Computational design of ligand-binding membrane receptors with high selectivity

Feng, Xinwei; Ambía, Joaquín; Chen, Kuang-Yui Michael; Young, M. E.; Barth, Patrick

doi:10.1038/nchembio.2371

Cited by 36 publications

(27 citation statements)

References 49 publications

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“…A remaining challenge is to create tools for membrane proteins (3): a class of molecules that constitute over 30% of all proteins (4) and are targets for 60% of pharmaceuticals (5). There have been several achievements in membrane protein design including a zinc-transporting tetramer Rocker (6), an ion-conducting protein based on the Escherichia Coli Wza transporter (7), β-barrel pores with increased selectivity (8), receptors with new ligand-binding properties (9,10), and designed de novo α-helical bundles that insert into the membrane (11). For these advances, design of lipid-facing positions often used a native sequence or restricted the chemistry and/or size of amino acids.…”

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confidence: 99%

Protein structure prediction and design in a biologically-realistic implicit membrane

Alford

Fleming

et al. 2019

Preprint

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Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets including prediction of protein orientations in the bilayer, ∆∆G calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Further, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.membrane proteins | lipid composition | energy function | structure prediction | protein design

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confidence: 99%

Protein structure prediction and design in a biologically-realistic implicit membrane

Alford

Fleming

et al. 2019

Preprint

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“…Thankfully, methods for multistate design are being developed [91][92][93][94]. For instance, Grigoryan et al use such an approach to design leucine zippers that selectively bind a single partner from 20 members of the bZIP family by modelling potential offtarget interactions as part of the design process [95]; Löffler et al engineer a (ba) 8 -barrel into a retro-aldolase with measurable, albeit low, catalytic efficiency [94]; and Feng et al use conformation ensembles to engineer ligand-binding G-protein-coupled receptors [96 ].…”

Section: Challenges Aheadmentioning

confidence: 99%

Towards functional de novo designed proteins

Dawson

Rhys

Woolfson

2019

Current Opinion in Chemical Biology

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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.

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“…engineering) of a GPCR protein, which shows how a protein design algorithm may guide the alteration of transmembrane proteins affinity toward a certain ligand [89]. However, due to the complexity of transmembrane proteins as mentioned before, the design procedure was limited to rely mostly on homology modeling and ligand docking [90]. …”

Section: The Scope Of Automated Protein Designmentioning

confidence: 99%

Recent advances in automated protein design and its future challenges

Setiawan

Brender

Zhang

2018

Expert Opinion on Drug Discovery

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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.

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Computational design of ligand-binding membrane receptors with high selectivity

Cited by 36 publications

References 49 publications

Protein structure prediction and design in a biologically-realistic implicit membrane

Protein structure prediction and design in a biologically-realistic implicit membrane

Towards functional de novo designed proteins

Recent advances in automated protein design and its future challenges

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