Computational design of peptide ligands

Discovery of Highly Bioactive Peptides through Hierarchical Structural Information and Molecular Dynamics Simulations

Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce PepHiRe, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied PepHiRe to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, PepHiRe effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC50 values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.

Target-Specific De Novo Peptide Binder Design with DiffPepBuilder

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

Prediction of promiscuous multiepitope-based peptide vaccine against RdRp of rotavirus using immunoinformatics studies

Rotavirus, a dsRNA virus in the Reoviridae family, shows a segmented genome. The VP1 gene encodes the RNA-dependent RNA polymerase (RdRp). This study aims to develop a multiepitope-based vaccine targeting RdRp using immunoinformatic approaches. In this study, 100 available nucleotide sequences of VP1-Rotavirus belonging to different strains across the world were retrieved from NCBI database. The selected sequences were aligned, and a global consensus sequence was developed by using CLC work bench. The study involved immunoinformatic approaches and molecular docking studies to reveal the promiscuous epitopes that can be eventually used as active vaccine candidates for Rotavirus. In total, 27 highly immunogenic, antigenic, and non-allergenic T-cell and B-cell epitopes were predicted for the Multiepitope vaccine (MEV) against rotavirus. It was also observed that MEV can prove to be effective worldwide due to its high population coverage, demonstrating the consistency of this vaccine. Moreover, there is a high docking interaction and immunological response with a binding score of -50.2 kcal/mol, suggesting the vaccine's efficacy. Toll-like receptors (TLRs) also suggest that the vaccine is physiologically and immunologically effective. Collectively, our data point to an effective MEV against rotavirus that can effectively reduce viral infections and improve the health status worldwide.

ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model demonstrates remarkable performance, achieving an AUC of 0.856 and 0.823 on the PepBCL Set_1 and Set_2 datasets, respectively. Additionally, it attains a Precision of 0.564 in PepBCL Set 1 and 0.527 in PepBCL Set 2, surpassing the performance of previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells, and other fields. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development.

Inhibition of DEF-p65 Interactions as a Potential Avenue to Suppress Tumor Growth in Pancreatic Cancer

The limited success of current targeted therapies for pancreatic cancer underscores an urgent demand for novel treatment modalities. The challenge in mitigating this malignancy can be attributed to the digestive organ expansion factor (DEF), a pivotal yet underexplored factor in pancreatic tumorigenesis. The study uses a blend of in vitro and in vivo approaches, complemented by the theoretical analyses, to propose DEF as a promising anti-tumor target. Analysis of clinical samples reveals that high expression of DEF is correlated with diminished survival in pancreatic cancer patients. Crucially, the depletion of DEF significantly impedes tumor growth. The study further discovers that DEF binds to p65, shielding it from degradation mediated by the ubiquitin-proteasome pathway in cancer cells. Based on these findings and computational approaches, the study formulates a DEF-mimicking peptide, peptide-031, designed to disrupt the DEF-p65 interaction. The effectiveness of peptide-031 in inhibiting tumor proliferation has been demonstrated both in vitro and in vivo. This study unveils the oncogenic role of DEF while highlighting its prognostic value and therapeutic potential in pancreatic cancer. In addition, peptide-031 is a promising therapeutic agent with potent anti-tumor effects.

Computational Modeling of the Interactions between DPP IV and Hemorphins

Type 2 diabetes is a chronic metabolic disorder characterized by high blood glucose levels due to either insufficient insulin production or ineffective utilization of insulin by the body. The enzyme dipeptidyl peptidase IV (DPP IV) plays a crucial role in degrading incretins that stimulate insulin secretion. Therefore, the inhibition of DPP IV is an established approach for the treatment of diabetes. Hemorphins are a class of short endogenous bioactive peptides produced by the enzymatic degradation of hemoglobin chains. Numerous in vitro and in vivo physiological effects of hemorphins, including DPP IV inhibiting activity, have been documented in different systems and tissues. However, the underlying molecular binding behavior of these peptides with DPP IV remains unknown. Here, computational approaches such as protein-peptide molecular docking and extensive molecular dynamics (MD) simulations were employed to identify the binding pose and stability of peptides in the active site of DPP IV. Findings indicate that hemorphins lacking the hydrophobic residues LVV and VV at the N terminal region strongly bind to the conserved residues in the active site of DPP IV. Furthermore, interactions with these critical residues were sustained throughout the duration of multiple 500 ns MD simulations. Notably, hemorphin 7 showed higher binding affinity and sustained interactions by binding to S1 and S2 pockets of DPP IV.

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.

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

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

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

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

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

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.

Modifying enzyme replacement therapy - A perspective

Several diseases are caused by the lack of functional proteins, including lysosomal storage diseases or haemophilia A and B. Patients suffering from one of these diseases are treated via enzyme replacement therapies to restore the missing protein. Although this treatment strategy prevents some disease symptoms, enzyme replacement therapies are very expensive and require very frequent infusions, which can cause infusion adverse reactions and massively impair the quality of life of the patients. This review proposes a technology to sustainably produce proteins within the patient to potentially make frequent protein-infusions redundant. This technology is based on blood circulating immune cells as producers of the needed therapeutic protein. To ensure a stable protein concentration over time the cells are equipped with a system, which induces cell proliferation when low therapeutic protein levels are detected and a system inhibiting cell proliferation when high therapeutic protein levels are detected.

Precise Assembly of Multiple Antigens on Nanoparticles with Specially Designed Affinity Peptides

Antigen proteins, assembled on nanoparticles, can be recognized by antigen-presenting cells effectively to enhance antigen immunogenicity. The ability to simultaneously display multiantigens on the same nanoparticle could have numerous applications but remained technical challenges. Here, we described a method for precise assembly of multiple antigens on nanoparticles with specially designed affinity peptides. First, we designed and screened affinity peptides with high affinity and specificity, which could respectively target the key amino acid residues of classical swine fever virus (CSFV) E2 protein or porcine circovirus type 2 capsid protein (PCV2 Cap) accurately. Then, we conjugated the antigen proteins to poly(lactic acid-glycolic acid) copolymer (PLGA) and Gram-positive enhancer matrix (GEM) nanoparticles through the peptides and perfectly assembled two kinds of multiantigen display nanoparticles with different particle sizes. Subsequently, the immunological properties of the assembled nanoparticles were tested. The results showed that the antigen display nanoparticles could promote the maturation, phagocytosis, and proinflammatory effects of antigen-presenting cells (APCs). Besides, compared with the antigen proteins, multiantigen display nanoparticles could induce much higher levels of antibodies and neutralizing antibodies in mice. This strategy may provide a technical support for the study of protein structure and the research and development of polyvalent vaccines.

Key aspects of the past 30 years of protein design

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

An anti-picornaviral strategy based on the crystal structure of foot-and-mouth disease virus 2C protein

The foot-and-mouth disease virus (FMDV) 2C protein shares conserved motifs with enterovirus 2Cs despite low sequence identity. Here, we determine the crystal structure of an FMDV 2C fragment to 1.83 Å resolution, which comprises an ATPase domain, a region equivalent to the enterovirus 2C zinc-finger (ZFER), and a C-terminal domain harboring a loop (PBL) that occupies a hydrophobic cleft (Pocket) in an adjacent 2C molecule. Mutations at ZFER, PBL, and Pocket affect FMDV 2C ATPase activity and are lethal to FMDV infectious clones. Because the PBL-Pocket interaction between FMDV 2C molecules is essential for its functions, we design an anti-FMDV peptide derived from PBL (PBL-peptide). PBL-peptide inhibits FMDV 2C ATPase activity, binds FMDV 2C with nanomolar affinity, and disrupts FMDV 2C oligomerization. FMDV 2C targets lipid droplets (LDs) and induces LD clustering in cells, and PBL-peptide disrupts FMDV 2C-induced LD clustering. Finally, we demonstrate that PBL-peptide exhibits anti-FMDV activity in cells.

Noble Metal Nanoparticle Biosensors: From Fundamental Studies toward Point-of-Care Diagnostics

Noble metal nanoparticles (NMNPs) have become firmly established as effective agents to detect various biomolecules with extremely high sensitivity. This ability stems from the collective oscillation of free electrons and extremely large electric field enhancement under exposure to light, leading to various light-matter interactions such as localized surface plasmon resonance (LSPR) and surface-enhanced Raman scattering. A remarkable feature of NMNPs is their customizability by mechanisms such as particle etching, growth, and aggregation/dispersion, yielding distinct color changes and excellent opportunities for colorimetric biosensing in user-friendly assays and devices. They are readily functionalized with a large variety of capping agents and biomolecules, with resultant bioconjugates often possessing excellent biocompatibility, which can be used to quantitatively detect analytes from physiological fluids. Furthermore, they can possess excellent catalytic properties that can achieve significant signal amplification through mechanisms such as the catalytic transformation of colorless substrates to colored reporters. The various excellent attributes of NMNP biosensors have put them in the spotlight for developing high-performance in vitro diagnostic (IVD) devices that are particularly well-suited to mitigate the societal threat that infectious diseases pose. This threat continues to dominate the global health care landscape, claiming millions of lives annually. NMNP IVDs possess the potential to sensitively detect infections even at very early stages with affordable and field-deployable devices, which will be key to strengthening infectious disease management. This has been the major focal point of current research, with a view to new avenues for early multiplexed detection of infectious diseases with portable devices such as smartphones, especially in resource-limited settings.In this Account, we provide an overview of our original inspiration and efforts in NMNP-based assay development, together with some more sophisticated IVD assays by ourselves and many others. Our work in the area has led to our recent efforts in developing IVDs for high-profile infectious diseases, including Ebola and HIV. We emphasize that integration with digital platforms represents an opportunity to establish and efficiently manage widespread testing, tracking, epidemiological intelligence, and data sharing backed by community participation. We highlight how digital technologies can address the limitations of conventional diagnostic technologies at the point of care (POC) and how they may be used to abate and contain the spread of infectious diseases. Finally, we focus on more recent integrations of noble metal nanoparticles with Raman spectroscopy for accurate, noninvasive POC diagnostics with improved sensitivity and specificity.

An antigen display system of GEM nanoparticles based on affinity peptide ligands

Gram-positive enhancer matrix (GEM) nanoparticles are often used in mucosal immunity, preparation of subunit vaccines or as an immune adjuvant due to its good immunological activities in recent years. Here, we designed and screened out a high affinity peptide ligand PL23, which could specifically target the non-epitope region of Classic Swine Fever Virus (CSFV) E2 protein, by virtual screening technology, enzyme linked immunosorbent assay (ELISA) and surface plasmon resonance (SPR) test. The OD value of PL23 at 450 nm was reached 1.982, and the K_D value of it was 90.12 nM. Its binding capacity to protein was verified by SDS-PAGE as well. PL23 was subsequently conjugated to GEM nanoparticles by dehydration synthesis generating GEM-PL23 particles, and the GEM-PL-E2 particles were assembled after incubated with CSFV E2 protein. The cytotoxic test indicated that PL23, CSFV E2 protein, GEM nanoparticles, GEM-PL23 particles and GEM-PL-E2 particles were not toxic to cells and GEM nanoparticles could significantly promote the growth of APCs at high concentration for 1 h, p<0.001. In addition, GEM nanoparticles could promote the uptake of antigen by APCs. The cytokines tests suggested that GEM-PL-E2 particles could promote innate immune responses, regulate adaptive immune responses generated by T cells and APCs, and promote the differentiation and maturation of dendritic cells without producing inflammasomes. The results of immunological activity identification showed GEM-PL-E2 particles induced higher levels of both neutralizing antibodies and anti-CSFV antibodies than CSFV E2 protein in mice. This strategy provided a new, simpler, faster and cheaper method for assembling GEM nanoparticles, using an affinity peptide ligand replaced the protein anchor (PA), and provided a better application prospect for the application of GEM particles.

HSymM-guided engineering of the immunodominant p53 transactivation domain putative peptide antigen for improved binding to its anti-p53 monoclonal antibody

A novel engineering strategy to improve autoantibody detection with peptide fragments derived from the parent antigen is presented. The model system studied was the binding of the putative p53 TAD peptide antigen (residues 46-55) to its cognate anti-p53 antibody, ab28. Each engineered peptide contained the full decapeptide epitope and differed only in the flanking regions. Since minimal structural information was available to guide the design, a simple epitope:paratope binding model was applied. The Hidden Symmetry Model, which we recently reported, was used to guide peptide design and estimate per-residue contributions to interaction free energy as a function of added C- and N-terminal flanking peptides. Twenty-four peptide constructs were designed, synthesized, and assessed for binding affinity to ab28 by surface plasmon resonance, and a subset of these peptides were evaluated in a simulated immunoassay for limit of detection. Many peptides exhibited over 200-fold enhancements in binding affinity and improved limits of detection. The epitope was reevaluated and is proposed to be the undecapeptide corresponding to residues 45-55. HSymM calculated binding free energy and experimental data were found to be in good agreement (R2 > 0.75).

Disulfide-stapled design of α-helical bundles to target the trimer-of-hairpins motif of human respiratory syncytial virus fusion protein

Human respiratory syncytial virus (RSV) is the major cause of acute lower respiratory tract infections worldwide in infants and young children. The RSV F glycoprotein is a class I fusion protein that mediates viral entry into host cells and is a major target of neutralizing antibodies. Targeting F glycoprotein has been recognized as a promising antiviral therapeutic strategy against RSV infection. Here, we reported the disulfide-stapled design of α-helical bundle to target the trimer-of-hairpins (TOH) motif of RSV F glycoprotein, which is the central regulatory module that triggers viral membrane fusion event. In TOH motif, three N-terminal heptad repeat (NtHR) helices form a trimeric coiled-coil core and other three C-terminal heptad repeat (CtHR) helices add to the core in an antiparallel manner. Interaction analysis between NtHR and CtHR revealed that the C-terminal tail of CtHR packs tightly against NtHR as compared to the N-terminal and middle regions of CtHR. A core binding site in CtHR C-terminus was identified, which represents a 13-mer chp peptide and can effectively interact with NtHR helix in native ordered conformation but would become largely disordered when splitting from the protein context of CtHR helix. Two chp helices were stapled together in a parallel manner with single, double or triple disulfide bridges, thus systematically resulting in seven disulfide-stapled α-helical bundles. Molecular simulations revealed that the double and triple stapling can effectively stabilize the structured conformation of α-helical bundles, whereas the free conformation of single-stapled bundles still remain intrinsically disordered in solvent. The double-stapled bundle chp-ds[508,516] and the triple-stapled bundle chp-ts[508,512,516] were rationally designed to have high potency; they can form a tight three-helix bundle with NtHR helix, thus potently targeting NtHR-CtHR interactions involved in RSV-F TOH motif through a competitive disruption mechanism.

Purification of Classical Swine Fever Virus E2 Subunit Vaccines Based on High Affinity Peptide Ligand

BACKGROUND

The purification of expressed proteins is the most critical part of subunit-- vaccine production. Protein-purification methods such as affinity chromatography and ion exchange still have the shortcomings of being time consuming and complicated. With the rapid development of computational molecular-simulation technology, structure-based peptide-ligand design has become feasible. Objection: We aimed to apply molecular docking for a peptide ligand designed for classical swine fever virus (CSFV) E2 purification.

METHODS

Computational-derived peptides were synthesized, and the in vitro binding interaction with E2 was investigated. The effects of purification on E2 were also evaluated.

RESULTS

The best peptide recognizing E2 was P₆, which had a sequence of KKFYWRYWEH. Based on kinetic surface plasmon resonance (SPR) analysis, the apparent affinity constant of P6 was found to be 148 nM. Importantly, P₆ showed suitable binding affinity and specificity for E2 purification from transgenic rice seeds. Evaluation of immune antibodies in mice showed that the antibody- blocking rate on day 42 after inoculation reached 86.18% and 90.68%.

CONCLUSION

The computational-designed peptide in this study has high sensitivity and selectivity and is thus useful for the purification of CSFV E2. The novel method of design provided a broad platform and powerful tool for protein-peptide screening, as well as new insights into CSFV vaccine design.

PepFun: Open Source Protocols for Peptide-Related Computational Analysis

Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.

Identification of Novel FNIN2 and FNIN3 Fibronectin-Derived Peptides That Promote Cell Adhesion, Proliferation and Differentiation in Primary Cells and Stem Cells

In recent years, a major rise in the demand for biotherapeutic drugs has centered on enhancing the quality and efficacy of cell culture and developing new cell culture techniques. Here, we report fibronectin (FN) derived, novel peptides fibronectin-based intergrin binding peptide (FNIN)2 (18-mer) and FNIN3 (20-mer) which promote cell adhesion proliferation, and the differentiation of primary cells and stem cells. FNIN2 and 3 were designed based on the in silico interaction studies between FN and its receptors (integrin α5β1, αvβ3, and αIIbβ3). Analysis of the proliferation of seventeen-cell types showed that the effects of FNINs depend on their concentration and the existence of expressed integrins. Significant rhodamine-labeled FNIN2 fluorescence on the membranes of HeLa, HepG2, A498, and Du145 cells confirmed physical binding. Double coating with FNIN2 or 3 after polymerized dopamine (pDa) or polymerized tannic acid (pTA) precoating increased HBEpIC cell proliferation by 30–40 percent, suggesting FNINs potently affect primary cells. Furthermore, the proliferation of C2C12 myoblasts and human mesenchymal stem cells (MSCs) treated with FNINs was significantly increased in 2D/3D culture. FNINs also promoted MSC differentiation into osteoblasts. The results of this study offer a new approach to the production of core materials (e.g., cell culture medium components, scaffolds) for cell culture.

Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening

BACKGROUND

Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2.

METHODS

In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis.

RESULTS

The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant.

CONCLUSION

Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

DPL: a comprehensive database on sequences, structures, sources and functions of peptide ligands

DPL (http://www.peptide-ligand.cn/) is a comprehensive database of peptide ligand (DPL). DPL1.0 holds 1044 peptide ligand entries and provides references for the study of the polypeptide platform. The data were collected from PubMed-NCBI, PDB, APD3, CAMPR3, etc. The lengths of the base sequences are varied from 3 to78. DPL database has 923 linear peptides and 88 cyclic peptides. The functions of peptides collected by DPL are very wide. It includes 540 entries of antiviral peptides (including SARS-CoV-2), 55 entries of signal peptides, 48 entries of protease inhibitors, 45 entries of anti-hypertension, 37 entries of anticancer peptides, etc. There are 270 different kinds of peptide targets. All peptides in DPL have clear binding targets. Most of the peptides and receptors have 3D structures experimentally verified or predicted by CYCLOPS, I-TASSER and SWISS-MODEL. With the rapid development of the COVID-2019 epidemic, this database also collects the research progress of peptides against coronavirus. In conclusion, DPL is a unique resource, which allows users easily to explore the targets, different structures as well as properties of peptides.

Targeting Strategies for Tissue-Specific Drug Delivery

Off-target effects of systemically administered drugs have been a major hurdle in designing therapies with desired efficacy and acceptable toxicity. Developing targeting strategies to enable site-specific drug delivery holds promise in reducing off-target effects, decreasing unwanted toxicities, and thereby enhancing a drug's therapeutic efficacy. Over the past three decades, a large body of literature has focused on understanding the biological barriers that hinder tissue-specific drug delivery and strategies to overcome them. These efforts have led to several targeting strategies that modulate drug delivery in both the preclinical and clinical settings, including small molecule-, nucleic acid-, peptide-, antibody-, and cell-based strategies. Here, we discuss key advances and emerging concepts for tissue-specific drug delivery approaches and their clinical translation.

Determine the Potential Epitope Based Peptide Vaccine Against Novel SARS-CoV-2 Targeting Structural Proteins Using Immunoinformatics Approaches

Coronaviruses (CoVs) belong to the Coronaviridae-family. The genus Beta-coronaviruses, are enveloped positive strand RNA viruses with club-like spikes at the surface with a unique replication process and a large RNA genome (∼25 kb). CoVs are known as one of the major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Recently, a new strain of coronavirus has been identified and named as SARS-CoV-2. A large number of COVID-19 (disease caused by SARS-CoV-2) cases are being diagnosed all over the World especially in China (Wuhan). COVID-19 showed high mortality rate exponentially, however, not even a single effective cure is being introduced yet against COVID-19. In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against COVID-19 for the development of a coronavirus peptide vaccine. Cytotoxic T-lymphocyte (CTL) and B-cell epitopes were predicted for SARS-CoV-2 coronavirus structural proteins (Spikes, Membrane, Envelope, and Nucleocapsid). The docking complexes of the top 10 epitopes having antigenic sites were analyzed led by binding affinity and binding interactional analyses of top ranked predicted peptides with the MHC-I HLA molecule. The predicted peptides may have potential to be used as peptide vaccine against COVID-19.

Targeting thermoTRP ion channels: in silico preclinical approaches and opportunities

INTRODUCTION

A myriad of cellular pathophysiological responses are mediated by polymodal ion channels that respond to chemical and physical stimuli such as thermoTRP channels. Intriguingly, these channels are pivotal therapeutic targets with limited clinical pharmacology. In silico methods offer an unprecedented opportunity for discovering new lead compounds targeting thermoTRP channels with improved pharmacological activity and therapeutic index.

AREAS COVERED

This article reviews the progress on thermoTRP channel pharmacology because of (i) advances in solving their atomic structure using cryo-electron microscopy and, (ii) progress on computational techniques including homology modeling, molecular docking, virtual screening, molecular dynamics, ADME/Tox and artificial intelligence. Together, they have increased the number of lead compounds with clinical potential to treat a variety of pathologies. We used original and review articles from Pubmed (1997-2020), as well as the clinicaltrials.gov database, containing the terms thermoTRP, artificial intelligence, docking, and molecular dynamics.

EXPERT OPINION

The atomic structure of thermoTRP channels along with computational methods constitute a realistic first line strategy for designing drug candidates with improved pharmacology and clinical translation. In silico approaches can also help predict potential side-effects that can limit clinical development of drug candidates. Together, they should provide drug candidates with upgraded therapeutic properties.

Targeting of interleukin-10 receptor by a potential human interleukin-10 peptide efficiently blocks interleukin-10 pathway-dependent cell proliferation

OBJECTIVE

Human interleukin-10 (IL-10) is a dimeric and pleiotropic cytokine that plays a crucial role in cellular immunoregulatory responses. As IL-10 binds to its receptors, IL-10Ra and IL-10Rb, it will suppress or induce the downstream cellular immune responses to protect from diseases.

MATERIALS AND METHODS

In this study, a potential peptide derived from IL-10 based on molecular docking and structural analysis was designed and validated by a series of cell assays to block IL-10 binding to receptor IL-10Ra for the inhibition of cell growth.

RESULTS

The simulation results indicate that the designed peptide IL10NM25 bound to receptor IL-10Ra is dominated by electrostatic interactions, whereas van der Waals (VDW) and hydrophobic interactions are minor. The cell experiments showed that IL10NM25 specifically binds to receptor IL-10Ra on the cell surface of two B-lineage cell lines, B lymphoma derived (BJAB), and lymphoblastoid cell line, whereas the mutant and scramble peptides are not able to suppress the binding of IL-10 to receptor IL-10Ra, consistent with the molecular simulation predictions.

CONCLUSION

This study demonstrates that structure-based peptide design can be effective in the development of peptide drug discovery.

Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We?

Peptides and peptidomimetics are strongly re-emerging as amenable candidates in the development of therapeutic strategies against a plethora of pathologies. In particular, these molecules are extremely suitable to treat diseases in which a major role is played by protein-protein interactions (PPIs). Unlike small organic compounds, peptides display both a high degree of specificity avoiding secondary off-targets effects and a relatively low degree of toxicity. Further advantages are provided by the possibility to easily conjugate peptides to functionalized nanoparticles, so improving their delivery and cellular uptake. In many cases, such molecules need to assume a specific three-dimensional conformation that resembles the bioactive one of the endogenous ligand. To this end, chemical modifications are introduced in the polypeptide chain to constrain it in a well-defined conformation, and to improve the drug-like properties. In this context, a successful strategy for peptide/peptidomimetics design and optimization is to combine different computational approaches ranging from structural bioinformatics to atomistic simulations. Here, we review the computational tools for peptide design, highlighting their main features and differences, and discuss selected protocols, among the large number of methods available, used to assess and improve the stability of the functional folding of the peptides. Finally, we introduce the simulation techniques employed to predict the binding affinity of the designed peptides for their targets.

The emerging role of computational design in peptide macrocycle drug discovery

Drug discovery is a laborious process with rising cost per new drug. Peptide macrocycles are promising therapeutics, though conformational flexibility can reduce target affinity and specificity. Recent computational advancements address this problem by enabling rational design of rigidly folded peptide macrocycles.

AREAS COVERED

This review summarizes currently approved peptide macrocycle therapeutics and discusses advantages of mesoscale drugs over small molecules or protein therapeutics. It describes the history, rationale, and state of the art of computational tools, such as Rosetta, that allow the design of rigidly structured peptide macrocycles. The emerging pipeline for designing peptide macrocycle drugs is described, including current challenges in designing permeable molecules that can emulate the chameleonic behavior of natural macrocycles. Prospects for reducing computational cost and improving accuracy with emerging computational technologies are also discussed.

EXPERT OPINION

To embrace computational design of peptide macrocycle drugs, we must shift current attitudes regarding the role of computation in drug discovery, and move beyond Lipinski's rules. This technology has the potential to shift failures to earlier in silico stages of the drug discovery process, improving success rates in costly clinical trials. Given the available tools, now is the time for drug developers to incorporate peptide macrocycle design into drug discovery pipelines.

Improving Protein-Peptide Docking Results via Pose-Clustering and Rescoring with a Combined Knowledge-Based and MM-GBSA Scoring Function

Protein-peptide docking, which predicts the complex structure between a protein and a peptide, is a valuable computational tool in peptide therapeutics development and the mechanistic investigation of peptides involved in cellular processes. Although current peptide docking approaches are often able to sample near-native peptide binding modes, correctly identifying those near-native modes from decoys is still challenging because of the extremely high complexity of the peptide binding energy landscape. In this study, we have developed an efficient postdocking rescoring protocol using a combined scoring function of knowledge-based ITScorePP potentials and physics-based MM-GBSA energies. Tested on five benchmark/docking test sets, our postdocking strategy showed an overall significantly better performance in binding mode prediction and score-rmsd correlation than original docking approaches. Specifically, our postdocking protocol outperformed original docking approaches with success rates of 15.8 versus 10.5% for pepATTRACT on the Global_57 benchmark, 5.3 versus 5.3% for CABS-dock on the Global_57 benchmark, 17.0 versus 11.3% for FlexPepDock on the LEADS-PEP data set, 40.3 versus 33.9% for HPEPDOCK on the Local_62 benchmark, and 64.2 versus 52.8% for HPEPDOCK on the LEADS-PEP data set when the top prediction was considered. These results demonstrated the efficacy and robustness of our postdocking protocol.

Identification of Protein Functional Regions

Protein sequence stores the information relative to both functionality and stability, thus making it difficult to disentangle the two contributions. However, the identification of critical residues for function and stability has important implications for the mapping of the proteome interactions, as well as for many pharmaceutical applications, e. g. the identification of ligand binding regions for targeted pharmaceutical protein design. In this work, we propose a computational method to identify critical residues for protein functionality and stability and to further categorise them in strictly functional, structural and intermediate. We evaluate single site conservation and use Direct Coupling Analysis (DCA) to identify co-evolved residues both in natural and artificial evolution processes. We reproduce artificial evolution using protein design and base our approach on the hypothesis that artificial evolution in the absence of any functional constraint would exclusively lead to site conservation and co-evolution events of the structural type. Conversely, natural evolution intrinsically embeds both functional and structural information. By comparing the lists of conserved and co-evolved residues, outcomes of the analysis on natural and artificial evolution, we identify the functional residues without the need of any a priori knowledge of the biological role of the analysed protein.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .

Molecular modeling and rational design of hydrocarbon-stapled/halogenated helical peptides targeting CETP self-binding site: Therapeutic implication for atherosclerosis

The human plasma cholesteryl ester transfer protein (CETP) collects triglycerides from very-/low-density lipoproteins (V/LDL) and exchanges them for cholesteryl esters from high-density lipoproteins (HDL), which has recognized as an important therapeutic target for atherosclerosis. The protein has a C-terminal amphipathic α-helix that serves as self-binding peptide to fulfill biological function by dynamically binding to/unbinding from its cognate site (termed self-binding site) in the same protein. Previously, we successfully derived and halogenated the helical peptide to competitively disrupt the self-binding behavior of CETP C-terminal tail. However, the halogenated peptides have only a limited affinity increase as compared to native helical peptide (∼3-fold), thus exhibiting only a moderate competitive potency. Here, instead of optimizing the direct intermolecular interaction of peptide with CETP self-binding site we attempt to further improve the peptide competitive potency by reducing its conformational flexibility with hydrocarbon-stapling technique. Computational analysis reveals that the helical peptide has large intrinsic disorder in unbound free state, which would incur a considerable entropy penalty upon rebinding to the self-binding site. All-hydrocarbon bridge is designed and optimized on native and halogenated peptides in terms of the helical pattern and binding mode of self-binding peptide. Dynamics simulation and circular dichroism indicate that the stapling can considerably reduce peptide disorder in free state. Energetics calculation and fluorescence assay conform that the binding affinity of stapled/halogenated peptides is improved substantially (by > 5-fold), thus exhibiting an effective competition potency with native peptide for the self-binding site. Structural examination suggests that the binding modes and nonbonded interactions of native and halogenated peptides are not influenced essentially due to the stapling.

A lanthanide-peptide-derived bacterium-like nanotheranostic with high tumor-targeting, -imaging and -killing properties

Nanostructures formed with bioactive peptides offer an exciting prospect in clinical oncology as a novel class of therapeutic agents for human cancers. Despite their therapeutic potential, however, peptide-based nanomedicines are often inefficacious in vivo due to low cargo-loading efficiency, poor tumor cell-targeting specificity and limited drug accumulation in tumor tissues. Here, we describe the design, via assembly of a p53-activating peptide termed PMI, functionalized PEG and fluorescent lanthanide oxyfluoride nanocrystals, of a novel nanotheranostic shaped in flexible rods. This lanthanide-peptide nanorod or LProd of bionic nature exhibited significantly enhanced tumor-targeting and -imaging properties compared to its spherical counterpart. Importantly, LProd potently inhibited tumor growth in a mouse model of human colon cancer through activating tumor suppressor protein p53 via MDM2/MDMX antagonism, while maintaining a highly favorable biosafety profile. Our data demonstrate that LProd as a multifunctional theranostic platform is ideally suited for tumor-specific peptide drug delivery with real-time disease tracking, thereby broadly impacting clinical development of antitumor peptides.

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

Background:

Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.

Methods:

We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.

Results:

Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.

Conclusion:

There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

Molecular Design of Peptide-Fc Fusion Drugs

Background:

Peptide-Fc fusion drugs, also known as peptibodies, are a category of biological therapeutics in which the Fc region of an antibody is genetically fused to a peptide of interest. However, to develop such kind of drugs is laborious and expensive. Rational design is urgently needed.

Methods:

We summarized the key steps in peptide-Fc fusion technology and stressed the main computational resources, tools, and methods that had been used in the rational design of peptide-Fc fusion drugs. We also raised open questions about the computer-aided molecular design of peptide-Fc.

Results:

The design of peptibody consists of four steps. First, identify peptide leads from native ligands, biopanning, and computational design or prediction. Second, select the proper Fc region from different classes or subclasses of immunoglobulin. Third, fuse the peptide leads and Fc together properly. At last, evaluate the immunogenicity of the constructs. At each step, there are quite a few useful resources and computational tools.

Conclusion:

Reviewing the molecular design of peptibody will certainly help make the transition from peptide leads to drugs on the market quicker and cheaper.

Affordable Microfluidic Bead-Sorting Platform for Automated Selection of Porous Particles Functionalized with Bioactive Compounds

The ability to rapidly and accurately evaluate bioactive compounds immobilized on porous particles is crucial in the discovery of drugs, diagnostic reagents, ligands, and catalysts. Existing options for solid phase screening of bioactive compounds, while highly effective and well established, can be cost-prohibitive for proof-of-concept and early stage work, limiting its applicability and flexibility in new research areas. Here, we present a low-cost microfluidics-based platform enabling automated screening of small porous beads from solid-phase peptide libraries with high sensitivity and specificity, to identify leads with high binding affinity for a biological target. The integration of unbiased computer assisted image processing and analysis tools, provided the platform with the flexibility of sorting through beads with distinct fluorescence patterns. The customized design of the microfluidic device helped with handling beads with different diameters (~100-300 µm). As a microfluidic device, this portable novel platform can be integrated with a variety of analytical instruments to perform screening. In this study, the system utilizes fluorescence microscopy and unsupervised image analysis, and can operate at a sorting speed of up to 125 beads/hr (~3.5 times faster than a trained operator) providing >90% yield and >90% bead sorting accuracy. Notably, the device has proven successful in screening a model solid-phase peptide library by showing the ability to select beads carrying peptides binding a target protein (human IgG).