A conjugate of chlorin e6 and cationic amphipathic peptoid: a dual antimicrobial and anticancer photodynamic therapy agent.

Cationic amphipathic structures are often utilized in natural membrane-active host-defense peptides. Negatively charged surface membranes of rapidly proliferating bacterial and cancer cells have been targeted by various synthetic peptides and peptidomimetics adopting the structural motif. Herein, we synthesized a set of conjugates composed of cationic amphipathic peptoids (i.e., oligo-N-substituted glycines) and a chlorin photosensitizer, named chlorin e6 (Ce6)-peptoid conjugates (CPCs). Among the nine CPCs, CPC 7, composed of Ce6, a PEG linker, and guanidine-rich helical amphipathic peptoids, exhibited a distinct photoresponsive inactivation of Gram-positive and Gram-negative bacteria. Subsequent studies showed that CPC 7 effectively killed various cancer cells after irradiation with red light (655 nm), suggesting the potential of CPC 7 as a dual antimicrobial and anticancer agent. Confocal laser scanning microscopy and flow cytometry data suggested that CPC 7 could induce apoptotic cell death. Our results show the potential of peptoid-based photosensitizer conjugates as a versatile platform for antimicrobial and anticancer photodynamic therapy agents and peptoid therapeutics.

Identification of Single-Atom Ni Site Active toward Electrochemical CO2 Conversion to CO.

Electrocatalytic conversion of CO2 into value-added products offers a new paradigm for a sustainable carbon economy. For active CO2 electrolysis, the single-atom Ni catalyst has been proposed as promising from experiments, but an idealized Ni-N4 site shows an unfavorable energetics from theory, leading to many debates on the chemical nature responsible for high activity. To resolve this conundrum, here we investigated CO2 electrolysis of Ni sites with well-defined coordination, tetraphenylporphyrin (N4-TPP) and 21-oxatetraphenylporphyrin (N3O-TPP). Advanced spectroscopic and computational studies revealed that the broken ligand-field symmetry is the key for active CO2 electrolysis, which subordinates an increase in the Ni redox potential yielding NiI. Along with their importance in activity, ligand-field symmetry and strength are directly related to the stability of the Ni center. This suggests the next quest for an activity-stability map in the domain of ligand-field strength, toward a rational ligand-field engineering of single-atom Ni catalysts for efficient CO2 electrolysis.

Photosensitizer-peptoid conjugates for photoinactivation of Gram-negative bacteria: structure-activity relationship and mechanistic studies.

Multitarget engagement is considered an effective strategy to overcome the threat of bacterial infection, and antimicrobials with multiple mechanisms of action have been successful as natural chemical weaponry. Here, we synthesized a library of photosensitizer-peptoid conjugates (PsPCs) as novel antimicrobial photodynamic therapy (aPDT) agents. The peptoids, linkers, and photosensitizers were varied, and their structure-antimicrobial activity relationships against Escherichia coli were evaluated; PsPC 9 was indicated to be the most promising photoresponsive antimicrobial agent among the synthesized PsPCs. Spectroscopic analyses indicated that 9 generated singlet oxygen upon absorption of visible light (420 nm) while maintaining the weakly helical conformation of the peptoid. Mechanistic studies suggested that damage to the bacterial membrane and cleavage of DNA upon light irradiation were the main causes of bactericidal activity, which was supported by flow cytometry and DNA gel electrophoresis experiments. We demonstrated that the optimal combination of membrane-active peptoids and photosensitizers can generate an efficient aPDT agent that targets multiple sites of bacterial components and kills bacteria by membrane disruption and reactive oxygen species generation.

Convolutional neural network model to predict causal risk factors that share complex regulatory features.

Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of potential causal noncoding variants in post-GWAS analyses.

Light polarization dependency existing in the biological photosystem and possible implications for artificial antenna systems.

The processes of biological photosynthesis provide inspiration and valuable lessons for artificial energy collection, transfer, and conversion systems. The extraordinary efficiency of each sequential process of light to biomass conversion originates from the unique architecture and mechanism of photosynthetic proteins. Near 100% quantum efficiency of energy transfer in biological photosystems is achieved by the chlorophyll assemblies in antenna complexes, which also exhibit a significant degree of light polarization. The three-dimensional chiral assembly of chlorophylls is an optimized biological architecture that enables maximum energy transfer efficiency with precisely designed coupling between chlorophylls. In this review, we summarize the key lessons from the photosynthetic processes in biological photosystems, and move our focus to energy transfer mechanisms and the chiral structure of the chlorophyll assembly. Then, we introduce recent approaches and possible implications to realize the biological energy transfer processes on bioinspired scaffold-based artificial antenna systems.

Peptoid Helix Displaying Flavone and Porphyrin: Synthesis and Intramolecular Energy Transfer.

Natural light-harvesting complexes (LHCs) absorb a broad spectrum of sunlight using a collection of photosynthetic pigments whose spatial arrangement is controlled by a protein matrix and exhibit efficient energy transfer. We constructed a novel light-harvesting protein mimic, which absorbs light in the UV to visible region (280-700 nm) by displaying flavone and porphyrin on a peptoid helix. First, an efficient synthesis of 4'-derivatized 7-methoxyflavone (7-MF, 3 and 4) was developed. The flavone-porphyrin-peptoid conjugate (FPPC) was then prepared via Miyaura borylation on a resin-bound peptoid followed by Suzuki coupling between the peptoid and pigment. Circular dichroism spectroscopy indicated that the FPPC underwent helix-to-loop conversion of the peptoid scaffold upon changing the solvent conditions. A distinct intramolecular energy transfer was observed from 7-MF to porphyrin with greater efficiency in the helix than that in the loop conformation of the peptoid, whereas no clear evidence of energy transfer was obtained for unstructured FPPC. We thus demonstrate the value of the helical peptoid, which provided a controlled orientation for 7-MF and porphyrin and modulated the energy transfer efficiency via conformational switching. Our work provides a way to construct a sophisticated LHC mimic with enhanced coverage of the solar spectrum and controllable energy transfer efficiency.

Control of porphyrin interactions via structural changes of a peptoid scaffold.

Nature utilizes optimally organized pigments in light-harvesting complexes. To mimic the natural photosynthetic proteins, effective control over inter-pigment interactions is necessary to attain the desired photophysical properties. Previously, we developed porphyrin-peptoid conjugates (PPCamide) and displayed two porphyrins at defined positions on an α-helical peptoid using a flexible n-butyl linker. Herein, we synthesized new porphyrin-peptoid conjugates (PPCC-C), where porphyrins are conjugated through a rigid C-C linkage to the helical peptoid via the Suzuki-Miyaura cross-coupling reaction. With PPCC-C, we studied the effects of backbone conformation, inter-porphyrin distance, and the linker flexibility on porphyrin interactions. When the rigid C-C linkage was used, conformational homogeneity of the PPC increased, providing more effective intramolecular excitonic couplings between the porphyrins; however, the intermolecular porphyrin J-aggregation decreased. In PPCC-C with a nonameric peptoid backbone, the formation of a threaded loop conformation was observed, which could be switched back to a helical conformation by N-terminal acetylation or by the addition of a protic solvent. This threaded loop-to-helix conversion restored the intramolecular porphyrin interactions. Our results suggest that PPCs represent an excellent system for control over porphyrin interactions and therefore are useful as a model system to elucidate pigment interactions in nature or as a molecular construct with switchable photophysical properties.

Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes.

Global network modeling of distal regulatory interactions is essential in understanding the overall architecture of gene expression programs. Here, we developed a Bayesian probabilistic model and computational method for global causal network construction with breast cancer as a model. Whereas physical regulator binding was well supported by gene expression causality in general, distal elements in intragenic regions or loci distant from the target gene exhibited particularly strong functional effects. Modeling the action of long-range enhancers was critical in recovering true biological interactions with increased coverage and specificity overall and unraveling regulatory complexity underlying tumor subclasses and drug responses in particular. Transcriptional cancer drivers and risk genes were discovered based on the network analysis of somatic and genetic cancer-related DNA variants. Notably, we observed that the risk genes were functionally downstream of the cancer drivers and were selectively susceptible to network perturbation by tumorigenic changes in their upstream drivers. Furthermore, cancer risk alleles tended to increase the susceptibility of the transcription of their associated genes. These findings suggest that transcriptional cancer drivers selectively induce a combinatorial misregulation of downstream risk genes, and that genetic risk factors, mostly residing in distal regulatory regions, increase transcriptional susceptibility to upstream cancer-driving somatic changes.

Predicting the recurrence of noncoding regulatory mutations in cancer.

BACKGROUND: One of the greatest challenges in cancer genomics is to distinguish driver mutations from passenger mutations. Whereas recurrence is a hallmark of driver mutations, it is difficult to observe recurring noncoding mutations owing to a limited amount of whole-genome sequenced samples. Hence, it is required to develop a method to predict potentially recurrent mutations. RESULTS: In this work, we developed a random forest classifier that predicts regulatory mutations that may recur based on the features of the mutations repeatedly appearing in a given cohort. With breast cancer as a model, we profiled 35 quantitative features describing genetic and epigenetic signals at the mutation site, transcription factors whose binding motif was disrupted by the mutation, and genes targeted by long-range chromatin interactions. A true set of mutations for machine learning was generated by interrogating publicly available pan-cancer genomes based on our statistical model of mutation recurrence. The performance of our random forest classifier was evaluated by cross validations. The variable importance of each feature in the classification of mutations was investigated. Our statistical recurrence model for the random forest classifier showed an area under the curve (AUC) of ~0.78 in predicting recurrent mutations. Chromatin accessibility at the mutation sites, the distance from the mutations to known cancer risk loci, and the role of the target genes in the regulatory or protein interaction network were among the most important variables. CONCLUSIONS: Our methods enable to characterize recurrent regulatory mutations using a limited number of whole-genome samples, and based on the characterization, to predict potential driver mutations whose recurrence is not found in the given samples but likely to be observed with additional samples.

A direct assay of butyrylcholinesterase activity using a fluorescent substrate.

In this study, we report a direct fluorometric assay for butyrylcholinesterase (BChE) activity and screening of its inhibitor, using a fluorescent substrate. 2-(2-(5,6-Dimethoxy-1,3-dioxoisoindolin-2-yl)acetoxy)-N,N,N-trimethylethan-1-ammonium iodide (1) was hydrolyzed by BChE, and its fluorescence was quenched by an intramolecular photoinduced electron transfer process. The resulting change in fluorescence provided a facile method for real-time BChE activity testing. Remarkably, 1 was selectively hydrolyzed by BChE, even in the presence of excess acetylcholinesterase, thereby facilitating the specific monitoring of BChE activity. This assay method is also useful for screening potential BChE inhibitors. Given its simplicity, selectivity, and higher assay speed, this method may be extended to high-throughput screening of BChE inhibitors and relevant drug discovery.

In Vitro Models for Investigating Intestinal Host-Pathogen Interactions.

Infectious diseases are increasingly recognized as a major threat worldwide due to the rise of antimicrobial resistance and the emergence of novel pathogens. In vitro models that can adequately mimic in vivo gastrointestinal physiology are in high demand to elucidate mechanisms behind pathogen infectivity, and to aid the design of effective preventive and therapeutic interventions. There exists a trade-off between simple and high throughput models and those that are more complex and physiologically relevant. The complexity of the model used shall be guided by the biological question to be addressed. This review provides an overview of the structure and function of the intestine and the models that are developed to emulate this. Conventional models are discussed in addition to emerging models which employ engineering principles to equip them with necessary advanced monitoring capabilities for intestinal host-pathogen interrogation. Limitations of current models and future perspectives on the field are presented.

Protein-mimetic peptoid nanoarchitectures for pathogen recognition and neutralization.

Recent outbreaks of both new and existing infectious pathogens have threatened healthcare systems around the world. Therefore, it is vital to detect and neutralize pathogens to prevent their spread and treat infected patients. This consideration has led to the development of biosensors and antibiotics inspired by the structure and function of antibodies and antimicrobial peptides (AMPs), which constitute adaptive and innate immunity, efficiently protecting the human body against invading pathogens. Herein, we provide an overview of recent advances in the detection and neutralization of pathogens using protein-mimetic peptoid nanoarchitectures. Peptoids are bio-inspired and sequence-defined polymers composed of repeating N-substituted glycine units. They can spontaneously fold into well-defined three-dimensional nanostructures that encode chemical information depending on their sequences. Loop-functionalized peptoid nanosheets have been constructed by mimicking antibodies containing chemically variable loops as binding motifs for their respective target pathogen. Furthermore, by mimicking the cationic amphipathic features of natural AMPs, helical peptoids and their assemblies have been developed to achieve selective anti-infective activity owing to their intrinsic ability to interact with bacterial membranes and viral envelopes. We believe that this mini-review furnishes in-depth insight into how to construct protein-like nanostructures via the self-assembly of peptoids for application in the detection of pathogens and the treatment of infectious diseases for future healthcare applications.

Prediction of long-term heavy metal leaching from dredged marine sediment applied inland as a construction material.

Column leaching studies have been suggested as a reference for site-specific prediction of the long-term leaching characteristics of trace constituents in granular materials used as construction materials. In this study, the concept of the long-term leaching prediction using column studies is applied for dredged marine sediment impacted by heavy metals. The column studies show tailing of the liquid to solid ratio-dependent heavy metal leaching for sediment after heavy metal treatment by acid washing. A dual-mode first-order decay model, applied for the first time in this study for column leaching studies, is able to reproduce the leaching characteristics observed. A procedure for long-term leaching prediction using the dual-mode model is developed and applied to a virtual field scenario for which the sediment is beneficially used as a construction material. The prediction results show that by more accurately reproducing the column study results, the dual-mode model generally predicts greater long-term heavy metal loading to the underlying soil layer and longer duration of leaching than the single-mode model. The heavy metal leaching observed in the columns does not show any correlation with the sequential extraction procedure and toxicity characteristic leaching procedure (TCLP) results, suggesting that the column leaching test should be considered to be independent of such batch test procedures.

Precisely tuneable energy transfer system using peptoid helix-based molecular scaffold.

The energy flow during natural photosynthesis is controlled by maintaining the spatial arrangement of pigments, employing helices as scaffolds. In this study, we have developed porphyrin-peptoid (pigment-helix) conjugates (PPCs) that can modulate the donor-acceptor energy transfer efficiency with exceptional precision by controlling the relative distance and orientation of the two pigments. Five donor-acceptor molecular dyads were constructed using zinc porphyrin and free base porphyrin (Zn(i + 2)-Zn(i + 6)), and highly efficient energy transfer was demonstrated with estimated efficiencies ranging from 92% to 96% measured by static fluorescence emission in CH2Cl2 and from 96.3% to 97.6% using femtosecond transient absorption measurements in toluene, depending on the relative spatial arrangement of the donor-acceptor pairs. Our results suggest that the remarkable precision and tunability exhibited by nature can be achieved by mimicking the design principles of natural photosynthetic proteins.

Chromatin structure-based prediction of recurrent noncoding mutations in cancer.

Recurrence is a hallmark of cancer-driving mutations. Recurrent mutations can arise at the same site or affect the same gene at different sites. Here we identified a set of mutations arising in individual samples and altering different cis-regulatory elements that converge on a common gene via chromatin interactions. The mutations and genes identified in this fashion showed strong relevance to cancer, in contrast to noncoding mutations with site-specific recurrence only. We developed a prediction method that identifies potentially recurrent mutations on the basis of the features shared by mutations whose recurrence is observed in a given cohort. Our method was capable of accurately predicting recurrent mutations at the level of target genes but not mutations recurring at the same site. We experimentally validated predicted mutations in distal regulatory regions of the TERT gene. In conclusion, we propose a novel approach to discovering potential cancer-driving mutations in noncoding regions.