Protein Engineering Design & Selection最新文献

Most proteins are flexible molecules that coexist in an ensemble of several conformations. Point mutations in the amino acid sequence of a protein can trigger structural changes that drive the protein population to a conformation distinct from the native state. Here, we report a protein engineering approach to better understand protein dynamics and ligand binding of the FK506-binding protein 51 (FKBP51), a prospective target for stress-related diseases, metabolic disorders, some types of cancers and chronic pain. By randomizing selected regions of its ligand-binding domain and sorting yeast display libraries expressing these variants, mutants with high affinity to conformation-specific FKBP51 selective ligands were identified. These improved mutants are valuable tools for the discovery of novel selective ligands that preferentially and specifically bind the FKBP51 active site in its open conformation state. Moreover, they will help us understand the conformational dynamics and ligand binding mechanics of the FKBP51 binding pocket.

Selection by phage display is a popular and widely used technique for the discovery of recombinant protein binders from large protein libraries for therapeutic use. The protein library is displayed on the surface of bacteriophages which are amplified using bacteria, preferably Escherichia coli, to enrich binders in several selection rounds. Traditionally, the so-called panning procedure during which the phages are incubated with the target protein, washed and eluted is done manually, limiting the throughput. High-throughput systems with automated panning already in use often require high-priced equipment. Moreover, the bottleneck of the selection process is usually the screening and characterization. Therefore, having a high-throughput panning procedure without a scaled screening platform does not necessarily increase the discovery rate. Here, we present an easy-to-use high-throughput selection system with automated panning using cost-efficient equipment integrated into a workflow with high-throughput sequencing and a tailored screening step using biolayer-interferometry. The workflow has been developed for selections using two recombinant libraries, ADAPT (Albumin-binding domain-derived affinity proteins) and CaRA (Calcium-regulated affinity) and has been evaluated for three new targets. The newly established semi-automated system drastically reduced the hands-on time and increased robustness while the selection outcome, when compared to manual handling, was very similar in deep sequencing analysis and generated binders in the nanomolar affinity range. The developed selection system has shown to be highly versatile and has the potential to be applied to other binding domains for the discovery of new protein binders.

Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.

Monoclonal antibody (mAb) therapies have rapidly become a powerful class of therapeutics with applications covering a diverse range of clinical indications. Though most widely used for the treatment of cancer, mAbs are also playing an increasing role in the defense of viral infections, most recently with palivizumab for prevention and treatment of severe RSV infections in neonatal and pediatric populations. In addition, during the COVID-19 pandemic, mAbs provided a bridge to the rollout of vaccines; however, their continued role as a therapeutic option for those at greatest risk of severe disease has become limited due to the emergence of neutralization resistant Omicron variants. Although there are many techniques for the identification of mAbs, including single B cell cloning and immunization of genetically engineered mice, the low cost, rapid throughput and technological simplicity of antibody phage display has led to its widespread adoption in mAb discovery efforts. Here we used our 27-billion-member naïve single-chain antibody (scFv) phage library to identify a panel of neutralizing anti-SARS-CoV-2 scFvs targeting diverse epitopes on the receptor binding domain (RBD). Although typically a routine process, we found that upon conversion to IgG, a number of our most potent clones failed to maintain their neutralization potency. Kinetic measurements confirmed similar affinity to the RBD; however, mechanistic studies provide evidence that the loss of neutralization is a result of structural limitations likely arising from initial choice of panning antigen. Thus this work highlights a risk of scFv-phage panning to mAb conversion and the importance of initial antigen selection.

Enzymatic degradation of plastics is currently limited to the use of engineered natural enzymes. As of yet, all engineering approaches applied to plastic degrading enzymes retain the natural $alpha /beta $-fold. While mutations can be used to increase thermostability, an inherent maximum likely exists for the $alpha /beta $-fold. It is thus of interest to introduce catalytic activity toward plastics in a different protein fold to escape the sequence space of plastic degrading enzymes. Here, a method for designing highly thermostable enzymes that can degrade plastics is described. With the help of Rosetta an active site catalysing the hydrolysis of polycarbonate is introduced into a set of thermostable scaffolds. Through computational evaluation, a potential PCase was selected and produced recombinantly in Escherichia coli. Thermal analysis suggests that the design has a melting temperature of >95$^{circ }$C. Activity toward polycarbonate was confirmed using atomic force spectroscopy (AFM), proving the successful design of a PCase.

Numerous technologies are currently in development for use in next-generation protein sequencing platforms. A notable published approach employs fluorescently-tagged binding proteins to identity the N-terminus of immobilized peptides, in-between rounds of digestion. This approach makes use of N-terminal amino acid binder (NAAB) proteins, which would identify amino acids by chemical and shape complementarity. One source of NAABs is the ClpS protein family, which serve to recruit proteins to bacterial proteosomes based on the identity of the N-terminal amino acid. In this study, a Thermosynechococcus vestitus (also known as Thermosynechococcus elongatus) ClpS2 protein was used as the starting point for direct evolution of an NAAB with affinity and specificity for N-terminal leucine. Enriched variants were analyzed and shown to improve the interaction between the ClpS surface and the peptide chain, without increasing promiscuity. Interestingly, interactions were found that were unanticipated which favor different charged residues located at position 5 from the N-terminus of a target peptide.

Enzyme design is an important application of computational protein design (CPD). It can benefit enormously from the additional chemistries provided by noncanonical amino acids (ncAAs). These can be incorporated into an 'expanded' genetic code, and introduced in vivo into target proteins. The key step for genetic code expansion is to engineer an aminoacyl-transfer RNA (tRNA) synthetase (aaRS) and an associated tRNA that handles the ncAA. Experimental directed evolution has been successfully used to engineer aaRSs and incorporate over 200 ncAAs into expanded codes. But directed evolution has severe limits, and is not yet applicable to noncanonical AA backbones. CPD can help address several of its limitations, and has begun to be applied to this problem. We review efforts to redesign aaRSs, studies that designed new proteins and functionalities with the help of ncAAs, and some of the method developments that have been used, such as adaptive landscape flattening Monte Carlo, which allows an enzyme to be redesigned with substrate or transition state binding as the design target.

After approximately 60 years of work, the protein folding problem has recently seen rapid advancement thanks to the inventions of AlphaFold and RoseTTAFold, which are machine-learning algorithms capable of reliably predicting protein structures from their sequences. A key component in their success was the inclusion of pairwise interaction information between residues. As research focus shifts towards developing algorithms to design and engineer binding proteins, it is likely that knowledge of interaction features at protein interfaces can improve predictions. Here, 574 protein complexes were analyzed to identify the stability features of their pairwise interactions, revealing that interactions between pre-stabilized residues are a selected feature in protein binding interfaces. In a retrospective analysis of 475 de novo designed binding proteins with an experimental success rate of 19%, inclusion of pairwise interaction pre-stabilization parameters increased the frequency of identifying experimentally successful binders to 40%.

Display technologies are powerful tools for discovering binding proteins against a broad range of biological targets. However, it remains challenging to adapt display technologies for the discovery of proteins that inhibit the enzymatic activities of targets. Here, we investigate approaches for discovering and characterizing inhibitory antibodies in yeast display format using a well-defined series of constructs and the target matrix metalloproteinase-9. Three previously reported antibodies were used to create model libraries consisting of inhibitory, non-inhibitory, and non-binding constructs. Conditions that preferentially enrich for inhibitory clones were identified for both magnetic bead-based enrichments and fluorescence-activated cell sorting. Half maximal inhibitory concentration (IC50) was obtained through yeast titration assays. The IC50 of the inhibitory antibody obtained in yeast display format falls within the confidence interval of the IC50 value determined in soluble form. Overall, this study identifies strategies for the discovery and characterization of inhibitory clones directly in yeast display format.

Computational protein design promises the ability to build tailor-made proteins de novo. While a range of de novo proteins have been constructed so far, the majority of these designs have idealized topologies that lack larger cavities which are necessary for the incorporation of small molecule binding sites or enzymatic functions. One attractive target for enzyme design is the TIM-barrel fold, due to its ubiquity in nature and capability to host versatile functions. With the successful de novo design of a 4-fold symmetric TIM barrel, sTIM11, an idealized, minimalistic scaffold was created. In this work, we attempted to extend this de novo TIM barrel by incorporating a helix-loop-helix motif into its βα-loops by applying a physics-based modular design approach using Rosetta. Further diversification was performed by exploiting the symmetry of the scaffold to integrate two helix-loop-helix motifs into the scaffold. Analysis with AlphaFold2 and biochemical characterization demonstrate the formation of additional α-helical secondary structure elements supporting the successful extension as intended.