Prediction Of Peptide Binding Research Articles

Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions.

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations.

Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering. PepBinding has been demonstrated on seven distinct model peptides. In peptide docking using HPEPDOCK, the peptide backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures ranged from 3.8 to 16.0 Å, corresponding to the medium to inaccurate quality models according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. The Pep-GaMD simulations performed for only 200 ns significantly improved the docking models, resulting in five medium and two acceptable quality models. Therefore, PepBinding is an efficient workflow for predicting peptide binding structures and is publicly available at https://github.com/MiaoLab20/PepBinding.

All-atom modeling of methacrylate-based multi-modal chromatography resins for Langmuir constant prediction of peptides

In downstream processing, the intricate nature of the interactions between biomolecules and adsorbent materials presents a significant challenge in the prediction of their binding and elution behaviors. This complexity is further heightened in multi-modal chromatography (MMC), which employs two distinct binding mechanisms. To gain a deeper understanding of the involved interactions, simulating the adsorption of biomolecules on resin surfaces is a focal point of ongoing research. However, previous studies often simplified the adsorbent surface, modeling it as a flat or slightly curved plane without including a realistic backbone structure. Here, we introduce and validate two novel workflows aimed at predicting peptide binding behaviors in MMC, specifically targeting methacrylate-based resins. Our first achievement was the development of an all-atom model of a commercial MMC resin surface, incorporating its polymethacrylic backbone. Furthermore, we established and tested a workflow for rapid calculations of binding free energies (ΔG) with 10 linear peptides as target molecules. These ΔG calculations were effectively used to predict Langmuir constants, achieving a high coefficient of determination (R²) of 0.96. In subsequent benchmarking tests, our model outperformed established, simpler resin surface models in terms of predictive capabilities.

DP-site: A dual deep learning-based method for protein-peptide interaction site prediction

BackgroundProtein-peptide interaction prediction is an important topic for several applications including various biological processes, understanding drug discovery, protein function abnormal cellular behaviors, and treating diseases. Over the years, studies have shown that experimental methods have improved the identification of this bio-molecular interaction. However, predicting protein-peptide interactions using these methods is laborious, time-consuming, dependent on third-party tools, and costly. MethodTo address these previous drawbacks, this study introduces a computational framework called DP-Site. The proposed framework concentrates on using a compound of a dual pipeline along with a combination predictor. A deep convolutional neural network for feature extraction and classification is embedded in pipeline 1. In addition, pipeline 2 includes a deep long-short-term memory-based and a random forest classifier for feature extraction and classification. In this investigation, the evolutionary, structure-based, sequence-based, and physicochemical information of proteins is utilized for identifying protein-peptide interaction at the residue level. ResultsThe proposed method is evaluated on both the ten-fold cross-validation and independent test sets. The robust and consistent results between cross-validation and independent test sets confirm the ability of the proposed method to predict peptide binding residues in proteins. Moreover, experimental findings demonstrate that DP-Site has significantly outperformed other state-of-the-art sequence-based and structure-based methods. The proposed method achieves a remarkable balance between a specificity of 0.799 and a sensitivity of 0.770, along with the best f-measure of 0.661 and the highest precision of 0.580 using an independent test set. ConclusionsThe outcome of various experiments confirms the proficiency of the proposed method and outperforms state-of-the-art sequence-based and structure-based methods in terms of the mentioned criteria. DP-Site can be accessed at https://github.com/shafiee 95/shima.shafiee.DP-Site.

The electrostatic landscape of MHC-peptide binding revealed using inception networks

Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.

ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model.

Peptide binding to major histocompatibility complex (MHC) proteins plays a critical role in T-cell recognition and the specificity of the immune response. Experimental validation such peptides is extremely resource-intensive. As a result, accurate computational prediction of binding peptides is highly important, particularly in the context of cancer immunotherapy applications, such as the identification of neoantigens. In recent years, there is a significant need to continually improve the existing prediction methods to meet the demands of this field. We developed ConvNeXt-MHC, a method for predicting MHC-I-peptide binding affinity. It introduces a degenerate encoding approach to enhance well-established panspecific methods and integrates transfer learning and semi-supervised learning methods into the cutting-edge deep learning framework ConvNeXt. Comprehensive benchmark results demonstrate that ConvNeXt-MHC outperforms state-of-the-art methods in terms of accuracy. We expect that ConvNeXt-MHC will help us foster new discoveries in the field of immunoinformatics in the distant future. We constructed a user-friendly website at http://www.combio-lezhang.online/predict/, where users can access our data and application.

Genome, HLA and polygenic risk score analyses for prevalent and persistent cervical human papillomavirus (HPV) infections

Genetic variants that underlie susceptibility to cervical high-risk human papillomavirus (hrHPV) infections are largely unknown. We conducted discovery genome-wide association studies (GWAS), replication, meta-analysis and colocalization, generated polygenic risk scores (PRS) and examined the association of classical HLA alleles and cervical hrHPV infections in a cohort of over 10,000 women. We identified genome-wide significant variants for prevalent hrHPV around LDB2 and for persistent hrHPV near TPTE2, SMAD2, and CDH12, which code for proteins that are significantly expressed in the human endocervix. Genetic variants associated with persistent hrHPV are in genes enriched for the antigen processing and presentation gene set. HLA-DRB1*13:02, HLA-DQB1*05:02 and HLA-DRB1*03:01 were associated with increased risk, and HLA-DRB1*15:03 was associated with decreased risk of persistent hrHPV. The analyses of peptide binding predictions showed that HLA-DRB1 alleles that were positively associated with persistent hrHPV showed weaker binding with peptides derived from hrHPV proteins and vice versa. The PRS for persistent hrHPV with the best model fit, had a P-value threshold (PT) of 0.001 and a p-value of 0.06 (-log10(0.06) = 1.22). The findings of this study expand our understanding of genetic risk factors for hrHPV infection and persistence and highlight the roles of MHC class II molecules in hrHPV infection.

Modeling Peptide-Protein Interactions by a Logo-Based Method: Application in Peptide-HLA Binding Predictions.

Peptide-protein interactions form a cornerstone in molecular biology, governing cellular signaling, structure, and enzymatic activities in living organisms. Improving computational models and experimental techniques to describe and predict these interactions remains an ongoing area of research. Here, we present a computational method for peptide-protein interactions' description and prediction based on leveraged amino acid frequencies within specific binding cores. Utilizing normalized frequencies, we construct quantitative matrices (QMs), termed 'logo models' derived from sequence logos. The method was developed to predict peptide binding to HLA-DQ2.5 and HLA-DQ8.1 proteins associated with susceptibility to celiac disease. The models were validated by more than 17,000 peptides demonstrating their efficacy in discriminating between binding and non-binding peptides. The logo method could be applied to diverse peptide-protein interactions, offering a versatile tool for predictive analysis in molecular binding studies.

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.

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features

Protein–peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein–peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git.

HLA variants and TCR diversity against SARS-CoV-2 in the pre-COVID-19 era.

HLA antigen presentation and T-cell mediated immunity are critical to control acute viral infection such as COVID-19 caused by SARS-CoV-2. Recent data suggest that both the depth of peptide presentation and the breadth of the T-cell repertoire are associated with disease outcome. It has also been shown that unexposed subjects can develop strong T-cell responses against SARS-CoV-2 due to heterologous immunity. In this study, we explored the anti-SARS-CoV-2 T-cell repertoire by analyzing previously published T-cell receptor (TCR) CDR3β immunosequencing data in a cohort of 116 healthy donors and in the context of immune reconstitution after allogeneic hematopoietic stem cell transplantation in 116 recipients collected during the pre-COVID-19 era. For this, 143,310 publicly available SARS-CoV-2 specific T-cell sequences were investigated among the 3.5 million clonotypes in the cohort. We also performed HLA class I peptide binding predictions using the reference proteome of the virus and high resolution genotyping data in these patients. We could demonstrate that individuals are fully equipped at the genetic level to recognize SARS-CoV-2. This is evidenced by the 5% median cumulative frequency of clonotypes having their sequence matched to a SARS-CoV-2 specific T-cell. In addition, any combination of HLA class I variants in this cohort is associated with a broad capacity of presenting hundreds of SARS-CoV-2 derived peptides. These results could be explained by heterologous immunity and random somatic TCR recombination. We speculate that these observations could explain the efficacy of the specific immune response against SARS-CoV-2 in individuals without risk factors of immunodeficiency and infected prior to vaccination.

HLA-Clus: HLA class I clustering based on 3D structure

BackgroundIn a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria.ResultsThe HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved.ConclusionsThe HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository (https://github.com/yshen25/HLA-Clus).

Effects of HLA single chain trimer design on peptide presentation and stability.

MHC class I "single-chain trimer" molecules, coupling MHC heavy chain, β2-microglobulin, and a specific peptide into a single polypeptide chain, are widely used in research. To more fully understand caveats associated with this design that may affect its use for basic and translational studies, we evaluated a set of engineered single-chain trimers with combinations of stabilizing mutations across eight different classical and non-classical human class I alleles with 44 different peptides, including a novel human/murine chimeric design. While, overall, single-chain trimers accurately recapitulate native molecules, care was needed in selecting designs for studying peptides longer or shorter than 9-mers, as single-chain trimer design could affect peptide conformation. In the process, we observed that predictions of peptide binding were often discordant with experiment and that yields and stabilities varied widely with construct design. We also developed novel reagents to improve the crystallizability of these proteins and confirmed novel modes of peptide presentation.

STMHCpan, an accurate Star-Transformer-based extensible framework for predicting MHC I allele binding peptides.

Peptide-major histocompatibility complex I (MHC I) binding affinity prediction is crucial for vaccine development, but existing methods face limitations such as small datasets, model overfitting due to excessive parameters and suboptimal performance. Here, we present STMHCPan (STAR-MHCPan), an open-source package based on the Star-Transformer model, for MHC I binding peptide prediction. Our approach introduces an attention mechanism to improve the deep learning network architecture and performance in antigen prediction. Compared with classical deep learning algorithms, STMHCPan exhibits improved performance with fewer parameters in receptor affinity training. Furthermore, STMHCPan outperforms existing ligand benchmark datasets identified by mass spectrometry. It can also handle peptides of arbitrary length and is highly scalable for predicting T-cell responses. Our software is freely available for use, training and extension through Github (https://github.com/Luckysoutheast/STMHCPan.git).

A comprehensive assessment and comparison of tools for HLA class I peptide-binding prediction.

Human leukocyte antigen class I (HLA-I) molecules bind intracellular peptides produced by protein hydrolysis and present them to the T cells for immune recognition and response. Prediction of peptides that bind HLA-I molecules is very important in immunotherapy. A growing number of computational predictors have been developed in recent years. We survey a comprehensive collection of 27 tools focusing on their input and output data characteristics, key aspects of the underlying predictive models and their availability. Moreover, we evaluate predictive performance for eight representative predictors. We consider a wide spectrum of relevant aspects including allele-specific analysis, influence of negative to positive data ratios and runtime. We also curate high-quality benchmark datasets based on analysis of the consistency of the data labels. Results reveal that each considered method provides accurate results, which can be explained by our analysis that finds that their predictive models capture meaningful binding motifs. Although some methods are overall more accurate than others, we find that none of them is universally superior. We provide a comprehensive comparison of the convenience as well as the accuracy of the methods under specific prediction scenarios, such as for specific alleles, metrics of predictive performance and constraints on runtime. Our systematic and broad analysis provides informative clues to the users to identify the most suitable tools for a given prediction scenario and for the developers to design future methods.

Peptide-binding specificity prediction using fine-tuned protein structure prediction networks

Peptide-binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence-only models and should be particularly powerful for systems where less experimental data are available.

Consensus Algorithm for Calculation of Protein Binding Affinity using Multiple Models

The major histocompatibility complex (MHC) molecules, which bind peptides for presentation on the cell surface, play an important role in cell-mediated immunity. In light of developing databases and technologies over the years, significant progress has been made in research on peptide binding affinity calculation. Several in techniques have been developed to predict peptide binding to MHC class I. Most of the research on MHC Class I due to its nature brings better performance and more. Considering the use of different methods and different technologies, and the approach of similar methods on different proteins, a classification was created according to the binding affinity of protein peptides. For this classification, MHC Class I was studied using the MHCflurry, NetMHCPan, NetMHC, NetMHCCons and ssmpmbec. In these simulations conducted within the scope of this thesis, no overall superiority was observed between the models. It has been determined that they are superior to each other in various points. Getting the best results may vary depending on the multiple uses of models. The important thing is to recognize the data and act with the appropriate model. But even that doesn’t make a huge difference. Since the consensus approach is directly related to the models, the better the models, the better. Xix

High-throughput, targeted MHC class I immunopeptidomics using a functional genetics screening platform.

Identification of CD8+ T cell epitopes is critical for the development of immunotherapeutics. Existing methods for major histocompatibility complex class I (MHC class I) ligand discovery are time intensive, specialized and unable to interrogate specific proteins on a large scale. Here, we present EpiScan, which uses surface MHC class I levels as a readout for whether a genetically encoded peptide is an MHC class I ligand. Predetermined starting pools composed of >100,000 peptides can be designed using oligonucleotide synthesis, permitting large-scale MHC class I screening. We exploit this programmability of EpiScan to uncover an unappreciated role for cysteine that increases the number of predicted ligands by 9-21%, reveal affinity hierarchies by analysis of biased anchor peptide libraries and screen viral proteomes for MHC class I ligands. Using these data, we generate and iteratively refine peptide binding predictions to create EpiScan Predictor. EpiScan Predictor performs comparably to other state-of-the-art MHC class I peptide binding prediction algorithms without suffering from underrepresentation of cysteine-containing peptides. Thus, targeted immunopeptidomics using EpiScan will accelerate CD8+ T cell epitope discovery toward the goal of individual-specific immunotherapeutics.

Docking-Based Prediction of Peptide Binding to MHC Proteins.

Major histocompatibility complex (MHC) proteins are the most polymorphic and polygenic proteins in humans. They bind peptides, derived from cleavage of different pathogenic antigens, and are responsible for presenting them to T cells. The peptides recognized by the T cell receptors are denoted as epitopes and they trigger an immune response.In this chapter, we describe a docking protocol for predicting the peptide binding to a given MHC protein using the software tool GOLD. The protocol starts with the construction of a combinatorial peptide library used in the docking and ends with the derivation of a quantitative matrix (QM) accounting for the contribution of each amino acid at each peptide position.

Identification of HLA-E Binding Mycobacterium tuberculosis-Derived Epitopes through Improved Prediction Models.

Tuberculosis (TB) remains one of the deadliest infectious diseases worldwide, posing great social and economic burden to affected countries. Novel vaccine approaches are needed to increase protective immunity against the causative agent Mycobacterium tuberculosis (Mtb) and to reduce the development of active TB disease in latently infected individuals. Donor-unrestricted T cell responses represent such novel potential vaccine targets. HLA-E-restricted T cell responses have been shown to play an important role in protection against TB and other infections, and recent studies have demonstrated that these cells can be primed in vitro. However, the identification of novel pathogen-derived HLA-E binding peptides presented by infected target cells has been limited by the lack of accurate prediction algorithms for HLA-E binding. In this study, we developed an improved HLA-E binding peptide prediction algorithm and implemented it to identify (to our knowledge) novel Mtb-derived peptides with capacity to induce CD8+ T cell activation and that were recognized by specific HLA-E-restricted T cells in Mycobacterium-exposed humans. Altogether, we present a novel algorithm for the identification of pathogen- or self-derived HLA-E-presented peptides.