Highly active enzymes by automated combinatorial backbone assembly and sequence design

Lapidoth, Gideon; Khersonsky, Olga; Lipsh, Rosalie; Dym, Orly; Albeck, Shira; Rogotner, S.; Fleishman, Sarel J.

doi:10.1038/s41467-018-05205-5

Cited by 50 publications

(81 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We recently showed that evolution-guided atomistic design calculations, which use phylogenetic analysis to guide atomistic design calculations 61 , enabled the automated, accurate and effective design of large and topologically complex soluble proteins. Designed proteins exhibited atomic accuracy, high expression levels, stability 54,55 , binding affinity, specificity 59 , and catalytic efficiency 57,58 . Membrane proteins are typically large and challenging targets for conventional protein-engineering and design methods.…”

Section: Discussionmentioning

confidence: 99%

“…Note that as observed in studies of mutational effects on stability in soluble proteins, the correlation coefficient between computed and observed values was low (Pearson r 2 =0.21 and 0.02 for ref2015_memb and RosettaMembrane, respectively) [51][52][53][54] . Such low correlation coefficients provide an impetus for improving the energy function; however, as we previously demonstrated, discriminating stabilizing from destabilizing mutations is sufficient to enable the design of accurate, stable, and functionally efficient proteins [54][55][56][57][58][59] . We next tested sequence-recovery rates using combinatorial sequence optimisation based on ref2015, ref2015_memb, and RosettaMembrane in a benchmark of 20 non-redundant structures (<80% sequence identity) ranging in size from 124-765 amino acids 60 .…”

Section: Ab Initio Structure Prediction In Membrane Proteinsmentioning

confidence: 93%

See 1 more Smart Citation

A lipophilicity-based energy function for membrane-protein modelling and design

Weinstein

Elazar

Fleishman

2019

Preprint

View full text Add to dashboard Cite

Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs. Design of topologically more complex proteins, such as most receptors, channels, and transporters, however, demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions. Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks. The plasma membrane, however, imposes different physical constraints on protein solvation. To understand these constraints, we recently developed a high-throughput experimental screen, called dsTβL, and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane. Here, we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks. Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2.5Å root-mean-square deviation within the top-predicted five models. Furthermore, in two sequence-design benchmarks, the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure, thereby recommending this new energy function for membrane-protein modelling and design.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Ab Initio Structure Prediction In Membrane Proteinsmentioning

confidence: 93%

A lipophilicity-based energy function for membrane-protein modelling and design

Weinstein

Elazar

Fleishman

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…1b). To account for this rugged energy landscape, protein designers have developed a variety of methods for performing backbone sampling along with sequence optimization 102 . One approach that has worked well when designing de novo proteins is to iterate between rotamer-based sequence optimization and gradient-based minimization of torsion angles (backbone and side chain) 72,77 (see also Box 1).…”

Section: Optimizing the Protein Sequencementioning

confidence: 99%

Advances in protein structure prediction and design

Kuhlman

Bradley

2019

Nat Rev Mol Cell Biol

618

433

View full text Add to dashboard Cite

The stunning diversity of molecular functions performed by naturally evolved proteins is made possible by their finely tuned three-dimensional structures, which are in turn determined by their genetically encoded amino acid sequences. A predictive understanding of the relationship between amino acid sequence and protein structure would therefore open up new avenues, both for the prediction of function from genome sequence data and also for the rational engineering of novel protein functions through the design of amino acid sequences with specific structures. The past decade has seen dramatic improvements in our ability to predict and design the three-dimensional structures of proteins, with potentially far-reaching implications for medicine and our understanding of biology. New machine-learning algorithms have been developed that analyse the patterns of correlated mutations in protein families, to predict structurally interacting residues from sequence information alone 1,2. Improved protein energy functions 3,4 have for the first time made it possible to start with an approximate structure prediction model and move it closer to the experimentally determined structure by an energy-guided refinement process 5,6. Advances in protein conformational sampling and sequence optimization have permitted the design of novel protein structures and complexes 7,8 , some of which show promise as therapeutics 9. These advances in protein structure prediction and design have been fuelled by technological breakthroughs as well as by a rapid growth in biological databases. Protein-modelling algorithms (Box 1) are computationally demanding both to develop and to apply. The rapid increase in computing power available to researchers (both CPU-based and, increasingly, GPU-based computing power) facilitates rapid benchmarking of new algorithms and enables their application to larger molecules and molecular assemblies. At the same time, next-generation sequencing has fuelled a dramatic increase in protein sequence databases as genomic and metagenomic sequencing efforts have expanded 10. Advances in software and automation have increased the pace of experimental structure determination, speeding the growth of the database of experimentally determined protein structures (the Protein Data Bank (PDB)) 11 , which now contains close to 150,000 macromolecular structures. Deep-learning algorithms 12 that have revolutionized image processing and speech recognition are now being adopted by protein modellers seeking to take advantage of these expanded sequence and structural databases. In this Review, we highlight a selection of recent breakthroughs that these technological advances have enabled. We describe current approaches to the prediction and design of protein structures, focusing primarily on template-free methods that do not require an

show abstract

“…However, the quality values (C-score = 0; TM = 0.71 ± 0.11; RMSD = 6.8 ± 4.1 Å) obtained during this second modelling round suggested some important structural differences with those PDB identified as structural neighbours. Curiously, 6FHF is an unusual GH10 sequence, because it was designed by using rational protein design approaches, and generated by automated combinatorial backbone assembly and sequence design 67 . This observation supports the importance of using metagenomic approaches for the discovery of enzymes with unusual sequences.…”

Section: D Modelling Analysis Of Glycosyl Hydrolasesmentioning

confidence: 99%

Neotropical termite microbiomes as sources of novel plant cell wall degrading enzymes

Victorica

Soria

Batista-García

et al. 2020

Sci Rep

View full text Add to dashboard Cite

In this study, we used shotgun metagenomic sequencing to characterise the microbial metabolic potential for lignocellulose transformation in the gut of two colonies of Argentine higher termite species with different feeding habits, Cortaritermes fulviceps and Nasutitermes aquilinus. Our goal was to assess the microbial community compositions and metabolic capacity, and to identify genes involved in lignocellulose degradation. Individuals from both termite species contained the same five dominant bacterial phyla (Spirochaetes, Firmicutes, Proteobacteria, Fibrobacteres and Bacteroidetes) although with different relative abundances. However, detected functional capacity varied, with C. fulviceps (a grass-wood-feeder) gut microbiome samples containing more genes related to amino acid metabolism, whereas N. aquilinus (a wood-feeder) gut microbiome samples were enriched in genes involved in carbohydrate metabolism and cellulose degradation. The C. fulviceps gut microbiome was enriched specifically in genes coding for debranching-and oligosaccharide-degrading enzymes. These findings suggest an association between the primary food source and the predicted categories of the enzymes present in the gut microbiomes of each species. To further investigate the termite microbiomes as sources of biotechnologically relevant glycosyl hydrolases, a putative GH10 endo-β-1,4xylanase, Xyl10E, was cloned and expressed in Escherichia coli. Functional analysis of the recombinant metagenome-derived enzyme showed high specificity towards beechwood xylan (288.1 IU/mg), with the optimum activity at 50 °C and a pH-activity range from 5 to 10. These characteristics suggest that Xy110E may be a promising candidate for further development in lignocellulose deconstruction applications.

show abstract

Highly active enzymes by automated combinatorial backbone assembly and sequence design

Cited by 50 publications

References 79 publications

A lipophilicity-based energy function for membrane-protein modelling and design

A lipophilicity-based energy function for membrane-protein modelling and design

Advances in protein structure prediction and design

Neotropical termite microbiomes as sources of novel plant cell wall degrading enzymes

Contact Info

Product

Resources

About