In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure-activity relationship approach

Virtual-screening of xanthine oxidase inhibitory peptides: Inhibition mechanisms and prediction of activity using machine-learning

Xanthine oxidase (XO) inhibitory peptides can prevent XO-mediated hyperuricemia. Currently, QSAR about XO inhibitory peptides with different lengths remains to be enriched. Here, XO inhibitory peptides were obtained from porcine visceral proteins through virtual-screening. A prediction model was established by machine-learning. Virtual-screening retained four lengths of peptides, including 3-6. Molecular-docking recognized their binding sites with XO and showed residues W, F, and G were the key amino acids. Datasets of XO inhibitory peptides therewith were established. The optimal model was used to generalize the peptides reported. Results showed that the R2 of the tripeptide, tetrapeptide, pentapeptide and hexapeptide in the generalisation test were R2 = 0.81, R2 = 0.82, R2 = 0.83 and R2 = 0.83, respectively. Overall, this work can serve as a reference for explaining the activity mechanism of XO inhibitory peptides and predicting the activity of XO inhibitory peptides.

CoDIAC: A comprehensive approach for interaction analysis reveals novel insights into SH2 domain function and regulation

Protein domains are conserved structural and functional units and are the functional building blocks of proteins. Evolutionary expansion means that domain families are often represented by many members in a species, which are found in various configurations with other domains, which have evolved new specificity for interacting partners. Here, we develop a structure-based interface analysis to comprehensively map domain interfaces from available experimental and predicted structures, including interfaces with other macromolecules and intraprotein interfaces (such as might exist between domains in a protein). We hypothesized that a comprehensive approach to contact mapping of domains could yield new insights. Specifically, we use it to gain information about how domains selectivity interact with ligands, whether domain-domain interfaces of repeated domain partnerships are conserved across diverse proteins, and identify regions of conserved post-translational modifications, using relationship to interaction interfaces as a method to hypothesize the effect of post-translational modifications (and mutations). We applied this approach to the human SH2 domain family, an extensive modular unit that is the foundation of phosphotyrosine-mediated signaling, where we identified a novel approach to understanding the binding selectivity of SH2 domains and evidence that there is coordinated and conserved regulation of multiple SH2 domain binding interfaces by tyrosine and serine/threonine phosphorylation and acetylation, suggesting that multiple signaling systems can regulate protein activity and SH2 domain interactions in a regulated manner. We provide the extensive features of the human SH2 domain family and this modular approach, as an open source Python package for COmprehensive Domain Interface Analysis of Contacts (CoDIAC).

Musical protein: Mapping the time sequence of music onto the spatial architecture of proteins

BACKGROUND AND OBJECTIVE

Music, the ubiquitous language across human cultures, is traditionally considered as a form of art but has been linked to biomolecules in recent years. However, previous efforts have only been addressed on sonification of nucleic acids and proteins to produce so-called life music, the soundscape from the basic building blocks of life. In this study, we attempted to, for the first time, conduct a reverse operation of this process, i.e. conversion of music to protein (CoMtP).

METHODS

A novel notion termed musical protein (MP) -- the protein defined by music -- was proposed and, on this basis, we described a computational strategy to map the time sequence of music onto the spatial architecture of proteins, which considered that each note in the stave of a music (target) can be simply characterized by two acoustical quantities and that each residue in the primary sequence of a protein (hit) was represented by amino acid descriptors.

RESULTS

A simulated annealing (SA) algorithm was applied to iteratively generate the best matched MP hit for a music target and structural bioinformatics was then used to model spatial advanced structure for the resulting MP. We also demonstrated that some small MPs derived from music segments may have potential biological functions, which, for example, can serve as antimicrobial peptides (AMPs) to inhibit clinical bacterial strains with moderate or high antibacterial potency.

CONCLUSIONS

This work may benefit many aspects; for example, it would open a door for the hearing-impaired persons to 'listen' music in a biological vision and could be a mean of exposing students to the concepts of biomolecules at an earlier age through the use of auditory characteristics. The CoMtP would also facilitate the rational design of proteins with biological and medicinal significance.

Machine learning methods for unveiling the potential of antioxidant short peptides in goat milk-derived proteins during in vitro gastrointestinal digestion

Milk serves as an important dietary source of bioactive peptides, offering notable benefits to individuals. Among the antioxidant short peptides (di- and tripeptides) generated from gastrointestinal digestion are characterized by enhanced bioavailability and bioaccessibility, while assessing them individually presents a labor-intensive and expensive challenge. Based on 4 distinct types of amino acid descriptors (physicochemical, 3D structural, quantum, and topological attributes) and genetic algorithms for feature selection, 1 and 4 machine learning predicted models separately for di- and tripeptides with ABTS radical scavenging capacity exhibited excellent fitting and prediction ability with random forest regression as machine learning algorithm. Intriguingly, the electronic properties of N-terminal amino acid were considered as only factor affecting the antioxidant capacity of dipeptides containing both tyrosine and tryptophan. Four peptides from the potential di- and tripeptides exhibited highly predicted values by the constructed predicted models. Subsequently, a total of 45 dipeptides and 52 tripeptides were screened by a customized workflow in goat milk during in vitro simulated digestion. In addition to 5 known antioxidant dipeptides, 9 peptides were quantified during digestion, falling within the range of 0.04 to 1.78 mg L-1. Particularly noteworthy was the promising in vivo functionality of antioxidant dipeptides with N-terminal tyrosine, supported by in silico assays. Overall, this investigation explored crucial molecular properties influencing antioxidant short peptides and high-throughput screening potential peptides with antioxidant activity from goat milk aided by machine learning, thereby facilitating the identification of novel bioactive peptides from milk-derived proteins and paving the way for understanding their metabolites during digestion.

Collagen derived Gly-Pro-type DPP-IV inhibitory peptides: Structure-activity relationship, inhibition kinetics and inhibition mechanism

Our previous study has demonstrated that both the amino acid at N3 position and peptide length affected the DPP-IV inhibitory activity of Gly-Pro-type peptides. To further elucidate their molecular mechanism, a combined approach of QSAR modeling, enzymatic kinetics and molecular docking was used. Results showed that the QSAR models of Gly-Pro-type tripeptides and Gly-Pro-type peptides containing 3-12 residues were successfully constructed by 5z-scale descriptor with R2 of 0.830 and 0.797, respectively. The lower values of electrophilicity, polarity, and side-chain bulk of amino acid at N3 position caused higher DPP-IV inhibitory activity of Gly-Pro-type peptides. Moreover, an appropriate increase in the length of Gly-Pro-type peptides did not change their competitive inhibition mode, but decreased their inhibition constants (Ki values) and increased interactions with DPP-IV. More importantly, the interactions between the residues at C-terminal of Gly-Pro-type peptides containing 5 ∼ 6 residues with S2 extensive subsites (Ser209, Phe357, Arg358) of DPP-IV increased the interactions of Gly residue at N1 position with the S2 subsites (Glu205, Glu206, Asn710, Arg125, Tyr662) and decreased the acylation level of DPP-IV-peptide complex, and thereby increasing peptides' DPP-IV inhibitory activity.

Bioaccessibility of novel antihypertensive short-chain peptides in goat milk using the INFOGEST static digestion model by effect-directed assays

Short-chain peptides (SCPs, 2-4 amino acids) offer potential health benefits. A customized workflow was designed to screen SCPs in goat milk during INFOGEST digestion in vitro and 186 SCPs were preliminarily identified. Based on a two-terminal position numbering method and genetic algorithm combined with a support vector machine, 22 SCPs with predicted IC50 values less than 10 μM were obtained using a quantitative structure-activity relationship (QSAR) model with satisfactory fitting and predictive capacity (R2, RMSE, Q2, and R2pre of 0.93, 0.27, 0.71, and 0.65, respectively). Four novel antihypertensive SCPs were confirmed by testing in vitro and molecular docking analysis, and their quantification results (0.06 to 1.53 mg L-1) suggested distinct metabolic fates. This study facilitated the discovery of unknown potential food-derived antihypertensive peptides and the understanding of bioaccessible peptides during digestion.

Virtual screening, identification, and potential antioxidant mechanism of novel bioactive peptides during aging by a short-chain peptidomics, quantitative structure-activity relationship analysis, and molecular docking

Antioxidant peptides have received a great deal of attention. However, only a few studies have been conducted on the antioxidant peptides originating from Baijiu. A total of 1490 features deemed potential short-chain peptides (the amino acid number between 2 and 4, SCPs) were screened and analyzed by a customized short-chain peptidomics approach in Feng-flavor Baijiu (FFB) during 14 years of aging, with an obvious discrepancy between FFB aged for 3 years and 6 years being observed. Thirty-nine characteristic SCPs in total were identified and accurately quantified by high-throughput parallel reaction monitoring-based synthetic standards, with the contents ranging from 0.16 to 279.33 μg L-1. Combined with the absorption, distribution, metabolism, excretion, and toxicity analysis model, PGRW, WK, SC, and PAW, four novel antioxidant peptides with high ABTS radical scavenging capacity, were obtained using a customized quantitative structure-activity relationship (QSAR) model based on a two terminal position numbering method, with satisfied coefficients of determination (R2), internal cross-validated R2 (Q2), and external R2 (R2pre) of 0.925, 0.808, and 0.665, respectively. Furthermore, these 4 antioxidant peptides could block the Keap-Nrf2 interaction and promote the accumulation of Nrf2 by molecular docking analysis, and the interaction energy between peptide PGRW and Keap1 was higher than that between epigallocatechin gallate and Keap1 based on CHARMm forced field. Overall, this study facilitated the discovery of functional peptides in Baijiu and the understanding of aging mechanisms.

PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships

Peptide quantitative structure-activity relationships (pQSARs) have been widely applied to the statistical modeling and empirical prediction of peptide activity, property and feature. In the procedure, the peptide structure is characterized at sequence level using amino acid descriptors (AADs) and then correlated with observations by machine learning methods (MLMs), consequently resulting in a variety of quantitative regression models used to explain the structural factors that govern peptide activities, to generalize peptide properties of unknown from known samples, and to design new peptides with desired features. In this study, we developed a comprehensive platform, termed PepQSAR database, which is a systematic collection and decomposition of various data sources and abundant information regarding the pQSARs, including AADs, MLMs, data sets, peptide sequences, measured activities, model statistics, and literatures. The database also provides a comparison function for the various previously built pQSAR models reported by different groups via distinct approaches. The structured and searchable PepQSAR database is expected to provide a useful resource and powerful tool for the computational peptidology community, which is freely available at http://i.uestc.edu.cn/PQsarDB .

Quantitative structure-activity relationship modeling reveals the minimal sequence requirement and amino acid preference of sirtuin-1's deacetylation substrates in diabetes mellitus

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide (NAD[Formula: see text]-dependent deacetylase involved in multiple glucose metabolism pathways and plays an important role in the pathogenesis of diabetes mellitus (DM). The enzyme specifically recognizes its deacetylation substrates' peptide segments containing a central acetyl-lysine residue as well as a number of amino acids flanking the central residue. In this study, we attempted to ascertain the minimal sequence requirement (MSR) around the central acetyl-lysine residue of SIRT1 substrate-recognition sites as well as the amino acid preference (AAP) at different residues of the MSR window through quantitative structure-activity relationship (QSAR) strategy, which would benefit our understanding of SIRT1 substrate specificity at the molecular level and is also helpful to rationally design substrate-mimicking peptidic agents against DM by competitively targeting SIRT1 active site. In this procedure, a large-scale dataset containing 6801 13-mer acetyl-lysine peptides (and their SIRT1-catalyized deacetylation activities) were compiled to train 10 QSAR regression models developed by systematic combination of machine learning methods (PLS and SVM) and five amino acids descriptors (DPPS, T-scale, MolSurf, [Formula: see text]-score, and FASGAI). The two best QSAR models (PLS+FASGAI and SVM+DPPS) were then employed to statistically examine the contribution of residue positions to the deacetylation activity of acetyl-lysine peptide substrates, revealing that the MSR can be represented by 5-mer acetyl-lysine peptides that meet a consensus motif X[Formula: see text]X[Formula: see text]X[Formula: see text](AcK)₀X[Formula: see text]. Structural analysis found that the X[Formula: see text] and (AcK)₀ residues are tightly packed against the enzyme active site and confer both stability and specificity for the enzyme-substrate complex, whereas the X[Formula: see text], X[Formula: see text] and X[Formula: see text] residues are partially exposed to solvent but can also effectively stabilize the complex system. Subsequently, a systematic deacetylation activity change profile (SDACP) was created based on QSAR modeling, from which the AAP for each residue position of MSR was depicted. With the profile, we were able to rationally design an SDACP combinatorial library with promising deacetylation activity, from which nine MSR acetyl-lysine peptides as well as two known SIRT1 acetyl-lysine peptide substrates were tested by using SIRT1 deacetylation assay. It is revealed that the designed peptides exhibit a comparable or even higher activity than the controls, although the former is considerably shorter than the latter.

Structure-guided discovery of antioxidant peptides bounded to the Keap1 receptor as hunter for potential dietary antioxidants

Human health can be damaged by free radicals, and antioxidant peptides are excellent radical scavengers. Antioxidant tripeptides data set based on 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulofnic acid) (ABTS) assay was created, 9 types of descriptors were integrated and 4 quantitative structure-activity relationship (QSAR) models were constructed in this study. Several structural factors influencing the activity of antioxidant tripeptides and the dominant amino acids at each position of tripeptides were revealed by the optimal model. Ten food-derived tripeptides with higher activity were selected for synthesis and activity determination. Molecular docking results demonstrated that these tripeptides were stably bound to the Keap1 receptor, further elucidating the antioxidant mechanism. It was known from the simulation of gastrointestinal digestion experiments that the model results possessed a guiding effect on the selection of proteins with high antioxidant activity. The performance of the model was proved to be robust after validation.

New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data

Protein domain interactions with short linear peptides, such as those of the Src homology 2 (SH2) domain with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and quantify these interactions has resulted in the development of high-throughput (HTP) quantitative measurement techniques, such as microarray or fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to covering 500,000 of the 5.5 million possible SH2-pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published data sets led us here to reevaluate the analysis methods and raw data of published SH2-pTyr HTP experiments. We identified several opportunities for improving the identification of positive and negative interactions and the accuracy of affinity measurements. We implemented model-fitting techniques that are more statistically appropriate for the nonlinear SH2-pTyr interaction data. We also developed a method to account for protein concentration errors due to impurities and degradation or protein inactivity and aggregation. Our revised analysis increases the reported affinity accuracy, reduces the false-negative rate, and increases the amount of useful data by adding reliable true-negative results. We demonstrate improvement in classification of binding versus nonbinding when using machine-learning techniques, suggesting improved coherence in the reanalyzed data sets. We present revised SH2-pTyr affinity results and propose a new analysis pipeline for future HTP measurements of domain-peptide interactions.

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

Quantitative Structure-Activity Relationship Study of Bitter Di-, Tri- and Tetrapeptides Using Integrated Descriptors

New quantitative structure–activity relationship (QSAR) models for bitter peptides were built with integrated amino acid descriptors. Datasets contained 48 dipeptides, 52 tripeptides and 23 tetrapeptides with their reported bitter taste thresholds. Independent variables consisted of 14 amino acid descriptor sets. A bootstrapping soft shrinkage approach was utilized for variable selection. The importance of a variable was evaluated by both variable selecting frequency and standardized regression coefficient. Results indicated model qualities for di-, tri- and tetrapeptides with R² and Q² at 0.950 ± 0.002, 0.941 ± 0.001; 0.770 ± 0.006, 0.742 ± 0.004; and 0.972 ± 0.002, 0.956 ± 0.002, respectively. The hydrophobic C-terminal amino acid was the key determinant for bitterness in dipeptides, followed by the contribution of bulky hydrophobic N-terminal amino acids. For tripeptides, hydrophobicity of C-terminal amino acids and the electronic properties of the amino acids at the second position were important. For tetrapeptides, bulky hydrophobic amino acids at N-terminus, hydrophobicity and partial specific volume of amino acids at the second position, and the electronic properties of amino acids of the remaining two positions were critical. In summary, this study not only constructs reliable models for predicting the bitterness in different groups of peptides, but also facilitates better understanding of their structure-bitterness relationships and provides insights for their future studies.

Protein-Catalyzed Capture Agents

Protein-catalyzed capture agents (PCCs) are synthetic and modular peptide-based affinity agents that are developed through the use of single-generation in situ click chemistry screens against large peptide libraries. In such screens, the target protein, or a synthetic epitope fragment of that protein, provides a template for selectively promoting the noncopper catalyzed azide-alkyne dipolar cycloaddition click reaction between either a library peptide and a known ligand or a library peptide and the synthetic epitope. The development of epitope-targeted PCCs was motivated by the desire to fully generalize pioneering work from the Sharpless and Finn groups in which in situ click screens were used to develop potent, divalent enzymatic inhibitors. In fact, a large degree of generality has now been achieved. Various PCCs have demonstrated utility for selective protein detection, as allosteric or direct inhibitors, as modulators of protein folding, and as tools for in vivo tumor imaging. We provide a historical context for PCCs and place them within the broader scope of biological and synthetic aptamers. The development of PCCs is presented as (i) Generation I PCCs, which are branched ligands engineered through an iterative, nonepitope-targeted process, and (ii) Generation II PCCs, which are typically developed from macrocyclic peptide libraries and are precisely epitope-targeted. We provide statistical comparisons of Generation II PCCs relative to monoclonal antibodies in which the protein target is the same. Finally, we discuss current challenges and future opportunities of PCCs.

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

Due to their beneficial effects on human health, antioxidant peptides have attracted much attention from researchers. However, the structure-activity relationships of antioxidant peptides have not been fully understood. In this paper, quantitative structure-activity relationships (QSAR) models were built on two datasets, i.e., the ferric thiocyanate (FTC) dataset and ferric-reducing antioxidant power (FRAP) dataset, containing 214 and 172 unique antioxidant tripeptides, respectively. Sixteen amino acid descriptors were used and model population analysis (MPA) was then applied to improve the QSAR models for better prediction performance. The results showed that, by applying MPA, the cross-validated coefficient of determination (Q²) was increased from 0.6170 to 0.7471 for the FTC dataset and from 0.4878 to 0.6088 for the FRAP dataset, respectively. These findings indicate that the integration of different amino acid descriptors provide additional information for model building and MPA can efficiently extract the information for better prediction performance.

Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides

There are emerging advancements in the strategies used for the discovery and development of food-derived bioactive peptides because of their multiple food and health applications. Bioinformatics and peptidomics are two computational and analytical techniques that have the potential to speed up the development of bioactive peptides from bench to market. Structure-activity relationships observed in peptides form the basis for bioinformatics and in silico prediction of bioactive sequences encrypted in food proteins. Peptidomics, on the other hand, relies on "hyphenated" (liquid chromatography-mass spectrometry-based) techniques for the detection, profiling, and quantitation of peptides. Together, bioinformatics and peptidomics approaches provide a low-cost and effective means of predicting, profiling, and screening bioactive protein hydrolysates and peptides from food. This article discuses the basis, strengths, and limitations of bioinformatics and peptidomics approaches currently used for the discovery and analysis of food-derived bioactive peptides.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

New Quantitative Structure-Activity Relationship Model for Angiotensin-Converting Enzyme Inhibitory Dipeptides Based on Integrated Descriptors

Angiotensin-converting enzyme (ACE) inhibitory peptides derived from food proteins have been widely reported for hypertension treatment. In this paper, a benchmark data set containing 141 unique ACE inhibitory dipeptides was constructed through database mining, and a quantitative structure-activity relationships (QSAR) study was carried out to predict half-inhibitory concentration (IC₅₀) of ACE activity. Sixteen descriptors were tested and the model generated by G-scale descriptor showed the best predictive performance with the coefficient of determination (R²) and cross-validated R² (Q²) of 0.6692 and 0.6220, respectively. For most other descriptors, R² were ranging from 0.52 to 0.68 and Q² were ranging from 0.48 to 0.61. A complex model combining all 16 descriptors was carried out and variable selection was performed in order to further improve the prediction performance. The quality of model using integrated descriptors (R² 0.7340 ± 0.0038, Q² 0.7151 ± 0.0019) was better than that of G-scale. An in-depth study of variable importance showed that the most correlated properties to ACE inhibitory activity were hydrophobicity, steric, and electronic properties and C-terminal amino acids contribute more than N-terminal amino acids. Five novel predicted ACE-inhibitory peptides were synthesized, and their IC₅₀ values were validated through in vitro experiments. The results indicated that the constructed model could give a reliable prediction of ACE-inhibitory activity of peptides, and it may be useful in the design of novel ACE-inhibitory peptides.

A computational method for prediction of matrix proteins in endogenous retroviruses

Human endogenous retroviruses (HERVs) encode active retroviral proteins, which may be involved in the progression of cancer and other diseases. Matrix protein (MA), in group-specific antigen genes (gag) of retroviruses, is associated with the virus envelope glycoproteins in most mammalian retroviruses and may be involved in virus particle assembly, transport and budding. However, the amount of annotated MAs in ERVs is still at a low level so far. No computational method to predict the exact start and end coordinates of MAs in gags has been proposed yet. In this paper, a computational method to identify MAs in ERVs is proposed. A divide and conquer technique was designed and applied to the conventional prediction model to acquire better results when dealing with gene sequences with various lengths. Initiation sites and termination sites were predicted separately and then combined according to their intervals. Three different algorithms were applied and compared: weighted support vector machine (WSVM), weighted extreme learning machine (WELM) and random forest (RF). G − mean (geometric mean of sensitivity and specificity) values of initiation sites and termination sites under 5-fold cross validation generated by random forest models are 0.9869 and 0.9755 respectively, highest among the algorithms applied. Our prediction models combine RF & WSVM algorithms to achieve the best prediction results. 98.4% of all the collected ERV sequences with complete MAs (125 in total) could be predicted exactly correct by the models. 94,671 HERV sequences from 118 families were scanned by the model, 104 new putative MAs were predicted in human chromosomes. Distributions of the putative MAs and optimizations of model parameters were also analyzed. The usage of our predicting method was also expanded to other retroviruses and satisfying results were acquired.

A computational model for predicting transmembrane regions of retroviruses

Transmembrane region (TR) is a conserved region of transmembrane (TM) subunit in envelope (env) glycoprotein of retrovirus. Evidences have shown that TR is responsible for anchoring the env glycoprotein on the lipid bilayer and substitution of the TR for a covalently linked lipid anchor abrogates fusion. However, universal software could not achieve sufficient accuracy as TM in env also has several motifs such as signal peptide, fusion peptide and immunosuppressive domain composed largely of hydrophobic residues. In this paper, a support vector machine-based (SVM) model is proposed to identify TRs in retroviruses. Firstly, physicochemical and evolutionary information properties were extracted as original features. And then, the feature importance was analyzed by minimum Redundancy Maximum Relevance (mRMR) feature selection criterion. Our model achieved an Sn of 0.955, Sp of 0.998, ACC of 0.995, MCC of 0.954 using 10-fold cross-validation on the training dataset. These results suggest that the proposed model can be used to predict TRs in non-annotation retroviruses and 11917, 3344, 2, 289 and 6 new putative TRs were found in HERV, HIV, HTLV, SIV, MLV, respectively.

SVM and SVR-based MHC-binding prediction using a mathematical presentation of peptide sequences

At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliability of prevailing methods. For this reason, the development of models with high accuracy are crucial. An accurate prediction of the peptides that bind to specific major histocompatibility complex class I and II (MHC-I and MHC-II) molecules is important for an understanding of the functioning of the immune system and the development of peptide-based vaccines. Peptide binding is the most selective step in identifying T-cell epitopes. In this paper, we present a new approach to predicting MHC-binding ligands that takes into account new weighting schemes for position-based amino acid frequencies, BLOSUM and VOGG substitution of amino acids, and the physicochemical and molecular properties of amino acids. We have made models for quantitatively and qualitatively predicting MHC-binding ligands. Our models are based on two machine learning methods support vector machine (SVM) and support vector regression (SVR), where our models have used for feature selection, several different encoding and weighting schemes for peptides. The resulting models showed comparable, and in some cases better, performance than the best existing predictors. The obtained results indicate that the physicochemical and molecular properties of amino acids (AA) contribute significantly to the peptide-binding affinity.

Molecular mechanism of the susceptibility difference between HLA-B*27:02/04/05 and HLA-B*27:06/09 to ankylosing spondylitis: substitution analysis, MD simulation, QSAR modelling, and in vitro assay

The human leukocyte antigen HLA-B27 is directly involved in the disease pathogenesis of ankylosing spondylitis (AS). HLA-B27 has a high degree of genetic polymorphism, with 105 currently known subtypes; the presence of aspartic acid at residue 116 (Asp116) has been found to play an essential role in AS susceptibility. Here, we systematically investigated the molecular mechanism of the susceptibility difference between the AS-associated subtypes HLA-B*27:02/04/05 and AS-unassociated subtypes HLA-B*27:06/09 to AS at sequence, structure, energetic and dynamic levels. In total seven variable residues were identified among the five studied HLA-B27 subtypes, in which Asp116 can be largely stabilized by a spatially vicinal, positively charged His114 through a salt bridge, while five other variable residues seem to have only a marginal effect on AS susceptibility. We also employed a quantitative structure-activity relationship approach to model the statistical correlation between peptide structure and affinity to HLA-B*27:05, a genetic ancestor of all other HLA-B27 subtypes and associated strongly with AS. The built regression predictor was verified rigorously through both internal cross-validation and external blind validation, and was then employed to identify potential HLA-B*27:05 binders from >20,000 cartilage-derived self-peptides. Subsequently, the binding potency of the top five antigenic peptides to HLA-B*27:05 was assayed in vitro using a FACS-based MHC stabilization experiment. Consequently, two (QRVGSDEFK and LRGAGTNEK) out of the five peptides were determined to have high affinity (BL50 = 5.5 and 15.8 nM, respectively) and, as expected, both of them possess positively charged Lys at the C-terminus.

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

QSAR studies may help to better understand structural requirements for peptide bioactivity and therefore to develop potent BAPs.

Accurate prediction of major histocompatibility complex class II epitopes by sparse representation via ℓ 1-minimization

BACKGROUND

The major histocompatibility complex (MHC) is responsible for presenting antigens (epitopes) on the surface of antigen-presenting cells (APCs). When pathogen-derived epitopes are presented by MHC class II on an APC surface, T cells may be able to trigger an specific immune response. Prediction of MHC-II epitopes is particularly challenging because the open binding cleft of the MHC-II molecule allows epitopes to bind beyond the peptide binding groove; therefore, the molecule is capable of accommodating peptides of variable length. Among the methods proposed to predict MHC-II epitopes, artificial neural networks (ANNs) and support vector machines (SVMs) are the most effective methods. We propose a novel classification algorithm to predict MHC-II called sparse representation via ℓ 1-minimization.

RESULTS

We obtained a collection of experimentally confirmed MHC-II epitopes from the Immune Epitope Database and Analysis Resource (IEDB) and applied our ℓ 1-minimization algorithm. To benchmark the performance of our proposed algorithm, we compared our predictions against a SVM classifier. We measured sensitivity, specificity abd accuracy; then we used Receiver Operating Characteristic (ROC) analysis to evaluate the performance of our method. The prediction performance of MHC-II epitopes of the ℓ 1-minimization algorithm was generally comparable and, in some cases, superior to the standard SVM classification method and overcame the lack of robustness of other methods with respect to outliers. While our method consistently favoured DPPS encoding with the alleles tested, SVM showed a slightly better accuracy when "11-factor" encoding was used.

CONCLUSIONS

ℓ 1-minimization has similar accuracy than SVM, and has additional advantages, such as overcoming the lack of robustness with respect to outliers. With ℓ 1-minimization no model selection dependency is involved.

EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information

Background

Epitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms.

Results

We have developed a novel epitope prediction method named EpicCapo and its variants, EpicCapo⁺ and EpicCapo^+REF. Nonapeptides were encoded numerically using a novel peptide-encoding scheme for machine learning algorithms by utilizing 40 amino acid pairwise contact potentials (referred to as AAPPs throughout this paper). The predictive performances of EpicCapo⁺ and EpicCapo^+REF outperformed other state-of-the-art methods without losing interpretability. Interestingly, the most informative AAPPs estimated by our study were those developed by Micheletti and Simons while previous studies utilized two AAPPs developed by Miyazawa & Jernigan and Betancourt & Thirumalai. In addition, we found that all amino acid positions in nonapeptides could effect on performances of the predictive models including non-anchor positions. Finally, EpicCapo^+REF was applied to identify candidates of promiscuous epitopes. As a result, 67.1% of the predicted nonapeptides epitopes were consistent with preceding studies based on immunological experiments.

Conclusions

Our method achieved high performance in testing with benchmark datasets. In addition, our study identified a number of candidates of promiscuous CTL epitopes consistent with previously reported immunological experiments. We speculate that our techniques may be useful in the development of new vaccines. The R implementation of EpicCapo^+REF is available at http://pirun.ku.ac.th/~fsciiok/EpicCapoREF.zip. Datasets are available at http://pirun.ku.ac.th/~fsciiok/Datasets.zip.