Protein Design Research Articles

PROSCA: an online platform for humanized scaffold mining facilitating rational protein engineering.

Protein scaffolds with small size, high stability and low immunogenicity show important applications in the field of protein engineering and design. However, no relevant computational platform has been reported yet to mining such scaffolds with the desired properties from massive protein structures in human body. Here, we developed PROSCA, a structure-based online platform dedicated to explore the space of the entire human proteome, and to discovery new privileged protein scaffolds with potential engineering value that have never been noticed. PROSCA accepts structure of protein as an input, which can be subsequently aligned with a certain class of protein structures (e.g. the human proteome either from experientially resolved or AlphaFold2 predicted structures, and the human proteins belonging to specific families or domains), and outputs humanized protein scaffolds which are structurally similar with the input protein as well as other related important information such as families, sequences, structures and expression level in human tissues. Through PROSCA, the user can also get excellent experience in visualizations of protein structures and expression overviews, and download the figures and tables of results which can be customized according to the user's needs. Along with the advanced protein engineering and selection technologies, PROSCA will facilitate the rational design of new functional proteins with privileged scaffolds. PROSCA is freely available at https://idrblab.org/prosca/.

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.

Computational design of non-porous pH-responsive antibody nanoparticles

AbstractProgramming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and is important for targeted delivery of biologics. Here we describe the design of octahedral non-porous nanoparticles with a targeting antibody on the two-fold symmetry axis, a designed trimer programmed to disassemble below a tunable pH transition point on the three-fold axis, and a designed tetramer on the four-fold symmetry axis. Designed non-covalent interfaces guide cooperative nanoparticle assembly from independently purified components, and a cryo-EM density map closely matches the computational design model. The designed nanoparticles can package protein and nucleic acid payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between 5.9 and 6.7. The ability to incorporate almost any antibody into a non-porous pH-dependent nanoparticle opens up new routes to antibody-directed targeted delivery.

Peptides and metal ions: A successful marriage for developing artificial metalloproteins.

The mutual relationship between peptides and metal ions enables metalloproteins to have crucial roles in biological systems, including structural, sensing, electron transport, and catalytic functions. The effort to reproduce or/and enhance these roles, or even to create unprecedented functions, is the focus of protein design, the first step toward the comprehension of the complex machinery of nature. Nowadays, protein design allows the building of sophisticated scaffolds, with novel functions and exceptional stability. Recent progress in metalloprotein design has led to the building of peptides/proteins capable of orchestrating the desired functions of different metal cofactors. The structural diversity of peptides allows proper selection of first- and second-shell ligands, as well as long-range electrostatic and hydrophobic interactions, which represent precious tools for tuning metal properties. The scope of this review is to discuss the construction of metal sites in de novo designed and miniaturized scaffolds. Selected examples of mono-, di-, and multi-nuclear binding sites, from the last 20 years will be described in an effort to highlight key artificial models of catalytic or electron-transfer metalloproteins. The authors' goal is to make readers feel like guests at the marriage between peptides and metal ions while offering sources of inspiration for future architects of innovative, artificial metalloproteins.

Immunoinformatics design of a structural proteins driven multi-epitope candidate vaccine against different SARS-CoV-2 variants based on fynomer

The ideal vaccines for combating diseases that may emerge in the future require more than simply inactivating a few pathogenic strains. This study aims to provide a peptide-based multi-epitope vaccine effective against various severe acute respiratory syndrome coronavirus 2 strains. To design the vaccine, a library of peptides from the spike, nucleocapsid, membrane, and envelope structural proteins of various strains was prepared. Then, the final vaccine structure was optimized using the fully protected epitopes and the fynomer scaffold. Using bioinformatics tools, the antigenicity, allergenicity, toxicity, physicochemical properties, population coverage, and secondary and three-dimensional structures of the vaccine candidate were evaluated. The bioinformatic analyses confirmed the high quality of the vaccine. According to further investigations, this structure is similar to native protein and there is a stable and strong interaction between vaccine and receptors. Based on molecular dynamics simulation, structural compactness and stability in binding were also observed. In addition, the immune simulation showed that the vaccine can stimulate immune responses similar to real conditions. Finally, codon optimization and in silico cloning confirmed efficient expression in Escherichia coli. In conclusion, the fynomer-based vaccine can be considered as a new style in designing and updating vaccines to protect against coronavirus disease.

Hypergraph Isomorphism Computation.

The isomorphism problem, crucial in network analysis, involves analyzing both low-order and high-order structural information. Graph isomorphism algorithms focus on structural equivalence to simplify solver space, aiding applications like protein design, chemical pathways, and community detection. However, they fall short in capturing complex high-order relationships, unlike hypergraph isomorphism methods. Traditional hypergraph methods face challenges like high memory use and inaccurate identification, leading to poor performance. To overcome these, we introduce a hypergraph Weisfeiler-Lehman (WL) test algorithm, extending the WL test from graphs to hypergraphs, and develop a hypergraph WL kernel framework with two variants: the Hypergraph WL Subtree Kernel and Hypergraph WL Hyperedge Kernel. The Hypergraph WL Subtree Kernel counts different types of rooted subtrees and generates the final feature vector for a given hypergraph by comparing the number of different types of rooted subtrees. The Subtree Kernel identifies different rooted subtrees, while the Hyperedge Kernel focuses on hyperedges' vertex labels, enhancing feature vector generation. In order to fulfill our research objectives, a comprehensive set of experiments was meticulously designed, including seven graph classification datasets and 12 hypergraph classification datasets. Results on graph classification datasets indicate that the Hypergraph WL Subtree Kernel can achieve the same performance compared with the classical Graph Weisfeiler-Lehman Subtree Kernel. Results on hypergraph classification datasets show significant improvements compared to other typical kernel-based methods, which demonstrates the effectiveness of the proposed methods. In our evaluation, our proposed methods outperform the second-best method in terms of runtime, running over 80 times faster when handling complex hypergraph structures. This significant speed advantage highlights the great potential of our methods in real-world applications.

Guiding a language-model based protein design method towards MHC Class-I immune-visibility targets in vaccines and therapeutics

Proteins have an arsenal of medical applications that include disrupting protein interactions, acting as potent vaccines, and replacing genetically deficient proteins. While therapeutics must avoid triggering unwanted immune-responses, vaccines should support a robust immune-reaction targeting a broad range of pathogen variants. Therefore, computational methods modifying proteins’ immunogenicity without disrupting function are needed. While many components of the immune-system can be involved in a reaction, we focus on Cytotoxic T-lymphocytes (CTLs). These target short peptides presented via the MHC Class I (MHC-I) pathway. To explore the limits of modifying the visibility of those peptides to CTLs within the distribution of naturally occurring sequences, we developed a novel machine learning technique, CAPE-XVAE. It combines a language model with reinforcement learning to modify a protein’s immune-visibility. Our results show that CAPE-XVAE effectively modifies the visibility of the HIV Nef protein to CTLs. We contrast CAPE-XVAE to CAPE-Packer, a physics-based method we also developed. Compared to CAPE-Packer, the machine learning approach suggests sequences that draw upon local sequence similarities in the training set. This is beneficial for vaccine development, where the sequence should be representative of the real viral population. Additionally, the language model approach holds promise for preserving both known and unknown functional constraints, which is essential for the immune-modulation of therapeutic proteins. In contrast, CAPE-Packer, emphasizes preserving the protein’s overall fold and can reach greater extremes of immune-visibility, but falls short of capturing the sequence diversity of viral variants available to learn from. Source code: https://github.com/hcgasser/CAPE (Branch: CAPE_1.1)

Design and Expression of Recombinant cell Penetrating Protein Based on Tat and pertussis-like Toxin A and Evaluation of its Effects on the lung cancer

Design and Expression of Recombinant cell Penetrating Protein Based on Tat and pertussis-like Toxin A and Evaluation of its Effects on the lung cancer

Enhancing prime editor flexibility with coiled-coil heterodimers

BackgroundPrime editing enables precise base substitutions, insertions, and deletions at targeted sites without the involvement of double-strand DNA breaks or exogenous donor DNA templates. However, the large size of prime editors (PEs) hampers their delivery in vivo via adeno-associated virus (AAV) due to the viral packaging limit. Previously reported split PE versions provide a size reduction, but they require intricate engineering and potentially compromise editing efficiency.ResultsHerein, we present a simplified split PE named as CC-PE, created through non-covalent recruitment of reverse transcriptase to the Cas9 nickase via coiled-coil heterodimers, which are widely used in protein design due to their modularity and well-understood sequence-structure relationship. We demonstrate that the CC-PE maintains or even surpasses the efficiency of unsplit PE in installing intended edits, with no increase in the levels of undesired byproducts within tested loci amongst a variety of cell types (HEK293T, A549, HCT116, and U2OS). Furthermore, coiled-coil heterodimers are used to engineer SpCas9-NG-PE and SpRY-PE, two Cas9 variants with more flexible editing scope. Similarly, the resulting NG-CC-PE and SpRY-CC-PE also achieve equivalent or enhanced efficiency of precise editing compared to the intact PE. When the dual AAV vectors carrying CC-PE are delivered into mice to target the Pcsk9 gene in the liver, CC-PE enables highly efficient precise editing, resulting in a significant reduction of plasma low-density lipoprotein cholesterol and total cholesterol.ConclusionsOur innovative, modular system enhances flexibility, thus potentially facilitating the in vivo applicability of prime editing.

The Damietta Server: a comprehensive protein design toolkit.

The growing importance of protein design to various research disciplines motivates the development of integrative computational platforms that enhance the accessibility and interoperability of different design tools. To this end, we describe a web-based toolkit that builds on the Damietta protein design engine, which deploys a tensorized energy calculation framework. The Damietta Server seamlessly integrates different design tools, in addition to other tools such as message-passing neural networks and molecular dynamics routines, allowing the user to perform multiple operations on structural models and forward them across tools. The toolkit can be used for tasks such as core or interface design, symmetric design, mutagenic scanning, or conformational sampling, through an intuitive user interface. With the envisioned integration of more tools, the Damietta Server will provide a central resource for protein design and analysis, benefiting basic and applied biomedical research communities.The toolkit isavailable with no loginrequirement through https://damietta.de/.

Amino acid propensities for secondary structures and its variation across protein structures using exhaustive PDB data

Amino acid propensities for protein secondary structures are vital for protein structure prediction, understanding folding, and design, and have been studied using various theoretical and experimental methods. Traditional assessments of average propensities using statistical methods have been done on relatively smaller dataset for only a few secondary structures. They also involve averaging out the environmental factors and lack insights into consistency of preferences across diverse protein structures. While a few studies have explored variations in propensities across protein structural classes and folds, exploration of such variations across protein structures remains to be carried out. In this work, we have revised the average propensities for all six different secondary structures, namely α-helix, β-strand, 310-helix, π-helix, turn and coil, analyzing the most exhaustive dataset available till date using two robust secondary structure assignment algorithms, DSSP and STRIDE. The propensities evaluated here can serve as a standard reference. Moreover, we present here, for the first time, the propensities within individual protein structures and investigated how the preferences of residues and more interestingly, of their groups formed based on their structural features, vary across different unique structures. We devised a novel approach- the minimal set analysis, based on the propensity distribution of residues, which along with the group propensities led us to the conclusion that a residue's preference for a specific secondary structure is primarily dictated by its side chain's structural features. The findings in this study provide a more insightful picture of residues propensities and can be useful in protein folding and design studies.

Protein design community balances openness, security

Protein design community balances openness, security

Design, development, and validation of multi-epitope proteins for serological diagnosis of Zika virus infections and discrimination from dengue virus seropositivity.

Zika virus (ZIKV), an arbovirus from the Flaviviridae family, is the causative agent of Zika fever, a mild and frequent oligosymptomatic disease in humans. Nonetheless, on rare occasions, ZIKV infection can be associated with Guillain-Barré Syndrome (GBS), and severe congenital complications, such as microcephaly. The oligosymptomatic disease, however, presents symptoms that are quite similar to those observed in infections caused by other frequent co-circulating arboviruses, including dengue virus (DENV). Moreover, the antigenic similarity between ZIKV and DENV, and even with other members of the Flaviviridae family, complicates serological testing due to the high cross-reactivity of antibodies. Here, we designed, produced in a prokaryotic expression system, and purified three multiepitope proteins (ZIKV-1, ZIKV-2, and ZIKV-3) for differential diagnosis of Zika. The proteins were evaluated as antigens in ELISA tests for the detection of anti-ZIKV IgG using ZIKV- and DENV-positive human sera. The recombinant proteins were able to bind and detect anti-ZIKV antibodies without cross-reactivity with DENV-positive sera and showed no reactivity with Chikungunya virus (CHIKV)- positive sera. ZIKV-1, ZIKV-2, and ZIKV-3 proteins presented 81.6%, 95%, and 66% sensitivity and 97%, 96%, and 84% specificity, respectively. Our results demonstrate the potential of the designed and expressed antigens in the development of specific diagnostic tests for the detection of IgG antibodies against ZIKV, especially in regions with the circulation of multiple arboviruses.

Design, Synthesis and Catalytic Activity of Protein Containing Thiotyrosine as an Active Site Residue

Design, Synthesis and Catalytic Activity of Protein Containing Thiotyrosine as an Active Site Residue

Self-Assembled Recombinant Elastin and Globular Protein Vesicles with Tunable Properties for Diverse Applications.

ConspectusVesicles are self-assembled structures comprised of a membrane-like exterior surrounding a hollow lumen with applications in drug delivery, artificial cells, and micro-bioreactors. Lipid or polymer vesicles are the most common and are made of lipids or polymers, respectively. They are highly useful structures for many applications but it can be challenging to decorate them with proteins or encapsulate proteins in them, owing to the use of organic solvent in their formation and the large size of proteins relative to lipid or polymer molecules. By utilization of recombinant fusion proteins to make vesicles, specific protein domains can be directly incorporated while also imparting tunability and stability. Protein vesicle assembly relies on the design and use of self-assembling amphiphilic proteins. A specific protein vesicle platform made in purely aqueous conditions of a globular, functional protein fused to a glutamate-rich leucine zipper (ZE) and a thermoresponsive elastin-like polypeptide (ELP) fused to an arginine-rich leucine zipper (ZR) is discussed here. The hydrophobic conformational change of the ELP above its transition temperature drives assembly, and strong ZE/ZR binding enables incorporation of the desired functional protein. Mixing the soluble proteins on ice induces zipper binding, and then warming above the ELP transition temperature (Tt) triggers the transition to and growth of protein-rich coacervates and, finally, reorganization of proteins into vesicles. Vesicle size is tunable based on salt concentration, rate of heating, protein concentration, size of the globular protein, molar ratio of the proteins, and the ELP sequence. Increasing the salt concentration decreases vesicle size by decreasing the Tt, resulting in a shorter coacervation transition stage. Likewise, directly changing the heating rate also changes this time and increasing protein concentration increases coalescence. Increasing globular protein size decreases the size of the vesicle due to steric hindrance. By changing the ELP sequence, which consists of (VPGXG)n, through the guest residue (X) or number of repeats (n), Tt is changed, affecting size. Additionally, the chemical nature of X variation has endowed vesicles with stimuli responsiveness and stability at physiological conditions.Protein vesicles have been used for biocatalysis, biomacromolecular drug delivery, and vaccine applications. Photo-cross-linkable vesicles were used to deliver small molecule cargo to cancer cells in vitro and antigen to immune cells in vivo. pH-responsive vesicles effectively delivered functional protein cargo, including cytochrome C, to the cytosol of cancer cells in vitro, using hydrophobic ion pairing to improve cargo distribution in the vesicles and release. The globular protein used to make the vesicles can be varied to achieve different functions. For example, enzyme vesicles exhibit biocatalysis, and antigen vesicles induce antibody and cellular immune responses after vaccination in mice. Collectively, the development and engineering of the protein vesicle platform has employed amphiphilic self-assembly strategies and rational protein engineering to control physical, chemical, and biological properties for biotechnology and nanomedicine applications.

In Solution Identification of the Lysine-Cysteine Redox Switch with a NOS Bridge in Transaldolase by Sulfur K-Edge X-ray Absorption Spectroscopy.

A novel covalent post-translational modification (lysine-NOS-cysteine) was discovered in proteins, initially in the enzyme transaldolase of Neisseria gonorrhoeae (NgTAL) [Nature 2021, 593, 460-464], acting as a redox switch. The identification of this novel linkage in solution was unprecedented until now. We present detection of the NOS redox switch in solution using sulfur K-edge X-ray absorption spectroscopy (XAS). The oxidized NgTAL spectrum shows a distinct shoulder on the low-energy side of the rising edge, corresponding to a dipole-allowed transition from the sulfur 1s core to the unoccupied σ* orbital of the S-O group in the NOS bridge. This feature is absent in the XAS spectrum of reduced NgTAL, where Lys-NOS-Cys is absent. Our experimental and calculated XAS data support the presence of a NOS bridge in solution, thus potentially facilitating future studies on enzyme activity regulation mediated by the NOS redox switches, drug discovery, biocatalytic applications, and protein design.

Zig, Zag, and ’Zyme: leveraging structural biology to engineer disease resistance

AbstractDynamic host–pathogen interactions determine whether disease will occur. Pathogen effector proteins are central players in such disease development. On one hand, they improve susceptibility by manipulating host targets; on the other hand, they can trigger immunity after recognition by host immune receptors. A major research direction in the study of molecular plant pathology is to understand effector-host interactions, which has informed the development and breeding of crops with enhanced disease resistance. Recent breakthroughs on experiment- and artificial intelligence-based structure analyses significantly accelerate the development of this research area. Importantly, the detailed molecular insight of effector–host interactions enables precise engineering to mitigate disease. Here, we highlight a recent study by Xiao et al., who describe the structure of an effector-receptor complex that consists of a fungal effector, with polygalacturonase (PG) activity, and a plant-derived polygalacturonase-inhibiting protein (PGIP). PGs weaken the plant cell wall and produce immune-suppressive oligogalacturonides (OGs) as a virulence mechanism; however, PGIPs directly bind to PGs and alter their enzymatic activity. When in a complex with PGIPs, PGs produce OG polymers with longer chains that can trigger immunity. Xiao et al. demonstrate that a PGIP creates a new active site tunnel, together with a PG, which favors the production of long-chain OGs. In this way, the PGIP essentially acts as both a PG receptor and enzymatic manipulator, converting virulence to defense activation. Taking a step forward, the authors used the PG-PGIP complex structure as a guide to generate PGIP variants with enhanced long-chain OG production, likely enabling further improved disease resistance. This study discovered a novel mechanism by which a plant receptor plays a dual role to activate immunity. It also demonstrates how fundamental knowledge, obtained through structural analyses, can be employed to guide the design of proteins with desired functions in agriculture.

Hydrophobic mismatch drives self-organization of designer proteins into synthetic membranes

The organization of membrane proteins between and within membrane-bound compartments is critical to cellular function. Yet we lack approaches to regulate this organization in a range of membrane-based materials, such as engineered cells, exosomes, and liposomes. Uncovering and leveraging biophysical drivers of membrane protein organization to design membrane systems could greatly enhance the functionality of these materials. Towards this goal, we use de novo protein design, molecular dynamic simulations, and cell-free systems to explore how membrane-protein hydrophobic mismatch could be used to tune protein cotranslational integration and organization in synthetic lipid membranes. We find that membranes must deform to accommodate membrane-protein hydrophobic mismatch, which reduces the expression and co-translational insertion of membrane proteins into synthetic membranes. We use this principle to sort proteins both between and within membranes, thereby achieving one-pot assembly of vesicles with distinct functions and controlled split-protein assembly, respectively. Our results shed light on protein organization in biological membranes and provide a framework to design self-organizing membrane-based materials with applications such as artificial cells, biosensors, and therapeutic nanoparticles.

Structural bioinformatics studies of glutamate transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L->Q, I->T, F->Y and Q->L, T->I and Y->F.

Glutamate transporters play key roles in nervous physiology by modulating excitatory neurotransmitter levels, when malfunctioning, involving in a wide range of neurological and physiological disorders. However, integral transmembrane proteins including the glutamate transporters remain notoriously difficult to study, due to their localization within the cell membrane. Here we present the structural bioinformatics studies of glutamate transporters and their water-soluble variants generated through QTY-code, a protein design strategy based on systematic amino acid substitutions. These include 2 structures determined by X-ray crystallography, cryo-EM, and 6 predicted by AlphaFold2, and their predicted water-soluble QTY variants. In the native structures of glutamate transporters, transmembrane helices contain hydrophobic amino acids such as leucine (L), isoleucine (I), and phenylalanine (F). To design water-soluble variants, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, namely glutamine (Q), threonine (T) and tyrosine (Y). The QTY variants exhibited water-solubility, with four having identical isoelectric focusing points (pI) and the other four having very similar pI. We present the superposed structures of the native glutamate transporters and their water-soluble QTY variants. The superposed structures displayed remarkable similarity with RMSD 0.528Å-2.456Å, despite significant protein transmembrane sequence differences (41.1%->53.8%). Additionally, we examined the differences of hydrophobicity patches between the native glutamate transporters and their QTY variants. Upon closer inspection, we discovered multiple natural variations of L->Q, I->T, F->Y and Q->L, T->I, Y->F in these transporters. Some of these natural variations were benign and the remaining were reported in specific neurological disorders. We further investigated the characteristics of hydrophobic to hydrophilic substitutions in glutamate transporters, utilizing variant analysis and evolutionary profiling. Our structural bioinformatics studies not only provided insight into the differences between the hydrophobic helices and hydrophilic helices in the glutamate transporters, but they are also expected to stimulate further study of other water-soluble transmembrane proteins.

Discovery of Potent Isoindolinone Inhibitors that Target an Active Conformation of PARP1 Using DNA-Encoded Libraries.

Inhibition of poly (ADP-ribose) polymerase-1 (PARP1), a DNA repair enzyme, has proven to be a successful strategy for the treatment of various cancers. With the appropriate selection conditions and protein design, DNA-encoded library (DEL) technology provides a powerful avenue to identify small molecules with the desired mechanism of action towards a target of interest. However, DNA-binding proteins, such as PARP1, can be challenging targets for DEL screening due to non-specific protein-DNA interactions. To overcome this, we designed and screened a PARP1 catalytic domain construct without the autoinhibitory helical domain. This allowed us to interrogate an active, functionally-relevant form of the protein resulting in the discovery of novel isoindolinone PARP1 inhibitors with single-digit nanomolar potency. These inhibitors also demonstrated little to no PARP1-DNA trapping, a property that could be advantageous in the clinic.