Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.

CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4+ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.

Contemplating immunopeptidomes to better predict them.

The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.

A quantitative model of temperature-dependent diapause progression.

Winter diapause in insects is commonly terminated through cold exposure, which, like vernalization in plants, prevents development before spring arrives. Currently, quantitative understanding of the temperature dependence of diapause termination is limited, likely because diapause phenotypes are generally cryptic to human eyes. We introduce a methodology to tackle this challenge. By consecutively moving butterfly pupae of the species Pieris napi from several different cold conditions to 20 °C, we show that diapause termination proceeds as a temperature-dependent rate process, with maximal rates at relatively cold temperatures and low rates at warm and extremely cold temperatures. Further, we show that the resulting thermal reaction norm can predict P. napi diapause termination timing under variable temperatures. Last, we show that once diapause is terminated in P. napi, subsequent development follows a typical thermal performance curve, with a maximal development rate at around 31 °C and a minimum at around 2 °C. The sequence of these thermally distinct processes (diapause termination and postdiapause development) facilitates synchronous spring eclosion in nature; cold microclimates where diapause progresses quickly do not promote fast postdiapause development, allowing individuals in warmer winter microclimates to catch up, and vice versa. The unveiling of diapause termination as one temperature-dependent rate process among others promotes a parsimonious, quantitative, and predictive model, wherein winter diapause functions both as an adaptation against premature development during fall and winter and for synchrony in spring.

Embedding electronic perpetual motion into single-atom catalysts for persistent Fenton-like reactions.

In our quest to leverage the capabilities of the emerging single-atom catalysts (SACs) for wastewater purification, we confronted fundamental challenges related to electron scarcity and instability. Through meticulous theoretical calculations, we identified optimal placements for nitrogen vacancies (Nv) and iron (Fe) single-atom sites, uncovering a dual-site approach that significantly amplified visible-light absorption and charge transfer dynamics. Informed by these computational insights, we cleverly integrated Nv into the catalyst design to boost electron density around iron atoms, yielding a potent and flexible photoactivator for benign peracetic acid. This exceptional catalyst exhibited remarkable stability and effectively degraded various organic contaminants over 20 cycles with self-cleaning properties. Specifically, the Nv sites captured electrons, enabling their swift transfer to adjacent Fe sites under visible light irradiation. This mechanism accelerated the reduction of the formed "peracetic acid-catalyst" intermediate. Theoretical calculations were used to elucidate the synergistic interplay of dual mechanisms, illuminating increased adsorption and activation of reactive molecules. Furthermore, electron reduction pathways on the conduction band were elaborately explored, unveiling the production of reactive species that enhanced photocatalytic processes. A six-flux model and associated parameters were also applied to precisely optimize the photocatalytic process, providing invaluable insights for future photocatalyst design. Overall, this study offers a molecule-level insight into the rational design of robust SACs in a photo-Fenton-like system, with promising implications for wastewater treatment and other high-value applications.

Predictions and rewards affect decision-making but not subjective experience.

To survive, organisms constantly make decisions to avoid danger and maximize rewards in information-rich environments. As a result, decisions about sensory input are not only driven by sensory information but also by other factors, such as the expected rewards of a decision (known as the payoff matrix) or by information about temporal regularities in the environment (known as cognitive priors or predictions). However, it is unknown to what extent these different types of information affect subjective experience or whether they merely result in nonperceptual response criterion shifts. To investigate this question, we used three carefully matched manipulations that typically result in behavioral shifts in decision criteria: a visual illusion (Müller-Lyer condition), a punishment scheme (payoff condition), and a change in the ratio of relevant stimuli (base rate condition). To gauge shifts in subjective experience, we introduce a task in which participants not only make decisions about what they have just seen but are also asked to reproduce their experience of a target stimulus. Using Bayesian ordinal modeling, we show that each of these three manipulations affects the decision criterion as intended but that the visual illusion uniquely affects sensory experience as measured by reproduction. In a series of follow-up experiments, we use computational modeling to show that although the visual illusion results in a distinct drift-diffusion (DDM) parameter profile relative to nonsensory manipulations, reliance on DDM parameter estimates alone is not sufficient to ascertain whether a given manipulation is perceptual or nonperceptual.

Speech Prosody Serves Temporal Prediction of Language via Contextual Entrainment.

Temporal prediction assists language comprehension. In a series of recent behavioral studies, we have shown that listeners specifically employ rhythmic modulations of prosody to estimate the duration of upcoming sentences, thereby speeding up comprehension. In the current human magnetoencephalography (MEG) study on participants of either sex, we show that the human brain achieves this function through a mechanism termed entrainment. Through entrainment, electrophysiological brain activity maintains and continues contextual rhythms beyond their offset. Our experiment combined exposure to repetitive prosodic contours with the subsequent presentation of visual sentences that either matched or mismatched the duration of the preceding contour. During exposure to prosodic contours, we observed MEG coherence with the contours, which was source-localized to right-hemispheric auditory areas. During the processing of the visual targets, activity at the frequency of the preceding contour was still detectable in the MEG; yet sources shifted to the (left) frontal cortex, in line with a functional inheritance of the rhythmic acoustic context for prediction. Strikingly, when the target sentence was shorter than expected from the preceding contour, an omission response appeared in the evoked potential record. We conclude that prosodic entrainment is a functional mechanism of temporal prediction in language comprehension. In general, acoustic rhythms appear to endow language for employing the brain's electrophysiological mechanisms of temporal prediction.

Predictable and unpredictable deviance detection in the human hippocampus and amygdala.

Our brains extract structure from the environment and form predictions given past experience. Predictive circuits have been identified in wide-spread cortical regions. However, the contribution of medial temporal structures in predictions remains under-explored. The hippocampus underlies sequence detection and is sensitive to novel stimuli, sufficient to gain access to memory, while the amygdala to novelty. Yet, their electrophysiological profiles in detecting predictable and unpredictable deviant auditory events remain unknown. Here, we hypothesized that the hippocampus would be sensitive to predictability, while the amygdala to unexpected deviance. We presented epileptic patients undergoing presurgical monitoring with standard and deviant sounds, in predictable or unpredictable contexts. Onsets of auditory responses and unpredictable deviance effects were detected earlier in the temporal cortex compared with the amygdala and hippocampus. Deviance effects in 1-20 Hz local field potentials were detected in the lateral temporal cortex, irrespective of predictability. The amygdala showed stronger deviance in the unpredictable context. Low-frequency deviance responses in the hippocampus (1-8 Hz) were observed in the predictable but not in the unpredictable context. Our results reveal a distributed network underlying the generation of auditory predictions and suggest that the neural basis of sensory predictions and prediction error signals needs to be extended.

Direct Eight-Electron N2O Electroreduction to NH3 Enabled by an Fe Double-Atom Catalyst.

N2O is a dominant atmosphere pollutant, causing ozone depletion and global warming. Currently, electrochemical reduction of N2O has gained increasing attention to remove N2O, but its product is worthless N2. Here, we propose a direct eight-electron (8e) pathway to electrochemically convert N2O into NH3. As a proof of concept, using density functional theory calculation, an Fe2 double-atom catalyst (DAC) anchored by N-doped porous graphene (Fe2@NG) was screened out to be the most active and selective catalyst for N2O electroreduction toward NH3 via the novel 8e pathway, which benefits from the unique bent N2O adsorption configuration. Guided by theoretical prediction, Fe2@NG DAC was fabricated experimentally, and it can achieve a high N2O-to-NH3 Faradaic efficiency of 77.8% with a large NH3 yield rate of 2.9 mg h-1 cm-2 at -0.6 V vs RHE in a neutral electrolyte. Our study offers a feasible strategy to synthesize NH3 from pollutant N2O with simultaneous N2O removal.

Random, de novo, and conserved proteins: How structure and disorder predictors perform differently.

Understanding the emergence and structural characteristics of de novo and random proteins is crucial for unraveling protein evolution and designing novel enzymes. However, experimental determination of their structures remains challenging. Recent advancements in protein structure prediction, particularly with AlphaFold2 (AF2), have expanded our knowledge of protein structures, but their applicability to de novo and random proteins is unclear. In this study, we investigate the structural predictions and confidence scores of AF2 and protein language model-based predictor ESMFold for de novo and conserved proteins from Drosophila and a dataset of comparable random proteins. We find that the structural predictions for de novo and random proteins differ significantly from conserved proteins. Interestingly, a positive correlation between disorder and confidence scores (pLDDT) is observed for de novo and random proteins, in contrast to the negative correlation observed for conserved proteins. Furthermore, the performance of structure predictors for de novo and random proteins is hampered by the lack of sequence identity. We also observe fluctuating median predicted disorder among different sequence length quartiles for random proteins, suggesting an influence of sequence length on disorder predictions. In conclusion, while structure predictors provide initial insights into the structural composition of de novo and random proteins, their accuracy and applicability to such proteins remain limited. Experimental determination of their structures is necessary for a comprehensive understanding. The positive correlation between disorder and pLDDT could imply a potential for conditional folding and transient binding interactions of de novo and random proteins.

QTY code designed antibodies for aggregation prevention: A structural bioinformatic and computational study.

Therapeutic monoclonal antibodies are the most rapidly growing class of molecular medicine, and they are beneficial to the treatment of a broad spectrum of human diseases. However, the aggregation of antibodies during the process of manufacture, distribution, and storage poses significant challenges, potentially compromising efficacy and inducing adverse immune responses. We previously conceived a QTY (glutamine, threonine, tyrosine) code, a simple tool for enhancing protein water-solubility by systematically pairwise replacing hydrophobic residues L (leucine), V (valine)/I (isoleucine), and F (phenylalanine). The QTY code offers a promising alternative to traditional methods of controlling aggregation in integral transmembrane proteins. In this study, we designed variants of four antibodies applying the QTY code, changing only the ß-sheets. Through the structure-based aggregation analysis, we found that these QTY antibody variants demonstrated significantly decreased aggregation propensity compared to their wild-type counter parts. Our results of molecular dynamics simulations showed that the design by QTY code is capable of maintaining the antigen-binding affinity and structural stability. Our structural informatic and computational study suggests that the QTY code offers a significant potential in mitigating antibody aggregation.

In silico prediction of the metabolism of Blastocrithidia nonstop, a trypanosomatid with non-canonical genetic code.

BACKGROUND: Almost all extant organisms use the same, so-called canonical, genetic code with departures from it being very rare. Even more exceptional are the instances when a eukaryote with non-canonical code can be easily cultivated and has its whole genome and transcriptome sequenced. This is the case of Blastocrithidia nonstop, a trypanosomatid flagellate that reassigned all three stop codons to encode amino acids. RESULTS: We in silico predicted the metabolism of B. nonstop and compared it with that of the well-studied human parasites Trypanosoma brucei and Leishmania major. The mapped mitochondrial, glycosomal and cytosolic metabolism contains all typical features of these diverse and important parasites. We also provided experimental validation for some of the predicted observations, concerning, specifically presence of glycosomes, cellular respiration, and assembly of the respiratory complexes. CONCLUSIONS: In an unusual comparison of metabolism between a parasitic protist with a massively altered genetic code and its close relatives that rely on a canonical code we showed that the dramatic differences on the level of nucleic acids do not seem to be reflected in the metabolisms. Moreover, although the genome of B. nonstop is extremely AT-rich, we could not find any alterations of its pyrimidine synthesis pathway when compared to other trypanosomatids. Hence, we conclude that the dramatic alteration of the genetic code of B. nonstop has no significant repercussions on the metabolism of this flagellate.

Unraveling the molecular basis for effective regulation of integrin α5ß1 for enhanced therapeutic interventions.

Cell attachment to the extracellular matrix significantly impacts the integrity of tissues and human health. The integrin α5ß1 is a heterodimer of α5 and ß1 subunits and has been identified as a crucial modulator in several human carcinomas. Integrin α5ß1 significantly regulates cell proliferation, angiogenesis, inflammation, tumor metastasis, and invasion. This regulatory role of integrin α5ß1 in tumor metastasis makes it an appealing target for cancer therapy. The majority of the drugs targeting integrin α5ß1 are limited only to clinical trials. In our study, we have performed 94287 compounds screening to determine potential drugs against α5ß1 integrin. We have used ATN-161 as a reference and employed combined bioinformatic methodologies, including molecular modelling, virtual screening, MM-GBSA, cell-line cytotoxicity prediction, ADMET, Density Functional Theory (DFT), Non-covalent Interactions (NCI) and molecular simulation, to identify putative integrin α5ß1 inhibitors. We found Taxifolin, PD133053, and Acebutolol that possess inhibitory activity against α5ß1 integrin and could act as effective drug for the cancer treatment. Taxifolin, PD133053, and Acebutolol exhibited excellent binding to the druggable pocket of integrin α5ß1, and also maintained a unique binding mechanism with extra hydrophobic contacts at molecular level. Overall, our study gives new pharmacological candidates that may act as a potential drug against integrin α5ß1.

toxCSM: comprehensive prediction of small molecule toxicity profiles.

Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high attrition rates in later development stages. Toxicity has been one of the main causes of failure during clinical trials, increasing drug development time and costs. To facilitate early identification and optimisation of toxicity profiles, several computational tools emerged aiming at improving success rates by timely pre-screening drug candidates. Despite these efforts, there is an increasing demand for platforms capable of assessing both environmental as well as human-based toxicity properties at large scale. Here, we present toxCSM, a comprehensive computational platform for the study and optimisation of toxicity profiles of small molecules. toxCSM leverages on the well-established concepts of graph-based signatures, molecular descriptors and similarity scores to develop 36 models for predicting a range of toxicity properties, which can assist in developing safer drugs and agrochemicals. toxCSM achieved an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of up to 0.99 and Pearson's correlation coefficients of up to 0.94 on 10-fold cross-validation, with comparable performance on blind test sets, outperforming all alternative methods. toxCSM is freely available as a user-friendly web server and API at http://biosig.lab.uq.edu.au/toxcsm.

Role of the Cerebellum in the Construction of Functional and Geometrical Spaces.

The perceptual and motor systems appear to have a set of movement primitives that exhibit certain geometric and kinematic invariances. Complex patterns and mental representations can be produced by (re)combining some simple motor elements in various ways using basic operations, transformations, and respecting a set of laws referred to as kinematic laws of motion. For example, point-to-point hand movements are characterized by straight hand paths with single-peaked-bell-shaped velocity profiles, whereas hand speed profiles for curved trajectories are often irregular and more variable, with speed valleys and inflections extrema occurring at the peak curvature. Curvature and speed are generically related by the 2/3 power law. Mathematically, such laws can be deduced from a combination of Euclidean, affine, and equi-affine geometries, whose neural correlates have been partially detected in various brain areas including the cerebellum and the basal ganglia. The cerebellum has been found to play an important role in the control of coordination, balance, posture, and timing over the past years. It is also assumed that the cerebellum computes forward internal models in relationship with specific cortical and subcortical brain regions but its motor relationship with the perceptual space is unclear. A renewed interest in the geometrical and spatial role of the cerebellum may enable a better understanding of its specific contribution to the action-perception loop and behavior's adaptation. In this sense, we complete this overview with an innovative theoretical framework that describes a possible implementation and selection by the cerebellum of geometries adhering to different mathematical laws.

Consensus Paper: Cerebellum and Reward.

Cerebellum is a key-structure for the modulation of motor, cognitive, social and affective functions, contributing to automatic behaviours through interactions with the cerebral cortex, basal ganglia and spinal cord. The predictive mechanisms used by the cerebellum cover not only sensorimotor functions but also reward-related tasks. Cerebellar circuits appear to encode temporal difference error and reward prediction error. From a chemical standpoint, cerebellar catecholamines modulate the rate of cerebellar-based cognitive learning, and mediate cerebellar contributions during complex behaviours. Reward processing and its associated emotions are tuned by the cerebellum which operates as a controller of adaptive homeostatic processes based on interoceptive and exteroceptive inputs. Lobules VI-VII/areas of the vermis are candidate regions for the cortico-subcortical signaling pathways associated with loss aversion and reward sensitivity, together with other nodes of the limbic circuitry. There is growing evidence that the cerebellum works as a hub of regional dysconnectivity across all mood states and that mental disorders involve the cerebellar circuitry, including mood and addiction disorders, and impaired eating behaviors where the cerebellum might be involved in longer time scales of prediction as compared to motor operations. Cerebellar patients exhibit aberrant social behaviour, showing aberrant impulsivity/compulsivity. The cerebellum is a master-piece of reward mechanisms, together with the striatum, ventral tegmental area (VTA) and prefrontal cortex (PFC). Critically, studies on reward processing reinforce our view that a fundamental role of the cerebellum is to construct internal models, perform predictions on the impact of future behaviour and compare what is predicted and what actually occurs.

Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage.

We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2700 unique chemical structures to train machine learning models. To that end multiple advanced training strategies are compared, including the integration of in vitro data and a novel self-supervised pretraining task. In addition to the predictions, our final model provides meaningful epistemic uncertainties for every data point. This allows us to successfully identify regions of exceptional predictive performance, with an absolute average fold error (AAFE/geometric mean fold error) of less than 2.5 across multiple end points. Together, these advancements represent a significant leap toward actionable PK predictions, which can be utilized early on in the drug design process to expedite development and reduce reliance on nonclinical studies.

Molecular Dynamic Simulations Reveal that Water-Soluble QTY-Variants of Glutamate Transporters EAA1, EAA2 and EAA3 Retain the Conformational Characteristics of Native Transporters.

OBJECTIVE: Glutamate transporters play a crucial role in neurotransmitter homeostasis, but studying their structure and function is challenging due to their membrane-bound nature. This study aims to investigate whether water-soluble QTY-variants of glutamate transporters EAA1, EAA2 and EAA3 retain the conformational characteristics and dynamics of native membrane-bound transporters. METHODS: Molecular dynamics simulations and comparative genomics were used to analyze the structural dynamics of both native transporters and their QTY-variants. Native transporters were simulated in lipid bilayers, while QTY-variants were simulated in aqueous solution. Lipid distortions, relative solvent accessibilities, and conformational changes were examined. Evolutionary conservation profiles were correlated with structural dynamics. Statistical analyses included multivariate analysis to account for confounding variables. RESULTS: QTY-variants exhibited similar residue-wise conformational dynamics to their native counterparts, with correlation coefficients of 0.73 and 0.56 for EAA1 and EAA3, respectively (p < 0.001). Hydrophobic interactions of native helices correlated with water interactions of QTY- helices (rs = 0.4753, p < 0.001 for EAA1). QTY-variants underwent conformational changes resembling the outward-to-inward transition of native transporters. CONCLUSIONS: Water-soluble QTY-variants retain key structural properties of native glutamate transporters and mimic aspects of native lipid interactions, including conformational flexibility. This research provides valuable insights into the conformational changes and molecular mechanisms of glutamate transport, potentially offering a new approach for studying membrane protein dynamics and drug interactions.

Comprehensive risk factor predictions for 3-year survival among HIV-associated and disseminated cryptococcosis involving lungs and central nervous system.

BACKGROUND: The global mortality rate resulting from HIV-associated cryptococcal disease is remarkably elevated, particularly in severe cases with dissemination to the lungs and central nervous system (CNS). Regrettably, there is a dearth of predictive analysis regarding long-term survival, and few studies have conducted longitudinal follow-up assessments for comparing anti-HIV and antifungal treatments. METHODS: A cohort of 83 patients with HIV-related disseminated cryptococcosis involving the lung and CNS was studied for 3 years to examine survival. Comparative analysis of clinical and immunological parameters was performed between deceased and surviving individuals. Subsequently, multivariate Cox regression models were utilized to validate mortality predictions at 12, 24, and 36 months. RESULTS: Observed plasma cytokine levels before treatment were significantly lower for IL-1RA (p < 0.001) and MCP-1 (p < 0.05) when in the survivor group. Incorporating plasma levels of IL-1RA, IL-6, and high-risk CURB-65 score demonstrated the highest area under curve (AUC) value (0.96) for predicting 1-year mortality. For 1-, 2- and 3-year predictions, the single-factor model with IL-1RA demonstrated superior performance compared to all multiple-variate models (AUC = 0.95/0.78/0.78). CONCLUSIONS: IL-1RA is a biomarker for predicting 3-year survival. Further investigations to explore the pathogenetic role of IL-1RA in HIV-associated disseminated cryptococcosis and as a potential therapeutic target are warranted.

New Drinking Water Genome Catalog Identifies a Globally Distributed Bacterial Genus Adapted to Disinfected Drinking Water Systems.

Genome-resolved insights into the structure and function of the drinking water microbiome can advance the effective management of drinking water quality. To enable this, we constructed and curated thousands of metagenome-assembled and isolate genomes from drinking water distribution systems globally to develop a Drinking Water Genome Catalog (DWGC). The current DWGC disproportionately represents disinfected drinking water systems due to a paucity of metagenomes from nondisinfected systems. Using the DWGC, we identify core genera of the drinking water microbiome including a genus (UBA4765) within the order Rhizobiales that is frequently detected and highly abundant in disinfected drinking water systems. We demonstrate that this genus has been widely detected but incorrectly classified in previous amplicon sequencing-based investigations of the drinking water microbiome. Further, we show that a single genome variant (genomovar) within this genus is detected in 75% of drinking water systems included in this study. We propose a name for this uncultured bacterium as "Raskinella chloraquaticus" and describe the genus as "Raskinella" (endorsed by SeqCode). Metabolic annotation and modeling-based predictions indicate that this bacterium is capable of necrotrophic growth, is able to metabolize halogenated compounds, proliferates in a biofilm-based environment, and shows clear indications of disinfection-mediated selection.

Identification of MYB gene family in medicinal tea tree Melaleuca alternifolia (Maiden and Betche) cheel and analysis of members regulating terpene biosynthesis.

BACKGROUND: The tea tree (Melaleuca alternifolia) is renowned for its production of tea tree oil, an essential oil primarily composed of terpenes extracted from its shoot. MYB transcription factors, which are one of the largest TF families, play a crucial role in regulating primary and secondary metabolite synthesis. However, knowledge of the MYB gene family in M. alternifolia is limited. METHODS AND RESULTS: Here, we conducted a comprehensive genome-wide analysis of MYB genes in M. alternifolia, referred to as MaMYBs, including phylogenetic relationships, structures, promoter regions, and GO annotations. Our findings classified 219 MaMYBs into four subfamilies: one 5R-MYB, four 3R-MYBs, sixty-one MYB-related, and the remaining 153 are all 2R-MYBs. Seven genes (MYB189, MYB146, MYB44, MYB29, MYB175, MYB162, and MYB160) were linked to terpenoid synthesis based on GO annotation. Phylogenetic analysis with Arabidopsis homologous MYB genes suggested that MYB193 and MYB163 may also be involved in terpenoid synthesis. Additionally, through correlation analysis of gene expression and metabolite content, we identified 42 MYB genes associated with metabolite content. CONCLUSION: The results provide valuable insights into the importance of MYB transcription factors in essential oil production in M. alternifolia. These findings lay the groundwork for a better understanding of the MYB regulatory network and the development of novel strategies to enhance essential oil synthesis in M. alternifolia.