Design of p53-derived peptides with cytotoxicity on breast cancer

The role of ETFS amino acids on the stability and inhibition of p53-MDM2 complex of anticancer p53-derivatives peptides: Density functional theory and molecular docking studies

Cancer is one of the leading causes of mortality in the world. Despite the existence of diverse antineoplastic treatments, these do not possess the expected efficacy in many cases. Knowledge of the molecular mechanisms involved in tumor processes allows the identification of a greater number of therapeutic targets employed in the study of new anticancer drugs. In the last decades, peptide-based therapy design using computational chemistry has gained importance in the field of oncology therapeutics. This work aims to evaluate the electronic structure, physicochemical properties, stability, and inhibition of ETFS amino acids and peptides derived from the p53-MDM2 binding domain with action in cancer cells; by means of chemical descriptors at the DFT-BHandHLYP level in an aqueous solution, and its intermolecular interactions through molecular docking studies. The results show that The ETFS fragment plays a critical role in the intermolecular interactions. Thus, the amino acids E17, T18 and S20 increase intermolecular interactions through hydrogen bonds and enhance structural stability. F19, W23 and V25 enhance the formation of the alpha-helix. The hydrogen bonds formed by the backbone atoms for PNC-27, PNC-27-B and PNC-28 stabilize the α-helices more than hydrogen bonds formed by the side chains atoms. Also, molecular docking indicated that the PNC27B-MDM2, PNC28B-MDM2, PNC27-MDM2 and PNC28A-MDM2 complexes show the best binding energy. Therefore, DFT and molecular docking studies showed that the proposed peptides: PNC-28B, PNC-27B and PNC-28A could inhibit the binding of MDM2 to the p53 protein, decreasing the translocation and degradation of p53 native protein.

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

Background:

Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.

Methods:

We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.

Results:

Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.

Conclusion:

There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

Molecular design and validation of halogen bonding orthogonal to hydrogen bonding in breast cancer MDM2-peptide complex

Peptide therapeutics has been raised as an attractive approach for the treatment of breast cancer by targeting the oncogenic protein MDM2 that inactivates p53 tumor suppressor. Here, we performed molecular design of halogen bonding orthogonal to hydrogen bonding at the complex interface of MDM2 protein with its cognate peptide ligand to improve the peptide binding affinity and specificity. Crystal structure analysis, high-level quantum chemistry (QC) calculations and combined quantum mechanics/molecular mechanics (QM/MM) modeling revealed that halogen substitution at position 3 of the benzene moiety of peptide Phe3 residue can constitute a putative halogen bonding, which is shown to be geometrically perpendicular to and energetically independent of a native hydrogen bonding that share a common carbonyl oxygen acceptor. The designed halogen bonding was then validated by surface plasmon resonance (SPR) assays, that is, substitution with bromine at position 3 can considerably improve peptide affinity by ∼4-fold, but the peptide binding does not change substantially upon the bromine substitution at other positions of the Phe3 benzene moiety (the negative controls that are theoretically unable to form the halogen bonding), indicating that the orthogonal molecular interaction (OMI) system between the designed halogen bonding and native hydrogen bonding can co-work well at the complex interface of MDM2 protein with its halogenated peptide ligands.

Structure-based rational design of peptide hydroxamic acid inhibitors to target tumor necrosis factor-α converting enzyme as potential therapeutics for hepatitis

The human tumor necrosis factor-α converting enzyme (TACE) has recently been raised as a new and promising therapeutic target of hepatitis and other inflammatory diseases. Here, we reported a successful application of the solved crystal structure of TACE complex with a peptide-like ligand INN for rational design of novel peptide hydroxamic acid inhibitors with high potency and selectivity to target and inhibit TACE. First, the intermolecular interactions between TACE catalytic domain and INN were characterized through an integrated bioinformatics approach, with which the key substructures of INN that dominate ligand binding were identified. Subsequently, the INN molecular structure was simplified to a chemical sketch of peptide hydroxamic acid compound, which can be regarded as a linear tripeptide capped by a N-terminal carboxybenzyl group (chemically protective group) and a C-terminal hydroxamate moiety (coordinated to the Zn(2+) at TACE active site). Based on the sketch, a virtual combinatorial library containing 180 peptide hydroxamic acids was generated, from which seven samples were identified as promising candidates by using a knowledge-based protein-peptide affinity predictor and were then tested in vitro with a standard TACE activity assay protocol. Consequently, three designed peptide hydroxamic acids, i.e. Cbz-Pro-Ile-Gln-hydroxamic acid, Cbz-Leu-Ile-Val-hydroxamic acid and Cbz-Phe-Val-Met-hydroxamic acid, exhibited moderate or high inhibitory activity against TACE, with inhibition constants Ki of 36 ± 5, 510 ± 46 and 320 ± 26 nM, respectively. We also examined the structural basis and non-bonded profile of TACE interaction with a designed peptide hydroxamic acid inhibitor, and found that the inhibitor ligand is tightly buried in the active pocket of TACE, forming a number of hydrogen bonds, hydrophobic forces and van der Waals contacts at the interaction interface, conferring both stability and specificity for TACE-inhibitor complex architecture.