Computational protein design with electrostatic focusing: Experimental characterization of a conditionally folded helical domain with a reduced amino acid alphabet

2013 ◽  
Vol 8 (7) ◽  
pp. 855-864 ◽  
Author(s):  
Maria Suárez-Diez ◽  
Anaïs M. Pujol ◽  
Manolis Matzapetakis ◽  
Alfonso Jaramillo ◽  
Olga Iranzo
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Computational Protein Design ◽  
Experimental Characterization ◽  
Reduced Amino Acid Alphabet ◽  
Helical Domain ◽  
Amino Acid Alphabet

scholarly journals Pushing the limits of automatic computational protein design: design, expression, and characterization of a large synthetic protein based on a fungal laccase scaffold

2011 ◽  
Vol 5 (1-2) ◽  
pp. 45-58 ◽  
Author(s):  
Doris J. Glykys ◽  
Géza R. Szilvay ◽  
Pablo Tortosa ◽  
María Suárez Diez ◽  
Alfonso Jaramillo ◽  
...  
Keyword(s):  
Protein Design ◽  
Computational Protein Design ◽  
Fungal Laccase ◽  
Synthetic Protein

Fast databank searching with a reduced amino-acid alphabet

1994 ◽  
Vol 10 (4) ◽  
pp. 453-454 ◽  
Author(s):  
Claudine Landès ◽  
Jean-Loup Risler
Keyword(s):  
Amino Acid ◽  
Reduced Amino Acid Alphabet ◽  
Amino Acid Alphabet

A structural homology approach for computational protein design with flexible backbone

2018 ◽  
Vol 35 (14) ◽  
pp. 2418-2426 ◽  
Author(s):  
David Simoncini ◽  
Kam Y J Zhang ◽  
Thomas Schiex ◽  
Sophie Barbe
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Protein Sequence ◽  
Critical Role ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Computational Protein Design ◽  
Supplementary Information ◽  
Structural Homology ◽  
Homologous Proteins

Abstract Motivation Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. Results We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%. Availability and implementation Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A method for computing the inter-residue interaction potentials for reduced amino acid alphabet

2007 ◽  
Vol 32 (S1) ◽  
pp. 883-889 ◽  
Author(s):  
Abhinav Luthra ◽  
Anupam Nath Jha ◽  
G. K. Ananthasuresh ◽  
Saraswathi Vishveswara
Keyword(s):  
Amino Acid ◽  
Interaction Potentials ◽  
Reduced Amino Acid Alphabet ◽  
Residue Interaction ◽  
Amino Acid Alphabet

scholarly journals Challenges in the computational design of proteins

2009 ◽  
Vol 6 (suppl_4) ◽  
Author(s):  
María Suárez ◽  
Alfonso Jaramillo
Keyword(s):  
Amino Acid ◽  
Structural Biology ◽  
Protein Design ◽  
Current Knowledge ◽  
Computational Design ◽  
Amino Acid Sequences ◽  
Computational Protein Design ◽  
Energy Functions ◽  
Physical Description ◽  
Atomic Interactions

Protein design has many applications not only in biotechnology but also in basic science. It uses our current knowledge in structural biology to predict, by computer simulations, an amino acid sequence that would produce a protein with targeted properties. As in other examples of synthetic biology, this approach allows the testing of many hypotheses in biology. The recent development of automated computational methods to design proteins has enabled proteins to be designed that are very different from any known ones. Moreover, some of those methods mostly rely on a physical description of atomic interactions, which allows the designed sequences not to be biased towards known proteins. In this paper, we will describe the use of energy functions in computational protein design, the use of atomic models to evaluate the free energy in the unfolded and folded states, the exploration and optimization of amino acid sequences, the problem of negative design and the design of biomolecular function. We will also consider its use together with the experimental techniques such as directed evolution. We will end by discussing the challenges ahead in computational protein design and some of their future applications.

Protein design depends on the size of the amino acid alphabet

2002 ◽  
Vol 66 (3) ◽  
Author(s):  
Robin C. Ball ◽  
Thomas M. A. Fink
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Amino Acid Alphabet

scholarly journals RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou’s five-step rule

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Lei Zheng ◽  
Shenghui Huang ◽  
Nengjiang Mu ◽  
Haoyue Zhang ◽  
Jiayu Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Sequence Analysis ◽  
Protein Sequence ◽  
Web Server ◽  
Protein Sequence Analysis ◽  
Evaluation Indexes ◽  
Reduced Amino Acid Alphabet ◽  
Correlation Parameters ◽  
Amino Acid Alphabet

Abstract By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook. Database URL: http://bioinfor.imu.edu.cn/raacbook

Sign in / Sign up

Export Citation Format

Share Document