Experimental Validation Research Articles

Time–Frequency Decomposition Based Protection Scheme for Power Transformer— Simulation and Experimental Validation

Time–Frequency Decomposition Based Protection Scheme for Power Transformer— Simulation and Experimental Validation

Computational and experimental identification of keystone interactions in Ebola virus matrix protein VP40 dimer formation.

The Ebola virus (EBOV) is a lipid-enveloped virus with a negative sense RNA genome that can cause severe and often fatal viral hemorrhagic fever. The assembly and budding of EBOV is regulated by the matrix protein, VP40, which is a peripheral protein that associates with anionic lipids at the inner leaflet of the plasma membrane. VP40 is sufficient to form virus-like particles (VLPs) from cells, which are nearly indistinguishable from authentic virions. Due to the restrictions of studying EBOV in BSL-4 facilities, VP40 has served as a surrogate in cellular studies to examine the EBOV assembly and budding process from the host cell plasma membrane. VP40 is a dimer where inhibition of dimer formation halts budding and formation of new VLPs as well as VP40 localization to the plasma membrane inner leaflet. To better understand VP40 dimer stability and critical amino acids to VP40 dimer formation, we integrated computational approaches with experimental validation. Site saturation/alanine scanning calculation, combined with molecular mechanics-based generalized Born with Poisson-Boltzmann surface area (MM-GB/PBSA) method and molecular dynamics simulations were used to predict the energetic contribution of amino acids to VP40 dimer stability and the hydrogen bonding network across the dimer interface. These studies revealed several previously unknown interactions and critical residues predicted to impact VP40 dimer formation. In vitro and cellular studies were then pursued for a subset of VP40 mutations demonstrating reduction in dimer formation (in vitro) or plasma membrane localization (in cells). Together, the computational and experimental approaches revealed critical residues for VP40 dimer stability in an alpha-helical interface (between residues 106-117) as well as in a loop region (between residues 52-61) below this alpha-helical region. This study sheds light on the structural origins of VP40 dimer formation and may inform the design of a small molecule that can disrupt VP40 dimer stability.

Identifying the Anti-inflammatory Effects of Astragalus Polysaccharides in Anti-N-Methyl-D-Aspartate Receptor Encephalitis: Network Pharmacology and Experimental Validation.

Astragalus polysaccharides (APS), a group of bioactive compounds obtained from the natural source Astragalus membranaceus(AM), exhibits numerous pharmacological actions in the central nervous system, such as anti-inflammatory, antioxidant, and immunomodulatory properties. Despite the remarkable benefits, the effectiveness of APS in treating anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and the corresponding mechanism have yet to be fully understood. As such, this study aims to investigate the impact of APS on anti-NMDAR encephalitis and explore the potential molecular network mechanism. The impact of APS intervention on mice with anti-NMDAR encephalitis was assessed, and the possible molecular network mechanism was investigated utilizing network pharmacology and bioinformatics techniques such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG),protein-protein interaction (PPI) network, and molecular docking. Enzyme-linked immunosorbent assay (ELISA) was applied to detect the expression of core target proteins. APS significantly ameliorated cognitive impairment and reduced susceptibility to PTZ-induced seizures in mice with anti-NMDAR encephalitis, confirming the beneficial effect of APS on anti-NMDAR encephalitis. Seventeen intersecting genes were identified between APS and anti-NMDAR encephalitis. GO and KEGG analyses revealed the characteristics of the intersecting gene networks. STRING interaction in the PPI network was applied to find crucial molecules. The results of molecular docking suggested that APS may regulate interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) as potential targets in anti-NMDAR encephalitis. Furthermore, the levels of IL-1β, IL-6, and TNF-α detected by ELISA in anti-NMDAR encephalitis mice were significantly downregulated in response to the administration of APS. The findings of this study demonstrate the significant role of APS in the treatment of anti-NMDAR encephalitis, as it effectively suppresses inflammatory cytokines. These results suggest that APS has the potential to be considered as a viable herbal medication for the treatment of anti-NMDAR encephalitis.

Video process detection for space electrostatic suspension material experiment in China’s Space Station

The purpose of video process detection is to identify key processes of interest and associated semantic categories, which is an important computer vision task for localizing key processes in space science experiments. To detect experimental processes in space electrostatic suspension material experiment on China’s Space Station, we propose a Slow-and-Fast Dual-Stream Network. It extracts multi-scale temporal semantic information by designing a Slow-and-Fast Stream structure and effectively detects the experimental processes with various temporal scales. In addition, the Two Path Temporal Pyramid Network (TP2N) enhances the bi-direction flow among multi-scale features on the temporal dimension to alleviate boundary sensitivity. The validation experiments on our established dataset demonstrate the effectiveness of our proposed method with 62.43% mAP, outperforming the related state-of-the-art temporal action localization methods with nearly 7% mAP gain. This paper is the first application of deep learning in space electrostatic suspension material experiments on China’s Space Station and further provides important technical support for the identification of key processes in space science experiments.

A Novel Leg-Integrated Switched Capacitor Inverter Topology for Three-Phase Induction Motor Drives

This paper presents an optimized leg-integrated switched capacitor inverter (LISCI) topology for 3-φ induction motor driven home appliance applications where a switched capacitor (SC) network, used for voltage boosting, is integrated with one of the inverter legs (phase-A leg). This results in a reduced semiconductor requirement as the voltage ratings of the phase-A switches are reduced by half. Moreover, due to the unique structure of the proposed topology, it can produce 3-level pole voltage corresponding to phase-A, even though the pole voltages of phases B and C remain 2-level. This kind of performance, referred to as quasi 3-level performance, results in an unusual space vector (SV) hexagon with 12 SVs, which is implemented by using a novel PWM method. A comparison study of the proposed topology with the existing SC based 3-φ inverter topologies reveals that the former has the lowest semiconductor requirement, highly improved harmonic performance and very low losses. All the claims are supported by experimental validations performed on a 3φ induction motor (3φIM) drive.

Lithium-ion battery SOH prediction based on VMD-PE and improved DBO optimized temporal convolutional network model

Accurately estimating the state of health (SOH) in lithium-ion batteries is crucial for optimizing performance, cost management, enhancing safety, extending lifespan, and promoting environmental sustainability. However, due to factors such as capacity regeneration, battery capacity degradation data exhibits nonlinear and non-stationary characteristics, making it challenging to accurately predict SOH. In order to improve prediction accuracy, this paper proposes a novel SOH prediction model based on VMD-PE-IDBO-TCN. Firstly, the variational mode decomposition-permutation entropy (VMD-PE) method is innovatively introduced to decompose the original battery SOH data and reorganize it into a series of subsequences, reducing the impact of non-stationary components on the prediction results. Moreover, temporal convolutional network (TCN) prediction models are used for each subsequence, and an improved dung beetle optimization (IDBO) algorithm is used to optimize the TCN parameters, where this IDBO algorithm incorporates SPM chaotic mapping, golden sine strategy, and adaptive Gaussian-Cauchy mixed mutation perturbation. The IDBO algorithm rapidly and accurately identifies the optimal parameter combination, significantly enhancing the predictive performance of the TCN model. Finally, the predicted values of each subsequence are superimposed to obtain the final prediction result. Two different lithium-ion battery datasets from NASA and CALCE are used for experimental validation. Experimental results show that the proposed model has higher prediction accuracy, with a maximum RMSE of only 0.0072 and a maximum MAPE of only 0.67 %, and has good adaptability to different types of batteries and batteries operating at high temperatures.

Design, Modeling, and Experimental Validation of a High Voltage Driving Circuit for Dielectric Elastomer Actuators

Design, Modeling, and Experimental Validation of a High Voltage Driving Circuit for Dielectric Elastomer Actuators

Dynamic compensation of the threading speed drop in rolling processes

This paper presents an approach for the dynamic speed drop compensation during threading in rolling processes. The feedforward control design exploits the differential flatness of the mechanical model and accelerates both the rolls and the drive train in a manner such that the acceleration torque is equal to the rolling torque during threading, while simultaneously maintaining the roll at the desired target speed. Ideally, this prevents the speed drop and enhances the quality and stability of the rolling process. The flatness-based feedforward trajectories are optimized in an online fashion to determine the optimal initial roll speed and duration of the acceleration process. An extensive experimental validation on a hot strip finishing mill shows superior performance in terms of various key performance indicators in comparison with a standard overspeed approach.

Desktop as a Service using Local Computers

Abstract: Desktop-as-a-Service (DaaS) has gained prominence as a cloud-based solution for delivering desktop environments to users. However, traditional DaaS models often rely on remote data centers, posing concerns regarding data security, latency, and dependence on external infrastructure. In this paper, we propose a paradigm shift by introducing a novel approach to DaaS that leverages the untapped potential of local computing resources.Our system harnesses the idle computing power available within the user's local environment, including desktops, laptops, and servers, to create a distributed DaaS platform. By integrating virtualization technologies and dynamic resource allocation strategies, we enable the provisioning of desktop environments ondemand, effectively reducing the reliance on centralized data centers. Key components of our system include a centralized management platform for orchestrating resource allocation and workload distribution, virtualization techniques for isolating and managing desktop instances, and robust security measures to safeguard sensitive data. Through experimental validation, we demonstrate the feasibility and performance of our approach, showcasing improvements in data privacy, latency reduction, and resource utilization compared to traditional DaaS models. Our research presents a significant step towards decentralized and resilient computing ecosystems, offering users greater control over their desktop environments while maintaining the scalability and accessibility inherent to DaaS paradigms

Cross-Domain Aspect-Based Sentiment Classification with a Pre-Training and Fine-Tuning Strategy for Low-Resource Domains

Aspect-based sentiment classification (ABSC) is a crucial sub-task of fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspects in a sentence as positive, negative, or neutral. Most existing ABSC methods are based on supervised learning. However, these methods rely heavily on fine-grained labeled training data, which can be scarce in low-resource domains, limiting their effectiveness. To overcome this challenge, we propose a low-resource cross-domain aspect-based sentiment classification (CDABSC) approach based on a pre-training and fine-tuning strategy. This approach applies the pre-training and fine-tuning strategy to an advanced deep learning method designed for ABSC, namely the attention-based encoding graph convolutional network (AEGCN) model. Specifically, a high-resource domain is selected as the source domain, and the AEGCN model is pre-trained using a large amount of fine-grained annotated data from the source domain. The optimal parameters of the model are preserved. Subsequently, a low-resource domain is used as the target domain, and the pre-trained model parameters are used as the initial parameters of the target domain model. The target domain is fine-tuned using a small amount of annotated data to adapt the parameters to the target domain model, improving the accuracy of sentiment classification in the low-resource domain. Finally, experimental validation on two domain benchmark datasets, restaurant and laptop, demonstrates significant outperformance of our approach over the baselines in CDABSC Micro-F1.

Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method.

Novel insights into immune-related genes associated with type 2 diabetes mellitus-related cognitive impairment

BACKGROUND The cognitive impairment in type 2 diabetes mellitus (T2DM) is a multifaceted and advancing state that requires further exploration to fully comprehend. Neuroinflammation is considered to be one of the main mechanisms and the immune system has played a vital role in the progression of the disease. AIM To identify and validate the immune-related genes in the hippocampus associated with T2DM-related cognitive impairment. METHODS To identify differentially expressed genes (DEGs) between T2DM and controls, we used data from the Gene Expression Omnibus database GSE125387. To identify T2DM module genes, we used Weighted Gene Co-Expression Network Analysis. All the genes were subject to Gene Set Enrichment Analysis. Protein-protein interaction network construction and machine learning were utilized to identify three hub genes. Immune cell infiltration analysis was performed. The three hub genes were validated in GSE152539 via receiver operating characteristic curve analysis. Validation experiments including reverse transcription quantitative real-time PCR, Western blotting and immunohistochemistry were conducted both in vivo and in vitro . To identify potential drugs associated with hub genes, we used the Comparative Toxicogenomics Database (CTD). RESULTS A total of 576 DEGs were identified using GSE125387. By taking the intersection of DEGs, T2DM module genes, and immune-related genes, a total of 59 genes associated with the immune system were identified. Afterward, machine learning was utilized to identify three hub genes (H2-T24 , Rac3 , and Tfrc ). The hub genes were associated with a variety of immune cells. The three hub genes were validated in GSE152539. Validation experiments were conducted at the mRNA and protein levels both in vivo and in vitro , consistent with the bioinformatics analysis. Additionally, 11 potential drugs associated with RAC3 and TFRC were identified based on the CTD. CONCLUSION Immune-related genes that differ in expression in the hippocampus are closely linked to microglia. We validated the expression of three hub genes both in vivo and in vitro , consistent with our bioinformatics results. We discovered 11 compounds associated with RAC3 and TFRC . These findings suggest that they are co-regulatory molecules of immunometabolism in diabetic cognitive impairment.

Causal roles of gut microbiota in cholangiocarcinoma etiology suggested by genetic study

BACKGROUND Cholangiocarcinoma (CCA) is a highly malignant biliary tract cancer with poor prognosis. Previous studies have implicated the gut microbiota in CCA, but evidence for causal mechanisms is lacking. AIM To investigate the causal relationship between gut microbiota and CCA risk. METHODS We performed a two-sample mendelian randomization study to evaluate potential causal associations between gut microbiota and CCA risk using genome-wide association study summary statistics for 196 gut microbial taxa and CCA. Genetic variants were used as instrumental variables. Multiple sensitivity analyses assessed result robustness. RESULTS Fifteen gut microbial taxa showed significant causal associations with CCA risk. Higher genetically predicted abundance of genus Eubacteriumnodatum group , genus Ruminococcustorques group , genus Coprococcus , genus Dorea , and phylum Actinobacteria were associated with reduced risk of gallbladder cancer and extrahepatic CCA. Increased intrahepatic CCA risk was associated with higher abundance of family Veillonellaceae , genus Alistipes , order Enterobacteriales , and phylum Firmicutes. Protective effects against CCA were suggested for genus Collinsella , genus Eisenbergiella , genus Anaerostipes , genus Paraprevotella , genus Parasutterella , and phylum Verrucomicrobia. Sensitivity analyses indicated these findings were reliable without pleiotropy. CONCLUSION This pioneering study provides novel evidence that specific gut microbiota may play causal roles in CCA risk. Further experimental validation of these candidate microbes is warranted to consolidate causality and mechanisms.

Discovery and in vitro Evaluation of Novel Serotonin 5-HT2A Receptor Ligands Identified Through Virtual Screening.

The 5-HT2A receptor is a molecular target of high pharmacological importance. Ligands of this protein, particularly atypical antipsychotics, are useful in the treatment of numerous mental disorders, including schizophrenia and major depressive disorder. Structure-based virtual screening using a 5-HT2A receptor complex was performed to identify novel ligands for the 5-HT2A receptor, serving as potential antidepressants. From the Enamine screening library, containing over 4 million compounds, 48 molecules were selected for subsequent experimental validation. These compounds were tested against the 5-HT2A receptor in radioligand binding assays. From the tested batch, six molecules were identified as ligands of the main molecular target and were forwarded to a more detailed in vitro profiling. This included radioligand binding assays at 5-HT1A, 5-HT7, and D2 receptors and functional studies at 5-HT2A receptors. These compounds were confirmed to show a binding affinity for at least one of the targets tested in vitro. Thus, the success rate for the inactive template-based screening reached 17%, while it was 9% for the active template-based screening. Similarity and fragment analysis indicated the structural novelty of the identified compounds. Pharmacokinetics for these molecules was determined using in silico approaches.

Guard cell and subsidiary cell sizes are key determinants for stomatal kinetics and drought adaptation in cereal crops.

Climate change-induced drought is a major threat to agriculture. C4 crops have a higher water use efficiency (WUE) and better adaptability to drought than C3 crops due to their smaller stomatal morphology and faster response. However, our understanding of stomatal behaviours in both C3 and C4 Poaceae crops is limited by knowledge gaps in physical traits of guard cell (GC) and subsidiary cell (SC). We employed infrared gas exchange analysis and a stomatal assay to explore the relationship between GC/SC sizes and stomatal kinetics across diverse drought conditions in two C3 (wheat and barley) and three C4 (maize, sorghum and foxtail millet) upland Poaceae crops. Through statistical analyses, we proposed a GCSC-τ model to demonstrate how morphological differences affect stomatal kinetics in C4 Poaceae crops. Our findings reveal that morphological variations specifically correlate with stomatal kinetics in C4 Poaceae crops, but not in C3 ones. Subsequent modelling and experimental validation provide further evidence that GC/SC sizes significantly impact stomatal kinetics, which affects stomatal responses to different drought conditions and thereby WUE in C4 Poaceae crops. These findings emphasize the crucial advantage of GC/SC morphological characteristics and stomatal kinetics for the drought adaptability of C4 Poaceae crops, highlighting their potential as future climate-resilient crops.

Vision-Based Object Manipulation for Activities of Daily Living Assistance Using Assistive Robot

The increasing prevalence of upper and lower extremity (ULE) functional deficiencies presents a significant challenge, as it restricts individuals’ ability to perform daily tasks independently. Robotic devices are emerging as assistive devices to assist individuals with limited ULE functionalities in activities of daily living (ADLs). While assistive manipulators are available, manual control through traditional methods like joysticks can be cumbersome, particularly for individuals with severe hand impairments and vision limitations. Therefore, autonomous/semi-autonomous control of a robotic assistive device to perform any ADL task is open to research. This study addresses the necessity of fostering independence in ADLs by proposing a creative approach. We present a vision-based control system for a six-degrees-of-freedom (DoF) robotic manipulator designed for semi-autonomous “pick-and-place” tasks, one of the most common activities among ADLs. Our approach involves selecting and training a deep-learning-based object detection model with a dataset of 47 ADL objects, forming the base for a 3D ADL object localization algorithm. The proposed vision-based control system integrates this localization technique to identify and manipulate ADL objects (e.g., apples, oranges, capsicums, and cups) in real time, returning them to specific locations to complete the “pick-and-place” task. Experimental validation involving an xArm6 (six DoF) robot from UFACTORY in diverse settings demonstrates the system’s adaptability and effectiveness, achieving an overall 72.9% success rate in detecting, localizing, and executing ADL tasks. This research contributes to the growing field of autonomous assistive devices, enhancing independence for individuals with functional impairments.

Predictive Modeling and Experimental Validation for Assessing the Mechanical Properties of Cementitious Composites Made with Silica Fume and Ground Granulated Blast Furnace Slag

Using sustainable cement-based alternatives, such as secondary cementitious raw materials (SCMs), could be a viable option to decrease CO2 emissions resulting from cement production. Previously conducted studies to determine the optimal mix designs of concrete primarily focused on either experimental approaches or empirical modeling techniques. However, in these experimental approaches, few tests could be performed for optimization due to time restrictions and lack of resources, and empirical modeling methods cannot be relied on without external validation. The machine learning-based approaches are further characterized by certain shortcomings, including a smaller number of data points, a less robust connection among the controlling factors, and a lack of comparative analyses among machine learning models. Furthermore, the literature on predicting the performance of concrete utilizing binary SCMs (silica fume (SF) and ground granulated blast furnace slag (GGBS)) is not available. Therefore, to address these drawbacks, this research aimed to integrate ML-based models with experimental validations for accurate predictions of the compressive strength (CS) and tensile strength (TS) of concrete that includes SF and GGBS as SCMs. Three soft computing techniques, namely the ANN, ANFIS, and GEP methods, were used for prediction purposes. Eight major input parameters, including the W/B ratio, cement, GGBS, SF, coarse aggregates, fine aggregates, superplasticizer, and the age of the specimens, were considered for modeling. The validity of the established models was assessed by using external experimental validation criteria, statistical metrics, and performance measures. In addition, sensitivity and parametric analyses were performed. Based on statistical measures, the ANFIS models outperformed other models with higher correlation and lower statistical error values. However, the GEP models exhibited superior performance compared to ANFIS and ANN with respect to the closeness of the RMSE, MAE, RSE, and R2 values between the training, validation, and testing sets for both the CS and TS models. Experimental validation showed strong evidence for the applicability of the proposed models with an R2 of 0.88 and error percentages of less than 10%. Sensitivity and parametric investigations demonstrated that the input variables exhibited the patterns described in the experimental dataset and the available literature. Hence, the proposed models are accurate, have better prediction performance, and can be used for design purposes.

Selecting potential biomarkers of plasma proteins in mares with endometritis.

Endometritis is a common condition in mares that causes significant economic loss. Lacking obvious clinical signs, the clinical diagnosis of endometritis in mares relies on case-by-case clinical examinations, which can be particularly inefficient in large-scale farms. Therefore, the identification of potential biomarkers can serve as a non-invasive and efficient screening technique for endometritis in mares. To compare the blood proteome between fertile mares and mares with endometritis to identify biomarkers potentially associated with the development of endometritis and validate their predictive potential. Observational and experimental study. Differentially expressed proteins were identified via Data Independent Acquisition (DIA) proteomic profiling in a screening cohort composed of eight healthy mares and eight mares with endometritis. Subsequently, enzyme-linked immunosorbent assay was employed that included a validation cohort of 40 healthy mares and 40 mares with endometritis to verify the accuracy and sensitivity of the identified proteins, thereby establishing a diagnostic threshold. In the screening cohort, 12 proteins were significantly differentially expressed between endometritis mares and healthy controls (p < 0.05, outside the 1/1.2 to 1.2-fold). In the validation experiment, all six screened proteins were assessed with area under the curve (AUC) >0.8. The samples displayed certain levels of individual heterogeneity, and the number of samples analysed was limited. Additionally, the identified biomarkers were primarily associated with generalised inflammation, which potentially limited their specificity for endometritis. Levels of plasma proteins are sensitive indicators of equine endometritis and potential tools for endometritis screening. In plasma, fetuin B, von Willebrand factor, vitamin K-dependent protein C, insulin-like growth factor binding protein 3, interleukin 1 receptor accessory protein, and type II cell cytoskeleton showed great predictive ability, with fetuin B being the best predictor (AUC = 0.93, 95% CI: 0.89-0.98), which performs better when combined with all six detected proteins (AUC = 1, 95% CI: 0.99-1.00).

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug–ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model’s exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.

One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse

The increasing availability of experimental and computational protein structures entices their use for function prediction. Here we develop an automated procedure to identify enzymes involved in metabolic reactions by assessing substrate conformations docked to a library of protein structures. By screening AlphaFold-modeled vitamin B6-dependent enzymes, we find that a metric based on catalytically favorable conformations at the enzyme active site performs best (AUROC Score=0.84) in identifying genes associated with known reactions. Applying this procedure, we identify the mammalian gene encoding hydroxytrimethyllysine aldolase (HTMLA), the second enzyme of carnitine biosynthesis. Upon experimental validation, we find that the top-ranked candidates, serine hydroxymethyl transferase (SHMT) 1 and 2, catalyze the HTMLA reaction. However, a mouse protein absent in humans (threonine aldolase; Tha1) catalyzes the reaction more efficiently. Tha1 did not rank highest based on the AlphaFold model, but its rank improved to second place using the experimental crystal structure we determined at 2.26 Å resolution. Our findings suggest that humans have lost a gene involved in carnitine biosynthesis, with HTMLA activity of SHMT partially compensating for its function.