Tertiary motifs as building blocks for the design of protein‐binding peptides

Glutamine-derived peptides: Current progress and future directions

Glutamine, the most abundant amino acid in the body, plays a critical role in preserving immune function, nitrogen balance, intestinal integrity, and resistance to infection. However, its limited solubility and instability present challenges for its use a functional nutrient. Consequently, there is a preference for utilizing glutamine-derived peptides as an alternative to achieve enhanced functionality. This article aims to review the applications of glutamine monomers in clinical, sports, and enteral nutrition. It compares the functional effectiveness of monomers and glutamine-derived peptides and provides a comprehensive assessment of glutamine-derived peptides in terms of their classification, preparation, mechanism of absorption, and biological activity. Furthermore, this study explores the potential integration of artificial intelligence (AI)-based peptidomics and synthetic biology in the de novo design and large-scale production of these peptides. The findings reveal that glutamine-derived peptides possess significant structure-related bioactivities, with the smaller molecular weight fraction serving as the primary active ingredient. These peptides possess the ability to promote intestinal homeostasis, exert hypotensive and hypoglycemic effects, and display antioxidant properties. However, our understanding of the structure-function relationships of glutamine-derived peptides remains largely exploratory at current stage. The combination of AI based peptidomics and synthetic biology presents an opportunity to explore the untapped resources of glutamine-derived peptides as functional food ingredients. Additionally, the utilization and bioavailability of these peptides can be enhanced through the use of delivery systems in vivo. This review serves as a valuable reference for future investigations of and developments in the discovery, functional validation, and biomanufacturing of glutamine-derived peptides in food science.

De novo design of stable proteins that efficaciously inhibit oncogenic G proteins

Many protein therapeutics are competitive inhibitors that function by binding to endogenous proteins and preventing them from interacting with native partners. One effective strategy for engineering competitive inhibitors is to graft structural motifs from a native partner into a host protein. Here, we develop and experimentally test a computational protocol for embedding binding motifs in de novo designed proteins. The protocol uses an "inside-out" approach: Starting with a structural model of the binding motif docked against the target protein, the de novo protein is built by growing new structural elements off the termini of the binding motif. During backbone assembly, a score function favors backbones that introduce new tertiary contacts within the designed protein and do not introduce clashes with the target binding partner. Final sequences are designed and optimized using the molecular modeling program Rosetta. To test our protocol, we designed small helical proteins to inhibit the interaction between Gαq and its effector PLC-β isozymes. Several of the designed proteins remain folded above 90°C and bind to Gαq with equilibrium dissociation constants tighter than 80 nM. In cellular assays with oncogenic variants of Gαq , the designed proteins inhibit activation of PLC-β isozymes and Dbl-family RhoGEFs. Our results demonstrate that computational protein design, in combination with motif grafting, can be used to directly generate potent inhibitors without further optimization via high throughput screening or selection.

Atligator Web: A Graphical User Interface for Analysis and Design of Protein-Peptide Interactions

A key functionality of proteins is based on their ability to form interactions with other proteins or peptides. These interactions are neither easily described nor fully understood, which is why the design of specific protein-protein interaction interfaces remains a challenge for protein engineering. We recently developed the software ATLIGATOR to extract common interaction patterns between different types of amino acids and store them in a database. The tool enables the user to better understand frequent interaction patterns and find groups of interactions. Furthermore, frequent motifs can be directly transferred from the database to a user-defined scaffold as a starting point for the engineering of new binding capabilities. Since three-dimensional visualization is a crucial part of ATLIGATOR, we created ATLIGATOR web-a web server offering an intuitive graphical user interface (GUI) available at https://atligator.uni-bayreuth.de. This new interface empowers users to apply ATLIGATOR by providing easy access with having all parts directly connected. Moreover, we extended the web by a design functionality so that, overall, ATLIGATOR web facilitates the use of ATLIGATOR with a more intuitive UI and advanced design options.

De novo design of stable proteins that efficaciously inhibit oncogenic G proteins

Many protein therapeutics are competitive inhibitors that function by binding to endogenous proteins and preventing them from interacting with native partners. One effective strategy for engineering competitive inhibitors is to graft structural motifs from a native partner into a host protein. Here, we develop and experimentally test a computational protocol for embedding binding motifs in de novo designed proteins. The protocol uses an "inside-out" approach: Starting with a structural model of the binding motif docked against the target protein, the de novo protein is built by growing new structural elements off the termini of the binding motif. During backbone assembly, a score function favors backbones that introduce new tertiary contacts within the designed protein and do not introduce clashes with the target binding partner. Final sequences are designed and optimized using the molecular modeling program Rosetta. To test our protocol, we designed small helical proteins to inhibit the interaction between Gα q and its effector PLC-β isozymes. Several of the designed proteins remain folded above 90°C and bind to Gα q with equilibrium dissociation constants tighter than 80 nM. In cellular assays with oncogenic variants of Gα q , the designed proteins inhibit activation of PLC-β isozymes and Dbl-family RhoGEFs. Our results demonstrate that computational protein design, in combination with motif grafting, can be used to directly generate potent inhibitors without further optimization via high throughput screening or selection.

statement for broader audience

Engineered proteins that bind to specific target proteins are useful as research reagents, diagnostics, and therapeutics. We used computational protein design to engineer de novo proteins that bind and competitively inhibit the G protein, Gα q , which is an oncogene for uveal melanomas. This computational method is a general approach that should be useful for designing competitive inhibitors against other proteins of interest.

Understanding a protein fold: the physics, chemistry, and biology of α-helical coiled coils

Protein science is being transformed by powerful computational methods for structure prediction and design: AlphaFold2 can predict many natural protein structures from sequence, and other AI methods are enabling the de novo design of new structures. This raises a question: how much do we understand the underlying sequence-to-structure/function relationships being captured by these methods? This perspective presents our current understanding of one class of protein assembly, the α-helical coiled coils. At first sight, these are straightforward: sequence repeats of hydrophobic (h) and polar (p) residues, (hpphppp)_n, direct the folding and assembly of amphipathic α helices into bundles. However, many different bundles are possible: they can have two or more helices (different oligomers); the helices can have parallel, antiparallel or mixed arrangements (different topologies); and the helical sequences can be the same (homomers) or different (heteromers). Thus, sequence-to-structure relationships must be present within the hpphppp repeats to distinguish these states. I discuss the current understanding of this problem at three levels: First, physics gives a parametric framework to generate the many possible coiled-coil backbone structures. Second, chemistry provides a means to explore and deliver sequence-to-structure relationships. Third, biology shows how coiled coils are adapted and functionalized in nature, inspiring applications of coiled coils in synthetic biology. I argue that the chemistry is largely understood; the physics is partly solved, though the considerable challenge of predicting even relative stabilities of different coiled-coil states remains; but there is much more to explore in the biology and synthetic biology of coiled coils.

Peptides from human BNIP5 and PXT1 and non-native binders of pro-apoptotic BAK can directly activate or inhibit BAK-mediated membrane permeabilization

Apoptosis is important for development and tissue homeostasis, and its dysregulation can lead to diseases, including cancer. As an apoptotic effector, BAK undergoes conformational changes that promote mitochondrial outer membrane disruption, leading to cell death. This is termed "activation" and can be induced by peptides from the human proteins BID, BIM, and PUMA. To identify additional peptides that can regulate BAK, we used computational protein design, yeast surface display screening, and structure-based energy scoring to identify 10 diverse new binders. We discovered peptides from the human proteins BNIP5 and PXT1 and three non-native peptides that activate BAK in liposome assays and induce cytochrome c release from mitochondria. Crystal structures and binding studies reveal a high degree of similarity among peptide activators and inhibitors, ruling out a simple function-determining property. Our results shed light on the vast peptide sequence space that can regulate BAK function and will guide the design of BAK-modulating tools and therapeutics.

ATLIGATOR: editing protein interactions with an atlas-based approach

MOTIVATION

Recognition of specific molecules by proteins is a fundamental cellular mechanism and relevant for many applications. Being able to modify binding is a key interest and can be achieved by repurposing established interaction motifs. We were specifically interested in a methodology for the design of peptide binding modules. By leveraging interaction data from known protein structures, we plan to accelerate the design of novel protein or peptide binders.

RESULTS

We developed ATLIGATOR-a computational method to support the analysis and design of a protein's interaction with a single side chain. Our program enables the building of interaction atlases based on structures from the PDB. From these atlases pocket definitions are extracted that can be searched for frequent interactions. These searches can reveal similarities in unrelated proteins as we show here for one example. Such frequent interactions can then be grafted onto a new protein scaffold as a starting point of the design process. The ATLIGATOR tool is made accessible through a python API as well as a CLI with python scripts.

AVAILABILITY AND IMPLEMENTATION

Source code can be downloaded at github (https://www.github.com/Hoecker-Lab/atligator), installed from PyPI ('atligator') and is implemented in Python 3.