Improving computational protein design by using structure-derived sequence profile

By Liang Dai, Yuedong Yang, Hyung Rae Kim, Yaoqi Zhou. 2010

25 citations recorded by the source corpus.

Designing a protein sequence that will fold into a predefined structure is of both practical and fundamental interest. Many successful, computational designs in the last decade resulted from improved understanding of hydrophobic and polar interactions between side chains of amino acid residues in stabilizing protein tertiary structures. However, the coupling between main‐chain backbone structure and local sequence has yet to be fully addressed. Here, we attempt to account for such coupling by using a sequence profile derived from the sequences of five residue fragments in a fragment library that are structurally matched to the five‐residue segments contained in a target structure. We further introduced a term to reduce low complexity regions of designed sequences. These two terms together with optimized reference states for amino‐acid residues were implemented in the RosettaDesign program. The new method, called RosettaDesign‐SR, makes a 12% increase (from 34 to 46%) in fraction of proteins whose designed sequences are more than 35% identical to wild‐type sequences. Meanwhile, it reduces 8% (from 22% to 14%) to the number of designed sequences that are not homologous to any known protein sequences according to psi‐blast. More importantly, the sequences designed by RosettaDesign‐SR have 2–3% more polar residues at the surface and core regions of proteins and these surface and core polar residues have about 4% higher sequence identity to wild‐type sequences than by RosettaDesign. Thus, the proteins designed by RosettaDesign‐SR should be less likely to aggregate and more likely to have unique structures due to more specific polar interactions. Proteins 2010. © 2010 Wiley‐Liss, Inc.

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. Yuedong Yang
2. Kuldip K. Paliwal
3. Yaoqi Zhou
4. Jack Hanson
5. Zhixiu Li
6. Rhys Heffernan
7. Jian Zhan
8. Liang Dai

In Molecular Biology

1. Kuldip K. Paliwal
2. Yaoqi Zhou
3. Jack Hanson
4. Yuedong Yang
5. Rhys Heffernan
6. Zhixiu Li
7. Jian Zhan
8. Liang Dai

Citations in the corpus

• ProDCoNN: Protein design using a convolutional neural network (2019, background): “23 The average sequence identity compare to wild-type sequence is 35% over 100 sequences designed for each protein for 33 training proteins.”
• SPIN2: Predicting sequence profiles from protein structures using deep neural networks (2018, background): “12 As deep learning methods allow for a high level of feature abstraction, adding new features allows the network to explore the deep hidden relationships between our features.”
• Energy functions in de novo protein design: current challenges and future prospects. (2013, methodology): “…energy terms is suggested from the success of employing SVM-trained scoring functions to separate binding from nonbinding designed interfaces (35) and of balancing local and nonlocal interactions to achieve higher recovery of native sequence, secondary structure, and solvent accessibility (19).”