Skip to main content

Illuminating the “Twilight Zone”: Advances in Difficult Protein Modeling

  • Protocol
  • First Online:
Homology Modeling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2627))

Abstract

Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The threshold value was long assumed to be around 20–30%. Below this level, obtained sequence identity was getting dangerously close to values that can be obtained by chance, after aligning any random, unrelated sequences. In these cases, other approaches, including ab initio folding simulations or fragment assembly, were usually employed. The most recent editions of the CASP and CAMEO community-wide modeling methods assessment have brought some surprising outcomes, proving that much more clues can be inferred from protein sequence analyses than previously thought. In this chapter, we focus on recent advances in the field of difficult protein modeling, pushing the threshold deep into the “twilight zone”, with particular attention devoted to improvements in applications of machine learning and model evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kendrew JC, Bodo G, Dintzis HM et al (1958) A three-dimensional model of the myoglobin molecule obtained by x-ray analysis. Nature 181:662–666. https://doi.org/10.1038/181662a0

    Article  CAS  PubMed  Google Scholar 

  2. Williamson MP, Havel TF, Wüthrich K (1985) Solution conformation of proteinase inhibitor IIA from bull seminal plasma by 1H nuclear magnetic resonance and distance geometry. J Mol Biol 182:295–315. https://doi.org/10.1016/0022-2836(85)90347-x

    Article  CAS  PubMed  Google Scholar 

  3. Cressey D, Callaway E (2017) Cryo-electron microscopy wins chemistry Nobel. Nature 550:167. https://doi.org/10.1038/nature.2017.22738

    Article  PubMed  Google Scholar 

  4. Yu X, Veesler D, Campbell MG et al (2017) Cryo-EM structure of human adenovirus D26 reveals the conservation of structural organization among human adenoviruses. Sci Adv 3:e1602670. https://doi.org/10.1126/sciadv.1602670

    Article  CAS  PubMed  Google Scholar 

  5. Stephens ZD, Lee SY, Faghri F et al (2015) Big data: astronomical or genomical? PLoS Biol 13:e1002195. https://doi.org/10.1371/journal.pbio.1002195

    Article  CAS  PubMed  Google Scholar 

  6. Berman HM, Coimbatore Narayanan B, Di Costanzo L et al (2013) Trendspotting in the Protein Data Bank. FEBS Lett 587:1036–1045. https://doi.org/10.1016/j.febslet.2012.12.029

    Article  CAS  PubMed  Google Scholar 

  7. Anfinsen CB (1972) The formation and stabilization of protein structure. Biochem J 128:737–749. https://doi.org/10.1042/bj1280737

    Article  CAS  PubMed  Google Scholar 

  8. Taniuchi H, Anfinsen CB (1969) An experimental approach to the study of the folding of staphylococcal nuclease. J Biol Chem 244:3864–3875

    Article  CAS  PubMed  Google Scholar 

  9. Bryngelson JD, Onuchic JN, Socci ND, Wolynes PG (1995) Funnels, pathways, and the energy landscape of protein folding: a synthesis. Proteins 21:167–195. https://doi.org/10.1002/prot.340210302

    Article  CAS  PubMed  Google Scholar 

  10. Onuchic JN, Luthey-Schulten Z, Wolynes PG (1997) Theory of protein folding: the energy landscape perspective. Annu Rev Phys Chem 48:545–600. https://doi.org/10.1146/annurev.physchem.48.1.545

    Article  CAS  PubMed  Google Scholar 

  11. Tzul FO, Vasilchuk D, Makhatadze GI (2017) Evidence for the principle of minimal frustration in the evolution of protein folding landscapes. Proc Natl Acad Sci U S A 114:E1627–E1632. https://doi.org/10.1073/pnas.1613892114

    Article  CAS  PubMed  Google Scholar 

  12. Lindorff-Larsen K, Piana S, Dror RO, Shaw DE (2011) How fast-folding proteins fold. Science 334:517–520. https://doi.org/10.1126/science.1208351

    Article  CAS  PubMed  Google Scholar 

  13. Best RB, Hummer G, Eaton WA (2013) Native contacts determine protein folding mechanisms in atomistic simulations. Proc Natl Acad Sci U S A 110:17874–17879. https://doi.org/10.1073/pnas.1311599110

    Article  PubMed  Google Scholar 

  14. Hartl FU (2017) Unfolding the chaperone story. Mol Biol Cell 28:2919–2923. https://doi.org/10.1091/mbc.E17-07-0480

    Article  CAS  PubMed  Google Scholar 

  15. Pang Y-P (2014) Low-mass molecular dynamics simulation: a simple and generic technique to enhance configurational sampling. Biochem Biophys Res Commun 452:588–592. https://doi.org/10.1016/j.bbrc.2014.08.119

    Article  CAS  PubMed  Google Scholar 

  16. Singh R, Bansal R, Rathore AS, Goel G (2017) Equilibrium ensembles for insulin folding from bias-exchange metadynamics. Biophys J 112:1571–1585. https://doi.org/10.1016/j.bpj.2017.03.015

    Article  CAS  PubMed  Google Scholar 

  17. Kamberaj H (2018) Faster protein folding using enhanced conformational sampling of molecular dynamics simulation. J Mol Graph Model 81:32–49. https://doi.org/10.1016/j.jmgm.2018.02.008

    Article  CAS  PubMed  Google Scholar 

  18. Okamoto Y (2019) Protein structure predictions by enhanced conformational sampling methods. Biophys Physicobiol 16:344–366. https://doi.org/10.2142/biophysico.16.0_344

    Article  CAS  PubMed  Google Scholar 

  19. Pal MK, Lahiri T, Tanwar G, Kumar R (2018) An improved protein structure evaluation using a semi-empirically derived structure property. BMC Struct Biol 18:16. https://doi.org/10.1186/s12900-018-0097-0

    Article  CAS  PubMed  Google Scholar 

  20. Zhao C, Shukla D (2018) SAXS-guided enhanced unbiased sampling for structure determination of proteins and complexes. Sci Rep 8:17748. https://doi.org/10.1038/s41598-018-36090-z

    Article  CAS  PubMed  Google Scholar 

  21. Moult J, Fidelis K, Kryshtafovych A et al (2018) Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins 86(Suppl 1):7–15. https://doi.org/10.1002/prot.25415

    Article  CAS  PubMed  Google Scholar 

  22. Webb B, Sali A (2016) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinformatics 54:5.6.1–5.6.37. https://doi.org/10.1002/cpbi.3

    Article  PubMed  Google Scholar 

  23. Šali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815. https://doi.org/10.1006/jmbi.1993.1626

    Article  PubMed  Google Scholar 

  24. Janson G, Grottesi A, Pietrosanto M et al (2019) Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling. PLoS Comput Biol 15:e1007219. https://doi.org/10.1371/journal.pcbi.1007219

    Article  CAS  PubMed  Google Scholar 

  25. Haas J, Gumienny R, Barbato A et al (2019) Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO). Proteins 87:1378–1387. https://doi.org/10.1002/prot.25815

    Article  CAS  PubMed  Google Scholar 

  26. Dill KA, MacCallum JL (2012) The protein-folding problem, 50 years on. Science 338:1042–1046. https://doi.org/10.1126/science.1219021

    Article  CAS  PubMed  Google Scholar 

  27. Chung SY, Subbiah S (1996) A structural explanation for the twilight zone of protein sequence homology. Structure 4:1123–1127. https://doi.org/10.1016/S0969-2126(96)00119-0

    Article  CAS  PubMed  Google Scholar 

  28. Rost B (1999) Twilight zone of protein sequence alignments. Protein Eng 12:85–94. https://doi.org/10.1093/protein/12.2.85

    Article  CAS  PubMed  Google Scholar 

  29. Kryshtafovych A, Monastyrskyy B, Fidelis K et al (2018) Evaluation of the template-based modeling in CASP12. Proteins 86(Suppl 1):321–334. https://doi.org/10.1002/prot.25425

    Article  CAS  PubMed  Google Scholar 

  30. Jones DT, McGuffin LJ (2003) Assembling novel protein folds from super-secondary structural fragments. Proteins 53(Suppl 6):480–485. https://doi.org/10.1002/prot.10542

    Article  CAS  PubMed  Google Scholar 

  31. Marks DS, Colwell LJ, Sheridan R et al (2011) Protein 3D structure computed from evolutionary sequence variation. PLoS One 6:e28766. https://doi.org/10.1371/journal.pone.0028766

    Article  CAS  PubMed  Google Scholar 

  32. Morcos F, Pagnani A, Lunt B et al (2011) Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc Natl Acad Sci U S A 108:E1293–E1301. https://doi.org/10.1073/pnas.1111471108

    Article  PubMed  Google Scholar 

  33. Jones DT, Buchan DWA, Cozzetto D, Pontil M (2012) PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28:184–190. https://doi.org/10.1093/bioinformatics/btr638

    Article  CAS  PubMed  Google Scholar 

  34. Eickholt J, Cheng J (2012) Predicting protein residue–residue contacts using deep networks and boosting. Bioinformatics 28:3066–3072. https://doi.org/10.1093/bioinformatics/bts598

    Article  CAS  PubMed  Google Scholar 

  35. Qian N, Sejnowski TJ (1988) Predicting the secondary structure of globular proteins using neural network models. J Mol Biol 202:865–884. https://doi.org/10.1016/0022-2836(88)90564-5

    Article  CAS  PubMed  Google Scholar 

  36. Holley LH, Karplus M (1989) Protein secondary structure prediction with a neural network. Proc Natl Acad Sci U S A 86:152–156. https://doi.org/10.1073/pnas.86.1.152

    Article  CAS  PubMed  Google Scholar 

  37. Cuff JA, Clamp ME, Siddiqui AS et al (1998) JPred: a consensus secondary structure prediction server. Bioinformatics 14:892–893. https://doi.org/10.1093/bioinformatics/14.10.892

    Article  CAS  PubMed  Google Scholar 

  38. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195–202. https://doi.org/10.1006/jmbi.1999.3091

    Article  CAS  PubMed  Google Scholar 

  39. Jones DT, Singh T, Kosciolek T, Tetchner S (2015) MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. Bioinformatics 31:999–1006. https://doi.org/10.1093/bioinformatics/btu791

    Article  CAS  PubMed  Google Scholar 

  40. Torrisi M, Pollastri G, Le Q (2020) Deep learning methods in protein structure prediction. Comput Struct Biotechnol J 18:1301–1310. https://doi.org/10.1016/j.csbj.2019.12.011

    Article  CAS  PubMed  Google Scholar 

  41. Bhattacharya S, Bhattacharya D (2020) Evaluating the significance of contact maps in low-homology protein modeling using contact-assisted threading. Sci Rep 10:2908. https://doi.org/10.1038/s41598-020-59834-2

    Article  CAS  PubMed  Google Scholar 

  42. Eickholt J, Cheng J (2013) A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks. BMC Bioinformatics 14(Suppl 14):S12. https://doi.org/10.1186/1471-2105-14-S14-S12

    Article  PubMed  Google Scholar 

  43. Fasel B (2003) An introduction to bio-inspired artificial neural network architectures. Acta Neurol Belg 103:6–12

    CAS  PubMed  Google Scholar 

  44. Tripp B (2019) Approximating the architecture of visual cortex in a convolutional network. Neural Comput 31:1551–1591. https://doi.org/10.1162/neco_a_01211

    Article  PubMed  Google Scholar 

  45. Xu J, Wang S (2019) Analysis of distance-based protein structure prediction by deep learning in CASP13. Proteins 87:1069–1081. https://doi.org/10.1002/prot.25810

    Article  CAS  PubMed  Google Scholar 

  46. Kryshtafovych A, Schwede T, Topf M et al (2019) Critical assessment of methods of protein structure prediction (CASP)—Round XIII. Proteins 87:1011–1020. https://doi.org/10.1002/prot.25823

    Article  CAS  PubMed  Google Scholar 

  47. Zheng W, Li Y, Zhang C et al (2019) Deep-learning contact-map guided protein structure prediction in CASP13. Proteins 87:1149–1164. https://doi.org/10.1002/prot.25792

    Article  CAS  PubMed  Google Scholar 

  48. Croll TI, Sammito MD, Kryshtafovych A, Read RJ (2019) Evaluation of template-based modeling in CASP13. Proteins 87:1113–1127. https://doi.org/10.1002/prot.25800

    Article  CAS  PubMed  Google Scholar 

  49. Senior AW, Evans R, Jumper J et al (2020) Improved protein structure prediction using potentials from deep learning. Nature 577:706–710. https://doi.org/10.1038/s41586-019-1923-7

    Article  CAS  PubMed  Google Scholar 

  50. AlQuraishi M (2019) AlphaFold at CASP13. Bioinformatics 35:4862–4865. https://doi.org/10.1093/bioinformatics/btz422

    Article  CAS  PubMed  Google Scholar 

  51. Brunger AT (2007) Version 1.2 of the crystallography and NMR system. Nat Protoc 2:2728–2733. https://doi.org/10.1038/nprot.2007.406

    Article  CAS  PubMed  Google Scholar 

  52. Billings WM, Hedelius B, Millecam T et al (2019) ProSPr: democratized implementation of Alphafold protein distance prediction network. BioRxiv. https://doi.org/10.1101/830273

  53. Yang J, Anishchenko I, Park H et al (2020) Improved protein structure prediction using predicted inter-residue orientations. Proc Natl Acad Sci U S A 117:1496–1503. https://doi.org/10.1073/pnas.1914677117

    Article  CAS  PubMed  Google Scholar 

  54. Heo L, Feig M (2020) High-accuracy protein structures by combining machine-learning with physics-based refinement. Proteins 88:637–642. https://doi.org/10.1002/prot.25847

    Article  CAS  PubMed  Google Scholar 

  55. Skolnick J, Gao M, Zhou H, Singh S (2021) AlphaFold 2: why it works and its implications for understanding the relationships of protein sequence, structure, and function. J Chem Inf Model 61:4827–4831. https://doi.org/10.1021/acs.jcim.1c01114

    Article  CAS  PubMed  Google Scholar 

  56. Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589. https://doi.org/10.1038/s41586-021-03819-2

    Article  CAS  PubMed  Google Scholar 

  57. Benkert P, Künzli M, Schwede T (2009) QMEAN server for protein model quality estimation. Nucleic Acids Res 37:W510–W514. https://doi.org/10.1093/nar/gkp322

    Article  CAS  PubMed  Google Scholar 

  58. di Luccio E, Koehl P (2011) A quality metric for homology modeling: the H-factor. BMC Bioinformatics 12:48. https://doi.org/10.1186/1471-2105-12-48

    Article  CAS  PubMed  Google Scholar 

  59. Sippl MJ (1995) Knowledge-based potentials for proteins. Curr Opin Struct Biol 5:229–235. https://doi.org/10.1016/0959-440x(95)80081-6

    Article  CAS  PubMed  Google Scholar 

  60. Fang Q, Shortle D (2005) A consistent set of statistical potentials for quantifying local side-chain and backbone interactions. Proteins 60:90–96. https://doi.org/10.1002/prot.20482

    Article  CAS  PubMed  Google Scholar 

  61. Summa CM, Levitt M, Degrado WF (2005) An atomic environment potential for use in protein structure prediction. J Mol Biol 352:986–1001. https://doi.org/10.1016/j.jmb.2005.07.054

    Article  CAS  PubMed  Google Scholar 

  62. Berglund A, Head RD, Welsh EA, Marshall GR (2004) ProVal: a protein-scoring function for the selection of native and near-native folds. Proteins 54:289–302. https://doi.org/10.1002/prot.10523

    Article  CAS  PubMed  Google Scholar 

  63. Wallner B, Elofsson A (2003) Can correct protein models be identified? Protein Sci 12:1073–1086. https://doi.org/10.1110/ps.0236803

    Article  CAS  PubMed  Google Scholar 

  64. Lovell SC, Davis IW, Arendall WB et al (2003) Structure validation by Calpha geometry: phi, psi and Cbeta deviation. Proteins 50:437–450. https://doi.org/10.1002/prot.10286

    Article  CAS  PubMed  Google Scholar 

  65. Moult J, Fidelis K, Kryshtafovych A et al (2007) Critical assessment of methods of protein structure prediction-Round VII. Proteins 69(Suppl 8):3–9. https://doi.org/10.1002/prot.21767

    Article  CAS  PubMed  Google Scholar 

  66. Benkert P, Biasini M, Schwede T (2011) Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics 27:343–350. https://doi.org/10.1093/bioinformatics/btq662

    Article  CAS  PubMed  Google Scholar 

  67. Benkert P, Schwede T, Tosatto SC (2009) QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information. BMC Struct Biol 9:35. https://doi.org/10.1186/1472-6807-9-35

    Article  CAS  PubMed  Google Scholar 

  68. Studer G, Biasini M, Schwede T (2014) Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane). Bioinformatics 30:i505–i511. https://doi.org/10.1093/bioinformatics/btu457

    Article  CAS  PubMed  Google Scholar 

  69. Studer G, Rempfer C, Waterhouse AM et al (2020) QMEANDisCo-distance constraints applied on model quality estimation. Bioinformatics 36:1765–1771. https://doi.org/10.1093/bioinformatics/btz828

    Article  CAS  PubMed  Google Scholar 

  70. Iwadate M, Kanou K, Terashi G et al (2010) Method for predicting homology modeling accuracy from amino acid sequence alignment: the power function. Chem Pharm Bull 58:1–10. https://doi.org/10.1248/cpb.58.1

    Article  CAS  Google Scholar 

  71. Zhang J, Zhang Y (2010) A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction. PLoS One 5:e15386. https://doi.org/10.1371/journal.pone.0015386

    Article  CAS  PubMed  Google Scholar 

  72. Shi X, Zhang J, He Z et al (2011) A sampling-based method for ranking protein structural models by integrating multiple scores and features. Curr Protein Pept Sci 12:540–548. https://doi.org/10.2174/138920311796957658

    Article  CAS  PubMed  Google Scholar 

  73. Wang Q, Vantasin K, Xu D, Shang Y (2011) MUFOLD-WQA: a new selective consensus method for quality assessment in protein structure prediction. Proteins 79(Suppl 10):185–195. https://doi.org/10.1002/prot.23185

    Article  PubMed  Google Scholar 

  74. He Z, Alazmi M, Zhang J, Xu D (2013) Protein structural model selection by combining consensus and single scoring methods. PLoS One 8:e74006. https://doi.org/10.1371/journal.pone.0074006

    Article  CAS  PubMed  Google Scholar 

  75. Mishra A, Rao S, Mittal A, Jayaram B (2013) Capturing native/native like structures with a physico-chemical metric (pcSM) in protein folding. Biochim Biophys Acta 1834:1520–1531. https://doi.org/10.1016/j.bbapap.2013.04.023

    Article  CAS  PubMed  Google Scholar 

  76. Dai W, Song T, Wang X et al (2014) Improvement in low-homology template-based modeling by employing a model evaluation method with focus on topology. PLoS One 9:e89935. https://doi.org/10.1371/journal.pone.0089935

    Article  CAS  PubMed  Google Scholar 

  77. Faraggi E, Kloczkowski A (2014) A global machine learning based scoring function for protein structure prediction. Proteins 82:752–759. https://doi.org/10.1002/prot.24454

    Article  CAS  PubMed  Google Scholar 

  78. Moult J, Fidelis K, Kryshtafovych A et al (2014) Critical assessment of methods of protein structure prediction (CASP)—Round X. Proteins 82:1–6. https://doi.org/10.1002/prot.24452

    Article  CAS  PubMed  Google Scholar 

  79. Roy A, Perez A, Dill KA, Maccallum JL (2014) Computing the relative stabilities and the per-residue components in protein conformational changes. Structure 22:168–175. https://doi.org/10.1016/j.str.2013.10.015

    Article  CAS  PubMed  Google Scholar 

  80. Moult J, Fidelis K, Kryshtafovych A, Tramontano A (2011) Critical assessment of methods of protein structure prediction (CASP)--Round IX. Proteins 79(Suppl 10):1–5. https://doi.org/10.1002/prot.23200

    Article  PubMed  Google Scholar 

  81. Nguyen SP, Shang Y, Xu D (2014) DL-PRO: a novel deep learning method for protein model quality assessment. Proc Int Jt Conf Neural Netw 2014:2071–2078. https://doi.org/10.1109/IJCNN.2014.6889891

    Article  PubMed  Google Scholar 

  82. Sarti E, Granata D, Seno F et al (2015) Native fold and docking pose discrimination by the same residue-based scoring function. Proteins 83:621–630. https://doi.org/10.1002/prot.24764

    Article  CAS  PubMed  Google Scholar 

  83. Singh A, Kaushik R, Mishra A et al (2016) ProTSAV: a protein tertiary structure analysis and validation server. Biochim Biophys Acta 1864:11–19. https://doi.org/10.1016/j.bbapap.2015.10.004

    Article  CAS  PubMed  Google Scholar 

  84. Moult J, Fidelis K, Kryshtafovych A et al (2016) Critical assessment of methods of protein structure prediction: progress and new directions in round XI. Proteins 84:4–14. https://doi.org/10.1002/prot.25064

    Article  CAS  PubMed  Google Scholar 

  85. Cao R, Cheng J (2016) Protein single-model quality assessment by feature-based probability density functions. Sci Rep 6:23990. https://doi.org/10.1038/srep23990

    Article  CAS  PubMed  Google Scholar 

  86. Miszta P, Pasznik P, Jakowiecki J et al (2018) GPCRM: a homology modeling web service with triple membrane-fitted quality assessment of GPCR models. Nucleic Acids Res 46:W387–W395. https://doi.org/10.1093/nar/gky429

    Article  CAS  PubMed  Google Scholar 

  87. Ogorzalek TL, Hura GL, Belsom A et al (2018) Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy. Proteins 86(Suppl 1):202–214. https://doi.org/10.1002/prot.25452

    Article  CAS  PubMed  Google Scholar 

  88. Pagès G, Charmettant B, Grudinin S (2019) Protein model quality assessment using 3D oriented convolutional neural networks. Bioinformatics 35:3313–3319. https://doi.org/10.1093/bioinformatics/btz122

    Article  CAS  PubMed  Google Scholar 

  89. McGuffin LJ, Adiyaman R, Maghrabi AHA et al (2019) IntFOLD: an integrated web resource for high performance protein structure and function prediction. Nucleic Acids Res 47:W408–W413. https://doi.org/10.1093/nar/gkz322

    Article  CAS  PubMed  Google Scholar 

  90. McGuffin LJ, Shuid AN, Kempster R et al (2018) Accurate template-based modeling in CASP12 using the IntFOLD4-TS, ModFOLD6, and ReFOLD methods. Proteins 86(Suppl 1):335–344. https://doi.org/10.1002/prot.25360

    Article  CAS  PubMed  Google Scholar 

  91. Wang X, Huang S-Y (2019) Integrating bonded and nonbonded potentials in the knowledge-based scoring function for protein structure prediction. J Chem Inf Model 59:3080–3090. https://doi.org/10.1021/acs.jcim.9b00057

    Article  CAS  PubMed  Google Scholar 

  92. Srivastava A, Adusumilli R, Boyce H et al (2019) Semantic workflows for benchmark challenges: enhancing comparability, reusability and reproducibility. Pac Symp Biocomput 24:208–219

    PubMed  Google Scholar 

  93. Adiyaman R, McGuffin LJ (2019) Methods for the refinement of protein structure 3D models. Int J Mol Sci 20:2301. https://doi.org/10.3390/ijms20092301

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The research was partially performed under OPUS grant from National Science Center (NCN, Poland), grant number UMO-2017/27/B/NZ7/01767 (to A.A.K).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Damian Bartuzi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Bartuzi, D., Kaczor, A.A., Matosiuk, D. (2023). Illuminating the “Twilight Zone”: Advances in Difficult Protein Modeling. In: Filipek, S. (eds) Homology Modeling. Methods in Molecular Biology, vol 2627. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2974-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2974-1_2

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2973-4

  • Online ISBN: 978-1-0716-2974-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics