Peptide-mediated interactions in biological systems: new discoveries and applications

Unveiling ODP4: A breakthrough in PCOS treatment via BAT transplantation

Women with polycystic ovary syndrome (PCOS) often face infertility due to endocrine disorders affecting their reproductive, metabolic, and endocrine systems. Although brown adipose tissue (BAT) transplantation has been established to treat polycystic ovaries and hyperandrogenism in PCOS rats, the underlying mechanism is still largely unclear owing to lacking effective clinical treatment. Peptides are believed to significantly contribute to PCOS pathogenesis, however, the specific effects of active peptides released by BAT on PCOS remain largely unexplored. This study sought to identify active peptides secreted in the recipient's BAT and investigate their potential biological functions in PCOS. We validated the impact of BAT transplantation and found that an overexpressed ovary derived peptide 4 (ODP4) in BAT transplantation rats could potentiate the inhibitory effect of dehydroepiandrosterone (DHEA) on granulosa cell (GC) development, yield a stimulatory effect on cell apoptosis and regulate ovulation genes and hormone synthesis. In DHEA-induced PCOS rats, ODP4 restored the estrous cycle and reduced cystic follicles, indicating its potential in PCOS treatment. Furthermore, transcriptomic analysis of KGN cells treated with ODP4 and DHEA showed changes in genes related to mitochondrial activity and oxidative damage. The mechanism results showed that ODP4 enhanced mitochondrial functionality, elevated ATP production, and decreased oxidative damage in KGN cells treatment with DHEA, suggesting its preventive role in mitochondrial malfunction and oxidative damage. These findings reveal unrecognized roles of ODP4 in PCOS pathogenesis. Our study substantiates that the connection between BAT transplantation and PCOS is related to peptidomics. Additionally, ODP4 has prospects for clinical application as an innovative therapeutic PCOS target.

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.

NCPepFold: Accurate Prediction of Noncanonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation

Artificial intelligence-based peptide structure prediction methods have revolutionized biomolecular science. However, restricting predictions to peptides composed solely of 20 natural amino acids significantly limits their practical application; as such, peptides often demonstrate poor stability under physiological conditions. Here, we present NCPepFold, a computational approach that can utilize a specific cyclic position matrix to directly predict the structure of cyclic peptides with noncanonical amino acids. By integrating multigranularity information at the residual and atomic level, along with fine-tuning techniques, NCPepFold significantly improves prediction accuracy, with the average peptide root-mean-square deviation (RMSD) for cyclic peptides being 1.640 Å. In summary, this is a novel deep learning model designed specifically for cyclic peptides with noncanonical amino acids, offering great potential for peptide drug design and advancing biomedical research.

Computational analysis and protein engineering of artificial PDZ domain/self-binding peptide fusion biomacromolecular system with molecular switch functionality

Self-binding peptides (SBPs) are peptide-dependent intramolecular interactions described by our group, which conceptualize the short peptide segment within a monomeric protein as able to perform biological function by its dynamic and reversible binding to a cognate domain partner in the same monomer. Previously, we offered evidences that the SBPs represent a new biomolecular dynamic phenomenon that works across folding and binding, and also proposed the SBPs as potential drug targets for pharmacological intervention. Here, we attempted to model and design artificial protein systems containing SBP with molecular switch functionality by using computational peptidology strategies and protein engineering methods. In the procedure, the free decapeptide ligands were fused to the C-terminal tail of human CAL PDZ domain through a flexible polypeptide linker, thus resulting in a number of PDZ/SBP fusion biomacromolecular systems. The intramolecular binding event of SBP to PDZ was observed in the fusion systems as well as between the free decapeptide and PDZ. We carefully examined linker effects on the intramolecular binding event and systematically optimized the length and composition of linker to improve binding. Computational analysis suggested dynamics, but not thermodynamics, primarily responsible for the binding of SBP to PDZ. The linker plays an important role, as it restrains the SBP within a small local spatial region nearby the binding site of PDZ. The term effective concentration (EC) was defined to characterize the high local concentration of SBP at a small region due to the linker restriction, which improves the binding probability of SBP to PDZ from a dynamics point of view. The CAL PDZ/SBP(iCAL36-K-6) fusion protein system with poly(G)8 linker can work as designed well, where the SBP(iCAL36-K-6) dynamically binds to/unbinds from PDZ in a regulatable manner by externally controlling its C-terminal deamidation/amidation, thus exhibiting a typical molecular switch functionality.

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.

Real-time analysis of the biomolecular interaction between gelsolin and Aβ1-42 monomer and its implication for Alzheimer's disease

Biomolecular interaction acts a pivotal part in understanding the mechanisms underlying the development of Alzheimer's disease (AD). Herein, we built a biosensing platform to explore the interaction between gelsolin (GSN) and different β-amyloid protein 1-42 (Aβ1-42) species, including Aβ1-42 monomer (m-Aβ), Aβ1-42 oligomers with both low and high levels of aggregation (LLo-Aβ and HLo-Aβ) via dual polarization interferometry (DPI). Real-time molecular interaction process and kinetic analysis showed that m-Aβ had the strongest affinity and specificity with GSN compared with LLo-Aβ and HLo-Aβ. The impact of GSN on inhibiting aggregation of Aβ1-42 and solubilizing Aβ1-42 aggregates was evaluated by circular dichroism (CD) spectroscopy. The maintenance of random coil structure of m-Aβ and the reversal of β-sheet structure in HLo-Aβ were observed, demonstrating the beneficial effects of GSN on preventing Aβ from aggregation. In addition, the structure of m-Aβ/GSN complex was analyzed in detail by molecular dynamics (MD) simulation and molecular docking. The specific binding sites and crucial intermolecular forces were identified, which are believed to stabilize m-Aβ in its soluble state and to inhibit the fibrilization of Aβ1-42. Combined theoretical simulations and experiment results, we speculate that the success of GSN sequestration mechanism and the balance of GSN levels in cerebrospinal fluid and plasma of AD subjects may contribute to a delay in AD progression. This research not only unveils the molecular basis of the interaction between GSN and Aβ1-42, but also provides clues to understanding the crucial functions of GSN in AD and drives the development of AD drugs and therapeutic approaches.

Directly monitoring the dynamic in vivo metabolisms of hyperpolarized 13C-oligopeptides

Peptides play essential roles in biological phenomena, and, thus, there is a growing interest in detecting in vivo dynamics of peptide metabolisms. Dissolution-dynamic nuclear polarization (d-DNP) is a state-of-the-art technology that can markedly enhance the sensitivity of nuclear magnetic resonance (NMR), providing metabolic and physiological information in vivo. However, the hyperpolarized state exponentially decays back to the thermal equilibrium, depending on the spin-lattice relaxation time (T1). Because of the limitation in T1, peptide-based DNP NMR molecular probes applicable in vivo have been limited to amino acids or dipeptides. Here, we report the direct detection of in vivo metabolic conversions of hyperpolarized 13C-oligopeptides. Structure-based T1 relaxation analysis suggests that the C-terminal [1-13C]Gly-d2 residue affords sufficient T1 for biological uses, even in relatively large oligopeptides, and allowed us to develop 13C-β-casomorphin-5 and 13C-glutathione. It was found that the metabolic response and perfusion of the hyperpolarized 13C-glutathione in the mouse kidney were significantly altered in a model of cisplatin-induced acute kidney injury.

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.

MicroProteinDB: A database to provide knowledge on sequences, structures and function of ncRNA-derived microproteins

Omics-based technologies have revolutionized our comprehension of microproteins encoded by ncRNAs, revealing their abundant presence and pivotal roles within complex functional landscapes. Here, we developed MicroProteinDB (http://bio-bigdata.hrbmu.edu.cn/MicroProteinDB), which offers and visualizes the extensive knowledge to aid retrieval and analysis of computationally predicted and experimentally validated microproteins originating from various ncRNA types. Employing prediction algorithms grounded in diverse deep learning approaches, MicroProteinDB comprehensively documents the fundamental physicochemical properties, secondary and tertiary structures, interactions with functional proteins, family domains, and inter-species conservation of microproteins. With five major analytical modules, it will serve as a valuable knowledge for investigating ncRNA-derived microproteins.

Interaction of SMAC with a survivin-derived peptide alters essential cancer hallmarks: Tumor growth, inflammation, and immunosuppression

Second mitochondrial-derived activator of caspase (SMAC), also known as direct inhibitor of apoptosis-binding proteins with low pI (Diablo), is known as a pro-apoptotic mitochondrial protein released into the cytosol in response to apoptotic signals. We recently reported SMAC overexpression in cancers as essential for cell proliferation and tumor growth due to non-apoptotic functions, including phospholipid synthesis regulation. These functions may be associated with its interactions with partner proteins. Using a peptide array with 768 peptides derived from 11 selected SMAC-interacting proteins, we identified SMAC-interacting sequences. These SMAC-binding sequences were produced as cell-penetrating peptides targeted to the cytosol, mitochondria, or nucleus, inhibiting cell proliferation and inducing apoptosis in several cell lines. For in vivo study, a survivin/baculoviral inhibitor of apoptosis repeat-containing 5 (BIRC5)-derived peptide was selected, due to its overexpression in many cancers and its involvement in mitosis, apoptosis, autophagy, cell proliferation, inflammation, and immune responses, as a target for cancer therapy. Specifically, a SMAC-targeting survivin/BIRC5-derived peptide, given intratumorally or intravenously, strongly inhibited lung tumor growth, cell proliferation, angiogenesis, and inflammation, induced apoptosis, and remodeled the tumor microenvironment. The peptide promoted tumor infiltration of CD-8+ cells and increased cell-intrinsic programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) expression, resulting in cancer cell self-destruction and increased tumor cell death, preserving immune cells. Thus, targeting the interaction between the multifunctional proteins SMAC and survivin represents an innovative therapeutic cancer paradigm.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach

Protein-peptide interactions (PPepIs) are vital to understanding cellular functions, which can facilitate the design of novel drugs. As an essential component in forming a PPepI, protein-peptide binding sites are the basis for understanding the mechanisms involved in PPepIs. Therefore, accurately identifying protein-peptide binding sites becomes a critical task. The traditional experimental methods for researching these binding sites are labor-intensive and time-consuming, and some computational tools have been invented to supplement it. However, these computational tools have limitations in generality or accuracy due to the need for ligand information, complex feature construction, or their reliance on modeling based on amino acid residues. To deal with the drawbacks of these computational algorithms, we describe a geometric attention-based network for peptide binding site identification (GAPS) in this work. The proposed model utilizes geometric feature engineering to construct atom representations and incorporates multiple attention mechanisms to update relevant biological features. In addition, the transfer learning strategy is implemented for leveraging the protein-protein binding sites information to enhance the protein-peptide binding sites recognition capability, taking into account the common structure and biological bias between proteins and peptides. Consequently, GAPS demonstrates the state-of-the-art performance and excellent robustness in this task. Moreover, our model exhibits exceptional performance across several extended experiments including predicting the apo protein-peptide, protein-cyclic peptide and the AlphaFold-predicted protein-peptide binding sites. These results confirm that the GAPS model is a powerful, versatile, stable method suitable for diverse binding site predictions.

Crosstalk Between NK Cell Receptors and Tumor Membrane Hsp70-Derived Peptide: A Combined Computational and Experimental Study

Natural killer (NK) cells are central components of the innate immunity system against cancers. Since tumor cells have evolved a series of mechanisms to escape from NK cells, developing methods for increasing the NK cell antitumor activity is of utmost importance. It is previously shown that an ex vivo stimulation of patient-derived NK cells with interleukin (IL)-2 and Hsp70-derived peptide TKD (TKDNNLLGRFELSG, aa450-461) results in a significant upregulation of activating receptors including CD94 and CD69 which triggers exhausted NK cells to target and kill malignant solid tumors expressing membrane Hsp70 (mHsp70). Considering that TKD binding to an activating receptor is the initial step in the cytolytic signaling cascade of NK cells, herein this interaction is studied by molecular docking and molecular dynamics simulation computational modeling. The in silico results showed a crucial role of the heterodimeric receptor CD94/NKG2A and CD94/NKG2C in the TKD interaction with NK cells. Antibody blocking and CRISPR/Cas9-mediated knockout studies verified the key function of CD94 in the TKD stimulation and activation of NK cells which is characterized by an increased cytotoxic capacity against mHsp70 positive tumor cells via enhanced production and release of lytic granules and pro-inflammatory cytokines.

N-substituting perturbation on the interaction affinity and recognition specificity between rheumatic immune-related Abl SH3 domain and its peptoid ligands

Abl is a nonreceptor tyrosine kinase involved in a variety of disease pathways such as rheumatic immune. Full-length Abl protein consists of a catalytic tyrosine kinase (TK) domain as well as two regulatory Src homology domains 2 and 3 (SH2 and SH3, respectively); the latter recognizes and binds to those natural proline-rich peptide segments containing a PxxP motif on the protein surface of its interacting partners. However, natural peptides cannot bind effectively to the modular domain in high affinity and strong selectivity due to their small size and broad specificity. Here, a synthetic proline-rich peptide p41 was used as template; its structural diversity was extended by combinationally replacing the Pro0 and Pro+3 residues with a number of N-substituted amino acids. Consequently, peptide affinity change upon the replacement was derived to create a systematic N-substituting perturbation profile, from which we identified several N-substitution combinations at the Pro0 and Pro+3 residues of p41 PxxP motif that may moderately or significantly improve the peptide binding potency to Abl; they represent potent peptoid binders of Abl SH3 domain, with affinity improved considerably relative to p41. More significantly, the designed potent peptoids were also found to exhibit a good SH3-selectivity for their cognate Abl over other noncognate nonreceptor tyrosine kinases, with S = 9.7-fold.

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 .

Antioxidant, Collagenase Inhibitory, and Antibacterial Effects of Bioactive Peptides Derived from Enzymatic Hydrolysate of Ulva australis

The protein extract of Ulva australis hydrolyzed with Alcalase and Flavourzyme was found to have multi-functional properties, including total antioxidant capacity (TAC), collagenase inhibitory, and antibacterial activities. The #5 fraction (SP5) and #7 fraction (SP7) of U. australis hydrolysate from cation-exchange chromatography displayed significantly high TAC, collagenase inhibitory, and antibacterial effects against Propionibacterium acnes, and only the Q3 fraction from anion-exchange chromatography displayed high multi-functional activities. Eight of 42 peptides identified by MALDI-TOF/MS and Q-TOF/MS/MS were selected from the results for screening with molecular docking on target proteins and were then synthesized. Thr-Gly-Thr-Trp (TGTW) displayed ABTS [2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)] radical scavenging activity. The effect of TAC as Trolox equivalence was dependent on the concentration of TGTW. Asn-Arg-Asp-Tyr (NRDY) and Arg-Asp-Arg-Phe (RDRF) exhibited collagenase inhibitory activity, which increased according to the increase in concentration, and their IC50 values were 0.95 mM and 0.84 mM, respectively. Peptides RDRF and His-Ala-Val-Tyr (HAVY) displayed anti-P. Acnes effects, with IC50 values of 8.57 mM and 13.23 mM, respectively. These results suggest that the U. australis hydrolysate could be a resource for the application of effective nutraceuticals and cosmetics.

The adipose-derived stem cell peptide ADSCP2 alleviates hypertrophic scar fibrosis via binding with pyruvate carboxylase and remodeling the metabolic landscape

AIM

The purpose of this study was to investigate the function and mechanism of a novel peptide derived from adipose-derived stem cell-conditioned medium (ADSC-CM).

METHODS

Mass spectrometry was applied to identify expressed peptides in ADSC-CM obtained at different time points. The cell counting kit-8 assay and quantitative reverse transcription polymerase chain reactions were performed to screen the functional peptides contained within ADSC-CM. RNA-seq, western blot, a back skin excisional model of BALB/c mice, the peptide pull-down assay, rescue experiments, untargeted metabolomics, and mixOmics analysis were performed to thoroughly understand the functional mechanism of selected peptide.

RESULTS

A total of 93, 827, 1108, and 631 peptides were identified in ADSC-CM at 0, 24, 48, and 72 h of conditioning, respectively. A peptide named ADSCP2 (DENREKVNDQAKL) derived from ADSC-CM inhibited collagen and ACTA2 mRNAs in hypertrophic scar fibroblasts. Moreover, ADSCP2 facilitated wound healing and attenuated collagen deposition in a mouse model. ADSCP2 bound with the pyruvate carboxylase (PC) protein and inhibited PC protein expression. Overexpressing PC rescued the reduction in collagen and ACTA2 mRNAs caused by ADSCP2. Untargeted metabolomics identified 258 and 447 differential metabolites in the negative and positive mode, respectively, in the ADSCP2-treated group. The mixOmics analysis, which integrated RNA-seq and untargeted metabolomics data, provided a more holistic view of the functions of ADSCP2.

CONCLUSION

Overall, a novel peptide derived from ADSC-CM, named ADSCP2, attenuated hypertrophic scar fibrosis in vitro and in vivo, and the novel peptide ADSCP2 might be a promising drug candidate for clinical scar therapy.

Computational design of dynamic receptor-peptide signaling complexes applied to chemotaxis

Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor-peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve strongly enhanced signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.

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.

Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Peptide-based therapeutics are increasingly pushing to the forefront of biomedicine with their promise of high specificity and low toxicity. Although noncanonical residues can always be used, employing only the natural 20 residues restricts the chemical space to a finite dimension allowing for comprehensive in silico screening. Towards this goal, the dataset comprising all possible di-, tri-, and tetra-peptide combinations of the canonical residues has been previously reported. However, with increasing computational power, the comprehensive set of pentapeptides is now also feasible for screening as the comprehensive set of cyclic peptides comprising four or five residues. Here, we provide both the complete and prefiltered libraries of all di-, tri-, tetra-, and penta-peptide sequences from 20 canonical amino acids and their homodetic (N-to-C-terminal) cyclic homologues. The FASTA, simplified molecular-input line-entry system (SMILES), and structure-data file (SDF)-three dimension (3D) libraries can be readily used for screening against protein targets. We also provide a simple method and tool for conducting identity-based filtering. Access to this dataset will accelerate small peptide screening workflows and encourage their use in drug discovery campaigns. As a case study, the developed library was screened against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease to identify potential small peptide inhibitors.

Rational peptide design for inhibition of the KIX-MLL interaction

The kinase-inducible domain interacting (KIX) domain is an integral part of the general transcriptional coactivator CREB-binding protein, and has been associated with leukemia, cancer, and various viral diseases. Hence, the KIX domain has attracted considerable attention in drug discovery and development. Here, we rationally designed a KIX inhibitor using a peptide fragment corresponding to the transactivation domain (TAD) of the transcriptional activator, mixed-lineage leukemia protein (MLL). We performed theoretical saturation mutagenesis using the Rosetta software to search for mutants expected to bind KIX more tightly than the wild-type MLL TAD. Mutant peptides with higher helical propensities were selected for experimental characterization. We found that the T2857W mutant of the MLL TAD peptide had the highest binding affinity for KIX compared to the other 12 peptides designed in this study. Moreover, the peptide had a high inhibitory effect on the KIX-MLL interaction with a half-maximal inhibitory concentration close to the dissociation constant for this interaction. To our knowledge, this peptide has the highest affinity for KIX among all previously reported inhibitors that target the MLL site of KIX. Thus, our approach may be useful for rationally developing helical peptides that inhibit protein-protein interactions implicated in the progression of various diseases.

In Silico Identification of Peptides with PPARγ Antagonism in Protein Hydrolysate from Rice (Oryza sativa)

At least half the population in industrialized countries suffers from obesity due to excessive accumulation of adipose tissue. Recently, rice (Oryza sativa) proteins have been considered valuable sources of bioactive peptides with antiadipogenic potential. In this study, the digestibility and bioaccessibility in vitro of a novel protein concentrate (NPC) from rice were determined through INFOGEST protocols. Furthermore, the presence of prolamin and glutelin was evaluated via SDS-PAGE, and their potential digestibility and the bioactivity of ligands against peroxisome proliferator-activated receptor gamma (PPARγ) were explored by BIOPEP UWM and HPEPDOCK. For the top candidates, molecular simulations were conducted using Autodock Vina to evaluate their binding affinity against the antiadipogenic region of PPARγ and their pharmacokinetics and drug-likeness using SwissADME. Simulating gastrointestinal digestion showed a recovery of 43.07% and 35.92% bioaccessibility. The protein banding patterns showed the presence of prolamin (57 kDa) and glutelin (12 kDa) as the predominant proteins in the NPC. The in silico hydrolysis predicts the presence of three and two peptide ligands in glutelin and prolamin fraction, respectively, with high affinity for PPARγ (≤160). Finally, the docking studies suggest that the prolamin-derived peptides QSPVF and QPY (−6.38 & −5.61 kcal/mol, respectively) have expected affinity and pharmacokinetic properties to act as potential PPARγ antagonists. Hence, according to our results, bioactive peptides resulting from NPC rice consumption might have an antiadipogenic effect via PPARγ interactions, but further experimentation and validation in suitable biological model systems are necessary to gain more insight and to provide evidence to support our in silico findings.

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.

Targeting peptide-mediated interactions in omics

Peptide-mediated interactions (PMIs) play a crucial role in cell signaling network, which are responsible for about half of cellular protein-protein associations in the human interactome and have recently been recognized as a new kind of promising druggable target for drug development and disease therapy. In this article, we give a systematic review regarding the proteome-wide discovery of PMIs and targeting druggable PMIs (dPMIs) with chemical drugs, self-inhibitory peptides (SIPs) and protein agents, particularly focusing on their implications and applications for therapeutic purpose in omics. We also introduce computational peptidology strategies used to model, analyze, and design PMI-targeted molecular entities and further extend the concepts of protein context, direct/indirect readout, and enthalpy/entropy effect involved in PMIs. Current issues and future perspective on this topic are discussed. There is still a long way to go before establishment of efficient therapeutic strategies to target PMIs on the omics scale.

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.

PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships

Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and empirical prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, and literatures. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB .

Structural modeling, energetic analysis and molecular design of a π-stacking system at the complex interface of pediatric respiratory syncytial virus nucleocapsid with the C-terminal peptide of phosphoprotein

Human respiratory syncytial virus (RSV) is a primary cause of lower respiratory tract infections and hospital visits during infancy and childhood. The RSV phosphoprotein (P) is a major polymerase cofactor that interacts with nucleoprotein (N) to promote the recognition of ribonucleoprotein complex (RNP) by viral RNA polymerase. The binding pocket of N protein is chemically diverse, in or around which a number of aromatic and charged amino acid residues are observed. Previously, a nonapeptide segment (P peptide, ²³³DNDLSLEDF²⁴¹) representing the C-terminal tail of P protein was identified to mediate the N-P interaction with a moderate affinity, in which the Phe241 at the end of P's C-terminus plays a critical role in the binding of P peptide to N protein. Here, we found that the side-chain aromatic phenyl moiety of P Phe241 residue can form short- and long-range cation-π interactions with N Arg132 and Arg150 residues, respectively, as well as T-shaped and parallel-displaced π-π stackings with N Tyr135 and His151 residues, respectively, which co-define a geometrically satisfactory π-stacking system at the complex interface of N protein with P peptide, thus largely stabilizing the complex architecture. The stacking effect was further optimized by systematically mutating the P Phe241 residue to other natural and non-natural aromatic amino acids with diverse chemical substitutions at the phenyl moiety to examine their structural and energetic effects on π-stacking system and on protein-peptide binding. The electron-donating mutations at the phenyl moiety of P Phe241 residue can effectively enhance the π-stacking system and then promote peptide binding, whereas the bulky and positively charged mutations would considerably impair the peptide potency by introducing steric hindrance and electrostatic repulsion. The [Tyr]P, [Thp]P and [Fph]P mutants were determined to have an increased affinity relative to wild-type P peptide, which could be used as self-inhibitory peptides to competitively disrupt the native interaction between N and P proteins.

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.

Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis

Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein-protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. PPIs in signaling and regulatory networks are known to be mediated by short linear motifs (SLiMs), which are conserved contiguous regions of 3-10 amino acids within interacting protein domains. SLiMs are the minimum sequences required for modulating cellular PPI networks. Thus, several in silico approaches have been developed to predict and analyze SLiM-mediated PPIs. In this review, we focus on emerging evidence supporting a crucial role for SLiMs in driver pathways that are disrupted in colorectal cancer (CRC) tumorigenesis and related PPI network alterations. As a result, SLiMs, along with short peptides, are attracting the interest of researchers to devise small molecules amenable to be used as novel anti-CRC targeted therapies. Overall, the characterization of SLiMs mediating crucial PPIs in CRC may foster the development of more specific combined pharmacological approaches.

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.

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.

High-Resolution Conformational Analysis of RGDechi-Derived Peptides Based on a Combination of NMR Spectroscopy and MD Simulations

The crucial role of integrin in pathological processes such as tumor progression and metastasis formation has inspired intense efforts to design novel pharmaceutical agents modulating integrin functions in order to provide new tools for potential therapies. In the past decade, we have investigated the biological proprieties of the chimeric peptide RGDechi, containing a cyclic RGD motif linked to an echistatin C-terminal fragment, able to specifically recognize αvβ3 without cross reacting with αvβ5 and αIIbβ3 integrin. Additionally, we have demonstrated using two RGDechi-derived peptides, called RGDechi1-14 and ψRGDechi, that chemical modifications introduced in the C-terminal part of the peptide alter or abolish the binding to the αvβ3 integrin. Here, to shed light on the structural and dynamical determinants involved in the integrin recognition mechanism, we investigate the effects of the chemical modifications by exploring the conformational space sampled by RGDechi1-14 and ψRGDechi using an integrated natural-abundance NMR/MD approach. Our data demonstrate that the flexibility of the RGD-containing cycle is driven by the echistatin C-terminal region of the RGDechi peptide through a coupling mechanism between the N- and C-terminal regions.

Structure-based analysis and rational design of human peroxiredoxin-1's C-terminus-derived peptides to target sulfiredoxin-1 in pancreatic cancer

Human peroxiredoxin (PRX) family of antioxidant enzymes reduces hydrogen peroxide and alkyl hydroperoxide involved in the redox signaling, among which the widely documented PRX1 is a versatile molecule regulating cell growth, differentiation and apoptosis, and has been implicated in the tumorigensis of pancreatic cancer. In this study, we systematically examined the complex crystal structure of PRX1 with its cognate interacting partner sulfiredoxin-1 (SRX1) at molecular level, and found that the PRX1-SRX1 association is a typical peptide-mediated protein-protein interaction, where a 18-mer C-terminal tail (CTT) segment of PRX1 was identified to be primarily responsible for the interaction, which contributes ~80% and ~ 55% to the total binding potency of SRX1 to PRX1 monomer and homodimer, respectively. We also demonstrated that the SRX1 exhibits a strong global selectivity for PRX1 CTT tail over other PRX family proteins. Next, the intermolecular interaction between PRX1 CTT tail and SRX1 was investigated at structural, energetic and dynamic levels, from which a 9-mer core region of PRX1 CTT tail was defined as the SRX1-binding hotspot. Biophysical assays substantiated that the CTT and CTTc peptides (out of PRX1 protein context) can bind in an independent manner and possess a close affinity to SRX1. Based on the CTTc sketch a computational combinatorial library consisting of 216 designed peptide derivatives was rationally generated, from which the top-5 hits were found to have comparable affinity with CTT peptide and improved affinity relative to CTTc peptide. They can be used as structurally reduced lead molecular entities to further develop new peptidic agents for therapeutic purpose to disrupt the native PRX1-SRX1 interaction by competing with PRX1 CTT tail for the peptide-binding pocket of SRX1.

An overview on the exploring the interaction of inorganic nanoparticles with microtubules for the advancement of cancer therapeutics

Targeting microtubules (MTs), dynamic and stable proteins in cells, by different ligands have been reported to be a potential strategy to combat cancer cells. Inorganic nanoparticles (NPs) have been widely used as anticancer, antibacterial and free radical scavenging agents, where they come in contact with biological macromolecules. The interaction between the NPs and biological macromolecules like MTs frequently occurs through different mechanisms. A prerequisite for a detailed exploration of MT structures and functions for biomedical applications like cancer therapy is to investigate profoundly the mechanisms involved in MT-NP interactions, for which the full explanation and characterization of the parameters that are responsible for the formation of a NP-protein complex are crucial. Therefore, in view of the fact that the goal of the rational NP-based future drug design and new therapies is to rely on the information of the structural details and protein-NPs binding mechanisms to manipulate the process of developing new potential drugs, a comprehensive investigation of the essence of the molecular recognition/interaction is also of considerable importance. In the present review, first, the microtubule (MT) structure and its binding sites upon interaction with MT stabilizing agents (MSAs) and MT destabilizing agents (MDAs) are introduced and rationalized. Next, MT targeting in cancer therapy and interaction of NPs with MTs are discussed. Furthermore, interaction of NPs with proteins and the manipulation of protein corona (PC), experimental techniques and direct interaction of NPs with MTs, are discussed, and finally the challenges and future perspective of the field are introduced. We envision this review can provide useful information on the manipulation of the MT lattice for the progress of cancer nanomedicine.

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.

Comprehensive binary interaction mapping of τ phosphotyrosine sites with SH2 domains in the human genome: Implications for the rational design of self-inhibitory phosphopeptides to target τ hyperphosphorylation signaling in Alzheimer's Disease

Human microtubule-associated protein Tau (τ) is abundant in the axons of neurons where it stabilizes microtubule bundles; abnormally hyperphosphorylated τ is a hallmark of Alzheimer's disease (AD) and related tauopathies. The hyperphosphorylation events can be recognized by phosphotyrosine-recognition domain SH2 (Src homology 2) to elicit downstream τ signaling in AD pathology. In this study, a comprehensive binary interaction map (CBIM) of all the 6 τ phosphotyrosine sites with 120 SH2 domains in the human genome was systematically created at structural level using computational analyses and binding assays, from which we were able to identify those of strong and moderate binding pairs of sites to domains. It is found that the SH2-recognition specificity of different τ phosphotyrosine sites has been evolutionally optimized to become roughly orthogonal to each other, and thus these site phosphorylations would regulate different but probably partially overlapped biological functions in τ signaling. Some SH2 groups such as SRC, RIN, PLCG, SOCS and SH2D were revealed to have effective binding potency as compared to others; they could be regarded as potential τ-associated proteins to transduce the downstream signaling. We further determined the systematic binding affinities of 6 τ-phosphopeptides to the 11 SH2 domains in SRC group, from which the FYN-τ¹⁸ and YES-τ²⁹ pairs were identified as strong binders. Subsequently, rational molecular design was performed on τ¹⁸ and τ²⁹ to derive a number of τ-phosphopeptide mutants with increased affinity; they are self-inhibitory candidates to competitively target τ hyperphosphorylation events in AD. In addition, it is revealed that the primary anchor pY₀ and secondary anchor X₊₃ of τ-phosphopeptides play an important role in SRC-group SH2 recognition, which confer stability and specificity to the SH2-phosphopeptide binding, respectively.

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/.

Characterizing the stabilization effects of stabilizers in protein-protein systems with end-point binding free energy calculations

Drug design targeting protein-protein interactions (PPIs) associated with the development of diseases has been one of the most important therapeutic strategies. Besides interrupting the PPIs with PPI inhibitors/blockers, increasing evidence shows that stabilizing the interaction between two interacting proteins may also benefit the therapy, such as the development of various types of molecular glues/stabilizers that mostly work by stabilizing the two interacting proteins to regulate the downstream biological effects. However, characterizing the stabilization effect of a stabilizer is usually hard or too complicated for traditional experiments since it involves ternary interactions [protein-protein-stabilizer (PPS) interaction]. Thus, developing reliable computational strategies will facilitate the discovery/design of molecular glues or PPI stabilizers. Here, by fully analyzing the energetic features of the binary interactions in the PPS ternary complex, we systematically investigated the performance of molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) methods on characterizing the stabilization effects of stabilizers in 14-3-3 systems. The results show that both MM/PBSA and MM/GBSA are powerful tools in distinguishing the stabilizers from the decoys (with area under the curves of 0.90-0.93 for all tested cases) and are reasonable for ranking protein-peptide interactions in the presence or absence of stabilizers as well (with the average Pearson correlation coefficient of ~0.6 at a relatively high dielectric constant for both methods). Moreover, to give a detailed picture of the stabilization effects, the stabilization mechanism is also analyzed from the structural and energetic points of view for individual systems containing strong or weak stabilizers. This study demonstrates a potential strategy to accelerate the discovery of PPI stabilizers.

Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level

Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein-protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein-peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom-residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised-supervised PpI affinity (usPpIA) predictor. Although both the ulPpI[a-r] potential and usPpIA predictor can be used to calculate PpI affinities from their complex structures, the latter seems to perform much better than the former, suggesting that the unsupervised potential can be improved substantially with a further correction by supervised statistical learning. We examine the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation. It is revealed that, despite developed solely based on solved structures, the integrated unsupervised-supervised method is also applicable for locally docked structures to reach a quantitative prediction but can only give a qualitative prediction on globally docked structures. The dynamics refinement seems not to change (or improve) the predictive results essentially, although it is computationally expensive and time-consuming relative to peptide docking. We also perform extrapolation of usPpIA predictor to the indirect affinity quantities of HLA-A*0201 binding epitope peptides and NHERF PDZ binding scaffold peptides, consequently resulting in a good and moderate correlation of the predicted KD with experimental IC50 and BLU on the two peptide sets, with Pearson's correlation coefficients Rp = 0.635 and 0.406, respectively.

Enterovirus 71 Activates GADD34 via Precursor 3CD to Promote IRES-Mediated Viral Translation

Enterovirus 71 (EV71) is the major pathogen of hand, foot, and mouth disease. In severe cases, it can cause life-threatening neurological complications, such as aseptic meningitis and polio-like paralysis. There are no specific antiviral treatments for EV71 infections. In a previous study, the host protein growth arrest and DNA damage-inducible protein 34 (GADD34) expression was upregulated during EV71 infection determined by ribosome profiling and RNA-sequencing. Here, we investigated the interactions of host protein GADD34 and EV71 during infections. Rhabdomyosarcoma (RD) cells were infected with EV71 resulting in a significant increase in expression of GADD34 mRNA and protein. Through screening of EV71 protein we determined that the non-structural precursor protein 3CD is responsible for upregulating GADD34. EV71 3CD increased the RNA and protein levels of GADD34, while the 3CD mutant Y441S could not. 3CD upregulated GADD34 translation via the upstream open reading frame (uORF) of GADD34 5'untranslated regions (UTR). EV71 replication was attenuated by the knockdown of GADD34. The function of GADD34 to dephosphorylate eIF2α was unrelated to the upregulation of EV71 replication, but the PEST 1, 2, and 3 regions of GADD34 were required. GADD34 promoted the EV71 internal ribosome entry site (IRES) activity through the PEST repeats and affected several other viruses. Finally, GADD34 amino acids 563 to 565 interacted with 3CD, assisting GADD34 to target the EV71 IRES. Our research reveals a new mechanism by which GADD34 promotes viral IRES and how the EV71 non-structural precursor protein 3CD regulates host protein expression to support viral replication.

IMPORTANCE Identification of host factors involved in viral replication is an important approach in discovering viral pathogenic mechanisms and identifying potential therapeutic targets. Previously, we screened host proteins that were upregulated by EV71 infection. Here, we report the interaction between the upregulated host protein GADD34 and EV71. EV71 non-structural precursor protein 3CD activates the RNA and protein expression of GADD34. Our study reveals that 3CD regulates the uORF of the 5′-UTR to increase GADD34 translation, providing a new explanation for how viral proteins regulate host protein expression. GADD34 is important for EV71 replication, and the key functional domains of GADD34 that promote EV71 are PEST 1, 2, and 3 regions. We report that GADD34 promotes viral IRES for the first time and this process is independent of its eIF2α phosphatase activity.

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.

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.

Systematic profiling and identification of the peptide-mediated interactions between human Yes-associated protein and its partners in esophageal cancer

Human Yes-associated protein (YAP) is involved in the Hippo signaling pathway and serves as a coactivator to modulate gene expression, which contains a transactivation domain (TD) responsible for binding to the downstream TEA domain family (TEAD) of transcription factors and two WW1/2 domains that recognize the proline-rich motifs (PRMs) present in a variety of upstream protein partners through peptide-mediated interactions (PMIs). The downstream YAP TD-TEAD interactions are closely associated with gastric cancer, and a number of therapeutic agents have been developed to target the interactions. In contrast, the upstream YAP WW1/2-partner interactions are thought to be involved in esophageal cancer but still remain largely unexplored. Here, we attempted to elucidate the complicated PMIs between the YAP WW1/2 domains and various PRMs of YAP-interacting proteins. A total of 106 peptide segments carrying the class I WW-binding motif [P/L]Px[Y/P] were extracted from 22 partner candidates, which are potential recognition sites of YAP WW1/2 domains. Structural and energetic analyses of the intermolecular interactions between the domains and peptides created a systematic domain-peptide binding profile, from which a number of biologically functional PMIs were identified and then substantiated in vitro using fluorescence spectroscopy assays. It is revealed that: (a) The sequence requirement for the partner recognition site binding to YAP WW1/2 domains is a decapeptide segment that contains a core PRM motif as well as two three-residue extensions from each side of the motif; the core motif and extended sections are responsible for the binding stability and recognition specificity of domain-peptide interaction, respectively. (b) There is an exquisite difference in the recognition specificity of the two domains; the LPxP and PPxP appear to more prefer WW1 than WW2, whereas the WW2 can bind more effectively to LPxY and PPxY than WW1. (c) WW2 generally exhibits a higher affinity to the panel of recognition site candidates than WW1. In addition, a number of partner peptides were found as promising recognition sites of the two domains and/or to have a good selectivity between the two domains. For example, the DVL1 peptide was determined to have moderate affinity to WW2 and strong selectivity for WW2 over WW1. Hydrogen bonds play a central role in selectivity.

Inhibition of Viral Membrane Fusion by Peptides and Approaches to Peptide Design

Fusion of lipid-enveloped viruses with the cellular plasma membrane or the endosome membrane is mediated by viral envelope proteins that undergo large conformational changes following binding to receptors. The HIV-1 fusion protein gp41 undergoes a transition into a "six-helix bundle" after binding of the surface protein gp120 to the CD4 receptor and a co-receptor. Synthetic peptides that mimic part of this structure interfere with the formation of the helix structure and inhibit membrane fusion. This approach also works with the S spike protein of SARS-CoV-2. Here we review the peptide inhibitors of membrane fusion involved in infection by influenza virus, HIV-1, MERS and SARS coronaviruses, hepatitis viruses, paramyxoviruses, flaviviruses, herpesviruses and filoviruses. We also describe recent computational methods used for the identification of peptide sequences that can interact strongly with protein interfaces, with special emphasis on SARS-CoV-2, using the PePI-Covid19 database.

InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network

Peptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modeling such interactions is to exhaustively sample the conformational space by fast-Fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection fast enough for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical potentials. We present InterPepRank for peptide-protein complex scoring and ranking. InterPepRank is a machine learning-based method which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary and sequence features as nodes. The graph network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys. InterPepRank is tested on a massive independent test set with no targets sharing CATH annotation nor 30% sequence identity with any target in training or validation data. On this set, InterPepRank has a median AUC of 0.86 for finding coarse peptide-protein complexes with LRMSD &lt; 4Å. This is an improvement compared to other state-of-the-art ranking methods that have a median AUC between 0.65 and 0.79. When included as a selection-method for selecting decoys for refinement in a previously established peptide docking pipeline, InterPepRank improves the number of medium and high quality models produced by 80% and 40%, respectively. The InterPepRank program as well as all scripts for reproducing and retraining it are available from: http://wallnerlab.org/InterPepRank.

Divide-and-link peptide docking: a fragment-based peptide docking protocol

Protein-peptide interactions are crucial for various important cellular regulations, and are also a basis for understanding protein-protein interactions, protein folding and peptide drug design. Due to the limited structural data obtained using experimental methods, it is necessary to predict protein-peptide interaction modes using computational methods. In the present work, we designed a fragment-based docking protocol, Divide-and-Link Peptide Docking (DLPepDock), to predict protein-peptide binding modes. This protocol contains the following steps: dividing the peptide into fragments and separately docking the fragments using a third-party small molecular docking tool, linking the docked fragmental poses to form the whole peptide conformations via fragmental coordinate transformation using our in-house program, removing unreasonable poses according to several geometrical filters, extracting representative conformations after clustering for further minimization using the steepest descent and conjugation gradient methods based on a full-atom molecular force field and finally scoring using the MM/PBSA binding energy calculation implemented in Amber. When tested on the LEADS-PEP benchmark data set of 26 diverse complexes with peptides of 6-12 residues, FlexPepDock ab initio and AutoDock CrankPep achieved superior results. DLPepDock performed better than the other 15 docking protocols implemented in nine docking programs (HPepDock, DockThor, rDock, Glide, LeDock, AutoDock, AutoDock Vina, Surflex, and GOLD). The Linux scripts to call the third-party tools and run all the calculations.

Design of Monovalent and Chimeric Tetravalent Dengue Vaccine Using an Immunoinformatics Approach

An immunoinformatics technique was used to predict a monovalent amide immunogen candidate capable of producing therapeutic antibodies as well as a potent immunogen candidate capable of acting as a universal vaccination against all dengue fever virus serotypes. The capsid protein is an attractive goal for anti-DENV due to its position in the dengue existence cycle. The widely accessible immunological data, advances in antigenic peptide prediction using reverse vaccinology, and the introduction of molecular docking in immunoinformatics have directed vaccine manufacturing. The C-proteins of DENV-1-4 serotypes were known as antigens to assist with logical design. Binding epitopes for TC cells, TH cells, and B cells is predicted from structural dengue virus capsid proteins. Each T cell epitope of C-protein integrated with a B cell as a templet was used as a vaccine and produce antibodies in contrast to serotype of the dengue virus. A chimeric tetravalent vaccine was created by combining four vaccines, each representing four dengue serotypes, to serve as a standard vaccine candidate for all four Sero groups. The LKRARNRVS, RGFRKEIGR, KNGAIKVLR, and KAINVLRGF from dengue 1, dengue 2, dengue 3, and dengue 4 epitopes may be essential immunotherapeutic representatives for controlling outbreaks.