New descriptors of amino acids and their application to peptide QSAR study

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.

Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). When we simplified the prediction problem to a binary classification, that is, whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with a 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/.

Preparation, identification, activity prediction, and protective effects on IR-HepG2 cells of five novel DPP-IV inhibitory peptides from protein hydrolysate of skipjack tuna dark muscles

To produce peptides with high dipeptidyl peptidase IV (DPP-IV) inhibitory activity, neutrase was selected from five proteases (trypsin, neutrase, pepsin, alcalase and flavor protease) with the highest degree of hydrolysis (DH) (18.23 ± 1.08%) and DPP-IV inhibitory rate (53.35 ± 4.02%) to produce protein hydrolysate (NPH) from the dark muscles of skipjack tuna (Katsuwonus pelamis). Then, NPH-1 was isolated from NPH by gel permeation chromatography and found to possess the highest DPP-IV inhibitory rate (65.12 ± 7.94% at 0.5 mg ml-1) in the separated components (including NPH-1, NPH-2, NPH-3 and NPH-4). Subsequently, the available prediction models of tripeptides and tetrapeptides with the DPP-IV inhibitory rate were established using an artificial neural network (ANN). The RMSE (0.56 and 0.33 for the model established through collected tripeptides and tetrapeptides, respectively) and R2 (0.95 and 0.99 for the model established through collected tripeptides and tetrapeptides, respectively) of the ANN model's parameters were within acceptable limits, indicating that this model is available. Next, the ANN model was applied to predict tripeptides and tetrapeptides from the hydrolysate of skipjack tuna dark muscles, and five peptides (Ala-Pro-Pro (APP), Pro-Pro-Pro (PPP), Asp-Pro-Leu-Leu (DPLL), Glu-Ala-Val-Pro (EAVP) and Glu-Ala-Iie-Pro (EAIP)) possessing a noticeable DPP-IV inhibitory rate (with DPP-IV IC50 values of 42.46 ± 5.02, 37.71 ± 9.17, 58.85 ± 14.42, 49.94 ± 6.69 and 57.15 ± 6.13 μM, respectively) were screened from the protein hydrolysate. The above five peptides were proved to effectively promote glucose consumption in the insulin resistant-HepG2 (IR-HepG2) cell model considering that the glucose consumption rates of APP, PPP, DPLL, EAVP and EAIP treatment groups are all more than twice that of the dexamethasone group. Accordingly, mechanistic studies showed that these peptides interacted with PI3K/AKT and AMPK signaling pathways and promoted the phosphorylation of PI3K p110, AKT and AMPK (the protein expressions of PI3K p110, p-AKT and p-AMPK in APP, PPP, DPLL, EAVP and EAIP treatment groups are 1.64-2.22 fold compared with that in the dexamethasone group), thereby enhancing glucose uptake and further alleviating insulin resistance. These findings demonstrated that skipjack tuna dark muscle is a potential DPP-IV inhibitory peptide source, and five DPP-IV inhibitory peptides from its hydrolysate may exert potent anti-diabetic activity. In comparison, PPP may be the most potential active ingredient for healthy food against type 2 diabetes mellitus in the five screened peptides considering synthetically the DPP-IV inhibitory rate, bioavailability and synthesis cost.

In Vivo and In Vitro Comparison of the DPP-IV Inhibitory Potential of Food Proteins from Different Origins after Gastrointestinal Digestion

Dipeptidyl-peptidase IV (DPP-IV) plays an essential role in glucose metabolism by inactivating incretins. In this context, food-protein-derived DPP-IV inhibitors are promising glycemic regulators which may act by preventing the onset of type 2 diabetes in personalized nutrition. In this study, the DPP-IV-inhibitory potential of seven proteins from diverse origins was compared for the first time in vitro and in vivo in rat plasma after the intestinal barrier (IB) passage of the indigested proteins. The DPP-IV-inhibitory potentials of bovine hemoglobin, caseins, chicken ovalbumin, fish gelatin, and pea proteins were determined in rat plasma thirty minutes after oral administration. In parallel, these proteins, together with bovine whey and gluten proteins, were digested using the harmonized INFOGEST protocol adapted for proteins. The DPP-IV half-maximal inhibitory concentration (IC50) was determined in situ using Caco-2 cells. The DPP-IV-inhibitory activity was also measured after IB passage using a Caco2/HT29-MTX mixed-cell model. The peptide profiles were analyzed using reversed-phase high-performance liquid chromatography tandem mass spectrometry (RP-HPLC-MS/MS) with MS data bioinformatics management, and the IC50 of the identified peptides was predicted in silico. The in vitro and in vivo DPP-IV-inhibitory activity of the proteins differed according to their origin. Vegetable proteins and hemoglobin yielded the highest DPP-IV-inhibitory activity in vivo. However, no correlation was found between the in vivo and in vitro results. This may be partially explained by the differences between the peptidome analysis and the in silico predictions, as well as the study complexity.

Identify Bitter Peptides by Using Deep Representation Learning Features

A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive process, before they can be removed or degraded. Here, we report the development of a machine learning prediction method, iBitter-DRLF, which is based on a deep learning pre-trained neural network feature extraction method. It uses three sequence embedding techniques, soft symmetric alignment (SSA), unified representation (UniRep), and bidirectional long short-term memory (BiLSTM). These were initially combined into various machine learning algorithms to build several models. After optimization, the combined features of UniRep and BiLSTM were finally selected, and the model was built in combination with a light gradient boosting machine (LGBM). The results showed that the use of deep representation learning greatly improves the ability of the model to identify bitter peptides, achieving accurate prediction based on peptide sequence data alone. By helping to identify bitter peptides, iBitter-DRLF can help research into improving the palatability of peptide therapeutics and dietary supplements in the future. A webserver is available, too.

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.

A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry

Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online.

Conformational insights into the C-terminal mutations of human rhodopsin in retinitispigmentosa

Rhodopsin is a light-sensitive transmembrane receptor involved in the visual transduction cascade. Among the several rhodopsin mutations related to retinitis pigmentosa (RP), those affecting the C-terminal VAPA-COOH motif that is implicated in rhodopsin trafficking from the Golgi to the rod outer segment are notably associated with more aggressive RP forms. However, molecular reasons for defective rhodopsin signaling due to VAPA-COOH mutations, which might include steric hindrance, physicochemical features and structural determinants, are yet unknown, thus limiting further drug design approaches. In this work, clinically relevant rhodopsin mutations at the P347 site within the VAPA-COOH motif were investigated by molecular dynamics (MD) simulations and compared to the wild-type (WT) system. In agreement with experimental evidence, conformational fluctuations of the intrinsically disordered C-terminal tail of WT and mutant rhodopsin were found not to affect the overall structure of the transmembrane domain, including binding to the retinal cofactor. The WT VAPA-COOH motif adopts a unique conformation that is not found in pathological mutants, suggesting that structural features could better explain the pathogenicity of P347 rhodopsin mutants than physicochemical or steric determinants. These results were confirmed by MD simulations in both membrane-embedded full-length opsin and membrane-free C-terminal deca-peptides, these latter becoming very useful and small-size model systems for further investigations of rhodopsin C-terminal mutations. Structural details elucidated in this work might facilitate the understanding of the pathological mechanisms of this class of rhodopsin mutants, which will be instrumental to the development of new therapeutic strategies.

In silico prediction of Severe Acute Respiratory Syndrome Coronavirus 2 main protease cleavage sites

One of the emerging subjects to combat the SARS-CoV-2 virus is to design accurate and efficient drug such as inhibitors against the viral protease to stop the viral spread. In addition to laboratory investigation of the viral protease, which is fundamental, the in silico research of viral protease such as the protease cleavage site prediction is critically important and urgent. However, this problem has yet to be addressed. This article has, for the first time, investigated this problem using the pattern recognition approaches. The article has shown that the pattern recognition approaches incorporating a specially tailored kernel function for dealing with amino acids has the outstanding performance in the accuracy of cleavage site prediction and the discovery of the prototype cleavage peptides.

iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.

Peptide-like and small-molecule inhibitors against Covid-19

Coronavirus disease strain (SARS-CoV-2) was discovered in 2019, and it is spreading very fast around the world causing the disease Covid-19. Currently, more than 1.6 million individuals are infected, and several thousand are dead across the globe because of Covid-19. Here, we utilized the in-silico approaches to identify possible protease inhibitors against SARS-CoV-2. Potential compounds were screened from the CHEMBL database, ZINC database, FDA approved drugs and molecules under clinical trials. Our study is based on 6Y2F and 6W63 co-crystallized structures available in the protein data bank (PDB). Seven hundred compounds from ZINC/CHEMBL databases and fourteen hundred compounds from drug-bank were selected based on positive interactions with the reported binding site. All the selected compounds were subjected to standard-precision (SP) and extra-precision (XP) mode of docking. Generated docked poses were carefully visualized for known interactions within the binding site. Molecular mechanics-generalized born surface area (MM-GBSA) calculations were performed to screen the best compounds based on docking scores and binding energy values. Molecular dynamics (MD) simulations were carried out on four selected compounds from the CHEMBL database to validate the stability and interactions. MD simulations were also performed on the PDB structure 6YF2F to understand the differences between screened molecules and co-crystallized ligand. We screened 300 potential compounds from various databases, and 66 potential compounds from FDA approved drugs. Cobicistat, ritonavir, lopinavir, and darunavir are in the top screened molecules from FDA approved drugs. The screened drugs and molecules may be helpful in fighting with SARS-CoV-2 after further studies.Communicated by Ramaswamy H. Sarma.

UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes

MOTIVATION

Single protein residue mutations may reshape the binding affinity of protein-protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein-protein complexes trained on interactome data.

RESULTS

Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein-protein mutations using UEP, a fast and reliable open-source code.

AVAILABILITY AND IMPLEMENTATION

UEP algorithm can be found at: https://github.com/pepamengual/UEP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches

Food-derived angiotensin I-converting enzyme (ACE) inhibitory peptides could potentially be used as safe supportive therapeutic products for high blood pressure. Theoretical approaches are promising methods with the advantage through exploring the relationships between peptide structures and their bioactivities. In this study, peptides with ACE inhibitory activity were collected and curated. Quantitative structure-activity relationship (QSAR) models were developed by using the combination of various machine learning approaches and chemical descriptors. The resultant models have revealed several structure features accounting for the ACE inhibitions. 14 new dipeptides predicted to lower blood pressure by inhibiting ACE were selected. Molecular docking indicated that these dipeptides formed hydrogen bonds with ACE. Five of these dipeptides were synthesized for experimental testing. The QSAR models developed were proofed to design and propose novel ACE inhibitory peptides. Machine learning algorithms and properly selected chemical descriptors can be promising modeling approaches for rational design of natural functional food components.

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

Predicting the bioactivity of peptides is an important challenge in drug development and peptide research. In this study, numerical descriptive vectors (NDVs) for peptide sequences were calculated based on the physicochemical properties of amino acids (AAs) and principal component analysis (PCA). The resulted NDV had the same length as the peptide sequence, so that each entry of NDV corresponded to one AA in the sequence. They were then applied to quantitative structure-activity relationship (QSAR) analysis of angiotensin-converting enzyme (ACE) inhibitor dipeptides, bitter-tasting dipeptides, and nonameric binding peptides of the human leukocyte antigens (HLA-A*0201). Multiple linear regression was used to construct the QSAR models. For each peptide set, a proper subset of physicochemical properties was chosen by the ant colony optimization algorithm. The leave-one-out cross-validation (q _loo ²) values were 0.855, 0.936, and 0.642 and the root-mean-square errors (RMSEs) were 0.450, 0.149, and 0.461. Our results revealed that the new numerical descriptive vector can afford extensive characterization of peptide sequence so that it can be easily employed in peptide QSAR studies. Moreover, the proposed numerical descriptive vectors were able to determine hot spot residues in the peptides under study.

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.

Mutated variants of squalene-hopene cyclase: enzymatic syntheses of triterpenes bearing oxygen-bridged monocycles and a new 6,6,6,6,6-fusded pentacyclic scaffold, named neogammacerane, from 2,3-oxidosqualene

Squalene-hopene cyclase (SHC) catalyzes the conversion of acyclic squalene molecule into a 6,6,6,6,5-fused pentacyclic hopene and hopanol. SHC is also able to convert (3S)-2,3-oxidosqualene into 3β-hydroxyhopene and 3β-hydroxyhopanol and can generate 3α-hydroxyhopene and 3α-hydroxyhopanol from (3R)-2,3-oxidosqualene. Functional analyses of active site residues toward the squalene cyclization reaction have been extensively reported, but investigations of the cyclization reactions of (3R,S)-oxidosqualene by SHC have rarely been reported. The cyclization reactions of oxidosqualene with W169X, G600F/F601G and F601G/P602F were examined. The variants of the W169L generated new triterpene skeletons possessing a 7-oxabicyclo[2.2.1]heptane moiety (oxygen-bridged monocycle) with (1S,2S,4R)- and (1R,2S,4S)-stereochemistry, which were produced from (3R)- and (3S)-oxidosqualenes, respectively. The F601G/P602F double mutant also furnished a novel triterpene, named neogammacer-21(22)-en-3β-ol, consisting of a 6,6,6,6,6-fused pentacyclic system, in which Me-29 at C-22 of the gammacerane skeleton migrated to C-21. We propose to name this novel scaffold neogammacerane. The formation mechanisms of the enzymatic products from 2,3-oxidosqualene are discussed.

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.

Unstimulated saliva: Background noise in taste molecules

Saliva is a highly complex bodily fluid composed of many proteins, peptides, small organic molecules, and ions. Saliva is produced and secreted by the major and minor salivary glands to protect the mouth and to participate in digestion. Generally, a distinction is made between unstimulated saliva that is a result of autonomic stimulation and stimulated saliva that is produced during chewing and taste stimulation. The link between saliva and sensory perception can thus be regarded in two ways: the role of unstimulated saliva as a background taste and the mechanistic role of stimulated saliva during eating. Indeed, unstimulated saliva (and its components) is continuously bathing our oral cavity and as such stimulates our taste receptors, thus playing a role in taste sensitivity. However, the role of unstimulated salivary components in mediating taste has been studied only in very few substances. To explore this question, this review attempts to compare data from the literature on unstimulated salivary composition with those on taste sensitivity. The main conclusion centres around the concept that the gustatory self-adaptation phenomenon may be relevant for only a few salivary compounds. Further studies at the level of the salivary Von Ebner glands and salivary pellicle are necessary before arriving at definitive conclusions on this subject. PRACTICAL APPLICATION: Unstimulated saliva contains taste substances that can influence sensory perception through taste adaptation. However, large inter-individual variability exists in unstimulated salivary composition both qualitatively and quantitatively. These differences may explain the variability in taste perception and thus the food choices and behaviors of an individual. Thus, in the context of providing personalized food and nutrition to the consumer, variability of unstimulated saliva should be considered for specific formulation of food products.

In Silico Approaches Applied to the Study of Peptide Analogs of Ile-Pro-Ile in Relation to Their Dipeptidyl Peptidase IV Inhibitory Properties

Inhibition of dipeptidyl peptidase IV (DPP-IV) may be exploited to maintain the incretin effect during the postprandial phase. As a result, glycemic regulation and energy homeostasis may be improved. Food protein-derived peptides have been identified as natural agents capable of inhibiting DPP-IV. Ile-Pro-Ile is the most potent DPP-IV inhibitory peptide identified to date. A minimum analog peptide set approach was used to study peptide analogs of Ile-Pro-Ile. The DPP-IV half maximal inhibitory concentration (IC50) values of the 25 peptides evaluated ranged from 3.9 ± 1.0 µM (Ile-Pro-Ile) to 247.0 ± 32.7 µM (Phe-Pro-Phe). The presence of Pro at position 2 of tripeptides was required to achieve high DPP-IV inhibition. Most peptides behaved as competitive inhibitors of DPP-IV with the exception of peptides with a N-terminal Trp, which were mixed-type inhibitors. While possessing the structure of preferred DPP-IV substrates, most peptides studied were particularly stable during 30 min incubation with DPP-IV. Molecular docking revealed that Ile-Pro-Ile and its peptide analogs interacted in a very similar manner with the active site of DPP-IV. In addition, no correlation was found between the Hydropathic INTeraction score and the DPP-IV IC50 values of the peptides studied. This outcome suggests that free energy may not be directly responsible for enzyme inhibition by the peptides. Finally, novel DPP-IV inhibitory peptides were identified using the strategy employed herein. These results may be relevant for the development of food protein-derived peptides with serum glucose lowering and food intake regulatory properties in humans.

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.

Identification of novel dipeptidyl peptidase IV (DPP-IV) inhibitory peptides in camel milk protein hydrolysates

Nine novel dipeptidyl peptidase IV (DPP-IV) inhibitory peptides (FLQY, FQLGASPY, ILDKEGIDY, ILELA, LLQLEAIR, LPVP, LQALHQGQIV, MPVQA and SPVVPF) were identified in camel milk proteins hydrolysed with trypsin. This was achieved using a sequential approach combining liquid chromatography tandem mass spectrometry (LC-MS/MS), qualitative/quantitative structure activity relationship (QSAR) and confirmatory studies with synthetic peptides. The most potent camel milk protein-derived DPP-IV inhibitory peptides, LPVP and MPVQA, had DPP-IV half maximal inhibitory concentrations (IC₅₀) of 87.0 ± 3.2 and 93.3 ± 8.0 µM, respectively. DPP-IV inhibitory peptide sequences identified within camel and bovine milk protein hydrolysates generated under the same hydrolysis conditions differ. This was linked to differences in enzyme selectivity for peptide bond cleavage of camel and bovine milk proteins as well as dissimilarities in their amino acid sequences. Camel milk proteins contain novel DPP-IV inhibitory peptides which may play a role in the regulation of glycaemia in humans.

Gene editing and genetic engineering approaches for advanced probiotics: A review

The applications of probiotics are significant and thus resulted in need of genome analysis of probiotic strains. Various omics methods and systems biology approaches enables us to understand and optimize the metabolic processes. These techniques have increased the researcher's attention towards gut microbiome and provided a new source for the revelation of uncharacterized biosynthetic pathways which enables novel metabolic engineering approaches. In recent years, the broad and quantitative analysis of modified strains relies on systems biology tools such as in silico design which are commonly used methods for improving strain performance. The genetic manipulation of probiotic microorganisms is crucial for defining their role in intestinal microbiota and exploring their beneficial properties. This review describes an overview of gene editing and systems biology approaches, highlighting the advent of omics methods which allows the study of new routes for studying probiotic bacteria. We have also summarized gene editing tools like TALEN, ZFNs and CRISPR-Cas that edits or cleave the specific target DNA. Furthermore, in this review an overview of proposed design of advanced customized probiotic is also hypothesized to improvise the probiotics.

Prediction of reversible disulfide based on features from local structural signatures

Background

Disulfide bonds are traditionally considered to play only structural roles. In recent years, increasing evidence suggests that the disulfide proteome is made up of structural disulfides and reversible disulfides. Unlike structural disulfides, reversible disulfides are usually of important functional roles and may serve as redox switches. Interestingly, only specific disulfide bonds are reversible while others are not. However, whether reversible disulfides can be predicted based on structural information remains largely unknown.

Methods

In this study, two datasets with both types of disulfides were compiled using independent approaches. By comparison of various features extracted from the local structural signatures, we identified several features that differ significantly between reversible and structural disulfides, including disulfide bond length, along with the number, amino acid composition, secondary structure and physical-chemical properties of surrounding amino acids. A SVM-based classifier was developed for predicting reversible disulfides.

Results

By 10-fold cross-validation, the model achieved accuracy of 0.750, sensitivity of 0.352, specificity of 0.953, MCC of 0.405 and AUC of 0.751 using the RevSS_PDB dataset. The robustness was further validated by using RevSS_RedoxDB as independent testing dataset. This model was applied to proteins with known structures in the PDB database. The results show that one third of the predicted reversible disulfide containing proteins are well-known redox enzymes, while the remaining are non-enzyme proteins. Given that reversible disulfides are frequently reported from functionally important non-enzyme proteins such as transcription factors, the predictions may provide valuable candidates of novel reversible disulfides for further experimental investigation.

Conclusions

This study provides the first comparative analysis between the reversible and the structural disulfides. Distinct features remarkably different between these two groups of disulfides were identified, and a SVM-based classifier for predicting reversible disulfides was developed accordingly. A web server named RevssPred can be accessed freely from: http://biocomputer.bio.cuhk.edu.hk/RevssPred.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3668-8) contains supplementary material, which is available to authorized users.

β-Amyrin synthase from Euphorbia tirucalli L. functional analyses of the highly conserved aromatic residues Phe413, Tyr259 and Trp257 disclose the importance of the appropriate steric bulk, and cation-π and CH-π interactions for the efficient catalytic action of the polyolefin cyclization cascade

Many of the functions of the active site residues in β-amyrin synthase and its catalytic mechanism remain unclear. Herein, we examined the functions of the highly conserved Phe413, Tyr259, and Trp257 residues in the β-amyrin synthase of Euphorbia tirucalli. The site-specific mutants F413V and F413M [corrected] showed nearly the same enzymatic activities as the wild type, indicating that π-electrons are not needed for the catalytic reaction. However, the F413A [corrected] mutant yielded a large amount of the tetracyclic dammarane skeleton, with decreased production of β-amyrin. This indicates that the Phe413 [corrected] residue is located near the D-ring formation site and works to position the oxidosqualene substrate correctly within the reaction cavity. On the other hand, the major catalysis-related function of the Tyr259 and Trp257 residues is to yield their π-electrons to the cationic intermediates. The Y259F variant showed nearly equivalent activity to that of the wild type, but aliphatic mutants such as the Ala, Val, and Leu variants showed significantly decreased the activity and yielded the tetracyclic dammarane scaffold, strongly demonstrating that the Tyr259 residue stabilizes the baccharenyl secondary cation via cation-π interaction. The aliphatic variants of Trp257 exhibited remarkably decreased enzymatic activity, and lupeol was produced in a high production ratio, indicating that Trp257 stabilizes the oleanyl cation via cation-π interaction. The aromatic Phe and Tyr mutants exhibited high activities owing to their more increased π-electron density relative to that of the aliphatic mutants, but lupeol was produced in a significantly high yield besides β-amyrin. The Trp residue is likely to be responsible for the robust binding of Me-30 through CH-π interaction. The decreased π-electron density of the Phe and Tyr mutants compared to that of Trp would have resulted in the high production of lupeol.

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features

Background

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used.

Results

In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins.

Conclusions

In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1185-4) contains supplementary material, which is available to authorized users.

Structure activity relationship modelling of milk protein-derived peptides with dipeptidyl peptidase IV (DPP-IV) inhibitory activity

Quantitative structure activity type models were developed in an attempt to predict the key features of peptide sequences having dipeptidyl peptidase IV (DPP-IV) inhibitory activity. The models were then employed to help predict the potential of peptides, which are currently reported in the literature to be present in the intestinal tract of humans following milk/dairy product ingestion, to act as inhibitors of DPP-IV. Two models (z- and v-scale) for short (2-5 amino acid residues) bovine milk peptides, behaving as competitive inhibitors of DPP-IV, were developed. The z- and the v-scale models (p<0.05, R(2) of 0.829 and 0.815, respectively) were then applied to 56 milk protein-derived peptides previously reported in the literature to be found in the intestinal tract of humans which possessed a structural feature of DPP-IV inhibitory peptides (P at the N2 position). Ten of these peptides were synthetized and tested for their in vitro DPP-IV inhibitory properties. There was no agreement between the predicted and experimentally determined DPP-IV half maximal inhibitory concentrations (IC50) for the competitive peptide inhibitors. However, the ranking for DPP-IV inhibitory potency of the competitive peptide inhibitors was conserved. Furthermore, potent in vitro DPP-IV inhibitory activity was observed with two peptides, LPVPQ (IC50=43.8±8.8μM) and IPM (IC50=69.5±8.7μM). Peptides present within the gastrointestinal tract of human may have promise for the development of natural DPP-IV inhibitors for the management of serum glucose.

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.

A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction

In this paper, we present a pipeline to perform improved QSAR analysis of peptides. The modeling involves a double selection procedure that first performs feature selection and then conducts sample selection before the final regression analysis. Five hundred and thirty-one physicochemical property parameters of amino acids were used as descriptors to characterize the structure of peptides. These high-dimensional descriptors then go through a feature selection process given by the binary matrix shuffling filter (BMSF) to obtain a set of important low-dimensional features. Each descriptor that passes the BMSF filtering also receives a weight defined through its contribution to reduce the estimation error. These selected features served as the predictors for subsequent sample selection and modeling. Based on the weighted Euclidean distances between samples, a common range was determined with high-dimensional semivariogram and then used as a threshold to select the near-neighbor samples from the training set. For each sample to be predicted, the QSAR model was established using SVR with the weighted, selected features based on the exclusive set of near-neighbor training samples. Prediction was conducted for each test sample accordingly. The performances of this pipeline are tested with the QSAR analysis of angiotensin-converting enzyme inhibitors and HLA-A*0201 data sets. Improved prediction accuracy was obtained in both applications. This pipeline can optimize the QSAR modeling from both the feature selection and sample selection perspectives. This leads to improved accuracy over single selection methods. We expect this pipeline to have extensive application prospect in the field of regression prediction.

β-Amyrin synthase from Euphorbia tirucalli. Steric bulk, not the π-electrons of Phe, at position 474 has a key role in affording the correct folding of the substrate to complete the normal polycyclization cascade

The importance of the steric bulk at 474 residue is described for completion of the cyclization cascade, but not the π-electrons of the Phe residue.

QSBR study of bitter taste of peptides: application of GA-PLS in combination with MLR, SVM, and ANN approaches

Detailed information about the relationships between structures and properties/activities of peptides as drugs and nutrients is useful in the development of drugs and functional foods containing peptides as active compounds. The bitterness of the peptides is an undesirable property which should be reduced during drug/nutrient production, and quantitative structure bitter taste relationship (QSBR) studies can help researchers to design less bitter peptides with higher target efficiency. Calculated structural parameters were used to develop three different QSBR models (i.e., multiple linear regression, support vector machine, and artificial neural network) to predict the bitterness of 229 peptides (containing 2–12 amino acids, obtained from the literature). The developed models were validated using internal and external validation methods, and the prediction errors were checked using mean percentage deviation and absolute average error values. All developed models predicted the activities successfully (with prediction errors less than experimental error values), whereas the prediction errors for nonlinear methods were less than those for linear methods. The selected structural descriptors successfully differentiated between bitter and nonbitter peptides.