Protein Engineering Design & Selection最新文献

Recent developments in cancer immunotherapy have highlighted the potential of harnessing natural killer (NK) cells in the treatment of neoplastic malignancies. Of these, bispecific antibodies, and NK cell engager (NKCE) protein therapeutics in particular, have been of interest. Here, we used phage display and yeast surface display to engineer RLN131, a unique cross-reactive antibody that binds to human, mouse, and cynomolgus NKp46, an activating receptor found on NK cells. RLN131 induced proliferation and activation of primary NK cells, and was used to create bispecific NKCE constructs of varying configurations and valency. All NKCEs were able to promote greater NK cell cytotoxicity against tumor cells than an unmodified anti-CD20 monoclonal antibody, and activity was observed irrespective of whether the constructs contained a functional Fc domain. Competition binding and fine epitope mapping studies were used to demonstrate that RLN131 binds to a conserved epitope on NKp46, underlying its species cross-reactivity.

Many proteins do not fold into a fixed three-dimensional structure, but rather function in a highly disordered state. These intrinsically disordered proteins pose a unique challenge to protein engineering and design: How can proteins be designed de novo if not by tailoring their structure? Here, we will review the nascent field of design of intrinsically disordered proteins with focus on applications in biotechnology and medicine. The design goals should not necessarily be the same as for de novo design of folded proteins as disordered proteins have unique functional strengths and limitations. We focus on functions where intrinsically disordered proteins are uniquely suited including disordered linkers, desiccation chaperones, sensors of the chemical environment, delivery of pharmaceuticals, and constituents of biomolecular condensates. Design of functional intrinsically disordered proteins relies on a combination of computational tools and heuristics gleaned from sequence-function studies. There are few cases where intrinsically disordered proteins have made it into industrial applications. However, we argue that disordered proteins can perform many roles currently performed by organic polymers, and that these proteins might be more designable due to their modularity.

Proline-rich antimicrobial peptides (PrAMPs) are attractive antibiotic candidates that target gram-negative bacteria ribosomes. We elucidated the sequence-function landscape of 43 000 variants of a recently discovered family member, Tur1a, using the validated SAMP-Dep platform that measures intracellular AMP potency in a high-throughput manner via self-depletion of the cellular host. The platform exhibited high replicate reproducibility (ρ = 0.81) and correlation between synonymous genetic variants (R2 = 0.93). Only two segments within Tur1a exhibited stringent mutational requirements to sustain potency: residues 9YLP11 and 19FP20. This includes the aromatic residue in the hypothesized binding domain but not the PRP domain. Along with unexpected mutational tolerance of PRP, the data contrast hypothesized importance of the 1RRIR4 motif and arginines in general. In addition to mutational tolerance of residue segments with presumed significance, 77% of mutations are functionally neutral. Multimutant performance mainly shows compounding effects from removed combinations of prolines and arginines in addition to the two segments of residues showing individual importance. Several variants identified as active from SAMP-Dep were externally produced and maintained activity when applied to susceptible species exogenously.

Protein developability is requisite for use in therapeutic, diagnostic, or industrial applications. Many developability assays are low throughput, which limits their utility to the later stages of protein discovery and evolution. Recent approaches enable experimental or computational assessment of many more variants, yet the breadth of applicability across protein families and developability metrics is uncertain. Here, three library-scale assays-on-yeast protease, split green fluorescent protein (GFP), and non-specific binding-were evaluated for their ability to predict two key developability outcomes (thermal stability and recombinant expression) for the small protein scaffolds affibody and fibronectin. The assays' predictive capabilities were assessed via both linear correlation and machine learning models trained on the library-scale assay data. The on-yeast protease assay is highly predictive of thermal stability for both scaffolds, and the split-GFP assay is informative of affibody thermal stability and expression. The library-scale data was used to map sequence-developability landscapes for affibody and fibronectin binding paratopes, which guides future design of variants and libraries.

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

Antibody discovery processes are continually advancing, with an ever-increasing number of potential binding sequences being identified out of in vivo, in vitro, and in silico sources. In this work we describe a rapid system for high yield recombinant antibody (IgG and Fab) expression using Gibson assembled linear DNA fragments (GLFs). The purified recombinant antibody yields from 1 ml expression for this process are approximately five to ten-fold higher than previous methods, largely due to novel usage of protecting flanking sequences on the 5' and 3' ends of the GLF. This method is adaptable for small scale (1 ml) expression and purification for rapid evaluation of binding and activity, in addition to larger scales (30 ml) for more sensitive assays requiring milligram quantities of antibody purified over two columns (Protein A and size exclusion chromatography). When compared to plasmid-based expression, these methods provide nearly equivalent yield of high-quality material across multiple applications, allowing for reduced costs and turnaround times to enhance the antibody discovery process.

We developed fluorescent protein sensors for nicotine with improved sensitivity. For iNicSnFR12 at pH 7.4, the proportionality constant for ∆F/F0vs [nicotine] (δ-slope, 2.7 μM-1) is 6.1-fold higher than the previously reported iNicSnFR3a. The activated state of iNicSnFR12 has a fluorescence quantum yield of at least 0.6. We measured similar dose-response relations for the nicotine-induced absorbance increase and fluorescence increase, suggesting that the absorbance increase leads to the fluorescence increase via the previously described nicotine-induced conformational change, the 'candle snuffer' mechanism. Molecular dynamics (MD) simulations identified a binding pose for nicotine, previously indeterminate from experimental data. MD simulations also showed that Helix 4 of the periplasmic binding protein (PBP) domain appears tilted in iNicSnFR12 relative to iNicSnFR3a, likely altering allosteric network(s) that link the ligand binding site to the fluorophore. In thermal melt experiments, nicotine stabilized the PBP of the tested iNicSnFR variants. iNicSnFR12 resolved nicotine in diluted mouse and human serum at 100 nM, the peak [nicotine] that occurs during smoking or vaping, and possibly at the decreasing levels during intervals between sessions. NicSnFR12 was also partially activated by unidentified endogenous ligand(s) in biofluids. Improved iNicSnFR12 variants could become the molecular sensors in continuous nicotine monitors for animal and human biofluids.

Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for the end user. Deep learning-based methods offer an attractive alternative, outperforming physics-based methods at a significantly lower computational cost. In this paper, we explore the application of Convolutional Neural Networks (CNNs) for sequence design. We describe the development and benchmarking of a range of networks, as well as reimplementations of previously described CNNs. We demonstrate the flexibility of representing proteins in a three-dimensional voxel grid by encoding additional design constraints into the input data. Finally, we describe TIMED-Design, a web application and command line tool for exploring and applying the models described in this paper. The user interface will be available at the URL: https://pragmaticproteindesign.bio.ed.ac.uk/timed. The source code for TIMED-Design is available at https://github.com/wells-wood-research/timed-design.