A Structure-based Data Set of Protein-Peptide Affinities and its Nonredundant Benchmark: Potential Applications in Computational Peptidology.

BACKGROUND: Peptides play crucial roles in diverse cellular functions and participate in many biological processes by interacting with a variety of proteins, which have also been exploited as a promising class of therapeutic agents to target druggable proteins over the past decades. Understanding the intrinsic association between the structure and affinity ofprotein-peptide interactions (PpIs) should be considerably valuable for the computational peptidology area, such as guiding protein-peptide docking calculations, developing protein-peptide affinity scoring functions, and designing peptide ligands for specific protein receptors. OBJECTIVE: We attempted to create a data source for relating PpI structure to affinity. METHODS: By exhaustively surveying the whole protein data bank (PDB) database as well as the ontologically enriched literature information, we manually curated a structure-based data set of protein-peptide affinities, PpI[S/A]DS, which assembled over 350 PpI complex samples with both the experimentally measured structure and affinity data. The data set was further reduced to a nonredundant benchmark consisting of 102 culled samples, PpI[S/A]BM, which only selected those of structurally reliable, functionally diverse and evolutionarily nonhomologous. RESULTS: The collected structures were resolved at a high-resolution level with either X-ray crystallography or solution NMR, while the deposited affinities were characterized by dissociation constant, i.e. Kd value, which is a direct biophysical measure of the intermolecular interaction strength between protein and peptide, ranging from subnanomolar to millimolar levels. The PpI samples in the set/benchmark were arbitrarily classified into α-helix, partial α-helix, ß-sheet formed through binding, ß-strand formed through self-folding, mixed, and other irregular ones, totally resulting in six classes according to the secondary structure of their peptide ligands. In addition, we also categorized these PpIs in terms of their biological function and binding behavior. CONCLUSION: The PpI[S/A]DS set and PpI[S/A]BM benchmark can be considered a valuable data source in the computational peptidology community, aiming to relate the affinity to structure for PpIs.

A SiO2 Hybrid Enzyme-Based Biosensor with Enhanced Electrochemical Stability for Accuracy Detection of Glucose.

A novel enzyme-based biosensor for glucose detection is successfully developed using layer-by-layer assembly technology. The introduction of commercially available SiO2 was found to be a facile way to improve overall electrochemical stability. After 30 CV cycles, the proposed biosensor could retain 95% of its original current. The biosensor presents good detection stability and reproducibility with the detection concentration range of 1.96 × 10-9 to 7.24 × 10-7 M. This study demonstrated that the hybridization of cheap inorganic nanoparticles was a useful method in preparing high-performance biosensors with a much lower cost.

Machine annealing-guided navigation of antihypertensive food peptide selectivity between human ACE N- and C-domains in structurally interacting diversity space.

Human angiotensin-converting enzyme (ACE) is a well-established druggable target for the treatment of hypertension (HTN), which contains two structurally homologous but functionally distinct N- and C-domains. Selective inhibition of the C-domain primarily contributes to the antihypertensive efficiency and can be exploited as medicinal agents and functional additives for regulating blood pressure with high safety. In this study, we used a machine annealing (MA) strategy to guide the navigation of antihypertensive peptides (AHPs) in structurally interacting diversity space with the two ACE domains based on their crystal/modeled complex structures and an in-house protein-peptide affinity scoring function, aiming to optimize the peptide selectivity for C-domain over N-domain. The strategy generated a panel of theoretically designed AHP hits with a satisfactory C-over-N (C > N) selectivity profile, from which several hits were found to have a good C > N selectivity, which is roughly comparable with or even better than the BPPb, a natural C > N-selective ACE-inhibitory peptide. Structural analysis and comparison of domain-peptide noncovalent interaction patterns revealed that (i) longer peptides (>4 amino aids) generally exhibit stronger selectivity than shorter peptides (<4 amino aids), (ii) peptide sequence can be divided into two, section I (including peptide C-terminal region) and section II (including peptide middle and N-terminal regions); the former contributes to both peptide affinity (primarily) and selectivity (secondarily), while the latter is almost only responsible for peptide selectivity, and (iii) charged/polar amino acids confer to peptide selectivity relative to hydrophobic/nonpolar amino acids (that confer to peptide affinity).

Targeting peptide-mediated interactions in omics.

Peptide-mediated interactions (PMIs) play a crucial role in cell signaling network, which are responsible for about half of cellular protein-protein associations in the human interactome and have recently been recognized as a new kind of promising druggable target for drug development and disease therapy. In this article, we give a systematic review regarding the proteome-wide discovery of PMIs and targeting druggable PMIs (dPMIs) with chemical drugs, self-inhibitory peptides (SIPs) and protein agents, particularly focusing on their implications and applications for therapeutic purpose in omics. We also introduce computational peptidology strategies used to model, analyze, and design PMI-targeted molecular entities and further extend the concepts of protein context, direct/indirect readout, and enthalpy/entropy effect involved in PMIs. Current issues and future perspective on this topic are discussed. There is still a long way to go before establishment of efficient therapeutic strategies to target PMIs on the omics scale.

Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level.

Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein-protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein-peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom-residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised-supervised PpI affinity (usPpIA) predictor. Although both the ulPpI[a-r] potential and usPpIA predictor can be used to calculate PpI affinities from their complex structures, the latter seems to perform much better than the former, suggesting that the unsupervised potential can be improved substantially with a further correction by supervised statistical learning. We examine the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation. It is revealed that, despite developed solely based on solved structures, the integrated unsupervised-supervised method is also applicable for locally docked structures to reach a quantitative prediction but can only give a qualitative prediction on globally docked structures. The dynamics refinement seems not to change (or improve) the predictive results essentially, although it is computationally expensive and time-consuming relative to peptide docking. We also perform extrapolation of usPpIA predictor to the indirect affinity quantities of HLA-A*0201 binding epitope peptides and NHERF PDZ binding scaffold peptides, consequently resulting in a good and moderate correlation of the predicted KD with experimental IC50 and BLU on the two peptide sets, with Pearson's correlation coefficients Rp = 0.635 and 0.406, respectively.

Systematic Comparison and Comprehensive Evaluation of 80 Amino Acid Descriptors in Peptide QSAR Modeling.

The peptide quantitative structure-activity relationship (QSAR), also known as the quantitative sequence-activity model (QSAM), has attracted much attention in the bio- and chemoinformatics communities and is a well developed computational peptidology strategy to statistically correlate the sequence/structure and activity/property relationships of functional peptides. Amino acid descriptors (AADs) are one of the most widely used methods to characterize peptide structures by decomposing the peptide into its residue building blocks and sequentially parametrizing each building block with a vector of amino acid principal properties. Considering that various AADs have been proposed over the past decades and new AADs are still emerging today, we herein query the following: is it necessary to develop so many AADs and do we need to continuously develop more new AADs? In this study, we exhaustively collect 80 published AADs and comprehensively evaluate their modeling performance (including fitting ability, internal stability, and predictive power) on 8 QSAR-oriented peptide sample sets (QPSs) by employing 2 sophisticated machine learning methods (MLMs), totally building and systematically comparing 1280 (80 AADs × 8 QPSs × 2 MLMs) peptide QSAR models. The following is revealed: (i) None of the AADs can work best on all or most peptide sets; an AAD usually performs well for some peptides but badly for others. (ii) Modeling performance is primarily determined by the peptide samples and then the MLMs used, while AADs have only a moderate influence on the performance. (iii) There is no essential difference between the modeling performances of different AAD types (physiochemical, topological, 3D-structural, etc.). (iv) Two random descriptors, which are separately generated randomly in standard normal distribution N(0, 1) and uniform distribution U(-1, +1), do not perform significantly worse than these carefully developed AADs. (v) A secondary descriptor, which carries major information involved in the 80 (primary) AADs, does not perform significantly better than these AADs. Overall, we conclude that since there are various AADs available to date and they already cover numerous amino acid properties, further development of new AADs is not an essential choice to improve peptide QSAR modeling; the traditional AAD methodology is believed to have almost reached the theoretical limit nowadays. In addition, the AADs are more likely to be a vector symbol but not informative data; they are utilized to mark and distinguish the 20 amino acids but do not really bring much original property information to these amino acids.

Systematic Modeling, Prediction, and Comparison of Domain-Peptide Affinities: Does it Work Effectively With the Peptide QSAR Methodology?

The protein-protein association in cellular signaling networks (CSNs) often acts as weak, transient, and reversible domain-peptide interaction (DPI), in which a flexible peptide segment on the surface of one protein is recognized and bound by a rigid peptide-recognition domain from another. Reliable modeling and accurate prediction of DPI binding affinities would help to ascertain the diverse biological events involved in CSNs and benefit our understanding of various biological implications underlying DPIs. Traditionally, peptide quantitative structure-activity relationship (pQSAR) has been widely used to model and predict the biological activity of oligopeptides, which employs amino acid descriptors (AADs) to characterize peptide structures at sequence level and then statistically correlate the resulting descriptor vector with observed activity data via regression. However, the QSAR has not yet been widely applied to treat the direct binding behavior of large-scale peptide ligands to their protein receptors. In this work, we attempted to clarify whether the pQSAR methodology can work effectively for modeling and predicting DPI affinities in a high-throughput manner? Over twenty thousand short linear motif (SLiM)-containing peptide segments involved in SH3, PDZ and 14-3-3 domain-medicated CSNs were compiled to define a comprehensive sequence-based data set of DPI affinities, which were represented by the Boehringer light units (BLUs) derived from previous arbitrary light intensity assays following SPOT peptide synthesis. Four sophisticated MLMs (MLMs) were then utilized to perform pQSAR modeling on the set described with different AADs to systematically create a variety of linear and nonlinear predictors, and then verified by rigorous statistical test. It is revealed that the genome-wide DPI events can only be modeled qualitatively or semiquantitatively with traditional pQSAR strategy due to the intrinsic disorder of peptide conformation and the potential interplay between different peptide residues. In addition, the arbitrary BLUs used to characterize DPI affinity values were measured via an indirect approach, which may not very reliable and may involve strong noise, thus leading to a considerable bias in the modeling. The R prd 2 = 0.7 can be considered as the upper limit of external generalization ability of the pQSAR methodology working on large-scale DPI affinity data.

Fe-based metal-organic frameworks as heterogeneous catalysts for highly efficient degradation of wastewater in plasma/Fenton-like systems.

Fe-based metal organic frameworks (Fe-MOFs) were successfully synthesized with the dielectric barrier discharge (DBD) plasma method and FeSO4·7H2O as the Fe precursor. Fe-MOFs were used as Fenton-like catalysts in DBD plasma/Fenton-like technology to treat wastewater, which addressed the issues with iron solubility. Since the valence state of iron will affect the catalytic performance, the Fe precursor FeSO4·7H2O was added to regulate the valence state and adjust the catalytic performance by improving the availability of active sites. The influences of discharge voltage, catalyst addition amount, H2O2 addition amount and pH on the degradation efficiency of methyl orange (MO) were systematically examined. Through free radical capture experiments, the reaction mechanism of the plasma/Fenton-like catalytic degradation process was deduced primarily as the coordinated oxidation process of hydroxyl radicals (·OH), photo-generated holes (h+) and superoxide radicals (·O2 -). The reusability experiments proved that the catalyst was stable and reusable. The possible degradation pathways were proposed based on the identification of intermediate products generated in the degradation process by liquid chromatography-mass spectrometry (LC-MS) analyses.