Protein-peptide Complexes Research Articles

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data.

Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection. CSP_Rank is a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.

Target-Specific De Novo Peptide Binder Design with DiffPepBuilder.

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

Machine learning, network pharmacology, and molecular dynamics reveal potent cyclopeptide inhibitors against dengue virus proteins.

The dengue virus is a major global health hazard responsible for an estimated 390 million diseases yearly. This study focused on identifying cyclopeptide inhibitors for envelope structural proteins E, NS1, NS3, and NS5. Additionally, 5579 cyclopeptides were individually screened against the four target proteins using a machine learning-based quantitative structure-activity relationship model. Subsequently, the best 10 cyclopeptides from each protein were selected for molecular docking with their corresponding proteins. Moreover, the protein-peptide complexes with the highest affinity were subjected to a 100-ns molecular dynamics simulation. The protein-protein complexes exhibited superior structural stability and binding interactions. Based on the results of the MD simulation analyses, which included checking values for Root Mean Square Deviation, Root Mean Square Fluctuation, Principal Component Analysis (PCA), free energy landscapes, and energetic components, it was found that NS5-CP03714 complex is more stable and has stronger binding interactions than NS3-CP02054. PCA and free energy landscape plots have confirmed the higher conformational stability of NS5-CP03714. Analysis of the energetic components revealed that NS5-CP03714 (total binding energy = -47.19kcal/mol) exhibits more favorable interaction energies and overall binding energy compared to NS3-CP02054 (total binding energy = -27.36kcal/mol), suggesting a stronger and more stable formation of the complex. In addition, the drug-target network of two specific peptides (CP02950 and CP05582) and their associated target proteins were analyzed. This analysis revealed valuable information about their ability to target several proteins and their potential for broad-spectrum activity. Additional experimental investigations are necessary to validate these computational results and assess the efficacy of identified peptide inhibitors in biological systems.

In-silico binding affinity of a phage display library screened novel peptide against various FABPs.

In accordance to the American Heart Association (AHA), cardiovascular diseases (CVDs) are the leading cause of death around the globe, causing more than 19.1million deaths in 2020. Heart-type fatty acid binding protein (H-FABP) is required for the metabolism of fatty acids (FA) inside cardiomyocytes is reported as a biomarker for myocardial damage. As early as one hour after an Acute myocardial infarction (AMI), H-FABP can be used to detect myocardial ischemia. Thus, H-FABP based detection can reduce the burden on the emergency department. A peptide-based detection system can provide point-of-care diagnostics for CVDs. There is a lot of research being done on peptide-based detection, and it has a lot of potential to help with unmet medical diagnostic needs. A twelve (12) amino acid peptide has been discovered using Phage Display Library Screening. The affinity of peptide with H-FABP and other FABPs has been done using molecular docking and ADMET profile has been done. Molecular docking of small peptides against the target protein can play a crucial role in recognizing peptide binding sites and poses. The docking study was done using the HDOCK server and the visualization of the docked complex was done using Pymol and UCSF chimera. The molecular simulation study of three protein-peptide complexes were done which also validated the binding affinity of peptide with the proteins. The RMSD, RMSF and radius of gyration are also analyzed. The results indicate that H-FABP shows higher level of binding interaction with the peptide having bond length ranging from 2.3 to 3.4 Å. The screened peptide is suitable for H-FABP binding and can be used for prognosis purposes in the heart ischemic conditions.

Current Computational Methods for Protein-peptide Complex Structure Prediction.

Peptide-mediated protein-protein interactions (PPIs) play an important role in various biological processes. The development of peptide-based drugs to modulate PPIs has attracted increasing attention due to the advantages of high specificity and low toxicity. In the development of peptide-based drugs, one of the most important steps is to determine the interaction details between the peptide and the target protein. In addition to experimental methods, recently developed computational methods provide a cost-effective way for studying protein-peptide interactions. In this article, we carefully reviewed recently developed protein-peptide docking methods, which were classified into three groups: template-based docking, template-free docking, and hybrid method. Then, we presented available benchmarking sets and evaluation metrics for assessing protein-peptide docking performance. Furthermore, we discussed the use of molecular dynamics simulations, as well as deep learning approaches in protein-peptide complex prediction.

A deep attention model for wide-genome protein-peptide binding affinity prediction at a sequence level

Peptides are pivotal in numerous biological activities by engaging in up to 40 % of protein-protein interactions in many cellular processes. Due to their exceptional specificity and effectiveness, peptides have emerged as promising candidates for drug design. However, accurately predicting protein-peptide binding affinity remains a challenging. Aiming at the problem, we develop a prediction model PepPAP based on convolutional neural network and multi-head attention, which relies solely on sequence features. These features include physicochemical properties, intrinsic disorder, sequence encoding, and especially interface propensity which is extracted from 16,689 non-redundant protein-peptide complexes. Notably, the adopted regression stratification cross-validation scheme proposed in our previous work is beneficial to improve the prediction for the cases with extreme binding affinity values. On three benchmark test datasets: T100, a series of peptides targeting to PDZ domain and CXCR4, PepPAP shows excellent performance, outperforming the existing methods and demonstrating its good generalization ability. Furthermore, PepPAP has good results in binary interaction prediction, and the analysis of the feature space distribution visualization highlights PepPAP's effectiveness. To the best of our knowledge, PepPAP is the first sequence-based deep attention model for wide-genome protein-peptide binding affinity prediction, and holds the potential to offer valuable insights for the peptide-based drug design.

Complex anti-relapse therapy of various variants of the course of the vulvar lichen sclerosus: A randomized prospective study

Aim. To develop and evaluate the effectiveness of complex therapy of vulvar lichen sclerosus (VLS), considering the clinical and immunological variant (CIV) of the disease. Materials and methods. A randomized prospective study included 292 patients 20-70 years old with different CIVs of the VLS course: atrophic (n=101), sclerosing (n=154), scleroatrophic (n=37), as well as 35 females with VLS in the comparison group of the same age category. Based on the clinical, immunological, and morphological features of the VLS course, a complex anti-relapse therapy was developed, the effectiveness of which was evaluated after a year of follow-up in comparison with the standard of care (SoC) of patients (from the comparison group) based on the number of disease relapses and the results of the Vulvar Quality of Life Index Questionnaire (VQLI) survey of women with vulvar diseases. Results. In patients with the sclerosing variant of VLS, short courses of topical glucocorticoids during exacerbations are justified; in patients with the scleroatrophic variant – topical calcineurin inhibitors, in the atrophic variant – a protein-peptide complex from porcine blood leukocytes (vaginal suppositories and cream-balsam with lanolin) containing interleukin-1, 6, tumor necrosis factor α, transforming growth factor, macrophage migration inhibitory factor. Patients with any variant of VLS are instructed to follow household and hygienic recommendations, use emollients daily, take prophylactic doses of vitamins A, E, and D to correct its deficiency or insufficiency, as well as use local enzyme therapy, as indicated, aimed at preventing or treatment of cicatricial changes. Local estrogens were prescribed only to women with genitourinary menopausal syndrome in the peri- and postmenopausal period. The developed complex anti-relapse therapy of VLS showed greater clinical efficacy compared to SoC: 3.7-fold decrease in episodes of disease exacerbation, as well as a significant (p=0.001) 1.3-fold decrease in the negative effect of VLS on the QoL of patients in the main clinical group (15.4 points according to the VQLI assessment – a mild effect on QoL) compared to SoC with the average score of 27.6, which corresponded to a strong negative effect of the disease on QoL. Conclusion. The results of the study support the distinction of VLS CIVs and the need to consider their features when choosing an effective therapy for the disease. Properly selected complex, supportive therapy of VLS can significantly increase the QoL of patients, minimize the number of relapses, and prevent the development of complications, including, probably, the risk of malignant transformation.

Probability Density Reweighting of High-Temperature Molecular Dynamics.

Molecular dynamics (MD) simulation is a popular method for elucidating the structures and functions of biomolecules. However, exploring the conformational space, especially for large systems with slow transitions, often requires enhanced sampling methods. Although conducting MD at high temperatures provides a straightforward approach, resulting conformational ensembles diverge significantly from those at low temperatures. To address this discrepancy, we propose a novel probability density-based reweighting (PDR) method. PDR exhibits robust performance across four distinct systems, including a miniprotein, a cyclic peptide, a protein loop, and a protein-peptide complex. It accurately restores the conformational distributions at high temperatures to those at low temperatures. Additionally, we apply PDR to reweight previously studied high-T MD simulations of 12 protein-peptide complexes, enabling a comprehensive investigation of the conformational space of protein-peptide complexes.

ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.

Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.

Pregravid preparation for women with bacterial vaginosis and a history of early pregnancy loss

Introduction. Statistics show that about 13 to 15 in 100 pregnancies end in early miscarriage. This represents both a medical and social issue, as such outcome may be associated with maternal depression and anxiety, which inevitably affects women’s activities, performance efficiency and social inclusion. Restoration of normobiocenosis and lactobacilli pool dominance is one of the key challenges for women planning pregnancy.Aim. To evaluate the effectiveness of probiotics at the pregravid preparation stage in women with a history of early pregnancy loss and bacterial vaginosis (BV).Materials and methods. We selected 46 patients of reproductive age (24–39 years) who applied for early pregnancy loss, with complaints of vaginal discharge with an unpleasant odour, pruritus, burning, and dyspareunia, using databases of 5 medical clinics in Moscow, Tyumen and Stavropol. To treat bacterial vaginosis, two-stage therapy aimed at eliminating the increased number of anaerobic microorganisms and restoring the vaginal microbiocenosis was used.Results. The effectiveness of combination therapy was assessed at 1 month. All patients on therapy experienced clinical improvement, normalization of vaginal microbiocenosis parameters based on Femoflor-16 test findings after the end of treatment. Physiological microbiocenosis of Lactobacillus spp. > 107 was detected in 43 (94.5%). In all patients, the average vaginal pH after two-stage therapy was 4.3 ± 0.7. The safety of Acilact Duo therapy was assessed in all study participants. No negative trends were recorded.Conclusion. Superlimflife (Acilact Duo), acidophilus bacteria + vitamin-mineral protein-peptide complex, quickly restores optimal vaginal acidity (pH), stimulates the growth of lactobacilli, and reduces the incidence of BV relapses.

Design of amyloidogenic peptide traps.

Segments of proteins with high β-strand propensity can self-associate to form amyloid fibrils implicated in many diseases. We describe a general approach to bind such segments in β-strand and β-hairpin conformations using de novo designed scaffolds that contain deep peptide-binding clefts. The designs bind their cognate peptides in vitro with nanomolar affinities. The crystal structure of a designed protein-peptide complex is close to the design model, and NMR characterization reveals how the peptide-binding cleft is protected in the apo state. We use the approach to design binders to the amyloid-forming proteins transthyretin, tau, serum amyloid A1 and amyloid β1-42 (Aβ42). The Aβ binders block the assembly of Aβ fibrils as effectively as the most potent of the clinically tested antibodies to date and protect cells from toxic Aβ42 species.

Folding of Class IIa HDAC Derived Peptides into α-helices Upon Binding to Myocyte Enhancer Factor-2 in Complex with DNA

Interaction of transcription factor myocyte enhancer factor-2 (MEF2) family members with class IIa histone deacetylases (HDACs) has been implicated in a wide variety of diseases. Though considerable knowledge on this topic has been accumulated over the years, a high resolution and detailed analysis of the binding mode of multiple class IIa HDAC derived peptides with MEF2D is still lacking. To fulfil this gap, we report here the crystal structure of MEF2D in complex with double strand DNA and four different class IIa HDAC derived peptides, namely HDAC4, HDAC5, HDAC7 and HDAC9. All class IIa HDAC derived peptides form extended amphipathic α-helix structures that fit snugly in the hydrophobic groove of MEF2D domain. Binding mode of class IIa HDAC derived peptides to MEF2D is very similar and occur primarily through nonpolar interactions mediated by highly conserved branched hydrophobic amino acids. Further studies revealed that class IIa HDAC derived peptides are unstructured in solution and appear to adopt a folded α-helix structure only upon binding to MEF2D. Comparison of our peptide-protein complexes with previously characterized structures of MEF2 bound to different co-activators and co-repressors, highlighted both differences and similarities, and revealed the adaptability of MEF2 in protein–protein interactions. The elucidation of the three-dimensional structure of MEF2D in complex with multiple class IIa HDAC derived peptides provide not only a better understanding of the molecular basis of their interactions but also have implications for the development of novel antagonist.

Molecular investigation of antimicrobial peptides against Helicobacter pylori proteins using a peptide-protein docking approach

The impact of H. pylori resistance on patient's treatment failure is a major concern. Therefore, the development of novel or alternative therapies for H. pylori is urgently needed. The purpose of this study was to investigate the molecular interactions of various antimicrobial peptides (AMPs) to H. pylori proteins. We performed the peptide-protein molecular docking using HADDOCK 2.4 webserver. Fourteen AMPs were tested for their binding efficacy against four H. pylori proteins. Simulation of the peptide-protein complex was performed using molecular dynamic software package AMBER20. From molecular docking analysis, five peptides (LL-37, Tilapia piscidin 4, napin, snakin-1 and EcAMP1) showed strong binding interactions against H. pylori proteins. The strongest binding affinity was observed in the interactions between Snakin-1 and PBP2, TP4 and type I HopQ and EcAMP1 and type I HopQ with −11.1, −13.6 and −13.8 kcal/mol, respectively. The dynamic simulation was performed for two complexes (snakin1-PBP2 and EcAMP1-HopQ). Results of the dynamics simulation showed that EcAMP1 had stable interaction and binding to type I HopQ protein without significant structural changes. In conclusion, both results of docking and simulation showed that EcAMP1 might be useful as a potential therapeutic agent for H. pylori treatment. This molecular approach provides deep understanding of the interaction insights between AMPs and H. pylori proteins. It paves the way for the development of novel anti-H. pylori using antimicrobial peptides.

From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2

The revolution brought about by AlphaFold2 opens promising perspectives to unravel the complexity of protein-protein interaction networks. The analysis of interaction networks obtained from proteomics experiments does not systematically provide the delimitations of the interaction regions. This is of particular concern in the case of interactions mediated by intrinsically disordered regions, in which the interaction site is generally small. Using a dataset of protein-peptide complexes involving intrinsically disordered regions that are non-redundant with the structures used in AlphaFold2 training, we show that when using the full sequences of the proteins, AlphaFold2-Multimer only achieves 40% success rate in identifying the correct site and structure of the interface. By delineating the interaction region into fragments of decreasing size and combining different strategies for integrating evolutionary information, we manage to raise this success rate up to 90%. We obtain similar success rates using a much larger dataset of protein complexes taken from the ELM database. Beyond the correct identification of the interaction site, our study also explores specificity issues. We show the advantages and limitations of using the AlphaFold2 confidence score to discriminate between alternative binding partners, a task that can be particularly challenging in the case of small interaction motifs.

Hydrogen-Deuterium Exchange Mass Spectrometry Identifies Local and Long-Distance Interactions within the Multicomponent Radical SAM Enzyme, PqqE.

Interactions among proteins and peptides are essential for many biological activities including the tailoring of peptide substrates to produce natural products. The first step in the production of the bacterial redox cofactor pyrroloquinoline quinone (PQQ) from its peptide precursor is catalyzed by a radical SAM (rSAM) enzyme, PqqE. We describe the use of hydrogen-deuterium exchange mass spectrometry (HDX-MS) to characterize the structure and conformational dynamics in the protein-protein and protein-peptide complexes necessary for PqqE function. HDX-MS-identified hotspots can be discerned in binary and ternary complex structures composed of the peptide PqqA, the peptide-binding chaperone PqqD, and PqqE. Structural conclusions are supported by size-exclusion chromatography coupled to small-angle X-ray scattering (SEC-SAXS). HDX-MS further identifies reciprocal changes upon the binding of substrate peptide and S-adenosylmethionine (SAM) to the PqqE/PqqD complex: long-range conformational alterations have been detected upon the formation of a quaternary complex composed of PqqA/PqqD/PqqE and SAM, spanning nearly 40 Å, from the PqqA binding site in PqqD to the PqqE active site Fe4S4. Interactions among the various regions are concluded to arise from both direct contact and distal communication. The described experimental approach can be readily applied to the investigation of protein conformational communication among a large family of peptide-modifying rSAM enzymes.

Study of Molecular Parameters and Antibacterial Activity of a Peptide Preparation Derived from Human Leukocytes

Background. The study of new antimicrobial substances isolated from the living organism tissues is of particular importance for medicine and the biopharmaceutical industry. Leukocyte peptides can act as an alternative to traditional antibiotics to overcome the bacterial antibiotic resistance problem.Aim. Evaluation of molecular weight and biological activity of ultrasound-derived leukocyte peptides.Methods. Separation of leukocyte peptide complexes into particular fractions by reverse phase chromatography and evaluation of their antibacterial activity by serial dilution and diffusion methods.Results. Studies have shown that one peptide fraction with a molecular weight of 440–570 Da has pronounced antibacterial activity.Conclusion. The obtained experimental data testify to the high therapeutic potential of the leukocyte protein-peptide complex, which has a wide spectrum of antibacterial activity.

Leveraging machine learning models for peptide-protein interaction prediction.

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Investigating Lycotoxin-An1a (An1a), a defense antiviral peptide from Alopecosa nagpag venom as prospective anti-dengue agent against DENV-2 NS2B-NS3 protease

Dengue fever is a global health concern with no effective therapy. Screening synthetic chemicals, animal-originated compounds, and phytocompounds against Dengue virus (DENV) targets has failed to find dengue antivirals. The current study examines animal drugs as antagonists against NS2B-NS3Pro, one of DENV's most promising therapeutic targets for dengue fever. Antiviral-Lycotoxin-An1a (An1a), a defence antiviral peptide isolated from the venom of Alopecosa nagpag, a toxic spider. Based on prior in vitro research, it was discovered that the venom peptide suppresses the action of DENV-2 NS2B-NS3Pro. An1a peptide with NS2B-NS3Pro wild type (WT) and two mutants (H51N and S135A) was tested for anti-dengue characteristics using in silico analysis. The WT NS2B-NS3Pro has a catalytic triad of His51, Asp75, and Ser135 in the active site, but the mutants have N51 instead of His51 and Ala135 instead of Ser135. The dynamic sites of the three proteases (WT, H51N, S135A) and the peptide toxin (An1a) were taken into account to achieve molecular docking of An1a with WT NS2B-NS3Pro in conjunction with H51N and S135A. Cluspro-2 performs rigid-flexible docking to predict peptide binding affinity, effectiveness, and inhibitory consistency. Since the ligand had a higher binding affinity, docking score, and molecular interaction network, MD simulations and MM-GBSA free energy calculations were used to investigate the stability of the three protein-peptide complexes. The computer-aided screening and manufacture of spider venom-based anti-dengue medicines yielded intriguing results in the preliminary studies. This study is significant in defining the ideal therapeutic candidate against dengue infections.

MM-GBSA and QM/MM simulation-based in silico approaches for the inhibition of Acinetobacter baumannii class D OXA-24 β-lactamase using antimicrobial peptides melittin and RP-1

The antimicrobial peptides (AMPs) have been regarded as the next generation antibiotics. This study aimed to explore the AMP inhibitors of β-lactamase enzyme employing computational biology methods. Protein-peptide docking of Acinetobacter baumannii OXA-24 class D β-lactamase with AMPs (melittin and RP-1) were performed using HawkDock webserver. The docked complexes were subjected to energy property analysis through MM/GBSA, and binding affinity (ΔG (kcal/mol)) and stability (dissociation constant, KD (M)) prediction using PRODIGY. Both the AMPs, melittin and RP-1, were well docked with A. baumannii OXA-24. The top ranked OXA-24-melittin and OXA-24-RP-1 complexes were detected on the basis of the HawkDock scores (-2974.08 and -2825.83, respectively), thereafter by rescoring with MM/GBSA-based binding free energy (BFE) of -33.85 and -29.29 kcal/mol, respectively. The PRODIGY-based respective BFE (-8.0 and -10.0 kcal/mol) and KD (1.4 × 10-6 and 5 × 10-8 M) of the complexes revealed excellent protein-peptide binding affinity and complex stability. The iMODS-based molecular dynamic simulation authenticated the stability and molecular motion flexibility of the protein-peptide complexes. The quantum mechanics and molecular mechanics energy estimated using density-functional theory with CP2K software, for the electrostatic interaction of OXA-24-RP-1 (-218862.95 kcal/mol), was more favourable than the H-bonded interaction of OXA-24-melittin (-200620.21 kcal/mol), nevertheless both the peptides were found effective to inhibit OXA-24. Both the AMPs had toxicity profiles within the acceptable limits as predicted through pkCSM. Current findings indicate the potential supplement or replacement of conventionally used antibiotics with melittin and RP-1 to treating or averting A. baumannii infection by escaping the β-lactamases-mediated resistance.

Predicting Binding Affinity Between MHC-I Receptor and Peptides Based on Molecular Docking and Protein-peptide Interaction Interface Characteristics

Background:Predicting protein-peptide binding affinity is one of the leading research subjects in peptide drug design and repositioning. In previous studies, models constructed by researchers just used features of peptide structures. These features had limited information and could not describe the proteinpeptide interaction mode. This made models and predicted results lack interpretability in pharmacy and biology, which led to the protein-peptide interaction mode not being reflected. Therefore, it was of little significance for the design of peptide drugs.Objective:Considering the protein-peptide interaction mode, we extracted protein-peptide interaction interface characteristics and built machine learning models to improve the performance and enhance the interpretability of models.Methods:Taking MHC-I protein and its binding peptides as the research object, protein-peptide complexes were obtained by molecular docking, and 94 protein-peptide interaction interface characteristics were calculated. Then ten important features were selected using recursive feature elimination to construct SVR, RF, and MLP models to predict protein-peptide binding affinity.Results:The MAE of the SVR, RF and MLP models constructed using protein-peptide interaction interface characteristics are 0.2279, 0.2939 and 0.2041, their MSE are 0.1289, 0.1308 and 0.0780, and their R2 reached 0.8711, 0.8692 and 0.9220, respectively.Conclusion:The model constructed using protein-peptide interaction interface characteristics showed better prediction results. The key features for predicting protein-peptide binding affinity are the bSASA of negatively charged species, hydrogen bond acceptor, hydrophobic group, planarity, and aromatic ring.