Protein structural motifs in prediction and design

Mackenzie, Craig O.; Grigoryan, Gevorg

doi:10.1016/j.sbi.2017.03.012

Cited by 36 publications

(27 citation statements)

References 60 publications

(88 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent methodological developments have shown that mining sequence-structure relationships from the PDB has the potential to improve the efficiency and efficacy of structure-based modeling and design (Debartolo et al, 2012;DeBartolo et al, 2014;Feng and Barth, 2016;Fernandez-Fuentes et al, 2006;Mackenzie and Grigoryan, 2017). It has long been recognized that proteins are composed of recurring structural elements (Jacobs et al, 2016;Vanhee et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1

et al. 2019

Self Cite

View full text Add to dashboard Cite

Graphical AbstractHighlights d Information in tertiary structural motifs is used to score protein complexes d Bcl-2 protein interactions are predicted and designed without atomistic modeling d Motif-based design generates high-affinity peptides quickly and reliably d High-resolution structures show how designs engage Bfl-1 and Mcl-1 SUMMARYUnderstanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%-38% sequence identity to any known native Bcl-2 family protein ligand. Highresolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering.

show abstract

Section: Introductionmentioning

confidence: 99%

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…By harnessing the power of computers, thousands of designs can be generated and analysed in silico at scales beyond minimal and rational design. The most widespread approach is fragmentbased design, which has three aspects: libraries of fragments or motifs are taken from structural databases, algorithms are developed to combine these to assemble target structures, and scoring functions are used to assess both the assembled structures and sequences that best fit onto them (Figure 1) [69][70][71][72]. This is epitomised by the Rosetta suite for computational protein design developed by the Baker group [73].…”

Section: Fragment-based Computational Design Beyond Protein Engineeringmentioning

confidence: 99%

Towards functional de novo designed proteins

Dawson

Rhys

Woolfson

2019

Current Opinion in Chemical Biology

View full text Add to dashboard Cite

Our ability to design completely de novo proteins is improving rapidly. This is true of all three main approaches to de novo protein design, which we define as: minimal, rational and computational design. Together, these have delivered a variety of protein scaffolds characterised to high resolution. This is truly impressive and a major advance from where the field was a decade or so ago. That all said, significant challenges in the field remain. Chief amongst these is the need to deliver functional de novo proteins. Such designs might include selective and/or tight binding of specified small molecules, or the catalysis of entirely new chemical transformations. We argue that, whilst progress is being made, solving such problems will require more than simply adding functional side chains to extant de novo structures. New approaches will be needed to target and build structure, stability and function simultaneously. Moreover, if we are to match the exquisite control and subtlety of natural proteins, design methods will have to incorporate multi-state modelling and dynamics. This will require more than black-box methodology, specifically increased understanding of protein conformational changes and dynamics will be needed.

show abstract

“…The sequence search can be limited strictly by only considering the amino acids observed at that position in homologs of the template sequence [96], or exclude conserved residues entirely and focus on optimization of the areas of the protein that are less conserved. Structural motifs that have been found to be stable [97] in wide variety of contexts may also be used. Alternatively, co-evolving residue pairs outside the main interacting surface may be an indication that long-range electrostatics are important and the scope of the design should be expanded [98].…”

Section: Challenges In Automated Protein Designmentioning

confidence: 99%

“…Almost every scoring function used in popular protein design algorithm, such as those used in RosettaDesign [128,153,171], FoldX [122], or OSPREY [106], falls into this category. Evodesign uses a combination of knowledge-based scores based in amino acid frequency at the corresponding location within structural related protein, machine-learning-based prediction of secondary and tertiary structure, and the physics-based FoldX force field [97,150]. …”

Section: Challenges In Automated Protein Designmentioning

confidence: 99%

Recent advances in automated protein design and its future challenges

Setiawan

Brender

Zhang

2018

Expert Opinion on Drug Discovery

View full text Add to dashboard Cite

Introduction Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

show abstract

Protein structural motifs in prediction and design

Cited by 36 publications

References 60 publications

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1

Towards functional de novo designed proteins

Recent advances in automated protein design and its future challenges

Contact Info

Product

Resources

About