Federico Comitani, Joshua O Nash, Sarah Cohen-Gogo, Astra I Chang, Timmy T Wen, Anant Maheshwari, Bipasha Goyal, Earvin S Tio, Kevin Tabatabaei, Chelsea Mayoh, Regis Zhao, Ben Ho, Ledia Brunga, John E G Lawrence, Petra Balogh, Adrienne M Flanagan, Sarah Teichmann, Annie Huang, Vijay Ramaswamy, Johann Hitzler, Jonathan D Wasserman, Rebecca A Gladdy, Brendan C Dickson, Uri Tabori, Mark J Cowley, Sam Behjati, David Malkin, Anita Villani, Meredith S Irwin, Adam Shlien <<<

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types. <<<

Ignaty Leshchiner, Edmund A Mroz, Justin Cha, Daniel Rosebrock, Oliver Spiro, Juliana Bonilla-Velez, William C Faquin, Armida Lefranc-Torres, Derrick T Lin, William A Michaud, Gad Getz, James W Rocco <<<

Analysis of premalignant tissue has identified the typical order of somatic events leading to invasive tumors in several cancer types. For other cancers, premalignant tissue is unobtainable, leaving genetic progression unknown. Here, we demonstrate how to infer progression from exome sequencing of primary tumors. Our computational method, PhylogicNDT, recapitulated the previous experimentally determined genetic progression of human papillomavirus-negative (HPV) head and neck squamous cell carcinoma (HNSCC). We then evaluated HPV HNSCC, which lacks premalignant tissue, and uncovered its previously unknown progression, identifying early drivers. We converted relative timing estimates of driver mutations and HPV integration to years before diagnosis based on a clock-like mutational signature. We associated the timing of transitions to aneuploidy with increased intratumor genetic heterogeneity and shorter overall survival. Our approach can establish previously unknown early genetic progression of cancers with unobtainable premalignant tissue, supporting development of experimental models and methods for early detection, interception and prognostication. <<<

The amygdala is assumed to contribute to a bottom-up attentional bias during visual processing of emotional faces. Still, how its response to emotion interacts with top-down attention is not fully understood. It is also unclear if amygdala activity and scalp EEG respond to emotion and attention in a similar way. Therefore, we studied the interaction of emotion and attention during face processing in oscillatory gamma-band activity (GBA) in the amygdala and on the scalp. Amygdala signals were recorded via intracranial EEG (iEEG) in 9 patients with epilepsy. Scalp recordings were collected from 19 healthy participants. Three randomized blocks of angry, neutral, and happy faces were presented, and either negative, neutral, or positive expressions were denoted as targets. Both groups detected happy faces fastest and most accurately. In the amygdala, the earliest effect was observed around 170 ms in high GBA (105-117.5 Hz) when neutral faces served as targets. Here, GBA was higher for emotional than neutral faces. During attention to negative faces, low GBA (< 90 Hz) increased specifically for angry faces both in the amygdala and over posterior scalp regions, albeit earlier on the scalp (60 ms) than in the amygdala (210 ms). From 570 ms, amygdala high GBA (117.5-145 Hz) was also increased for both angry and neutral, compared to happy, faces. When positive faces were the targets, GBA did not differentiate between expressions. The present data reveal that attention-independent emotion detection in amygdala high GBA may only occur during a neutral focus of attention. Top-down threat vigilance coordinates widespread low GBA, biasing stimulus processing in favor of negative faces. These results are in line with a multi-pathway model of emotion processing and help specify the role of GBA in this process by revealing how attentional focus can tune timing and amplitude of emotional GBA responses. <<<

Zheng Sun, Shi Huang, Meng Zhang, Qiyun Zhu, Niina Haiminen, Anna Paola Carrieri, Yoshiki Vázquez-Baeza, Laxmi Parida, Ho-Cheol Kim, Rob Knight, Yang-Yu Liu <<<

Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Nevertheless, benchmarking of metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that were analyzed and interpreted and clearly stating the strategy used for metagenomic profiling. <<<

Deformable image registration is one of the fundamental tasks in medical imaging. Classical registration algorithms usually require a high computational cost for iterative optimizations. Although deep-learning-based methods have been developed for fast image registration, it is still challenging to obtain realistic continuous deformations from a moving image to a fixed image with less topological folding problem. To address this, here we present a novel diffusion-model-based image registration method, called DiffuseMorph. DiffuseMorph not only generates synthetic deformed images through reverse diffusion but also allows image registration by deformation fields. Specifically, the deformation fields are generated by the conditional score function of the deformation between the moving and fixed images, so that the registration can be performed from continuous deformation by simply scaling the latent feature of the score. Experimental results on 2D facial and 3D medical image registration tasks demonstrate that our method provides flexible deformations with topology preservation capability. <<<

Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data. <<<

Alexandra Mousset, Enora Lecorgne, Isabelle Bourget, Pascal Lopez, Kitti Jenovai, Julien Cherfils-Vicini, Chloé Dominici, Géraldine Rios, Cédric Girard-Riboulleau, Bodu Liu, David L Spector, Sidse Ehmsen, Shufang Renault, Caroline Hego, Fatima Mechta-Grigoriou, François-Clément Bidard, Mikkel Green Terp, Mikala Egeblad, Cédric Gaggioli, Jean Albrengues <<<

Metastasis is the major cause of cancer death, and the development of therapy resistance is common. The tumor microenvironment can confer chemotherapy resistance (chemoresistance), but little is known about how specific host cells influence therapy outcome. We show that chemotherapy induces neutrophil recruitment and neutrophil extracellular trap (NET) formation, which reduces therapy response in mouse models of breast cancer lung metastasis. We reveal that chemotherapy-treated cancer cells secrete IL-1β, which in turn triggers NET formation. Two NET-associated proteins are required to induce chemoresistance: integrin-αvβ1, which traps latent TGF-β, and matrix metalloproteinase 9, which cleaves and activates the trapped latent TGF-β. TGF-β activation causes cancer cells to undergo epithelial-to-mesenchymal transition and correlates with chemoresistance. Our work demonstrates that NETs regulate the activities of neighboring cells by trapping and activating cytokines and suggests that chemoresistance in the metastatic setting can be reduced or prevented by targeting the IL-1β-NET-TGF-β axis. <<<

Long Song, Jie Liu, Beilu Cao, Bin Liu, Xiaoping Zhang, Zhaoyan Chen, Chaoqun Dong, Xiangqing Liu, Zhaoheng Zhang, Wenxi Wang, Lingling Chai, Jing Liu, Jun Zhu, Shubin Cui, Fei He, Huiru Peng, Zhaorong Hu, Zhenqi Su, Weilong Guo, Mingming Xin, Yingyin Yao, Yong Yan, Yinming Song, Guihua Bai, Qixin Sun, Zhongfu Ni <<<

Modern green revolution varieties of wheat (Triticum aestivum L.) confer semi-dwarf and lodging-resistant plant architecture owing to the Reduced height-B1b (Rht-B1b) and Rht-D1b alleles. However, both Rht-B1b and Rht-D1b are gain-of-function mutant alleles encoding gibberellin signalling repressors that stably repress plant growth and negatively affect nitrogen-use efficiency and grain filling. Therefore, the green revolution varieties of wheat harbouring Rht-B1b or Rht-D1b usually produce smaller grain and require higher nitrogen fertilizer inputs to maintain their grain yields. Here we describe a strategy to design semi-dwarf wheat varieties without the need for Rht-B1b or Rht-D1b alleles. We discovered that absence of Rht-B1 and ZnF-B (encoding a RING-type E3 ligase) through a natural deletion of a haploblock of about 500 kilobases shaped semi-dwarf plants with more compact plant architecture and substantially improved grain yield (up to 15.2%) in field trials. Further genetic analysis confirmed that the deletion of ZnF-B induced the semi-dwarf trait in the absence of the Rht-B1b and Rht-D1b alleles through attenuating brassinosteroid (BR) perception. ZnF acts as a BR signalling activator to facilitate proteasomal destruction of the BR signalling repressor BRI1 kinase inhibitor 1 (TaBKI1), and loss of ZnF stabilizes TaBKI1 to block BR signalling transduction. Our findings not only identified a pivotal BR signalling modulator but also provided a creative strategy to design high-yield semi-dwarf wheat varieties by manipulating the BR signal pathway to sustain wheat production. <<<

Zachariah H. Foda , Akshaya V. Annapragada , Kavya Boyapati , Daniel C. Bruhm , Nicholas A. Vulpescu , Jamie E. Medina , Dimitrios Mathios , Stephen Cristiano , Noushin Niknafs , Harry T. Luu , Michael G. Goggins , Robert A. Anders , Jing Sun , Shruti H. Mehta , David L. Thomas , Gregory D. Kirk , Vilmos Adleff , Jillian Phallen , Robert B. Scharpf , Amy K. Kim , Victor E. Velculescu <<<

Abstract Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high-risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate. In this study, we used whole-genome cell-free DNA fragmentome analyses to evaluate 724 individuals from the US, EU, or Hong Kong with hepatocellular carcinoma (HCC) or who were at average or high-risk for HCC. Using a machine learning model that incorporated multi-feature fragmentome data, the sensitivity for detecting cancer was 88% in an average risk population at 98% specificity, and 85% among high-risk individuals at 80% specificity. We validated these results in an independent population. cfDNA fragmentation changes reflected genomic and chromatin changes in liver cancer, including from transcription factor binding sites. These findings provide a biological basis for changes in cfDNA fragmentation in patients with liver cancer and provide an accessible approach for non-invasive cancer detection. <<<

Andrew Chow, Sara Schad, Michael D Green, Matthew D Hellmann, Viola Allaj, Nicholas Ceglia, Giulia Zago, Nisargbhai S Shah, Sai Kiran Sharma, Marissa Mattar, Joseph Chan, Hira Rizvi, Hong Zhong, Cailian Liu, Yonina Bykov, Dmitriy Zamarin, Hongyu Shi, Sadna Budhu, Corrin Wohlhieter, Fathema Uddin, Aditi Gupta, Inna Khodos, Jessica J Waninger, Angel Qin, Geoffrey J Markowitz, Vivek Mittal, Vinod Balachandran, Jennifer N Durham, Dung T Le, Weiping Zou, Sohrab P Shah, Andrew McPherson, Katherine Panageas, Jason S Lewis, Justin S A Perry, Elisa de Stanchina, Triparna Sen, John T Poirier, Jedd D Wolchok, Charles M Rudin, Taha Merghoub <<<

Immune checkpoint blockade (ICB) has been a remarkable clinical advance for cancer; however, the majority of patients do not respond to ICB therapy. We show that metastatic disease in the pleural and peritoneal cavities is associated with poor clinical outcomes after ICB therapy. Cavity-resident macrophages express high levels of Tim-4, a receptor for phosphatidylserine (PS), and this is associated with reduced numbers of CD8 T cells with tumor-reactive features in pleural effusions and peritoneal ascites from patients with cancer. We mechanistically demonstrate that viable and cytotoxic anti-tumor CD8 T cells upregulate PS and this renders them susceptible to sequestration away from tumor targets and proliferation suppression by Tim-4 macrophages. Tim-4 blockade abrogates this sequestration and proliferation suppression and enhances anti-tumor efficacy in models of anti-PD-1 therapy and adoptive T cell therapy in mice. Thus, Tim-4 cavity-resident macrophages limit the efficacy of immunotherapies in these microenvironments. <<<

Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity. <<<

Individual amino acids are widely popular as supplements because of various perceived and real health benefits. However, currently, there are no recommendations set by national health agencies for tolerable upper intake levels (UL) for amino acids because of a lack of well-conducted human dose-response trials. In the past decade, under the initiative of the International Council on Amino Acid Science, a nonprofit organization, a series of UL human clinical studies were conducted. The goal of this narrative review is to summarize the studies on 6 essential amino acids (leucine, tryptophan, methionine, lysine, histidine, and phenylalanine), 2 nonessential amino acids (arginine and serine), and 2 nonproteinogenic amino acids (ornithine and citrulline) and provide the first set of ULs. A brief background of the concept of the DRI framework of UL, the concept of UL for amino acids, and a perspective of the results are also provided. The data suggest that in relatively healthy adult individuals, the tested amino acids are well tolerated, and ULs, or the no-observed-adverse-effect-level (NOAEL), lowest-observed-adverse-effect-level (LOAEL), can be determined. The ULs were for leucine-young (35 g/d), tryptophan (4.5 g/d), and leucine-elderly (30 g/d); NOAEL and LOAEL for methionine at 3.2 and 6.4 g/d, respectively; NOAEL for arginine (30 g/d); NOAEL and LOAEL for lysine at 6 and 7.5 g/d, respectively; NOAEL and LOAEL for histidine at 8 and 12 g/d, respectively; and NOAEL for phenylalanine (12 g/d), serine (12 g/d), ornithine (12 g/d) and citrulline (24 g/d). This first set of human UL data are hoped to help national and international agencies set safety standards for supplemental amino acids. <<<

A high-throughput lithographic method with 25-nanometer resolution and smooth vertical sidewalls is proposed and demonstrated. The technique uses compression molding to create a thickness contrast pattern in a thin resist film carried on a substrate, followed by anisotropic etching to transfer the pattern through the entire resist thickness. Metal patterns with a feature size of 25 nanometers and a period of 70 nanometers were fabricated with the use of resist templates created by imprint lithography in combination with a lift-off process. With further development, imprint lithography should allow fabrication of sub-10-nanometer structures and may become a commercially viable technique for manufacturing integrated circuits and other nanodevices. <<<

Lukas Heumos, Anna C Schaar, Christopher Lance, Anastasia Litinetskaya, Felix Drost, Luke Zappia, Malte D Lücken, Daniel C Strobl, Juan Henao, Fabiola Curion, Single-cell Best Practices Consortium, Herbert B Schiller, Fabian J Theis <<<

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices. <<<

Joseph L. Watson , David Juergens , Nathaniel R. Bennett , Brian L. Trippe , Jason Yim , Helen E. Eisenach , Woody Ahern , Andrew J. Borst , Robert J. Ragotte , Lukas F. Milles , Basile I. M. Wicky , Nikita Hanikel , Samuel J. Pellock , Alexis Courbet , William Sheffler , Jue Wang , Preetham Venkatesh , Isaac Sappington , Susana Vázquez Torres , Anna Lauko , Valentin De Bortoli , Emile Mathieu , Regina Barzilay , Tommi S. Jaakkola , Frank DiMaio , Minkyung Baek , David Baker <<<

AbstractThere has been considerable recent progress in designing new proteins using deep learning methods1–9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, includingde novobinder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusionenables the design of diverse, complex, functional proteins from simple molecular specifications. <<<

The data volume of global information has grown exponentially in recent years, but the development of silicon-based memory has entered a bottleneck period. Deoxyribonucleic acid (DNA) storage is drawing attention owing to its advantages of high storage density, long storage time, and easy maintenance. However, the base utilization and information density of existing DNA storage methods are insufficient. Therefore, this study proposes a rotational coding based on blocking strategy (RBS) for encoding digital information such as text and images in DNA data storage. This strategy satisfies multiple constraints and produces low error rates in synthesis and sequencing. To illustrate the superiority of the proposed strategy, it was compared and analyzed with existing strategies in terms of entropy value change, free energy size, and Hamming distance. The experimental results show that the proposed strategy has higher information storage density and better coding quality in DNA storage, so it will improve the efficiency, practicality, and stability of DNA storage. <<<

Caiying Zhu, Yu Lian, Chenchen Wang, Peng Wu, Xuan Li, Yan Gao, Sibin Fan, Lanlan Ai, Liwei Fang, Hong Pan, Tao Cheng, Jun Shi, Ping Zhu <<<

Aplastic anemia (AA) is a T cell-mediated autoimmune disorder of the hematopoietic system manifested by severe depletion of the hematopoietic stem and progenitor cells (HSPCs). Nonetheless, our understanding of the complex relationship between HSPCs and T cells is still obscure, mainly limited by techniques and the sparsity of HSPCs in the context of bone marrow failure. Here we performed single-cell transcriptome analysis of residual HSPCs and T cells to identify the molecular players from patients with AA. We observed that residual HSPCs in AA exhibited lineage-specific alterations in gene expression and transcriptional regulatory networks, indicating a selective disruption of distinct lineage-committed progenitor pools. In particular, HSPCs displayed frequently altered alternative splicing events and skewed patterns of polyadenylation in transcripts related to DNA damage and repair, suggesting a likely role in AA progression to myelodysplastic syndromes. We further identified cell type-specific ligand-receptor interactions as potential mediators for ongoing HSPCs destruction by T cells. By tracking patients after immunosuppressive therapy (IST), we showed that hematopoiesis remission was incomplete accompanied by IST insensitive interactions between HSPCs and T cells as well as sustained abnormal transcription state. These data collectively constitute the transcriptomic landscape of disrupted hematopoiesis in AA at single-cell resolution, providing new insights into the molecular interactions of engaged T cells with residual HSPCs and render novel therapeutic opportunities for AA. <<<

Yaqi Zhao, Ibrahim Aldoss, Chunxu Qu, Jeremy Chase Crawford, Zhaohui Gu, Emma K Allen, Anthony E Zamora, Thomas B Alexander, Jeremy Wang, Hiroaki Goto, Toshihiko Imamura, Koshi Akahane, Guido Marcucci, Anthony S Stein, Ravi Bhatia, Paul G Thomas, Stephen J Forman, Charles G Mullighan, Kathryn G Roberts <<<

Blinatumomab, a bispecific antibody that directs CD3+ T cells to CD19+ tumor cells, shows variable efficacy in B-progenitor acute lymphoblastic leukemia (B-ALL). To determine tumor-intrinsic and -extrinsic determinants of response, we studied 44 adults with relapsed or refractory B-ALL (including 2 minimal residual disease positive) treated with blinatumomab using bulk tumor and single-cell sequencing. The overall response rate in patients with hematological disease was 55%, with a high response rate in those with CRLF2-rearranged Philadelphia chromosome-like ALL (12 [75%] of 16). Pretreatment samples of responders exhibited a tumor-intrinsic transcriptomic signature of heightened immune response. Multiple mechanisms resulted in loss of CD19 expression, including CD19 mutations, CD19-mutant allele-specific expression, low CD19 RNA expression, and mutations in CD19 signaling complex member CD81. Patients with low hypodiploid ALL were prone to CD19- relapse resulting from aneuploidy-mediated loss of the nonmutated CD19 allele. Increased expression of a CD19 isoform with intraexonic splicing of exon 2, CD19 ex2part, at baseline or during therapy was associated with treatment failure. These analyses demonstrate both tumor-intrinsic and -extrinsic factors influence blinatumomab response. We show that CD19 mutations are commonly detected in CD19- relapse during blinatumomab treatment. Identification of the CD19 ex2part splice variant represents a new biomarker predictive of blinatumomab therapy failure. <<<

Pablo Gainza, Sarah Wehrle, Alexandra Van Hall-Beauvais, Anthony Marchand, Andreas Scheck, Zander Harteveld, Stephen Buckley, Dongchun Ni, Shuguang Tan, Freyr Sverrisson, Casper Goverde, Priscilla Turelli, Charlène Raclot, Alexandra Teslenko, Martin Pacesa, Stéphane Rosset, Sandrine Georgeon, Jane Marsden, Aaron Petruzzella, Kefang Liu, Zepeng Xu, Yan Chai, Pu Han, George F Gao, Elisa Oricchio, Beat Fierz, Didier Trono, Henning Stahlberg, Michael Bronstein, Bruno E Correia <<<

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function. <<<

Seborrheic dermatitis (SD) is the most prevalent dermatological disease, occurring in up to 50% of newborns, children, and adults around the world. The antibacterial and antifungal resistance contributed to the search for new natural substances and the development of a novel substance based on () leaf oil (TTO), 1,8-cineole (eucalyptol), and α-(-)-bisabolol. Thus, this work aimed to determine the chemical composition of the novel plant-based substance and to evaluate its antimicrobial activity against standard microorganisms involved in the pathogenesis of SD. Moreover, the chemical composition of the substance was analyzed by gas chromatography coupled with mass spectrometry (GC/MS). (), (), (), and () were used for antimicrobial and antifungal assays by means of the broth microdilution method to determine the minimal inhibitory concentration (MIC). Finally, the substance's ability to inhibit () was evaluated. Eighteen compounds from different chemical groups were identified by GC/MS. The major biologically active compounds of the substance were terpinen-4-ol (20.88%), 1,8-cineole (22.28%), (-)-α-bisabolol (25.73%), and o-cymene (8.16%). The results showed that the substance has a synergistic antimicrobial and antifungal activity, while and strains were the most susceptible. Furthermore, the substance inhibited , which is a main pathogen involved in the pathogenesis of SD and clinical manifestations. It can be concluded that the novel plant-based substance has a promising potential against and scalp commensal bacteria and may be helpful for the development of new drugs for treatment of dandruff and SD. <<<