Accurate bond and angle parameters for X-ray protein structure refinement

Structural insight of a bi-functional isoprenyl diphosphate synthase Rv0562 from Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis. Mtb uses MK-9(II-H2), which consists of an isoprenyl side chain containing nine isoprene units, with one being hydrogenated in the β-position, as an essential element in the electron transport system. Rv0562 that can operate geranylgeranyl diphosphate synthase (GGPPs) activity catalyzes the synthesis of MK-9(II-H2) by condensing one molecule of DMAPP with eight molecules of isopentenyl diphosphate (IPP) to form a C45 long-chain isoprenoid product. In this study, the structures of Rv0562 were determined in the apo-form at a resolution of 2.54 Å and in complex with IPP and Mg2+ at a resolution of 1.89 Å, revealing detailed interactions between the enzyme and substrates. Moreover, the crystal structure of the Rv0562-DM variant was determined at 2.27 Å resolution in complex with polyethylene glycol (PEG), which occupies the substrate binding tunnel, mimicking the long-chain product. The chain length determination mechanism of Rv0562 is also probed through mutagenesis experiments. The obtained structures help us understand how Rv0562 catalyzes isoprenyl chain elongation, showing implications in developing new anti-Mtb treatments.

Peptide bonds revisited

Understanding the structural and chemical properties of peptide bonds within protein secondary structures is vital for elucidating their roles in protein folding, stability and function. This study examines the distinct characteristics of peptide bonds in α-helices and β-strands using a nonredundant data set comprising 1024 high-resolution protein crystal structures from the Protein Data Bank (PDB). The analysis reveals surprising and intriguing insights into bond lengths, angles, dihedral angles, electron-density distributions and hydrogen bonding within α-helices and β-strands. While the respective bond lengths (CN and CO) do not differ much between helices and strands, the bond angles (∠CNCα and ∠OCN) are significantly larger in strands compared with helices. Furthermore, the peptide dihedral angle (ω) in helices clusters around 180° and follows a sharp Gaussian distribution with a standard deviation of 4.1°. In contrast, the distribution of dihedral angles in strands spans a much wider range, with a more flattened Gaussian peak around 180°. This distinct difference in the distribution of dihedral angles reflects the unique structural characteristics of helices and strands, highlighting their respective conformational preferences. Additionally, if the ratio of the electron-density values (2mFo - DFc) at the midpoint of the CO bond and of the CN bond is calculated, a skewed distribution is observed, with the ratio being lower for helices than for strands. Moreover, higher normalized mean atomic displacement parameters (ADPs) for peptide atoms in helices relative to strands suggest increased flexibility or a more dynamic structure within helical regions. Analysis of hydrogen-bond distances between O and N atoms of the main chain reveals larger distances in helices compared with strands, indicative of distinct hydrogen-bonding patterns associated with different secondary structures. All of these observations taken together led us to conclude that peptide bonds in α-helices are different from peptide bonds in β-strands. Overall, α-helical peptide bonds seem to display a more enol-like character. This suggests that peptide oxygen atoms in helices are more likely to be protonated. These findings have several important implications for refining protein structures, particularly in regions susceptible to enol-like transitions or protonation. By recognizing the distinct bond-angle and bond-length variations associated with protonated carbonyl oxygen atoms, current refinement protocols can be adapted to apply more flexible restraints in these regions. This could improve the accuracy of modelling local geometries, where protonation or enol forms lead to subtle structural deviations from the canonical bond parameters typically enforced in refinement strategies.

How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology

The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL-E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditional Score Networks (NCSNs), and Graph NNs (GNNs) have impacted computer vision, language editing and translation, automated conversation, image generation, and social network management. Proteins can be viewed as texts written with the alphabet of amino acids, as images, or as graphs of interacting residues. Each of these perspectives suggests the use of tools from a different area of deep learning for protein structural biology. Here, I review how CNNs, LLMs, DDPMs/NCSNs, and GNNs have led to major advances in protein structure prediction, inverse folding, protein design, and small molecule design. This review is primarily intended as a deep learning primer for practicing experimental structural biologists. However, extensive references to the deep learning literature should also make it relevant to readers who have a background in machine learning, physics or statistics, and an interest in protein structural biology.

In Silico Discovery and Predictive Modeling of Novel Acetylcholinesterase (AChE) Inhibitors for Alzheimer's Treatment

INTRODUCTION

Alzheimer's disease, akin to coronary artery disease of the heart, is a progressive brain disorder driven by nerve cell damage.

METHODS

This study utilized computational methods to explore 14 anti-acetylcholinesterase (AChE) derivatives (1 ̶ 14) as potential treatments. By scrutinizing their interactions with 11 essential target proteins (AChE, Aβ, BChE, GSK-3β, MAO B, PDE-9, Prion, PSEN-1, sEH, Tau, and TDP-43) and comparing them with established drugs such as donepezil, galantamine, memantine, and rivastigmine, ligand 14 emerged as notable. During molecular dynamics simulations, the protein boasting the strongest bond with the critical 1QTI protein and exceeding drug-likeness criteria also exhibited remarkable stability within the enzyme's pocket across diverse temperatures (300- 320 K). In addition, we utilized density functional theory (DFT) to compute dipole moments and molecular orbital properties, including assessing the thermodynamic stability of AChE derivatives.

RESULT

This finding suggests a well-defined, potentially therapeutic interaction further supported by theoretical and future in vitro and in vivo investigations.

CONCLUSION

Ligand 14 thus emerges as a promising candidate in the fight against Alzheimer's disease.

Hydralazine inhibits cysteamine dioxygenase to treat preeclampsia and senesce glioblastoma

The vasodilator hydralazine (HYZ) has been used clinically for ~ 70 years and remains on the World Health Organization's List of Essential Medicines as a therapy for preeclampsia. Despite its longstanding use and the concomitant progress toward a general understanding of vasodilation, the target and mechanism of HYZ have remained unknown. We show that HYZ selectively targets 2-aminoethanethiol dioxygenase (ADO) by chelating its metal cofactor and alkylating one of its ligands. This covalent inactivation slows entry of proteins into the Cys/N-degron pathway that ADO initiates. HYZ's capacity to stabilize regulators of G-protein signaling (RGS4/5) normally marked for degradation by ADO explains its effect on blood vessel tension and comports with prior associations of insufficient RGS levels with human preeclampsia and analogous symptoms in mice. The established importance of ADO in glioblastoma led us to test HYZ in these cell types. Indeed, a single treatment induced senescence, suggesting a potential new HYZ-based therapy for this deadly brain cancer.

Evaluating the Anticancer Properties of Novel Piscidinol A Derivatives: Insights from DFT, Molecular Docking, and Molecular Dynamics Studies

Cancer is characterized by uncontrolled cell growth and spreading throughout the body. This study employed computational approaches to investigate 18 naturally derived anticancer piscidinol A derivatives (1-18) as potential therapeutics. By examining their interactions with 15 essential target proteins (HIF-1α, RanGAP, FOXM1, PARP2, HER2, ERα, NGF, FAS, GRP78, PRDX2, SCF complex, EGFR, Bcl-xL, ERG, and HSP70) and comparing them with established drugs such as camptothecin, docetaxel, etoposide, irinotecan, paclitaxel, and teniposide, compound 10 emerged as noteworthy. In molecular dynamics simulations, the protein with the strongest binding to the crucial 1A52 protein exceeded druglikeness criteria and displayed extraordinary stability within the enzyme's pocket over varied temperatures (300-320 K). Additionally, density functional theory was used to calculate dipole moments and molecular orbital characteristics, as well as analyze the thermodynamic stability of the putative anticancer derivatives. This finding reveals a well-defined, potentially therapeutic relationship supported by theoretical analysis, which is in good agreement with subsequent assessments of their potential in vitro cytotoxic effects of piscidinol A derivatives (6-18) against various cancer cell lines. Future in vivo and clinical studies are required to validate these findings further. Compound 10 thus emerges as an intriguing contender in the fight against cancer.

Influence of Stereochemistry in a Local Approach for Calculating Protein Conformations

Protein structure prediction is generally based on the use of local conformational information coupled with long-range distance restraints. Such restraints can be derived from the knowledge of a template structure or the analysis of protein sequence alignment in the framework of models arising from the physics of disordered systems. The accuracy of approaches based on sequence alignment, however, is limited in the case where the number of aligned sequences is small. Here, we derive protein conformations using only local conformations knowledge by means of the interval Branch-and-Prune algorithm. The computation efficiency is directly related to the knowledge of stereochemistry (bond angle and ω values) along the protein sequence and, in particular, to the variations of the torsion angle ω. The impact of stereochemistry variations is particularly strong in the case of protein topologies defined from numerous long-range restraints, as in the case of protein of β secondary structures. The systematic enumeration of the conformations improves the efficiency of the calculations. The analysis of DNA codons permits to connect the variations of torsion angle ω to the positions of rare DNA codons.

Quantum refinement in real and reciprocal space using the Phenix and ORCA software

X-ray and neutron crystallography, as well as cryogenic electron microscopy (cryo-EM), are the most common methods to obtain atomic structures of biological macromolecules. A feature they all have in common is that, at typical resolutions, the experimental data need to be supplemented by empirical restraints, ensuring that the final structure is chemically reasonable. The restraints are accurate for amino acids and nucleic acids, but often less accurate for substrates, inhibitors, small-molecule ligands and metal sites, for which experimental data are scarce or empirical potentials are harder to formulate. This can be solved using quantum mechanical calculations for a small but interesting part of the structure. Such an approach, called quantum refinement, has been shown to improve structures locally, allow the determination of the protonation and oxidation states of ligands and metals, and discriminate between different interpretations of the structure. Here, we present a new implementation of quantum refinement interfacing the widely used structure-refinement software Phenix and the freely available quantum mechanical software ORCA. Through application to manganese superoxide dismutase and V- and Fe-nitrogenase, we show that the approach works effectively for X-ray and neutron crystal structures, that old results can be reproduced and structural discrimination can be performed. We discuss how the weight factor between the experimental data and the empirical restraints should be selected and how quantum mechanical quality measures such as strain energies should be calculated. We also present an application of quantum refinement to cryo-EM data for particulate methane monooxygenase and show that this may be the method of choice for metal sites in such structures because no accurate empirical restraints are currently available for metals.

α-Amino-β-carboxymuconate-ε-semialdehyde decarboxylase catalyzes enol/keto tautomerization of oxaloacetate

ACMSD (α-amino-β-carboxymuconate-ε-semialdehyde decarboxylase) is a key metalloenzyme critical for regulating de novo endogenous NAD+/NADH biosynthesis through the tryptophan-kynurenine pathway. This decarboxylase is a recognized target implicated in mitochondrial diseases and neurodegenerative disorders. However, unraveling its enzyme-substrate complex has been challenging due to its high catalytic efficiency. Here, we present a combined biochemical and structural study wherein we determined the crystal structure of ACMSD in complex with malonate. Our analysis revealed significant rearrangements in the active site, particularly in residues crucial for ACMS decarboxylation, including Arg51, Arg239∗ (a residue from an adjacent subunit), His228, and Trp194. Docking modeling studies proposed a putative ACMS binding mode. Additionally, we found that ACMSD catalyzes oxaloacetic acid (OAA) tautomerization at a rate of 6.51 ± 0.42 s-1 but not decarboxylation. The isomerase activity of ACMSD on OAA warrants further investigation in future biological studies. Subsequent mutagenesis studies and crystallographic analysis of the W194A variant shed light on the roles of specific second-coordination sphere residues. Our findings indicate that Arg51 and Arg239∗ are crucial for OAA tautomerization. Moreover, our comparative analysis with related isomerase superfamily members underscores a general strategy employing arginine residues to promote OAA isomerization. Given the observed isomerase activity of ACMSD on OAA and its structural similarity to ACMS, we propose that ACMSD may facilitate isomerization to ensure ACMS is in the optimal tautomeric form for subsequent decarboxylation initiated by the zinc-bound hydroxide ion. Overall, these findings deepen the understanding of the structure and function of ACMSD, offering insights into potential therapeutic interventions.

Solvent organization in the ultrahigh-resolution crystal structure of crambin at room temperature

Ultrahigh-resolution structures provide unprecedented details about protein dynamics, hydrogen bonding and solvent networks. The reported 0.70 Å, room-temperature crystal structure of crambin is the highest-resolution ambient-temperature structure of a protein achieved to date. Sufficient data were collected to enable unrestrained refinement of the protein and associated solvent networks using SHELXL. Dynamic solvent networks resulting from alternative side-chain conformations and shifts in water positions are revealed, demonstrating that polypeptide flexibility and formation of clathrate-type structures at hydrophobic surfaces are the key features endowing crambin crystals with extraordinary diffraction power.

Generative artificial intelligence performs rudimentary structural biology modeling

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between the anti-viral nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the current capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.

AQuaRef: Machine learning accelerated quantum refinement of protein structures

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical restraints, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions relying solely on nonbonded repulsions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. We present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 neural network potential mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data.

Development of an FeII Complex Exhibiting Intermolecular Proton Shifting Coupled Spin Transition

In this study, we investigated reversible intermolecular proton shifting (IPS) coupled with spin transition (ST) in a novel FeII complex. The host FeII complex and the guest carboxylic acid anion were connected by intermolecular hydrogen bonds (IHBs). We extended the intramolecular proton transfer coupled ST phenomenon to the intermolecular system. The dynamic phenomenon was confirmed by variable-temperature single-crystal X-ray diffraction, neutron crystallography, and infrared spectroscopy. The mechanism of IPS was further validated using density functional theory calculations. The discovery of IPS-coupled ST in crystalline molecular materials provides good insights into fundamental processes and promotes the design of novel multifunctional materials with tunable properties for various applications, such as optoelectronics, information storage, and molecular devices.

Generative artificial intelligence performs rudimentary structural biology modeling

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.

Disentangling the formation, mechanism, and evolvement of the covalent methanesulfonyl fluoride acetylcholinesterase adduct: Insights into an aged-like inactive complex susceptible to reactivation by a combination of nucleophiles

Chemical warfare nerve agents and pesticides, known as organophosphorus compounds inactivate cholinesterases (ChEs) by phosphorylating the serine hydroxyl group located at the active site of ChEs. Over the course of time, phosphorylation is followed by loss of an organophosphate-leaving group and the bond with ChEs becomes irreversible, a process known as aging. Differently, structurally related irreversible catalytic poisons bearing sulfur instead of phosphorus convert ChEs in its aged form only by covalently binding to the key catalytic serine. Kinetic and crystallographic studies of the interaction between Torpedo californica acetylcholinesterase (TcAChE) and a small organosulfonate, methanesulfonyl fluoride (MSF), indeed revealed irreversibly methylsulfonylated serine 200, to be isosteric with the bound aged sarin/soman analogues. The potent bulky reversible inhibitor 7-bis-tacrine (BTA) adopts, in the active site of the crystal structure of the MSF-enzyme adduct, a location and an orientation that closely resemble the one being found in the crystal structure of the BTA-enzyme complex. Remarkably, the presence of BTA accelerates the rate of methanesulfonylation by a factor of two. This unexpected result can be explained on the basis of two facts: i) the steric hindrance exerted by BTA to MSF in accessing the active site and ii) the acceleration of the MSF-enzyme adduct formation as a consequence of the lowering of the rotational and translational degrees of freedom in the proximity of the catalytic serine. It is well known that pralidoxime (2-Pyridine Aldoxime Methyl chloride, 2-PAM) alone or in the presence of the substrate acetylcholine cannot reactivate the active site serine of the TcAChE-MSF adduct. We show that the simultaneous presence of 2-PAM and the additional neutral oxime, 2-[(hydroxyimino)methyl]-l-methylimidazol (2-HAM), triggers the reactivation process of TcAChE within the hour timescale. Overall, our results pave the way toward the likely use of a cocktail of distinctive oximes as a promising recipe for an effective and fast reactivation of aged cholinesterases.

Xplor-NIH: Better parameters and protocols for NMR protein structure determination

The present work describes an update to the protein covalent geometry and atomic radii parameters in the Xplor-NIH biomolecular structure determination package. In combination with an improved treatment of selected non-bonded interactions between atoms three bonds apart, such as those involving methyl hydrogens, and a previously developed term that affects the system's gyration volume, the new parameters are tested using structure calculations on 30 proteins with restraints derived from nuclear magnetic resonance data. Using modern structure validation criteria, including several formally adopted by the Protein Data Bank, and a clear measure of structural accuracy, the results show superior performance relative to previous Xplor-NIH implementations. Additionally, the Xplor-NIH structures compare favorably against originally determined NMR models.

Flexible active-site loops fine-tune substrate specificity of hyperthermophilic metallo-oxidases

Hyperthermophilic ('superheat-loving') archaea found in high-temperature environments such as Pyrobaculum aerophilum contain multicopper oxidases (MCOs) with remarkable efficiency for oxidizing cuprous and ferrous ions. In this work, directed evolution was used to expand the substrate specificity of P. aerophilum McoP for organic substrates. Six rounds of error-prone PCR and DNA shuffling followed by high-throughput screening lead to the identification of a hit variant with a 220-fold increased efficiency (kcat/Km) than the wild-type for 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) without compromising its intrinsic activity for metal ions. The analysis of the X-ray crystal structure reveals four proximal mutations close to the T1Cu active site. One of these mutations is within the 23-residues loop that occludes this site, a distinctive feature of prokaryotic MCOs. The increased flexibility of this loop results in an enlarged tunnel and one additional pocket that facilitates bulky substrate-enzyme interactions. These findings underscore the synergy between mutations that modulate the dynamics of the active-site loop enabling enhanced catalytic function. This study highlights the potential of targeting loops close to the T1Cu for engineering improvements suitable for biotechnological applications.

Feeling the Strain: Quantifying Ligand Deformation in Photosynthesis

Structural distortion of protein-bound ligands can play a critical role in enzyme function by tuning the electronic and chemical properties of the ligand molecule. However, quantifying these effects is difficult due to the limited resolution of protein structures and the difficulty of generating accurate structural restraints for nonprotein ligands. Here, we seek to quantify these effects through a statistical analysis of ligand distortion in chlorophyll proteins (CP), where ring deformation is thought to play a role in energy and electron transfer. To assess the accuracy of ring-deformation estimates from available structural data, we take advantage of the C2 symmetry of photosystem II (PSII), comparing ring-deformation estimates for equivalent sites both within and between 113 distinct X-ray and cryogenic electron microscopy PSII structures. Significantly, we find that several deformation modes exhibit considerable variability in predictions, even for equivalent monomers, down to a 2 Å resolution, to an extent that probably prevents their utilization in optical calculations. We further find that refinement restraints play a critical role in determining deformation values to resolution as low as 2 Å. However, for those modes that are well-resolved in the structural data, ring deformation in PSII is strongly conserved across all species tested from cyanobacteria to algae. These results highlight both the opportunities and limitations inherent in structure-based analyses of the bioenergetic and optical properties of CPs and other protein-ligand complexes.

Remodeling the polymer-binding cavity to improve the efficacy of PBAT-degrading enzyme

Poly(butylene adipate-co-terephthalate) (PBAT) is among the most widely applied synthetic polyesters that are utilized in the packaging and agricultural industries, but the accumulation of PBAT wastes has posed a great burden to ecosystems. Using renewable enzymes to decompose PBAT is an eco-friendly solution to tackle this problem. Recently, we demonstrated that cutinase is the most effective PBAT-degrading enzyme and that an engineered cutinase termed TfCut-DM could completely decompose PBAT film to terephthalate (TPA). Here, we report crystal structures of a variant of leaf compost cutinase in complex with soluble fragments of PBAT, including BTa and TaBTa. In the TaBTa complex, one TPA moiety was located at a polymer-binding site distal to the catalytic center that has never been experimentally validated. Intriguingly, the composition of the distal TPA-binding site shows higher diversity relative to the one proximal to the catalytic center in various cutinases. We thus modified the distal TPA-binding site of TfCut-DM and obtained variants that exhibit higher activity. Notably, the time needed to completely degrade the PBAT film to TPA was shortened to within 24 h by TfCut-DM Q132Y (5813 mol per mol protein). Taken together, the structural information regarding the substrate-binding behavior of PBAT-degrading enzymes could be useful guidance for direct enzyme engineering.

Investigation of how gate residues in the main channel affect the catalytic activity of Scytalidium thermophilum catalase

Catalase is an antioxidant enzyme that breaks down hydrogen peroxide (H2O2) into molecular oxygen and water. In all monofunctional catalases the pathway that H2O2 takes to the catalytic centre is via the `main channel'. However, the structure of this channel differs in large-subunit and small-subunit catalases. In large-subunit catalases the channel is 15 Å longer and consists of two distinct parts, including a hydrophobic lower region near the heme and a hydrophilic upper region where multiple H2O2 routes are possible. Conserved glutamic acid and threonine residues are located near the intersection of these two regions. Mutations of these two residues in the Scytalidium thermophilum catalase had no significant effect on catalase activity. However, the secondary phenol oxidase activity was markedly altered, with kcat and kcat/Km values that were significantly increased in the five variants E484A, E484I, T188D, T188I and T188F. These variants also showed a lower affinity for inhibitors of oxidase activity than the wild-type enzyme and a higher affinity for phenolic substrates. Oxidation of heme b to heme d did not occur in most of the studied variants. Structural changes in solvent-chain integrity and channel architecture were also observed. In summary, modification of the main-channel gate glutamic acid and threonine residues has a greater influence on the secondary activity of the catalase enzyme, and the oxidation of heme b to heme d is predominantly inhibited by their conversion to aliphatic and aromatic residues.

X-ray Crystallography Module in MD Simulation Program Amber 2023. Refining the Models of Protein Crystals

The MD simulation package Amber offers an attractive platform to refine crystallographic structures of proteins: (i) state-of-the-art force fields help to regularize protein coordinates and reconstruct the poorly diffracting elements of the structure, such as flexible loops; (ii) MD simulations restrained by the experimental diffraction data provide an effective strategy to optimize structural models of protein crystals, including explicitly modeled interstitial solvent as well as crystal contacts. Here, we present the new crystallography module xray, released as a part of the Amber 2023 package. This module contains functions to calculate and scale structure factors (including the contributions from bulk solvent), evaluate the maximum-likelihood-type crystallographic potential, and compute its derivative forces. The X-ray functionality of Amber no longer relies on external dependencies so that the full advantage of GPU acceleration can be taken. This makes it possible to refine in a short time hundreds of crystal models, including supercell models comprised of multiple unit cells. The new automated Amber-based refinement procedure leads to an appreciable improvement in Rfree (in some cases, by as much as 0.067) as well as MolProbity scores.

Structural insights into a novel nonheme iron-dependent oxygenase in selenoneine biosynthesis

Selenoneine (SEN) is a natural histidine derivative with radical-scavenging activity and shows higher antioxidant potential than its sulfur-containing isolog ergothioneine (EGT). Recently, the SEN biosynthetic pathway in Variovorax paradoxus was reported. Resembling EGT biosynthesis, the committed step of SEN synthesis is catalyzed by a nonheme Fe-dependent oxygenase termed SenA. This enzyme catalyzes oxidative carbon‑selenium (C-Se) bond formation to conjugate N-α-trimethyl histidine (TMH) and selenosugar to yield selenoxide; the process parallels the EGT biosynthetic route, in which sulfoxide synthases known as EgtB members catalyze the conjugation of TMH and cysteine or γ-glutamylcysteine to afford sulfoxides. Here, we report the crystal structures of SenA and its complex with TMH and thioglucose (SGlc), an analog of selenoglucose (SeGlc) at high resolution. The overall structure of SenA adopts the archetypical fold of EgtB, which comprises a DinB-like domain and an FGE-like domain. While the TMH-binding site is highly conserved to that of EgtB, a various substrate-enzyme interaction network in the selenosugar-binding site of SenA features a number of water-mediated hydrogen bonds. The obtained structural information is beneficial for understanding the mechanism of SenA-mediated C-Se bond formation.

Self-assembly of glycoprotein nanostructured filaments for modulating extracellular networks at long range

The intriguing capability of branched glycoprotein filaments to change their hierarchical organization, mediated by external biophysical stimuli, continues to expand understanding of self-assembling strategies that can dynamically rearrange networks at long range. Previous research has explored the corresponding biological, physiological and genetic mechanisms, focusing on protein assemblies within a limited range of nanometric units. Using direct microscopy bio-imaging, we have determined the morpho-structural changes of self-assembled filament networks of the zona pellucida, revealing controlled levels of structured organizations to join distinct evolved stages of the oocyte (Immature, Mature, and Fertilized). This natural soft network reorganizes its corresponding hierarchical network to generate symmetric, asymmetric, and ultimately a state with the lowest asymmetry of the outer surface roughness, and internal pores reversibly changed from elliptical to circular configurations at the corresponding stages. These elusive morpho-structural changes are regulated by the nanostructured polymorphisms of the branched filaments by self-extension/-contraction/-bending processes, modulated by determinate theoretical angles among repetitive filament units. Controlling the nanoscale self-assembling properties by delivering a minimum number of activation bio-signals may be triggered by these specific nanostructured polymorphic organizations. Finally, this research aims to guide this soft biomaterial into a desired state to protect oocytes, eggs, and embryos during development, to favour/prevent the fertilization/polyspermy processes and eventually to impact interactions with bacteria/virus at multiscale levels.

Accurate prediction of protein tertiary structural changes induced by single-site mutations with equivariant graph neural networks

Predicting the change of protein tertiary structure caused by singlesite mutations is important for studying protein structure, function, and interaction. Even though computational protein structure prediction methods such as AlphaFold can predict the overall tertiary structures of most proteins rather accurately, they are not sensitive enough to accurately predict the structural changes induced by single-site amino acid mutations on proteins. Specialized mutation prediction methods mostly focus on predicting the overall stability or function changes caused by mutations without attempting to predict the exact mutation-induced structural changes, limiting their use in protein mutation study. In this work, we develop the first deep learning method based on equivariant graph neural networks (EGNN) to directly predict the tertiary structural changes caused by single-site mutations and the tertiary structure of any protein mutant from the structure of its wild-type counterpart. The results show that it performs substantially better in predicting the tertiary structures of protein mutants than the widely used protein structure prediction method AlphaFold.

Site-specific relaxation of peptide bond planarity induced by electrically attracted proton/deuteron observed by neutron crystallography

In structural biology, peptide bonds, fundamental linkages between hundreds of amino acids, of which a protein molecule is composed, have been commonly treated as a plane structure just as Linus Pauling et al. proposed. In this paper, a site-specific peptide bond relaxation mechanism by deuterons whose localization has been suggested by neutron crystallography is proposed. Such deuteron was observed as an arm of neutron scattering length density protruding from the carbonyl oxygen atoms in the main chain in the omit map drawn by neutron crystallography of human lysozyme. Our comprehensive study using x-ray and neutron diffraction and 15 N chemical shifts of individual amide nitrogen atoms within the same peptide bond strongly suggests the relaxation of the electronic resonance structure because of site-specific modulation by protons/deuterons localized on the electron orbital of the carbonyl oxygen. All experimental data used in this examination were obtained at room temperature, which is preferable for enzymatic activity. Such a close interaction between the electron resonance structure of a peptide bond and the exchangeable protons/deuterons well agreed with that observed in an intermediate state in an amide hydrolytic reaction simulated by the ab-initio calculation including water molecules.

An In Silico Bioremediation Study to Identify Essential Residues of Metallothionein Enhancing the Bioaccumulation of Heavy Metals in Pseudomonas aeruginosa

Microorganisms are ubiquitously present in the environment and exert significant influence on numerous natural phenomena. The soil and groundwater systems, precipitation, and effluent outfalls from factories, refineries, and waste treatment facilities are all sources of heavy metal contamination. For example, Madinah, Saudi Arabia, has alarmingly high levels of lead and cadmium. The non-essential minerals cadmium (Cd) and lead (Pb) have been linked to damage to vital organs. Bioremediation is an essential component in the process of cleaning up polluted soil and water where biological agents such as bacteria are used to remove the contaminants. It is demonstrated that Pseudomonas aeruginosa (P. aeruginosa) isolated from activated sludge was able to remove Cd and Pb from water. The protein sequence of metallothionein from P. aeruginosa was retrieved to explore it for physicoparameters, orthologs, domain, family, motifs, and conserved residues. The homology structure was generated, and models were validated. Docking of the best model with the heavy metals was carried out to inspect the intramolecular interactions. The target protein was found to belong to the "metallothionein_pro" family, containing six motifs, and showed a close orthologous relationship with other heavy metal-resistant bacteria. The best model was generated by Phyre2. In this study, three key residues of metallothionein were identified that participate in heavy metal (Pb and Cd) binding, viz., Ala33, Ser34, and Glu59. In addition, the study provides an essential basis to explore protein engineering for the optimum use of metallothionein protein to reduce/remove heavy metals from the environment.

Molecular architecture and conservation of an immature human endogenous retrovirus

The human endogenous retrovirus K (HERV-K) is the most recently acquired endogenous retrovirus in the human genome and is activated and expressed in many cancers and amyotrophic lateral sclerosis. We present the immature HERV-K capsid structure at 3.2 Å resolution determined from native virus-like particles using cryo-electron tomography and subtomogram averaging. The structure shows a hexamer unit oligomerized through a 6-helix bundle, which is stabilized by a small molecule analogous to IP6 in immature HIV-1 capsid. The HERV-K immature lattice is assembled via highly conserved dimer and trimer interfaces, as detailed through all-atom molecular dynamics simulations and supported by mutational studies. A large conformational change mediated by the linker between the N-terminal and the C-terminal domains of CA occurs during HERV-K maturation. Comparison between HERV-K and other retroviral immature capsid structures reveals a highly conserved mechanism for the assembly and maturation of retroviruses across genera and evolutionary time.

Molecular architecture and conservation of an immature human endogenous retrovirus

A significant part of the human genome consists of endogenous retroviruses sequences. Human endogenous retrovirus K (HERV-K) is the most recently acquired endogenous retrovirus, is activated and expressed in many cancers and amyotrophic lateral sclerosis and possibly contributes to the aging process. To understand the molecular architecture of endogenous retroviruses, we determined the structure of immature HERV-K from native virus-like particles (VLPs) using cryo-electron tomography and subtomogram averaging (cryoET STA). The HERV-K VLPs show a greater distance between the viral membrane and immature capsid lattice, correlating with the presence of additional peptides, SP1 and p15, between the capsid (CA) and matrix (MA) proteins compared to the other retroviruses. The resulting cryoET STA map of the immature HERV-K capsid at 3.2 Å resolution shows a hexamer unit oligomerized through a 6-helix bundle which is further stabilized by a small molecule in the same way as the IP6 in immature HIV-1 capsid. The HERV-K immature CA hexamer assembles into the immature lattice via highly conserved dimmer and trimer interfaces, whose interactions were further detailed through all-atom molecular dynamics simulations and supported by mutational studies. A large conformational change mediated by the flexible linker between the N-terminal and the C-terminal domains of CA occurs between the immature and the mature HERV-K capsid protein, analogous to HIV-1. Comparison between HERV-K and other retroviral immature capsid structures reveals a highly conserved mechanism for the assembly and maturation of retroviruses across genera and evolutionary time.

Extent of N-Terminus Folding of Semenogelin 1 Cleavage Product Determines Tendency to Amyloid Formation

It is known that four peptide fragments of predominant protein in human semen Semenogelin 1 (SEM1) (SEM1(86-107), SEM1(68-107), SEM1(49-107) and SEM1(45-107)) are involved in fertilization and amyloid formation processes. In this work, the structure and dynamic behavior of SEM1(45-107) and SEM1(49-107) peptides and their N-domains were described. According to ThT fluorescence spectroscopy data, it was shown that the amyloid formation of SEM1(45-107) starts immediately after purification, which is not observed for SEM1(49-107). Seeing that the peptide amino acid sequence of SEM1(45-107) differs from SEM1(49-107) only by the presence of four additional amino acid residues in the N domain, these domains of both peptides were obtained via solid-phase synthesis and the difference in their dynamics and structure was investigated. SEM1(45-67) and SEM1(49-67) showed no principal difference in dynamic behavior in water solution. Furthermore, we obtained mostly disordered structures of SEM1(45-67) and SEM1(49-67). However, SEM1(45-67) contains a helix (E58-K60) and helix-like (S49-Q51) fragments. These helical fragments may rearrange into β-strands during amyloid formation process. Thus, the difference in full-length peptides' (SEM1(45-107) and SEM1(49-107)) amyloid-forming behavior may be explained by the presence of a structured helix at the SEM1(45-107) N-terminus, which contributes to an increased rate of amyloid formation.

Enhanced conformational exploration of protein loops using a global parameterization of the backbone geometry

Flexible loops are paramount to protein functions, with action modes ranging from localized dynamics contributing to the free energy of the system, to large amplitude conformational changes accounting for the repositioning whole secondary structure elements or protein domains. However, generating diverse and low energy loops remains a difficult problem. This work introduces a novel paradigm to sample loop conformations, in the spirit of the hit-and-run (HAR) Markov chain Monte Carlo technique. The algorithm uses a decomposition of the loop into tripeptides, and a novel characterization of necessary conditions for Tripeptide Loop Closure to admit solutions. Denoting m the number of tripeptides, the algorithm works in an angular space of dimension 12 m. In this space, the hyper-surfaces associated with the aforementioned necessary conditions are used to run a HAR-like sampling technique. On classical loop cases up to 15 amino acids, our parameter free method compares favorably to previous work, generating more diverse conformational ensembles. We also report experiments on a 30 amino acids long loop, a size not processed in any previous work.

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies

Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge. Here, we present IgFold, a fast deep learning method for antibody structure prediction. IgFold consists of a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks that directly predict backbone atom coordinates. IgFold predicts structures of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s). Accurate structure prediction on this timescale makes possible avenues of investigation that were previously infeasible. As a demonstration of IgFold's capabilities, we predicted structures for 1.4 million paired antibody sequences, providing structural insights to 500-fold more antibodies than have experimentally determined structures.

Functional tailoring of a PET hydrolytic enzyme expressed in Pichia pastoris

Using enzymes to hydrolyze and recycle poly(ethylene terephthalate) (PET) is an attractive eco-friendly approach to manage the ever-increasing PET wastes, while one major challenge to realize the commercial application of enzyme-based PET degradation is to establish large-scale production methods to produce PET hydrolytic enzyme. To achieve this goal, we exploited the industrial strain Pichia pastoris to express a PET hydrolytic enzyme from Caldimonas taiwanensis termed CtPL-DM. In contrast to the protein expressed in Escherichia coli, CtPL-DM expressed in P. pastoris is inactive in PET degradation. Structural analysis indicates that a putative N-glycosylation site N181 could restrain the conformational change of a substrate-binding Trp and hamper the enzyme action. We thus constructed N181A to remove the N-glycosylation and found that the PET hydrolytic activity of this variant was restored. The performance of N181A was further enhanced via molecular engineering. These results are of valuable in terms of PET hydrolytic enzyme production in industrial strains in the future.

A Fijivirus Major Viroplasm Protein Shows RNA-Stimulated ATPase Activity by Adopting Pentameric and Hexameric Assemblies of Dimers

Fijiviruses replicate and package their genomes within viroplasms in a process involving RNA-RNA and RNA-protein interactions. Here, we demonstrate that the 24 C-terminal residues (C-arm) of the P9-1 major viroplasm protein of the mal de Río Cuarto virus (MRCV) are required for its multimerization and the formation of viroplasm-like structures. Using an integrative structural approach, the C-arm was found to be dispensable for P9-1 dimer assembly but essential for the formation of pentamers and hexamers of dimers (decamers and dodecamers), which favored RNA binding. Although both P9-1 and P9-1ΔC-arm catalyzed ATP with similar activities, an RNA-stimulated ATPase activity was only detected in the full-length protein, indicating a C-arm-mediated interaction between the ATP catalytic site and the allosteric RNA binding sites in the (do)decameric assemblies. A stronger preference to bind phosphate moieties in the decamer was predicted, suggesting that the allosteric modulation of ATPase activity by RNA is favored in this structural conformation. Our work reveals the structural versatility of a fijivirus major viroplasm protein and provides clues to its mechanism of action. IMPORTANCE The mal de Río Cuarto virus (MRCV) causes an important maize disease in Argentina. MRCV replicates in several species of Gramineae plants and planthopper vectors. The viral factories, also called viroplasms, have been studied in detail in animal reovirids. This work reveals that a major viroplasm protein of MRCV forms previously unidentified structural arrangements and provides evidence that it may simultaneously adopt two distinct quaternary assemblies. Furthermore, our work uncovers an allosteric communication between the ATP and RNA binding sites that is favored in the multimeric arrangements. Our results contribute to the understanding of plant reovirids viroplasm structure and function and pave the way for the design of antiviral strategies for disease control.

Protein structure prediction using the evolutionary algorithm USPEX

Protein structure prediction is one of major problems of modern biophysics: current attempts to predict the tertiary protein structure from amino acid sequence are successful mostly when the use of big data and machine learning allows one to reduce the "prediction problem" to the "problem of recognition". Compared with recent successes of deep learning, classical predictive methods lag behind in their accuracy for the prediction of stable conformations. Therefore, in this work we extended the evolutionary algorithm USPEX to predict protein structure based on global optimization starting with the amino acid sequence. Moreover, we compared frequently used force fields for the task of protein structure prediction. Protein structure relaxation and energy calculations were performed using Tinker (with several different force fields) and Rosetta (with REF2015 force field) codes. To create new protein structure models in the USPEX algorithm, we developed novel variation operators. The test of the new method on seven proteins having (for simplicity) no cis-proline (with ω ≈ 0°) residues, and a length of up to 100 residues, revealed that our algorithm predicts tertiary structures of proteins with high accuracy. The comparison of the final potential energies of the predicted protein structures obtained using the USPEX and the Rosetta Abinitio approach showed that in most cases the developed algorithm found structures with close or even lower energy (Amber/Charmm/Oplsaal) and scoring function (REF2015). While USPEX has clearly demonstrated its ability to find very deep energy minima, our study showed that the existing force fields are not sufficiently accurate for accurate blind prediction of protein structures without further experimental verification.

In situ ligand restraints from quantum-mechanical methods

In macromolecular crystallographic structure refinement, ligands present challenges for the generation of geometric restraints due to their large chemical variability, their possible novel nature and their specific interaction with the binding pocket of the protein. Quantum-mechanical approaches are useful for providing accurate ligand geometries, but can be plagued by the number of minima in flexible molecules. In an effort to avoid these issues, the Quantum Mechanical Restraints (QMR) procedure optimizes the ligand geometry in situ, thus accounting for the influence of the macromolecule on the local energy minima of the ligand. The optimized ligand geometry is used to generate target values for geometric restraints during the crystallographic refinement. As demonstrated using a sample of >2330 ligand instances in >1700 protein-ligand models, QMR restraints generally result in lower deviations from the target stereochemistry compared with conventionally generated restraints. In particular, the QMR approach provides accurate torsion restraints for ligands and other entities.

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation

Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.

Molecular dynamics simulation study on the structures of fascin mutants

Fascin is a filamentous actin (F-actin) bundling protein, which cross-links F-actin into bundles and becomes an important component of filopodia on the cell surface. Fascin is overexpressed in many types of cancers. The mutation of fascin affects its ability to bind to F-actin and the progress of cancer. In this paper, we have studied the effects of residues of K22, K41, K43, K241, K358, K399, and K471 using molecular dynamics (MD) simulation. For the strong-effect residues, that is, K22, K41, K43, K358, and K471, our results show that the mutation of K to A leads to large values of root mean square fluctuation (RMSF) around the mutated residues, indicating those residues are important for the flexibility and thermal stability. On the other hand, based on residue cross-correlation analysis, alanine mutations of these residues reinforce the correlation between residues. Together with the RMSF data, the local flexibility is extended to the entire protein by the strong correlations to influence the dynamics and function of fascin. By contrast, for the mutants of K241A and K399A those do not affect the function of fascin, the RMSF data do not show significant differences compared with wild-type fascin. These findings are in a good agreement with experimental studies.

Conformational ensemble of amyloid-forming semenogelin 1 peptide SEM1(68-107) by NMR spectroscopy and MD simulations

SEM1(68-107) is a peptide corresponding to the region of semenogelin 1 protein from 68 to 107 amino acid position. SEM1(68-107) is an abundant component of semen, which participates in HIV infection enhanced by amyloid fibrils forming. To understand the causes influencing amyloid fibril formation, it is necessary to determine the spatial structure of SEM1(68-107). It was shown that the determination of SEM1(68-107) structure is complicated by the non-informative NMR spectra due to the high intramolecular mobility of peptides. The complementary approach based on the geometric restrictions of individual peptide fragments and molecular modeling was used for the determination of the spatial structure of SEM1(68-107). The N- (SEM1(68-85)) and C-terminuses (SEM1(86-107)) of SEM1(68-107) were chosen as two individual peptide fragments. SEM1(68-85) and SEM1(86-107) structures were established with NMR and circular dichroism CD spectroscopies. These regions were used as geometric restraints for the SEM1(68-107) structure modeling. Even though most of the SEM1(68-107) peptide is unstructured, our detailed analysis revealed the following structured elements: N-terminus (70His-84Gln) forms an α-helix, (86Asp-94Thr) and (101Gly-103Ser) regions fold into 3₁₀-helixes. The absence of a SEM1(68-107) rigid conformation leads to instability of these secondary structure regions. The calculated SEM1(68-107) structure is in good agreement with experimental values of hydrodynamic radius and dihedral angles obtained by NMR spectroscopy. This testifies the adequacy of a combined approach based on the use of peptide fragment structures for the molecular modeling formation of full-size peptide spatial structure.

Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach

This work extends the multi-scale computational scheme for the quantum mechanics (QM) calculations of Nuclear Magnetic Resonance (NMR) chemical shifts (CSs) in proteins that lack a well-defined 3D structure. The scheme couples the sampling of an intrinsically disordered protein (IDP) by classical molecular dynamics (MD) with protein fragmentation using the adjustable density matrix assembler (ADMA) and density functional theory (DFT) calculations. In contrast to our early investigation on IDPs (Pavlíková Přecechtělová et al., J. Chem. Theory Comput., 2019, 15, 5642-5658) and the state-of-the art NMR calculations for structured proteins, a partial re-optimization was implemented on the raw MD geometries in vibrational normal mode coordinates to enhance the accuracy of the MD/ADMA/DFT computational scheme. In addition, machine-learning based cluster analysis was performed on the scheme to explore its potential in producing protein structure ensembles (CLUSTER ensembles) that yield accurate CSs at a reduced computational cost. The performance of the cluster-based calculations is validated against results obtained with conventional structural ensembles consisting of MD snapshots extracted from the MD trajectory at regular time intervals (REGULAR ensembles). CS calculations performed with the refined MD/ADMA/DFT framework employed the 6-311++G(d,p) basis set that outperformed IGLO-III calculations with the same density functional approximation (B3LYP) and both explicit and implicit solvation. The partial geometry optimization did not universally improve the agreement of computed CSs with the experiment but substantially decreased errors associated with the ensemble averaging. A CLUSTER ensemble with 50 structures yielded ensemble averages close to those obtained with a REGULAR ensemble consisting of 500 MD frames. The cluster based calculations thus required only a fraction of the computational time.

Structural analysis and engineering of aldo-keto reductase from glyphosate-resistant Echinochloa colona

Glyphosate is a dominant organophosphate herbicide that inhibits 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) of the shikimate pathway. Glyphosate is extensively applied since manufactured, which has led to the emergence of various glyphosate-resistant crops and weeds. However, the molecular mechanism of many glyphosate-resistance machineries remains unclear. Recently, the upregulated expression of two homologous aldo-keto reductases (AKRs), designated as AKR4C16 and AKR4C17, were found to contribute to the glyphosate resistance in Echinochloa colona. This represents the first naturally evolved glyphosate-degrading machinery reported in plants. Here, we report the three-dimensional structure of these two AKR enzymes in complex with cofactor by performing X-ray crystallography. Furthermore, the binding-mode of glyphosate were elucidated in a ternary complex of AKR4C17. Based on the structural information and the previous study, we proposed a possible mechanism of action of AKR-mediated glyphosate degradation. In addition, a variant F291D of AKR4C17 that was constructed based on structure-based engineering showed a 70% increase in glyphosate degradation. In conclusion, these results demonstrate the structural features and glyphosate-binding mode of AKR4C17, which increases our understanding of the enzymatic mechanism of glyphosate bio-degradation and provides an important basis for the designation of AKR-based glyphosate-resistance for further applications.

Unveiling molecular details behind improved activity at neutral to alkaline pH of an engineered DyP-type peroxidase

DyP-type peroxidases (DyPs) are microbial enzymes that catalyze the oxidation of a wide range of substrates, including synthetic dyes, lignin-derived compounds, and metals, such as Mn²⁺ and Fe²⁺, and have enormous biotechnological potential in biorefineries. However, many questions on the molecular basis of enzyme function and stability remain unanswered. In this work, high-resolution structures of PpDyP wild-type and two engineered variants (6E10 and 29E4) generated by directed evolution were obtained. The X-ray crystal structures revealed the typical ferredoxin-like folds, with three heme access pathways, two tunnels, and one cavity, limited by three long loops including catalytic residues. Variant 6E10 displays significantly increased loops' flexibility that favors function over stability: despite the considerably higher catalytic efficiency, this variant shows poorer protein stability compared to wild-type and 29E4 variants. Constant-pH MD simulations revealed a more positively charged microenvironment near the heme pocket of variant 6E10, particularly in the neutral to alkaline pH range. This microenvironment affects enzyme activity by modulating the pK _a of essential residues in the heme vicinity and should account for variant 6E10 improved activity at pH 7-8 compared to the wild-type and 29E4 that show optimal enzymatic activity close to pH 4. Our findings shed light on the structure-function relationships of DyPs at the molecular level, including their pH-dependent conformational plasticity. These are essential for understanding and engineering the catalytic properties of DyPs for future biotechnological applications.

Antibody binding modulates the dynamics of the membrane-bound prion protein

Misfolding of the cellular prion protein (PrPC) is associated with lethal neurodegeneration. PrPC consists of a flexible tail (residues 23-123) and a globular domain (residues 124-231) whose C-terminal end is anchored to the cell membrane. The neurotoxic antibody POM1 and the innocuous antibody POM6 recognize the globular domain. Experimental evidence indicates that POM1 binding to PrPC emulates the influence on PrPC of the misfolded prion protein (PrPSc) while the binding of POM6 has the opposite biological response. Little is known about the potential interactions between flexible tail, globular domain, and the membrane. Here, we used atomistic simulations to investigate how these interactions are modulated by the binding of the Fab fragments of POM1 and POM6 to PrPC and by interstitial sequence truncations to the flexible tail. The simulations show that the binding of the antibodies restricts the range of orientations of the globular domain with respect to the membrane and decreases the distance between tail and membrane. Five of the six sequence truncations influence only marginally this distance and the contact patterns between tail and globular domain. The only exception is a truncation coupled to a charge inversion mutation of four N-terminal residues, which increases the distance of the flexible tail from the membrane. The interactions of the flexible tail and globular domain are modulated differently by the two antibodies.

Extended DNA binding interfaces beyond the canonical SAP domain contribute to the function of replication stress regulator SDE2 at DNA replication forks

Elevated DNA replication stress causes instability of the DNA replication fork and increased DNA mutations, which underlies tumorigenesis. The DNA replication stress regulator silencing-defective 2 (SDE2) is known to bind to TIMELESS (TIM), a protein of the fork protection complex, and enhances its stability, thereby supporting replisome activity at DNA replication forks. However, the DNA-binding activity of SDE2 is not well defined. Here, we structurally and functionally characterize a new conserved DNA-binding motif related to the SAP (SAF-A/B, Acinus, PIAS) domain in human SDE2 and establish its preference for ssDNA. Our NMR solution structure of the SDE2^SAP domain reveals a helix-extended loop-helix core with the helices aligned parallel to each other, consistent with known canonical SAP folds. Notably, we have shown that the DNA interaction of this SAP domain extends beyond the core SAP domain and is augmented by two lysine residues in the C-terminal tail, which is uniquely positioned adjacent to the SAP motif and conserved in the pre-mRNA splicing factor SF3A3. Furthermore, we found that mutation in the SAP domain and extended C terminus not only disrupts ssDNA binding but also impairs TIM localization at replication forks, thus inhibiting efficient fork progression. Taken together, our results establish SDE2^SAP as an essential element for SDE2 to exert its role in preserving replication fork integrity via fork protection complex regulation and highlight the structural diversity of the DNA–protein interactions achieved by a specialized DNA-binding motif.

Disentangling Unusual Catalytic Properties and the Role of the [4Fe-4S] Cluster of Three Endonuclease III from the Extremophile D. radiodurans

Endonuclease III (EndoIII) is a bifunctional DNA glycosylase with specificity for a broad range of oxidized DNA lesions. The genome of an extremely radiation- and desiccation-resistant bacterium, Deinococcus radiodurans, possesses three genes encoding for EndoIII-like enzymes (DrEndoIII1, DrEndoIII2 and DrEndoIII3), which reveal different types of catalytic activities. DrEndoIII2 acts as the main EndoIII in this organism, while DrEndoIII1 and 3 demonstrate unusual and no EndoIII activity, respectively. In order to understand the role of DrEndoIII1 and DrEndoIII3 in D. radiodurans, we have generated mutants which target non-conserved residues in positions considered essential for classic EndoIII activity. In parallel, we have substituted residues coordinating the iron atoms in the [4Fe-4S] cluster in DrEndoIII2, aiming at elucidating the role of the cluster in these enzymes. Our results demonstrate that the amino acid substitutions in DrEndoIII1 reduce the enzyme activity without altering the overall structure, revealing that the residues found in the wild-type enzyme are essential for its unusual activity. The attempt to generate catalytic activity of DrEndoIII3 by re-designing its catalytic pocket was unsuccessful. A mutation of the iron-coordinating cysteine 199 in DrEndoIII2 appears to compromise the structural integrity and induce the formation of a [3Fe-4S] cluster, but apparently without affecting the activity. Taken together, we provide important structural and mechanistic insights into the three EndoIIIs, which will help us disentangle the open questions related to their presence in D. radiodurans and their particularities.

CELLOPT: improved unit-cell parameters for electron diffraction data of small-molecule crystals

Electron diffraction enables structure determination of organic small molecules using crystals that are too small for conventional X-ray crystallography. However, because of uncertainties in the experimental parameters, notably the detector distance, the unit-cell parameters and the geometry of the structural models are typically less accurate and precise compared with results obtained by X-ray diffraction. Here, an iterative procedure to optimize the unit-cell parameters obtained from electron diffraction using idealized restraints is proposed. The cell optimization routine has been implemented as part of the structure refinement, and a gradual improvement in lattice parameters and data quality is demonstrated. It is shown that cell optimization, optionally combined with geometrical corrections for any apparent detector distortions, benefits refinement of electron diffraction data in small-molecule crystallography and leads to more accurate structural models.

Distal Mutations Shape Substrate-Binding Sites during Evolution of a Metallo-Oxidase into a Laccase

Laccases are in increasing demand as innovative solutions in the biorefinery fields. Here, we combine mutagenesis with structural, kinetic, and in silico analyses to characterize the molecular features that cause the evolution of a hyperthermostable metallo-oxidase from the multicopper oxidase family into a laccase (k _cat 273 s^-1 for a bulky aromatic substrate). We show that six mutations scattered across the enzyme collectively modulate dynamics to improve the binding and catalysis of a bulky aromatic substrate. The replacement of residues during the early stages of evolution is a stepping stone for altering the shape and size of substrate-binding sites. Binding sites are then fine-tuned through high-order epistasis interactions by inserting distal mutations during later stages of evolution. Allosterically coupled, long-range dynamic networks favor catalytically competent conformational states that are more suitable for recognizing and stabilizing the aromatic substrate. This work provides mechanistic insight into enzymatic and evolutionary molecular mechanisms and spots the importance of iterative experimental and computational analyses to understand local-to-global changes.