APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures

Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models

This review explores state of the art machine learning and deep learning models for peptide property prediction in mass spectrometry-based proteomics, including, but not limited to, models for predicting digestibility, retention time, charge state distribution, collisional cross section, fragmentation ion intensities, and detectability. The combination of these models enables not only the in silico generation of spectral libraries but also finds many additional use cases in the design of targeted assays or data-driven rescoring. This review serves as both an introduction for newcomers and an update for experienced researchers aiming to develop accessible and reproducible models for peptide property predictions. Key limitations of the current models, including difficulties in handling diverse post-translational modifications and instrument variability, highlight the need for large-scale, harmonized datasets, and standardized evaluation metrics for benchmarking.

Protein structure prediction via deep learning: an in-depth review

The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental methods like X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy have been the gold standard for determining protein structures. However, these approaches are often costly, inefficient, and time-consuming. At the same time, the number of known protein sequences far exceeds the number of experimentally determined structures, creating a gap that necessitates the use of computational approaches. Deep learning has emerged as a promising solution to address this challenge over the past decade. This review provides a comprehensive guide to applying deep learning methodologies and tools in protein structure prediction. We initially outline the databases related to the protein structure prediction, then delve into the recently developed large language models as well as state-of-the-art deep learning-based methods. The review concludes with a perspective on the future of predicting protein structure, highlighting potential challenges and opportunities.

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.

Structural information in therapeutic peptides: Emerging applications in biomedicine

Peptides are attracting a growing interest as therapeutic agents. This trend stems from their cost-effectiveness and reduced immunogenicity, compared to antibodies or recombinant proteins, but also from their ability to dock and interfere with large protein-protein interaction surfaces, and their higher specificity and better biocompatibility relative to organic molecules. Many tools have been developed to understand, predict, and engineer peptide function. However, most state-of-the-art approaches treat peptides only as linear entities and disregard their structural arrangement. Yet, structural details are critical for peptide properties such as solubility, stability, or binding affinities. Recent advances in peptide structure prediction have successfully addressed the scarcity of confidently determined peptide structures. This review will explore different therapeutic and biotechnological applications of peptides and their assemblies, emphasizing the importance of integrating structural information to advance these endeavors effectively.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Advances of deep Neural Networks (DNNs) in the development of peptide drugs

Peptides are able to bind to difficult disease targets with high potency and specificity, providing great opportunities to meet unmet medical requirements. Nevertheless, the unique features of peptides, such as their small size, high structural flexibility, and scarce data availability, bring extra challenges to the design process. Firstly, this review sums up the application of peptide drugs in treating diseases. Then, the review probes into the advantages of Deep Neural Networks (DNNs) in predicting and designing peptide structures. DNNs have demonstrated remarkable capabilities in structural prediction, enabling accurate three-dimensional modeling of peptide drugs through models like AlphaFold and its successors. Finally, the review deliberates on the challenges and coping strategies of DNNs in the development of peptide drugs, along with future research directions. Future research directions focus on further improving the accuracy and efficiency of DNN-based peptide drug design, exploring novel applications of peptide drugs, and accelerating their clinical translation. With continuous advancements in technology and data accumulation, DNNs are poised to play an increasingly crucial role in the field of peptide drug development.

Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis

Protein engineering alters the polypeptide chain to obtain a novel protein with improved functional properties. This field constantly evolves with advanced in silico tools and techniques to design novel proteins and peptides. Rational incorporating mutations, unnatural amino acids, and post-translational modifications increases the applications of engineered proteins and peptides. It aids in developing drugs with maximum efficacy and minimum side effects. Currently, the engineering of peptides is gaining attention due to their high stability, binding specificity, less immunogenic, and reduced toxicity properties. Engineered peptides are potent candidates for drug development due to their high specificity and low cost of production compared with other biologics, including proteins and antibodies. Therefore, understanding the current perception of designing and engineering peptides with the help of currently available in silico tools is crucial. This review extensively studies various in silico tools available for protein engineering in the prospect of designing peptides as therapeutics, followed by in vitro aspects. Moreover, a discussion on the chemical synthesis and purification of peptides, a case study, and challenges are also incorporated.

Evaluating the Antimicrobial Efficacy of a Designed Synthetic peptide against Pathogenic Bacteria

Recent research has focused on discovering peptides that effectively target multidrug-resistant bacteria while leaving healthy cells unharmed. In this work, we describe the antimicrobial properties of RK8, a peptide composed of eight amino acid residues. Its activity was tested against multidrug-resistant Gram-negative and Gram-positive bacteria. RK8's efficacy in eradicating mature biofilm and increasing membrane permeability was assessed using Sytox Green. Cytotoxicity assays were conducted both in vitro and in vivo models. Circular dichroism analysis revealed that RK8 adopted an extended structure in water and sodium dodecyl sulfate (SDS). RK8 exhibited MICs of 8-64 μM and MBCs of 4-64 μM against various bacteria, with higher effectiveness observed in Methicillin-resistant Staphylococcus aureus (MRSA) and E. coli KPC+ strains than others. Ciprofloxacin and Vancomycin showed varying MIC and MBC values lower than RK8 for Gram-positive bacteria, but competitive for Gram-negative bacteria. The combination of RK8 and ciprofloxacin showed a synergistic effect. The RK8 peptides could reduce 38% of the mature Acinetobacter baumannii biofilm. Sytox Green reagent achieved 100% membrane permeation of Gram-positive and Gram-negative bacteria. The RK8 peptide did not show cytotoxic effects against murine macrophages (64 μM), erythrocytes (100 μM) or Galleria mellanella larvae (960 μM). In the stability test against peptidases, the RK8 peptide was stable, maintaining around 60% of the molecule intact after 120 min of incubation. These results highlight the potential of RK8 to be a promising strategy for developing a new antimicrobial and antibiofilm agent, inspiring and motivating further research in antimicrobial peptides.

Generative Adversarial Network-Based Augmentation With Noval 2-Step Authentication for Anti-Coronavirus Peptide Prediction

The virus poses a longstanding and enduring danger to various forms of life. Despite the ongoing endeavors to combat viral diseases, there exists a necessity to explore and develop novel therapeutic options. Antiviral peptides are bioactive molecules with a favorable toxicity profile, making them promising alternatives for viral infection treatment. Therefore, this article employed a generative adversarial network for antiviral peptide augmentation and a novel two-step authentication process for augmented synthetic peptides to enhance antiviral activity prediction. Additionally, five widely utilized deep learning models were employed for classification purposes. Initially, a GAN was used to augment the antiviral peptide. In a two-step authentication process, the NCBI-BLAST was utilized to identify the antiviral activity resemblance between the synthetic and real peptide. Subsequently, the hydrophobicity, hydrophilicity, hydroxylic nature, positive charge, and negative charge of synthetic and authentic antiviral peptides were compared before their utilization. Later, to examine the impact of authenticated peptide augmentation in the prediction of antiviral peptides, a comparison is conducted with the outcomes of non-peptide augmented prediction. The study demonstrates that the 1-D convolution neural network with augmented peptide exhibits superior performance compared to other employed classifiers and state-of-the-art models. The network attains a mean classification accuracy of 95.41%, an AUC value of 0.95, and an MCC value of 0.90 on the benchmark antiviral and anti-corona peptides dataset. Thus, the performance of the proposed model indicates its efficacy in predicting the antiviral activity of peptides.

Antibacterial and Anti-Inflammatory Activity of Branched Peptides Derived from Natural Host Defense Sequences

Antibiotic resistance is a major global health threat, necessitating the development of new treatments and diverse molecules to combat severe infections and preserve the efficacy of existing drugs. Antimicrobial peptides (AMPs) offer a versatile arsenal against bacteria, and peptide structure branching can enhance their resistance to proteases and improve their overall efficacy. A small library of peptides derived from natural host defense peptides and synthesized in a tetrabranched form was selected against E. coli. Six selected branched peptides were further studied for antibacterial activity against a panel of strains, biofilm inhibition, protease resistance, and cytotoxicity. Their structure was predicted computationally and their mechanism of action was investigated by electron microscopy and by using fluorescent dyes. The peptide BAMP2 showed promise in a mouse skin infection model, indicating the potential for local infection treatment.

Exploring protein functions from structural flexibility using CABS-flex modeling

Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS-flex method has been developed as an efficient modeling tool that combines coarse-grained simulations with all-atom detail. Available both as a web server and a standalone package, CABS-flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command-line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS-flex across various structure-function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS-flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges.

Conjugation with the Carrier Helped to Reveal acidification-Induced Structural Shift in the Peptide from Phospholipase Domain of Parvovirus B19

Spectroscopic studies on domains and peptides of large proteins are complicated because of the tendency of short peptides to form oligomers in aquatic buffers, but conjugation of a peptide with a carrier protein may be helpful. In this study we approved that a fragment of SK30 peptide from phospholipase A2 domain of VP1 Parvovirus B19 capsid protein (residues: 144-159; 164; 171-183; sequence: SAVDSAARIHDFRYSQLAKLGINPYTHWTVADEELLKNIK) turns from random coil to alpha helix in the acidic medium only in case if it had been conjugated with BSA (through additional N-terminal Cys residue, turning it into CSK31 peptide, and SMCC linker) according to CD-spectroscopy results. In contrast, unconjugated SK30 peptide does not undergo such shift because it forms stable oligomers connected by intermolecular antiparallel beta sheet, according to IR-spectroscopy, CD-spectroscopy, blue native gel electrophoresis and centrifugal ultrafiltration, as, probably, the whole isolated phospholipase domain of VP1 protein does. However, being a part of the long VP1 capsid protein, phospholipase domain may change its fold during the acidification of the medium in the endolysosome by the way of the formation of contacts between protonated His153 and Asp175, promoting the shift from random coil to alpha helix in its N-terminal part. This study opens up a perspective of vaccine development, since rabbit polyclonal antibodies against the conjugate of CSK31 peptide with BSA, in which the structure of the second alpha helix from the phospholipase A2 domain should be reproduced, can bind epitopes of the complete recombinant unique part of VP1 Parvovirus B19 capsid (residues: 1-227).

Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.

Machine learning for antimicrobial peptide identification and design

Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues. Recent advances in AI and ML have led to breakthroughs in our abilities to predict biomolecular properties and structures and to generate new molecules. The ML-based modelling of peptides may overcome some of the disadvantages associated with traditional drug discovery and aid the rapid development and translation of AMPs. Here, we provide an introduction to this emerging field and survey ML approaches that can be used to address issues currently hindering AMP development. We also outline important limitations that can be addressed for the broader adoption of AMPs in clinical practice, as well as new opportunities in data-driven peptide design.

NeuroPep 2.0: An Updated Database Dedicated to Neuropeptide and Its Receptor Annotations

Neuropeptides not only work through nervous system but some of them also work peripherally to regulate numerous physiological processes. They are important in regulation of numerous physiological processes including growth, reproduction, social behavior, inflammation, fluid homeostasis, cardiovascular function, and energy homeostasis. The various roles of neuropeptides make them promising candidates for prospective therapeutics of different diseases. Currently, NeuroPep has been updated to version 2.0, it now holds 11,417 unique neuropeptide entries, which is nearly double of the first version of NeuroPep. When available, we collected information about the receptor for each neuropeptide entry and predicted the 3D structures of those neuropeptides without known experimental structure using AlphaFold2 or APPTEST according to the peptide sequence length. In addition, DeepNeuropePred and NeuroPred-PLM, two neuropeptide prediction tools developed by us recently, were also integrated into NeuroPep 2.0 to help to facilitate the identification of new neuropeptides. NeuroPep 2.0 is freely accessible at https://isyslab.info/NeuroPepV2/.

Elucidation of conserved multi-epitope vaccine against Leishmania donovani using reverse vaccinology

Visceral leishmaniasis (VL) is a tropical disease that causes severe public health problems in humans when untreated. As no licensed vaccine exists against VL, we aimed to formulate a potential MHC-restricted chimeric vaccine construct against this dreadful parasitic disease. Amastin-like protein derived from L. donovani is considered to be stable, immunogenic and non-allergic. A comprehensive established framework was used to explore the set of immunogenic epitopes with estimated population coverage of 96.08% worldwide. The rigorous assessment revealed 6 promiscuous T-epitopes which can plausibly be presented by more than 66 diverse HLA alleles. Further docking and simulation study of peptide receptor complexes identified a strong and stable binding interaction with better structural compactness. The predicted epitopes were combined with appropriate linkers and adjuvant molecules and their translation efficiency was evaluated in pET28+(a), an bacterial expression vector using in-silico cloning. Molecular docking followed by MD simulation study revealed a stable interaction between chimeric vaccine construct with TLRs. Immune simulation of the chimeric vaccine constructs showed an elevated Th1 immune response against both B and T epitopes. With this, the detailed computational analysis suggested that the chimeric vaccine construct can evoke a robust immune response against Leishmania donovani infection. Future studies are required to validate the role of amastin as a promising vaccine target.Communicated by Ramaswamy H. Sarma.

Structure prediction of linear and cyclic peptides using CABS-flex

The structural modeling of peptides can be a useful aid in the discovery of new drugs and a deeper understanding of the molecular mechanisms of life. Here we present a novel multiscale protocol for the structure prediction of linear and cyclic peptides. The protocol combines two main stages: coarse-grained simulations using the CABS-flex standalone package and an all-atom reconstruction-optimization process using the Modeller program. We evaluated the protocol on a set of linear peptides and two sets of cyclic peptides, with cyclization through the backbone and disulfide bonds. A comparison with other state-of-the-art tools (APPTEST, PEP-FOLD, ESMFold and AlphaFold implementation in ColabFold) shows that for most cases, AlphaFold offers the highest resolution. However, CABS-flex is competitive, particularly when it comes to short linear peptides. As demonstrated, the protocol performance can be further improved by combination with the residue-residue contact prediction method or more efficient scoring. The protocol is included in the CABS-flex standalone package along with online documentation to aid users in predicting the structure of peptides and mini-proteins.

A novel opsonic eCIRP inhibitor for lethal sepsis

Sepsis is a life-threatening inflammatory condition partly orchestrated by the release of various damage-associated molecular patterns such as extracellular cold-inducible RNA-binding protein (eCIRP). Despite advances in understanding the pathogenic role of eCIRP in inflammatory diseases, novel therapeutic strategies to prevent its excessive inflammatory response are lacking. Milk fat globule-epidermal growth factor-VIII (MFG-E8) is critical for the opsonic clearance of apoptotic cells, but its potential involvement in the removal of eCIRP was previously unknown. Here, we report that MFG-E8 can strongly bind eCIRP to facilitate αvβ3-integrin-dependent internalization and lysosome-dependent degradation of MFG-E8/eCIRP complexes, thereby attenuating excessive inflammation. Genetic disruption of MFG-E8 expression exaggerated sepsis-induced systemic accumulation of eCIRP and other cytokines, and consequently exacerbated sepsis-associated acute lung injury. In contrast, MFG-E8-derived oligopeptide recapitulated its eCIRP binding properties, and significantly attenuated eCIRP-induced inflammation to confer protection against sepsis. Our findings suggest a novel therapeutic approach to attenuate eCIRP-induced inflammation to improve outcomes of lethal sepsis.

Structural Shifts of the Parvovirus B19 Capsid Receptor-binding Domain: A Peptide Study

BACKGROUND

Binding appropriate cellular receptors is a crucial step of a lifecycle for any virus. Structure of receptor-binding domain for a viral surface protein has to be determined before the start of future drug design projects.

OBJECTIVES

Investigation of pH-induced changes in the secondary structure for a capsid peptide with loss of function mutation can shed some light on the mechanism of entrance.

METHODS

Spectroscopic methods were accompanied by electrophoresis, ultrafiltration, and computational biochemistry.

RESULTS

In this study, we showed that a peptide from the receptor-binding domain of Parvovirus B19 VP1 capsid (residues 13-31) is beta-structural at pH=7.4 in 0.01 M phosphate buffer, but alpha- helical at pH=5.0, according to the circular dichroism (CD) spectroscopy results. Results of infra- red (IR) spectroscopy showed that the same peptide exists in both alpha-helical and beta-structural conformations in partial dehydration conditions both at pH=7.4 and pH=5.0. In contrast, the peptide with Y20W mutation, which is known to block the internalization of the virus, forms mostly alpha-helical conformation in partial dehydration conditions at pH=7.4. According to our hypothesis, an intermolecular antiparallel beta structure formed by the wild-type peptide in its tetramers at pH=7.4 is the prototype of the similar intermolecular antiparallel beta structure formed by the corresponding part of Parvovirus B19 receptor-binding domain with its cellular receptor (AXL).

CONCLUSION

Loss of function Y20W substitution in VP1 capsid protein prevents the shift into the beta-structural state by the way of alpha helix stabilization and the decrease of its ability to turn into the disordered state.

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.

Exploration of DPP-IV Inhibitory Peptide Design Rules Assisted by the Deep Learning Pipeline That Identifies the Restriction Enzyme Cutting Site

The mining of antidiabetic dipeptidyl peptidase IV (DPP-IV) inhibitory peptides (DPP-IV-IPs) is currently a costly and laborious process. Due to the absence of rational peptide design rules, it relies on cumbersome screening of unknown enzyme hydrolysates. Here, we present an enhanced deep learning model called bidirectional encoder representation (BERT)-DPPIV, specifically designed to classify DPP-IV-IPs and explore their design rules to discover potent candidates. The end-to-end model utilizes a fine-tuned BERT architecture to extract structural/functional information from input peptides and accurately identify DPP-IV-Ips from input peptides. Experimental results in the benchmark data set showed BERT-DPPIV yielded state-of-the-art accuracy and MCC of 0.894 and 0.790, surpassing the 0.797 and 0.594 obtained by the sequence-feature model. Furthermore, we leveraged the attention mechanism to uncover that our model could recognize the restriction enzyme cutting site and specific residues that contribute to the inhibition of DPP-IV. Moreover, guided by BERT-DPPIV, proposed design rules for DPP-IV inhibitory tripeptides and pentapeptides were validated, and they can be used to screen potent DPP-IV-IPs.

Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function

When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.

Comparative Evaluation of Existing and Rationally Designed Novel Antimicrobial Peptides for Treatment of Skin and Soft Tissue Infections

Skin and soft tissue infections (SSTIs) and acne are among the most common skin conditions in primary care. SSTIs caused by ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter sp.) can range in severity, and treating them is becoming increasingly challenging due to the growing number of antibiotic-resistant pathogens. There is also a rise in antibiotic-resistant strains of Cutibacterium acne, which plays a role in the development of acne. Antimicrobial peptides (AMPs) are considered to be a promising solution to the challenges posed by antibiotic resistance. In this study, six new AMPs were rationally designed and compared to five existing peptides. The MIC values against E. coli, P. aeruginosa, K. pneumoniae, E. faecium, S. aureus, and C. acnes were determined, and the peptides were evaluated for cytotoxicity using Balb/c 3T3 cells and dermal fibroblasts, as well as for hemolytic activity. The interaction with bacterial membranes and the effect on TNF-α and IL-10 secretion were also evaluated for selected peptides. Of the tested peptides, RP556 showed high broad-spectrum antibacterial activity without inducing cytotoxicity or hemolysis, and it stimulated the production of IL-10 in LPS-stimulated peripheral blood mononuclear cells. Four of the novel AMPs showed pronounced specificity against C. acnes, with MIC values (0.3–0.5 μg/mL) below the concentrations that were cytotoxic or hemolytic.

Identification of B and T Cell Epitopes to Design an Epitope-Based Peptide Vaccine against the Cell Surface Binding Protein of Monkeypox Virus: An Immunoinformatics Study

Background

Although the monkeypox virus-associated illness was previously confined to Africa, recently, it has started to spread across the globe and become a significant threat to human lives. Hence, this study was designed to identify the B and T cell epitopes and develop an epitope-based peptide vaccine against this virus's cell surface binding protein through an in silico approach to combat monkeypox-associated diseases.

Results

The analysis revealed that the cell surface binding protein of the monkeypox virus contains 30 B cell and 19 T cell epitopes within the given parameter. Among the T cell epitopes, epitope "ILFLMSQRY" was found to be one of the most potential peptide vaccine candidates. The docking analysis revealed an excellent binding affinity of this epitope with the human receptor HLA-B∗15:01 with a very low binding energy (-7.5 kcal/mol).

Conclusion

The outcome of this research will aid the development of a T cell epitope-based peptide vaccine, and the discovered B and T cell epitopes will facilitate the creation of other epitope and multi-epitope-based vaccines in the future. This research will also serve as a basis for further in vitro and in vivo analysis to develop a vaccine that is effective against the monkeypox virus.

A refined pH-dependent coarse-grained model for peptide structure prediction in aqueous solution

Introduction: Peptides carry out diverse biological functions and the knowledge of the conformational ensemble of polypeptides in various experimental conditions is important for biological applications. All fast dedicated softwares perform well in aqueous solution at neutral pH. Methods: In this study, we go one step beyond by combining the Debye-Hückel formalism for charged-charged amino acid interactions and a coarse-grained potential of the amino acids to treat pH and salt variations. Results: Using the PEP-FOLD framework, we show that our approach performs as well as the machine-leaning AlphaFold2 and TrRosetta methods for 15 well-structured sequences, but shows significant improvement in structure prediction of six poly-charged amino acids and two sequences that have no homologous in the Protein Data Bank, expanding the range of possibilities for the understanding of peptide biological roles and the design of candidate therapeutic peptides.

Benchmarking AlphaFold2 on peptide structure prediction

Recent advancements in computational tools have allowed protein structure prediction with high accuracy. Computational prediction methods have been used for modeling many soluble and membrane proteins, but the performance of these methods in modeling peptide structures has not yet been systematically investigated. We benchmarked the accuracy of AlphaFold2 in predicting 588 peptide structures between 10 and 40 amino acids using experimentally determined NMR structures as reference. Our results showed AlphaFold2 predicts α-helical, β-hairpin, and disulfide-rich peptides with high accuracy. AlphaFold2 performed at least as well if not better than alternative methods developed specifically for peptide structure prediction. AlphaFold2 showed several shortcomings in predicting Φ/Ψ angles, disulfide bond patterns, and the lowest RMSD structures failed to correlate with lowest pLDDT ranked structures. In summary, computation can be a powerful tool to predict peptide structures, but additional steps may be necessary to analyze and validate the results.

Antimicrobial Peptides Active in In Vitro Models of Endodontic Bacterial Infections Modulate Inflammation in Human Cardiac Fibroblasts

Endodontic and periodontal disease are conditions of infectious origin that can lead to tooth loss or develop into systemic hyperinflammation, which may be associated with a wide variety of diseases, including cardiovascular. Endodontic and periodontal treatment often relies on antibiotics. Since new antimicrobial resistances are a major threat, the use of standard antibiotics is not recommended when the infection is only local. Antimicrobial peptides were recently demonstrated to be valid alternatives for dental treatments. The antimicrobial peptide M33D is a tetrabranched peptide active against Gram-negative and Gram-positive bacteria. It has a long life, unusual for peptides, because its branched form provides resistance to proteases. Here the efficacy of M33D and of its analog M33i/l as antibiotics for local use in dentistry was evaluated. M33D and M33i/l were active against reference strains and multidrug-resistant clinical isolates of Gram-negative and Gram-positive species. Their minimum inhibitory concentration against different strains of dental interest was between 0.4 and 6.0 μM. Both peptides acted rapidly on bacteria, impairing membrane function. They also disrupted biofilm effectively. Disinfection of the root canal is crucial for endodontic treatments. M33D and M33i/l reduced E. faecalis colonies to one-twentieth in a dentin slices model reproducing root canal irrigation. They both captured and neutralized lipopolysaccharide (LPS), a bacterial toxin responsible for inflammation. The release of IL-1β and TNFα by LPS-stimulated murine macrophages was reduced by both peptides. Human cardiac fibroblasts respond to different insults with the release of proinflammatory cytokines, and consequently, they are considered directly involved in atherogenic cardiovascular processes, including those triggered by infections. The presence of M33D and M33i/l at MIC concentration reduced IL6 release from LPS- stimulated human cardiac fibroblasts, hence proving to be promising in preventing bacteria-induced atherogenesis. The two peptides showed low toxicity to mammalian cells, with an EC50 one order of magnitude higher than the average MIC and low hemolytic activity. The development of antimicrobial peptides for dental irrigations and medication is a very promising new field of research that will provide tools to fight dental infections and their severe consequences, while at the same time protecting standard antibiotics from new outbreaks of antimicrobial resistance.

Structural and Functional Annotation and Molecular Docking Analysis of a Hypothetical Protein from Neisseria gonorrhoeae: An In-Silico Approach

Background

Worldwide, Neisseria gonorrhoeae-related sexually transmitted infections (STIs) continue to be of significant public health concern. This obligate-human pathogen has developed a number of defenses against both innate and adaptive immune responses during infection, some of which are mediated by the pathogen's proteins. Hence, the uncharacterized proteins of N. gonorrhoeae can be annotated to get insight into the unique functions of this organism related to its pathogenicity and to find a more efficient therapeutic target.

Methods

In this study, a hypothetical protein (HP) of N. gonorrhoeae was chosen for analysis and an in-silico approach was used to explore various properties such as physicochemical characteristics, subcellular localization, secondary structure, 3D structures, and functional annotation of that HP. Finally, a molecular docking analysis was performed to design an epitope-based vaccine against that HP.

Results

This study has identified the potential role of the chosen HP of N. gonorrhoeae in plasmid transfer, cell cycle control, cell division, and chromosome partitioning. Acidic nature, thermal stability, cytoplasmic localization of the protein, and some of its other physicochemical properties have also been identified through this study. Molecular docking analysis has demonstrated that one of the T cell epitopes of the protein has a significant binding affinity with the human leukocyte antigen HLA-B∗15 : 01.

Conclusions

The in-silico characterization of this protein will help us understand molecular mechanism of action of N. gonorrhoeae and get an insight into novel therapeutic identification processes. This research will, therefore, enhance our knowledge to find new medications to tackle this potential threat to humankind.

From a Cone Snail Toxin to a Competitive MC4R Antagonist

The melanocortin 4 receptor (MC4R) plays a role in energy homeostasis and represents a target for treating energy balance disorders. For decades, synthetic ligands have been derived from MC4R endogenous agonists and antagonists, such as setmelanotide used to treat rare forms of genetic obesity. Recently, animal venoms have demonstrated their capacity to provide melanocortin ligands with toxins from a scorpion and a spider. Here, we described a cone snail toxin, N-CTX-Ltg1a, with a nanomolar affinity for hMC4R but unrelated to any known toxins or melanocortin ligands. We then derived from the conotoxin the linear peptide HT1-0, a competitive antagonist of G_s, G₁₅, and β-arrestin2 pathways with a low nanomolar affinity for hMC4R. Similar to endogenous ligands, HT1-0 needs hydrophobic and basic residues to bind hMC4R. Altogether, it represents the first venom-derived peptide of high affinity on MC4R and paves the way for the development of new MC4R antagonists.

A Generalized Attraction-Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction

Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for non-bonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small β-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.

Conformation and membrane interaction studies of the potent antimicrobial and anticancer peptide palustrin-Ca

Palustrin-Ca (GFLDIIKDTGKEFAVKILNNLKCKLAGGCPP) is a host defence peptide with potent antimicrobial and anticancer activities, first isolated from the skin of the American bullfrog Lithobates catesbeianus. The peptide is 31 amino acid residues long, cationic and amphipathic. Two-dimensional NMR spectroscopy was employed to characterise its three-dimensional structure in a 50/50% water/2,2,2-trifluoroethanol-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{3}$$\end{document}d3 mixture. The structure is defined by an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α-helix that spans between Ile\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{6}$$\end{document}6-Ala\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{26}$$\end{document}26, and a cyclic disulfide-bridged domain at the C-terminal end of the peptide sequence, between residues 23 and 29. A molecular dynamics simulation was employed to model the peptide’s interactions with sodium dodecyl sulfate micelles, a widely used bacterial membrane-mimicking environment. Throughout the simulation, the peptide was found to maintain its \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α-helical conformation between residues Ile\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{6}$$\end{document}6-Ala\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{26}$$\end{document}26, while adopting a position parallel to the surface to micelle, which is energetically-favourable due to many hydrophobic and electrostatic contacts with the micelle.

Biophysical study of the structure and dynamics of the antimicrobial peptide maximin 1

Maximin 1 is a cationic, amphipathic antimicrobial peptide found in the skin secretions and brains of the Chinese red belly toad Bombina maxima. The 27 amino acid residue-long peptide is biologically interesting as it possesses a variety of biological activities, including antibacterial, antifungal, antiviral, antitumour and spermicidal activities. Its three-dimensional structural model was obtained in a 50/50% water/2,2,2-trifluoroethanol-d₃ mixture using two-dimensional NMR spectroscopy. Maximin 1 was found to adopt an α-helical structure from residue Ile² to Ala²⁶ . The peptide is amphipathic, showing a clear separation between polar and non-polar residues. The interactions with sodium dodecyl sulfate micelles, a widely-used bacterial membrane-mimicking environment, were modelled using molecular dynamics simulations. The peptide maintains an α-helical conformation, occasionally displaying a flexibility around the Gly⁹ and Gly¹⁶ residues, which is likely responsible for the peptide's low haemolytic activity. It is found to preferentially adopt a position parallel to the micellar surface, establishing a number of hydrophobic and electrostatic interactions with the micelle.