The structural basis of peptide-protein binding strategies

Investigating the Nature of PRM:SH3 Interactions Using Artificial Intelligence and Molecular Dynamics

Understanding the binding interactions within protein-peptide complexes is crucial for elucidating key physicochemical phenomena in biological systems. Among the outcomes of these interactions, biomolecular condensates have recently emerged as vital players in various cellular functions including signaling. Complexes such as PRM:SH3 are known to undergo condensation, yet the chemical interactions and governing factors driving these behaviors remain poorly understood. In this study, we combine AlphaFold2 and molecular dynamics simulations to investigate the binding nature of PRM:SH3. Our findings reveal that proline-to-alanine mutations enhance flexibility, weakening the binding affinity, while charge-altering mutations modify the binding mode and influence the binding strength. Notably, the PRM(H) series shows that binding is primarily driven by local flexibility and the hydrophobic effect. Furthermore, we demonstrate that the root-mean-square deviation and dendrogram height are correlated to experimental dissociation constants. These insights provide a framework for understanding the binding behaviors of protein-peptide complexes and offer an effective approach for studying similar systems.

A Computational Approach for Designing a Peptide-Based Acetyl-CoA Synthetase 2 Inhibitor: A New Horizon for Anticancer Development

Acetyl-CoA Synthetase 2 (ACSS2) has emerged as a new target for anticancer development owing to its high expression in various tumours and its enhancement of malignancy. Stressing the growing interest in peptide-derived drugs featuring better selectivity and efficacy, a computational protocol was applied to design a peptide inhibitor for ACSS2. Herein, 3600 peptide sequences derived from ACSS2 nucleotide motif were generated by classifying the 20 amino acids into six physiochemical groups. De novo modeling maintained essential binding interactions, and a refined library of 16 peptides was derived using Support Vector Machine filters to ensure proper bioavailability, toxicity, and therapeutic relevance. Structural and folding predictions, along with molecular docking, identified the top candidate, Pep16, which demonstrated significantly higher binding affinity (91.1 ± 1.6 kcal/mol) compared to a known inhibitor (53.7 ± 0.7 kcal/mol). Further molecular dynamics simulations and binding free energy calculations revealed that Pep16 enhances ACSS2 conformational variability, occupies a larger binding interface, and achieved firm binding. MM/GBSA analysis highlighted key electrostatic interactions with specific ACSS2 residues, including ARG 373, ARG 526, ARG 628, ARG 631, and LYS 632. Overall, Pep16 appears to lock the ACSS2 nucleotide pocket into a compact, rigid conformation, potentially blocking ATP binding and catalytic activity, and may serve as a novel specific ACSS2 inhibitor. Though, we urge further research to confirm and compare its therapeutic potential to existing inhibitors. We also believe that this systematic methodology would represent an indispensable tool for prospective peptide-based drug discovery.

VHI-Pred: A Multi-Feature-Based Tool for Predicting Human-Virus Protein-Protein Interactions

Viral diseases pose a significant threat to public health, highlighting the importance of understanding protein-protein interactions between hosts and viruses for therapeutic development. However, this process is often expensive and time-consuming, especially given the rapid evolution of viruses. Machine learning algorithms and artificial intelligence have emerged as powerful tools for efficiently identifying these interactions. This study aims to develop a machine learning-based model to predict protein interactions between viral pathogens and human hosts while analyzing the factors influencing these interactions. The prediction model was constructed using three machine learning algorithms: Random Forest (RF), XGBoost (XGB), and Artificial Neural Networks (ANN). Each algorithm underwent rigorous testing. The modeling features included physicochemical properties, motifs, and amino acid sequences. Model performance was evaluated using fitness, accuracy, precision, sensitivity, and specificity metrics, with validation conducted via the K-fold method. The accuracy of the RF, XGB, and ANN models was 87%, 86%, and 86%, respectively. By integrating dimensionality reduction and clustering techniques, the accuracy of the RF model improved to 90%. Traditionally, studying host-pathogen interactions is labor intensive and costly. The integration of machine learning algorithms into this field significantly enhances the efficiency of analyzing viral pathogen-human host interactions. This study demonstrates the effectiveness of such an approach and provides valuable insights for future research. The results are accessible to researchers through a web application at http://vhi.sysbiomed.ir .

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

PCANN Program for Structure-Based Prediction of Protein-Protein Binding Affinity: Comparison With Other Neural-Network Predictors

In this communication, we introduce a new structure-based affinity predictor for protein-protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM-2 language model to encode the information about protein binding interfaces and graph attention network (GAT) to parlay this information intoK d $$ {K}_{\mathrm{d}} $$ predictions. In the tests employing two previously unused literature-extracted datasets, PCANN performed better than the best of the publicly available predictors, BindPPI, with mean absolute error (MAE) of 1.3 versus 1.4 kcal/mol. Further progress in the development ofK d $$ {K}_{\mathrm{d}} $$ predictors using deep learning models is faced with two problems: (i) the amount of experimental data available to train and test new predictors is limited and (ii) the availableK d $$ {K}_{\mathrm{d}} $$ data are often not very accurate and lack internal consistency with respect to measurement conditions. These issues can be potentially addressed through an AI-leveraged literature search followed by careful human curation and by introducing additional parameters to account for variations in experimental conditions.

Protein-Based Degraders: From Chemical Biology Tools to Neo-Therapeutics

The nascent field of targeted protein degradation (TPD) could revolutionize biomedicine due to the ability of degrader molecules to selectively modulate disease-relevant proteins. A key limitation to the broad application of TPD is its dependence on small-molecule ligands to target proteins of interest. This leaves unstructured proteins or those lacking defined cavities for small-molecule binding out of the scope of many TPD technologies. The use of proteins, peptides, and nucleic acids (otherwise known as "biologics") as the protein-targeting moieties in degraders addresses this limitation. In the following sections, we provide a comprehensive and critical review of studies that have used proteins and peptides to mediate the degradation and hence the functional control of otherwise challenging disease-relevant protein targets. We describe existing platforms for protein/peptide-based ligand identification and the drug delivery systems that might be exploited for the delivery of biologic-based degraders. Throughout the Review, we underscore the successes, challenges, and opportunities of using protein-based degraders as chemical biology tools to spur discoveries, elucidate mechanisms, and act as a new therapeutic modality.

Structural basis of promiscuous inhibition of Listeria virulence activator PrfA by oligopeptides

The facultative pathogen Listeria monocytogenes uses a master regulator, PrfA, to tightly control the fitness-costly expression of its virulence factors. We found that PrfA activity is repressed via competitive occupancy of the binding site for the PrfA-activating cofactor, glutathione, by exogenous nutritional oligopeptides. The inhibitory peptides show different sequence and physicochemical properties, but how such a wide variety of oligopeptides can bind PrfA was unclear. Using crystal structure analysis of PrfA complexed with inhibitory tri- and tetrapeptides, we show here that the binding promiscuity is due to the ability of PrfA β5 in the glutathione-binding inter-domain tunnel to establish parallel or antiparallel β sheet-like interactions with the peptide backbone. Spacious tunnel pockets provide additional flexibility for unspecific peptide accommodation while providing selectivity for hydrophobic residues. Hydrophobic contributions from two adjacent peptide residues appear to be critical for efficient PrfA inhibitory binding. In contrast to glutathione, peptide binding prevents the conformational change required for the correct positioning of the DNA-binding helix-turn-helix motifs of PrfA, effectively inhibiting virulence gene expression.

Robustly interrogating machine learning-based scoring functions: what are they learning?

MOTIVATION

Machine learning-based scoring functions (MLBSFs) have been found to exhibit inconsistent performance on different benchmarks and be prone to learning dataset bias. For the field to develop MLBSFs that learn a generalizable understanding of physics, a more rigorous understanding of how they perform is required.

RESULTS

In this work, we compared the performance of a diverse set of popular MLBSFs (RFScore, SIGN, OnionNet-2, Pafnucy, and PointVS) to our proposed baseline models that can only learn dataset biases on a range of benchmarks. We found that these baseline models were competitive in accuracy to these MLBSFs in almost all proposed benchmarks, indicating these models only learn dataset biases. Our tests and provided platform, ToolBoxSF, will enable researchers to robustly interrogate MLBSF performance and determine the effect of dataset biases on their predictions.

AVAILABILITY AND IMPLEMENTATION

https://github.com/guydurant/toolboxsf.

Inhibition Clathrin Mediated Endocytosis: Pitstop 1 and Pitstop 2 Chimeras

Twenty-five chimera compounds of Pitstop 1 and 2 were synthesised and screened for their ability to block the clathrin terminal domain-amphiphysin protein-protein interaction (NTD-PPI using an ELISA) and clathrin mediated endocytosis (CME) in cells. Library 1 was based on Pitstop 2, but no notable clathrin PPI or in-cell activity was observed. With the Pitstop 1, 16 analogues were produced with 1,8-naphthalic imide core as a foundation. Analogues with methylene spaced linkers and simple amides showed a modest to good range of PPI inhibition (7.6-42.5 μM, naphthyl 39 and 4-nitrophenyl 40 respectively) activity. These data reveal the importance of the naphthalene sulfonate moiety, with no des-SO3 analogue displaying PPI inhibition. This was consistent with the observed analogue docked poses within the clathrin terminal domain Site 1 binding pocket. Further modifications targeted the naphthalene imide moiety, with the installation of 5-Br (45 a), 5-OH (45 c) and 5-propyl ether (45 d) moieties. Among them, the OH 45 c and propyl ether 45 d retained PPI inhibition, with propyl ether 45 d being the most active with a PPI inhibition IC50=7.3 μM. This is 2x more potent than Pitstop 2 and 3x more potent than Pitstop 1.

Stereocontrolled and time-honored access to piperidine- and pyrrolidine-fused 3-methylenetetrahydropyrans using lactam-tethered alkenols

Polycyclic oxygen-heterocycles bearing the 3-methylenetetrahydropyran (i.e., 3-MeTHP) motif are resident in bioactive molecules such as hodgsonox and iridoid. Meanwhile, the δ- and γ-lactam topologies as well as their reduced variants (i.e., piperidines and pyrrolidines) are at the core of several pharmaceuticals and fragrances. A stereocontrolled, time-honored, and cost-effective strategy that merges a 3-MeTHP motif with the aforementioned azaheterocyclic scaffolds could exponentially expand the 3D-structural space for the discovery of new small molecules with medicinal value. In these studies, readily affordable lactam-tethered alkenols have been interrogated in two complementary cascade approaches, leading to the regioselective and stereocontrolled synthesis of lactam-fused 3-MeTHPs. The first approach hinges on regioselective 6-endo-trig bromoetherification of the alkenols and concomitant elimination to arrive at the desired 3-MeTHPs. The methylene portion of the 3-MeTHP is unveiled at a late stage, which is noteworthy since all existing approaches to 3-MeTHPs rely on early-stage introduction of the methylene group. The second strategy involves transition metal-catalyzed alkoxylation of the tethered alkenol followed by base-induced double bond isomerization. The lactam-fused 3-MeTHPs are obtained in high site- and diastereo-selectivities. Post-modification of the bicycles has led to the construction of 3-MeTHP-fused saturated piperidines and pyrrolidines as well as 3-MeTHPs bearing four contiguous stereocenters.

Retinal Degeneration Response to Graphene Quantum Dots: Disruption of the Blood-Retina Barrier Modulated by Surface Modification-Dependent DNA Methylation

Graphene quantum dots (GQDs) are used in diverse fields from chemistry-related materials to biomedicines, thus causing their substantial release into the environment. Appropriate visual function is crucial for facilitating the decision-making process within the nervous system. Given the direct interaction of eyes with the environment and even nanoparticles, herein, GQDs, sulfonic acid-doped GQDs (S-GQDs), and amino-functionalized GQDs (A-GQDs) were employed to understand the potential optic neurotoxicity disruption mechanism by GQDs. The negatively charged GQDs and S-GQDs disturbed the response to light stimulation and impaired the structure of the retinal nuclear layer of zebrafish larvae, causing vision disorder and retinal degeneration. Albeit with sublethal concentrations, a considerably reduced expression of the retinal vascular sprouting factor sirt1 through increased DNA methylation damaged the blood-retina barrier. Importantly, the regulatory effect on vision function was influenced by negatively charged GQDs and S-GQDs but not positively charged A-GQDs. Moreover, cluster analysis and computational simulation studies indicated that binding affinities between GQDs and the DNMT1-ligand binding might be the dominant determinant of the vision function response. The previously unknown pathway of blood-retinal barrier interference offers opportunities to investigate the biological consequences of GQD-based nanomaterials, guiding innovation in the industry toward environmental sustainability.

Collapsed Star Copolymers Exhibiting Near Perfect Mimicry of the Therapeutic Protein "TRAIL"

Here we introduce amphiphilic star polymers as versatile protein mimics capable of approximating the activity of certain native proteins. Our study focuses on designing a synthetic polymer capable of replicating the biological activity of TRAIL, a promising anticancer protein that shows very poor circulation half-life. Successful protein mimicry requires precise control over the presentation of receptor-binding peptides from the periphery of the polymer scaffold while maintaining enough flexibility for protein-peptide binding. We show that this can be achieved by building hydrophobic blocks into the core of a star-shaped polymer, which drives unimolecular collapse in water. By screening a library of diblock copolymer stars, we were able to design structures with IC50's of ∼4 nM against a colon cancer cell line (COLO205), closely approximating the activity of the native TRAIL protein. This finding highlights the broad potential for simple synthetic polymers to mimic the biological activity of complex proteins.

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.

Modular binder technology by NGS-aided, high-resolution selection in yeast of designed armadillo modules

Establishing modular binders as diagnostic detection agents represents a cost- and time-efficient alternative to the commonly used binders that are generated one molecule at a time. In contrast to these conventional approaches, a modular binder can be designed in silico from individual modules to, in principle, recognize any desired linear epitope without going through a selection and hit-validation process, given a set of preexisting, amino acid-specific modules. Designed armadillo repeat proteins (dArmRP) have been developed as modular binder scaffolds, and we report here the generation of highly specific dArmRP modules by yeast surface display selection, performed on a rationally designed dArmRP library. A selection strategy was developed to distinguish the binding difference resulting from a single amino acid mutation in the target peptide. Our reverse-competitor strategy introduced here employs the designated target as a competitor to increase the sensitivity when separating specific from cross-reactive binders that show similar affinities for the target peptide. With this switch in selection focus from affinity to specificity, we found that the enrichment during this specificity sort is indicative of the desired phenotype, regardless of the binder abundance. Hence, deep sequencing of the selection pools allows retrieval of phenotypic hits with only 0.1% abundance in the selectivity sort pool from the next-generation sequencing data alone. In a proof-of-principle study, a binder was created by replacing all corresponding wild-type modules with a newly selected module, yielding a binder with very high affinity for the designated target that has been successfully validated as a detection agent in western blot analysis.

Intramolecular chaperone-assisted dual-anchoring activation (ICDA): a suitable preorganization for electrophilic halocyclization

The halocyclization reaction represents one of the most common methodologies for the synthesis of heterocyclic molecules. Many efforts have been made to balance the relationship between structure, reactivity and selectivity, including the design of new electrophilic halogenation reagents and the utilization of activating strategies. However, discovering universal reagents or activating strategies for electrophilic halocyclization remains challenging due to the case-by-case practice for different substrates or different cyclization models. Here we report an intramolecular chaperone-assisted dual-anchoring activation (ICDA) model for electrophilic halocyclization, taking advantage of the non-covalent dual-anchoring orientation as the driving force. This protocol allows a practical, catalyst-free and rapid approach to access seven types of small-sized, medium-sized, and large-sized heterocyclic units and to realize polyene-like domino halocyclizations, as exemplified by nearly 90 examples, including a risk-reducing flow protocol for gram-scale synthesis. DFT studies verify the crucial role of ICDA in affording a suitable preorganization for transition state stabilization and X+ transfer acceleration. The utilization of the ICDA model allows a spatiotemporal adjustment to straightforwardly obtain fast, selective and high-yielding synthetic transformations.

Prediction of virus-host interactions and identification of hot spot residues of DENV-2 and SH3 domain interactions

Dengue virus, particularly serotype 2 (DENV-2), poses a significant global health threat, and understanding the molecular basis of its interactions with host cell proteins is imperative for developing targeted therapeutic strategies. This study elucidated the interactions between proline-enriched motifs and Src homology 3 (SH3) domain. The SH3 domain is pivotal in mediating protein-protein interactions, particularly by recognizing and binding to proline-rich regions in partner proteins. Through a computational pipeline, we analyzed the interactions and binding modes of proline-enriched motifs with SH3 domains, identified new potential DENV-2 interactions with the SH3 domain, and revealed potential hot spot residues, underscoring their significance in the viral life cycle. This comprehensive analysis provides crucial insights into the molecular basis of DENV-2 infection, highlighting conserved and serotype-specific interactions. The identified hot spot residues offer potential targets for therapeutic intervention, laying the foundation for developing antiviral strategies against Dengue virus infection. These findings contribute to the broader understanding of viral-host interactions and provide a roadmap for future research on Dengue virus pathogenesis and treatment.

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features

Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein-peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git .

Unexpected dynamics in femtomolar complexes of binding proteins with peptides

Ultra-tight binding is usually observed for proteins associating with rigidified molecules. Previously, we demonstrated that femtomolar binders derived from the Armadillo repeat proteins (ArmRPs) can be designed to interact very tightly with fully flexible peptides. Here we show for ArmRPs with four and seven sequence-identical internal repeats that the peptide-ArmRP complexes display conformational dynamics. These dynamics stem from transient breakages of individual protein-residue contacts that are unrelated to overall unbinding. The labile contacts involve electrostatic interactions. We speculate that these dynamics allow attaining very high binding affinities, since they reduce entropic losses. Importantly, only NMR techniques can pick up these local events by directly detecting conformational exchange processes without complications from changes in solvent entropy. Furthermore, we demonstrate that the interaction surface of the repeat protein regularizes upon peptide binding to become more compatible with the peptide geometry. These results provide novel design principles for ultra-tight binders.

Computed Protein-Protein Enthalpy Signatures as a Tool for Identifying Conformation Sampling Problems

Understanding the thermodynamic signature of protein-peptide binding events is a major challenge in computational chemistry. The complexity generated by both components possessing many degrees of freedom poses a significant issue for methods that attempt to directly compute the enthalpic contribution to binding. Indeed, the prevailing assumption has been that the errors associated with such approaches would be too large for them to be meaningful. Nevertheless, we currently have no indication of how well the present methods would perform in terms of predicting the enthalpy of binding for protein-peptide complexes. To that end, we carefully assembled and curated a set of 11 protein-peptide complexes where there is structural and isothermal titration calorimetry data available and then computed the absolute enthalpy of binding. The initial "out of the box" calculations were, as expected, very modest in terms of agreement with the experiment. However, careful inspection of the outliers allows for the identification of key sampling problems such as distinct conformations of peptide termini not being sampled or suboptimal cofactor parameters. Additional simulations guided by these aspects can lead to a respectable correlation with isothermal titration calorimetry (ITC) experiments (R2 of 0.88 and an RMSE of 1.48 kcal/mol overall). Although one cannot know prospectively whether computed ITC values will be correct or not, this work shows that if experimental ITC data are available, then this in conjunction with computed ITC, can be used as a tool to know if the ensemble being simulated is representative of the true ensemble or not. That is important for allowing the correct interpretation of the detailed dynamics of the system with respect to the measured enthalpy. The results also suggest that computational calorimetry is becoming increasingly feasible. We provide the data set as a resource for the community, which could be used as a benchmark to help further progress in this area.

DeepBSRPred: deep learning-based binding site residue prediction for proteins

MOTIVATION

Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand the binding affinities and functions of protein-protein complexes.

RESULTS

We have developed a deep neural network-based method, DeepBSRPred, for predicting the binding sites using protein sequence information and predicted structures from AlphaFold2. Specific sequence and structure-based features include position-specific scoring matrix (PSSM), solvent accessible surface area, conservation score and amino acid properties, and residue depth, respectively. Our method predicted the binding sites with an average F1 score of 0.73 in a dataset of 1236 proteins. Further, we compared the performance with other existing methods in the literature using four benchmark datasets and our method outperformed those methods.

AVAILABILITY AND IMPLEMENTATION

The DeepBSRPred web server can be found at https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html , along with all datasets used in this study. The trained models, the DeepBSRPred standalone source code, and the feature computation pipeline are freely available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html .

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.

A comprehensive review on the pancreatic lipase inhibitory peptides: A future anti-obesity strategy

Dysregulation of lipid homeostasis contributes to obesity and can directly lead to several critical public health concerns globally. This paper aimed to present a brief review of related properties and the use of pancreatic lipase inhibitors as the future weight loss drug discovery and development procured from a wide range of natural sources. A total of 176 pancreatic lipase inhibitory peptides were identified from recent publications and peptide databases. These peptides were classified into three categories according to their peptide length and further analyzed using bioinformatic approaches to identify their structural activity relationship. Molecular docking analyses were conducted for each amino acid at the terminal position of the peptides to predict the binding affinity between peptide-enzyme protein complexes based on intermolecular contact interactions. Overall, the observations revealed the features of the inhibitory peptides and their inhibitory mechanisms and interactions. These findings strived to benefit scientists whose research may be relevant to anti-obesity drug development and/or discovery thereby support effective translation of preclinical research for humans’ health being.

De novo design of modular peptide-binding proteins by superhelical matching

General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1-3. Here, inspired by natural and re-engineered protein-peptide systems4-11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Systematic analysis and comparison of peptide specificity and selectivity between their cognate receptors and noncognate decoys

Protein-peptide interactions (PpIs) play an important role in cell signaling networks and have been exploited as new and attractive therapeutic targets. The affinity and specificity are two unity-of-opposite aspects of PpIs (and other biomolecular interactions); the former indicates the absolute binding strength between the peptide ligand and its cognate protein receptor in a PpI, while the latter represents the relative recognition selectivity of the peptide ligand for its cognate protein receptor in a PpI over those noncognate decoys that could be potentially encountered by the peptide in cell. Although the PpI binding affinity has been widely investigated over the past decades, the peptide recognition specificity (and selectivity) still remains largely unexplored to date. In this study, we classified PpI specificity into three types: (i) class-I specificity: peptide selectivity for its cognate wild-type protein receptor over the noncognate mutant decoys of this receptor, (ii) class-II specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are homologous with this receptor, and (iii) class-III specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are the cognate receptors of other peptides. We performed affinity and selectivity analysis for the three types of PpI specificity and revealed that the PpIs generally exhibit a moderate or modest specificity; peptide selectivity increases in the order: class-I < class-II < class-III. All the three types of PpI specificity were observed to have no statistically significant correlation with peptide length and hydrophobicity, but the class-I and class-II specificities can be influenced considerably by peptide secondary structures; the high specificity is preferentially associated with ordered structure types as compared to undefined structure types. In addition, the mutation distribution (for class-I specificity), sequence conservation (for class-II specificity), and structural similarity (for class-III specificity) seem also to address effects on peptide selectivity.

Glycomimetic Peptides as Therapeutic Tools

The entry of peptides into glycobiology has led to the development of a unique class of therapeutic tools. Although numerous and well-known peptides are active as endocrine regulatory factors that bind to specific receptors, and peptides have been used extensively as epitopes for vaccine production, the use of peptides that mimic sugars as ligands of lectin-type receptors has opened a unique approach to modulate activity of immune cells. Ground-breaking work that initiated the use of peptides as tools for therapy identified sugar mimetics by screening phage display libraries. The peptides that have been discovered show significant potential as high-avidity, therapeutic tools when synthesized as multivalent structures. Advantages of peptides over sugars as drugs for immune modulation will be illustrated in this review.

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.

AmiA and AliA peptide ligands are secreted by Klebsiella pneumoniae and inhibit growth of Streptococcus pneumoniae

Streptococcus pneumoniae colonizes the human nasopharynx, a multi-species microbial niche. Pneumococcal Ami-AliA/AliB oligopeptide permease is an ABC transporter involved in environmental sensing with peptides AKTIKITQTR, FNEMQPIVDRQ, and AIQSEKARKHN identified as ligands of its substrate binding proteins AmiA, AliA, and AliB, respectively. These sequences match ribosomal proteins of multiple bacterial species, including Klebsiella pneumoniae. By mass spectrometry, we identified such peptides in the Klebsiella pneumoniae secretome. AmiA and AliA peptide ligands suppressed pneumococcal growth, but the effect was dependent on peptide length. Growth was suppressed for diverse pneumococci, including antibiotic-resistant strains, but not other bacterial species tested, with the exception of Streptococcus pseudopneumoniae, whose growth was suppressed by the AmiA peptide ligand. By multiple sequence alignments and protein and peptide binding site predictions, for AmiA we have identified the location of an amino acid in the putative binding site whose mutation appears to result in loss of response to the peptide. Our results indicate that pneumococci sense the presence of Klebsiella pneumoniae peptides in the environment.

Intrinsic protein disorder uncouples affinity from binding specificity

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins often function by molecular recognition, in which they undergo induced folding. Based on prior generalizations, the idea prevails in the IDP field that due to the entropic penalty of induced folding, the major functional advantage associated with this binding mode is "uncoupling" specificity from binding strength. Nevertheless, both weaker binding and high specificity of IDPs/IDRs rest on limited experimental observations, making these assumptions more speculations than evidence-supported facts. The issue is also complicated by the rather vague concept of specificity that lacks an exact measure, such as the Kd for binding strength. We addressed these issues by creating and analyzing a comprehensive dataset of well-characterized ID/globular protein complexes, for which both the atomic structure of the complex and free energy (ΔG, Kd ) of interaction is known. Through this analysis, we provide evidence that the affinity distributions of IDP/globular and globular/globular complexes show different trends, whereas specificity does not connote to weaker binding strength of IDPs/IDRs. Furthermore, protein disorder extends the spectrum in the direction of very weak interactions, which may have important regulatory consequences and suggest that, in a biological sense, strict correlation of specificity and binding strength are uncoupled by structural disorder.

To Improve Prediction of Binding Residues With DNA, RNA, Carbohydrate, and Peptide Via Multi-Task Deep Neural Networks

MOTIVATION

The interactions of proteins with DNA, RNA, peptide, and carbohydrate play key roles in various biological processes. The studies of uncharacterized protein-molecules interactions could be aided by accurate predictions of residues that bind with partner molecules. However, the existing methods for predicting binding residues on proteins remain of relatively low accuracies due to the limited number of complex structures in databases. As different types of molecules partially share chemical mechanisms, the predictions for each molecular type should benefit from the binding information with other molecule types.

RESULTS

In this study, we employed a multiple task deep learning strategy to develop a new sequence-based method for simultaneously predicting binding residues/sites with multiple important molecule types named MTDsite. By combining four training sets for DNA, RNA, peptide, and carbohydrate-binding proteins, our method yielded accurate and robust predictions with AUC values of 0.852, 0836, 0.758, and 0.776 on their respective independent test sets, which are 0.52 to 6.6% better than other state-of-the-art methods. To my best knowledge, this is the first method using multi-task framework to predict multiple molecular binding sites simultaneously.

Binary combinatorial scanning reveals potent poly-alanine-substituted inhibitors of protein-protein interactions

Establishing structure-activity relationships is crucial to understand and optimize the activity of peptide-based inhibitors of protein-protein interactions. Single alanine substitutions provide limited information on the residues that tolerate simultaneous modifications with retention of biological activity. To guide optimization of peptide binders, we use combinatorial peptide libraries of over 4,000 variants-in which each position is varied with either the wild-type residue or alanine-with a label-free affinity selection platform to study protein-ligand interactions. Applying this platform to a peptide binder to the oncogenic protein MDM2, several multi-alanine-substituted analogs with picomolar binding affinity were discovered. We reveal a non-additive substitution pattern in the selected sequences. The alanine substitution tolerances for peptide ligands of the 12ca5 antibody and 14-3-3 regulatory protein are also characterized, demonstrating the general applicability of this new platform. We envision that binary combinatorial alanine scanning will be a powerful tool for investigating structure-activity relationships.

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.

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.

Overcoming the shortcomings of peptide-based therapeutics

Peptides have traditionally been perceived as poor drug candidates due to unfavorable characteristics mainly regarding their pharmacokinetic behavior, including plasma stability, membrane permeability and circulation half-life. Nonetheless, in recent years, general strategies to tackle those shortcomings have been established, and peptides are subsequently gaining increasing interest as drugs due to their unique ability to combine the advantages of antibodies and small molecules. Macrocyclic peptides are a special focus of drug development efforts due to their ability to address so called ‘undruggable’ targets characterized by large and flat protein surfaces lacking binding pockets. Here, the main strategies developed to date for adapting peptides for clinical use are summarized, which may soon help usher in an age highly shaped by peptide-based therapeutics. Nonetheless, limited membrane permeability is still to overcome before peptide therapeutics will be broadly accepted.

Tertiary motifs as building blocks for the design of protein-binding peptides

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre-defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface-complementing fragments or "seeds." We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM-based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide-protein complexes can be covered by seeds generated from single-chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide-covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher-quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide-binding site. These results demonstrate that known peptide-binding structures can be constructed from TERMs in single-chain structures and suggest that TERM information can be applied to efficiently design novel target-complementing binders.

Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses

Hydrogen bonds play important roles in protein folding and protein-ligand interactions, particularly in specific protein-DNA recognition. However, the distributions of hydrogen bonds, especially hydrogen bond energy (HBE) in different types of protein-ligand complexes, is unknown. Here we performed a comparative analysis of hydrogen bonds among three non-redundant datasets of protein-protein, protein-peptide, and protein-DNA complexes. Besides comparing the number of hydrogen bonds in terms of types and locations, we investigated the distributions of HBE. Our results indicate that while there is no significant difference of hydrogen bonds within protein chains among the three types of complexes, interfacial hydrogen bonds are significantly more prevalent in protein-DNA complexes. More importantly, the interfacial hydrogen bonds in protein-DNA complexes displayed a unique energy distribution of strong and weak hydrogen bonds whereas majority of the interfacial hydrogen bonds in protein-protein and protein-peptide complexes are of predominantly high strength with low energy. Moreover, there is a significant difference in the energy distributions of minor groove hydrogen bonds between protein-DNA complexes with different binding specificity. Highly specific protein-DNA complexes contain more strong hydrogen bonds in the minor groove than multi-specific complexes, suggesting important role of minor groove in specific protein-DNA recognition. These results can help better understand protein-DNA interactions and have important implications in improving quality assessments of protein-DNA complex models.

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.

LRH-1 (liver receptor homolog-1) derived affinity peptide ligand to inhibit interactions between β-catenin and LRH-1 in pancreatic cancer cells: from computational design to experimental validation

Poor prognosis, rapid progression and the lack of an effective treatment make pancreatic cancer one of the most lethal malignancies. Recent studies point to a role for liver receptor homolog-1 (LRH-1) in pathogenesis of pancreatic cancer and suggest prevention of the β-catenin/LRH-1 complex formation as a potential strategy for inhibition of the pancreas cancer cells progression. In the current investigation, we have followed a biomimetic strategy and designed an affinity peptide with sequence DEMEEPQQTE to inhibit formation of the β-catenin/LRH-1 complex. Quantitative real-time PCR experiments on the AsPC-1 pancreatic metastatic cells showed that the peptide has an inhibitory effect on the Wnt signaling proliferation line by reducing the expression levels of the CCND1, CCNE1, and MYC genes. Furthermore, the increased expression level of BAX gene showed that AsPC-1 cells were directed to the apoptosis pathway. At last, POU5F1, KLF4, and CD44 gene expression levels suggested that the peptide has an inhibitory effect on the stemness feature of the AsPC-1 cells. Here, we introduced a novel peptide inhibitor targeting an important protein-protein interaction, the β-catenin/LRH-1 complex, which may provide highly promising starting points for subsequent drug design. Communicated by Ramaswamy H. Sarma.

The emerging role of dipeptidyl peptidase 3 in pathophysiology

Dipeptidyl peptidase 3 (DPP3), a zinc-dependent aminopeptidase, is a highly conserved enzyme among higher animals. The enzyme cleaves dipeptides from the N-terminus of tetra- to decapeptides, thereby taking part in activation as well as degradation of signalling peptides critical in physiological and pathological processes such as blood pressure regulation, nociception, inflammation and cancer. Besides its catalytic activity, DPP3 moonlights as a regulator of the cellular oxidative stress response pathway, e.g., the Keap1-Nrf2 mediated antioxidative response. The enzyme is also recognized as a key modulator of the renin-angiotensin system. Recently, DPP3 has been attracting growing attention within the scientific community, which has significantly augmented our knowledge of its physiological relevance. Herein, we review recent advances in our understanding of the structure and catalytic activity of DPP3, with a focus on attributing its molecular architecture and catalytic mechanism to its wide-ranging biological functions. We further highlight recent intriguing reports that implicate a broader role for DPP3 as a valuable biomarker in cardiovascular and renal pathologies and furthermore discuss its potential as a promising drug target.

Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions

Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.

Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach

Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization.

Effect of FKBP12-Derived Intracellular Peptides on Rapamycin-Induced FKBP-FRB Interaction and Autophagy

Intracellular peptides (InPeps) generated by proteasomes were previously suggested as putative natural regulators of protein-protein interactions (PPI). Here, the main aim was to investigate the intracellular effects of intracellular peptide VFDVELL (VFD7) and related peptides on PPI. The internalization of the peptides was achieved using a C-terminus covalently bound cell-penetrating peptide (cpp; YGRKKRRQRRR). The possible inhibition of PPI was investigated using a NanoBiT^® luciferase structural complementation reporter system, with a pair of plasmids vectors each encoding, simultaneously, either FK506-binding protein (FKBP) or FKBP-binding domain (FRB) of mechanistic target of rapamycin complex 1 (mTORC1). The interaction of FKBP-FRB within cells occurs under rapamycin induction. Results shown that rapamycin-induced interaction between FKBP-FRB within human embryonic kidney 293 (HEK293) cells was inhibited by VFD7-cpp (10-500 nM) and FDVELLYGRKKRRQRRR (VFD6-cpp; 1-500 nM); additional VFD7-cpp derivatives were either less or not effective in inhibiting FKBP-FRB interaction induced by rapamycin. Molecular dynamics simulations suggested that selected peptides, such as VFD7-cpp, VFD6-cpp, VFAVELLYGRKKKRRQRRR (VFA7-cpp), and VFEVELLYGRKKKRRQRRR (VFA7-cpp), bind to FKBP and to FRB protein surfaces. However, only VFD7-cpp and VFD6-cpp induced changes on FKBP structure, which could help with understanding their mechanism of PPI inhibition. InPeps extracted from HEK293 cells were found mainly associated with macromolecular components (i.e., proteins and/or nucleic acids), contributing to understanding InPeps' intracellular proteolytic stability and mechanism of action-inhibiting PPI within cells. In a model of cell death induced by hypoxia-reoxygenation, VFD6-cpp (1 µM) increased the viability of mouse embryonic fibroblasts cells (MEF) expressing mTORC1-regulated autophagy-related gene 5 (Atg5), but not in autophagy-deficient MEF cells lacking the expression of Atg5. These data suggest that VFD6-cpp could have therapeutic applications reducing undesired side effects of rapamycin long-term treatments. In summary, the present report provides further evidence that InPeps have biological significance and could be valuable tools for the rational design of therapeutic molecules targeting intracellular PPI.

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.

Structural Prediction of Peptide-MHC Binding Modes

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8⁺ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

Computational Screening for the Anticancer Potential of Seed-Derived Antioxidant Peptides: A Cheminformatic Approach

Some seed-derived antioxidant peptides are known to regulate cellular modulators of ROS production, including those proposed to be promising targets of anticancer therapy. Nevertheless, research in this direction is relatively slow owing to the inevitable time-consuming nature of wet-lab experimentations. To help expedite such explorations, we performed structure-based virtual screening on seed-derived antioxidant peptides in the literature for anticancer potential. The ability of the peptides to interact with myeloperoxidase, xanthine oxidase, Keap1, and p47^phox was examined. We generated a virtual library of 677 peptides based on a database and literature search. Screening for anticancer potential, non-toxicity, non-allergenicity, non-hemolyticity narrowed down the collection to five candidates. Molecular docking found LYSPH as the most promising in targeting myeloperoxidase, xanthine oxidase, and Keap1, whereas PSYLNTPLL was the best candidate to bind stably to key residues in p47^phox. Stability of the four peptide-target complexes was supported by molecular dynamics simulation. LYSPH and PSYLNTPLL were predicted to have cell- and blood-brain barrier penetrating potential, although intolerant to gastrointestinal digestion. Computational alanine scanning found tyrosine residues in both peptides as crucial to stable binding to the targets. Overall, LYSPH and PSYLNTPLL are two potential anticancer peptides that deserve deeper exploration in future.