Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors.

Neural network (NN)-based protein modeling methods have improved significantly in recent years. Although the overall accuracy of the two non-homology-based modeling methods, AlphaFold and RoseTTAFold, is outstanding, their performance for specific protein families has remained unexamined. G-protein-coupled receptor (GPCR) proteins are particularly interesting since they are involved in numerous pathways. This work directly compares the performance of these novel deep learning-based protein modeling methods for GPCRs with the most widely used template-based software-Modeller. We collected the experimentally determined structures of 73 GPCRs from the Protein Data Bank. The official AlphaFold repository and RoseTTAFold web service were used with default settings to predict five structures of each protein sequence. The predicted models were then aligned with the experimentally solved structures and evaluated by the root-mean-square deviation (RMSD) metric. If only looking at each program's top-scored structure, Modeller had the smallest average modeling RMSD of 2.17 Å, which is better than AlphaFold's 5.53 Å and RoseTTAFold's 6.28 Å, probably since Modeller already included many known structures as templates. However, the NN-based methods (AlphaFold and RoseTTAFold) outperformed Modeller in 21 and 15 out of the 73 cases with the top-scored model, respectively, where no good templates were available for Modeller. The larger RMSD values generated by the NN-based methods were primarily due to the differences in loop prediction compared to the crystal structures.

Making Use of Averaging Methods in MODELLER for Protein Structure Prediction.

Recent advances in protein structure prediction, driven by AlphaFold 2 and machine learning, demonstrate proficiency in static structures but encounter challenges in capturing essential dynamic features crucial for understanding biological function. In this context, homology-based modeling emerges as a cost-effective and computationally efficient alternative. The MODELLER (version 10.5, accessed on 30 November 2023) algorithm can be harnessed for this purpose since it computes intermediate models during simulated annealing, enabling the exploration of attainable configurational states and energies while minimizing its objective function. There have been a few attempts to date to improve the models generated by its algorithm, and in particular, there is no literature regarding the implementation of an averaging procedure involving the intermediate models in the MODELLER algorithm. In this study, we examined MODELLER's output using 225 target-template pairs, extracting the best representatives of intermediate models. Applying an averaging procedure to the selected intermediate structures based on statistical potentials, we aimed to determine: (1) whether averaging improves the quality of structural models during the building phase; (2) if ranking by statistical potentials reliably selects the best models, leading to improved final model quality; (3) whether using a single template versus multiple templates affects the averaging approach; (4) whether the "ensemble" nature of the MODELLER building phase can be harnessed to capture low-energy conformations in holo structures modeling. Our findings indicate that while improvements typically fall short of a few decimal points in the model evaluation metric, a notable fraction of configurations exhibit slightly higher similarity to the native structure than MODELLER's proposed final model. The averaging-building procedure proves particularly beneficial in (1) regions of low sequence identity between the target and template(s), the most challenging aspect of homology modeling; (2) holo protein conformations generation, an area in which MODELLER and related tools usually fall short of the expected performance.

Investigating impact of CORDEX-based predicted climatic and LCM-based LULC scenarios on hydrologic response of a semi-gauged Indian catchment.

The present study aims at documenting the impact of different climate and land use change scenarios on runoff in the Kangsabati River basin. While the study relies on India Meteorological Department (IMD), National Oceanic and Atmospheric Administration's Physical Sciences Laboratory (NOAA-PSL), and a multi-model ensemble of six driving models from Coordinated Regional Downscaling Experiment-Regional Climate Models (CORDEX RCM) for climate data input, it depends on IDRISI Selva's Land Change Modeller (LCM) and Soil and Water Assessment Tool (SWAT) model to generate projected land use land change maps and simulate its streamflow response, respectively. A total of four land use and land cover (LULC) scenarios, representing four projected land use change, were modelled across three climatic scenarios, called Representative Concentration Pathways (RCPs). With runoff being predominantly impacted more by climate change than LULC, volumetric runoff is expected to be 12-46% higher than the baseline period of 1982-2017. Conversely, while surface runoff is expected to decrease by 4-28% in lower parts of the basin, it will increase by 2-39% in the rest of it, depending on the subtle alterations in land use and climatic variability.

In silico structural characterization of Cytochrome c oxidase Subunit 1: A transmembrane protein from Aedes aegypti.

BACKGROUND & OBJECTIVES: The present study proposed a series of computational techniques such as homology modelling, molecular simulation, and molecular docking to be performed to explore the structural features and binding mechanism of Cytochrome c oxidase subunit I (COX1) protein with known inhibitors. METHODS: Elucidation of the three-dimensional structure of COX1 protein was carried out by using MODELLER software. The modelled protein was validated using GROMACS, structural qualitative tools and web servers. Finally the model was docked with carbon monoxide (CO) and nitric oxide (NO) using Auto Dock Tools. RESULTS: The three-dimensional structure of mitochondrial transmembrane protein COX1 was built using homology modelling based on high-resolution crystal structures of Bos taurus. Followed by inserting the lipid bilayer, molecular dynamics simulation was performed on the modelled protein structure. The modelled protein was validated using qualitative structural indices. Known inhibitors such as carbon monoxide (CO) and nitric oxide (NO) inhibit their active binding sites of mitochondrial COX1 and the inhibitors were docked into the active site of attained model. A structure-based virtual screening was performed on the basis of the active site inhibition with best scoring hits. The COX1 model was submitted and can be accessible from the Model Archive site through the following link https://www.modelarchive.org/doi/10.5452/ma-at44v. INTERPRETATION & CONCLUSION: Structural characterization and active site identification can be further used as target for the planning of potent mosquitocidal compounds, thereby assisting the information in the field of research.

Conservation Reserve Program effects on floodplain land cover management.

Growing populations and industrialized agriculture practices have eradicated much of the United States wetlands along river floodplains. One program available for the restoration of floodplains is the Conservation Reserve Program (CRP). The current research explores the effects CRP land change has on flooding zones, utilizing Flood Modeller and HEC-RAS. Flood Modeller is proven a viable tool for flood modeling within the United States when compared to HEC-RAS. Application of the software is used in the Nodaway River system located in the western halves of Iowa and Missouri to model effects of introducing new forest areas within the region. Flood stage during the conversion first decreases in the early years, before rising to produce greater heights. Flow velocities where CRP land is present are reduced for long-term scopes. Velocity reduction occurs as the Manning's roughness increases due to tree diameter and brush density. Flood zones become more widespread with the implementation of CRP. Future model implementations are recommended to witness the effects of smaller flood recurrence intervals.

Molecular dynamics and protein interaction studies of lipopeptide (Iturin A) on α- amylase of Spodoptera litura.

The small mottled willow moth (Spodoptera litura) is one of the best-known agricultural pest insects. To understand the insecticidal activity, we have selected iturin A compound produced by Bacillus amyloliquefaciens RHNK22 which showed the strongest and most common inhibitory effect on the Spodoptera litura protein. In this work we have identified the action of iturin A on α- amylase is a major digestive enzyme of Spodoptera litura using docking studies. A 3D model of α- amylase from Spodoptera litura was generated using 2HPH as a template with the help of Modeller7v7. With the aid of the molecular mechanics and molecular dynamics methods, the final model is obtained and is further checked by Procheck and Verify 3D graph programs, which showed that the final refined model is reliable. With this model, a adjustable docking study was performed with iturin A using GOLD software. The results indicated that ARG 18, THR15, LEU42 in α- amylase are important determinant residues in binding as they have strong hydrogen bonding interactions with iturin A. These hydrogen binding interactions play an important role for the stability of the complex.

A comparative study of protein structure prediction tools for challenging targets: Snake venom toxins.

Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein structure prediction tools, such as AlphaFold2 and ColabFold. However, their performance has primarily been evaluated on well-characterised proteins and their ability to predict sturtctures of proteins lacking experimental structures, such as many snake venom toxins, has been less scrutinised. In this study, we evaluated three modelling tools on their prediction of over 1000 snake venom toxin structures for which no experimental structures exist. Our findings show that AlphaFold2 (AF2) performed the best across all assessed parameters. We also observed that ColabFold (CF) only scored slightly worse than AF2, while being computationally less intensive. All tools struggled with regions of intrinsic disorder, such as loops and propeptide regions, and performed well in predicting the structure of functional domains. Overall, our study highlights the importance of exercising caution when working with proteins with no experimental structures available, particularly those that are large and contain flexible regions. Nonetheless, leveraging computational structure prediction tools can provide valuable insights into the modelling of protein interactions with different targets and reveal potential binding sites, active sites, and conformational changes, as well as into the design of potential molecular binders for reagent, diagnostic, or therapeutic purposes.

An automated pipeline integrating AlphaFold 2 and MODELLER for protein structure prediction.

The ability to predict a protein's three-dimensional conformation represents a crucial starting point for investigating evolutionary connections with other members of the corresponding protein family, examining interactions with other proteins, and potentially utilizing this knowledge for the purpose of rational drug design. In this work, we evaluated the feasibility of improving AlphaFold2's three-dimensional protein predictions by developing a novel pipeline (AlphaMod) that incorporates AlphaFold2 with MODELLER, a template-based modeling program. Additionally, our tool can drive a comprehensive quality assessment of the tertiary protein structure by incorporating and comparing a set of different quality assessment tools. The outcomes of selected tools are combined into a composite score (BORDASCORE) that exhibits a meaningful correlation with GDT_TS and facilitates the selection of optimal models in the absence of a reference structure. To validate AlphaMod's results, we conducted evaluations using two distinct datasets summing up to 72 targets, previously used to independently assess AlphaFold2's performance. The generated models underwent evaluation through two methods: i) averaging the GDT_TS scores across all produced structures for a single target sequence, and ii) a pairwise comparison of the best structures generated by AlphaFold2 and AlphaMod. The latter, within the unsupervised setups, shows a rising accuracy of approximately 34% over AlphaFold2. While, when considering the supervised setup, AlphaMod surpasses AlphaFold2 in 18% of the instances. Finally, there is an 11% correspondence in outcomes between the diverse methodologies. Consequently, AlphaMod's best-predicted tertiary structures in several cases exhibited a significant improvement in the accuracy of the predictions with respect to the best models obtained by AlphaFold2. This pipeline paves the way for the integration of additional data and AI-based algorithms to further improve the reliability of the predictions.

Analysis Of Inhibitory Effect Of Transcriptional Regulatory Protein (Sty 4289) Of Salmonella Typhi Ct18by Homology Modelling.

BACKGROUND: Salmonella typhi cause typhoid fever which is life threatening disease. It affects approximately 600,000 people per annum around the world. Food and water are the integral components through which this disease is transmitted and becomes base of typhoid. It spreads widely where cleanliness is very poor. Objective was to analyse three-dimensional structure of transcriptional regulator of Salmonella typhi CT18 by homology modelling to inhibit virulent effect of Salmonella typhi. METHODS: Bioinformatics tools and programs like comprehensive Microbial resource (CMR). Interproscan, Basic Local Alignment Search tool (BLAST), Modeller 9.10, Procheck and Prosa were used as bioinformatic tools for effective study of protein. RESULTS: Homology modelling is an appropriate and precise method to find three-dimensional transcriptional regulator to stop its virulency. CONCLUSIONS: Homology modelling is computational and accurate method to find 3D structure of transcriptional regulator to inhibit its virulence effect of causing disease.

In-silico Structural Modeling of Human Immunodeficiency Virus Proteins.

Human immunodeficiency virus (HIV) is an infectious virus that depletes the CD4+ T lymphocytes of the immune system and causes a chronic life-treating disease-acquired immunodeficiency syndrome (AIDS). The HIV genome encodes different structural and accessory proteins involved in viral entry and life cycle. Determining the 3D structure of HIV proteins is essential for new target position finding, structure-based drug designing, and future planning for computational and laboratory experimentations. Hence, the study aims to predict the 3D structures of all the HIV structural and accessory proteins using computational homology modeling to understand better the structural basis of HIV proteins interacting with host cells and viral replication. The sequences of HIV capsid, matrix, nucleocapsid, p6, reverse transcriptase, invertase, protease, gp120, gp41, virus protein r, viral infectivity factor, virus protein unique, RNA splicing regulator, transactivator protein, negative regulating factor, and virus protein x proteins were retrieved from UniProt. The primary and secondary structures of HIV proteins were predicted by Expasy ProtParam and SOPMA web servers. For the homology modeling, the MODELLER predicted the 3D structures of HIV proteins using templates. Then, the modeled structures were validated by the Ramachandran plot, local and global quality estimation scores, QMEAN scores, and Z-scores. Most of the amino acid residues of HIV proteins were present in the most favored and generously allowed regions in the Ramachandran plots. The local and global quality scores and Z-scores of the HIV proteins confirmed the good quality of modeled structures. The 3D modeled structures of HIV proteins might help further investigate the possible treatment.

Homology Modeling of Class A G-Protein-Coupled Receptors in the Age of the Structure Boom.

With 700 members, G protein-coupled receptors (GPCRs) of the rhodopsin family (class A) form the largest membrane receptor family in humans and are the target of about 30% of presently available pharmaceutical drugs. The recent boom in GPCR structures led to the structural resolution of 57 unique receptors in different states (39 receptors in inactive state only, 2 receptors in active state only and 16 receptors in different activation states). In spite of these tremendous advances, most computational studies on GPCRs, including molecular dynamics simulations, virtual screening and drug design, rely on GPCR models obtained by homology modeling. In this protocol, we detail the different steps of homology modeling with the MODELLER software, from template selection to model evaluation. The present structure boom provides closely related templates for most receptors. If, in these templates, some of the loops are not resolved, in most cases, the numerous available structures enable to find loop templates with similar length for equivalent loops. However, simultaneously, the large number of putative templates leads to model ambiguities that may require additional information based on multiple sequence alignments or molecular dynamics simulations to be resolved. Using the modeling of the human bradykinin receptor B1 as a case study, we show how several templates are managed by MODELLER, and how the choice of template(s) and of template fragments can improve the quality of the models. We also give examples of how additional information and tools help the user to resolve ambiguities in GPCR modeling.

In silico modelling of acute toxicity of 1, 2, 4-triazole antifungal agents towards zebrafish (Danio rerio) embryos: Application of the Small Dataset Modeller tool.

Nowadays, there is a widespread use of triazole antifungal agents to kill broad classes of fungi in farming lands and to protect herbs, fruits and grains. These agents further deposit into the aquatic systems causing toxicity to the living aquatic creatures, which can then affect human beings. Considering this issue, risk assessment of these toxic chemicals is a very essential task. Due to the inadequate experimental data on acute toxicity of antifungal agents containing the 1, 2, 4-triazole ring, higher testing costs along with the regulatory restrictions and the international regulations to lessen animal testing emphasize on in silico techniques such as quantitative structure-activity relationship (QSAR) studies. The application of QSAR modelling has created an easier avenue to predict activity/property/toxicity of newly synthesized compounds. In the present study, we have used 23 antifungal agents containing the 1, 2, 4-triazole ring to develop 2D-QSAR models and explored their structural attributes crucial for acute toxicity towards embryonic phase of zebrafish (Danio rerio). Here, we have employed simple 2D descriptors to develop the QSAR models. The models were evolved by executing the Small Dataset Modeller tool (https://dtclab.webs.com/software-tools), and the validation of the models was achieved by employing different precise validation principles. The statistical validation metrics confirm that built models are robust, useful and well predictive to forecast the acute toxicity of new compounds.

Protein-protein Interaction and Molecular Dynamics of Iturin A Gene on Effector Proteins of Phytophthora infestans.

AIM AND OBJECTIVES: Phytophthora infestans (Mont.) de Bary, the fungal pathogen causes late blight, which results in devastating economic loss among the Solanaceae. The bacillus lipopeptides show the antagonistic activity against the many plant pathogens, among bacillus lipopeptides reported as the antifungal gene. Hence, to understand the in silico antifungal activity, we have selected gene iturin A (AXN89987) produced by Bacillus spp to check the molecular dynamics study with the effector proteins of the P. infestanse. In this concern, known effector proteins of P. infestans were subjected to the protein-protein interaction followed by simulation. MATERIALS AND METHODS: Iturin A gene was amplified using the soil bacterium Bacillus subtilis with gene-specific primers, cloned into pTZ 57R/T vector and confirmed by sequencing. To get better insights, the protein model was developed for Iturin A using Modeller 9.17, using PDB structure of ID 4MRT (Phosphopantetheine transferase Sfp) and 1QR0 (4'-phosphopantetheinyl moiety of coenzyme A) as a template, it shared the identity 72% and expected P-value: 3e-121, respectively. The model quality was assessed using ProSA and PROCHECK programs. RESULTS: The potency of modelled protein against effector proteins of P. infestans were evaluated in silico using the HADDOCK server and the results showed the high affinity of towards the effector protein Host ATG8 (PDB-5L83). Finally, the simulation was performed to the docked conformation of with Host ATG8 to further understand the stability of the complex using the Desmond program. CONCLUSION: Altogether, the protein-protein interaction and simulation study propose a new methodology and to uncover possible antifungal activity of iturin A against effector proteins of P. infestans.

Development of a new quantitative structure-activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™.

BACKGROUND: Food flavors are relatively low molecular weight chemicals with unique odor-related functional groups that may also be associated with mutagenicity. These chemicals are often difficult to test for mutagenicity by the Ames test because of their low production and peculiar odor. Therefore, application of the quantitative structure-activity relationship (QSAR) approach is being considered. We used the StarDrop™ Auto-Modeller™ to develop a new QSAR model. RESULTS: In the first step, we developed a new robust Ames database of 406 food flavor chemicals consisting of existing Ames flavor chemical data and newly acquired Ames test data. Ames results for some existing flavor chemicals have been revised by expert reviews. We also collected 428 Ames test datasets for industrial chemicals from other databases that are structurally similar to flavor chemicals. A total of 834 chemicals' Ames test datasets were used to develop the new QSAR models. We repeated the development and verification of prototypes by selecting appropriate modeling methods and descriptors and developed a local QSAR model. A new QSAR model "StarDrop NIHS 834_67" showed excellent performance (sensitivity: 79.5%, specificity: 96.4%, accuracy: 94.6%) for predicting Ames mutagenicity of 406 food flavors and was better than other commercial QSAR tools. CONCLUSIONS: A local QSAR model, StarDrop NIHS 834_67, was customized to predict the Ames mutagenicity of food flavor chemicals and other low molecular weight chemicals. The model can be used to assess the mutagenicity of food flavors without actual testing.

Decoding the vital segments in human ATP-dependent RNA helicase.

An analysis of the ATP-dependent RNA helicase using known functionally close analogs helps disclose the structural and functional information of the enzyme. The enzyme plays several interlinked biological functions and there is an urgent need to interpret its key active-site residues to infer function and establish role. The human protein q96c10.1 is annotated using tools such as interpro, go and cdd. The physicochemical properties are estimated using the tool protparam. We describe the enzyme protein model developed using modeller to identify active site residues. We used consurf to estimate the structural conservation and is evolutionary relationship is inferred using known close sequence homologs. The active site is predicted using castp and its topological flexibility is estimated through cabs-flex. The protein is annotated as a hydrolase using available data and ddx58 is found as its top-ranked interacting protein partner. We show that about 124 residues are found to be highly conserved among 259 homologs, clustered in 7 clades with the active-site showing low sequence conservation. It is further shown that only 9 loci among the 42 active-site residues are conserved with limited structural fluctuation from the wild type structure. Thus, we document various useful information linked to function, sequence similarity and phylogeny of the enzyme for annotation as potential helicase as designated by uniprot. Data shows limited degree of conserved sequence segments with topological flexibility unlike in other subfamily members of the protein.

Homology Modeling of P2X Receptors.

Since the X-ray structure of the zebra fish P2X4 receptor in the closed state was published in 2009 homology modeling has been used to generate structural models for P2X receptors. In this chapter, we outline how to use the MODELLER software to generate such structural models for P2X receptors whose structures have not been solved yet.

Homology Modeling of Protein Targets with MODELLER.

Homology modeling is a computational approach to generate three-dimensional structures of protein targets when experimental data about similar proteins are available. Although experimental methods such as X-ray crystallography and nuclear magnetic resonance spectroscopy successfully solved the structures of nearly 150,000 macromolecules, there is still a gap in our structural knowledge. We can fulfill this gap with computational methodologies. Our goal in this chapter is to explain how to perform homology modeling of protein targets for drug development. We choose as a homology modeling tool the program MODELLER. To illustrate its use, we describe how to model the structure of human cyclin-dependent kinase 3 using MODELLER. We explain the modeling procedure of CDK3 apoenzyme and the structure of this enzyme in complex with roscovitine.

Land use change and conversion effects on ground water quality trends: An integration of land change modeler in GIS and a new Ground Water Quality Index developed by fuzzy multi-criteria group decision-making models.

This study aggregated Land Change Modeller (LCM) as a useful model in GIS with an extended Groundwater Quality Index (GWQI) developed by fuzzy Multi-Criteria Group Decision-Making models to investigate the effect of land use change and conversion on groundwater quality being supplied for drinking. The model's performance was examined through an applied study in Shiraz, Iran, in a five year period (2011 to 2015). Four land use maps including urban, industrial, garden, and bare were employed in LCM model and the impact of change in area and their conversion to each other on GWQI changes was analysed. The correlation analysis indicated that increase in the urban land use area and conversion of bare to the residential/industrial land uses, had a relation with water quality decrease. Integration of LCM and GWQI can accurately and logically provide a numerical analysis of the possible impact of land use change and conversion, as one of the influencing factors, on the groundwater quality. Hence, the methodology could be used in urban development planning and management in macro level.

Computer-aided Drug Designing for the Identification of High-Affinity Small Molecule Targeting CD20 for the Clinical Treatment of Chronic Lymphocytic Leukemia (CLL).

BACKGROUND: Chronic lymphocytic leukemia (CLL) is a B-lineage lymphoid malignancy of self-reactive cells that are focused to produce polyreactive natural autoantibodies. Its surface protein marker CD20 plays an important role in the humoral immune response targeting which has emerged as an attractive therapeutic option for the treatment of CLL. The present study explains the interaction of the CD20 with its established inhibitors and to discover the compound having high binding affinity against the target protein receptor. Technically, during the development of new compound through docking studies, best drug among all pre-exist drugs got filtered, hence in reference to docked best drug study moved ahead. METHODS: The 3D structure of CD20 was built using homology base fold recognition method using Smith waterman's Local alignment and standalone Delta Blast algorithms. 23 established inhibitors towards CD20 were selected in this present investigation. Among these inhibitors, etoposide (RMSD value -96.6481) showed high binding capacity with the receptor CD20 which was further subjected to virtual screening. The said screening presented 380 possible drugs having structural similarity to etoposide. RESULTS: The docking studies of the screened drugs separated the compound having PubChem CID: 11753896 (RMSD value -98.5416). Toxicity and interaction profile validated this compound for having a better affinity with the target protein. Conclusively, this research study says that according to ADMET profile and BOILED-Egg plot, the compound (PubChem CID: 11753896) obtained from Virtual Screen could be the best drug in future during the prevention of Chronic Lymphocytic Leukemia. CONCLUSION: The compound identified in the present investigation can be subjected further for in vitro and in vivo studies for ADMET properties and it could optimize a good profile in the field of pharmacy and bioavailable for suppressing cancer. The pharmacophore study revealed that the drug CID11753896 is a non-inhibitor of CYP450 microsomal enzymes and was found to be non-toxic, similar to the established compound CID36462. It has a lower LD50 value of 2.5423mol/kg as compared to the established compound whose LD50 value is 2.9588mol/kg. Also, the compound was found to be non-carcinogenic.

Analysis of the Interactions Between Thioredoxin and 20 Selenoproteins in Chicken.

Thioredoxin (Trx) is a small molecular protein with complicated functions in a number of processes, including inflammation, apoptosis, embryogenesis, cardiovascular disease, and redox regulation. Some selenoproteins, such as glutathione peroxidase (Gpx), iodothyronine deiodinase (Dio), and thioredoxin reductase (TR), are involved in redox regulation. However, whether there are interactions between Trx and selenoproteins is still not known. In the present paper, we used a Modeller, Hex 8.0.0, and the KFC2 Server to predict the interactions between Trx and selenoproteins. We used the Modeller to predict the target protein in objective format and assess the accuracy of the results. Molecular interaction studies with Trx and selenoproteins were performed using the molecular docking tools in Hex 8.0.0. Next, we used the KFC2 Server to further test the protein binding sites. In addition to the selenoprotein physiological functions, we also explored potential relationships between Trx and selenoproteins beyond all the results we got. The results demonstrate that Trx has the potential to interact with 19 selenoproteins, including iodothyronine deiodinase 1 (Dio1), iodothyronine deiodinase 3 (Dio3), glutathione peroxidase 1 (Gpx1), glutathione peroxidase 2 (Gpx2), glutathione peroxidase 3 (Gpx3), glutathione peroxidase 4 (Gpx4), selenoprotein H (SelH), selenoprotein I (SelI), selenoprotein M (SelM), selenoprotein N (SelN), selenoprotein T (SelT), selenoprotein U (SelU), selenoprotein W (SelW), selenoprotein 15 (Sep15), methionine sulfoxide reductase B (Sepx1), selenophosphate synthetase 1 (SPS1), TR1, TR2, and TR3, among which TR1, TR2, TR3, SPS1, Sep15, SelN, SelM, SelI, Gpx2, Gpx3, Gpx4, and Dio3 exhibited intense correlations with Trx. However, additional experiments are needed to verify them.