Abstract
Predicting a protein’s structure from its amino acid sequence remains an unsolved problem after several decades of efforts. If the query protein has a homolog of known structure, the task is relatively easy and high-resolution models can often be built by copying and refining the framework of the solved structure. However, a template-based modeling procedure does not help answer the questions of how and why a protein adopts its specific structure. In particular, if structural homologs do not exist, or exist but cannot be identified, models have to be constructed from scratch. This procedure, called ab initio modeling, is essential for a complete solution to the protein structure prediction problem; it can also help us understand the physicochemical principle of how proteins fold in nature. Currently, the accuracy of ab initio modeling is low and the success is generally limited to small proteins (<120 residues). With the help of co-evolution based contact map predictions, success in folding larger-size proteins was recently witnessed in blind testing experiments. In this chapter, we give a review on the field of ab initio structure modeling. Our focus will be on three key components of the modeling algorithms: energy function design, conformational search, and model selection. Progress and advances of several representative algorithms will be discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bairoch A, Apweiler R, Wu CH et al (2005). The universal protein resource (UniProt). Nucleic Acids Res 33(Database issue): D154–159
Battey JN, Kopp J, Bordoli L et al (2007) Automated server predictions in CASP7. Proteins 69(S8):68–82
Berendsen HJC, Postma JPM, van Gunsteren WF et al (1981) Interaction models for water in relation to protein hydration. Intermolecular forces, Reidel, The Netherlands
Berg BA, Neuhaus T (1992) Multicanonical ensemble: a new approach to simulate first-order phase transitions. Physical Review Letters 68(1):9–12
Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Research 28(1):235–242
Berrera M, Molinari H, Fogolari F (2003) Amino acid empirical contact energy definitions for fold recognition in the space of contact maps. BMC Bioinform 4:8
Best RB, Buchete NV, Hummer G (2008) Are current molecular dynamics force fields too helical? Biophysical Journal 95(1):L07–09
Best RB, Hummer G (2009) Optimized molecular dynamics force fields applied to the helix-coil transition of polypeptides. J Phys Chem B 113(26):9004–9015
Bowie JU, Eisenberg D (1994) An evolutionary approach to folding small alpha-helical proteins that uses sequence information and an empirical guiding fitness function. Proc Natl Acad Sci U S A 91(10):4436–4440
Bradley P, Malmstrom L, Qian B et al (2005a) Free modeling with Rosetta in CASP6. Proteins 61(Suppl 7):128–134
Bradley P, Misura KM, Baker D (2005b) Toward high-resolution de novo structure prediction for small proteins. Science 309(5742):1868–1871
Brooks BR, Bruccoleri RE, Olafson BD et al (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. Journal of Computational Chemistry 4(2):187–217
Bryant SH, Lawrence CE (1993) An empirical energy function for threading protein sequence through the folding motif. Proteins 16(1):92–112
Cao R, Bhattacharya D, Adhikari B et al (2015). Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11. Proteins 84:247–259
Case DA, Pearlman DA, Caldwell JA et al (1997). AMBER 5.0, University of California, San Francisco
Chen J, Brooks CL 3rd (2007) Can molecular dynamics simulations provide high-resolution refinement of protein structure? Proteins 67(4):922–930
Chowdhury S, Lee MC, Xiong GM et al (2003) Ab initio folding simulation of the Trp-cage mini-protein approaches NMR resolution. Journal of Molecular Biology 327(3):711–717
Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Science 2(9):1511–1519
Cornell WD, Cieplak P, Bayly CI et al (1995) A Second generation force field for the simulation of proteins, nucleic acids, and organic molecules. Journal of the American Chemical Society 117:5179–5197
Cozzetto D, Kryshtafovych A, Fidelis K et al (2009) Evaluation of template-based models in CASP8 with standard measures. Proteins 77(Suppl 9):18–28
Das R, Qian B, Raman S et al (2007) Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home. Proteins 69(S8):118–128
Deng H, Jia Y, Zhang Y (2016) 3DRobot: automated generation of diverse and well-packed protein structure decoys. Bioinformatics 32(3):378–387
Deng HY, Jia Y, Wei YY et al (2012) What is the best reference state for designing statistical atomic potentials in protein structure prediction? Proteins-Structure Function and Bioinformatics 80(9):2311–2322
Dominy BN, Brooks CL (2002) Identifying native-like protein structures using physics-based potentials. Journal of Computational Chemistry 23(1):147–160
Duan Y, Kollman PA (1998) Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. Science 282(5389):740–744
Eisenberg D, Luthy R, Bowie JU (1997) VERIFY3D: assessment of protein models with three-dimensional profiles. Methods in Enzymology 277:396–404
Ensign DL, Kasson PM, Pande VS (2007) Heterogeneity even at the speed limit of folding: large-scale molecular dynamics study of a fast-folding variant of the villin headpiece. Journal of Molecular Biology 374(3):806–816
Ezkurdia I, Grana O, Izarzugaza JM et al (2009) Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8. Proteins 77(Suppl 9):196–209
Fan H, Mark AE (2004) Refinement of homology-based protein structures by molecular dynamics simulation techniques. Protein Science 13(1):211–220
Feig M, Brooks CL 3rd (2002) Evaluating CASP4 predictions with physical energy functions. Proteins 49(2):232–245
Feig M, Mirjalili V (2015). Protein structure refinement via molecular-dynamics simulations: what works and what does not? Proteins 84:282–292
Felts AK, Gallicchio E, Wallqvist A et al (2002) Distinguishing native conformations of proteins from decoys with an effective free energy estimator based on the OPLS all-atom force field and the Surface Generalized Born solvent model. Proteins 48(2):404–422
Fischer D (2006) Servers for protein structure prediction. Current Opinion in Structural Biology 16(2):178–182
Freddolino PL, Harrison CB, Liu Y et al (2010) Challenges in protein folding simulations: timescale, representation, and analysis. Nature Physics 6(10):751–758
Freddolino PL, Liu F, Gruebele M et al (2008) Ten-microsecond molecular dynamics simulation of a fast-folding WW domain. Biophysical Journal 94(10):L75–77
Freddolino PL, Park S, Roux B et al (2009) Force field bias in protein folding simulations. Biophysical Journal 96(9):3772–3780
Freddolino PL, Schulten K (2009) Common structural transitions in explicit-solvent simulations of villin headpiece folding. Biophysical Journal 97(8):2338–2347
Fujitsuka Y, Chikenji G, Takada S (2006) SimFold energy function for de novo protein structure prediction: consensus with Rosetta. Proteins 62(2):381–398
Ginalski K, Elofsson A, Fischer D et al (2003) 3D-Jury: a simple approach to improve protein structure predictions. Bioinformatics 19(8):1015–1018
Hagler A, Euler E, Lifson S (1974) Energy functions for peptides and proteins i. derivation of a consistent force field including the hydrogen bond from amide crystals. Journal of the American Chemical Society 96:5319–5327
Hamelberg D, Mongan J, McCammon JA (2004) Enhanced sampling of conformational transitions in proteins using full atomistic accelerated molecular dynamics simulations. Protein Science 13:76
Helles G (2008) A comparative study of the reported performance of ab initio protein structure prediction algorithms. Journal of the Royal Society, Interface 5(21):387–396
Hendlich M, Lackner P, Weitckus S et al (1990) Identification of native protein folds amongst a large number of incorrect models. The calculation of low energy conformations from potentials of mean force. Journal of Molecular Biology 216(1):167–180
Hills RD Jr, Brooks CL 3rd (2009) Insights from coarse-grained go models for protein folding and dynamics. International Journal of Molecular Sciences 10(3):889–905
Hsieh MJ, Luo R (2004) Physical scoring function based on AMBER force field and poisson-boltzmann implicit solvent for protein structure prediction. Proteins 56(3):475–486
Im W, Lee MS, Brooks CL 3rd (2003) Generalized born model with a simple smoothing function. Journal of Computational Chemistry 24(14):1691–1702
Jagielska A, Wroblewska L, Skolnick J (2008) Protein model refinement using an optimized physics-based all-atom force field. Proceedings of the National Academy of Sciences of the United States of America 105(24):8268–8273
Jauch R, Yeo HC, Kolatkar PR et al (2007) Assessment of CASP7 structure predictions for template free targets. Proteins 69(Suppl 8):57–67
Jonassen I, Klose D, Taylor WR (2006) Protein model refinement using structural fragment tessellation. Computational Biology and Chemistry 30(5):360–366
Jones DT (1999) GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. Journal of Molecular Biology 287(4):797–815
Jones DT, Buchan DW, Cozzetto D et al (2012) PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28(2):184–190
Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935
Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. Journal of the American Chemical Society 118:11225–11236
Jorgensen WL, Tirado-Rives J (1988) The OPLS potential functions for proteins. energy minimizations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society 110:1657–1666
Kaminski GA, Friesner RA, Tirado-Rives J et al (2001) Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J Phys Chem B 105:6474–6487
Karplus K, Barrett C, Hughey R (1998) Hidden markov models for detecting remote protein homologies. Bioinformatics 14:846–856
Kihara D, Lu H, Kolinski A et al (2001) TOUCHSTONE: an ab initio protein structure prediction method that uses threading-based tertiary restraints. Proc Natl Acad Sci U S A 98(18):10125–10130
Kinch L, Yong Shi S, Cong Q et al (2011) CASP9 assessment of free modeling target predictions. Proteins 79(Suppl 10):59–73
Kinch LN, Li W, Monastyrskyy B, et al. (2015). Evaluation of free modeling targets in CASP11 and ROLL. Proteins 84: 51–66
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Klepeis JL, Floudas CA (2003) ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence. Biophysical Journal 85(4):2119–2146
Klepeis JL, Wei Y, Hecht MH et al (2005) Ab initio prediction of the three-dimensional structure of a de novo designed protein: a double-blind case study. Proteins 58(3):560–570
Kocher JP, Rooman MJ, Wodak SJ (1994) Factors influencing the ability of knowledge-based potentials to identify native sequence-structure matches. Journal of Molecular Biology 235(5):1598–1613
Kosciolek T, Jones DT (2014) De novo structure prediction of globular proteins aided by sequence variation-derived contacts. PLoS ONE 9(3):e92197
Kryshtafovych A, Barbato A, Fidelis K et al (2014) Assessment of the assessment: evaluation of the model quality estimates in CASP10. Proteins 82(Suppl 2):112–126
Kryshtafovych A, Barbato A, Monastyrskyy B, et al (2015) Methods of model accuracy estimation can help selecting the best models from decoy sets: Assessment of model accuracy estimations in CASP11. Proteins 84: 349–369
Kryshtafovych A, Fidelis K, Tramontano A (2011) Evaluation of model quality predictions in CASP9. Proteins 79(Suppl 10):91–106
Larsson P, Skwark MJ, Wallner B et al (2009) Assessment of global and local model quality in CASP8 using Pcons and ProQ. Proteins 77(Suppl 9):167–172
Lazaridis T, Karplus M (1999a) Discrimination of the native from misfolded protein models with an energy function including implicit solvation. Journal of Molecular Biology 288(3):477–487
Lazaridis T, Karplus M (1999b) Effective energy function for proteins in solution. Proteins 35(2):133–152
Lee J (1993) New monte carlo algorithm: entropic sampling. Physical Review Letters 71(2):211–214
Lee J, Kim SY, Joo K et al (2004) Prediction of protein tertiary structure using PROFESY, a novel method based on fragment assembly and conformational space annealing. Proteins 56(4):704–714
Lee J, Scheraga HA, Rackovsky S (1998) Conformational analysis of the 20-residue membrane-bound portion of melittin by conformational space annealing. Biopolymers 46(2):103–116
Lee MC, Duan Y (2004) Distinguish protein decoys by using a scoring function based on a new AMBER force field, short molecular dynamics simulations, and the generalized born solvent model. Proteins 55(3):620–634
Lee MR, Tsai J, Baker D et al (2001) Molecular dynamics in the endgame of protein structure prediction. Journal of Molecular Biology 313(2):417–430
Lei HX, Wu C, Liu HG et al (2007) Folding free-energy landscape of villin headpiece subdomain from molecular dynamics simulations. Proceedings of the National Academy of Sciences of the United States of America 104(12):4925–4930
Levitt M, Hirshberg M, Sharon R et al (1995) Potential-energy function and parameters for simulations of the molecular-dynamics of proteins and nucleic-acids in solution. Computer Physics Communications 91(1–3):215–231
Li Z, Scheraga HA (1987) Monte carlo-minimization approach to the multiple-minima problem in protein folding. Proc Natl Acad Sci U S A 84(19):6611–6615
Lindahl E, Hess B, van der Spoel D (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Modeling 7:306–317
Lindorff-Larsen K, Maragakis P, Piana S et al (2012) Systematic validation of protein force fields against experimental data. PLoS ONE 7(2):e32131
Lindorff-Larsen K, Piana S, Dror RO et al (2011) How fast-folding proteins fold. Science 334(6055):517–520
Lindorff-Larsen K, Piana S, Palmo K et al (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78(8):1950–1958
Liwo A, Khalili M, Scheraga HA (2005) Ab initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains. Proc Natl Acad Sci U S A 102(7):2362–2367
Liwo A, Lee J, Ripoll DR et al (1999) Protein structure prediction by global optimization of a potential energy function. Proc Natl Acad Sci U S A 96(10):5482–5485
Liwo A, Pincus MR, Wawak RJ et al (1993) Calculation of protein backbone geometry from alpha-carbon coordinates based on peptide-group dipole alignment. Protein Science 2(10):1697–1714
Lu H, Skolnick J (2001) A distance-dependent atomic knowledge-based potential for improved protein structure selection. Proteins 44(3):223–232
Luthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356(6364):83–85
MacKerell AD Jr, Bashford D, Bellott M et al (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102(18):3586–3616
Mariani V, Kiefer F, Schmidt T et al (2011) Assessment of template based protein structure predictions in CASP9. Proteins 79(Suppl 10):37–58
Marks DS, Colwell LJ, Sheridan R et al (2011) Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6(12):e28766
Marks DS, Hopf TA, Sander C (2012) Protein structure prediction from sequence variation. Nature Biotechnology 30(11):1072–1080
McGuffin LJ (2007) Benchmarking consensus model quality assessment for protein fold recognition. BMC Bioinformatics 8:345
Melo F, Sanchez R, Sali A (2002) Statistical potentials for fold assessment. Protein Science 11(2):430–448
Metropolis N, Rosenbluth AW, Rosenbluth MN et al (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092
Miao YL, Feixas F, Eun CS et al (2015) Accelerated molecular dynamics simulations of protein folding. Journal of Computational Chemistry 36(20):1536–1549
Mirjalili V, Feig M (2013) Protein structure refinement through structure selection and averaging from molecular dynamics ensembles. Journal of Chemical Theory and Computation 9(2):1294–1303
Mitchell M (1996). An Introduction to Genetic Algorithms. Cambridge, MIT Press
Mittal J, Best RB (2010) Tackling force-field bias in protein folding simulations: folding of Villin HP35 and Pin WW domains in explicit water. Biophysical Journal 99(3):L26–28
Montelione GT (2012). Template based modeling assessment in CASP10. 10th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction. Gaeta, Italy
Moult J, Fidelis K, Zemla A et al (2001) Critical assessment of methods of protein structure prediction (CASP): round IV. Proteins Suppl 5:2–7
Nemethy G, Gibson KD, Palmer KA et al (1992) Energy parameters in polypeptides. 10. Improved geometric parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides. J Phys Chem B 96:6472–6484
Neria E, Fischer S, Karplus M (1996) Simulation of activation free energies in molecular systems. J Chem Phys 105(5):1902–1921
Nguyen H, Maier J, Huang H et al (2014) Folding simulations for proteins with diverse topologies are accessible in days with a physics-based force field and implicit solvent. Journal of the American Chemical Society 136(40):13959–13962
Nilges M, Brunger AT (1991) Automated modeling of coiled coils: application to the GCN4 dimerization region. Protein Engineering 4(6):649–659
Oldziej S, Czaplewski C, Liwo A et al (2005) Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment in two blind tests. Proceedings of the National Academy of Sciences of the United States of America 102(21):7547–7552
Ovchinnikov S, Kim DE, Wang RY, et al (2015) Improved de novo structure prediction in CASP11 by incorporating Co-evolution information into rosetta. Proteins 84:67–75
Park B, Levitt M (1996) Energy functions that discriminate X-ray and near native folds from well-constructed decoys. Journal of Molecular Biology 258(2):367–392
Petrey D, Honig B (2000) Free energy determinants of tertiary structure and the evaluation of protein models. Protein Science 9(11):2181–2191
Pettitt CS, McGuffin LJ, Jones DT (2005) Improving sequence-based fold recognition by using 3D model quality assessment. Bioinformatics 21(17):3509–3515
Piana S, Klepeis JL, Shaw DE (2014) Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. Current Opinion in Structural Biology 24:98–105
Piana S, Lindorff-Larsen K, Shaw DE (2011) How robust are protein folding simulations with respect to force field parameterization? Biophysical Journal 100(9):L47–49
Piana S, Lindorff-Larsen K, Shaw DE (2012) Protein folding kinetics and thermodynamics from atomistic simulation. Proceedings of the National Academy of Sciences of the United States of America 109(44):17845–17850
Piana S, Lindorff-Larsen K, Shaw DE (2013a) Atomic-level description of ubiquitin folding. Proc Natl Acad Sci U S A 110(15):5915–5920
Piana S, Lindorff-Larsen K, Shaw DE (2013b) Atomistic description of the folding of a dimeric protein. J Phys Chem B 117(42):12935–12942
Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols 5(4):725–738
Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology 234(3):779–815
Samudrala R, Moult J (1998) An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. Journal of Molecular Biology 275(5):895–916
Shen MY, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Science 15(11):2507–2524
Shortle D, Simons KT, Baker D (1998) Clustering of low-energy conformations near the native structures of small proteins. Proc Natl Acad Sci U S A 95(19):11158–11162
Simons KT, Kooperberg C, Huang E et al (1997) Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions. Journal of Molecular Biology 268(1):209–225
Sippl MJ (1990) Calculation of conformational ensembles from potentials of mean force. an approach to the knowledge-based prediction of local structures in globular proteins. Journal of Molecular Biology 213(4):859–883
Sippl MJ (1993) Recognition of errors in three-dimensional structures of proteins. Proteins 17(4):355–362
Skolnick J (2006) In quest of an empirical potential for protein structure prediction. Current Opinion in Structural Biology 16(2):166–171
Skolnick J, Jaroszewski L, Kolinski A et al (1997) Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct? Protein Science 6:676–688
Skolnick J, Kihara D, Zhang Y (2004) Development and large scale benchmark testing of the PROSPECTOR 3.0 threading algorithm. Protein 56:502–518
Skolnick J, Zhang Y, Arakaki AK et al (2003) TOUCHSTONE: A unified approach to protein structure prediction. Proteins 53(Suppl 6):469–479
Soding J (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics 21(7):951–960
Sorin EJ, Pande VS (2005) Exploring the helix-coil transition via all-atom equilibrium ensemble simulations. Biophysical Journal 88(4):2472–2493
Sugita Y, Okamoto Y (1999) Replica-exchange molecular dynamics method for protein folding. Chemical Physics Letters 314(1–2):141–151
Summa CM, Levitt M (2007) Near-native structure refinement using in vacuo energy minimization. Proc Natl Acad Sci U S A 104(9):3177–3182
Swendsen RH, Wang JS (1986) Replica Monte Carlo simulation of spin glasses. Physical Review Letters 57(21):2607–2609
Tai CH, Bai H, Taylor TJ et al (2014) Assessment of template-free modeling in CASP10 and ROLL. Proteins 82(Suppl 2):57–83
Taylor WR, Bartlett GJ, Chelliah V et al (2008) Prediction of protein structure from ideal forms. Proteins 70(4):1610–1619
Thomas PD, Dill KA (1996) Statistical potentials extracted from protein structures: how accurate are they? Journal of Molecular Biology 257(2):457–469
Tosatto SC (2005) The victor/FRST function for model quality estimation. Journal of Computational Biology 12(10):1316–1327
Tozzini V (2005) Coarse-grained models for proteins. Current Opinion in Structural Biology 15(2):144–150
Tsai J, Bonneau R, Morozov AV et al (2003) An improved protein decoy set for testing energy functions for protein structure prediction. Proteins 53(1):76–87
van Gunsteren WF, Billeter SR, Eising AA et al (1996). Biomolecular simulation: The GROMOS96 Manual and User Guide Univ Publ House, Zurich
Vieth M, Kolinski A, Brooks CL et al (1994) Prediction of the folding pathways and structure of the GCN4 leucine zipper. Journal of Molecular Biology 237(4):361–367
Wallner B, Elofsson A (2003) Can correct protein models be identified? Protein Science 12(5):1073–1086
Wallner B, Elofsson A (2007) Prediction of global and local model quality in CASP7 using Pcons and ProQ. Proteins 69(S8):184–193
Wang JM, Cieplak P, Kollman PA (2000) How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? Journal of Computational Chemistry 21(12):1049–1074
Wang K, Fain B, Levit M et al (2004). Improved protein structure selection using decoy-dependent discriminatory functions. BMC Structural Biology 4(8)
Weiner SJ, Kollman PA, Case DA et al (1984) A new force field for molecular mechanical simulation of nucleic acids and proteins. Journal of the American Chemical Society 106:765–784
Wiederstein M, Sippl MJ (2007). ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35(Web Server issue): W407–410
Wroblewska L, Skolnick J (2007) Can a physics-based, all-atom potential find a protein’s native structure among misfolded structures? i. large scale AMBER benchmarking. Journal of Computational Chemistry 28(12):2059–2066
Wu S, Skolnick J, Zhang Y (2007) Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biology 5:17
Wu S, Szilagyi A, Zhang Y (2011) Improving protein structure prediction using multiple sequence-based contact predictions. Structure 19(8):1182–1191
Wu S, Zhang Y (2007) LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Research 35(10):3375–3382
Wu S, Zhang Y (2008a) A comprehensive assessment of sequence-based and template-based methods for protein contact prediction. Bioinformatics 24(7):924–931
Wu S, Zhang Y (2008b) MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 72(2):547–556
Wu S, Zhang Y (2010) Recognizing protein substructure similarity using segmental threading. Structure 18(7):858–867
Xu D, Zhang J, Roy A et al (2011) Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement. Proteins 79(Suppl 10):147–160
Xu D, Zhang Y (2012) Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80(7):1715–1735
Xu D, Zhang Y (2013) Toward optimal fragment generations for ab initio protein structure assembly. Proteins 81(2):229–239
Yang J, Yan R, Roy A et al (2015a) The I-TASSER Suite: protein structure and function prediction. Nature Methods 12(1):7–8
Yang J, Zhang W, He B, et al (2015) Template-based protein structure prediction in CASP11 and retrospect of I-TASSER in the last decade. Proteins 84: 233–246
Yang Y, Faraggi E, Zhao H et al (2011) Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates. Bioinformatics 27(15):2076–2082
Zagrovic B, Snow CD, Shirts MR et al (2002) Simulation of folding of a small alpha-helical protein in atomistic detail using worldwide-distributed computing. Journal of Molecular Biology 323(5):927–937
Zhang C, Kim SH (2000) Environment-dependent residue contact energies for proteins. Proc Natl Acad Sci U S A 97(6):2550–2555
Zhang C, Liu S, Zhou H et al (2004) An accurate, residue-level, pair potential of mean force for folding and binding based on the distance-scaled, ideal-gas reference state. Protein Science 13(2):400–411
Zhang J, Liang Y, Zhang Y (2011) Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling. Structure 19(12):1784–1795
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(10):e15386
Zhang W, Yang J, He B et al (2015). Integration of QUARK and I-TASSER for Ab initio protein structure prediction in CASP11. Proteins 84: 76–86
Zhang Y (2008). Progress and Challenges in protein structure prediction. Curr Opin Struct Biol: In press
Zhang Y (2009) I-TASSER: Fully automated protein structure prediction in CASP8. Proteins 77(S9):100–113
Zhang Y (2014) Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10. Proteins 82(Suppl 2):175–187
Zhang Y, Hubner I, Arakaki A et al (2006) On the origin and completeness of highly likely single domain protein structures. Proc Natl Acad Sci U S A 103:2605–2610
Zhang Y, Kihara D, Skolnick J (2002) Local energy landscape flattening: parallel hyperbolic monte carlo sampling of protein folding. Proteins-Struct Func Genet 48(2):192–201
Zhang Y, Kolinski A, Skolnick J (2003) TOUCHSTONE II: A new approach to ab initio protein structure prediction. Biophysical Journal 85(2):1145–1164
Zhang Y, Skolnick J (2004a) Automated structure prediction of weakly homologous proteins on a genomic scale. Proceedings of the National Academy of Sciences of the United States of America 101:7594–7599
Zhang Y, Skolnick J (2004b) SPICKER: a clustering approach to identify near-native protein folds. Journal of Computational Chemistry 25(6):865–871
Zhang Y, Skolnick J (2005a) The protein structure prediction problem could be solved using the current PDB library. Proc Natl Acad Sci U S A 102:1029–1034
Zhang Y, Skolnick J (2005b) TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Research 33(7):2302–2309
Zhang Y, Skolnick J (2013) Segment assembly, structure alignment and iterative simulation in protein structure prediction. BMC Biology 11:44
Zhou H, Skolnick J (2007) Ab initio protein structure prediction using chunk-TASSER. Biophysical Journal 93(5):1510–1518
Zhou H, Skolnick J (2011) GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction. Biophysical Journal 101(8):2043–2052
Zhou H, Zhou Y (2002) Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Science 11(11):2714–2726
Zhou R (2003) Free energy landscape of protein folding in water: explicit vs. implicit solvent. Proteins 53(2):148–161
Acknowledgements
Authors want to thank Drs. Sitao Wu and Haiyou Deng for their contribution to the article. The project is supported in part by the National Institute of General Medical Sciences (GM083107, GM116960, and GM097033).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Lee, J., Freddolino, P.L., Zhang, Y. (2017). Ab Initio Protein Structure Prediction. In: J. Rigden, D. (eds) From Protein Structure to Function with Bioinformatics. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1069-3_1
Download citation
DOI: https://doi.org/10.1007/978-94-024-1069-3_1
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-024-1067-9
Online ISBN: 978-94-024-1069-3
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)