Beyond glycan barriers: non-cognate ligands and protein mimicry approaches to elicit broadly neutralizing antibodies for HIV-1.

Human immunodeficiency virus type 1 (HIV-1) vaccine immunogens capable of inducing broadly neutralizing antibodies (bNAbs) remain obscure. HIV-1 evades immune responses through enormous diversity and hides its conserved vulnerable epitopes on the envelope glycoprotein (Env) by displaying an extensive immunodominant glycan shield. In elite HIV-1 viremic controllers, glycan-dependent bNAbs targeting conserved Env epitopes have been isolated and are utilized as vaccine design templates. However, immunological tolerance mechanisms limit the development of these antibodies in the general population. The well characterized bNAbs monoclonal variants frequently exhibit extensive levels of somatic hypermutation, a long third heavy chain complementary determining region, or a short third light chain complementarity determining region, and some exhibit poly-reactivity to autoantigens. This review elaborates on the obstacles to engaging and manipulating the Env glycoprotein as an effective immunogen and describes an alternative reverse vaccinology approach to develop a novel category of bNAb-epitope-derived non-cognate immunogens for HIV-1 vaccine design.

Predicting mutant outcome by combining deep mutational scanning and machine learning.

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted "targets": (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding ß1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment-the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.

Alpha repeat proteins (αRep) as expression and crystallization helpers.

We have previously described a highly diverse library of artificial repeat proteins based on thermostable HEAT-like repeats, named αRep. αReps binding specifically to proteins difficult to crystallize have been selected and in several examples, they made possible the crystallization of these proteins. To further simplify the production and crystallization experiments we have explored the production of chimeric proteins corresponding to covalent association between the targets and their specific binders strengthened by a linker. Although chimeric proteins with expression partners are classically used to enhance expression, these fusions cannot usually be used for crystallization. With specific expression partners like a cognate αRep this is no longer true, and chimeric proteins can be expressed purified and crystallized. αRep selection by phage display suppose that at least a small amount of the target protein should be produced to be used as a bait for selection and this might, in some cases, be difficult. We have therefore transferred the αRep library in a new construction adapted to selection by protein complementation assay (PCA). This new procedure allows to select specific binders by direct interaction with the target in the cytoplasm of the bacteria and consequently does not require preliminary purification of target protein. αRep binders selected by PCA or by phage display can be used to enhance expression, stability, solubility and crystallogenesis of proteins that are otherwise difficult to express, purify and/or crystallize.

One step DNA assembly for combinatorial metabolic engineering.

The rapid and efficient assembly of multi-step metabolic pathways for generating microbial strains with desirable phenotypes is a critical procedure for metabolic engineering, and remains a significant challenge in synthetic biology. Although several DNA assembly methods have been developed and applied for metabolic pathway engineering, many of them are limited by their suitability for combinatorial pathway assembly. The introduction of transcriptional (promoters), translational (ribosome binding site (RBS)) and enzyme (mutant genes) variability to modulate pathway expression levels is essential for generating balanced metabolic pathways and maximizing the productivity of a strain. We report a novel, highly reliable and rapid single strand assembly (SSA) method for pathway engineering. The method was successfully optimized and applied to create constructs containing promoter, RBS and/or mutant enzyme libraries. To demonstrate its efficiency and reliability, the method was applied to fine-tune multi-gene pathways. Two promoter libraries were simultaneously introduced in front of two target genes, enabling orthogonal expression as demonstrated by principal component analysis. This shows that SSA will increase our ability to tune multi-gene pathways at all control levels for the biotechnological production of complex metabolites, achievable through the combinatorial modulation of transcription, translation and enzyme activity.

Synthesis cost-optimal targeted mutant protein libraries.

Protein variant libraries produced by site-directed mutagenesis are a useful tool utilized by protein engineers to explore variants with potentially improved properties, such as activity and stability. These libraries are commonly built by selecting residue positions and alternative beneficial mutations for each position. All possible combinations are then constructed and screened, by incorporating degenerate codons at mutation sites. These degenerate codons often encode additional unwanted amino acids or even STOP codons. Our study aims to take advantage of annealing based recombination of oligonucleotides during synthesis and utilize multiple degenerate codons per mutation site to produce targeted protein libraries devoid of unwanted variants. Toward this goal we created an algorithm to calculate the minimum number of degenerate codons necessary to specify any given amino acid set, and a dynamic programming method that uses this algorithm to optimally partition a DNA target sequence with degeneracies into overlapping oligonucleotides, such that the total cost of synthesis of the target mutant protein library is minimized. Computational experiments show that, for a modest increase in DNA synthesis costs, beneficial variant yields in produced mutant libraries are increased by orders of magnitude, an effect particularly pronounced in large combinatorial libraries.

The importance of residue-level filtering and the Top2018 best-parts dataset of high-quality protein residues.

We have curated a high-quality, "best-parts" reference dataset of about 3 million protein residues in about 15,000 PDB-format coordinate files, each containing only residues with good electron density support for a physically acceptable model conformation. The resulting prefiltered data typically contain the entire core of each chain, in quite long continuous fragments. Each reference file is a single protein chain, and the total set of files were selected for low redundancy, high resolution, good MolProbity score, and other chain-level criteria. Then each residue was critically tested for adequate local map quality to firmly support its conformation, which must also be free of serious clashes or covalent-geometry outliers. The resulting Top2018 prefiltered datasets have been released on the Zenodo online web service and are freely available for all uses under a Creative Commons license. Currently, one dataset is residue filtered on main chain plus Cß atoms, and a second dataset is full-residue filtered; each is available at four different sequence-identity levels. Here, we illustrate both statistics and examples that show the beneficial consequences of residue-level filtering. That process is necessary because even the best of structures contain a few highly disordered local regions with poor density and low-confidence conformations that should not be included in reference data. Therefore, the open distribution of these very large, prefiltered reference datasets constitutes a notable advance for structural bioinformatics and the fields that depend upon it.

Combinatorial Approaches for Efficient Design of Photoswitchable Protein-Protein Interactions as In Vivo Actuators.

Light switchable two-component protein dimerization systems offer versatile manipulation and dissection of cellular events in living systems. Over the past 20 years, the field has been driven by the discovery of photoreceptor-based interaction systems, the engineering of light-actuatable binder proteins, and the development of photoactivatable compounds as dimerization inducers. This perspective is to categorize mechanisms and design approaches of these dimerization systems, compare their advantages and limitations, and bridge them to emerging applications. Our goal is to identify new opportunities in combinatorial protein design that can address current engineering challenges and expand in vivo applications.

Proteins mimicking epitope of HIV-1 virus neutralizing antibody induce virus-neutralizing sera in mice.

BACKGROUND: The development of an effective vaccine preventing HIV-1 infection is hindered by the enormous antigenic variability and unique biochemical and immunological properties of HIV-1 Env glycoprotein, the most promising target for HIV-1 neutralizing antibody. Functional studies of rare elite neutralizers led to the discovery of broadly neutralizing antibodies. METHODS: We employed a highly complex combinatorial protein library derived from a 5 kDa albumin-binding domain scaffold, fused with support protein of total 38 kDa, to screen for binders of broadly neutralizing antibody VRC01 paratope. The most specific binders were used for immunization of experimental mice to elicit Env-specific antibodies and to test their neutralization activity using a panel of HIV-1 clade C and B pseudoviruses. FINDINGS: Three most specific binders designated as VRA017, VRA019, and VRA177 exhibited high specificity to VRC01 antibody. Immunized mice produced Env-binding antibodies which neutralize eight of twelve HIV-1 Tier 2 pseudoviruses. Molecular modelling revealed a shape complementarity between VRA proteins and a part of VRC01 gp120 interacting surface. INTERPRETATION: This strategy based on the identification of protein replicas of broadly neutralizing antibody paratope represents a novel approach in HIV-1 vaccine development. This approach is not affected by low immunogenicity of neutralization-sensitive epitopes, variability, and unique biochemical properties of HIV-1 Env used as a crucial antigen in the majority of contemporary tested vaccines. FUND: Czech Health Research Council 15-32198A, Ministry of Health, Czech Republic.

Shuffle Optimizer: A Program to Optimize DNA Shuffling for Protein Engineering.

DNA shuffling is a powerful tool to develop libraries of variants for protein engineering. Here, we present a protocol to use our freely available and easy-to-use computer program, Shuffle Optimizer. Shuffle Optimizer is written in the Python computer language and increases the nucleotide homology between two pieces of DNA desired to be shuffled together without changing the amino acid sequence. In addition we also include sections on optimal primer design for DNA shuffling and library construction, a small-volume ultrasonicator method to create sheared DNA, and finally a method to reassemble the sheared fragments and recover and clone the library. The Shuffle Optimizer program and these protocols will be useful to anyone desiring to perform any of the nucleotide homology-dependent shuffling methods.

A Structure-Based Design Protocol for Optimizing Combinatorial Protein Libraries.

Protein variant libraries created via site-directed mutagenesis are a powerful approach to engineer improved proteins for numerous applications such as altering enzyme substrate specificity. Conventional libraries commonly use a brute force approach: saturation mutagenesis via degenerate codons that encode all 20 natural amino acids. In contrast, this chapter describes a protocol for designing "smarter" degenerate codon libraries via direct combinatorial optimization in "library space." Several case studies illustrate how it is possible to design degenerate codon libraries that are highly enriched for favorable, low-energy sequences as assessed using a standard all-atom scoring function. There is much to gain for experimental protein engineering laboratories willing to think beyond site saturation mutagenesis. In the common case that the exact experimental screening budget is not fixed, it is particularly helpful to perform a Pareto analysis to inspect favorable libraries at a range of possible library sizes.

Engineering growth factors for regenerative medicine applications.

Growth factors are important morphogenetic proteins that instruct cell behavior and guide tissue repair and renewal. Although their therapeutic potential holds great promise in regenerative medicine applications, translation of growth factors into clinical treatments has been hindered by limitations including poor protein stability, low recombinant expression yield, and suboptimal efficacy. This review highlights current tools, technologies, and approaches to design integrated and effective growth factor-based therapies for regenerative medicine applications. The first section describes rational and combinatorial protein engineering approaches that have been utilized to improve growth factor stability, expression yield, biodistribution, and serum half-life, or alter their cell trafficking behavior or receptor binding affinity. The second section highlights elegant biomaterial-based systems, inspired by the natural extracellular matrix milieu, that have been developed for effective spatial and temporal delivery of growth factors to cell surface receptors. Although appearing distinct, these two approaches are highly complementary and involve principles of molecular design and engineering to be considered in parallel when developing optimal materials for clinical applications. STATEMENT OF SIGNIFICANCE: Growth factors are promising therapeutic proteins that have the ability to modulate morphogenetic behaviors, including cell survival, proliferation, migration and differentiation. However, the translation of growth factors into clinical therapies has been hindered by properties such as poor protein stability, low recombinant expression yield, and non-physiological delivery, which lead to suboptimal efficacy and adverse side effects. To address these needs, researchers are employing clever molecular and material engineering and design strategies to both improve the intrinsic properties of growth factors and effectively control their delivery into tissue. This review highlights examples of interdisciplinary tools and technologies used to augment the therapeutic potential of growth factors for clinical applications in regenerative medicine.