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At least 181 records · Page 10

Gate-based quantum computing for protein design

Protein design is a technique to engineer proteins by permuting amino acids in the sequence to obtain novel functionalities. However, exploring all possible combinations of amino acids is generally impossible due to the exponential growth of possibilities with the number of designable sites. The present work introduces circuits implementing a pure quantum approach, Grover’s algorithm, to solve protein design problems. Our algorithms can adjust to implement any custom pair-wise energy tables and protein structure models. Moreover, the algorithm’s oracle is designed to consist of only adder functions. Quantum computer simulators validate the practicality of our circuits, containing up to 234 qubits. However, a smaller circuit is implemented on real quantum devices. Our results show that using iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover’s algorithm over classical methods (i.e.,).

59 BASIC BIOLOGICAL SCIENCES↗

Robust deep learning–based protein sequence design using ProteinMPNN

Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning–based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo–electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.

59 BASIC BIOLOGICAL SCIENCES↗

De novo protein design of photochemical reaction centers

Abstract Natural photosynthetic protein complexes capture sunlight to power the energetic catalysis that supports life on Earth. Yet these natural protein structures carry an evolutionary legacy of complexity and fragility that encumbers protein reengineering efforts and obfuscates the underlying design rules for light-driven charge separation. De novo development of a simplified photosynthetic reaction center protein can clarify practical engineering principles needed to build new enzymes for efficient solar-to-fuel energy conversion. Here, we report the rational design, X-ray crystal structure, and electron transfer activity of a multi-cofactor protein that incorporates essential elements of photosynthetic reaction centers. This highly stable, modular artificial protein framework can be reconstituted in vitro with interchangeable redox centers for nanometer-scale photochemical charge separation. Transient absorption spectroscopy demonstrates Photosystem II-like tyrosine and metal cluster oxidation, and we measure charge separation lifetimes exceeding 100 ms, ideal for light-activated catalysis. This de novo-designed reaction center builds upon engineering guidelines established for charge separation in earlier synthetic photochemical triads and modified natural proteins, and it shows how synthetic biology may lead to a new generation of genetically encoded, light-powered catalysts for solar fuel production.

59 BASIC BIOLOGICAL SCIENCES↗

SARS-CoV2 billion-compound docking

Abstract This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.

60 APPLIED LIFE SCIENCES↗

Structural and functional analyses of SARS-CoV-2 Nsp3 and its specific interactions with the 5’ UTR of the viral genome

ABSTRACT Non-structural protein 3 (Nsp3) is the largest open reading frame encoded in the SARS-CoV-2 genome, essential for the formation of double-membrane vesicles (DMV) wherein viral RNA replication occurs. We conducted an extensive structure-function analysis of Nsp3 and determined the crystal structures of the ubiquitin-like 1 (Ubl1), nucleic acid binding (NAB), β-coronavirus-specific marker (βSM) domains, and a sub-region of the Y domain of this protein. We show that the Ubl1, ADP-ribose phosphatase (ADRP), human SARS Unique (HSUD), NAB, and Y domains of Nsp3 bind the 5’ UTR of the viral genome and that the Ubl1 and Y domains possess affinity for recognition of this region, suggesting high specificity. The Ubl1-Nucleocapsid (N) protein complex binds the 5’ UTR with greater affinity than the individual proteins alone. Our results suggest that multiple domains of Nsp3, particularly Ubl1 and Y, shepherd the 5’ UTR of the viral genome during translocation through the DMV membrane, priming the Ubl1 domain to load the genome onto N protein. IMPORTANCE The largest protein encoded by the SARS-CoV-2 genome is Nsp3. In infected cells, this multi-domain protein forms a pore structure in the virus-induced double-membrane vesicles (DMV). We have incomplete data on Nsp3 molecular structure, and here, we describe crystal structures for multiple domains of Nsp3. It is thought that newly replicated viral RNA transits through the DMV pore; however, we possess incomplete data on which regions of Nsp3 actually interact with RNA. Here, we present data showing that five domains of Nsp3 interact with the 5’ UTR of the SARS-CoV-2 RNA, including the Y domain for which no function has ever been discovered. These data suggest that the pore structure plays an active role in recognizing the terminal end of the genome, transiting and loading the viral RNA onto the cytoplasmic nucleocapsid protein. These data help expand our knowledge of Nsp3 structure and function and the SARS-CoV-2 replication cycle.

Microbiology↗

Polymer-based microfluidic device for on-chip counter-diffusive crystallization and in situ X-ray crystallography at room temperature

Proteins are long chains of amino acid residues that perform a myriad of functions in living organisms, including enzymatic reactions, signalling, and maintaining structural integrity. Protein function is determined directly by the protein structure. X-ray crystallography is the primary technique for determining the 3D structure of proteins, and facilitates understanding the effects of protein structure on function. The first step towards structure determination is crystallizing the protein of interest. We have developed a centrifugally-actuated microfluidic device that incorporates the fluid handling and metering necessary for protein crystallization. Liquid handling takes advantage of surface forces to control fluid flow and enable metering, without the need for any fluidic or pump connections. Our approach requires only the simple steps of pipetting the crystallization reagents into the device followed by either spinning or shaking to set up counter-diffusive protein crystallization trials. The use of thin, UV-curable polymers with a high level of X-ray transparency allows for in situ X-ray crystallography, eliminating the manual handling of fragile protein crystals and streamlining the process of protein structure analysis. We demonstrate the utility of our device using hen egg white lysozyme as a model system, followed by the crystallization and in situ, room temperature structural analysis of the hub domain of calcium–calmodulin dependent kinase II (CaMKIIβ).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mitigating the allergenicity of peanut allergen Ara h 1 by cold atmospheric pressure argon plasma jet

Abstract BACKGROUND Peanut allergy is recognized as a major food allergy that triggers severe and even fatal symptoms. Avoidance of peanuts in the diet is the main option for current safety management. Processing techniques reducing peanut allergenicity are required to develop other options. Cold plasma is currently considered as a novel non‐thermal approach to alter protein structure and has the potential to alleviate immunoreactivity of protein allergen. RESULTS The application of a cold argon plasma jet to peanut protein extract could reduce the amount of a 64 kDa protein band corresponding to a major peanut allergen Ara h 1 using sodium dodecyl sulfate–polyacrylamide gel electrophoresis, but the overall protein size distribution did not change significantly. A decrease in peanut protein solubility was a possible cause that led to the loss of protein content in the soluble fraction. Immunoblotting and enzyme‐linked immunosorbent assay elucidated that the immunoreactivity of Ara h 1 was significantly decreased with the time treated with plasma. Ara h 1 antigenicity reduced by 38% after five scans (approximately 3 min) of cold argon plasma jet treatment, and the reduction was up to 66% after approximately 15 min of treatment. CONCLUSION The results indicate that cold argon plasma jet treatment could be a suitable platform for alleviating the immunoreactivity of peanut protein. This work demonstrates an efficient, compact, and rapid platform for mitigating the allergenicity of peanuts, and shows great potential for the plasma platform as a non‐thermal technique in the food industry. © 2023 Society of Chemical Industry.

Hsu, Fu‐Chiun↗

Crystallographic Characterization of Sodium Ions in a Bacterial Leucine/Sodium Symporter

Na + is the most abundant ion in living organisms and plays essential roles in regulating nutrient uptake, muscle contraction, and neurotransmission. The identification of Na + in protein structures is crucial for gaining a deeper understanding of protein function in a physiological context. LeuT, a bacterial homolog of the neurotransmitter:sodium symporter family, uses the Na + gradient to power the uptake of amino acids into cells and has been used as a paradigm for the study of Na + -dependent transport systems. We have devised a low-energy multi-crystal approach for characterizing low-Z (Z ≤ 20) anomalous scattering ions such as Na + , Mg 2+ , K + , and Ca 2+ by combining Bijvoet-difference Fourier syntheses for ion detection and f” refinements for ion speciation. Using the approach, we experimentally identify two Na + bound near the central leucine binding site in LeuT. Using LeuT microcrystals, we also demonstrate that Na + may be depleted to study conformational changes in the LeuT transport cycle.

59 BASIC BIOLOGICAL SCIENCES↗

Defining the S-Glutathionylation Proteome by Biochemical and Mass Spectrometric Approaches

Protein S-glutathionylation (SSG) is a reversible post-translational modification (PTM) featuring the conjugation of glutathione to a protein cysteine thiol. SSG can alter protein structure, activity, subcellular localization, and interaction with small molecules and other proteins. Thus, it plays a critical role in redox signaling and regulation in various physiological activities and pathological events. In this review, we summarize current biochemical and analytical approaches for characterizing SSG at both the proteome level and at individual protein levels. To illustrate the mechanism underlying SSG-mediated redox regulation, we highlight recent examples of functional and structural consequences of SSG modifications. Finally, we discuss the analytical challenges in characterizing SSG and the thiol PTM landscape, future directions for understanding of the role of SSG in redox signaling and regulation and its interplay with other PTMs, and the potential role of computational approaches to accelerate functional discovery.

59 BASIC BIOLOGICAL SCIENCES↗

In Situ Proteolysis Condition-Induced Crystallization of the XcpVWX Complex in Different Lattices

Although prevalent in the determination of protein structures; crystallography always has the bottleneck of obtaining high-quality protein crystals for characterizing a wide range of proteins; especially large protein complexes. Stable fragments or domains of proteins are more readily to crystallize; which prompts the use of in situ proteolysis to remove flexible or unstable structures for improving crystallization and crystal quality. In this work; we investigated the effects of in situ proteolysis by chymotrypsin on the crystallization of the XcpVWX complex from the Type II secretion system of Pseudomonas aeruginosa. Different proteolysis conditions were found to result in two distinct lattices in the same crystallization solution. With a shorter chymotrypsin digestion at a lower concentration; the crystals exhibited a P3 hexagonal lattice that accommodates three complex molecules in one asymmetric unit. By contrast; a longer digestion with chymotrypsin of a 10-fold higher concentration facilitated the formation of a compact P212121 orthorhombic lattice with only one complex molecule in each asymmetric unit. The molecules in the hexagonal lattice have shown high atomic displacement parameter values compared with the ones in the orthorhombic lattice. Taken together; our results clearly demonstrate that different proteolysis conditions can result in the generation of distinct lattices in the same crystallization solution; which can be exploited in order to obtain different crystal forms of a better quality

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

De novo design of high-affinity binders of bioactive helical peptides

Many peptide hormones form an α-helix on binding their receptors, and sensitive methods for their detection could contribute to better clinical management of disease. De novo protein design can now generate binders with high affinity and specificity to structured proteins. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

59 BASIC BIOLOGICAL SCIENCES↗

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here, in this work, we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

36 MATERIALS SCIENCE↗

Dissecting the structural heterogeneity of proteins by native mass spectrometry

Abstract A single gene yields many forms of proteins via combinations of posttranscriptional/posttranslational modifications. Proteins also fold into higher‐order structures and interact with other molecules. The combined molecular diversity leads to the heterogeneity of proteins that manifests as distinct phenotypes. Structural biology has generated vast amounts of data, effectively enabling accurate structural prediction by computational methods. However, structures are often obtained heterologously under homogeneous states in vitro. The lack of native heterogeneity under cellular context creates challenges in precisely connecting the structural data to phenotypes. Mass spectrometry (MS) based proteomics methods can profile proteome composition of complex biological samples. Most MS methods follow the “bottom‐up” approach, which denatures and digests proteins into short peptide fragments for ease of detection. Coupled with chemical biology approaches, higher‐order structures can be probed via incorporation of covalent labels on native proteins that are maintained at the peptide level. Alternatively, native MS follows the “top‐down” approach and directly analyzes intact proteins under nondenaturing conditions. Various tandem MS activation methods can dissect the intact proteins for in‐depth structural elucidation. Herein, we review recent native MS applications for characterizing heterogeneous samples, including proteins binding to mixtures of ligands, homo/hetero‐complexes with varying stoichiometry, intrinsically disordered proteins with dynamic conformations, glycoprotein complexes with mixed modification states, and active membrane protein complexes in near‐native membrane environments. We summarize the benefits, challenges, and ongoing developments in native MS, with the hope to demonstrate an emerging technology that complements other tools by filling the knowledge gaps in understanding the molecular heterogeneity of proteins.

59 BASIC BIOLOGICAL SCIENCES↗

CHARMM-GUI Drude prepper for molecular dynamics simulation using the classical Drude polarizable force field

Explicit treatment of electronic polarizability in empirical force fields (FFs) represents an extension over a traditional additive or pairwise FF and provides a more realistic model of the variations in electronic structure in condensed phase, macromolecular simulations. To facilitate utilization of the polarizable FF based on the classical Drude oscillator model, Drude Prepper has been developed in CHARMM-GUI. Drude Prepper ingests additive CHARMM protein structures file (PSF) and pre-equilibrated coordinates in CHARMM, PDB, or NAMD format, from which the molecular components of the system are identified. These include all residues and patches connecting those residues along with water, ions, and other solute molecules. This information is then used to construct the Drude FF-based PSF using molecular generation capabilities in CHARMM, followed by minimization and equilibration. In addition, inputs are generated for molecular dynamics (MD) simulations using CHARMM, GROMACS, NAMD, and OpenMM. Validation of the Drude Prepper protocol and inputs is performed through conversion and MD simulations of various heterogeneous systems that include proteins, nucleic acids, lipids, polysaccharides, and atomic ions using the aforementioned simulation packages. Stable simulations are obtained in all studied systems, including 5 μs simulation of ubiquitin, verifying the integrity of the generated Drude PSFs. Additionally, the ability of the Drude FF to model variations in electronic structure is shown through dipole moment analysis in selected systems. Finally, the capabilities and availability of Drude Prepper in CHARMM-GUI is anticipated to greatly facilitate the application of the Drude FF to a range of condensed phase, macromolecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The immune-evasive proline-283 substitution in influenza nucleoprotein increases aggregation propensity without altering the native structure

Nucleoprotein (NP) is a key structural protein of influenza ribonucleoprotein complexes and is central to viral RNA packing and trafficking. NP also determines the sensitivity of influenza to myxovirus resistance protein 1 (MxA), an innate immunity factor that restricts influenza replication. A few critical MxA-resistant mutations have been identified in NP, including the highly conserved proline-283 substitution. This essential proline-283 substitution impairs influenza growth, a fitness defect that becomes particularly prominent at febrile temperature (39°C) when host chaperones are depleted. Here, we biophysically characterize proline-283 NP and serine-283 NP to test whether the fitness defect is caused by the proline-283 substitution introducing folding defects. We show that the proline-283 substitution changes the folding pathway of NP, making NP more aggregation prone during folding, but does not alter the native structure of the protein. These findings suggest that influenza has evolved to hijack host chaperones to promote the folding of otherwise biophysically incompetent viral proteins that enable innate immune system escape.

60 APPLIED LIFE SCIENCES↗