Rationally seeded computational protein design of ɑ-helical barrels.

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

A de novo designed coiled coil-based switch regulates the microtubule motor kinesin-1.

Many enzymes are allosterically regulated via conformational change; however, our ability to manipulate these structural changes and control function is limited. Here we install a conformational switch for allosteric activation into the kinesin-1 microtubule motor in vitro and in cells. Kinesin-1 is a heterotetramer that accesses open active and closed autoinhibited states. The equilibrium between these states centers on a flexible elbow within a complex coiled-coil architecture. We target the elbow to engineer a closed state that can be opened with a de novo designed peptide. The alternative states are modeled computationally and confirmed by biophysical measurements and electron microscopy. In cells, peptide-driven activation increases kinesin transport, demonstrating a primary role for conformational switching in regulating motor activity. The designs are enabled by our understanding of ubiquitous coiled-coil structures, opening possibilities for controlling other protein activities.

Management of Huanglongbing of Citrus: Lessons from São Paulo and Florida.

São Paulo, Brazil, and Florida, USA, were the two major orange production areas in the world until Huanglongbing (HLB) was discovered in São Paulo in 2004 and Florida in 2005. In the absence of resistant citrus varieties, HLB is the most destructive citrus disease known because of the lack of effective tools to reduce spread of the vector, Diaphorina citri (Asian citrus psyllid), and transmission of the associated pathogen, Candidatus Liberibacter asiaticus. In both countries, a three-pronged management approach was recommended and begun: planting only disease-free nursery trees, effective psyllid control, and removal of all symptomatic trees. In Brazil, these management procedures were continued and improved and resulted in relatively little overall loss of production. In contrast, in Florida the citrus industry has been devastated with annual production reduced by approximately 80%. This review compares and contrasts various cultural and pest management strategies that have been used to reduce infection by the pathogen and increase tolerance of HLB in the main orange-growing regions in the world.

Sorting drug conformers in enzyme active sites: the XTB way.

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

Positivity preserving density matrix minimization at finite temperatures via square root.

We present a Wave Operator Minimization (WOM) method for calculating the Fermi-Dirac density matrix for electronic structure problems at finite temperature while preserving physicality by construction using the wave operator, i.e., the square root of the density matrix. WOM models cooling a state initially at infinite temperature down to the desired finite temperature. We consider both the grand canonical (constant chemical potential) and canonical (constant number of electrons) ensembles. Additionally, we show that the number of steps required for convergence is independent of the number of atoms in the system. We hope that the discussion and results presented in this article reinvigorate interest in density matrix minimization methods.

Predicting potential SARS-CoV-2 mutations of concern via full quantum mechanical modelling.

Ab initio quantum mechanical models can characterize and predict intermolecular binding, but only recently have models including more than a few hundred atoms gained traction. Here, we simulate the electronic structure for approximately 13 000 atoms to predict and characterize binding of SARS-CoV-2 spike variants to the human ACE2 (hACE2) receptor using the quantum mechanics complexity reduction (QM-CR) approach. We compare four spike variants in our analysis: Wuhan, Omicron, and two Omicron-based variants. To assess binding, we mechanistically characterize the energetic contribution of each amino acid involved, and predict the effect of select single amino acid mutations. We validate our computational predictions experimentally by comparing the efficacy of spike variants binding to cells expressing hACE2. At the time we performed our simulations (December 2021), the mutation A484K which our model predicted to be highly beneficial to ACE2 binding had not been identified in epidemiological surveys; only recently (August 2023) has it appeared in variant BA.2.86. We argue that our computational model, QM-CR, can identify mutations critical for intermolecular interactions and inform the engineering of high-specificity interactors.

Gene silencing of cathepsins B and L using CTV-based, plant-mediated RNAi interferes with ovarial development in Asian citrus psyllid (ACP), Diaphorina citri.

Diaphorina citri Kuwayama (Hemiptera: Liviidae) is a vector of the bacteria Candidatus Liberibacter americanus (CLam) and Candidatus Liberibacter asiaticus (CLas), which are phloem-restricted and associated with the most important and destructive worldwide citrus disease, Huanglongbing (HLB). Currently, no cure for HLB has been described. Therefore, measures have focused on reducing D. citri populations. In these insects, cathepsin B (DCcathB) and L (DCcathL) enzymes play an important role in digestion, and are involved in embryogenesis, immune defense, and ecdysis. In this study, we used a CTV-based vector to deliver dsRNA (CTV-dsRNA) into Citrus macrophylla plants targeting DCcathB and DCcathL genes in D. citri that fed on the phloem of these CTV-RNAi infected plants. Subsequently, we evaluated expression of DCcathB and DCcathL genes as well as the Vitellogenin (Vg) gene by RT-qPCR in D. citri fed on CTV-dsRNA occurring in plant phloem. It was found that a defective phenotype in D. citri females as a result of knockdown of DCcathB and DCcathL genes mediated by CTV dsRNA. These results showed that Psyllids fed on plants treated with the CTV-dsRNA exhibited downregulation of the Vg gene, one of the most important genes associated with embryogenic and female development, which was associated with dsRNA-mediated silencing of the two cathepsin genes. Based on our findings, a CTV-based strategy for delivering RNAi via plants that targets DCcathB and DCcathL genes may represent a suitable avenue for development of dsRNA-based tools to manage D. citri that limits the spread of HLB.

CC+ : A searchable database of validated coiled coils in PDB structures and AlphaFold2 models.

α-Helical coiled coils are common tertiary and quaternary elements of protein structure. In coiled coils, two or more α helices wrap around each other to form bundles. This apparently simple structural motif can generate many architectures and topologies. Coiled coil-forming sequences can be predicted from heptad repeats of hydrophobic and polar residues, hpphppp, although this is not always reliable. Alternatively, coiled-coil structures can be identified using the program SOCKET, which finds knobs-into-holes (KIH) packing between side chains of neighboring helices. SOCKET also classifies coiled-coil architecture and topology, thus allowing sequence-to-structure relationships to be garnered. In 2009, we used SOCKET to create a relational database of coiled-coil structures, CC+ , from the RCSB Protein Data Bank (PDB). Here, we report an update of CC+ following an update of SOCKET (to Socket2) and the recent explosion of structural data and the success of AlphaFold2 in predicting protein structures from genome sequences. With the most-stringent SOCKET parameters, CC+ contains ≈12,000 coiled-coil assemblies from experimentally determined structures, and ≈120,000 potential coiled-coil structures within single-chain models predicted by AlphaFold2 across 48 proteomes. CC+ allows these and other less-stringently defined coiled coils to be searched at various levels of structure, sequence, and side-chain interactions. The identified coiled coils can be viewed directly from CC+ using the Socket2 application, and their associated data can be downloaded for further analyses. CC+ is available freely at http://coiledcoils.chm.bris.ac.uk/CCPlus/Home.html. It will be updated automatically. We envisage that CC+ could be used to understand coiled-coil assemblies and their sequence-to-structure relationships, and to aid protein design and engineering.

Protein-ligand interactions from a quantum fragmentation perspective: The case of the SARS-CoV-2 main protease interacting with α-ketoamide inhibitors.

We present a hybrid, multi-method, computational scheme for protein/ligand systems well suited to be used on modern and forthcoming massively parallel computing systems. The scheme relies on a multi-scale polarizable molecular modeling, approach to perform molecular dynamics simulations, and on an efficient Density Functional Theory (DFT) linear scaling method to post-process simulation snapshots. We use this scheme to investigate recent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2 virus. We assessed the reliability and the coherence of the hybrid scheme, in particular, by checking the ability of MM and DFT to reproduce results from high-end ab initio computations regarding such inhibitors. The DFT approach enables an a posteriori fragmentation of the system and an investigation into the strength of interaction among identified fragment pairs. We show the necessity of accounting for a large set of plausible protease/inhibitor conformations to generate reliable interaction data. Finally, we point out ways to further improve α-ketoamide inhibitors to more strongly interact with particular protease domains neighboring the active site.

Complexity reduction in density functional theory: Locality in space and energy.

We present recent developments of the NTChem program for performing large scale hybrid density functional theory calculations on the supercomputer Fugaku. We combine these developments with our recently proposed complexity reduction framework to assess the impact of basis set and functional choice on its measures of fragment quality and interaction. We further exploit the all electron representation to study system fragmentation in various energy envelopes. Building off this analysis, we propose two algorithms for computing the orbital energies of the Kohn-Sham Hamiltonian. We demonstrate that these algorithms can efficiently be applied to systems composed of thousands of atoms and as an analysis tool that reveals the origin of spectral properties.

Experimental-theoretical study of laccase as a detoxifier of aflatoxins.

We investigate laccase-mediated detoxification of aflatoxins, fungal carcinogenic food contaminants. Our experimental comparison between two aflatoxins with similar structures (AFB1 and AFG2) shows significant differences in laccase-mediated detoxification. A multi-scale modeling approach (Docking, Molecular Dynamics, and Density Functional Theory) identifies the highly substrate-specific changes required to improve laccase detoxifying performance. We employ a large-scale density functional theory-based approach, involving more than 7000 atoms, to identify the amino acid residues that determine the affinity of laccase for aflatoxins. From this study we conclude: (1) AFB1 is more challenging to degrade, to the point of complete degradation stalling; (2) AFG2 is easier to degrade by laccase due to its lack of side products and favorable binding dynamics; and (3) ample opportunities to optimize laccase for aflatoxin degradation exist, especially via mutations leading to π-π stacking. This study identifies a way to optimize laccase for aflatoxin bioremediation and, more generally, contributes to the research efforts aimed at rational enzyme optimization.

Differential sensing with arrays of de novo designed peptide assemblies.

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings.

NDP52 acts as a redox sensor in PINK1/Parkin-mediated mitophagy.

Mitophagy, the elimination of mitochondria via the autophagy-lysosome pathway, is essential for the maintenance of cellular homeostasis. The best characterised mitophagy pathway is mediated by stabilisation of the protein kinase PINK1 and recruitment of the ubiquitin ligase Parkin to damaged mitochondria. Ubiquitinated mitochondrial surface proteins are recognised by autophagy receptors including NDP52 which initiate the formation of an autophagic vesicle around the mitochondria. Damaged mitochondria also generate reactive oxygen species (ROS) which have been proposed to act as a signal for mitophagy, however the mechanism of ROS sensing is unknown. Here we found that oxidation of NDP52 is essential for the efficient PINK1/Parkin-dependent mitophagy. We identified redox-sensitive cysteine residues involved in disulphide bond formation and oligomerisation of NDP52 on damaged mitochondria. Oligomerisation of NDP52 facilitates the recruitment of autophagy machinery for rapid mitochondrial degradation. We propose that redox sensing by NDP52 allows mitophagy to function as a mechanism of oxidative stress response.

Probing disorder in 2CzPN using core and valence states.

Molecules which exhibit thermally activated delayed fluorescence (TADF) show great promise for use in efficient, environmentally-friendly OLEDs, and thus the design of new TADF emitters is an active area of research. However, when used in devices, they are typically in the form of disordered thin films, where both the external molecular environment and thermally-induced internal variations in parameters such as the torsion angle can strongly influence their electronic structure. In this work, we use density functional theory and X-ray photoelectron spectroscopy to investigate the impact of disorder on both core and valence states in the TADF emitter 2CzPN (1,2-bis(carbazol-9-yl)-4,5-dicyanobenzene). By simulating gas phase molecules displaying varying levels of disorder, we assess the relative sensitivity of the different states to factors such as varying torsion angle. The theoretical results for both core and valence states show good agreement with experiment, thereby also highlighting the advantages of our approach for interpreting experimental spectra of large aromatic molecules, which are too complex to interpret based solely on experimental data.

De novo designed peptides for cellular delivery and subcellular localisation.

Increasingly, it is possible to design peptide and protein assemblies de novo from first principles or computationally. This approach provides new routes to functional synthetic polypeptides, including designs to target and bind proteins of interest. Much of this work has been developed in vitro. Therefore, a challenge is to deliver de novo polypeptides efficiently to sites of action within cells. Here we describe the design, characterisation, intracellular delivery, and subcellular localisation of a de novo synthetic peptide system. This system comprises a dual-function basic peptide, programmed both for cell penetration and target binding, and a complementary acidic peptide that can be fused to proteins of interest and introduced into cells using synthetic DNA. The designs are characterised in vitro using biophysical methods and X-ray crystallography. The utility of the system for delivery into mammalian cells and subcellular targeting is demonstrated by marking organelles and actively engaging functional protein complexes.

Searching for a Reliable Density Functional for Molecule-Environment Interactions, Found B97M-V/def2-mTZVP.

In the present study, we have examined density functional theory methods for the calculation of the interaction energy between a small molecule and its environment. For simple systems such as a neutral solute in a neutral solvent, good accuracy can be attained using low-cost "3c" methods, in particular r2SCAN-3c. When part(s) of the system is charged, the accurate computation of the interactions is more challenging. In these cases, we find the B97M-V/def2-mTZVP method to agree well with reference values; it also shows good accuracy for the more straightforward neutral systems. Thus, B97M-V/def2-mTZVP provides a means for accurate and low-cost computation of interaction energies, notably the binding between a substrate or a drug molecule and an enzyme, which may facilitate rational drug design.

Modeling the Conformational Preference of the Lignocellulose Interface and Its Interaction with Weak Acids.

We have examined the conformational space of model systems for the hydrogen-bonded and covalent linkages between the sugar and lignin components of lignocellulose. Specifically, glucose and paracoumaryl alcohol moieties are used in our models. Multistage screening protocols are used to identify and validate a set of lowest-energy isomers. We found a cost-effective screening process involving an initial screening with DFTB3/3ob using a 20 kJ mol-1 threshold, a refinement with the SCANh/6-31+G(2d,p) method with a 10 kJ mol-1 cutoff, a third step at the DSD-PBEP86/ma-def2-TZVP//MS1-D3/6-31+G(2d,p) level with the same 10 kJ mol-1 threshold, and a last step at the CCSD(T)/CBS//B3LYP/cc-pVTZ level with a tighter 5 kJ mol-1 threshold. The use of machine learning (with the London and Axilrod-Teller-Muto potential) can further accelerate the screening process. In general, all low-energy conformers are characterized by hydrogen bonding between the sugar and lignin moieties. We examined the interactions of covalently bonded sugar-lignin models with weak acids (HSO3-, H2PO3-, HSeO3-, H2citrate-, etc.) and found that they interacted strongly with the oxygen of the sugar-O-lignin linkage. Our results suggest that acids such as dihydrogen citrate may be attractive alternatives to the commonly used HSO3- for lignocellulose processing.

Neighborhood poverty, intergenerational mobility, and early developmental health in a population birth cohort.

Living in a neighborhood with high levels of intergenerational mobility is associated with better childhood cognition and behavior as well as adult health. Nevertheless, it is unclear if such differences originate earlier, and thus if neighborhood intergenerational mobility is associated with health differences at birth. To address this question, we examined whether neighborhood intergenerational mobility, independent of neighborhood poverty, was associated with low birth weight (LBW) in a population-based cohort of singleton children born in California in 2017 (n = 426,873). Although increased neighborhood mobility was associated with a decreased likelihood of LBW, it was no longer associated with LBW (OR = 0.98, CI = 0.96, 1.00) after adjusting for neighborhood poverty. Meanwhile, neighborhood poverty was associated with LBW (OR = 1.04, CI = 1.02, 1.05) after accounting for mobility, with the odds of LBW 9.4% higher among children born where neighborhood poverty was in the 90th percentile compared with children born where neighborhood poverty was in the 10th percentile. Findings indicate that neighborhood poverty, but not intergenerational mobility, is a robust and independent correlate of increased LBW births, and thus early developmental health. These findings also suggest that the role of neighborhood intergenerational mobility in child and adult health outcomes may emerge later in development, independent of LBW, or that the role of neighborhood intergenerational mobility in LBW may be indirectly mediated through exposure to neighborhood poverty.

Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures.

We employ a recently developed complexity-reduction quantum mechanical (QM-CR) approach, based on complexity reduction of density functional theory calculations, to characterize the interactions of the SARS-CoV-2 spike receptor binding domain (RBD) with ACE2 host receptors and antibodies. QM-CR operates via ab initio identification of individual amino acid residue's contributions to chemical binding and leads to the identification of the impact of point mutations. Here, we especially focus on the E484K mutation of the viral spike protein. We find that spike residue 484 hinders the spike's binding to the human ACE2 receptor (hACE2). In contrast, the same residue is beneficial in binding to the bat receptor Rhinolophus macrotis ACE2 (macACE2). In agreement with empirical evidence, QM-CR shows that the E484K mutation allows the spike to evade categories of neutralizing antibodies like C121 and C144. The simulation also shows how the Delta variant spike binds more strongly to hACE2 compared to the original Wuhan strain, and predicts that a E484K mutation can further improve its binding. Broad agreement between the QM-CR predictions and experimental evidence supports the notion that ab initio modeling has now reached the maturity required to handle large intermolecular interactions central to biological processes.

Taking Advantage of a Systematic Energy Non-linearity Error in Density Functional Theory for the Calculation of Electronic Energy Levels.

We present an approximate approach for the calculation of ionization potential (IP) and electron affinity (EA) by exploiting the complementary energy non-linearity errors for a species M and its one-electron-ionized counterpart (M+). Reasonable IPs and EAs are thus obtained by averaging the orbital energies of M and M+, even with a low-level method such as BLYP/6-31G(d). By combining the corrected IPs and EAs, we can further obtain reasonable excitation energies. The errors in uncorrected valence IPs and uncorrected virtual-orbital energies show systematic trends. These characteristics provide a convenient and computationally efficient avenue for qualitative estimation of these properties with single corrections for multiple IPs and excitation energies.