Dawn of a New Era for Membrane Protein Design

Generative Landscapes and Dynamics to Design Multidomain Artificial Transmembrane Transporters

Protein design is challenging as it requires simultaneous consideration of interconnected factors, such as fold, dynamics, and function. These evolutionary constraints are encoded in protein sequences and can be learned through the latent generative landscape (LGL) framework to predict functional sequences by leveraging evolutionary patterns, enabling exploration of uncharted sequence space. By simulating designed proteins through molecular dynamics (MD), we gain deeper insights into the interdependencies governing structure and dynamics. We present a synergized workflow combining LGL with MD and biochemical characterization, allowing us to explore the sequence space effectively. This approach has been applied to design and characterize two artificial multidomain ATP-driven transmembrane copper transporters, with native-like functionality. This integrative approach proved effective in unraveling the intricate relationships between sequence, structure, and function.

Advances in the understanding of the production, modification and applications of xylanases in the food industry

Xylanases have broad applications in the food industry to decompose the complex carbohydrate xylan. This is applicable to enhance juice clarity, improve dough softness, or reduce beer turbidity. It can also be used to produce prebiotics and increase the nutritional value in foodstuff. However, the low yield and poor stability of most natural xylanases hinders their further applications. Therefore, it is imperative to explore higher-quality xylanases to address the potential challenges that appear in the food industry and to comprehensively improve the production, modification, and utilization of xylanases. Xylanases, due to their various sources, exhibit diverse characteristics that affect production and activity. Most fungi are suitable for solid-state fermentation to produce xylanases, but in liquid fermentation, microbial metabolism is more vigorous, resulting in higher yield. Fungi produce higher xylanase activity, but bacterial xylanases perform better than fungal ones under certain extreme conditions (high temperature, extreme pH). Gene and protein engineering technology helps to improve the production efficiency of xylanases and enhances their thermal stability and catalytic properties.

Progress and limitations in engineering cellular adhesion for research and therapeutics

Intercellular interactions form the cornerstone of multicellular biology. Despite advances in protein engineering, researchers artificially directing physical cell interactions still rely on endogenous cell adhesion molecules (CAMs) alongside off-target interactions and unintended signaling. Recently, methods for directing cellular interactions have been developed utilizing programmable domains such as coiled coils (CCs), nanobody-antigen, and single-stranded DNA (ssDNA). We first discuss desirable molecular- and systems-level properties in engineered CAMs, using the helixCAM platform as a benchmark. Next, we propose applications for engineered CAMs in immunology, developmental biology, tissue engineering, and neuroscience. Biologists in various fields can readily adapt current engineered CAMs to establish control over cell interactions, and their utilization in basic and translational research will incentivize further expansion in engineered CAM capabilities.

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels

Recent deep-learning (DL) protein design methods have been successfully applied to a range of protein design problems, including the de novo design of novel folds, protein binders, and enzymes. However, DL methods have yet to meet the challenge of de novo membrane protein (MP) and the design of complex β-sheet folds. We performed a comprehensive benchmark of one DL protein sequence design method, ProteinMPNN, using transmembrane and water-soluble β-barrel folds as a model, and compared the performance of ProteinMPNN to the new membrane-specific Rosetta Franklin2023 energy function. We tested the effect of input backbone refinement on ProteinMPNN performance and found that given refined and well-defined inputs, ProteinMPNN more accurately captures global sequence properties despite complex folding biophysics. It generates more diverse TMB sequences than Franklin2023 in pore-facing positions. In addition, ProteinMPNN generated TMB sequences that passed state-of-the-art in silico filters for experimental validation, suggesting that the model could be used in de novo design tasks of diverse nanopores for single-molecule sensing and sequencing. Lastly, our results indicate that the low success rate of ProteinMPNN for the design of β-sheet proteins stems from backbone input accuracy rather than software limitations.

Discovery and Visualization of the Hidden Relationships among N-Glycosylation, Disulfide Bonds, and Membrane Topology

Membrane proteins (MPs) are functionally important but structurally complex. In particular, MPs often carry three structural features, i.e., transmembrane domains (TMs), disulfide bonds (SSs), and N-glycosylation (N-GLYCO). All three features have been intensively studied; however, how the three features potentially correlate has been less addressed in the literature. With the growing accuracy from computational prediction, we used publicly available information on SSs and N-GLYCO and analyzed the potential relationships among post-translational modifications (PTMs) and the predicted membrane topology in the human proteome. Our results suggested a very close relationship between SSs and N-GLYCO that behaved similarly, whereas a complementary relation between the TMs and the two PTMs was also revealed, in which the high SS and/or N-GLYCO presence is often accompanied by a low TM occurrence in a protein. Furthermore, the occurrence of SSs and N-GLYCO in a protein heavily relies on the protein length; however, TMs seem not to possess such length dependence. Finally, SSs exhibits larger potential dynamics than N-GLYCO, which is confined by the presence of sequons. The special classes of proteins possessing extreme or unique patterns of the three structural features are comprehensively identified, and their structural features and potential dynamics help to identify their susceptibility to different physiological and pathophysiological insults, which could help drug development and protein engineering.

Protein Fusion Strategies for Membrane Protein Stabilization and Crystal Structure Determination

Crystal structures of membrane proteins are highly desired for their use in the mechanistic understanding of their functions and the designing of new drugs. However, obtaining the membrane protein structures is difficult. One way to overcome this challenge is with protein fusion methods, which have been successfully used to determine the structures of many membrane proteins, including receptors, enzymes and adhesion molecules. Existing fusion strategies can be categorized into the N or C terminal fusion, the insertion fusion and the termini restraining. The fusions facilitate protein expression, purification, crystallization and phase determination. Successful applications often require further optimization of protein fusion linkers and interactions, whose design can be facilitated by a shared helix strategy and by AlphaFold prediction in the future.