Accelerating the Screening of Small Peptide Ligands by Combining Peptide-Protein Docking and Machine Learning.

This research introduces a novel pipeline that couples machine learning (ML), and molecular docking for accelerating the process of small peptide ligand screening through the prediction of peptide-protein docking. Eight ML algorithms were analyzed for their potential. Notably, Light Gradient Boosting Machine (LightGBM), despite having comparable F1-score and accuracy to its counterparts, showcased superior computational efficiency. LightGBM was used to classify peptide-protein docking performance of the entire tetrapeptide library of 160,000 peptide ligands against four viral envelope proteins. The library was classified into two groups, 'better performers' and 'worse performers'. By training the LightGBM algorithm on just 1% of the tetrapeptide library, we successfully classified the remaining 99%with an accuracy range of 0.81-0.85 and an F1-score between 0.58-0.67. Three different molecular docking software were used to prove that the process is not software dependent. With an adjustable probability threshold (from 0.5 to 0.95), the process could be accelerated by a factor of at least 10-fold and still get 90-95% concurrence with the method without ML. This study validates the efficiency of machine learning coupled to molecular docking in rapidly identifying top peptides without relying on high-performance computing power, making it an effective tool for screening potential bioactive compounds.

An electronic nose based on 2D group VI transition metal dichalcogenides/organic compounds sensor array.

Rapid volatile organic compounds (VOCs) detection is a hot topic today; in this framework nanomaterials and their tailorable chemistry offer a plethora of compelling opportunities. In this work, Group VI transition metal dichalcogenides (TMDs, i.e., MoS2, WSe2, MoSe2, and WSe2) were functionalized with organic compounds (ellagic acid, tannic acid, catechin, and sodium cholate) able to assist their sonochemical exfoliation in water. The 16 resulting water-dispersed 2D hybrid inorganic/organic TMDs resulted in a few-layer nanoflakes conformation and were used to modify quartz crystal microbalances (QCMs) to equip an e-nose for VOCs determination. The ability of the sensors for the detection of VOCs was assessed on alcohols, terpenes, esters, and aldehydes; the responses were significatively different, confirming the synergic effect of TMD and the organic compound in the interaction with VOCs. The 16 sensors exhibited quantitative responses for VOCs (R2≥0.978) with fast signals recovery (<100 s) and repeatable (RSD ≤9.3%, n = 5), reproducible (RSD ≤12.8%, n = 3) and stable (RSD ≤14.6%, 3 months) signals. As proof of applicability, in an e-nose format, banana aroma evolution during post-harvest ripening was successfully monitored using the 2D TMDs-based sensors array. These data demonstrate that TMDs exfoliated in water with different organic compounds are sustainable functional nanomaterials, able to offer new opportunities in nano-bioelectronic applications.

Cannabinoid Profile in Cannabis sativa L. Samples by Means of LC-MRM/IDA/EPI Analysis: A New Approach for Cultivar Classification.

A new multitarget screening procedure for 36 cannabinoids in 12 Cannabis sativa L. cultivars (hemp) was developed using multiple reaction monitoring (MRM) coupled with an enhanced product ion (EPI) scan in an information-dependent acquisition (IDA) experiment, which can be performed by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS)/MS analysis. The MRM-IDA-EPI experiment was used for the analysis of hemp samples and the identification of the compounds of interest. It was performed through the comparison of EPI spectra with literature data and with the in-house library. The results, processed by multivariate statistical analysis, showed an accurate classification of the 12 C. sativa cultivars, emphasizing the synergic contribution of the new cannabinoids recently discovered and showing how the traditional classification based on a common cannabinoid is limiting.

A new class of sensing elements for sensors: Clamp peptides for Zika virus.

The design of a new class of selective and high affinity antibody mimetics termed clamp peptide (CP) that incorporate three short peptides structurally and mechanically mimicking a clamp is proposed as sensing elements for a reliable detection sensor platform. The CPs consist of two short peptides functioning as arms that recognize two different epitopes in the target protein and are connected by a third short peptide that acts as a hinge between the peptide arms. For the construction of CPs, we employed a rational design combined with computational methods. To illustrate our approach, we designed a CP that binds selectively to the envelope protein of the Zika virus (ZIKV). The virtual docking cycles were run maximizing the discrimination between ZIKV and Dengue virus (DENV) envelope proteins. DENV was chosen among the flavivirus family because it has high structural similarity with ZIKV. When employed in a colorimetric binding assay or in label-free electrochemical impedance sensor format, the CP was selective for ZIKV vs DENV particles showing detection limit under 104 copies/mL, comparable to anti-ZIKV antibodies. Apparent dissociation binding constants (Kd) confirmed a better performance of CPs than mono-arm peptides (Kd of best CP = 162 nM ± 23 nM; Kd of best mono-arm peptide = 11.15 ± 2.76 µM). The performance of the assays based on CPs was also verified in serum and urine (diluted 1:10 and 1:1 respectively). The detection limits of CPs decreased about one order of magnitude for ZIKV detection in serum or urine, with a distinct analytical signal starting from 105 copies/mL of ZIKV.

Cryotolerance of equine spermatozoa correlates with specific fatty acid pattern: A pilot study.

Sperm cryopreservation represents a powerful tool for horse breeding. To improve the efficiency of artificial insemination in the horse using cryopreserved spermatozoa, an adequate understanding of the underlying biophysical properties that affect sperm cryosurvival needs to be reached yet. In this pilot study, we described isolation and analysis of the main fatty acids from sperms of stallions classified as good and poor freezers (7 GF and 5 PF, according to sperm motility and viability, before and after cryopreservation). Fatty acid profiles were only assessed in pre-thaw sperms. Eight main fatty acids were identified, using gas chromatography, and their contents were expressed as percentage of the total lipid content. We found that lauric, myristic and oleic acid (C12:0, C14:0 and C18:1n9c) turned out to be about 2-fold more abundant in the sperm cells of the GFs compared with PFs. Moreover, we described for the first time the presence of a very high amount of a trans geometrical isomer of linoleic acid, linolelaidic acid (C18:2n6t), in pre-thaw PF spermatozoa. Notably, we found in fresh sperms of PF stallions a ratio of unsaturated fatty acids to saturated fatty acids which was twice that of those of GF group, suggesting a positive effect of a high saturated-to-unsaturated fatty acid ratio for the "freezability" of equine spermatozoa. Finally, principal component analysis (PCA) confirmed the relationships between specific fatty acids and cryotolerance of equine spermatozoa, also providing a graphical classification and additional information about the dominant variables governing the classification process.

Peptides, DNA and MIPs in Gas Sensing. From the Realization of the Sensors to Sample Analysis.

Detection and monitoring of volatiles is a challenging and fascinating issue in environmental analysis, agriculture and food quality, process control in industry, as well as in 'point of care' diagnostics. Gas chromatographic approaches remain the reference method for the analysis of volatile organic compounds (VOCs); however, gas sensors (GSs), with their advantages of low cost and no or very little sample preparation, have become a reality. Gas sensors can be used singularly or in array format (e.g., e-noses); coupling data output with multivariate statical treatment allows un-target analysis of samples headspace. Within this frame, the use of new binding elements as recognition/interaction elements in gas sensing is a challenging hot-topic that allowed unexpected advancement. In this review, the latest development of gas sensors and gas sensor arrays, realized using peptides, molecularly imprinted polymers and DNA is reported. This work is focused on the description of the strategies used for the GSs development, the sensing elements function, the sensors array set-up, and the application in real cases.

Identification of Cannabis sativa L. (hemp) Retailers by Means of Multivariate Analysis of Cannabinoids.

In this work, the concentration of nine cannabinoids, six neutral cannabinoids (THC, CBD, CBC, CBG, CBN and CBDV) and three acidic cannabinoids (THCA CBGA and CBDA), was used to identify the Italian retailers of Cannabis sativa L. (hemp), reinforcing the idea that the practice of categorizing hemp samples only using THC and CBD is inadequate. A high-performance liquid chromatography/high-resolution mass spectrometry (HPLC-MS/MS) method was developed for screening and simultaneously analyzing the nine cannabinoids in 161 hemp samples sold by four retailers located in different Italian cities. The hemp samples dataset was analyzed by univariate and multivariate analysis with the aim to identify the hemp retailers without any other information on the hemp samples like Cannabis strains, seeds, soil and cultivation characteristics, geographical origin, product storage, etc. The univariate analysis highlighted that the hemp samples could not be differentiated by using any of the nine cannabinoids analyzed. To evaluate the real efficiency of the discrimination among the four hemp retailers a partial least squares discriminant analysis (PLS-DA) was applied. The PLS-DA results showed a very good discrimination between the four hemp retailers with an explained variance of 100% and low classification errors in both calibration (5%) and cross validation (6%). A total of 92% of the hemp samples were correctly classified by the cannabinoid variables in both fitting and cross validation. This work contributed to show that an analytical method coupled with multivariate analysis can be used as a powerful tool for forensic purposes.

Computationally Designed Peptides for Zika Virus Detection: An Incremental Construction Approach.

Herein, and in contrast to current production of anti-Zika virus antibodies, we propose a semi-combinatorial virtual strategy to select short peptides as biomimetic antibodies/binding agents for the detection of intact Zika virus (ZIKV) particles. The virtual approach was based on generating different docking cycles of tetra, penta, hexa, and heptapeptide libraries by maximizing the discrimination between the amino acid motif in the ZIKV and dengue virus (DENV) envelope protein glycosylation site. Eight peptides, two for each length (tetra, penta, hexa, and heptapeptide) were then synthesized and tested vs. intact ZIKV particles by using a direct enzyme linked immunosorbent assay (ELISA). As a reference, we employed a well-established anti-ZIKV antibody, the antibody 4G2. Three peptide-based assays had good detection limits with dynamic range starting from 105 copies/mL of intact ZIKV particles; this was one order magnitude lower than the other peptides or antibodies. These three peptides showed slight cross-reactivity against the three serotypes of DENV (DENV-1, -2, and -3) at a concentration of 106 copies/mL of intact virus particles, but the discrimination between the DENV and ZIKV was lost when the coating concentration was increased to 107 copies/mL of the virus. The sensitivity of the peptides was tested in the presence of two biological matrices, serum and urine diluted 1:10 and 1:1, respectively. The detection limits decreased about one order of magnitude for ZIKV detection in serum or urine, albeit still having for two of the three peptides tested a distinct analytical signal starting from 106 copies/mL, the concentration of ZIKV in acute infection.

Headspace Volatile Evaluation of Carrot Samples-Comparison of GC/MS and AuNPs-hpDNA-Based E-Nose.

The performances of a quartz crystal microbalances (QCMs) based on an electronic nose (E-nose), modified with hairpin-DNA (hpDNA) for carrot aroma profiling has been evaluated. Solid phase micro-extraction (SPME) headspace sampling, combined with gas chromatography (GC), was used as a reference method. The changes in carrot aroma profiles stored at different temperatures (-18 °C, 4 °C, 25 °C, and 40 °C) were monitored during time up to 26 days. The principal component analysis of the data evidenced the different aroma patterns related to the presence of different key compounds. The output data achieved with the hpDNA-based E-nose were able to detect aroma patterns similar to gas chromatography with mass spectrometry (GC-MS). This work demonstrates that hpDNA has different sizes of loops that can be used for the development of sensor arrays able to detect aroma patterns in food and their changes with advantages in terms of easiness of usage, rapidity, and cost of analysis versus classical methods.

Hairpin DNA-AuNPs as molecular binding elements for the detection of volatile organic compounds.

Hairpin DNA (hpDNA) loops were used for the first time as molecular binding elements in gas analysis. The hpDNA loops sequences of unpaired bases were studied in-silico to evaluate the binding versus four chemical classes (alcohols, aldehydes, esters and ketones) of volatile organic compounds (VOCs). The virtual binding score trend was correlated to the oligonucleotide size and increased of about 25% from tetramer to hexamer. Two tetramer and pentamer and three hexamer loops were selected to test the recognition ability of the DNA motif. The selection was carried out trying to maximize differences among chemical classes in order to evaluate the ability of the sensors to work as an array. All oligonucleotides showed similar trends with best binding scores for alcohols followed by esters, aldehydes and ketones. The seven ssDNA loops (CCAG, TTCT, CCCGA, TAAGT, ATAATC, CATGTC and CTGCAA) were then extended with the same double helix stem of four base pair DNA (GAAG to 5' end and CTTC to 3' end) and covalently bound to gold nanoparticles (AuNPs) using a thiol spacer attached to 5' end of the hpDNA. HpDNA-AuNPs were deposited onto 20 MHz quartz crystal microbalances (QCMs) to form the gas piezoelectric sensors. An estimation of relative binding affinities was obtained using different amounts of eight VOCs (ethanol, 3-methylbutan-1-ol, 1-pentanol, octanal, nonanal, ethyl acetate, ethyl octanoate, and butane-2,3-dione) representative of the four chemical classes. In agreement with the predicted simulation, hexamer DNA loops improved by two orders of magnitude the binding affinity highlighting the key role of the hpDNA loop size. Using the sensors as an array a clear discrimination of VOCs on the basis of molecular weight and functional groups was achieved, analyzing the experimental with principal components analysis (PCA) demonstrating that HpDNA is a promising molecular binding element for analysis of VOCs.

Peptide Modified ZnO Nanoparticles as Gas Sensors Array for Volatile Organic Compounds (VOCs).

In this work a peptide based gas sensor array based of ZnO nanoparticles (ZnONPs) has been realized. Four different pentapeptides molecularly modeled for alcohols and esters having cysteine as a common spacer have been immobilized onto ZnONPs. ZnONPs have been morphologically and spectroscopically characterized. Modified nanoparticles have been then deposited onto quartz crystal microbalances (QCMs) and used as gas sensors with nitrogen as carrier gas. Analysis of the pure compounds modeled demonstrated a nice fitting of modeling with real data. The peptide based ZnONPs had very low sensitivity to water, compared to previously studied AuNPs peptide based gas sensors allowing the use of the array on samples with high water content. Real samples of fruit juices have been assayed; stability of the signal, good repeatability, and discrimination ability of the array was achieved.

Simple and rapid silver nanoparticles based antioxidant capacity assays: Reactivity study for phenolic compounds.

A single-step, rapid (10 min), sensitive silver nanoparticles (AgNPs) based spectrophotometric method for antioxidant capacity (AOC) assay has been developed. The assay is based on the ability of natural polyphenols to reduce Ag(I) and stabilize the produced AgNPs(0) at room temperature. Localized surface plasmon resonance (LSPR) of AgNPs at ≈420 nm is then measured. Using different conditions of pH (8.4) and temperature (45 °C) a further assay based on the production of AgNPs with selectivity for flavonols was also developed. The reactivity of the two AgNPs based assays vs. 15 polyphenols belonging to different chemical classes and 9 different samples has been studied and compared with ABTS, Folin and AuNPs based methods for AOC. The proposed assays had good reproducibility (RSD ≤ 13) and are simple, sensitive and cost effective. Moreover, used in conjunction with the classical AOC assays, can improve the information on the polyphenolic pool of food samples.

Selective solid phase extraction of JWH synthetic cannabinoids by using computationally designed peptides.

The objective of the present work is to demonstrate a rational way to prepare selective sorbents able to extract simultaneously several structural analogs. For this purpose the binding specificity of two hexapeptides computationally designed (VYWLVW and YYIGGF) versus four synthetic cannabinoids Naphthalen-1-yl-(1-pentylindol-3-yl)methanone (JWH 018), naphthalen-1-yl-(1-butylindol-3-yl)methanone (JWH 073), (R)-(1-((1-methylpiperidin-2-yl)methyl)-1H-indol-3-yl)(naphthalen-1-yl)methanone (AM 1220) and (R)-(+)-[2,3-Dihydro-5-methyl-3-(4-morpholinylmethyl)pyrrolo[1,2,3-de]-1,4-benzoxazin-6-yl]-1-napthalenylmethanone (WIN 55) was computationally studied and then experimentally tested by solid-phase extraction (SPE) clean-up and ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) analysis. The two peptides were chosen using a semi combinatorial virtual technique by generating 4 cycles of peptide libraries (around 2.3×104 elements). To select the two peptides, the simulated binding scores between synthetic cannabinoids and peptides was used by maximizing the recognition properties of amino acid motif between the two JWH and the other synthetic cannabinoids. In particular, the peptide YYIGGF, having also affinity for AM 120, was selected as control because it was the only one without tryptophan residues within the best peptides obtained from simulation. Experimentally, the two hexapeptides were tested as SPE sorbent using nanomolar solutions of the four drugs. After optimization of best retentions the binding constants were calculated by loading synthetic cannabinoids solutions at different concentrations. The results indicated a strong interaction between hexapeptide VYWLVW and JWH 018 (15.58±2.03×106M-1), 3-fold and 40-fold larger compared to the analog JWH 073 and both AM 1220 and the WIN 55. Similar trend was observed for the hexapeptide YYIGGF but the binding constants were at least three times lower highlighting the key role of the tryptophan. To demonstrate the hexapeptides specific interaction with only synthetic cannabinoids, a cross-reactivity study was carried out using other drugs (cocaine, morphine, phencyclidine and methamphetamine) in the same SPE condition. Finally the practical utility of these peptide modified sorbent materials was further demonstrated by detecting the synthetic cannabinoids in real samples using hair matrix.

Rapid micromotor-based naked-eye immunoassay.

A dynamic micromotor-based immunoassay, exemplified by cortisol detection, based on the use of tubular micromotors functionalized with a specific antibody is described. The use of antibody-functionalized micromotors offers huge acceleration of both direct and competitive cortisol immunoassays, along with greatly enhanced sensitivity of direct and competitive immunoassays. The dramatically improved speed and sensitivity reflect the greatly increased likelihood of antibody-cortisol contacts and fluid mixing associated with the dynamic movement of these microtube motors and corresponding bubble generation that lead to a highly efficient and rapid recognition process. Rapid naked-eye detection of cortisol in the sample is achieved in connection to use of horseradish peroxidase (HRP) tag and TMB/H2O2 system. Key parameters of the competitive immunoassay (e.g., incubation time and reaction volume) were optimized. This fast visual micromotor-based sensing approach enables "on the move" specific detection of the target cortisol down to 0.1µgmL-1 in just 2min, using ultrasmall (50µL) sample volumes.

Tailoring gas sensor arrays via the design of short peptides sequences as binding elements.

A semi-combinatorial virtual approach was used to prepare peptide-based gas sensors with binding properties towards five different chemical classes (alcohols, aldehydes, esters, hydrocarbons and ketones). Molecular docking simulations were conducted for a complete tripeptide library (8000 elements) versus 58 volatile compounds belonging to those five chemical classes. By maximizing the differences between chemical classes, a subset of 120 tripeptides was extracted and used as scaffolds for generating a combinatorial library of 7912 tetrapeptides. This library was processed in an analogous way to the former. Five tetrapeptides (IHRI, KSDS, LGFD, TGKF and WHVS) were chosen depending on their virtual affinity and cross-reactivity for the experimental step. The five peptides were covalently bound to gold nanoparticles by adding a terminal cysteine to each tetrapeptide and deposited onto 20MHz quartz crystal microbalances to construct the gas sensors. The behavior of peptides after this chemical modification was simulated at the pH range used in the immobilization step. ΔF signals analyzed by principal component analysis matched the virtually screened data. The array was able to clearly discriminate the 13 volatile compounds tested based on their hydrophobicity and hydrophilicity molecules as well as the molecular weight.

Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands.

A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.

Fatty acid composition and δ13 C of bulk and individual fatty acids as marker for authenticating Italian PDO/PGI extra virgin olive oils by means of isotopic ratio mass spectrometry.

European Regulation (EEC) 2568/91 has been setting the minimum requirements in order to allow labeling of oil as extra virgin. These general requirements, are based on physical-chemical and organoleptic parameters directly linked to the freshness and quality of the product. Isotope ratio mass spectrometry (IRMS) was demonstrated to be a useful tool for the discrimination of the origin of unknown samples, because the obtained data are practically independent of the cultivar employed and the production technique. In this work, the evaluation of the composition of fatty acid methyl esters (FAME) alongside with the determination of stable isotope ratio of C in bulk oils and in main FAME constituents have been investigated as a tool to improve geographical discrimination of Italian Protected Designation of Origin/Protected Geographical Indication (PDO/PGI) samples. For this purpose, authentic PDO/PGI extra virgin olive oils were sampled at oil mills and grouped into different sets according to their areas of provenience. The use of principal component analysis and partial least squares discriminant analysis multivariate analysis techniques demonstrated that discrimination of olive oil samples can be done using geographical and pedoclimatic parameters predominantly by using δ(13) C results of bulk and individual fatty acids. Results showed that δ(13) C values are a more reliable marker of origin with respect to fatty acid composition.

Bio-inspired solid phase extraction sorbent material for cocaine: a cross reactivity study.

The binding specificity of a bio-inspired hexapeptide (QHWWDW) versus cocaine and four other drugs such as 3,4-methylenedioxy-N-methylamphetamine (MDMA), 3,4-methylenedioxy-N-ethylamphetamine (MDEA), phencyclidine and morphine was computationally studied and then experimentally confirmed in solid phase extraction (SPE) followed by liquid chromatography-mass spectrometry (LC/MS) detection. In simulation, the hexapeptide-drug complexes were docked with different scoring functions and considering pH chemical environment. In experimental, the cross reactivity of the selected hexapeptide was tested as SPE sorbent versus cocaine and other four drugs using buffer solutions at pH 4 and 7. Significant differences in specific retention were found between cocaine (97% of recovery) and both morphine (45% of recovery) and phencyclidine (60% of recovery), but less for ecstasies (average recovery 69%). In agreement with docking simulation, the hexapeptide showed the highest recovery with best specificity versus cocaine at pH 7 with an experimentally binding constant of 2.9 × 10(6)M(-1). The bio-inspired sorbent material analytical performances were compared with a commercial reversed phase cartridge confirming the hexapeptide specificity to cocaine and validating simulated data.

Selection of peptide ligands for piezoelectric peptide based gas sensors arrays using a virtual screening approach.

Virtual and experimental affinity binding properties of 5 different peptides (cysteinylglycine, glutathione, Cys-Ile-His-Asn-Pro, Cys-Ile-Gln-Pro-Val, Cys-Arg-Gln-Val-Phe) vs. 14 volatile compounds belonging to relevant chemical classes were evaluated. The peptides were selected in order to have a large variability in physicochemical characteristics (including length). In virtual screening a rapid and cost-effective computational methodology for predicting binding scores of small peptide receptors vs. volatile compounds is proposed. Flexibility was considered for both ligands and peptides and each peptide conformer was treated as a possible receptor, generating a dedicated box and then running a docking process vs. all possible conformers of the 14 volatile compounds. The 5 peptides were covalently bound to gold nanoparticles and deposited onto 20 MHz quartz crystal microbalances to realize gas sensors. Gas sensing confirmed that each of the peptide conferred to the gold nanoparticles a particular selectivity pattern able to discriminate the 14 volatile compounds. The largest response was obtained for the pentapeptides Cys-Ile-His-Asn-Pro and Cys-Ile-Gln-Pro-Val while low response was achieved for the dipeptide. The comparative study, carried using a two-tailed T test, demonstrated that virtual screening was able to predict reliably the sensing ability of the pentapeptides. The dipeptide receptor exhibited 29% of virtual-experimental matching vs. 71% of glutathione and up to 93% for the pentapeptides. This virtual screening approach was proved to be a promising tool in predicting the behaviour of sensors array for gas detection.

Design, synthesis and characterization of a hexapeptide bio-inspired by acetylcholinesterase and its interaction with pesticide dichlorvos.

This paper describes the molecular modeling design, synthesis and characterization of a new bio-inspired hexapeptide of acetylcholinesterase enzyme and its interaction with the organophosphate pesticide dichlorvos monitored by UV-Vis spectroscopy and mass spectrometry. This strategy can contribute to the development of synthetic receptors to be coupled to biosensor transducers, avoiding the issues associated with proteins such as low stability under different pH and temperature conditions and high production cost. The resulting data of this work indicate a strong interaction between the pesticide dichlorvos and the hexapeptide (NH3(+)-Glu-His-Gly-Gly-Pro-Ser-COO(-)) with a binding constant of 4.10 × 10(5) M(-1) and the formation of an adduct by covalent binding on the serine residue from the hexapeptide.