PepComposer: computational design of peptides binding to a given protein surface

A fast and efficient virtual screening and identification strategy for helix peptide binders based on finDr webserver: A case study of bovine serum albumin (BSA)

Peptides offer unique advantages, including strong specificity, rapid action, and low side effects, making them a prominent focus in the development of new drugs and functional materials. However, the rapid and efficient screening and identification of high-affinity peptides for specific targets remains a significant challenge. In this study, we successfully screened 12-helix candidate peptides using bovine serum albumin (BSA) as the target protein, employing the computer-aided peptide virtual screening webserver finDr. Among the top five candidate peptides, we identified E4-TP2 (GVATVVARLFLL) as the peptide capable of binding BSA with high affinity constant (KD = 39.4 nM), confirmed through an in vitro molecular interaction instrument. The interaction mode of the peptide-BSA complex was analyzed using Ligplot software, revealing that the primary interactions involved hydrophobic forces and hydrogen bonds. Additionally, molecular dynamics simulations further elucidated the molecular mechanisms underlying the high-affinity peptide interactions, the results demonstrated that the complex exhibited good conformational stability and strong binding free energy (MM/PBSA: -21.075 ± 5.471 kJ/mol). In conclusion, the finDr virtual screening strategy and the molecular interaction identification method employed in this study provide a robust technical approach for the rapid and efficient acquisition of high-affinity binding peptides for target proteins of interest.

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.

Protein-peptide binding residue prediction based on protein language models and cross-attention mechanism

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.

Discovery of bioactive peptides as therapeutic agents for skin wound repair

Short sequences of amino acids called peptides have a wide range of biological functions and the potential to treat a number of diseases. Bioactive peptides can be derived from different sources, including marine organisms, and synthetic design, making them versatile candidates for production of therapeutic agents. Their therapeutic effects span across areas such as antimicrobial activity, cells proliferation and migration, synthesis of collagen, and more. This current review explores the fascinating realm of bioactive peptides as promising therapeutic agents for skin wound healing. This review focuses on the multifaceted biological effects of specific peptides, shedding light on their potential to revolutionize the field of dermatology and regenerative medicine. It delves into how these peptides stimulate collagen synthesis, inhibit inflammation, and accelerate tissue regeneration, ultimately contributing to the effective repair of skin wounds. The findings underscore the significant role several types of bioactive peptides can play in enhancing wound healing processes and offer promising insights for improving the quality of life for individuals with skin injuries and dermatological conditions. The versatility of peptides allows for the development of tailored treatments catering to specific wound types and patient needs. As continuing to delve deeper into the realm of bioactive peptides, there is immense potential for further exploration and innovation. Future endeavors may involve the optimization of peptide formulations, elucidation of underlying molecular and cellular mechanisms.

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.

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide-protein interactions (PepPIs) and protein-protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

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

BACKGROUND

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

AREAS COVERED

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

EXPERT OPINION

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

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Computer- and NMR-Aided Design of Small-Molecule Inhibitors of the Hub1 Protein

By binding to the spliceosomal protein Snu66, the human ubiquitin-like protein Hub1 is a modulator of the spliceosome performance and facilitates alternative splicing. Small molecules that bind to Hub1 would be of interest to study the protein-protein interaction of Hub1/Snu66, which is linked to several human pathologies, such as hypercholesterolemia, premature aging, neurodegenerative diseases, and cancer. To identify small molecule ligands for Hub1, we used the interface analysis, peptide modeling of the Hub1/Snu66 interaction and the fragment-based NMR screening. Fragment-based NMR screening has not proven sufficient to unambiguously search for fragments that bind to the Hub1 protein. This was because the Snu66 binding pocket of Hub1 is occupied by pH-sensitive residues, making it difficult to distinguish between pH-induced NMR shifts and actual binding events. The NMR analyses were therefore verified experimentally by microscale thermophoresis and by NMR pH titration experiments. Our study found two small peptides that showed binding to Hub1. These peptides are the first small-molecule ligands reported to interact with the Hub1 protein.

Protocol for iterative optimization of modified peptides bound to protein targets

Peptides are commonly used as therapeutic agents. However, they suffer from easy degradation and instability. Replacing natural by non-natural amino acids can avoid these problems, and potentially improve the affinity towards the target protein. Here, we present a computational pipeline to optimize peptides based on adding non-natural amino acids while improving their binding affinity. The workflow is an iterative computational evolution algorithm, inspired by the PARCE protocol, that performs single-point mutations on the peptide sequence using modules from the Rosetta framework. The modifications can be guided based on the structural properties or previous knowledge of the biological system. At each mutation step, the affinity to the protein is estimated by sampling the complex conformations and applying a consensus metric using various open protein-ligand scoring functions. The mutations are accepted based on the score differences, allowing for an iterative optimization of the initial peptide. The sampling/scoring scheme was benchmarked with a set of protein-peptide complexes where experimental affinity values have been reported. In addition, a basic application using a known protein-peptide complex is also provided. The structure- and dynamic-based approach allows users to optimize bound peptides, with the option to personalize the code for further applications. The protocol, called mPARCE, is available at: https://github.com/rochoa85/mPARCE/.

Tools and techniques for rational designing of antimicrobial peptides for aquaculture

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

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

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

Advances of research of Fc-fusion protein that activate NK cells for tumor immunotherapy

The rapid development of bioengineering technology has introduced Fc-fusion proteins, representing a novel kind of recombinant protein, as promising biopharmaceutical products in tumor therapy. Numerous related anti-tumor Fc-fusion proteins have been investigated and are in different stages of development. Fc-fusion proteins are constructed by fusing the Fc-region of the antibody with functional proteins or peptides. They retain the bioactivity of the latter and partial properties of the former. This structural and functional advantage makes Fc-fusion proteins an effective tool in tumor immunotherapy, especially for the recruitment and activation of natural killer (NK) cells, which play a critical role in tumor immunotherapy. Even though tumor cells have developed mechanisms to circumvent the cytotoxic effect of NK cells or induce defective NK cells, Fc-fusion proteins have been proven to effectively activate NK cells to kill tumor cells in different ways, such as antibody-dependent cell-mediated cytotoxicity (ADCC), activate NK cells in different ways in order to promote killing of tumor cells. In this review, we focus on NK cell-based immunity for cancers and current research progress of the Fc-fusion proteins for anti-tumor therapy by activating NK cells.

Structure and activity of the DHNA Coenzyme-A Thioesterase from Staphylococcus aureus providing insights for innovative drug development

Humanity is facing an increasing health threat caused by a variety of multidrug resistant bacteria. Within this scenario, Staphylococcus aureus, in particular methicillin resistant S. aureus (MRSA), is responsible for a number of hospital-acquired bacterial infections. The emergence of microbial antibiotic resistance urgently requires the identification of new and innovative strategies to treat antibiotic resistant microorganisms. In this context, structure and function analysis of potential drug targets in metabolic pathways vital for bacteria endurance, such as the vitamin K₂ synthesis pathway, becomes interesting. We have solved and refined the crystal structure of the S. aureus DHNA thioesterase (SaDHNA), a key enzyme in the vitamin K₂ pathway. The crystallographic structure in combination with small angle X-ray solution scattering data revealed a functional tetramer of SaDHNA. Complementary activity assays of SaDHNA indicated a preference for hydrolysing long acyl chains. Site-directed mutagenesis of SaDHNA confirmed the functional importance of Asp16 and Glu31 for thioesterase activity and substrate binding at the putative active site, respectively. Docking studies were performed and rational designed peptides were synthesized and tested for SaDHNA inhibition activity. The high-resolution structure of SaDHNA and complementary information about substrate binding will support future drug discovery and design investigations to inhibit the vitamin K₂ synthesis pathway.

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.

Computational Evolution Protocol for Peptide Design

Computational peptide design is useful for therapeutics, diagnostics, and vaccine development. To select the most promising peptide candidates, the key is describing accurately the peptide-target interactions at the molecular level. We here review a computational peptide design protocol whose key feature is the use of all-atom explicit solvent molecular dynamics for describing the different peptide-target complexes explored during the optimization. We describe the milestones behind the development of this protocol, which is now implemented in an open-source code called PARCE. We provide a basic tutorial to run the code for an antibody fragment design example. Finally, we describe three additional applications of the method to design peptides for different targets, illustrating the broad scope of the proposed approach.

The Effects of Tetrapeptides Designed to Fit the Androgen Binding Site of ZIP9 on Myogenic and Osteogenic Cells

Simple Summary

Pro-androgenic substances such as testosterone are often used to treat muscle- or bone-related disorders. Their interactions with the classical androgen receptor, however, can trigger a number of undesirable effects. It would therefore be of great benefit if the positive androgenic effects could be obtained by circumventing the classical androgen receptor. ZIP9 is a recently identified membrane-bound androgen receptor of physiological significance. Using in silico methods, we identified and verified the extracellular localization of its androgen binding site and designed small peptides that fit in it that do not interact with the AR. All peptides were found to be pro-androgenic; they stimulate mineralization in osteoblastic cells and myogenesis in myoblasts. Thus, these peptides might serve as testosterone surrogates in the treatment of osteogenic or myogenic disorders.

Abstract

ZIP9 is a recently identified membrane-bound androgen receptor of physiological significance that may mediate certain physiological responses to androgens. Using in silico methods, six tetrapeptides with the best docking properties at the testosterone binding site of ZIP9 were synthesized and further investigated. All tetrapeptides displaced T-BSA-FITC, a membrane-impermeable testosterone analog, from the surface of mouse myogenic L6 cells that express ZIP9 but not the classical androgen receptor (AR). Silencing the expression of ZIP9 with siRNA prevented this labeling. All tetrapeptides were found to be pro-androgenic; in L6 cells they stimulated the expression of myogenin, triggered activation of focal adhesion kinase, and prompted the fusion of L6 myocytes to syncytial myotubes. In human osteoblastic SAOS-2 cells that express AR and ZIP9, they reduced the expression of alkaline phosphatase and stimulated mineralization. These latter effects were prevented by silencing ZIP9 expression, indicating that the osteoblast/osteocyte conversion is exclusively mediated through ZIP9. Our results demonstrate that the synthetic tetrapeptides, by acting as ZIP9-specific androgens, have the potential to replace testosterone or testosterone analogs in the treatment of bone- or muscle-related disorders by circumventing the undesirable effects mediated through the classical AR.

Insights on peptide topology in the computational design of protein ligands: the example of lysozyme binding peptides

Herein, we compared the ability of linear and cyclic peptides generated in silico to target different protein sites: internal pockets and solvent-exposed sites. We selected human lysozyme (HuL) as a model target protein combined with the computational evolution of linear and cyclic peptides. The sequence evolution of these peptides was based on the PARCE algorithm. The generated peptides were screened based on their aqueous solubility and HuL binding affinity. The latter was evaluated by means of scoring functions and atomistic molecular dynamics (MD) trajectories in water, which allowed prediction of the structural features of the protein-peptide complexes. The computational results demonstrated that cyclic peptides constitute the optimal choice for solvent exposed sites, while both linear and cyclic peptides are capable of targeting the HuL pocket effectively. The most promising binders found in silico were investigated experimentally by surface plasmon resonance (SPR), nuclear magnetic resonance (NMR), and electrospray ionization mass spectrometry (ESI-MS) techniques. All tested peptides displayed dissociation constants in the micromolar range, as assessed by SPR; however, both NMR and ESI-MS suggested multiple binding modes, at least for the pocket binding peptides. A detailed NMR analysis confirmed that both linear and cyclic pocket peptides correctly target the binding site they were designed for.

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.

Toward Development of a Label-Free Detection Technique for Microfluidic Fluorometric Peptide-Based Biosensor Systems

The problems of chronic or noncommunicable diseases (NCD) that now kill around 40 million people each year require multiparametric combinatorial diagnostics for the selection of effective treatment tactics. This could be implemented using the biosensor principle based on peptide aptamers for spatial recognition of corresponding protein markers of diseases in biological fluids. In this paper, a low-cost label-free principle of biomarker detection using a biosensor system based on fluorometric registration of the target proteins bound to peptide aptamers was investigated. The main detection principle considered includes the re-emission of the natural fluorescence of selectively bound protein markers into a longer-wavelength radiation easily detectable by common charge-coupled devices (CCD) using a specific luminophore. Implementation of this type of detection system demands the reduction of all types of stray light and background fluorescence of construction materials and aptamers. The latter was achieved by careful selection of materials and design of peptide aptamers with substituted aromatic amino acid residues and considering troponin T, troponin I, and bovine serum albumin as an example. The peptide aptamers for troponin T were designed in silico using the «Protein 3D» (SPB ETU, St. Petersburg, Russia) software. The luminophore was selected from the line of ZnS-based solid-state compounds. The test microfluidic system was arranged as a flow through a massive of four working chambers for immobilization of peptide aptamers, coupled with the optical detection system, based on thick film technology. The planar optical setup of the biosensor registration system was arranged as an excitation-emission cascade including 280 nm ultraviolet (UV) light-emitting diode (LED), polypropylene (PP) UV transparent film, proteins layer, glass filter, luminophore layer, and CCD sensor. A laboratory sample has been created.

In-Silico Evidence for a Two Receptor Based Strategy of SARS-CoV-2

We propose a computational investigation on the interaction mechanisms between SARS-CoV-2 spike protein and possible human cell receptors. In particular, we make use of our newly developed numerical method able to determine efficiently and effectively the relationship of complementarity between portions of protein surfaces. This innovative and general procedure, based on the representation of the molecular isoelectronic density surface in terms of 2D Zernike polynomials, allows the rapid and quantitative assessment of the geometrical shape complementarity between interacting proteins, which was unfeasible with previous methods. Our results indicate that SARS-CoV-2 uses a dual strategy: in addition to the known interaction with angiotensin-converting enzyme 2, the viral spike protein can also interact with sialic-acid receptors of the cells in the upper airways.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Structural Characterization of Receptor-Receptor Interactions in the Allosteric Modulation of G Protein-Coupled Receptor (GPCR) Dimers

G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein-protein interactions (PPI).

Cyclic Peptide Inhibitors of the Tsg101 UEV Protein Interactions Refined through Global Docking and Gaussian Accelerated Molecular Dynamics Simulations

Tsg101 UEV domain proteins are potential targets for virus infection therapy, especially for HIV and Ebola viruses. Peptides are key in curbing virus transmission, and cyclic peptides have a greater survival time than their linear peptides. To date, the accurate prediction of cyclic peptide-protein receptors binding conformations still is challenging because of high peptide flexibility. Here, a useful approach combined the global peptide docking, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF), normal molecular dynamics (cMD), and solvated interaction energy (SIE) techniques. Then we used this approach to investigate the binding conformations of UEV domain proteins with three cyclic peptides inhibitors. We reported the possible cyclic peptide-UEV domain protein binding conformations via 2D PMF free energy profiles and SIE free energy calculations. The residues Trp145, Tyr147, and Trp148 of the native cyclic peptide (CP1) indeed play essential roles in the cyclic peptides-UEV domain proteins interactions. Our findings might increase the accuracy of cyclic peptide-protein conformational prediction, which may facilitate cyclic peptide inhibitor design. Our approach is expected to further aid in addressing the challenges in cyclic peptide inhibitor design.

A novel strategy for molecular interfaces optimization: The case of Ferritin-Transferrin receptor interaction

Protein-protein interactions regulate almost all cellular functions and rely on a fine tune of surface amino acids properties involved on both molecular partners. The disruption of a molecular association can be caused even by a single residue mutation, often leading to a pathological modification of a biochemical pathway. Therefore the evaluation of the effects of amino acid substitutions on binding, and the ad hoc design of protein-protein interfaces, is one of the biggest challenges in computational biology. Here, we present a novel strategy for computational mutation and optimization of protein-protein interfaces. Modeling the interaction surface properties using the Zernike polynomials, we describe the shape and electrostatics of binding sites with an ordered set of descriptors, making possible the evaluation of complementarity between interacting surfaces. With a Monte Carlo approach, we obtain protein mutants with controlled molecular complementarities. Applying this strategy to the relevant case of the interaction between Ferritin and Transferrin Receptor, we obtain a set of Ferritin mutants with increased or decreased complementarity. The extensive molecular dynamics validation of the method results confirms its efficacy, showing that this strategy represents a very promising approach in designing correct molecular interfaces.

Computational modeling of cyclic peptide inhibitor-MDM2/MDMX binding through global docking and Gaussian accelerated molecular dynamics simulations

MDM2 and MDMX are potential targets for p53-dependent cancer therapy. Peptides are key in cellular immunology and oncology, and cyclic peptides generally have higher half-life than their linear counterparts. However, prediction of cyclic peptide-protein binding is challenging with normal molecular simulation approaches because of high peptide flexibility. Here, we used global peptide docking, normal molecular dynamics, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF) profiles, and solvated interaction energy (SIE) techniques to investigate the interactions of MDM2/MDMX with three N-to-C-terminal cyclic peptide-based inhibitors. We determined the possible cyclic peptide-MDM2/MDMX complex structures via 2D PMF profiles and SIE calculations. Our findings increase the accuracy of peptide-protein structural prediction, which may facilitate cyclic peptide drug design. Advancements in the computational methods and computing power may further aid in addressing the challenges in cyclic peptide drug design. Communicated by Ramaswamy H. Sarma.

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.

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/ .

Discovery of novel helix binding sites at protein-protein interfaces

Protein-protein interactions (PPIs) play a key role in numerous biological processes. Many efforts have been undertaken to develop PPI modulators for therapeutic applications; however, to date, most of the peptide binders designed to target PPIs are derived from native binding helices or using the native helix binding site, which has limited the applications of protein-protein interface binding peptide design. Here, we developed a general computational algorithm, HPer (Helix Positioner), that locates single-helix binding sites at protein-protein interfaces based on the structure of protein targets. HPer performed well on known single-helix-mediated PPIs and recaptured the key interactions and hot-spot residues of native helical binders. We also screened non-helical-mediated PPIs in the PDBbind database and identified 17 PPIs that were suitable for helical peptide binding, and the helical binding sites in these PPIs were also predicted for designing novel peptide ligands. The L2 domain of EGFR, which was the top ranked, was selected as an example to show the protocol and results of designing novel helical peptide ligands on the searched binding site. The binding stability of the designed sequences were further investigated using molecular dynamics simulations.

The pepATTRACT web server for blind, large-scale peptide-protein docking

Peptide–protein interactions are ubiquitous in the cell and form an important part of the interactome. Computational docking methods can complement experimental characterization of these complexes, but current protocols are not applicable on the proteome scale. pepATTRACT is a novel docking protocol that is fully blind, i.e. it does not require any information about the binding site. In various stages of its development, pepATTRACT has participated in CAPRI, making successful predictions for five out of seven protein–peptide targets. Its performance is similar or better than state-of-the-art local docking protocols that do require binding site information. Here we present a novel web server that carries out the rigid-body stage of pepATTRACT. On the peptiDB benchmark, the web server generates a correct model in the top 50 in 34% of the cases. Compared to the full pepATTRACT protocol, this leads to some loss of performance, but the computation time is reduced from ∼18 h to ∼10 min. Combined with the fact that it is fully blind, this makes the web server well-suited for large-scale in silico protein–peptide docking experiments. The rigid-body pepATTRACT server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/pepATTRACT.

A Comprehensive Review on Current Advances in Peptide Drug Development and Design

Protein-protein interactions (PPIs) execute many fundamental cellular functions and have served as prime drug targets over the last two decades. Interfering intracellular PPIs with small molecules has been extremely difficult for larger or flat binding sites, as antibodies cannot cross the cell membrane to reach such target sites. In recent years, peptides smaller size and balance of conformational rigidity and flexibility have made them promising candidates for targeting challenging binding interfaces with satisfactory binding affinity and specificity. Deciphering and characterizing peptide-protein recognition mechanisms is thus central for the invention of peptide-based strategies to interfere with endogenous protein interactions, or improvement of the binding affinity and specificity of existing approaches. Importantly, a variety of computation-aided rational designs for peptide therapeutics have been developed, which aim to deliver comprehensive docking for peptide-protein interaction interfaces. Over 60 peptides have been approved and administrated globally in clinics. Despite this, advances in various docking models are only on the merge of making their contribution to peptide drug development. In this review, we provide (i) a holistic overview of peptide drug development and the fundamental technologies utilized to date, and (ii) an updated review on key developments of computational modeling of peptide-protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.

Combinatorial engineering of N-TIMP2 variants that selectively inhibit MMP9 and MMP14 function in the cell

Developing selective inhibitors for proteolytic enzymes that share high sequence homology and structural similarity is important for achieving high target affinity and functional specificity. Here, we used a combination of yeast surface display and dual-color selective library screening to obtain selective inhibitors for each of the matrix metalloproteinases (MMPs) MMP14 and MMP9 by modifying the non-specific N-terminal domain of the tissue inhibitor of metalloproteinase-2 (N-TIMP2). We generated inhibitor variants with 30- to 1175-fold improved specificity to each of the proteases, respectively, relative to wild type N-TIMP2. These biochemical results accurately predicted the selectivity and specificity obtained in cell-based assays. In U87MG cells, the activation of MMP2 by MMP14 was inhibited by MMP14-selective blockers but not MMP9-specific inhibitors. Target specificity was also demonstrated in MCF-7 cells stably expressing either MMP14 or MMP9, with only the MMP14-specific inhibitors preventing the mobility of MMP14-expressing cells. Similarly, the mobility of MMP9-expressing cells was inhibited by the MMP9-specific inhibitors, yet was not altered by the MMP14-specific inhibitors. The strategy developed in this study for improving the specificity of an otherwise broad-spectrum inhibitor will likely enhance our understanding of the basis for target specificity of inhibitors to proteolytic enzymes, in general, and to MMPs, in particular. We, moreover, envision that this study could serve as a platform for the development of next-generation, target-specific therapeutic agents. Finally, our methodology can be extended to other classes of proteolytic enzymes and other important target proteins.

Understanding and designing head-to-tail cyclic peptides

Cyclic peptides (CPs) are an exciting class of molecules with a variety of applications. However, design strategies for CP therapeutics, for example, are generally limited by a poor understanding of their sequence-structure relationships. This knowledge gap often leads to a trial-and-error approach for designing CPs for a specific purpose, which is both costly and time-consuming. Herein, we describe the current experimental and computational efforts in understanding and designing head-to-tail CPs along with their respective challenges. In addition, we provide several future directions in the field of computational CP design to improve its accuracy, efficiency and applicability. These advances, combined with experimental techniques, shall ultimately provide a better understanding of these interesting molecules and a reliable working platform to rationally design CPs with desired characteristics.

Protein-peptide docking: opportunities and challenges

Peptides have recently attracted much attention as promising drug candidates. Rational design of peptide-derived therapeutics usually requires structural characterization of the underlying protein-peptide interaction. Given that experimental characterization can be difficult, reliable computational tools are needed. In recent years, a variety of approaches have been developed for 'protein-peptide docking', that is, predicting the structure of the protein-peptide complex, starting from the protein structure and the peptide sequence, including variable degrees of information about the peptide binding site and/or conformation. In this review, we provide an overview of protein-peptide docking methods and outline their capabilities, limitations, and applications in structure-based drug design. Key challenges are also briefly discussed, such as modeling of large-scale conformational changes upon binding, scoring of predicted models, and optimal inclusion of varied types of experimental data and theoretical predictions into an integrative modeling process.

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

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