The Stability Landscape of de novo TIM Barrels Explored by a Modular Design Approach

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

Molecular handcraft of a well-folded protein chimera

Modular assembly is a compelling pathway to create new proteins, a concept supported by protein engineering and millennia of evolution. Natural evolution provided a repository of building blocks, known as domains, which trace back to even shorter segments that underwent numerous 'copy-paste' processes culminating in the scaffolds we see today. Utilizing the subdomain-database Fuzzle, we constructed a fold-chimera by integrating a flavodoxin-like fragment into a periplasmic binding protein. This chimera is well-folded and a crystal structure reveals stable interfaces between the fragments. These findings demonstrate the adaptability of α/β-proteins and offer a stepping stone for optimization. By emphasizing the practicality of fragment databases, our work pioneers new pathways in protein engineering. Ultimately, the results substantiate the conjecture that periplasmic binding proteins originated from a flavodoxin-like ancestor.

Diversifying de novo TIM barrels by hallucination

De novo protein design expands the protein universe by creating new sequences to accomplish tailor-made enzymes in the future. A promising topology to implement diverse enzyme functions is the ubiquitous TIM-barrel fold. Since the initial de novo design of an idealized four-fold symmetric TIM barrel, the family of de novo TIM barrels is expanding rapidly. Despite this and in contrast to natural TIM barrels, these novel proteins lack cavities and structural elements essential for the incorporation of binding sites or enzymatic functions. In this work, we diversified a de novo TIM barrel by extending multiple βα-loops using constrained hallucination. Experimentally tested designs were found to be soluble upon expression in Escherichia coli and well-behaved. Biochemical characterization and crystal structures revealed successful extensions with defined α-helical structures. These diversified de novo TIM barrels provide a framework to explore a broad spectrum of functions based on the potential of natural TIM barrels.

Hydrophobic Clusters Regulate Surface Hydration Dynamics of Bacillus subtilis Lipase A

The surface hydration diffusivity of Bacillus subtilis Lipase A (BSLA) has been characterized by low-field Overhauser dynamic nuclear polarization (ODNP) relaxometry using a series of spin-labeled constructs. Sites for spin-label incorporation were previously designed via an atomistic computational approach that screened for surface exposure, reflective of the surface hydration comparable to other proteins studied by this method, as well as minimal impact on protein function, dynamics, and structure of BSLA by excluding any surface site that participated in greater than 30% occupancy of a hydrogen bonding network within BSLA. Experimental ODNP relaxometry coupling factor results verify the overall surface hydration behavior for these BSLA spin-labeled sites similar to other globular proteins. Here, by plotting the ODNP parameters of relative diffusive water versus the relative bound water, we introduce an effective "phase-space" analysis, which provides a facile visual comparison of the ODNP parameters of various biomolecular systems studied to date. We find notable differences when comparing BSLA to other systems, as well as when comparing different clusters on the surface of BSLA. Specifically, we find a grouping of sites that correspond to the spin-label surface location within the two main hydrophobic core clusters of the branched aliphatic amino acids isoleucine, leucine, and valine cores observed in the BSLA crystal structure. The results imply that hydrophobic clustering may dictate local surface hydration properties, perhaps through modulation of protein conformations and samplings of the unfolded states, providing insights into how the dynamics of the hydration shell is coupled to protein motion and fluctuations.

Stepwise introduction of stabilizing mutations reveals nonlinear additive effects in de novo TIM barrels

Over the past decades, the TIM-barrel fold has served as a model system for the exploration of how changes in protein sequences affect their structural, stability, and functional characteristics, and moreover, how this information can be leveraged to design proteins from the ground up. After numerous attempts to design de novo proteins with this specific fold, sTIM11 was the first validated de novo design of an idealized four-fold symmetric TIM barrel. Subsequent efforts to enhance the stability of this initial design resulted in the development of DeNovoTIMs, a family of de novo TIM barrels with various stabilizing mutations. In this study, we present an investigation into the biophysical and thermodynamic effects upon introducing a varying number of stabilizing mutations per quarter along the sequence of a four-fold symmetric TIM barrel. We compared the base design DeNovoTIM0 without any stabilizing mutations with variants containing mutations in one, two, three, and all four quarters-designated TIM1q, TIM2q, TIM3q, and DeNovoTIM6, respectively. This analysis revealed a stepwise and nonlinear change in the thermodynamic properties that correlated with the number of mutated quarters, suggesting positive nonadditive effects. To shed light on the significance of the location of stabilized quarters, we engineered two variants of TIM2q which contain the same number of mutations but positioned in different quarter locations. Characterization of these TIM2q variants revealed that the mutations exhibit varying effects on the overall protein stability, contingent upon the specific region in which they are introduced. These findings emphasize that the amount and location of stabilized interfaces among the four quarters play a crucial role in shaping the conformational stability of these four-fold symmetric TIM barrels. Analysis of de novo proteins, as described in this study, enhances our understanding of how sequence variations can finely modulate stability in both naturally occurring and computationally designed proteins.

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing

Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-art deep-learning-based methods cannot provide interpretability from an energy perspective, affecting the accuracy of the design. Here we propose a new systematic approach, including both a posterior probability and a joint probability parts, to solve the two essential questions once for all. This approach takes the physicochemical property of amino acids into consideration and uses the joint probability model to ensure the convergence between structure and amino acid type. Our results demonstrated that this method could generate feasible, high-confidence sequences with low-energy side conformations. The designed sequences can fold into target structures with high confidence and maintain relatively stable biochemical properties. The side chain conformation has a significantly lower energy landscape without delegating to a rotamer library or performing the expensive conformational searches. Overall, we propose an end-to-end method that combines the advantages of both deep learning and energy-based methods. The design results of this model demonstrate high efficiency, and precision, as well as a low energy state and good interpretability.

Template-free prediction of a new monotopic membrane protein fold and assembly by AlphaFold2

AlphaFold2 (AF2) has revolutionized the field of protein structural prediction. Here, we test its ability to predict the tertiary and quaternary structure of a previously undescribed scaffold with new folds and unusual architecture, the monotopic membrane protein caveolin-1 (CAV1). CAV1 assembles into a disc-shaped oligomer composed of 11 symmetrically arranged protomers, each assuming an identical new fold, and contains the largest parallel β-barrel known to exist in nature. Remarkably, AF2 predicts both the fold of the protomers and the interfaces between them. It also assembles between seven and 15 copies of CAV1 into disc-shaped complexes. However, the predicted multimers are energetically strained, especially the parallel β-barrel. These findings highlight the ability of AF2 to correctly predict new protein folds and oligomeric assemblies at a granular level while missing some elements of higher-order complexes, thus positing a new direction for the continued development of deep-learning protein structure prediction approaches.

Anti-human immunodeficiency virus-1 activity of MoMo30 protein isolated from the traditional African medicinal plant Momordica balsamina

BACKGROUND

Plants are used in traditional healing practices of many cultures worldwide. Momordica balsamina is a plant commonly used by traditional African healers as a part of a treatment for HIV/AIDS. It is typically given as a tea to patients with HIV/AIDS. Water-soluble extracts of this plant were found to contain anti-HIV activity.

METHODS

We employed cell-based infectivity assays, surface plasmon resonance, and a molecular-cell model of the gp120-CD4 interaction to study the mechanism of action of the MoMo30-plant protein. Using Edman degradation results of the 15 N-terminal amino acids, we determined the gene sequence of the MoMo30-plant protein from an RNAseq library from total RNA extracted from Momordica balsamina.

RESULTS

Here, we identify the active ingredient of water extracts of the leaves of Momordica balsamina as a 30 kDa protein we call MoMo30-plant. We have identified the gene for MoMo30 and found it is homologous to a group of plant lectins known as Hevamine A-like proteins. MoMo30-plant is distinct from other proteins previously reported agents from the Momordica species, such as ribosome-inactivating proteins such as MAP30 and Balsamin. MoMo30-plant binds to gp120 through its glycan groups and functions as a lectin or carbohydrate-binding agent (CBA). It inhibits HIV-1 at nanomolar levels and has minimal cellular toxicity at inhibitory levels.

CONCLUSIONS

CBAs like MoMo30 can bind to glycans on the surface of the enveloped glycoprotein of HIV (gp120) and block entry. Exposure to CBAs has two effects on the virus. First, it blocks infection of susceptible cells. Secondly, MoMo30 drives the selection of viruses with altered glycosylation patterns, potentially altering their immunogenicity. Such an agent could represent a change in the treatment strategy for HIV/AIDS that allows a rapid reduction in viral loads while selecting for an underglycosylated virus, potentially facilitating the host immune response.

Design of Artificial Enzymes: Insights into Protein Scaffolds

The design of artificial enzymes has emerged as a promising tool for the generation of potent biocatalysts able to promote new-to-nature reactions with improved catalytic performances, providing a powerful platform for wide-ranging applications and a better understanding of protein functions and structures. The selection of an appropriate protein scaffold plays a key role in the design process. This review aims to give a general overview of the most common protein scaffolds that can be exploited for the generation of artificial enzymes. Several examples are discussed and categorized according to the strategy used for the design of the artificial biocatalyst, namely the functionalization of natural enzymes, the creation of a new catalytic site in a protein scaffold bearing a wide hydrophobic pocket and de novo protein design. The review is concluded by a comparison of these different methods and by our perspective on the topic.

Physics-based approach to extend a de novo TIM barrel with rationally designed helix-loop-helix motifs

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.

De Novo Design of a Highly Stable Ovoid TIM Barrel: Unlocking Pocket Shape towards Functional Design

The ability to finely control the structure of protein folds is an important prerequisite to functional protein design. The TIM barrel fold is an important target for these efforts as it is highly enriched for diverse functions in nature. Although a TIM barrel protein has been designed de novo, the ability to finely alter the curvature of the central beta barrel and the overall architecture of the fold remains elusive, limiting its utility for functional design. Here, we report the de novo design of a TIM barrel with ovoid (twofold) symmetry, drawing inspiration from natural beta and TIM barrels with ovoid curvature. We use an autoregressive backbone sampling strategy to implement our hypothesis for elongated barrel curvature, followed by an iterative enrichment sequence design protocol to obtain sequences which yield a high proportion of successfully folding designs. Designed sequences are highly stable and fold to the designed barrel curvature as determined by a 2.1 Å resolution crystal structure. The designs show robustness to drastic mutations, retaining high melting temperatures even when multiple charged residues are buried in the hydrophobic core or when the hydrophobic core is ablated to alanine. As a scaffold with a greater capacity for hosting diverse hydrogen bonding networks and installation of binding pockets or active sites, the ovoid TIM barrel represents a major step towards the de novo design of functional TIM barrels.

ProtGPT2 is a deep unsupervised language model for protein design

Protein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in Transformer-based architectures has enabled the implementation of language models capable of generating text with human-like capabilities. Here, motivated by this success, we describe ProtGPT2, a language model trained on the protein space that generates de novo protein sequences following the principles of natural ones. The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yields well-folded non-idealized structures with embodiments and large loops and reveals topologies not captured in current structure databases. ProtGPT2 generates sequences in a matter of seconds and is freely available.

Protein sequence design with a learned potential

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

Perspective on the Structural Basis for Human Aldo-Keto Reductase 1B10 Inhibition

Human aldo-keto reductase 1B10 (AKR1B10) is overexpressed in many cancer types and is involved in chemoresistance. This makes AKR1B10 to be an interesting drug target and thus many enzyme inhibitors have been investigated. High-resolution crystallographic structures of AKR1B10 with various reversible inhibitors were deeply analyzed and compared to those of analogous complexes with aldose reductase (AR). In both enzymes, the active site included an anion-binding pocket and, in some cases, inhibitor binding caused the opening of a transient specificity pocket. Different structural conformers were revealed upon inhibitor binding, emphasizing the importance of the highly variable loops, which participate in the transient opening of additional binding subpockets. Two key differences between AKR1B10 and AR were observed regarding the role of external loops in inhibitor binding. The first corresponded to the alternative conformation of Trp112 (Trp111 in AR). The second difference dealt with loop A mobility, which defined a larger and more loosely packed subpocket in AKR1B10. From this analysis, the general features that a selective AKR1B10 inhibitor should comply with are the following: an anchoring moiety to the anion-binding pocket, keeping Trp112 in its native conformation (AKR1B10-like), and not opening the specificity pocket in AR.

A newly introduced salt bridge cluster improves structural and biophysical properties of de novo TIM barrels

Protein stability can be fine‐tuned by modifying different structural features such as hydrogen‐bond networks, salt bridges, hydrophobic cores, or disulfide bridges. Among these, stabilization by salt bridges is a major challenge in protein design and engineering since their stabilizing effects show a high dependence on the structural environment in the protein, and therefore are difficult to predict and model. In this work, we explore the effects on structure and stability of an introduced salt bridge cluster in the context of three different de novo TIM barrels. The salt bridge variants exhibit similar thermostability in comparison with their parental designs but important differences in the conformational stability at 25°C can be observed such as a highly stabilizing effect for two of the proteins but a destabilizing effect to the third. Analysis of the formed geometries of the salt bridge cluster in the crystal structures show either highly ordered salt bridge clusters or only single salt bridges. Rosetta modeling of the salt bridge clusters results in a good prediction of the tendency on stability changes but not the geometries observed in the three‐dimensional structures. The results show that despite the similarities in protein fold, the salt bridge clusters differently influence the structural and stability properties of the de novo TIM barrel variants depending on the structural background where they are introduced.

PDB Code(s): 7OSU, 7OT7, 7OSV, 7OT8 and 7P12;