and protection against peptidases when compared to their native peptide counterparts. Already utilized in treating diverse diseases like neurodegenerative disorders, cancer and infectious diseases, their future in medicine seems bright, with many peptidomimetics in clinical trials or development stages. Peptidomimetics are well-suited for addressing disturbed protein-protein interactions (PPIs), which often underlie various pathologies. Structural biology and computational methods like molecular dynamics simulations facilitate rational design, whereas machine learning algorithms accelerate protein structure prediction, enabling efficient drug development. Experimental validation via various spectroscopic, biophysical, and biochemical assays confirms computational predictions and guides further optimization. Peptidomimetics, with their tailored constrained structures, represent a frontier in drug design focused on targeting PPIs. In this overview, we present a comprehensive landscape of peptidomimetics, encompassing perspectives on involvement in pathologies, chemical strategies, and methodologies for their characterization, spanning in silico, in vitro and in cell approaches. With increasing interest from pharmaceutical sectors, peptidomimetics hold promise for revolutionizing therapeutic approaches, marking a new era of precision drug discovery.

Leveraging machine learning models for peptide-protein interaction prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Peptriever: a Bi-Encoder approach for large-scale protein-peptide binding search

MOTIVATION

Peptide therapeutics hinge on the precise interaction between a tailored peptide and its designated receptor while mitigating interactions with alternate receptors is equally indispensable. Existing methods primarily estimate the binding score between protein and peptide pairs. However, for a specific peptide without a corresponding protein, it is challenging to identify the proteins it could bind due to the sheer number of potential candidates.

RESULTS

We propose a transformers-based protein embedding scheme in this study that can quickly identify and rank millions of interacting proteins. Furthermore, the proposed approach outperforms existing sequence- and structure-based methods, with a mean AUC-ROC and AUC-PR of 0.73.

AVAILABILITY AND IMPLEMENTATION

Training data, scripts, and fine-tuned parameters are available at https://github.com/RoniGurvich/Peptriever. The proposed method is linked with a web application available for customized prediction at https://peptriever.app/.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Design and functional characterization of Salmo salar TLR5 agonist peptides derived from high mobility group B1 acidic tail

Toll-like receptor 5 (TLR5) responds to the monomeric form of flagellin and induces the MyD88-depending signaling pathway, activating proinflammatory transcription factors such as NF-κB and the consequent induction of cytokines. On the other hand, HMGB1 is a highly conserved non-histone chromosomal protein shown to interact with and activate TLR5. The present work aimed to design and characterize TLR5 agonist peptides derived from the acidic tail of Salmo salar HMGB1 based on the structural knowledge of the TLR5 surface using global molecular docking platforms. Peptide binding poses complexed on TLR5 ectodomain model from each algorithm were filtrated based on docking scoring functions and predicted theoretical binding affinity of the complex. Circular dichroism spectra were recorded for each peptide selected for synthesis. Only intrinsically disordered peptides (6W, 11W, and SsOri) were selected for experimental functional assay. The functional characterization of the peptides was performed by NF-κB activation assays, RT-qPCR gene expression assays, and Piscirickettsia salmonis challenge in SHK-1 cells. The 6W and 11W peptides increased the nuclear translation of p65 and phosphorylation. In addition, the peptides induced the expression of genes related to the TLR5 pathway activation, pro- and anti-inflammatory response, and differentiation and activation of T lymphocytes towards phenotypes such as TH1, TH17, and TH2. Finally, it was shown that the 11W peptide protects immune cells against infection with P. salmonis bacteria. Overall, the results indicate the usefulness of novel peptides as potential immunostimulants in salmonids.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

MinD-RNase E interplay controls localization of polar mRNAs in E. coli

The E. coli transcriptome at the cell's poles (polar transcriptome) is unique compared to the membrane and cytosol. Several factors have been suggested to mediate mRNA localization to the membrane, but the mechanism underlying polar localization of mRNAs remains unknown. Here, we combined a candidate system approach with proteomics to identify factors that mediate mRNAs localization to the cell poles. We identified the pole-to-pole oscillating protein MinD as an essential factor regulating polar mRNA localization, although it is not able to bind RNA directly. We demonstrate that RNase E, previously shown to interact with MinD, is required for proper localization of polar mRNAs. Using in silico modeling followed by experimental validation, the membrane-binding site in RNase E was found to mediate binding to MinD. Intriguingly, not only does MinD affect RNase E interaction with the membrane, but it also affects its mode of action and dynamics. Polar accumulation of RNase E in ΔminCDE cells resulted in destabilization and depletion of mRNAs from poles. Finally, we show that mislocalization of polar mRNAs may prevent polar localization of their protein products. Taken together, our findings show that the interplay between MinD and RNase E determines the composition of the polar transcriptome, thus assigning previously unknown roles for both proteins.

From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2

The revolution brought about by AlphaFold2 opens promising perspectives to unravel the complexity of protein-protein interaction networks. The analysis of interaction networks obtained from proteomics experiments does not systematically provide the delimitations of the interaction regions. This is of particular concern in the case of interactions mediated by intrinsically disordered regions, in which the interaction site is generally small. Using a dataset of protein-peptide complexes involving intrinsically disordered regions that are non-redundant with the structures used in AlphaFold2 training, we show that when using the full sequences of the proteins, AlphaFold2-Multimer only achieves 40% success rate in identifying the correct site and structure of the interface. By delineating the interaction region into fragments of decreasing size and combining different strategies for integrating evolutionary information, we manage to raise this success rate up to 90%. We obtain similar success rates using a much larger dataset of protein complexes taken from the ELM database. Beyond the correct identification of the interaction site, our study also explores specificity issues. We show the advantages and limitations of using the AlphaFold2 confidence score to discriminate between alternative binding partners, a task that can be particularly challenging in the case of small interaction motifs.

Discovering peptide inhibitors of thrombin as a strategy for anticoagulation

Unusual blood clots can cause serious health problems, such as lung embolism, stroke, and heart attack. Inhibiting thrombin activity was adopted as an effective strategy for preventing blood clots. In this study, we explored computational-based method for designing peptide inhibitors of human thrombin therapeutic peptides to prevent platelet aggregation. The random peptides and their 3-dimentional structures were generated to build a virtual peptide library. The generated peptides were docked into the binding pocket of human thrombin. The designed strong binding peptides were aligned with the native binder by comparative study, and we showed the top 5 peptide binders display strong binding affinity against human thrombin. The 5 peptides were synthesized and validated their inhibitory activity. Our result showed the 5-mer peptide AEGYA, EVVNQ, and FASRW with inhibitory activity against thrombin, range from 0.53 to 4.35 μM. In vitro anti-platelet aggregation assay was carried out, suggesting the 3 peptides can inhibit the platelet aggregation induced by thrombin. This study showed computer-aided peptide inhibitor design can be a robust method for finding potential binders for thrombin, which provided solutions for anticoagulation.

Agonistic effect of peptides derived from a truncated HMGB1 acidic tail sequence in TLR5 from Salmo salar

Based on the structural knowledge of TLR5 surface and using blind docking platforms, peptides derived from a truncated HMGB1 acidic tail from Salmo salar was designed as TLR5 agonistic. Additionally, a template peptide with the native N-terminal of the acidic tail sequence as a reference was included (SsOri). Peptide binding poses complexed on TLR5 ectodomain model from each algorithm were filtrated based on docking scoring functions and predicted theoretical binding affinity of the complex. The best peptides, termed 6WK and 5LWK, were selected for chemical synthesis and experimental functional assay. The agonist activity by immunoblotting and immunocytochemistry was determined following the NF-κBp65 phosphorylation (p-NF-κBp65) and the nuclear translocation of the NF-κBp65 subunit from the cytosol, respectively. HeLa cells stably expressing a S. salar TLR5 chimeric form (TLR5c7) showed increased p-NF-κBp65 levels regarding extracts from flagellin-treated cells. No statistically significant differences (p > 0.05) were found in the detected p-NF-κBp65 levels between cellular extracts treated with peptides or flagellin by one-way ANOVA. The image analysis of NF-κBp65 immunolabeled cells obtained by confocal microscopy showed increased nuclear NF-κBp65 co-localization in cells both 5LWK and flagellin stimulated, while 6WK and SsOri showed less effect on p65 nuclear translocation (p < 0.05). Also, an increased transcript expression profile of proinflammatory cytokines such as TNFα, IL-1β, and IL-8 in HKL cells isolated from Salmo salar was evidenced in 5LWK - stimulated by RT-PCR analysis. Overall, the result indicates the usefulness of novel peptides as a potential immunostimulant in S. salar.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

Rational design peptide inhibitors of Cyclophilin D as a potential treatment for acute pancreatitis

Cyclophilin D (CypD) is a mitochondrial matrix peptidyl prolidase that regulates the mitochondrial permeability transition pore. Inhibition of CypD was suggested as a therapeutic strategy for acute pancreatitis. Peptide inhibitors emerged as novel binding ligand for blocking receptor activity. In this study, we present our computational approach for designing peptide inhibitors of CypD. The 3-D structure of random peptides were built, and docked into the active center of CypD using Rosetta script integrated FlexPepDock module. The peptide displayed the lowest binding energy against CypD was further selected for virtual iterative mutation based on virtual mutagenesis and molecular docking. Finally, the top 5 peptides with the lowest binding energy was selected for validating their affinity against CypD using inhibitory assay. We showed 4 out of the selected 5 peptides were capable for blocking the activity of CypD, while WACLQ display the strongest affinity against CypD, which reached 0.28 mM. The binding mechanism between WACLQ and CypD was characterized using molecular dynamics simulation. Here, we proved our approach can be a robust method for screening peptide inhibitors.

The role of intrinsic disorder in binding of plant microtubule-associated proteins to the cytoskeleton

Microtubules (MTs) represent one of the main components of the eukaryotic cytoskeleton and support numerous critical cellular functions. MTs are in principle tube-like structures that can grow and shrink in a highly dynamic manner; a process largely controlled by microtubule-associated proteins (MAPs). Plant MAPs are a phylogenetically diverse group of proteins that nonetheless share many common biophysical characteristics and often contain large stretches of intrinsic protein disorder. These intrinsically disordered regions are determinants of many MAP-MT interactions, in which structural flexibility enables low-affinity protein-protein interactions that enable a fine-tuned regulation of MT cytoskeleton dynamics. Notably, intrinsic disorder is one of the major obstacles in functional and structural studies of MAPs and represents the principal present-day challenge to decipher how MAPs interact with MTs. Here, we review plant MAPs from an intrinsic protein disorder perspective, by providing a complete and up-to-date summary of all currently known members, and address the current and future challenges in functional and structural characterization of MAPs.

pyPept: a python library to generate atomistic 2D and 3D representations of peptides

We present pyPept, a set of executables and underlying python-language classes to easily create, manipulate, and analyze peptide molecules using the FASTA, HELM, or recently-developed BILN notations. The framework enables the analysis of both pure proteinogenic peptides as well as those with non-natural amino acids, including support to assemble a customizable monomer library, without requiring programming. From line notations, a peptide is transformed into a molecular graph for 2D depiction tasks, the calculation of physicochemical properties, and other systematic analyses or processing pipelines. The package includes a module to rapidly generate approximate peptide conformers by incorporating secondary structure restraints either given by the user or predicted via pyPept, and a wrapper tool is also provided to automate the generation and output of 2D and 3D representations of a peptide directly from the line notation. HELM and BILN notations that include circular, branched, or stapled peptides are fully supported, eliminating errors in structure creation that are prone during manual drawing and connecting. The framework and common workflows followed in pyPept are described together with illustrative examples. pyPept has been released at: https://github.com/Boehringer-Ingelheim/pyPept .

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25μM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

Structure-Activity Relationship Study of the High-Affinity Neuropeptide Y4 Receptor Positive Allosteric Modulator VU0506013

Positive allosteric modulators targeting the Y₄ receptor (Y₄R), a G protein-coupled receptor (GPCR) involved in the regulation of satiety, offer great potential in anti-obesity research. In this study, we selected 603 compounds by using quantitative structure-activity relationship (QSAR) models and tested them in high-throughput screening (HTS). Here, the novel positive allosteric modulator (PAM) VU0506013 was identified, which exhibits nanomolar affinity and pronounced selectivity toward the Y₄R in engineered cell lines and mouse descending colon mucosa natively expressing the Y₄R. Based on this lead structure, we conducted a systematic SAR study in two regions of the scaffold and presented a series of 27 analogues with modifications in the N- and C-terminal heterocycles of the molecule to obtain insight into functionally relevant positions. By mutagenesis and computational docking, we present a potential binding mode of VU0506013 in the transmembrane core of the Y₄R. VU0506013 presents a promising scaffold for developing in vivo tools to move toward anti-obesity drug research focused on the Y₄R.

Elucidation of Short Linear Motif-Based Interactions of the FERM Domains of Ezrin, Radixin, Moesin, and Merlin

The ERM (ezrin, radixin, and moesin) family of proteins and the related protein merlin participate in scaffolding and signaling events at the cell cortex. The proteins share an N-terminal FERM [band four-point-one (4.1) ERM] domain composed of three subdomains (F1, F2, and F3) with binding sites for short linear peptide motifs. By screening the FERM domains of the ERMs and merlin against a phage library that displays peptides representing the intrinsically disordered regions of the human proteome, we identified a large number of novel ligands. We determined the affinities for the ERM and merlin FERM domains interacting with 18 peptides and validated interactions with full-length proteins through pull-down experiments. The majority of the peptides contained an apparent Yx[FILV] motif; others show alternative motifs. We defined distinct binding sites for two types of similar but distinct binding motifs (YxV and FYDF) using a combination of Rosetta FlexPepDock computational peptide docking protocols and mutational analysis. We provide a detailed molecular understanding of how the two types of peptides with distinct motifs bind to different sites on the moesin FERM phosphotyrosine binding-like subdomain and uncover interdependencies between the different types of ligands. The study expands the motif-based interactomes of the ERMs and merlin and suggests that the FERM domain acts as a switchable interaction hub.

Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking

The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.

Peptides from human BNIP5 and PXT1 and non-native binders of pro-apoptotic BAK can directly activate or inhibit BAK-mediated membrane permeabilization

Apoptosis is important for development and tissue homeostasis, and its dysregulation can lead to diseases, including cancer. As an apoptotic effector, BAK undergoes conformational changes that promote mitochondrial outer membrane disruption, leading to cell death. This is termed "activation" and can be induced by peptides from the human proteins BID, BIM, and PUMA. To identify additional peptides that can regulate BAK, we used computational protein design, yeast surface display screening, and structure-based energy scoring to identify 10 diverse new binders. We discovered peptides from the human proteins BNIP5 and PXT1 and three non-native peptides that activate BAK in liposome assays and induce cytochrome c release from mitochondria. Crystal structures and binding studies reveal a high degree of similarity among peptide activators and inhibitors, ruling out a simple function-determining property. Our results shed light on the vast peptide sequence space that can regulate BAK function and will guide the design of BAK-modulating tools and therapeutics.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors

Post-translational modification (PTM) of antigens provides an additional source of specificities targeted by immune responses to tumors or pathogens, but identifying antigen PTMs and assessing their role in shaping the immunopeptidome is challenging. Here we describe the Protein Modification Integrated Search Engine (PROMISE), an antigen discovery pipeline that enables the analysis of 29 different PTM combinations from multiple clinical cohorts and cell lines. We expanded the antigen landscape, uncovering human leukocyte antigen class I binding motifs defined by specific PTMs with haplotype-specific binding preferences and revealing disease-specific modified targets, including thousands of new cancer-specific antigens that can be shared between patients and across cancer types. Furthermore, we uncovered a subset of modified peptides that are specific to cancer tissue and driven by post-translational changes that occurred in the tumor proteome. Our findings highlight principles of PTM-driven antigenicity, which may have broad implications for T cell-mediated therapies in cancer and beyond.

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.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

Reverse engineering approach: a step towards a new era of vaccinology with special reference to Salmonella

INTRODUCTION

Salmonella is responsible for causing enteric fever, septicemia, and gastroenteritis in humans. Due to high disease burden and emergence of multi- and extensively drug-resistant Salmonella strains, it is becoming difficult to treat the infection with existing battery of antibiotics as we are not able to discover newer antibiotics at the same pace at which the pathogens are acquiring resistance. Though vaccines against Salmonella are available commercially, they have limited efficacy. Advancements in genome sequencing technologies and immunoinformatics approaches have solved the problem significantly by giving rise to a new era of vaccine designing, i.e. 'Reverse engineering.' Reverse engineering/vaccinology has expedited the vaccine identification process. Using this approach, multiple potential proteins/epitopes can be identified and constructed as a single entity to tackle enteric fever.

AREAS COVERED

This review provides details of reverse engineering approach and discusses various protein and epitope-based vaccine candidates identified using this approach against typhoidal Salmonella.

EXPERT OPINION

Reverse engineering approach holds great promise for developing strategies to tackle the pathogen(s) by overcoming the limitations posed by existing vaccines. Progressive advancements in the arena of reverse vaccinology, structural biology, and systems biology combined with an improved understanding of host-pathogen interactions are essential components to design new-generation vaccines.

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.

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.

Accurate Prediction for Protein-Peptide Binding Based on High-Temperature Molecular Dynamics Simulations

The structural characterization of protein-peptide interactions is fundamental to elucidating biological processes and designing peptide drugs. Molecular dynamics (MD) simulations are extensively used to study biomolecular systems. However, simulating the protein-peptide binding process is usually quite expensive. Based on our previous studies, herein, we propose a simple and effective method to predict the binding site and pose of the peptide simultaneously using high-temperature (high-T) MD simulations with the RSFF2C force field. Thousands of binding events (nonspecific or specific) can be sampled during microseconds of high-T MD. From density-based clustering analysis, the structures of all of the 12 complexes (nine with linear peptides and three with cyclic peptides) can be successfully predicted with root-mean-square deviation (RMSD) < 2.5 Å. By directly simulating the process of the ligand binding onto the receptor, our method approaches experimental precision for the first time, significantly surpassing previous protein-peptide docking methods in terms of accuracy.

Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

MOTIVATION

Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization.

RESULTS

Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark dataset of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 min on a single CPU core, compared with 496 min for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/hpepdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Tools and techniques for rational designing of antimicrobial peptides for aquaculture

Fisheries and aquaculture industries remain essential sources of food and nutrition for millions of people worldwide. Indiscriminate use of antibiotics has led to the emergence of antimicrobial-resistant bacteria and posed a severe threat to public health. Researchers have opined that antimicrobial peptides (AMPs) can be the best possible alternative to curb the rising tide of antimicrobial resistance in aquaculture. AMPs may also help to achieve the objectives of one health approach. The natural AMPs are associated with several shortcomings, like less in vivo stability, toxicity to host cell, high cost of production and low potency in a biological system. In this review, we have provided a comprehensive outline about the strategies for designing synthetic mimics of natural AMPs with high potency. Moreover, the freely available AMP databases and the information about the molecular docking tools are enlisted. We also provided in silico template for rationally designing the AMPs from fish piscidins or other peptides. The rationally designed piscidin (rP1 and rp2) may be used to tackle microbial infections in aquaculture. Further, the protocol can be used to develop the truncated mimics of natural AMPs having more potency and protease stability.

Structural Features of Antibody-Peptide Recognition

Antibody recognition of antigens is a critical element of adaptive immunity. One key class of antibody-antigen complexes is comprised of antibodies targeting linear epitopes of proteins, which in some cases are conserved elements of viruses and pathogens of relevance for vaccine design and immunotherapy. Here we report a detailed analysis of the structural and interface features of this class of complexes, based on a set of nearly 200 nonredundant high resolution antibody-peptide complex structures that were assembled from the Protein Data Bank. We found that antibody-bound peptides adopt a broad range of conformations, often displaying limited secondary structure, and that the same peptide sequence bound by different antibodies can in many cases exhibit varying conformations. Propensities of contacts with antibody loops and extent of antibody binding conformational changes were found to be broadly similar to those for antibodies in complex with larger protein antigens. However, antibody-peptide interfaces showed lower buried surface areas and fewer hydrogen bonds than antibody-protein antigen complexes, while calculated binding energy per buried interface area was found to be higher on average for antibody-peptide interfaces, likely due in part to a greater proportion of buried hydrophobic residues and higher shape complementarity. This dataset and these observations can be of use for future studies focused on this class of interactions, including predictive computational modeling efforts and the design of antibodies or epitope-based vaccine immunogens.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Targeting Protein Interaction Hotspots Using Structured and Disordered Chimeric Peptide Inhibitors

The main challenge in inhibiting protein-protein interactions (PPI) for therapeutic purposes is designing molecules that bind specifically to the interaction hotspots. Adding to the complexity, such hotspots can be within both structured and disordered interaction interfaces. To address this, we present a strategy for inhibiting the structured and disordered hotspots of interactions using chimeric peptides that contain both structured and disordered parts. The chimeric peptides we developed are comprised of a cyclic structured part and a disordered part, which target both disordered and structured hotspots. We demonstrate our approach by developing peptide inhibitors for the interactions of the antiapoptotic iASPP protein. First, we developed a structured, α-helical stapled peptide inhibitor, derived from the N-terminal domain of MDM2. The peptide bound two hotspots on iASPP at the low micromolar range and had a cytotoxic effect on A2780 cancer cells with a half-maximal inhibitory concentration (IC₅₀) value of 10 ± 1 μM. We then developed chimeric peptides comprising the structured stapled helical peptide and the disordered p53-derived LinkTer peptide that we previously showed to inhibit iASPP by targeting its disordered RT loop. The chimeric peptide targeted both structured and disordered domains in iASPP with higher affinity compared to the individual structured and disordered peptides and caused cancer cell death. Our strategy overcomes the inherent difficulty in inhibiting the interactions of proteins that possess structured and disordered regions. It does so by using chimeric peptides derived from different interaction partners that together target a much wider interface covering both the structured and disordered domains. This paves the way for developing such inhibitors for therapeutic purposes.

InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol

MOTIVATION

Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide-protein docking or structure prediction. One such method is Rosetta FlexPepDock which consistently refines coarse peptide-protein models into sub-Ångström precision using Monte-Carlo simulations and statistical potentials. Deep learning has recently seen increased use in protein structure prediction, with graph neural networks used for protein model quality assessment.

RESULTS

Here, we introduce a graph neural network, InterPepScore, as an additional scoring term to complement and improve the Rosetta FlexPepDock refinement protocol. InterPepScore is trained on simulation trajectories from FlexPepDock refinement starting from thousands of peptide-protein complexes generated by a wide variety of docking schemes. The addition of InterPepScore into the refinement protocol consistently improves the quality of models created, and on an independent benchmark on 109 peptide-protein complexes its inclusion results in an increase in the number of complexes for which the top-scoring model had a DockQ-score of 0.49 (Medium quality) or better from 14.8% to 26.1%.

AVAILABILITY AND IMPLEMENTATION

InterPepScore is available online at http://wallnerlab.org/InterPepScore.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of the probable structure of the sAPPα-GABABR1a complex and theoretical solutions for such cases

Amyloid precursor protein (APP) is the core of the pathogenesis of Alzheimer's disease (AD). Existing studies have shown that the soluble secreted APP (sAPPα) fragment obtained from the hydrolysis of APP by α-secretase has a synaptic function. Thereinto, a nine-residue fragment (APP9mer) of the extension domain region of sAPPα can bind directly and selectively to the N-terminal sushi1 domain (SD1) of the γ-aminobutyric acid type B receptor subunit 1a (GABA_BR1a) protein, which can influence synaptic transmission and plasticity by changing the GABA_BR1a conformation. APP9mer is a highly flexible, disordered region, and as such it is difficult to experimentally determine the optimal APPmer-SD1 binding complex. In this study we constructed two types of APP9mer-SD1 complexes through molecular docking and molecular dynamics simulation, aiming to explore the recognition function and mechanism of the specific binding of APP9mer with SD1, from which the most probable APPmer-SD1 model conformation is predicted. All the data from the analyses of RMSD, RMSF, PCA, DCCM and MM/PBSA binding energy as well as comparison with the experimental dissociation constant K_d suggest that 2NC is the most likely conformation to restore the crystal structure of the experimental APP9mer-SD1 complex. Of note, the key recognition residues of APP9mer are D24, D25, D27, W29 and W30, which mainly act on the 9-45 residue domain of SD1 (consisting of two loops and three short β-chains at the N-terminus of SD1). The mini-model with key residues identified establishes the molecular basis with deep insight into the interaction between APP and GABA_BR1a and provides a target for the development of therapeutic strategies for modulating GABA_BR1a-specific signaling in neurological and psychiatric disorders. More importantly, the study offers a theoretical solution for how to determine a biomolecular structure with a highly flexible, disordered fragment embedded within. The flexible fragment involved in a protein structure has to be deserted usually during the structural determination with experimental methods (e.g. X-ray crystallography, etc.).

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

Modeling interactions between short peptides and their receptors is a challenging docking problem due to the peptide flexibility, resulting in a formidable sampling problem of peptide conformation in addition to its orientation. Alternatively, the peptide can be viewed as a piece that complements the receptor monomer structure. Here, we show that the peptide conformation can be determined based on the receptor backbone only and sampled using local structural motifs found in solved protein monomers and interfaces, independent of sequence similarity. This approach outperforms current peptide docking protocols and promotes new directions for peptide interface design.

Peptide docking can be perceived as a subproblem of protein–protein 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 protein–peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide–protein 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 peptide–protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

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.

Harnessing protein folding neural networks for peptide-protein docking

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment

Predicting protein-peptide complex structures is crucial to the understanding of a vast variety of peptide-mediated cellular processes and to peptide-based drug development. Peptide flexibility and binding mode ranking are the two major challenges for protein-peptide complex structure prediction. Peptides are highly flexible molecules, and therefore, brute-force modeling of peptide conformations of interest in protein-peptide docking is beyond current computing power. Inspired by the fact that the protein-peptide binding process is like protein folding, we developed a novel strategy, named MDockPeP2, which tries to address these challenges using physicochemical information embedded in abundant monomeric proteins with an exhaustive search strategy, in combination with an integrated global search and a local flexible minimization method. Only the peptide sequence and the protein crystal structure are required. The method was systemically assessed using a newly constructed structural database of 89 nonredundant protein-peptide complexes with the peptide sequence length ranging from 5 to 29 in which about half of the peptides are longer than 15 residues. MDockPeP2 yielded a total success rate of 58.4% (70.8, 79.8%) for the bound docking (i.e., with the bound receptor and fully flexible peptides) and 19.0% (44.8, 70.7%) for the challenging unbound docking when top 10 (100, 1000) models were considered for each prediction. MDockPeP2 achieved significantly higher success rates on two other datasets, peptiDB and LEADS-PEP, which contain only short- and medium-size peptides (≤ 15 residues). For peptiDB, our method obtained a success rate of 62.0% for the bound docking and 35.9% for the unbound docking when the top 10 models were considered. For LEADS-PEP, MDockPeP2 achieved a success rate of 69.8% when the top 10 models were considered. The program is available at https://zougrouptoolkit.missouri.edu/mdockpep2/download.html.