PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS

Elif Altunkulah; Yunus Ensari

doi:10.18036/estubtdc.1378676

Research Article

PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS

Year 2024, Volume: 13 Issue: 1, 31 - 51, 30.01.2024

Elif Altunkulah Yunus Ensari

https://doi.org/10.18036/estubtdc.1378676

Abstract

Proteins play crucial roles, including biocatalysis, transportation, and receptor activity, in living organisms. Moreover, their functional efficacy is influenced by their structural properties. Determining the three-dimensional structure of a protein is crucial to comprehending its catalytic mechanism, identifying potentially beneficial mutations for industrial applications, and enhancing its properties, including stability, activity, and substrate affinity. Although X-ray crystallography, nuclear magnetic resonance (NMR), and electron microscopy are employed to ascertain protein structures, many researchers have turned to bioinformatics modeling tools because of the high cost and time demands of these techniques. For structure prediction, there are three basic methods: ab initio (de novo), homology-based, and threading-based modeling techniques.

In this study, 11 modeling tools belong to different approaches were compared through modeling of various proteins; Geobacillus kaustophilus ksilan alpha-1,2-glucuronidase, Actinosynnema pretiosum bifunctional cytochrome P450/NADPH-P450 reductase, human high affinity cationic amino acid transporter 1 (SLC7A), human proton-coupled zinc antiporter (SLC30A) and Bacillus subtilis RNA polymerase sigma factor (sigY). Generated models were validated through QMEAN, QMEANDisCo, ProSA, ERRAT and PROCHECK tools. All of the studied proteins could be successfully modeled using homology modeling techniques, while some of the proteins could not be effectively modeled using threading or ab initio-based methods. YASARA generated reliable models for proteins that contain heteroatoms, such as P450 monooxygenases, because other tools exclude heteroatoms in their produced structures. Among approaches for modeling without templates, AlphaFold is a potent tool. On the other side, well-known template-based tools like YASARA, Robetta, and SWISS-MODEL have arisen. These results will help scientists choose the best protein modeling strategy and tool to guarantee high-quality structures.

Keywords

Protein structure prediction, Homology modeling, Alphafold, YASARA, Ab initio modeling

References

[1] Smith GM. The Nature of Enzymes. In: Biotechnology. 1995. p. 4–72.
[2] Benítez CMV, Lopes HS. Protein structure prediction with the 3D-HP side-chain model using a master–slave parallel genetic algorithm. J Brazilian Comput Soc. 2010;16(1):69–78.
[3] Divya M, Jain SJMN, Phadke SR, Kishore R, Kamate M, Gupta N, et al. Protein structure prediction for novel mutations in Arylsulfatase-A gene. Mol Cytogenet. 2014;7(1):P62.
[4] Alford RF, Fleming PJ, Fleming KG, Gray JJ. Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. Biophys J. 2020 Apr;118(8):2042–55.
[5] Batbat T, Öztürk C. Ayrık Yapay Arı Kolonisi Algoritması İle Protein Yapısı Tahmini. Bilişim Teknol Derg. 2016 Sep 30;9(3):260–3.
[6] Li X, Hu C, Liang J. Simplicial edge representation of protein structures and alpha contact potential with confidence measure. Proteins. 2003 Dec;53(4):792–805.
[7] Torrisi M, Pollastri G, Le Q. Deep learning methods in protein structure prediction. Comput Struct Biotechnol J. 2020;18:1301–10.
[8] Aydin Z, Singh A, Bilmes J, Noble WS. Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure. BMC Bioinformatics. 2011;12(1):154.
[9] Pearce R, Zhang Y. Toward the solution of the protein structure prediction problem. J Biol Chem. 2021;297(1):100870.
[10] ANFINSEN CB, HABER E, SELA M, WHITE FHJ. The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc Natl Acad Sci U S A. 1961 Sep;47(9):1309–14.
[11] Lee J, Wu S, Zhang Y. Ab Initio Protein Structure Prediction. In: From Protein Structure to Function with Bioinformatics. Dordrecht: Springer Netherlands; 2009. p. 3–25.
[12] Abbass J, Nebel JC, Mansour N. Ab Initio Protein Structure Prediction: Methods and challenges. In: Biological Knowledge Discovery Handbook. 2013. p. 703–24.
[13] Liwo A, Lee J, Ripoll DR, Pillardy J, Scheraga HA. Protein structure prediction by global optimization of a potential energy function. Proc Natl Acad Sci U S A. 1999 May;96(10):5482–5.
[14] Simons KT, Strauss C, Baker D. Prospects for ab initio protein structural genomics. J Mol Biol. 2001 Mar;306(5):1191–9.
[15] Zhang Y, Kolinski A, Skolnick J. TOUCHSTONE II: A New Approach to Ab Initio Protein Structure Prediction. Biophys J. 2003;85(2):1145–64.
[16] Bradley P, Misura KMS, Baker D. Toward high-resolution de novo structure prediction for small proteins. Science. 2005 Sep;309(5742):1868–71.
[17] Wu D, Wu T, Liu Q, Yang Z. The SARS-CoV-2 outbreak: What we know. Int J Infect Dis IJID Off Publ Int Soc Infect Dis. 2020 May;94:44–8.
[18] Rashid MA, Shatabda S, Newton MAH, Hoque MT, Sattar A. A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction. Adv Bioinformatics. 2014;2014:985968.
[19] Abbass J, Nebel JC. Customised fragments libraries for protein structure prediction based on structural class annotations. BMC Bioinformatics. 2015;16(1):136.
[20] Akdel M, Pires DE V, Pardo EP, Jänes J, Zalevsky AO, Mészáros B, et al. A structural biology community assessment of AlphaFold2 applications. Nat Struct Mol Biol. 2022;29(11):1056–67.
[21] Nikolaev DM, Shtyrov AA, Panov MS, Jamal A, Chakchir OB, Kochemirovsky VA, et al. A Comparative Study of Modern Homology Modeling Algorithms for Rhodopsin Structure Prediction. ACS Omega. 2018;3(7):7555–66.
[22] Chivian D, Baker D. Homology modeling using parametric alignment ensemble generation with consensus and energy-based model selection. Nucleic Acids Res. 2006;34(17):e112.
[23] Battey JND, Kopp J, Bordoli L, Read RJ, Clarke ND, Schwede T. Automated server predictions in CASP7. Proteins. 2007;69 Suppl 8:68–82.
[24] Heneghan MN, McLoughlin L, Murray PG, Tuohy MG. Cloning, characterisation and expression analysis of α-glucuronidase from the thermophilic fungus Talaromyces emersonii. Enzyme Microb Technol. 2007;41(6):677–82.
[25] Xu Y, Liu Z, Cai L, Xu D. Protein Structure Prediction by Protein Threading BT - Computational Methods for Protein Structure Prediction and Modeling: Volume 2: Structure Prediction. In: Xu Y, Xu D, Liang J, editors. New York, NY: Springer New York; 2007. p. 1–42.
[26] Eswar N, John B, Mirkovic N, Fiser A, Ilyin VA, Pieper U, et al. Tools for comparative protein structure modeling and analysis. Nucleic Acids Res. 2003 Jul;31(13):3375–80.
[27] Shao M, Wang S, Wang C, Yuan X, Li SC, Zheng W, et al. Incorporating Ab Initio energy into threading approaches for protein structure prediction. BMC Bioinformatics. 2011 Feb;12 Suppl 1(Suppl 1):S54.
[28] Shi J, Blundell TL, Mizuguchi K. FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J Mol Biol. 2001 Jun;310(1):243–57.
[29] Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022 Jan;50(D1):D439–44.
[30] Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9.
[31] Jayaram B, Bhushan K, Shenoy SR, Narang P, Bose S, Agrawal P, et al. Bhageerath: an energy based web enabled computer software suite for limiting the search space of tertiary structures of small globular proteins. Nucleic Acids Res. 2006;34(21):6195–204.
[32] Jabeen A, Mohamedali A, Ranganathan S. Protocol for Protein Structure Modelling. In: Ranganathan S, Gribskov M, Nakai K, Schönbach CBTE of B and CB, editors. Oxford: Academic Press; 2019. p. 252–72.
[33] Chen CC, Hwang JK, Yang JM. (PS)2-v2: template-based protein structure prediction server. BMC Bioinformatics. 2009;10(1):366.
[34] Chandra Sekhar Mukhopadhyay, Ratan Kumar Choudhary MAI. Basic Applied Bioinformatics. Wiley-Blackwell; 2017. 472 p.
[35] Guex N, Peitsch MC, Schwede T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective. Electrophoresis. 2009 Jun;30 Suppl 1:S162-73.
[36] Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018 Jul;46(W1):W296–303.
[37] Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, et al. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014 Jul;42(Web Server issue):W252-8.
[38] Roche DB, Buenavista MT, Tetchner SJ, McGuffin LJ. The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction. Nucleic Acids Res. 2011 Jul;39(Web Server issue):W171-6.
[39] Roche DB, Tetchner SJ, McGuffin LJ. FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins. BMC Bioinformatics. 2011;12(1):160.
[40] Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10(6):845–58.
[41] Pieper U, Webb BM, Dong GQ, Schneidman-Duhovny D, Fan H, Kim SJ, et al. ModBase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res. 2014 Jan;42(Database issue):D336-46.
[42] Krieger E, Vriend G. YASARA View - molecular graphics for all devices - from smartphones to workstations. Bioinformatics. 2014;
[43] Krieger E, Vriend G. New ways to boost molecular dynamics simulations. J Comput Chem. 2015 May;36(13):996–1007.
[44] Joosten RP, te Beek TAH, Krieger E, Hekkelman ML, Hooft RWW, Schneider R, et al. A series of PDB related databases for everyday needs. Nucleic Acids Res. 2011 Jan 1;39(suppl_1):D411–9.
[45] Krieger E, Vriend G. Models@Home: distributed computing in bioinformatics using a screensaver based approach. Bioinformatics. 2002 Feb;18(2):315–8.
[46] Zheng W, Zhang C, Li Y, Pearce R, Bell EW, Zhang Y. Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell reports methods. 2021 Jul;1(3).
[47] Wu S, Zhang Y. LOMETS: A local meta-threading-server for protein structure prediction. Nucleic Acids Res. 2007 May 15;35(10):3375–82.
[48] Bienert S, Waterhouse A, de Beer TAP, Tauriello G, Studer G, Bordoli L, et al. The SWISS-MODEL Repository-new features and functionality. Nucleic Acids Res. 2017 Jan;45(D1):D313–9.
[49] Studer G, Rempfer C, Waterhouse AM, Gumienny R, Haas J, Schwede T. QMEANDisCo—distance constraints applied on model quality estimation. Bioinformatics. 2020 Mar 15;36(6):1765–71.
[50] Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007 Jul;35(Web Server issue):W407-10.
[51] Sippl MJ. Recognition of errors in three-dimensional structures of proteins. Proteins. 1993 Dec;17(4):355–62.
[52] Sippl MJ. Knowledge-based potentials for proteins. Curr Opin Struct Biol. 1995 Apr;5(2):229–35.
[53] Colovos C, Yeates TO. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci. 1993 Sep 1;2(9):1511–9.
[54] Ramachandran GN, Sasisekharan V. Conformation of Polypeptides and Proteins In: Anfinsen CB, Anson ML, Edsall JT, Richards FMBTA in PC, editors. Academic Press; 1968. p. 283–437.
[55] MacArthur MW, Thornton JM. Deviations from planarity of the peptide bond in peptides and proteins. J Mol Biol. 1996 Dec;264(5):1180–95.
[56] Hooft RWW, Sander C, Vriend G. Objectively judging the quality of a protein structure from a Ramachandran plot. Bioinformatics. 1997 Aug 1;13(4):425–30.
[57] Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr. 1993 Apr 1;26(2):283–91.
[58] Morris AL, MacArthur MW, Hutchinson EG, Thornton JM. Stereochemical quality of protein structure coordinates. Proteins. 1992 Apr;12(4):345–64.
[59] Gligorijević V, Renfrew PD, Kosciolek T, Leman JK, Berenberg D, Vatanen T, et al. Structure-based protein function prediction using graph convolutional networks. Nat Commun. 2021;12(1).
[60] Jang WD, Lee SM, Kim HU, Lee SY. Systematic and Comparative Evaluation of Software Programs for Template-Based Modeling of Protein Structures. Biotechnol J. 2020;1–21.

PROTEİN YAPISI TAHMİNİ: YAKLAŞIMLARIN VE ARAÇLARIN DERİNLEMESINE KARŞILAŞTIRILMASI

Year 2024, Volume: 13 Issue: 1, 31 - 51, 30.01.2024

Elif Altunkulah Yunus Ensari

https://doi.org/10.18036/estubtdc.1378676

Abstract

Proteinler, canlı organizmalarda biyokataliz, taşıma ve reseptör aktivitesi gibi çok önemli roller oynar. Dahası, işlevsel etkinlikleri yapısal özelliklerinden etkilenir. Bir proteinin üç boyutlu yapısının belirlenmesi, katalitik mekanizmasının anlaşılması, endüstriyel uygulamalar için potansiyel olarak faydalı mutasyonların belirlenmesi ve stabilite, aktivite ve substrat afinitesi gibi özelliklerinin geliştirilmesi için çok önemlidir. Protein yapılarını tespit etmek için X-ışını kristalografisi, nükleer manyetik rezonans (NMR) ve elektron mikroskopisi kullanılsa da, birçok araştırmacı bu tekniklerin yüksek maliyet ve zaman talepleri nedeniyle biyoinformatik modelleme araçlarına yönelmiştir. Yapı tahmini için üç temel yöntem vardır: ab initio (de novo), homoloji tabanlı ve threading tabanlı modelleme teknikleri.

Bu çalışmada, farklı yaklaşımlara ait 11 modelleme aracı, çeşitli proteinlerin modellenmesi yoluyla karşılaştırılmıştır; Geobacillus kaustophilus ksilan alfa-1,2-glukuronidaz, Actinosynnema pretiosum bifonksiyonel sitokrom P450/NADPH-P450 redüktaz, insan yüksek afiniteli katyonik amino asit taşıyıcı 1 (SLC7A), insan proton-bağlı çinko antiporter (SLC30A) ve Bacillus subtilis RNA polimeraz sigma faktörü (sigY). Oluşturulan modeller QMEAN, QMEANDisCo, ProSA, ERRAT ve PROCHECK araçları ile doğrulanmıştır. Çalışılan tüm proteinler homoloji modelleme teknikleri kullanılarak başarılı bir şekilde modellenebilirken, bazı proteinler threading veya ab initio tabanlı yöntemler kullanılarak etkili bir şekilde modellenememiştir. YASARA, P450 monooksijenazlar gibi heteroatom içeren proteinler için güvenilir modeller üretmiştir, çünkü diğer araçlar üretilen modellerde heteroatomları hariç tutmuştur. Kalıptan bağımsız modelleme yaklaşımları arasında AlphaFold güçlü bir araçtır. Diğer taraftan, YASARA, Robetta ve SWISS-MODEL gibi iyi bilinen kalıp tabanlı araçlar ön plana çıkmıştır. Bu sonuçlar, bilim insanlarının yüksek kaliteli protein yapılarını elde etmeleri için en iyi protein modelleme stratejisini ve aracını seçmelerine yardımcı olacaktır.

Keywords

Protein yapı tahmini, homoloji modelleme, YASARA, Ab initio modelleme, Alphafold

References

[1] Smith GM. The Nature of Enzymes. In: Biotechnology. 1995. p. 4–72.
[2] Benítez CMV, Lopes HS. Protein structure prediction with the 3D-HP side-chain model using a master–slave parallel genetic algorithm. J Brazilian Comput Soc. 2010;16(1):69–78.
[3] Divya M, Jain SJMN, Phadke SR, Kishore R, Kamate M, Gupta N, et al. Protein structure prediction for novel mutations in Arylsulfatase-A gene. Mol Cytogenet. 2014;7(1):P62.
[4] Alford RF, Fleming PJ, Fleming KG, Gray JJ. Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. Biophys J. 2020 Apr;118(8):2042–55.
[5] Batbat T, Öztürk C. Ayrık Yapay Arı Kolonisi Algoritması İle Protein Yapısı Tahmini. Bilişim Teknol Derg. 2016 Sep 30;9(3):260–3.
[6] Li X, Hu C, Liang J. Simplicial edge representation of protein structures and alpha contact potential with confidence measure. Proteins. 2003 Dec;53(4):792–805.
[7] Torrisi M, Pollastri G, Le Q. Deep learning methods in protein structure prediction. Comput Struct Biotechnol J. 2020;18:1301–10.
[8] Aydin Z, Singh A, Bilmes J, Noble WS. Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure. BMC Bioinformatics. 2011;12(1):154.
[9] Pearce R, Zhang Y. Toward the solution of the protein structure prediction problem. J Biol Chem. 2021;297(1):100870.
[10] ANFINSEN CB, HABER E, SELA M, WHITE FHJ. The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. Proc Natl Acad Sci U S A. 1961 Sep;47(9):1309–14.
[11] Lee J, Wu S, Zhang Y. Ab Initio Protein Structure Prediction. In: From Protein Structure to Function with Bioinformatics. Dordrecht: Springer Netherlands; 2009. p. 3–25.
[12] Abbass J, Nebel JC, Mansour N. Ab Initio Protein Structure Prediction: Methods and challenges. In: Biological Knowledge Discovery Handbook. 2013. p. 703–24.
[13] Liwo A, Lee J, Ripoll DR, Pillardy J, Scheraga HA. Protein structure prediction by global optimization of a potential energy function. Proc Natl Acad Sci U S A. 1999 May;96(10):5482–5.
[14] Simons KT, Strauss C, Baker D. Prospects for ab initio protein structural genomics. J Mol Biol. 2001 Mar;306(5):1191–9.
[15] Zhang Y, Kolinski A, Skolnick J. TOUCHSTONE II: A New Approach to Ab Initio Protein Structure Prediction. Biophys J. 2003;85(2):1145–64.
[16] Bradley P, Misura KMS, Baker D. Toward high-resolution de novo structure prediction for small proteins. Science. 2005 Sep;309(5742):1868–71.
[17] Wu D, Wu T, Liu Q, Yang Z. The SARS-CoV-2 outbreak: What we know. Int J Infect Dis IJID Off Publ Int Soc Infect Dis. 2020 May;94:44–8.
[18] Rashid MA, Shatabda S, Newton MAH, Hoque MT, Sattar A. A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction. Adv Bioinformatics. 2014;2014:985968.
[19] Abbass J, Nebel JC. Customised fragments libraries for protein structure prediction based on structural class annotations. BMC Bioinformatics. 2015;16(1):136.
[20] Akdel M, Pires DE V, Pardo EP, Jänes J, Zalevsky AO, Mészáros B, et al. A structural biology community assessment of AlphaFold2 applications. Nat Struct Mol Biol. 2022;29(11):1056–67.
[21] Nikolaev DM, Shtyrov AA, Panov MS, Jamal A, Chakchir OB, Kochemirovsky VA, et al. A Comparative Study of Modern Homology Modeling Algorithms for Rhodopsin Structure Prediction. ACS Omega. 2018;3(7):7555–66.
[22] Chivian D, Baker D. Homology modeling using parametric alignment ensemble generation with consensus and energy-based model selection. Nucleic Acids Res. 2006;34(17):e112.
[23] Battey JND, Kopp J, Bordoli L, Read RJ, Clarke ND, Schwede T. Automated server predictions in CASP7. Proteins. 2007;69 Suppl 8:68–82.
[24] Heneghan MN, McLoughlin L, Murray PG, Tuohy MG. Cloning, characterisation and expression analysis of α-glucuronidase from the thermophilic fungus Talaromyces emersonii. Enzyme Microb Technol. 2007;41(6):677–82.
[25] Xu Y, Liu Z, Cai L, Xu D. Protein Structure Prediction by Protein Threading BT - Computational Methods for Protein Structure Prediction and Modeling: Volume 2: Structure Prediction. In: Xu Y, Xu D, Liang J, editors. New York, NY: Springer New York; 2007. p. 1–42.
[26] Eswar N, John B, Mirkovic N, Fiser A, Ilyin VA, Pieper U, et al. Tools for comparative protein structure modeling and analysis. Nucleic Acids Res. 2003 Jul;31(13):3375–80.
[27] Shao M, Wang S, Wang C, Yuan X, Li SC, Zheng W, et al. Incorporating Ab Initio energy into threading approaches for protein structure prediction. BMC Bioinformatics. 2011 Feb;12 Suppl 1(Suppl 1):S54.
[28] Shi J, Blundell TL, Mizuguchi K. FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J Mol Biol. 2001 Jun;310(1):243–57.
[29] Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022 Jan;50(D1):D439–44.
[30] Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9.
[31] Jayaram B, Bhushan K, Shenoy SR, Narang P, Bose S, Agrawal P, et al. Bhageerath: an energy based web enabled computer software suite for limiting the search space of tertiary structures of small globular proteins. Nucleic Acids Res. 2006;34(21):6195–204.
[32] Jabeen A, Mohamedali A, Ranganathan S. Protocol for Protein Structure Modelling. In: Ranganathan S, Gribskov M, Nakai K, Schönbach CBTE of B and CB, editors. Oxford: Academic Press; 2019. p. 252–72.
[33] Chen CC, Hwang JK, Yang JM. (PS)2-v2: template-based protein structure prediction server. BMC Bioinformatics. 2009;10(1):366.
[34] Chandra Sekhar Mukhopadhyay, Ratan Kumar Choudhary MAI. Basic Applied Bioinformatics. Wiley-Blackwell; 2017. 472 p.
[35] Guex N, Peitsch MC, Schwede T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: a historical perspective. Electrophoresis. 2009 Jun;30 Suppl 1:S162-73.
[36] Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018 Jul;46(W1):W296–303.
[37] Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, et al. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014 Jul;42(Web Server issue):W252-8.
[38] Roche DB, Buenavista MT, Tetchner SJ, McGuffin LJ. The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction. Nucleic Acids Res. 2011 Jul;39(Web Server issue):W171-6.
[39] Roche DB, Tetchner SJ, McGuffin LJ. FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins. BMC Bioinformatics. 2011;12(1):160.
[40] Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10(6):845–58.
[41] Pieper U, Webb BM, Dong GQ, Schneidman-Duhovny D, Fan H, Kim SJ, et al. ModBase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res. 2014 Jan;42(Database issue):D336-46.
[42] Krieger E, Vriend G. YASARA View - molecular graphics for all devices - from smartphones to workstations. Bioinformatics. 2014;
[43] Krieger E, Vriend G. New ways to boost molecular dynamics simulations. J Comput Chem. 2015 May;36(13):996–1007.
[44] Joosten RP, te Beek TAH, Krieger E, Hekkelman ML, Hooft RWW, Schneider R, et al. A series of PDB related databases for everyday needs. Nucleic Acids Res. 2011 Jan 1;39(suppl_1):D411–9.
[45] Krieger E, Vriend G. Models@Home: distributed computing in bioinformatics using a screensaver based approach. Bioinformatics. 2002 Feb;18(2):315–8.
[46] Zheng W, Zhang C, Li Y, Pearce R, Bell EW, Zhang Y. Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell reports methods. 2021 Jul;1(3).
[47] Wu S, Zhang Y. LOMETS: A local meta-threading-server for protein structure prediction. Nucleic Acids Res. 2007 May 15;35(10):3375–82.
[48] Bienert S, Waterhouse A, de Beer TAP, Tauriello G, Studer G, Bordoli L, et al. The SWISS-MODEL Repository-new features and functionality. Nucleic Acids Res. 2017 Jan;45(D1):D313–9.
[49] Studer G, Rempfer C, Waterhouse AM, Gumienny R, Haas J, Schwede T. QMEANDisCo—distance constraints applied on model quality estimation. Bioinformatics. 2020 Mar 15;36(6):1765–71.
[50] Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007 Jul;35(Web Server issue):W407-10.
[51] Sippl MJ. Recognition of errors in three-dimensional structures of proteins. Proteins. 1993 Dec;17(4):355–62.
[52] Sippl MJ. Knowledge-based potentials for proteins. Curr Opin Struct Biol. 1995 Apr;5(2):229–35.
[53] Colovos C, Yeates TO. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci. 1993 Sep 1;2(9):1511–9.
[54] Ramachandran GN, Sasisekharan V. Conformation of Polypeptides and Proteins In: Anfinsen CB, Anson ML, Edsall JT, Richards FMBTA in PC, editors. Academic Press; 1968. p. 283–437.
[55] MacArthur MW, Thornton JM. Deviations from planarity of the peptide bond in peptides and proteins. J Mol Biol. 1996 Dec;264(5):1180–95.
[56] Hooft RWW, Sander C, Vriend G. Objectively judging the quality of a protein structure from a Ramachandran plot. Bioinformatics. 1997 Aug 1;13(4):425–30.
[57] Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr. 1993 Apr 1;26(2):283–91.
[58] Morris AL, MacArthur MW, Hutchinson EG, Thornton JM. Stereochemical quality of protein structure coordinates. Proteins. 1992 Apr;12(4):345–64.
[59] Gligorijević V, Renfrew PD, Kosciolek T, Leman JK, Berenberg D, Vatanen T, et al. Structure-based protein function prediction using graph convolutional networks. Nat Commun. 2021;12(1).
[60] Jang WD, Lee SM, Kim HU, Lee SY. Systematic and Comparative Evaluation of Software Programs for Template-Based Modeling of Protein Structures. Biotechnol J. 2020;1–21.

There are 60 citations in total.

Details

Primary Language	English
Subjects	Translational and Applied Bioinformatics, Bioinformatics and Computational Biology (Other), Structural Biology, Biomolecular Modelling and Design
Journal Section	Articles
Authors	Elif Altunkulah 0000-0003-1347-9914 Yunus Ensari 0000-0002-4757-4197
Publication Date	January 30, 2024
Submission Date	October 20, 2023
Acceptance Date	November 29, 2023
Published in Issue	Year 2024 Volume: 13 Issue: 1

Cite

APA	Altunkulah, E., & Ensari, Y. (2024). PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS. Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji, 13(1), 31-51. https://doi.org/10.18036/estubtdc.1378676
AMA	Altunkulah E, Ensari Y. PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji. January 2024;13(1):31-51. doi:10.18036/estubtdc.1378676
Chicago	Altunkulah, Elif, and Yunus Ensari. “PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS”. Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji 13, no. 1 (January 2024): 31-51. https://doi.org/10.18036/estubtdc.1378676.
EndNote	Altunkulah E, Ensari Y (January 1, 2024) PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji 13 1 31–51.
IEEE	E. Altunkulah and Y. Ensari, “PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS”, Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji, vol. 13, no. 1, pp. 31–51, 2024, doi: 10.18036/estubtdc.1378676.
ISNAD	Altunkulah, Elif - Ensari, Yunus. “PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS”. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji 13/1 (January 2024), 31-51. https://doi.org/10.18036/estubtdc.1378676.
JAMA	Altunkulah E, Ensari Y. PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji. 2024;13:31–51.
MLA	Altunkulah, Elif and Yunus Ensari. “PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS”. Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji, vol. 13, no. 1, 2024, pp. 31-51, doi:10.18036/estubtdc.1378676.
Vancouver	Altunkulah E, Ensari Y. PROTEIN STRUCTURE PREDICTION: AN IN-DEPTH COMPARISON OF APPROACHES AND TOOLS. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi - C Yaşam Bilimleri Ve Biyoteknoloji. 2024;13(1):31-5.

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