Skip to main content
SpringerLink
Log in

Protein Structure Annotations

  • Chapter
  • First Online:
Essentials of Bioinformatics, Volume I

Abstract

This chapter aims to introduce to the specifics of protein structure annotations and their fundamental position in structural bioinformatics, bioinformatics in general. Proteins are profoundly characterised by their structure in every aspect of their functioning and, while over the last decades there has been a close to exponential growth of known protein sequences, the growth of known protein structures has been closer to linear because of the high complexity and cost of determining them. Thus, protein structure predictors are among the most thoroughly assessed tools in bioinformatics (in venues such as CASP or CAMEO) because they allow the structural study of proteins on a large scale. This chapter presents the key types of protein structure annotation and the methods and algorithms for predicting them, with the aim to give both a historical perspective on their development and a snapshot of their current state of the art. From one-dimensional protein annotations – i.e. secondary structure, solvent accessibility and torsion angles – to more complex and informative two-dimensional protein abstractions, i.e. contact maps, both mature and currently developing methods for protein structure annotations are introduced. The aim of this overview is to facilitate the adoption and development of state-of-the-art protein structural predictors. Particular attention is given to some of the best performing and freely available web servers and standalone programmes to predict protein structure annotations.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
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
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

  • Adhikari B, Hou J, Cheng J (2017) DNCON2: improved protein contact prediction using two-level deep convolutional neural networks. Bioinformatics 34(9):1466–1472

    Article  PubMed Central  CAS  Google Scholar 

  • Ahmad S, Gromiha M, Fawareh H, Sarai A (2004) ASAView: database and tool for solvent accessibility representation in proteins. BMC Bioinformatics 5:51

    Article  PubMed  PubMed Central  Google Scholar 

  • Aloy P, Stark A, Hadley C, Russell RB (2003) Predictions without templates: new folds, secondary structure, and contacts in CASP5. Proteins Struct Funct Bioinforma 53(S6):436–456

    Article  CAS  Google Scholar 

  • Altschul SF et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Baldi P, Brunak S, Frasconi P, Soda G, Pollastri G (1999) Exploiting the past and the future in protein secondary structure prediction. Bioinforma Oxf Engl 15(11):937–946

    Article  CAS  Google Scholar 

  • Bartoli L, Capriotti E, Fariselli P, Martelli PL, Casadio R (2008) The pros and cons of predicting protein contact maps. Methods Mol Biol Clifton NJ 413:199–217

    CAS  Google Scholar 

  • Baú D, Martin AJ, Mooney C, Vullo A, Walsh I, Pollastri G (2006) Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins. BMC Bioinformatics 7:402

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Berman HM et al (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Buchan DWA, Jones DT (2018) Improved protein contact predictions with the MetaPSICOV2 server in CASP12. Proteins 86(Suppl 1):78–83

    Article  CAS  PubMed  Google Scholar 

  • Buchan DWA, Ward SM, Lobley AE, Nugent TCO, Bryson K, Jones DT (2010) Protein annotation and modelling servers at University College London. Nucleic Acids Res 38(suppl_2):W563–W568

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bystroff C, Thorsson V, Baker D (2000) HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. J Mol Biol 301(1):173–190

    Article  CAS  PubMed  Google Scholar 

  • Cheng J, Baldi P (2007) Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics 8:113

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Cheng J, Randall AZ, Sweredoski MJ, Baldi P (2005) SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 33(suppl_2):W72–W76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chou PY, Fasman GD (1974) Prediction of protein conformation. Biochemistry (Mosc) 13(2):222–245

    Article  CAS  Google Scholar 

  • Cornette JL, Cease KB, Margalit H, Spouge JL, Berzofsky JA, DeLisi C (1987) Hydrophobicity scales and computational techniques for detecting amphipathic structures in proteins. J Mol Biol 195(3):659–685

    Article  CAS  PubMed  Google Scholar 

  • Cuff JA, Clamp ME, Siddiqui AS, Finlay M, Barton GJ (1998) JPred: a consensus secondary structure prediction server. Bioinformatics 14(10):892–893

    Article  CAS  PubMed  Google Scholar 

  • De Brevern AG, Etchebest C, Hazout S (2000) Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks. Proteins Struct Funct Bioinforma 41(3):271–287

    Article  Google Scholar 

  • Di Lena P, Fariselli P, Margara L, Vassura M, Casadio R (2010) Fast overlapping of protein contact maps by alignment of eigenvectors. Bioinformatics 26(18):2250–2258

    Article  PubMed  CAS  Google Scholar 

  • Di Lena P, Fariselli P, Margara L, Vassura M, Casadio R (2011) Is there an optimal substitution matrix for contact prediction with correlated mutations? IEEEACM Trans Comput Biol Bioinforma 8(4):1017–1028

    Article  Google Scholar 

  • Di Lena P, Nagata K, Baldi P (2012) Deep architectures for protein contact map prediction. Bioinformatics 28(19):2449–2457

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Drozdetskiy A, Cole C, Procter J, Barton GJ (2015) JPred4: a protein secondary structure prediction server. Nucleic Acids Res 43(W1):W389–W394

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Eickholt J, Cheng J (2012) Predicting protein residue–residue contacts using deep networks and boosting. Bioinformatics 28(23):3066–3072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Faraggi E, Yang Y, Zhang S, Zhou Y (2009) Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction. Structure 17(11):1515–1527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fariselli P, Olmea O, Valencia A, Casadio R (2001) Prediction of contact maps with neural networks and correlated mutations. Protein Eng Des Sel 14(11):835–843

    Article  CAS  Google Scholar 

  • Fauchère JL, Charton M, Kier LB, Verloop A, Pliska V (1988) Amino acid side chain parameters for correlation studies in biology and pharmacology. Int J Pept Protein Res 32(4):269–278

    Article  PubMed  Google Scholar 

  • Finn RD, Clements J, Eddy SR (2011) HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 39(Web Server issue):W29–W37

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Göbel U, Sander C, Schneider R, Valencia A (1994) Correlated mutations and residue contacts in proteins. Proteins 18(4):309–317

    Article  PubMed  Google Scholar 

  • Haas J et al Continuous Automated Model Evaluation (CAMEO) complementing the critical assessment of structure prediction in CASP12. Proteins: Struct Funct Bioinf p. n/a-n/a

    Google Scholar 

  • Heffernan R et al (2015) Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Sci Rep 5:11476

    Article  PubMed  PubMed Central  Google Scholar 

  • Heffernan R et al (2016) Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins. Bioinformatics 32(6):843–849

    Article  CAS  PubMed  Google Scholar 

  • Heffernan R, Yang Y, Paliwal K, Zhou Y (2017) Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility. Bioinformatics 33(18):2842–2849

    Article  CAS  PubMed  Google Scholar 

  • Holbrook SR, Muskal SM, Kim SH (1990) Predicting surface exposure of amino acids from protein sequence. Protein Eng 3(8):659–665

    Article  CAS  PubMed  Google Scholar 

  • Huang Y, Bystroff C (2006) Improved pairwise alignments of proteins in the Twilight Zone using local structure predictions. Bioinformatics 22(4):413–422

    Article  CAS  PubMed  Google Scholar 

  • Johnson LS, Eddy SR, Portugaly E (2010) Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinformatics 11:431

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202

    Article  CAS  PubMed  Google Scholar 

  • Jones DT, Swindells MB (2002) Getting the most from PSI–BLAST. Trends Biochem Sci 27(3):161–164

    Article  CAS  PubMed  Google Scholar 

  • 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(2):184–190

    Article  CAS  PubMed  Google Scholar 

  • 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(7):999–1006

    Article  CAS  PubMed  Google Scholar 

  • Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12):2577–2637

    Article  CAS  PubMed  Google Scholar 

  • Kaján L, Hopf TA, Kalaš M, Marks DS, Rost B (2014) FreeContact: fast and free software for protein contact prediction from residue co-evolution. BMC Bioinformatics 15:85

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kendrew JC et al (1960) Structure of myoglobin: a three-dimensional Fourier synthesis at 2 A. resolution. Nature 185(4711):422–427

    Article  CAS  PubMed  Google Scholar 

  • Kim DE, DiMaio F, Wang RY-R, Song Y, Baker D (2014) One contact for every twelve residues allows robust and accurate topology-level protein structure modeling. Proteins 82(2):208–218

    Article  CAS  PubMed  Google Scholar 

  • Kinch LN, Li W, Monastyrskyy B, Kryshtafovych A, Grishin NV (2016) Assessment of CASP11 contact-assisted predictions. Proteins 84(Suppl 1):164–180

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kosciolek T, Jones DT (2014) De Novo structure prediction of globular proteins aided by sequence variation-derived contacts. PLoS One 9(3):e92197

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kosciolek T, Jones DT (2016) Accurate contact predictions using covariation techniques and machine learning. Proteins 84(Suppl 1):145–151

    Article  PubMed  CAS  Google Scholar 

  • Kuang R, Leslie CS, Yang A-S (2004) Protein backbone angle prediction with machine learning approaches. Bioinformatics 20(10):1612–1621

    Article  CAS  PubMed  Google Scholar 

  • Kukic P, Mirabello C, Tradigo G, Walsh I, Veltri P, Pollastri G (2014) Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks. BMC Bioinformatics 15:6

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  PubMed  Google Scholar 

  • Lyons J et al (2014) Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. J Comput Chem 35(28):2040–2046

    Article  CAS  PubMed  Google Scholar 

  • MacCallum RM (2004) Striped sheets and protein contact prediction. Bioinformatics 20(suppl_1):i224–i231

    Article  CAS  PubMed  Google Scholar 

  • Magnan CN, Baldi P (2014) SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics 30(18):2592–2597

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Martin J, Letellier G, Marin A, Taly J-F, de Brevern AG, Gibrat J-F (2005) Protein secondary structure assignment revisited: a detailed analysis of different assignment methods. BMC Struct Biol 5:17

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Martin AJ, Mooney C, Walsh I, Pollastri G (2010) Contact map prediction by machine learning. In: Pan Y, Zomaya A, Rangwala H, Karypis G (eds) Introduction to protein structure prediction. Wiley. https://doi.org/10.1002/9780470882207.ch7

  • Mirabello C, Pollastri G (2013) Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility. Bioinformatics 29(16):2056–2058

    Article  CAS  PubMed  Google Scholar 

  • Monastyrskyy B, D’Andrea D, Fidelis K, Tramontano A, Kryshtafovych A (2014) Evaluation of residue–residue contact prediction in CASP10. Proteins Struct Funct Bioinforma 82:138–153

    Article  CAS  Google Scholar 

  • Monastyrskyy B, D’Andrea D, Fidelis K, Tramontano A, Kryshtafovych A (2016) New encouraging developments in contact prediction: Assessment of the CASP11 results. Proteins 84(Suppl 1):131–144

    Article  PubMed  CAS  Google Scholar 

  • Mooney C, Pollastri G (2009) Beyond the twilight zone: automated prediction of structural properties of proteins by recursive neural networks and remote homology information. Proteins Struct Funct Bioinforma 77(1):181–190

    Article  CAS  Google Scholar 

  • Mooney C, Vullo A, Pollastri G (2006) Protein structural motif prediction in multidimensional ø-ψ space leads to improved secondary structure prediction. J Comput Biol 13(8):1489–1502

    Article  CAS  PubMed  Google Scholar 

  • Mooney C, Cessieux A, Shields DC, Pollastri G (2013) SCL-Epred: a generalised de novo eukaryotic protein subcellular localisation predictor. Amino Acids 45(2):291–299

    Article  CAS  PubMed  Google Scholar 

  • Murzin AG, Brenner SE, Hubbard T, Chothia C (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247(4):536–540

    CAS  PubMed  Google Scholar 

  • Olmea O, Valencia A (1997) Improving contact predictions by the combination of correlated mutations and other sources of sequence information. Fold Des 2:S25–S32

    Article  CAS  PubMed  Google Scholar 

  • Pascarella S, Persio RD, Bossa F, Argos P (1998) Easy method to predict solvent accessibility from multiple protein sequence alignments. Proteins Struct Funct Bioinforma 32(2):190–199

    Article  CAS  Google Scholar 

  • Pauling L, Corey RB (1951) Configurations of polypeptide chains with favored orientations around single bonds. Proc Natl Acad Sci U S A 37(11):729–740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pazos F, Helmer-Citterich M, Ausiello G, Valencia A (1997) Correlated mutations contain information about protein-protein interaction. J Mol Biol 271(4):511–523

    Article  CAS  PubMed  Google Scholar 

  • Perutz MF, Rossmann MG, Cullis AF, Muirhead H, Will G, North AC (1960) Structure of haemoglobin: a three-dimensional Fourier synthesis at 5.5-A. resolution, obtained by X-ray analysis. Nature 185(4711):416–422

    Article  CAS  PubMed  Google Scholar 

  • Pollastri G, Baldi P (2002) Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners. Bioinformatics 18(suppl_1):S62–S70

    Article  PubMed  Google Scholar 

  • Pollastri G, McLysaght A (2005) Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 21(8):1719–1720

    Article  CAS  PubMed  Google Scholar 

  • Pollastri G, Baldi P, Fariselli P, Casadio R (2001) Improved prediction of the number of residue contacts in proteins by recurrent neural networks. Bioinformatics 17(suppl_1):S234–S242

    Article  PubMed  Google Scholar 

  • Pollastri G, Baldi P, Fariselli P, Casadio R (2002) Prediction of coordination number and relative solvent accessibility in proteins. Proteins 47(2):142–153

    Article  CAS  PubMed  Google Scholar 

  • Pollastri G, Martin AJ, Mooney C, Vullo A (2007) Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information. BMC Bioinformatics 8:201

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Remmert M, Biegert A, Hauser A, Söding J (2012) HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 9(2):173–175

    Article  CAS  Google Scholar 

  • Rost B (2001) Review: protein secondary structure prediction continues to rise. J Struct Biol 134(2):204–218

    Article  CAS  PubMed  Google Scholar 

  • Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232(2):584–599

    Article  CAS  PubMed  Google Scholar 

  • Rost B, Sander C (1994) Conservation and prediction of solvent accessibility in protein families. Proteins 20(3):216–226

    Article  CAS  PubMed  Google Scholar 

  • Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5(4):725–738

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schaarschmidt J, Monastyrskyy B, Kryshtafovych A, Bonvin AMJJ (2018) Assessment of contact predictions in CASP12: co-evolution and deep learning coming of age. Proteins Struct Funct Bioinforma 86:51–66

    Article  CAS  Google Scholar 

  • Schäffer AA et al (2001) Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res 29(14):2994–3005

    Article  PubMed  PubMed Central  Google Scholar 

  • Schlessinger A, Punta M, Rost B (2007) Natively unstructured regions in proteins identified from contact predictions. Bioinforma Oxf Engl 23(18):2376–2384

    Article  CAS  Google Scholar 

  • Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  PubMed  Google Scholar 

  • Seemayer S, Gruber M, Söding J (2014) CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations. Bioinformatics 30(21):3128–3130

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sims GE, Choi I-G, Kim S-H (2005) Protein conformational space in higher order ϕ-Ψ maps. Proc Natl Acad Sci U S A 102(3):618–621

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tegge AN, Wang Z, Eickholt J, Cheng J (2009) NNcon: improved protein contact map prediction using 2D-recursive neural networks. Nucleic Acids Res 37(suppl_2):W515–W518

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • The UniProt Consortium (2016) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45(D1):D158–D169

    Article  CAS  Google Scholar 

  • Thomas H (2005) An amino acid has two sides: a new 2D measure provides a different view of solvent exposure. Proteins Struct Funct Bioinforma 59(1):38–48

    Article  CAS  Google Scholar 

  • Ting D, Wang G, Shapovalov M, Mitra R, Jordan MI, Jr RLD (2010) Neighbor-dependent Ramachandran probability distributions of amino acids developed from a Hierarchical Dirichlet process model. PLoS Comput Biol 6(4):e1000763

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Torrisi M, Kaleel M, Pollastri G (2018) Porter 5: state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes. bioRxiv:289033

    Google Scholar 

  • Vassura M, Margara L, Di Lena P, Medri F, Fariselli P, Casadio R (2008) Reconstruction of 3D structures from protein contact maps. IEEEACM Trans Comput Biol Bioinforma 5(3):357–367

    Article  CAS  Google Scholar 

  • Vassura M et al (2011) Blurring contact maps of thousands of proteins: what we can learn by reconstructing 3D structure. BioData Min 4:1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Vendruscolo M, Kussell E, Domany E (1997) Recovery of protein structure from contact maps. Fold Des 2(5):295–306

    Article  CAS  PubMed  Google Scholar 

  • Vullo A, Walsh I, Pollastri G (2006) A two-stage approach for improved prediction of residue contact maps. BMC Bioinformatics 7:180

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Walsh I, Baù D, Martin AJ, Mooney C, Vullo A, Pollastri G (2009) Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks. BMC Struct Biol 9:5

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Walsh I, Pollastri G, Tosatto SCE (2016) Correct machine learning on protein sequences: a peer-reviewing perspective. Brief Bioinform 17(5):831–840

    Article  CAS  PubMed  Google Scholar 

  • Wang S, Li W, Liu S, Xu J (2016) RaptorX-property: a web server for protein structure property prediction. Nucleic Acids Res 44(W1):W430–W435

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang S, Sun S, Li Z, Zhang R, Xu J (2017) Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput Biol 13(1):e1005324

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Wang S, Sun S, Xu J (2018) Analysis of deep learning methods for blind protein contact prediction in CASP12. Proteins 86(Suppl 1):67–77

    Article  CAS  PubMed  Google Scholar 

  • Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ (2009) Jalview version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics 25(9):1189–1191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wood MJ, Hirst JD (2005) Protein secondary structure prediction with dihedral angles. Proteins Struct Funct Bioinforma 59(3):476–481

    Article  CAS  Google Scholar 

  • Xia L, Pan X-M (2000) New method for accurate prediction of solvent accessibility from protein sequence. Proteins Struct Funct Bioinforma 42(1):1–5

    Google Scholar 

  • Yang Y, Faraggi E, Zhao H, Zhou Y (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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yang Y et al (2016) Sixty-five years of the long march in protein secondary structure prediction: the final stretch? Brief Bioinform 19(3):482–494

    PubMed Central  Google Scholar 

  • Yuan Z (2005) Better prediction of protein contact number using a support vector regression analysis of amino acid sequence. BMC Bioinformatics 6:248

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zemla A (2003) LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res 31(13):3370–3374

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zemla A, Venclovas Č, Fidelis K, Rost B (1999) A modified definition of Sov, a segment-based measure for protein secondary structure prediction assessment. Proteins Struct Funct Bioinforma 34(2):220–223

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, University College Dublin, Dublin, Ireland

    Mirko Torrisi & Gianluca Pollastri

Authors
  1. Mirko Torrisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gianluca Pollastri
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

    Noor Ahmad Shaik

  2. Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia

    Khalid Rehman Hakeem

  3. Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

    Babajan Banaganapalli

  4. Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

    Ramu Elango

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Torrisi, M., Pollastri, G. (2019). Protein Structure Annotations. In: Shaik, N., Hakeem, K., Banaganapalli, B., Elango, R. (eds) Essentials of Bioinformatics, Volume I. Springer, Cham. https://doi.org/10.1007/978-3-030-02634-9_10

Download citation

Publish with us

Policies and ethics

Search

Navigation