Multidimensional Design of Anticancer Peptides

The Scent of Change: Sustainable Fragrances Through Industrial Biotechnology

Current environmental and safety considerations urge innovation to address the need for sustainable high-value chemicals that are embraced by consumers. This review discusses the concept of sustainable fragrances, as high-value, everyday and everywhere chemicals. Current and emerging technologies represent an opportunity to produce fragrances in an environmentally and socially responsible way. Biotechnology, including fermentation, biocatalysis, and genetic engineering, has the potential to reduce the environmental footprint of fragrance production while maintaining quality and consistency. Computational and in silico methods, including machine learning (ML), are also likely to augment the capabilities of sustainable fragrance production. Continued innovation and collaboration will be crucial to the future of sustainable fragrances, with a focus on developing novel sustainable ingredients, as well as ethical sourcing practices.

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.

Natural Peptides Inducing Cancer Cell Death: Mechanisms and Properties of Specific Candidates for Cancer Therapeutics

Nowadays, cancer has become the second highest leading cause of death, and it is expected to continue to affect the population in forthcoming years. Additionally, treatment options will become less accessible to the public as cases continue to grow and disease mechanisms expand. Hence, specific candidates with confirmed anticancer effects are required to develop new drugs. Among the novel therapeutic options, proteins are considered a relevant source, given that they have bioactive peptides encrypted within their sequences. These bioactive peptides, which are molecules consisting of 2–50 amino acids, have specific activities when administered, producing anticancer effects. Current databases report the effects of peptides. However, uncertainty is found when their molecular mechanisms are investigated. Furthermore, analyses addressing their interaction networks or their directly implicated mechanisms are needed to elucidate their effects on cancer cells entirely. Therefore, relevant peptides considered as candidates for cancer therapeutics with specific sequences and known anticancer mechanisms were accurately reviewed. Likewise, those features which turn certain peptides into candidates and the mechanisms by which peptides mediate tumor cell death were highlighted. This information will make robust the knowledge of these candidate peptides with recognized mechanisms and enhance their non-toxic capacity in relation to healthy cells and further avoid cell resistance.

Natural Peptides Inducing Cancer Cell Death: Mechanisms and Properties of Specific Candidates for Cancer Therapeutics

Nowadays, cancer has become the second highest leading cause of death, and it is expected to continue to affect the population in forthcoming years. Additionally, treatment options will become less accessible to the public as cases continue to grow and disease mechanisms expand. Hence, specific candidates with confirmed anticancer effects are required to develop new drugs. Among the novel therapeutic options, proteins are considered a relevant source, given that they have bioactive peptides encrypted within their sequences. These bioactive peptides, which are molecules consisting of 2–50 amino acids, have specific activities when administered, producing anticancer effects. Current databases report the effects of peptides. However, uncertainty is found when their molecular mechanisms are investigated. Furthermore, analyses addressing their interaction networks or their directly implicated mechanisms are needed to elucidate their effects on cancer cells entirely. Therefore, relevant peptides considered as candidates for cancer therapeutics with specific sequences and known anticancer mechanisms were accurately reviewed. Likewise, those features which turn certain peptides into candidates and the mechanisms by which peptides mediate tumor cell death were highlighted. This information will make robust the knowledge of these candidate peptides with recognized mechanisms and enhance their non-toxic capacity in relation to healthy cells and further avoid cell resistance.

Synthetic Molecular Evolution of Cell Penetrating Peptides

Rational design and optimization of cell penetrating peptides (CPPs) is difficult to accomplish because of the lack of quantitative sequence-structure-function rules describing the activity and because of the complex, poorly understood mechanisms of CPPs. Synthetic molecular evolution is a powerful method to identify gain-of-function cell penetrating peptide variants in this situation. Synthetic molecular evolution requires the design and synthesis of iterative, knowledge-based peptide libraries and the screening of such libraries in complex orthogonal cell-based screens for improved activity. In this chapter, we describe methods for synthesizing powerful combinatorial peptide libraries for synthetic molecular evolution.

Correlation between the secondary structure and surface activity of β-sheet forming cationic amphiphilic peptides and their anticancer activity

Cancer is one of the main causes of death worldwide. The current cancer treatment strategies often lack selectivity for cancer cells resulting in dose-limiting adverse effects and reduced quality of life. Recently, anticancer peptides (ACPs) have emerged as an alternative treatment with higher selectivity, less adverse effects, and lower propensity for drug resistance. However, most of the current studies on the ACPs are focused on α-helical ACPs and there is lack of systematic studies on β-sheet forming ACPs. Herein we report the development of a new series of rationally designed short cationic amphiphilic β-sheet forming ACPs and their structure activity relationship. The peptides had the general formula (XY₁XY₂)₃, with X representing hydrophobic amino acids (isoleucine (I) or leucine (L)), Y₁ and Y₂ representing cationic amino acids (arginine (R) or lysine (K)). The cytotoxicity of the designed ACPs in HCT 116 colorectal cancer, HeLa cervical cancer and human dermal fibroblast (HDF) cells was assessed by MTT test. The physicochemical properties of the peptides were characterized by various techniques including RP-HPLC, LC-MS, and Circular Dichroism (CD) spectroscopy. The surface activity of the peptides at the air-water interface and their interaction with the lipid monolayers as models for cell membranes were studied by Langmuir trough. The peptides consisting of I with R and K had selective anticancer activity while the combination of L and R diminished the anticancer activity of the peptides but rendered them more toxic to HDFs. The anticancer activity of the peptides was directed by their surface activity (amphiphilicity) and their secondary structure in hydrophobic surfaces including cancer cell membranes. The selectivity of the peptides for cancer cells was a result of their higher penetration into cancer cell membranes compared to normal cell membranes. The peptides exerted their anticancer activity by disrupting the mitochondrial membranes and eventually apoptosis. The results presented in this study provide an insight into the structure-activity relationship of this class of ACPs which can be employed as guidance to design new ACPs with improved anticancer activity and lower toxicity against normal cells.

Rationally designed short cationic α-helical peptides with selective anticancer activity

HYPOTHESIS

Naturally derived or synthetic anticancer peptides (ACPs) have emerged as a new generation of anticancer agents with higher selectivity for cancer cells and less propensity for drug resistance. Despite the structural diversity of ACPs, α-helix is the most common secondary structure among them. Herein we report the development of a new library of short cationic amphiphilic α-helical ACPs with selective cytotoxicity against colorectal and cervical cancer.

EXPERIMENTS

The peptides had a general formula C(XXYY)₃ with C representing amino acid cysteine (providing a -SH group for molecular conjugation), X representing hydrophobic amino acids (isoleucine (I) or leucine (L)), and Y representing cationic amino acids (arginine (R) or lysine (K)). Two variants of the peptides were synthesized by adding additional Isoleucine residues to the C-terminal and replacing the N-terminal cysteine with LC-propargylglycine (LC-G) to investigate the effect of N-terminal and C-terminal variation on the anticancer activity. The structure and physicochemical properties of the peptides were determined by RP-HPLC, LC-MS and CD spectroscopy. The cytotoxicity of the peptides in different cell lines was assessed by MTT test, cell proliferation assay and mitochondrial damage assay. The mechanism of cell selectivity of the peptides was investigated by studying their interfacial behaviour at the air/water and lipid/water interface using Langmuir trough.

FINDINGS

The peptides consisting of K residues in their hydrophilic domains exhibited more selective anticancer activity whereas the peptides containing R exhibited strong toxicity in normal cells. The anticancer activity of the peptides was a function of their helical content and their hydrophobicity. Therefore, the addition of two I residues at C-terminal enhanced the anticancer activity of the peptides by increasing their hydrophobicity and their helical content. These two variants also exhibited strong anticancer activity against colorectal cancer multicellular tumour spheroids (MCTS). The higher toxicity of the peptides in cancer cells compared to normal cells was the result of higher penetration into the negatively charged cancer cell membranes, leading to higher cellular uptake, and their cytotoxic effect was mainly exerted by damaging the mitochondrial membranes leading to apoptosis. The results from this study provide a basis for rational design of new α-helical ACPs with enhanced anticancer activity and selectivity.

Geometry encoded functional programming of tumor homing peptides for targeted drug delivery

Poly-peptide molecules have shown promising applications in drug delivery and tumor targeting. A series of tumor homing peptides were designed by exhaustively sampling low energy geometrical basins of amino acids at specific sites of a peptide molecule to induce a conformational lock. This peptide library was pruned to a limited set of eight molecules, employing electrostatic interactions, docking, and molecular dynamics simulations. These designed and optimized peptides were synthesized and tested on various cell lines, including breast cancer (MDA-MB-231), cervical cancer (HeLa), osteosarcoma (U2-OS), and non-cancerous mammary epithelial cells (MCF-10A) using confocal microscopy and flow cytometry. Peptides show differential uptake in cancerous MDA-MB-231, HeLa, U2-OS, and non-cancerous MCF-10A cells. Confocal imaging verified their ability to penetrate even in 3D tumorospheres of MDA-MB-231 cells. Further, experiments of mitochondrial membrane potential depolarization and Caspase-3 activation confirmed that their cytotoxic effects are by apoptosis. Homing ability of the designed peptides in in vivo system and fluorescence imaging with clinical samples of human origin have further confirmed that the in vitro studies are qualitatively identical and quantitatively comparable in their ability to selectively recognize tumor cells. Overall, we present a roadmap for the functional programming of peptide-based homing and penetrating molecules that can perform selective tumor targeting.

The Advent of Generative Chemistry

Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or "imagine" new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.

EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides

As cancer remains one of the main threats of human life, developing efficient cancer treatments is urgent. Anticancer peptides, which could overcome the significant side effects and poor results of traditional cancer treatments, have become a new potential alternative these years. However, identifying anticancer peptides by experimental methods is time consuming and resource consuming, it is of great significance to develop effective computational tools to quickly and accurately identify potential anticancer peptides from amino acid sequences. For most current computational methods, feature representation plays a key role in their final successes. This study proposes a novel fast and accurate approach to identify anticancer peptides using diversified feature representations and ensemble learning method. For the feature representations, the information is encoded from multidimensional feature spaces, including sequence composition, sequence-order, physicochemical properties, etc. In order to better model the potential relationships of peptides, multiple ensemble classifiers, LightGBMs, are applied to detect the different feature sets at first. Then the obtained multiple outputs are used as inputs of the support vector machine classifier, which effectively identifies anticancer peptides. Experimental results on cross validation and independent test sets demonstrate that our method can achieve better or comparable performances compared with other state-of-the-art methods.

Fluorescent RGD-based pro-apoptotic peptide conjugates as mitochondria-targeting probes for enhanced anticancer activities

We have designed 2-domain anticancer peptides with RGD-based KLAK bi-functional short motifs (linear and cyclic analogues). RGD tripeptide acts as tumor blood vessel 'homing' motif while KLAK tetrapeptide internalized in mitochondria and causes cell apoptosis. All three peptides (RGDKLAK; HM, cyclic-RGDKLAK; HMC-1, and RGD-cyclic-KLAK; HMC-2) were conjugated with fluorescein isothiocyanate isomer-I (5-FITC; F) for in-vivo and in-vitro optical imaging studies. These fluorescent-peptide (FL-peptide) analogues were analyzed to possess α_vβ₃-integrin targeting affinity, high uptake in in-vitro cell binding assays followed by in-vivo tumor xenograft mice studies. Pharmacological profile reveals that F-HMC-1 analogue exhibited selectively and specifically higher affinity for α_vβ₃-integrin than other analogues in U87MG cells in comparison with HeLa cells. The subcutaneous U87MG tumor xenograft mice models clearly visualized the uptake of F-HMC-1 in tumor tissue in contrast with normal tissues with tumor-to-normal tissue ratio (T/NT = 15.9 ± 1.1) at 2 h post-injection. These results suggested that F-HMC-1 peptide has potential diagnostic applications for targeting α_vβ₃-integrin assessed by optical imaging study in U87MG tumor xenograft mice models.

In Vitro and MD Simulation Study to Explore Physicochemical Parameters for Antibacterial Peptide to Become Potent Anticancer Peptide

Although the physicochemical properties of antimicrobial peptides (AMPs) and anticancer peptides (ACPs) are very similar, it remains unclear which specific parameter(s) of ACPs confer the major anticancer activity. By answering how to construct a short AMP/ACP that could easily be synthesized in the most cost effective way plus conferring a maximum anticancer effect is a very important scientific breakthrough in the development of protein/peptide drugs. In this study, an 18-amino-acids antimicrobial peptide, AcrAP1 (named AP1-Z1), was used as a template. Bioinformatics algorithms were then performed to design its six mutants (AP1-Z3a, AP1-Z3b, AP1-Z5a, AP1-Z5b, AP1-Z7, and AP1-Z9). After a series of in vitro experiments plus intensive computational analysis, the data demonstrated that AP1-Z5a and AP1-Z5b induced both apoptosis and anti-angiogenic effects to achieve the maximum anticancer activity. Specifically, the most effective mutant, AP1-Z5b, exhibited high selectivity for the charged membrane in molecular dynamics simulations. These findings clearly demonstrated that both charge and hydrophobicity play an important role and are necessary to reach an optimum equilibrium for optimizing the anticancer activity of AMPs. Overall, the present study provides a very crucial theoretical basis and important scientific evidence on the key physicochemical parameters of ACP drugs development.

Chemical and Biological Characteristics of Antimicrobial α-Helical Peptides Found in Solitary Wasp Venoms and Their Interactions with Model Membranes

Solitary wasps use their stinging venoms for paralyzing insect or spider prey and feeding them to their larvae. We have surveyed bioactive substances in solitary wasp venoms, and found antimicrobial peptides together with some other bioactive peptides. Eumenine mastoparan-AF (EMP-AF) was the first to be found from the venom of the solitary eumenine wasp Anterhynchium flavomarginatum micado, showing antimicrobial, histamine-releasing, and hemolytic activities, and adopting an α-helical secondary structure under appropriate conditions. Further survey of solitary wasp venom components revealed that eumenine wasp venoms contained such antimicrobial α-helical peptides as the major peptide component. This review summarizes the results obtained from the studies of these peptides in solitary wasp venoms and some analogs from the viewpoint of (1) chemical and biological characterization; (2) physicochemical properties and secondary structure; and (3) channel-like pore-forming properties.

In silico design and optimization of selective membranolytic anticancer peptides

Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.

Syndiotactic peptides for targeted delivery

Lack of cell-type specificity and proteolytic susceptibility have long been the major bottlenecks for the development of peptide-based biomaterials for targeted drug delivery. Though a poly-l backbone provides the adaptability to re-conform the peptide structure to bind to a receptor, it also makes the peptide more susceptible to proteolytic cleavage. We have attempted to address this issue by designing a set of syndiotactic peptides de novo, with alternating l- and d-amino acids in succession. The designed peptides have higher rates of cellular uptake than the Tat (48-60) peptide in breast and cervical cancer cells. The uptake is independent of concentration, temperature and endocytosis (clathrin mediated). Importantly, the peptides are stable in both human plasma and bovine serum. The peptide-drug conjugates are much less toxic to the non-cancerous cells than cancer cells. The designed peptides are a step forward towards the development of targeted drug delivery vectors on peptide templates. STATEMENT OF SIGNIFICANCE: Present options in chemotherapy have multiple side effects arising from the lack of cell-type specificity, which makes them synonymous with "a Pyrrhic victory". Proteolytic susceptibility and non-specificity towards cancer cells has stunted the development of peptide-based biomaterials for targeted drug delivery. We have designed a set of peptides, addressing the above-mentioned roadblocks at an in vitro level. The peptides were designed on the template of a naturally existing peptide antibiotic from Bacillus brevis. The designed peptides have higher rates of cellular transduction than the model peptide (Tat), and is majorly membrane based. The peptides are stable in serum and selective towards cancer cells. Observations presented in this work can potentially take the discipline of de novo design of biomaterial conjugates forward.

Mechanistic Landscape of Membrane-Permeabilizing Peptides

Membrane permeabilizing peptides (MPPs) are as ubiquitous as the lipid bilayer membranes they act upon. Produced by all forms of life, most membrane permeabilizing peptides are used offensively or defensively against the membranes of other organisms. Just as nature has found many uses for them, translational scientists have worked for decades to design or optimize membrane permeabilizing peptides for applications in the laboratory and in the clinic ranging from antibacterial and antiviral therapy and prophylaxis to anticancer therapeutics and drug delivery. Here, we review the field of membrane permeabilizing peptides. We discuss the diversity of their sources and structures, the systems and methods used to measure their activities, and the behaviors that are observed. We discuss the fact that "mechanism" is not a discrete or a static entity for an MPP but rather the result of a heterogeneous and dynamic ensemble of structural states that vary in response to many different experimental conditions. This has led to an almost complete lack of discrete three-dimensional active structures among the thousands of known MPPs and a lack of useful or predictive sequence-structure-function relationship rules. Ultimately, we discuss how it may be more useful to think of membrane permeabilizing peptides mechanisms as broad regions of a mechanistic landscape rather than discrete molecular processes.

Linking sequence patterns and functionality of alpha-helical antimicrobial peptides

Motivation

The rational design of antimicrobial peptides (AMPs) with increased therapeutic potential requires deep understanding of the determinants of their activities. Inspired by the computational linguistic approach, we hypothesized that sequence patterns may encode the functional features of AMPs.

Results

We found that α-helical and β-sheet peptides have non-intersecting pattern sets and therefore constructed new sequence templates using only helical patterns. Designed peptides adopted an α-helical conformation upon binding to lipids, confirming that the method captures structural and biophysical properties. In the antimicrobial assay, 5 of 7 designed peptides exhibited activity against Gram(+) and Gram(–) bacteria, with most potent candidate comparable to best natural peptides. We thus conclude that sequence patterns comprise the structural and functional features of α-helical AMPs and guide their efficient design.

Supplementary information

Supplementary data are available at Bioinformatics online.

Synthetic molecular evolution of hybrid cell penetrating peptides

Peptides and analogs such as peptide nucleic acids (PNA) are promising tools and therapeutics, but the cell membrane remains a barrier to intracellular targets. Conjugation to classical cell penetrating peptides (CPPs) such as pTat_48–60 (tat) and pAntp_43–68 (penetratin) facilitates delivery; however, efficiencies are low. Lack of explicit design principles hinders rational improvement. Here, we use synthetic molecular evolution (SME) to identify gain-of-function CPPs with dramatically improved ability to deliver cargoes to cells at low concentration. A CPP library containing 8192 tat/penetratin hybrid peptides coupled to an 18-residue PNA is screened using the HeLa pTRE-LucIVS2 splice correction reporter system. The daughter CPPs identified are one to two orders of magnitude more efficient than the parent sequences at delivery of PNA, and also deliver a dye cargo and an anionic peptide cargo. The significant increase in performance following a single iteration of SME demonstrates the power of this approach to peptide sequence optimization.

Therapeutic peptide nucleic acids can be delivered into cells by conjugation to cell penetrating peptides (CPPs), but efficiency is usually low. Here the authors use synthetic molecular evolution and a luciferase-based library screen to generate new CPPs with improved efficiency and lower toxicity.

Recurrent Neural Network Model for Constructive Peptide Design

We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequences represent a suitable input for these machine-learning models. Generative models trained on peptide sequences could therefore facilitate the design of bespoke peptide libraries. We trained RNNs with LSTM units on pattern recognition of helical antimicrobial peptides and used the resulting model for de novo sequence generation. Of these sequences, 82% were predicted to be active antimicrobial peptides compared to 65% of randomly sampled sequences with the same amino acid distribution as the training set. The generated sequences also lie closer to the training data than manually designed amphipathic helices. The results of this study showcase the ability of LSTM RNNs to construct new amino acid sequences within the applicability domain of the model and motivate their prospective application to peptide and protein design without the need for the exhaustive enumeration of sequence libraries.

Rational Design of Membrane-Pore-Forming Peptides

Specific interactions of peptides with lipid membranes are essential for cellular communication and constitute a central aspect of the innate host defense against pathogens. A computational method for generating innovative membrane-pore-forming peptides inspired by natural templates is presented. Peptide representation in terms of sequence- and topology-dependent hydrophobic moments is introduced. This design concept proves to be appropriate for the de novo generation of first-in-class membrane-active peptides with the anticipated mode of action. The designed peptides outperform the natural template in terms of their antibacterial activity. They form a kinked helical structure and self-assemble in the membrane by an entropy-driven mechanism to form dynamically growing pores that are dependent on the lipid composition. The results of this study demonstrate the unique potential of natural template-based peptide design for chemical biology and medicinal chemistry.

Peptides with Dual Antimicrobial and Anticancer Activities

In recent years, the number of people suffering from cancer and multi-resistant infections has increased, such that both diseases are already seen as current and future major causes of death. Moreover, chronic infections are one of the main causes of cancer, due to the instability in the immune system that allows cancer cells to proliferate. Likewise, the physical debility associated with cancer or with anticancer therapy itself often paves the way for opportunistic infections. It is urgent to develop new therapeutic methods, with higher efficiency and lower side effects. Antimicrobial peptides (AMPs) are found in the innate immune system of a wide range of organisms. Identified as the most promising alternative to conventional molecules used nowadays against infections, some of them have been shown to have dual activity, both as antimicrobial and anticancer peptides (ACPs). Highly cationic and amphipathic, they have demonstrated efficacy against both conditions, with the number of nature-driven or synthetically designed peptides increasing year by year. With similar properties, AMPs that can also act as ACPs are viewed as future chemotherapeutic drugs, with the advantage of low propensity to resistance, which started this paradigm in the pharmaceutical market. These peptides have already been described as molecules presenting killing mechanisms at the membrane level, but also acting toward intracellular targets, which increases their success compartively to one-target specific drugs. This review will approach the desirable characteristics of small peptides that demonstrated dual activity against microbial infections and cancer, as well as the peptides engaged in clinical trials.

Breast Cancer Targeting Peptide Binds Keratin 1: A New Molecular Marker for Targeted Drug Delivery to Breast Cancer

The biomarkers or receptors expressed on cancer cells and the targeting ligands with high binding affinity for biomarkers play a key role in early detection and treatment of breast cancer. The breast cancer targeting peptide p160 (12-mer) and its enzymatically stable analogue 18-4 (10-mer) showed marked potential for breast cancer drug delivery using cell studies and animal models. Herein, we used affinity purification, liquid chromatography-tandem mass spectrometry, and proteomics to identify keratin 1 (KRT1) as the target receptor highly expressed on breast cancer cells for p160 peptide(s). Western blot and immunocytochemistry in MCF-7 breast cancer cells confirmed the identity of KRT1. We demonstrate that the p160 or 18-4 binding to MCF-7 breast cancer cells is dependent on the expression of KRT1, and we confirm peptide-KRT1 binding specificity using SPR experiments (K_d ∼ 1.1 μM and 0.98 μM for p160 and 18-4, respectively). Furthermore, we assessed the ability of peptide 18-4 to improve the cellular uptake and anticancer activity of a pro-apoptotic antimicrobial peptide, microcin J25 (MccJ25), in breast cancer cells. A covalent conjugate of peptide 18-4 with MccJ25 showed preferential cytotoxicity toward breast cancer cells with minimal cytotoxicity against normal HUVEC cells. The conjugate inhibited the growth of MDA-MB-435 MDR multidrug-resistant cells with an IC₅₀ comparable to that of nonresistant cells. Conjugation improved selective cellular uptake of MccJ25, and the conjugate triggered cancer cell death by apoptosis. Our findings establish KRT1 as a new marker for breast cancer targeting. Additionally, it pinpoints the potential use of antimicrobial lasso peptides as a novel class of anticancer therapeutics.

Tuning the anticancer activity of a novel pro-apoptotic peptide using gold nanoparticle platforms

Pro-apoptotic peptides induce intrinsic apoptosis pathway in cancer cells. However, poor cellular penetration of the peptides is often associated with limited therapeutic efficacy. In this report, a series of peptide-gold nanoparticle platforms were developed to evaluate the anticancer activity of a novel alpha-lipoic acid-peptide conjugate, LA-WKRAKLAK, with respect to size and shape of nanoparticles. Gold nanoparticles (AuNPs) were found to enhance cell internalization as well as anticancer activity of the peptide conjugates. The smaller nanospheres showed a higher cytotoxicity, morphological change and cellular uptake compared to larger nanospheres and nanorods, whereas nanorods showed more hemolytic activity compared to nanospheres. The findings suggested that the anticancer and biological effects of the peptides induced by intrinsic apoptotic pathway were tuned by peptide-functionalized gold nanoparticles (P-AuNPs) as a function of their size and shape.

Membranolytic anticancer peptides

Understanding the structure–activity relationships and mechanisms of action of membranolytic anticancer peptides could help them advance to therapeutic success.

Novel tubulin-targeted cell penetrating antimitotic octapeptide

Here, we have discovered an antimitotic cell penetrating octapeptide containing single Arg amino acid.