Synthesis,Biological Evaluation and Molecular Modeling of Cyclic Tetrapeptide Based Inhibitors HDAC

Histone deacetylases（HDACs） are considered to be among the most promising targets for the development of anti-cancer drugs,and HDAC inhibitors（HDACIs） have become a promising class of anti-cancer drugs.To explore whether thioacetyl group as the zinc binding group（ZBG） and a slight change in the hydrophobicity of the recognition domain of HDACIs could alter their activities,we synthesized a series of cyclo[-L-Am7（SAc）-Aib-L-Phe（n-Cl）D-Pro-] and evaluated their HDAC-inhibitory and antiproliferative activities.The results show that these peptides could inhibit HDAC at 10-9 mol/L level,and could selectively inhibit the proliferation of three human cancer cell lines with IC 50 at 10-6 mol/L level.Docking study was conducted to examine the mechanisms by which these peptides interact with HDAC2.It appeared that a zinc ion in the active site of HDAC was coordinated by the carbonyl oxygen atom of the ZBG in the inhibitor.Both the ZBG domain of all the peptides and the surface recognition domain of cyclo[-L-Am7（SAc）-Aib-L-Phe（o-Cl）-D-Pro-] and that of cyclo[-L-Am7（SAc）-Aib-L-Phe（m-Cl）-D-Pro-] interacted with HDAC2 via hydrogen bonding.Hydrophobic interaction has been considered to provide favorable contributions to stabilizing the complexes,and the introduction of a chlorine atom at the aromatic ring on the L-Phe position of these peptides affected the interaction between each of these inhibitors and the enzyme,resulting in slight change in the structure of the surface recognition domain of the peptides.

Synthesis of Protected Tetrapeptide, N-o-Ns-N(Me)-Val-N(Me)-Val-N(Me)-Val-N(Me)-Phe-O^tBu

The protected tetrapeptide, N-o-Ns-N （ Me ） -Val-N （ Me ） -Val-N （ Me ） -Val- N （ Me ） -Phe- OtBu, was prepared from L-valine and L-phenylalanine. Ted-butyl acetate and HClO4 were used to protect carbonyl group, o-nitrobenzenesulfonyl chloride and triethyl amine were used to protect amino group, and N-alkylation was finished with iodomethane. Then the protected amino acid was turned into acid chloride which was taken as coupling reagent. After 14 steps, such as protection, alkylation, deprotection and coupling, the protected tetrapeptide was obtained with a yield of 26.9%. The structures of intermediates and target compound were identified with NMR spectra and high resolution mass spectra.

Tetrapeptide self-assembled multicolor fluorescent nanoparticles for bioimaging applications

The biocompatibility and biodegradability of peptide self-assembled materials makes them suitable for many biological applications,such as targeted drug delivery,bioimaging,and tracking of therapeutic agents.According to our previous research,self-assembled fluorescent peptide nanoparticles can overcome the intrinsic optical properties of peptides.However,monochromatic fluorescent nanomaterials have many limitations as luminescent agents in biomedical applications.Therefore,combining different fluorescent species into one nanostructure to prepare fluorescent nanoparticles with multiple emission wavelengths has become a very attractive research area in the bioimaging field.In this study,the tetrapeptide Trp-Trp-Trp-Trp(WWWW)was self-assembled into multicolor fluorescent nanoparticles(TPNPs).The results have demonstrated that TPNPs have the blue,green,red and near infrared(NIR)fluorescence emission wavelength.Moreover,TPNPs have shown excellent performance in multicolor bioimaging,biocompatibility,and photostability.The facile preparation and multicolor fluorescence features make TPNPs potentially useful in multiplex bioanalysis and diagnostics.

Colorimetric Recognition of 3,4-Dihydroxy-D,L-phenylalanine with Tetrapeptide-modified Copper Nanoparticles as Chiral Nanozymes

The construct of artificial nanocatalyts by simulating natural enzymes and thereby bringing new properties for practical applications is still a challenging task to date.In this study,chiral tetrapeptide(L-phenylalanine-L-phenylalanine-L-cysteine-L-histidine)-engineered copper nanoparticles(FFCH@CuNPs)were fabricated as an artificial peroxidase(POD).More interestingly,the nano-catalysts exhibited chiral identification function.In comparison with other nanocatalysts like L-cysteine-,L-histidine-,chiral dipeptide(L-cysteine-L-histidine)-,or chiral tripeptide(L-phenylalanine-L-cysteine-L-histidine)-modified CuNPs,FFCH@CuNPs demonstrated higher POD-mimetic catalytic activity in the 3,3',5,5'-tetramethylbenzidine(TMB)-H_(2)O_(2) system and stronger enantioselectivity in the recognition of 3,4-dihydroxy-D,L-phenylalanine(D,L-DOPA)enantiomers.Considering the strength difference between the intermolecular hydrogen bonding and theπ-πinteractions,the principle behind the chiral discrimination of D,L-DOPA was explored.Furthermore,higher contents of surface Cu2+ions and hydroxyl radicals were found in the FFCH@CuNPs-D-DOPA-TMB-H_(2)O_(2) system than in the FFCH@CuNPs-L-DOPA-TMB-H_(2)O_(2) system.Based on these results,a protocol for distinguishing between D,L-DOPA enantiomers through colorimetric recognition was established.This study provides a new insight into the design and fabrication of oligopeptides@CuNPs-based chiral nanozymes with improved catalytic performance and features additional to those of natural enzymes.

Overexpression of the angiogenic tetrapeptide AcSDKP and the prolyl oligopeptidase(POP) is a common feature of cancer

Prolyl oligopeptidase (POP) is a cytosolic enzyme involved in the metabolism of many peptide hormones and neuropeptides (1). It was recently reported that POP is responsible

Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Peptide-based therapeutics are increasingly pushing to the forefront of biomedicine with their promise of high specificity and low toxicity.Although noncanonical residues can always be used,employing only the natural 20 residues restricts the chemical space to a finite dimension allowing for comprehensive in silico screening.Towards this goal,the dataset comprising all possible di-,tri-,and tetra-peptide combinations of the canonical residues has been previously reported.However,with increasing computational power,the comprehensive set of pentapeptides is now also feasible for screening as the comprehensive set of cyclic peptides comprising four or five residues.Here,we provide both the complete and prefiltered libraries of all di-,tri-,tetra-,and penta-peptide sequences from 20 canonical amino acids and their homodetic(N-to-C-terminal)cyclic homologues.The FASTA,simplified molecular-input line-entry system(SMILES),and structure-data file(SDF)-three dimension(3D)libraries can be readily used for screening against protein targets.We also provide a simple method and tool for conducting identity-based filtering.Access to this dataset will accelerate small peptide screening workflows and encourage their use in drug discovery campaigns.As a case study,the developed library was screened against severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)main protease to identify potential small peptide inhibitors.

VICMpred: An SVM-based Method for the Prediction of Functional Proteins of Gram-negative Bacteria Using Amino Acid Patterns and Composition

In this study, an attempt has been made to predict the major functions of gramnegative bacterial proteins from their amino acid sequences. The dataset used for training and testing consists of 670 non-redundant gram-negative bacterial proteins （255 of cellular process, 60 of information molecules, 285 of metabolism, and 70 of virulence factors）. First we developed an SVM-based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39% and 47.01%, respectively. We introduced a new concept for the classification of proteins based on tetrapeptides, in which we identified the unique tetrapeptides significantly found in a class of proteins. These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66%. We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75%. A five-fold cross validation was used to evaluate the performance of these methods. The web server VICMpred has been developed for predicting the function of gram-negative bacterial proteins （http：//www.imtech.res.in/raghava/vicmpred/）.