Prediction of amino acid sequence from structure

By K. Raha, Andrew M. Wollacott, Michael J. Italia, J. Desjarlais. 2000

93 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. N. V. Dokholyan
2. Feng Ding
3. I. Samish
4. Chun-chao Lo
5. Xiuwen Liu
6. Wei Wu
7. Jinfeng Zhang
8. Yang Chen

In Molecular Biology

1. I. Samish
2. N. V. Dokholyan
3. Feng Ding
4. Chun-chao Lo
5. Xiuwen Liu
6. Wei Wu
7. Jinfeng Zhang
8. Yang Chen

Citations in the corpus

• ProDCoNN: Protein design using a convolutional neural network (2019, background): “16 The agreement between the predicted sequences and the native sequence of each backbone template ranges from 24% to 28% on four protein superfamily motifs: SH3 domain, the homeodomain (HM), the fibronectin type III (FNIII) domain, and the RNA recognition motif (RRM).”
• Emergence of Protein Fold Families through Rational Design (2006, background): “The native sequence recapitulation rate is an indicator of the performance of a protein design approach [21,32,42].”
• Achievements and Challenges in Computational Protein Design. (2017, methodology): “The CPD was conducted via the Repacking of Core (ROC) program, later developed to their Sequence Prediction Algorithm (SPA) [128].”
• A de novo redesign of the WW domain (2003, methodology): “We report here the automated design of a -sheet protein devoid of ligands and cross-links through the implementation of a sequence prediction algorithm, or SPA (Raha et al. 2000), coupled with a novel sampling procedure that integrates information from an ensemble of backbone structures.”
• RosettaAntibodyDesign (RAbD): A general framework for computational antibody design (2017, unknown): “A common method for computational benchmarking of protein design methods is the use of the concept of sequence recovery [72].”