Quantum Annealing Designs Nonhemolytic Antimicrobial Peptides in a Discrete Latent Space.

Increasing the variety of antimicrobial peptides is crucial in meeting the global challenge of multi-drug-resistant bacterial pathogens. While several deep-learning-based peptide design pipelines are reported, they may not be optimal in data efficiency. High efficiency requires a well-compressed latent space, where optimization is likely to fail due to numerous local minima. We present a multi-objective peptide design pipeline based on a discrete latent space and D-Wave quantum annealer with the aim of solving the local minima problem. To achieve multi-objective optimization, multiple peptide properties are encoded into a score using non-dominated sorting. Our pipeline is applied to design therapeutic peptides that are antimicrobial and non-hemolytic at the same time. From 200 000 peptides designed by our pipeline, four peptides proceeded to wet-lab validation. Three of them showed high anti-microbial activity, and two are non-hemolytic. Our results demonstrate how quantum-based optimizers can be taken advantage of in real-world medical studies.

Axillary accessory breast cancer reconstructed by a thoracodorsal artery perforator flap: A case report.

INTRODUCTION: Primary accessory breast cancer is rare and most commonly occurs in the axilla. Due to its low incidence, few studies have discussed axillary reconstruction after accessory breast cancer resection. In the present report, we describe a patient who underwent axillary reconstruction with a thoracodorsal artery perforator (TAP) flap after resection, and reconstruction methods after resection of axillary accessory breast cancer are discussed based on current and previous reports. PATIENT CONCERNS: A 60-year-old woman presented with a 7-year history of a gradually growing lump in the left axilla. DIAGNOSIS: The patient was diagnosed with latent breast cancer, axillary lymph node metastasis, or carcinoma of the accessory axillary breast with axillary lymph node metastasis. INTERVENTIONS: After preoperative chemotherapy, tumor resection and axillary lymph node dissection were performed, followed by immediate axillary reconstruction using a TAP flap. The patient received postoperative adjuvant endocrine and radiation therapy (50 Gy). OUTCOMES: No recurrence or metastasis was observed for 5 years postoperatively. The reconstructed axilla was not bulky, and scar contracture was not observed, with a full range of motion of the shoulder joint. CONCLUSION: We described a patient who underwent immediate TAP flap reconstruction after resection of accessory breast cancer and axillary lymph node dissection, followed by postoperative radiation, which could cause scar contracture. The patient was followed up for more than 5 years after the operation and radiation therapy, and the appearance of the axilla and range of motion of the shoulder were good despite postoperative radiation.

Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides.

Cell-penetrating peptides have important therapeutic applications in drug delivery, but the variety of known cell-penetrating peptides is still limited. With a promise to accelerate peptide development, artificial intelligence (AI) techniques including deep generative models are currently in spotlight. Scientists, however, are often overwhelmed by an excessive number of unannotated sequences generated by AI and find it difficult to obtain insights to prioritize them for experimental validation. To avoid this pitfall, we leverage molecular dynamics (MD) simulations to obtain mechanistic information to prioritize and understand AI-generated peptides. A mechanistic score of permeability is computed from five steered MD simulations starting from different initial structures predicted by homology modelling. To compensate for variability of predicted structures, the score is computed with sample variance penalization so that a peptide with consistent behaviour is highly evaluated. Our computational pipeline involving deep learning, homology modelling, MD simulations and synthesizability assessment generated 24 novel peptide sequences. The top-scoring peptide showed a consistent pattern of conformational change in all simulations regardless of initial structures. As a result of wet-lab-experiments, our peptide showed better permeability and weaker toxicity in comparison to a clinically used peptide, TAT. Our result demonstrates how MD simulations can support de novo peptide design by providing mechanistic information supplementing statistical inference.

Selection of Ovalbumin-specific Binding Peptides through Instant Translation in Ribosome Display Using E. coli Extract.

In vitro selection has been widely used to generate molecular-recognition elements in analytical sciences. Although reconstituted types of in vitro transcription and translation (IVTT) system, such as PURE system, are nowadays widely used for ribosome display and mRNA/cDNA display, use of E. coli extract is often avoided, presumably because it contains unfavorable contaminants, such as ribonuclease. Nevertheless, the initial speed of protein translation in E. coli extract is markedly faster than that of PURE system. We thus hypothesized that E. coli extract is more appropriate for instant translation in ribosome display than PURE system. Here, we first revisit the potency of E. coli extract for ribosome display by shortening the translation time, and then applied the optimized condition for selecting peptide aptamers for ovalbumin (OVA). The OVA-binding peptides selected using E. coli extract exhibited specific binding to OVA, even in the presence of 50% serum. We conclude that instant translation in ribosome display using E. coli extract has the potential to generate easy-to-use and economical molecular-recognition elements in analytical sciences.

Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks.

Antimicrobial peptides are a potential solution to the threat of multidrug-resistant bacterial pathogens. Recently, deep generative models including generative adversarial networks (GANs) have been shown to be capable of designing new antimicrobial peptides. Intuitively, a GAN controls the probability distribution of generated sequences to cover active peptides as much as possible. This paper presents a peptide-specialized model called PepGAN that takes the balance between covering active peptides and dodging nonactive peptides. As a result, PepGAN has superior statistical fidelity with respect to physicochemical descriptors including charge, hydrophobicity, and weight. Top six peptides were synthesized, and one of them was confirmed to be highly antimicrobial. The minimum inhibitory concentration was 3.1 µg/mL, indicating that the peptide is twice as strong as ampicillin.

Escherichia coli expression, purification, and refolding of human folate receptor α (hFRα) and ß (hFRß).

Human folate receptors (hFRα and hFRß) are membrane proteins anchored to the cell surface by glycosylphosphatidylinositol. They play an important role in cell growth by taking up folate for de novo synthesis of purines and methylation of DNA, lipids, and proteins. Thus, controlling folate uptake through hFRs may lead to the development of anti-cancer drugs. Development of hFRs-targeting drug requires a large amount of hFRs. However, it is difficult to prepare active forms of hFRs from prokaryotic cells because of their high content of cysteine residues that form disulfide bonds. Here, we prepared active forms of hFRα and hFRß from inclusion bodies of Escherichia coli. The crucial steps in our preparation were intensive washing of the inclusion bodies to remove impurities derived from E. coli and gradual dropping of solubilized hFRs into refolding buffers to correctly reform disulfide bonds. The binding activity of prepared hFRs to folate was confirmed by biolayer interferometry measurements. Finally, we successfully prepared the active form of 2.52 mg hFRα and 2.4 mg hFRß from 10 g of E. coli cell bodies.