Computational Intelligence Tools for Protein Modeling

Protein modeling plays a quite important role in computational aid drug discovery and designing. Advancements in computational tools make easy to model a protein structure from a large amount of sequence data. Protein modeling and structural prediction speed up the drug designing and synthesizing processes. Computational approaches help in the study of biophysiochemical properties. These tools identify the structure and the function of the proteins from the sequence comparison and the relationship with other proteins in the database. Now a days, with the improvements in tools, the protein secondary structures can also be predicated from sequences alone. This make a major breakthrough in protein modeling process. Similar sequenced proteins have similar secondary structure, which can perform similar task. From this, same drug can be used for similar structured proteins. This review paper provides a comprehensive detail to different protein modeling tools.

[1]  R. Samudrala,et al.  De Novo Protein Structure Prediction , 2007 .

[2]  D. Baker,et al.  Improved recognition of native‐like protein structures using a combination of sequence‐dependent and sequence‐independent features of proteins , 1999, Proteins.

[3]  T L Blundell,et al.  An evaluation of the performance of an automated procedure for comparative modelling of protein tertiary structure. , 1993, Protein engineering.

[4]  Torsten Schwede,et al.  BIOINFORMATICS Bioinformatics Advance Access published November 12, 2005 The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling , 2022 .

[5]  G. Vriend,et al.  Prediction of protein conformational freedom from distance constraints , 1997, Proteins.

[6]  Torsten Schwede,et al.  The SWISS-MODEL Repository and associated resources , 2008, Nucleic Acids Res..

[7]  C. Anfinsen,et al.  The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain. , 1961, Proceedings of the National Academy of Sciences of the United States of America.

[8]  A. Sali,et al.  Modeling of loops in protein structures , 2000, Protein science : a publication of the Protein Society.

[9]  A. Sali,et al.  Comparative protein structure modeling of genes and genomes. , 2000, Annual review of biophysics and biomolecular structure.

[10]  Claudio N. Cavasotto,et al.  Homology modeling in drug discovery: current trends and applications. , 2009, Drug discovery today.

[11]  Liam J McGuffin,et al.  Assembling novel protein folds from super‐secondary structural fragments , 2003, Proteins.

[12]  E. Koonin,et al.  The structure of the protein universe and genome evolution , 2002, Nature.

[13]  Lars Malmström,et al.  Automated prediction of CASP‐5 structures using the Robetta server , 2003, Proteins.

[14]  D. Baker,et al.  Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[15]  G J Williams,et al.  The Protein Data Bank: a computer-based archival file for macromolecular structures. , 1977, Journal of molecular biology.

[16]  Torsten Schwede,et al.  Automated comparative protein structure modeling with SWISS‐MODEL and Swiss‐PdbViewer: A historical perspective , 2009, Electrophoresis.

[17]  Ben M. Webb,et al.  Comparative Protein Structure Modeling Using MODELLER , 2007, Current protocols in protein science.

[18]  J Lundström,et al.  Pcons: A neural‐network–based consensus predictor that improves fold recognition , 2001, Protein science : a publication of the Protein Society.

[19]  David T. Jones Successful ab initio prediction of the tertiary structure of NK‐lysin using multiple sequences and recognized supersecondary structural motifs , 1997, Proteins.

[20]  Marco Biasini,et al.  SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information , 2014, Nucleic Acids Res..

[21]  Richard Bonneau,et al.  Rosetta in CASP4: Progress in ab initio protein structure prediction , 2001, Proteins.

[22]  Tim J. P. Hubbard,et al.  SCOP database in 2004: refinements integrate structure and sequence family data , 2004, Nucleic Acids Res..

[23]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[24]  M. Karplus,et al.  Effective energy function for proteins in solution , 1999, Proteins.

[25]  Paula Herber,et al.  Verification of Embedded Real-time Systems , 2015, SyDe Summer School.

[26]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[27]  Richard Bonneau,et al.  Ab initio protein structure prediction of CASP III targets using ROSETTA , 1999, Proteins.

[28]  David C. Jones Predicting novel protein folds by using FRAGFOLD , 2001, Proteins.

[29]  A G Murzin,et al.  SCOP: a structural classification of proteins database for the investigation of sequences and structures. , 1995, Journal of molecular biology.

[30]  Edward W. Lowe,et al.  Computational Methods in Drug Discovery , 2014, Pharmacological Reviews.

[31]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[32]  C Kooperberg,et al.  Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. , 1997, Journal of molecular biology.