Assessing and enhancing foldability in designed proteins

htFuncLib: Designing Libraries of Active-site Multipoint Mutants for Protein Optimization

Protein function relies on accurate and densely packed constellations of amino acids within the active site. The high density in the active site optimizes activity but reduces tolerance to mutations, thereby frustrating efforts to engineer or design new or dramatically improved activity. Introducing new activities may therefore require simultaneous multipoint mutations. Still, in a phenomenon known as epistasis, the outcome of combinations of mutations can differ significantly-and even reverse-the impact of the individual mutations, limiting predictability. To address these challenges we previously developed FuncLib, a method for the computational design of multipoint mutants in active sites. We recently extended FuncLib to enable the design of large combinatorial mutation libraries for high-throughput screening in a method called htFuncLib that generates compatible sets of mutations likely to yield functional multipoint mutants. htFuncLib enables scalable library design and experimental screening of hundreds and up to millions of active-site variants. This approach has generated thousands of active enzymes and fluorescent proteins with diverse functional properties. We have updated the FuncLib web server (https://FuncLib.weizmann.ac.il/) to support htFuncLib and introduced an electronic notebook (https://github.com/Fleishman-Lab/htFuncLib-web-server) for customizable library design, making those tools easily accessible for protein engineering and design. The new FuncLib web server enables reliable and scalable design of function for low-, medium- and high-throughput experiments through a single computational platform. We envision that this server will accelerate the optimization and discovery of function in enzymes, antibodies, and other proteins.

Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering

Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.

Addressing epistasis in the design of protein function

Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence- and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity.

Computer-driven Evolution of Myrosinase from the Cabbage Aphid for Efficient Production of (R)-Sulforaphane

Myrosinase (Myr) catalyzes the hydrolysis of glucosinolates, yielding biologically active metabolites. In this study, glucoraphanin (GRA) extracted from broccoli seeds was effectively hydrolyzed using a Myr-obtained cabbage aphid (Brevicoryne brassicae) (BbMyr) to produce (R)-sulforaphane (SFN). The gene encoding BbMyr was successfully heterologously expressed in Escherichia coli, resulting in the production of 1.6 g/L (R)-SFN, with a remarkable yield of 20.8 mg/gbroccoli seeds, achieved using recombination E. coli whole-cell catalysis under optimal conditions (pH 4.5, 45 °C). Subsequently, BbMyr underwent combinatorial simulation-driven mutagenesis, yielding a mutant, DE9 (N321D/Y426S), showing a remarkable 2.91-fold increase in the catalytic efficiency (kcat/KM) compared with the original enzyme. Molecular dynamics simulations demonstrated that the N321D mutation in loopA of mutant DE9 enhanced loopA stability by inducing favorable alterations in hydrogen bonds, while the Y426S mutation in loopB decreased spatial resistance. This research lays a foundation for the environmentally sustainable enzymatic (R)-SFN synthesis.

Engineered Zea mays phenylalanine ammonia-lyase for improve the catalytic efficiency of biosynthesis trans-cinnamic acid and p-coumaric acid

Phenylalanine ammonia-lyase (PAL) plays a pivotal role in the biosynthesis of phenylalanine. PAL from Zea mays (ZmPAL2) exhibits a bi-function of direct deamination of L-phenylalanine (L-Phe) or L-tyrosine(-L-Tyr) to form trans-cinnamic acid or p-coumaric acid. trans-Cinnamic acid and p-coumaric acid are mainly used in flavors and fragrances, food additives, pharmaceutical and other fields. Here, the Activity of ZmPAL2 toward L-Phe or L-Tyr was improved by using semi-rational and rational designs. The catalytic efficiency (kcat/Km) of mutant PT10 (V258I/I459V/Q484N) against L-Phe was 30.8 μM-1 s-1, a 4.5-fold increase compared to the parent, and the catalytic efficiency of mutant PA1 (F135H/I459L) to L-tyrosine exhibited 8.6 μM-1 s-1, which was 1.6-fold of the parent. The yield of trans-cinnamic acid in PT10 reached 30.75 g/L with a conversion rate of 98%. Meanwhile, PA1 converted L-Tyr to yield 3.12 g/L of p-coumaric acid with a conversion rate of 95%. Suggesting these two engineered ZmPAL2 to be valuable biocatalysts for the synthesis of trans-cinnamic acid and p-coumaric acid. In addition, MD simulations revealed that the underlying mechanisms of the increased catalytic efficiency of both mutant PT10 and PA1 are attributed to the substrate remaining stable within the pocket and closer to the catalytically active site. This also provides a new perspective on engineered PAL.

Enhancing the Catalytic Efficiency of D-lactonohydrolase through the Synergy of Tunnel Engineering, Evolutionary Analysis, and Force-Field Calculations

Computational design advances enzyme evolution and their use in biocatalysis in a faster and more efficient manner. In this study, a synergistic approach integrating tunnel engineering, evolutionary analysis, and force-field calculations has been employed to enhance the catalytic activity of D-lactonohydrolase (D-Lac), which is a pivotal enzyme involved in the resolution of racemic pantolactone during the production of vitamin B5. The best mutant, N96S/A271E/F274Y/F308G (M3), was obtained and its catalytic efficiency (kcat/KM) was nearly 23-fold higher than that of the wild-type. The M3 whole-cell converted 20 % of DL-pantolactone into D-pantoic acid (D-PA, >99 % e.e.) with a conversion rate of 47 % and space-time yield of 107.1 g L-1 h-1, demonstrating its great potential for industrial-scale D-pantothenic acid production. Molecular dynamics (MD) simulations revealed that the reduction in the steric hindrance within the substrate tunnel and conformational reconstruction of the distal loop resulted in a more favourable"catalytic" conformation, making it easier for the substrate and enzyme to enter their pre-reaction state. This study illustrates the potential of the distal residue on the pivotal loop at the entrance of the D-Lac substrate tunnel as a novel modification hotspot capable of reshaping energy patterns and consequently influencing the enzymatic activity.

De novo design of knotted tandem repeat proteins

De novo protein design methods can create proteins with folds not yet seen in nature. These methods largely focus on optimizing the compatibility between the designed sequence and the intended conformation, without explicit consideration of protein folding pathways. Deeply knotted proteins, whose topologies may introduce substantial barriers to folding, thus represent an interesting test case for protein design. Here we report our attempts to design proteins with trefoil (31) and pentafoil (51) knotted topologies. We extended previously described algorithms for tandem repeat protein design in order to construct deeply knotted backbones and matching designed repeat sequences (N = 3 repeats for the trefoil and N = 5 for the pentafoil). We confirmed the intended conformation for the trefoil design by X ray crystallography, and we report here on this protein's structure, stability, and folding behaviour. The pentafoil design misfolded into an asymmetric structure (despite a 5-fold symmetric sequence); two of the four repeat-repeat units matched the designed backbone while the other two diverged to form local contacts, leading to a trefoil rather than pentafoil knotted topology. Our results also provide insights into the folding of knotted proteins.

De Novo Design of Polyhedral Protein Assemblies: Before and After the AI Revolution

Self-assembling polyhedral protein biomaterials have gained attention as engineering targets owing to their naturally evolved sophisticated functions, ranging from protecting macromolecules from the environment to spatially controlling biochemical reactions. Precise computational design of de novo protein polyhedra is possible through two main types of approaches: methods from first principles, using physical and geometrical rules, and more recent data-driven methods based on artificial intelligence (AI), including deep learning (DL). Here, we retrospect first principle- and AI-based approaches for designing finite polyhedral protein assemblies, as well as advances in the structure prediction of such assemblies. We further highlight the possible applications of these materials and explore how the presented approaches can be combined to overcome current challenges and to advance the design of functional protein-based biomaterials.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Combinatorial assembly and design of enzymes

The design of structurally diverse enzymes is constrained by long-range interactions that are necessary for accurate folding. We introduce an atomistic and machine learning strategy for the combinatorial assembly and design of enzymes (CADENZ) to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a 10-fold improved hit rate and more than 10,000 recovered enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.

What Have We Learned from Design of Function in Large Proteins?

The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.