Protein Engineering Design & Selection最新文献

The Fv region of the antibody (comprising VH and VL domains) is the area responsible for target binding and thus the antibody's specificity. The orientation, or packing, of these two domains relative to each other influences the topography of the Fv region, and therefore can influence the antibody's binding affinity. We present abYpap, an improved method for predicting the packing angle between the VH and VL domains. With the large data set now available, we were able to expand greatly the number of features that could be used compared with our previous work. The machine-learning model was tuned for improved performance using 37 selected residues (previously 13) and also by including the lengths of the most variable 'complementarity determining regions' (CDR-L1, CDR-L2 and CDR-H3). Our method shows large improvements from the previous version, and also against other modeling approaches, when predicting the packing angle.

Lignocellulosic biomass is a renewable source of energy, chemicals and materials. Many applications of this resource require the depolymerization of one or more of its polymeric constituents. Efficient enzymatic depolymerization of cellulose to glucose by cellulases and accessory enzymes such as lytic polysaccharide monooxygenases is a prerequisite for economically viable exploitation of this biomass. Microbes produce a remarkably diverse range of cellulases, which consist of glycoside hydrolase (GH) catalytic domains and, although not in all cases, substrate-binding carbohydrate-binding modules (CBMs). As enzymes are a considerable cost factor, there is great interest in finding or engineering improved and robust cellulases, with higher activity and stability, easy expression, and minimal product inhibition. This review addresses relevant engineering targets for cellulases, discusses a few notable cellulase engineering studies of the past decades and provides an overview of recent work in the field.

Oxidoreductases catalyze essential redox reactions, and many require a diffusible cofactor for electron transport, such as NAD(H). Non-canonical cofactor analogs have been explored as a means to create enzymatic reactions that operate orthogonally to existing metabolism. Here, we aimed to engineer the formate dehydrogenase from Candid boidinii (CbFDH) for activity with the non-canonical cofactor nicotinamide adenine dinucleotide 3'-phosphate (3'-NADP(H)). We used PyRosetta, the Cofactor Specificity Reversal Structural Analysis and Library Design (CSR-SALAD), and structure-guided saturation mutagenesis to identify mutations that enable CbFDH to use 3'-NADP+. Two single mutants, D195A and D195G, had the highest activities with 3'-NADP+, while the double mutant D195G/Y196S exhibited the highest cofactor selectivity reversal behavior. Steady state kinetic analyses were performed; the D195A mutant exhibited the highest KTS value with 3'-NADP+. This work compares the utility of computational approaches for cofactor specificity engineering while demonstrating the engineering of an important enzyme for novel non-canonical cofactor selectivity.

Self-supervised pretraining on protein sequences has led to state-of-the art performance on protein function and fitness prediction. However, sequence-only methods ignore the rich information contained in experimental and predicted protein structures. Meanwhile, inverse folding methods reconstruct a protein's amino-acid sequence given its structure, but do not take advantage of sequences that do not have known structures. In this study, we train a masked inverse folding protein masked language model parameterized as a structured graph neural network. During pretraining, this model learns to reconstruct corrupted sequences conditioned on the backbone structure. We then show that using the outputs from a pretrained sequence-only protein masked language model as input to the inverse folding model further improves pretraining perplexity. We evaluate both of these models on downstream protein engineering tasks and analyze the effect of using information from experimental or predicted structures on performance.

Organophosphorus (OP) pesticides are still widely applied but pose a severe toxicological threat if misused. For in vivo detoxification, the application of hydrolytic enzymes potentially offers a promising treatment. A well-studied example is the phosphotriesterase of Brevundimonas diminuta (BdPTE). Whereas wild-type BdPTE can hydrolyse pesticides like paraoxon, chlorpyrifos-oxon and mevinphos with high catalytic efficiencies, kcat/KM >2 × 107 M-1 min-1, degradation of malaoxon is unsatisfactory (kcat/KM ≈ 1 × 104 M-1 min-1). Here, we report the rational engineering of BdPTE mutants with improved properties and their efficient production in Escherichia coli. As result, the mutant BdPTE(VRNVVLARY) exhibits 37-fold faster malaoxon hydrolysis (kcat/KM = 4.6 × 105 M-1 min-1), together with enhanced expression yield, improved thermal stability and reduced susceptibility to oxidation. Therefore, this BdPTE mutant constitutes a powerful candidate to develop a biocatalytic antidote for the detoxification of this common pesticide metabolite as well as related OP compounds.

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.

Biocatalysis has become a powerful alternative for green chemistry. Expanding the range of amino acids used in protein biosynthesis can improve industrially appealing properties such as enantioselectivity, activity and stability. This review will specifically delve into the thermal stability improvements that non-canonical amino acids (ncAAs) can confer to enzymes. Methods to achieve this end, such as the use of halogenated ncAAs, selective immobilization and rational design, will be discussed. Additionally, specific enzyme design considerations using ncAAs are discussed along with the benefits and limitations of the various approaches available to enhance the thermal stability of enzymes.

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.