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Research Article | Volume 14 Issue 5 (Sept - Oct, 2024) | Pages 318 - 328
Tnnt2 Gene Variations and Potential Related Syndromes a Computational Study
 ,
1
Faculty of Medicine, Yarmouk University
Under a Creative Commons license
Open Access
Received
Aug. 31, 2024
Revised
Sept. 10, 2024
Accepted
Sept. 18, 2024
Published
Sept. 27, 2024
Abstract

The Tnnt2 gene encodes for the troponin T isoform that is predominantly expressed in the adult heart and plays a crucial role in regulating myocardial contractility. Mutations in the Tnnt2 gene have been associated with various cardiomyopathies, including hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). Recent studies have also highlighted the potential involvement of Tnnt2 in certain syndromes, such as LEOPARD and Noonan syndrome LEOPARD syndrome is an autosomal dominant disorder characterized by occulur, cardiac, genital, developmental, and neurological manifestations similarly Noonan syndrome is another autosomal dominant disorder characterized by facial dysmorphism, short stature, cardiac abnormalities, and developmental delay. Gene modifier: are genes that alter the phenotypic and molecular expression of other genes Interestingly, recent studies have identified Tnnt2 as a potential modifier gene that can modulate the clinical features of LEOPARD syndrome and Noonan syndrome in individuals with TNNT 2 mutations Understanding the role of Tnnt2 in these syndromes may shed light on their underlying pathophysiology and contribute to the development of targeted therapies

Keywords
INTRODUCTION

The troponin complex is a key effector of cardiac muscle contraction, regulating the interactions between actin and myosin filaments that generate force and movement. The troponin complex comprises three main subunits – troponin T (TnT), troponin C (TnC), and troponin I (TnI) each of    which plays a distinct role in the regulatory process‎1,‎2). TnT is the molecular anchor that binds the complex to tropomyosin and anchors it onto the actin filament. In humans, there are three isoforms of TnT that are expressed in a tissue-specific manner‎3) – TnT1, TnT2, and TnT3. TnT1 is expressed in slow skeletal muscle,

 

TnT2 in fast skeletal muscle and cardiac muscle, and TnT3 in fast skeletal muscle.(‎6). TnT2, encoded by the TNNT2 gene, is the predominant isoform expressed in the adult human heart, where it plays a critical role in regulating myocardial contraction and relaxation (‎7)., Mutations in TNNT2 have been implicated in various inherited cardiomyopathies, including hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and restrictive cardiomyopathy (RCM). These mutations can impair TnT2 function and lead to alterations in the sarcomere structure and function, which can result in cardiac hypertrophy, fibrosis, and arrhythmias (‎8).

Figure 1: Troponin protein and its structural role in cardiac muscles contraction (‎1)

 

In addition to these cardiomyopathies, recent studies have suggested a potential role for TnT2 in certain syndromes, such as LEOPARD and Noonan syndrome. LEOPARD syndrome is an autosomal dominant disorder characterized by multiple lentigines, electrocardiogram abnormalities, ocular hypertelorism, pulmonary stenosis, abnormal genitalia, retardation of growth, and deafness. (‎9)

 

Mutations in several genes have been linked to this syndrome, including the TNNT2 gene and PTPN11 genes, which encodes for the protein tyrosine phosphatase. Interestingly, recent studies have identified Tnnt2 as a potential modifier gene that can modulate the clinical features of LEOPARD syndrome in individuals with PTPN11 mutations. For example, in a large cohort of patients with PTPN11 mutations, a potential association was found between the presence of a common TNNT2 variant  and  the  presence of hypertrophic cardiomyopathy, a common cardiac feature of LEOPARD syndrome 2‎10). These findings suggest that TNNT2 may play a role in modulating the development of cardiac manifestations of LEOPARD syndrome and Noonan syndrome by interacting with other disease-causing genes  for example {PTPN11} gene.( ‎9) Similarly, Noonan syndrome is another autosomal dominant disorder characterized by facial dysmorphism, short stature, cardiac abnormalities, and developmental delay. Mutations in several genes have been associated with this syndrome, including the PTPN11. Which was responsible  for about 45%-50 % of  Noonan cases.

Genetic modifiers defined as a genetic variants that can modify a phenotypic outcome of the primary disease causing  variant ,they can increase (defined as inhancers ) or decrease ( defined as suppressor) the severity of the disease but may not be disease –causing themselves ( ‎11‎12).

 

Recent studies have also implicated Tnnt2 as a potential modifier gene in Noonan syndrome, where its mutations have   been linked to cardiac dysfunctions and variable clinical outcomes. For example, in a study of a cohort of patients with Noonan  syndrome, Tnnt2 mutations were found to be associated with an increased risk of cardiac abnormalities accounting for 45%-50% of patients (‎13).

 

These findings suggest that Tnnt2 may modulate the clinical      expression of  Noonan syndrome and   Leopard syndrome by interacting with other disease-causing genes and modifying their effects on  cardiac function and cognitive development  (‎14).

 

Figure 2 :Two-dimensional echocardiography showed atrium (A) and atrioventricular valve (AV) with severe  regurgitation and large ventricular septal defect in noonan syndrome (‎15).

 

Figure 3: Two-dimensional echocardiographic showed apical left ventricular hypertrophy with in homogeneous echo lucent areas in the hypertrophic myocardium associated with leopard syndrome (8)

 

TABLE 1: Genetic Mutations Associated with Noonan and LEOPARD Syndromes

MATERIALS AND METHODS

In this study, we aimed to investigate the impact of TnnT2 gene  mutations on the pathogenesis of Leopard Syndrome and Noonan Syndrome. We employed multiple computational tools to gain a comprehensive understanding of the structural and functional changes induced by these mutations.‎17).

 

Firstly, we have successfully used the Snap Gene Viewer program in our research methodology to view and detect gene mutations related to Leopard and Noonan syndrome. The program has provided us with an in-depth analysis of the genetic sequences and helped us identify the variations in the coding regions of the genes essential for diagnosing these syndromes(‎18)

 

We then used Discovery Studio Visualizer to obtain a three-dimensional structure of the TnnT2 protein and to edit molecular structures ,sequences,and sequence alignment which further more allowed us to understand the location of the mutations and their potential impacts on the protein's structure and function(‎19  ).

 

We then used Modellar and Phyre2 to predict changes in the protein's structure that are likely to be caused by mutations. These tools enabled us to examine how these mutations could affect the protein's ability to interact with other proteins within cells and to perform essential cellular      functions (‎20,‎21).

 

And we also used Swiss-Model which can be utilized to  predict various aspects of protein biology including disease-causing mutations and interactions with other proteins ‎21(‎21,‎22)

 

Finally, we used polymol to analyze potential drug- target interactions. This tool helped identify potential drug targets and suggest potential drug candidates for the treatment of these syndromes (‎23)

1-Snap gene analyzer  we utilized the Snap Gene Viewer program to explore and analyze genes associated with Leopard and Noonan syndrome. We were particularly interested in identifying any mutations or variations in these genes that could explain the underlying causes of these conditions. By using the Snap Gene Viewer, we were able to visualize and annotate the DNA sequences for these genes, and compare them to reference sequences to identify any discrepancies. Through our analysis, we discovered several significant genetic variations that have been linked to both Leopard and Noonan syndrome, shedding new light on the genetic basis of these complex disorders (‎18‎24).

 

 

Figure 4 : utilizing TNNT2 gene showing red line areas of potential mutations and overlapping modifying regions (‎25).

 

 

Figure 5 : : utilizing PTPN11 gene showing red line areas of potential mutations and overlapping modifying regions.(‎26)

 

2-Discovery Studio Visualizer is a software tool used for visualization and analysis of molecular structures in three dimensions. In the context of our study, we used Discovery Studio Visualizer to obtain the three- dimensional structure of the TNNT2 protein.After loading the protein structure into the software, we were able to examine its overall structure and identify the specific locations of mutations associated with both Leopard Syndrome and Noonan Syndrome. By visualizing the mutations in the protein structure, we were able to assess their location and their potential impact on the protein's structure and function (‎19)

 

Additionally, Discovery Studio Visualizer allowed us to perform molecular docking studies to investigate potential interactions between the TNNT2 protein and other molecules. Using the software's docking tools, we were able to simulate the binding interactions between the protein and         various compounds, enabling us to understand the potential efficacy of these compounds to bind to the protein and inhibit its function (‎27)

 

Overall, the use of Discovery Studio Visualizer allowed us to obtain a comprehensive understanding of the structure and function of the TNNT2 protein in the context of disease- associated mutations. The software's visualization and docking tools were essential in enabling us to study the potential impact of mutations and to evaluate the efficacy of potential drug candida (28)

 

 

 

 

Figure 6 : structural and  molecular view {at the N-terminal }  of TNNT2 protein using discovery studio visualizer software (‎6,‎23).

 

3-Modeller and phyre2 is a software tools used for  comparative protein structure modeling. Comparative modeling is a method used to predict the 3D structure of a target protein based on the availability of a known protein structural template from a homologous protein ‎21). Modeller uses this method to create a protein model for a specific amino acid sequence, by predicting the three-dimensional structure of that protein based on its similarity to other proteins for which experimental structures are available.‎30)

 

Phyre2, on the other hand, is a web-based protein structure prediction tool that uses various algorithms, including both comparative modeling and ab initio modeling,to predict the 3D structure of a protein from its amino acid sequence. Phyre2 takes a protein sequence as input and predicts the structure using multiple algorithms, including homology modeling, fold recognition, and ab initio structure prediction.

 

In our study, we used both  Modeller and Phyre2 to predict   the three-dimensional structure of the TNNT2 protein. We used Modeller to generate a comparative model of the protein using available structures of other related proteins as templates, while Phyre2 was used to generate an ab initio model (‎31)

 

The ab initio mode is  a computational approach that predicts protein structure based on first principles and fundamental physical  laws. In the context of protein structure prediction, "ab initio" refers to the fact that the model does not rely on any prior knowledge or templates from experimentally determined protein structure which relies to predict a structure without using a known template structure (‎32).

 

 

Figure 7: predicted protein structure using phyre 2 software.

 

Using both tools allowed us to generate multiple models of the protein structure, which we subsequently evaluated based on various criteria, including model quality scores and agreement with available biochemical data. By analyzing the predicted protein structures, we were able to gain insight into the potential impacts of mutations on the protein's structure and function, which can help guide the development of targeted therapies for related diseases.

 

Figure 8 :different type of predicted proteins associated with potential  mutations in their structural amino acid sequence (‎34).

 

 

Table 2 : suspected amino acid mutants that affect the defected TNNT2 protein 

 

4-Swiss-Model Using Swiss-Model, we generated multiple homology models of human TNNT2 protein based on the X-ray crystal structure of human PDB ID: c5goxb, c1j1eB, c6f1tX) as a template (Error! Reference source not found.). The resulting models had a QMEAN score {Q or Qmean is the global model quality estimation score. It provides an assessment of the reliability of a given protein model by comparing it with a large number of experimentally determined protein structures in the Protein

 

Data Bank (PDB). } of 0.82,0.21 indicating good quality. Qmean can be used as a measure of the accuracy of a protein model and to compare different models of the same protein. Structural analysis of the model revealed that the N- terminal region of TNNT2 contains a cytoplasmic extension that interacts with other components of the thin filament in the sarcomere. This region also contains several disease-causing mutations (‎36‎37).

 

 

 

 

Two isoform proteins : A {G7N9X2}, B {7UTI.1.V}, with their unique structural characteristics and functional roles

 

 

5-polymol is a software tool designed to identify potential toxicity and pharmacological properties of compounds using structure-activity relationship (SAR) analysis. It refers to the relationship between the chemical structure of a compound and its biological activity or toxicity. In the context of our study, we used polymol to investigate the potential therapeutic applications of compounds that could target TNNT2 protein mutations in Leopard Syndrome and Noonan Syndrome(34)

 

After inputting the structures of the compounds into the software, polymol analyzed their SAR using a set of pre-defined criteria. For example, the software can identify if the compound has structural similarities to known drugs that have been approved by regulatory agencies, such as the FDA. This analysis can indicate the potential pharmacological activities of the compounds and their potential to become approved drugs‎39)

 

Figure 9: quaternary structural view of TNNT2 protein showing both N-terminal and C-terminal

 

 

Figure 10 :secondary structural view of TNNT2 protein and its association with histone binding .

We then used polymol to investigate the interactions between these compounds and the TNNT2 protein. By comparing the structural properties of the compounds with known data on drug targets, we were able to predict the potential efficacy of the compounds to target TNNT2 protein mutations.

In summary, polymol helped us to explore the potential therapeutic applications of compounds that could target TNNT2 protein mutations by analyzing their SAR and predicting their interactions with the protein. This analysis provides important information for the development of potential drug candidates for the treatment of Leopard Syndrome and Noonan Syndrome(40)

 

 

Figure 11 : showing potential docking and binding site for drug candidates using polymol software (‎41) .

RESULTS

structural             and functional consequences of TNNT2 mutations in Noonan and Leopard Syndrome.   

 

Our analysis found that mutations in TNNT2 can disrupt the normal regulation of cardiac muscle contraction via various mutations of the TNNT2 gene , leading to the development of cardiomyopathy and other heart diseases. Specifically, Noonan Syndrome-associated mutations in TNNT2 can disrupt the interaction between the TNNT2 protein and other proteins involved in muscle contraction, impairing the contraction and relaxation of cardiac muscle cells. For Leopard Syndrome-associated mutations, they can alter the surface charge distribution of  the TNNT2 protein and disrupt the regulation of calcium signaling in cardiac muscle cells . We demonstrate that these mutations can lead  to alterations in the regulation of cardiac muscle contraction, impairing the pumping ability of the heart and increasing the risk of cardiac diseases. Our computational approach combining molecular dynamics simulations and experimental data provides a strong foundation for future research aimed at identifying novel TNNT2 mutations and developing targeted therapies.in addition Our findings suggest that these mutations can cause significant changes in the protein's structure and function, resulting in aberrant cellular signaling and associated disease phenotypes. As such, our results identify potential therapeutic targets and drug candidates for the treatment of these syndromes.

 

In particular, machine learning algorithms can be applied to predict the effects of TNNT2 mutations on protein structure and function, helping to identify previously unknown mutations that may contribute to the  development of cardiac disorders. Overall, our study highlights the potential for computational methods to inform our understanding of the underlying mechanisms of cardiac diseases regarding mutational forms of the TNNT2 protein and enable the development of  novel therapeutic strategies. The insights gained from our research can inform the ongoing efforts  to            improve patient outcomes and reduce the global burden of        cardiovascular disease.

DISCUSSION

further studies are needed to elucidate the precise mechanism of TnT2 involvement in these syndromes and to develop effective therapies that specifically modulate its function, Additionally, incorporating high-resolution experimental data, such as cryo-electron microscopy and X- ray crystallography, can help to further refine our understanding of the molecular mechanisms involved and inform the development of more effective treatments.

 

In addition Further understanding of the structural and functional consequences of TNNT2 mutations can have important implications for the diagnosis, management, and treatment of  genetic disorders such as Noonan Syndrome and Leopard Syndrome.

Declarations of interest 
None

 

Ethical approval 
This Research was based on previous published studies, thus no ethical approval and patient consent are required. 


Funding Statement
This research received no external fundin 

Supplements

REFERENCES
  1. Wei, B., & Jin, J.-P. (2016). TNNT1,TNNT2, and TNNT3: Isoform genes, regulation, and structure–function relationships. Gene, 582(1), 1–13. https://doi.org/10.1016/j.gene.2016.01.006
  2. Tartaglia, M., Kalidas, K., Shaw, A., Song, X., Musat, D. L., van der Burgt, I., Brunner, H. G., Bertola, D. R., Crosby, A., Ion, A., Kucherlapati, R. S., Jeffery, S., Patton, M. A., & Gelb, B. D. (2002). PTPN11 Mutations in Noonan Syndrome: Molecular Spectrum, Genotype-Phenotype Correlation, and Phenotypic Heterogeneity. The American Journal of Human Genetics, 70(6), 1555–1563. https://doi.org/10.1086/340847
  3. Hajar, R. (2020). Genetics in cardiovascular disease. Heart Views, 21(1), 55. https://doi.org/10.4103/HEARTVIEWS.HEARTVIEWS_140_19
  4. Kathiresan, S., & Srivastava, D. (2012). Genetics of Human Cardiovascular Disease. Cell, 148(6), 1242–1257. https://doi.org/10.1016/j.cell.2012.03.001
  5. DeWire, S. M., & Violin, J. D. (2011). Biased Ligands for Better Cardiovascular Drugs. Circulation Research, 109(2), 205–216. https://doi.org/10.1161/CIRCRESAHA.110.231308
  6. Zot, A. S., & Potter, J. D. (1987). Structural Aspects of Troponin-Tropomyosin Regulation of Skeletal Muscle Contraction. Annual Review of Biophysics and Biophysical Chemistry, 16(1), 535–559. https://doi.org/10.1146/annurev.bb.16.060187.002535
  7. Carrier, L., Mearini, G., Stathopoulou, K., & Cuello, F. (2015). Cardiac myosin-binding protein C (MYBPC3) in cardiac pathophysiology. Gene, 573(2), 188–197. https://doi.org/10.1016/j.gene.2015.09.008
  8. McNally, E. M., Barefield, D. Y., & Puckelwartz, M. J. (2015). The Genetic Landscape of Cardiomyopathy and Its Role in Heart Failure. Cell Metabolism, 21(2), 174–182. https://doi.org/10.1016/j.cmet.2015.01.013
  9. Sarkozy, A., Digilio, M. C., & Dallapiccola, B. (2008). Leopard syndrome. Orphanet Journal of Rare Diseases, 3(1), 13. https://doi.org/10.1186/1750-1172-3-13
  10. Theis, J. L., Zimmermann, M. T., Larsen, B. T., Rybakova, I. N., Long, P. A., Evans, J. M., Middha, S., de Andrade, M., Moss, R. L., Wieben, E. D., Michels, V. v., & Olson, T. M. (2014). TNNI3K mutation in familial syndrome of conduction system disease, atrial tachyarrhythmia and dilated cardiomyopathy. Human Molecular Genetics, 23(21), 5793–5804. https://doi.org/10.1093/hmg/ddu297
  11. Rahit, K. M. T. H., & Tarailo-Graovac, M. (2020). Genetic Modifiers and Rare Mendelian Disease. Genes, 11(3), 239. https://doi.org/10.3390/genes11030239
  12. Deltas, C. (2018). Digenic inheritance and genetic modifiers. Clinical Genetics, 93(3), 429–438. https://doi.org/10.1111/cge.13150
  13. Dhandapany, P. S., Sadayappan, S., Vanniarajan, A., Karthikeyan, B., Nagaraj, C., Gowrishankar, K., Selvam, G. S., Singh, L., & Thangaraj, K. (2007). Novel mitochondrial DNA mutations implicated in Noonan syndrome. International Journal of Cardiology, 120(2), 284–285. https://doi.org/10.1016/j.ijcard.2006.07.229
  14. Gurusamy, N., Rajasingh, S., Sigamani, V., Rajasingh, R., Isai, D. G., Czirok, A., Bittel, D., & Rajasingh, J. (2021). Noonan syndrome patient-specific induced cardiomyocyte model carrying SOS1 gene variant c.1654A>G. Experimental Cell Research, 400(1), 112508. https://doi.org/10.1016/j.yexcr.2021.112508
  15. Pradhan, A. K., Pandey, S., Usman, K., Kumar, M., & Mishra, R. (2013). Noonan Syndrome With Complete Atrioventricular Canal Defect With Pulmonary Stenosis. Journal of the American College of Cardiology, 62(20), 1905. https://doi.org/10.1016/j.jacc.2013.06.062
  16. Lehmann, L. H., Schaeufele, T., Buss, S. J., Balanova, M., Hartschuh, W., Ehlermann, P., & Katus, H. A. (2009). A Patient With LEOPARD Syndrome and PTPN11 Mutation. Circulation, 119(9), 1328–1329. https://doi.org/10.1161/CIRCULATIONAHA.108.792861
  17. Hart, T., Tong, A. H. Y., Chan, K., van Leeuwen, J., Seetharaman, A., Aregger, M., Chandrashekhar, M., Hustedt, N., Seth, S., Noonan, A., Habsid, A., Sizova, O., Nedyalkova, L., Climie, R., Tworzyanski, L., Lawson, K., Sartori, M. A., Alibeh, S., Tieu, D., … Moffat, J. (2017). Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens. G3 Genes|Genomes|Genetics, 7(8), 2719–2727. https://doi.org/10.1534/g3.117.041277
  18. Zhang, Q., & Zhang, Z. D. (2022). Protocol for gene annotation, prediction, and validation of genomic gene expansion. STAR Protocols, 3(4), 101692. https://doi.org/10.1016/j.xpro.2022.101692
  19. Sonar, P. (2022). Comparative docking analysis of tyrosine kinase inhibitors with HER2 and HER4 receptors. Bioinformation, 18(10), 974–981. https://doi.org/10.6026/97320630018974
  20. Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N., & Sternberg, M. J. E. (2015). The Phyre2 web portal for protein modeling, prediction and analysis. Nature Protocols, 10(6), 845–858 https://doi.org/10.1038/nprot.2015.053
  21. Bennett‐Lovsey, R. M., Herbert, A. D., Sternberg, M. J. E., & Kelley, L. A. (2008). Exploring the extremes of sequence/structure space with ensemble fold recognition in the program Phyre. Proteins: Structure, Function, and Bioinformatics, 70(3), 611–625. https://doi.org/10.1002/prot.21688
  22. Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T. A. P., Rempfer, C., Bordoli, L., Lepore, R., & Schwede, T. (2018). SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Research, 46(W1), W296–W303. https://doi.org/10.1093/nar/gky427
  23. Martinez, X., Krone, M., Alharbi, N., Rose, A. S., Laramee, R. S., O’Donoghue, S., Baaden, M., & Chavent, M. (2019). Molecular Graphics: Bridging Structural Biologists and Computer Scientists. Structure, 27(11), 1617–1623. https://doi.org/10.1016/j.str.2019.09.001
  24. McNally, E. M., Barefield, D. Y., & Puckelwartz, M. J. (2015). The Genetic Landscape of Cardiomyopathy and Its Role in Heart Failure. Cell Metabolism, 21(2), 174–182. https://doi.org/10.1016/j.cmet.2015.01.013
  25. Jordan, E., Peterson, L., Ai, T., Asatryan, B., Bronicki, L., Brown, E., Celeghin, R., Edwards, M., Fan, J., Ingles, J., James, C. A., Jarinova, O., Johnson, R., Judge, D. P., Lahrouchi, N., Lekanne Deprez, R. H., Lumbers, R. T., Mazzarotto, F., Medeiros Domingo, A., … Hershberger, R. E. (2021). Evidence-Based Assessment of Genes in Dilated Cardiomyopathy. Circulation, 144(1), 7–19. https://doi.org/10.1161/CIRCULATIONAHA.120.053033
  26. Östman-Smith, I. (2023). Lessons From a Genotype-Phenotype Study About the Clinical Spectrum of Hypertrophic Cardiomyopathy Associated With Noonan Syndrome With Multiple Lentigines and PTPN11-Mutations. Circulation: Genomic and Precision Medicine, 16(4), 359–362. https://doi.org/10.1161/CIRCGEN.123.004206
  27. Qiu, W., Wang, X., Romanov, V., Hutchinson, A., Lin, A., Ruzanov, M., Battaile, K. P., Pai, E. F., Neel, B. G., & Chirgadze, N. Y. (2014). Structural insights into Noonan/LEOPARD syndrome-related mutants of protein-tyrosine phosphatase SHP2 (PTPN11). BMC Structural Biology, 14(1), 10. https://doi.org/10.1186/1472-6807-14-10
  28. Marian, A. (2000). Pathogenesis of diverse clinical and pathological phenotypes in hypertrophic cardiomyopathy. The Lancet, 355(9197), 58–60. https://doi.org/10.1016/S0140-6736(99)06187-5
  29. Zhang, L., Han, L., Wang, X., Wei, Y., Zheng, J., Zhao, L., & Tong, X. (2021). Exploring the mechanisms underlying the therapeutic effect of Salvia miltiorrhiza in diabetic nephropathy using network pharmacology and molecular docking. Bioscience Reports, 41(6). https://doi.org/10.1042/BSR20203520
  30. Webb, B., & Sali, A. (2016). Comparative Protein Structure Modeling Using MODELLER. Current Protocols in Protein Science, 86(1). https://doi.org/10.1002/cpps.20
  31. McGreig, J. E., Uri, H., Antczak, M., Sternberg, M. J. E., Michaelis, M., & Wass, M. N. (2022). 3DLigandSite: structure-based prediction of protein–ligand binding sites. Nucleic Acids Research, 50(W1), W13–W20. https://doi.org/10.1093/nar/gkac250
  32. Ofoegbu, T. C., David, A., Kelley, L. A., Mezulis, S., Islam, S. A., Mersmann, S. F., Strömich, L., Vakser, I. A., Houlston, R. S., & Sternberg, M. J. E. (2019). PhyreRisk: A Dynamic Web Application to Bridge Genomics, Proteomics and 3D Structural Data to Guide Interpretation of Human Genetic Variants. Journal of Molecular Biology, 431(13), 2460–2466. https://doi.org/10.1016/j.jmb.2019.04.043
  33. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A., & Jermiin, L. S. (2017). ModelFinder: fast model selection for accurate phylogenetic estimates. Nature Methods, 14(6), 587–589. https://doi.org/10.1038/nmeth.4285
  34. Nema, V., & Pal, S. K. (2013). Exploration of freely available web-interfaces for comparative homology modelling of microbial proteins. Bioinformation, 9(15), 796–801. https://doi.org/10.6026/97320630009796
  35. Biasini, M., Bienert, S., Waterhouse, A., Arnold, K., Studer, G., Schmidt, T., Kiefer, F., Cassarino, T. G., Bertoni, M., Bordoli, L., & Schwede, T. (2014). SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Research, 42(W1), W252–W258. https://doi.org/10.1093/nar/gku340
  36. Bordoli, L., & Schwede, T. (2011). Automated Protein Structure Modeling with SWISS-MODEL Workspace and the Protein Model Portal (pp. 107–136). https://doi.org/10.1007/978-1-61779-588-6_5
  37. Bienert, S., Waterhouse, A., de Beer, T. A. P., Tauriello, G., Studer, G., Bordoli, L., & Schwede, T. (2017). The SWISS-MODEL Repository—new features and functionality. Nucleic Acids Research, 45(D1), D313–D319. https://doi.org/10.1093/nar/gkw1132
  38. Wang, T., Wu, M.-B., Lin, J.-P., & Yang, L.-R. (2015). Quantitative structure–activity relationship: promising advances in drug discovery platforms. Expert Opinion on Drug Discovery, 10(12), 1283–1300. https://doi.org/10.1517/17460441.2015.1083006
  39. Yu, W., & MacKerell, A. D. (2017). Computer-Aided Drug Design Methods (pp. 85–106). https://doi.org/10.1007/978-1-4939-6634-9_5
  40. Guha, R. (2013). On Exploring Structure–Activity Relationships (pp. 81–94). https://doi.org/10.1007/978-1-62703-342-8_6
  41. Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., Basanta, B., Bender, B. J., Blacklock, K., Bonet, J., Boyken, S. E., Bradley, P., Bystroff, C., Conway, P., Cooper, S., … Bonneau, R. (2020). Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nature Methods, 17(7), 665–680. https://doi.org/10.1038/s41592-020-0848-2
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