Rational Design of Self-assembling Artificial Proteins Utilizing a Micelle-Assisted Protein Labeling Technology (MAPLabTech): Testing the Scope

Self-assembling artificial proteins (SAPs) have gained enormous interest in recent years due to their applications in different fields. Synthesis of well-defined monodisperse SAPs is accomplished predominantly through genetic methods. However, the last decade witnessed the use of few chemical technologies for that purpose. In particular, micelle-assisted protein labeling technology (MAPLabTech) has made huge progress in this area. The first generation MAPLabTech focused on site-specific labeling of the active-site residue of serine proteases to make SAPs. Further, this methodology was exploited for labeling of N-terminal residue of a globular protein to make functional SAPs. In this study, we describe the synthesis of novel SAPs by developing a chemical method for site-specific labeling of a surface-exposed cysteine residue of globular proteins. In addition, we disclose the synthesis of redox- and pH-sensitive SAPs and their systematic self-assembly and dis-assembly studies using complementary biophysical studies. Altogether these studies further expand the scope of MAPLabTech in different fields such as vaccine design, targeted drug delivery, diagnostic imaging, biomaterials, and tissue engineering.

Deep neural language modeling enables functional protein generation across families

Bypassing nature's evolutionary trajectory, de novo protein generation - defined as creating artificial protein sequences from scratch - could enable breakthrough solutions for biomedical and environmental challenges. Viewing amino acid sequences as a language, we demonstrate that a deep learning-based language model can generate functional artificial protein sequences across families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. Our protein language model is trained by simply learning to predict the next amino acid for over 280 million protein sequences from thousands of protein families, without biophysical or coevolutionary modeling. We experimentally evaluate model-generated artificial proteins on five distinct antibacterial lysozyme families. Artificial proteins show similar activities and catalytic efficiencies as representative natural lysozymes, including hen egg white lysozyme, while reaching as low as 44% identity to any known naturally-evolved protein. The X-ray crystal structure of an enzymatically active artificial protein recapitulates the conserved fold and positioning of active site residues found in natural proteins. We demonstrate our language model's ability to be adapted to different protein families by accurately predicting the functionality of artificial chorismate mutase and malate dehydrogenase proteins. These results indicate that neural language models successfully perform de novo protein generation across protein families and may prove to be a tool to shortcut evolution.

Design of buried charged networks in artificial proteins

Soluble proteins are universally packed with a hydrophobic core and a polar surface that drive the protein folding process. Yet charged networks within the central protein core are often indispensable for the biological function. Here, we show that natural buried ion-pairs are stabilised by amphiphilic residues that electrostatically shield the charged motif from its surroundings to gain structural stability. To explore this effect, we build artificial proteins with buried ion-pairs by combining directed computational design and biophysical experiments. Our findings illustrate how perturbation in charged networks can introduce structural rearrangements to compensate for desolvation effects. We validate the physical principles by resolving high-resolution atomic structures of the artificial proteins that are resistant towards unfolding at extreme temperatures and harsh chemical conditions. Our findings provide a molecular understanding of functional charged networks and how point mutations may alter the protein’s conformational landscape.

Decorated Networks of Native Proteins: Nanomaterials with Tunable Mesoscopic Domain Size

Natural and artificial proteins with designer properties and functionalities offer unparalleled opportunity for functional nanoarchitectures formed through self-assembly. However, to exploit this potential we need to design the system such...

Molecular Dynamics Simulation and Kinetic Study of Fluoride Binding to V21C/V66C Myoglobin with a Cytoglobin-like Disulfide Bond

Protein design is able to create artificial proteins with advanced functions, and computer simulation plays a key role in guiding the rational design. In the absence of structural evidence for cytoglobin (Cgb) with an intramolecular disulfide bond, we recently designed a de novo disulfide bond in myoglobin (Mb) based on structural alignment (i.e., V21C/V66C Mb double mutant). To provide deep insight into the regulation role of the Cys21-Cys66 disulfide bond, we herein perform molecular dynamics (MD) simulation of the fluoride–protein complex by using a fluoride ion as a probe, which reveals detailed interactions of the fluoride ion in the heme distal pocket, involving both the distal His64 and water molecules. Moreover, we determined the kinetic parameters of fluoride binding to the double mutant. The results agree with the MD simulation and show that the formation of the Cys21-Cys66 disulfide bond facilitates both fluoride binding to and dissociating from the heme iron. Therefore, the combination of theoretical and experimental studies provides valuable information for understanding the structure and function of heme proteins, as regulated by a disulfide bond. This study is thus able to guide the rational design of artificial proteins with tunable functions in the future.

Hybrid gold nanoparticle–quantum dot self-assembled nanostructures driven by complementary artificial proteins

Hybrid nanostructures are constructed by the direct coupling of fluorescent quantum dots and plasmonic gold nanoparticles.

Insight into microtubule nucleation from tubulin-capping proteins

Nucleation is one of the least understood steps of microtubule dynamics. It is a kinetically unfavorable process that is templated in the cell by the γ-tubulin ring complex or by preexisting microtubules; it also occurs in vitro from pure tubulin. Here we study the nucleation inhibition potency of natural or artificial proteins in connection with their binding mode to the longitudinal surface of α- or β-tubulin. The structure of tubulin-bound CopN, a Chlamydia protein that delays nucleation, suggests that this protein may interfere with two protofilaments at the (+) end of a nucleus. Designed ankyrin repeat proteins that share a binding mode similar to that of CopN also impede nucleation, whereas those that target only one protofilament do not. In addition, an αRep protein predicted to target two protofilaments at the (−) end does not delay nucleation, pointing to different behaviors at both ends of the nucleus. Our results link the interference with protofilaments at the (+) end and the inhibition of nucleation.

Ultrafast flavin/tryptophan radical pair kinetics in a magnetically sensitive artificial protein

Flavin/tryptophan radical pairs in artificial proteins demonstrate that singlet- and triplet-born pairs can be exploited as biological magnetic sensors.