A consensus view of protein dynamics

Genetic evidence for splicing-dependent structural and functional plasticity in CASK protein

BACKGROUND

Pontocerebellar hypoplasia (PCH) may present with supratentorial phenotypes and is often accompanied by microcephaly. Damaging mutations in the X-linked gene CASK produce self-limiting microcephaly with PCH in females but are often lethal in males. CASK deficiency leads to early degeneration of cerebellar granule cells but its role in other regions of the brain remains uncertain.

METHOD

We generated a conditional Cask knockout mice and deleted Cask ubiquitously after birth at different times. We examined the clinical features in several subjects with damaging mutations clustered in the central part of the CASK protein. We have performed phylogenetic analysis and RT-PCR to assess the splicing pattern within the same protein region and performed in silico structural analysis to examine the effect of splicing on the CASK's structure.

RESULT

We demonstrate that deletion of murine Cask after adulthood does not affect survival but leads to cerebellar degeneration and ataxia over time. Intriguingly, damaging hemizygous CASK mutations in boys who display microcephaly and cerebral dysfunction but without PCH are known. These mutations are present in two vertebrate-specific CASK exons. These exons are subject to alternative splicing both in forebrain and hindbrain. Inclusion of these exons differentially affects the molecular structure and hence possibly the function/s of the CASK C-terminus.

CONCLUSION

Loss of CASK function disproportionately affects the cerebellum. Clinical data, however, suggest that CASK may have additional vertebrate-specific function/s that play a role in the mammalian forebrain. Thus, CASK has an ancient function shared between invertebrates and vertebrates as well as novel vertebrate-specific function/s.

A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort

Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).

Molecular insights into the anti-inflammatory activity of fermented pineapple juice using multimodal computational studies

Pineapple has been recognized for its potential to enhance health and well-being. This study aimed to gain molecular insights into the anti-inflammatory properties of fermented pineapple juice using multimodal computational studies. In this study, pineapple juice was fermented using Lactobacillus paracasei, and the solution underwent liquid chromatography-mass spectrometry analysis. Network pharmacology was applied to investigate compound interactions and targets. In silico methods assessed compound bioactivities. Protein-protein interactions, network topology, and enrichment analysis identified key compounds. Molecular docking explored compound-receptor interactions in inflammation regulation. Molecular dynamics simulations were conducted to confirm the stability of interactions between the identified crucial compounds and their respective receptors. The study revealed several compounds including short-chain fatty acids, peptides, dihydroxyeicosatrienoic acids, and glycerides that exhibited promising anti-inflammatory properties. Leucyl-leucyl-norleucine and Leu-Leu-Tyr exhibited robust and stable interactions with mitogen-activated protein kinase 14 and IκB kinase β, respectively, indicating their potential as promising therapeutic agents for inflammation modulation. This proposition is grounded in the pivotal involvement of these two proteins in inflammatory signaling pathways. These findings provide valuable insights into the anti-inflammatory potential of these compounds, serving as a foundation for further experimental validation and exploration. Future studies can build upon these results to advance the development of these compounds as effective anti-inflammatory agents.

Molecular docking-based interaction studies on imidazo[1,2-a] pyridine ethers and squaramides as anti-tubercular agents

Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (r_ext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC₅₀ values in the range of 0.0508-0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.

Network Analysis of Molecular Dynamics Sectors in the p53 Protein

Design of allosteric regulators is an emergent field in the area of drug discovery holding promise for currently untreated diseases. Allosteric regulators bind to a protein in one location and affect a distant site. The ubiquitous presence of allosteric effectors in biology and the success of serendipitously identified allosteric compounds point to the potential they hold. Although the mechanism of transmission of an allosteric signal is not unequivocally determined, one hypothesis suggests that groups of evolutionarily covarying residues within a protein, termed sectors, are conduits. A long-term goal of our lab is to allosterically modulate the activity of proteins by binding small molecules at points of allosteric control. However, methods to consistently identify such points remain unclear. Sector residues on the surfaces of proteins are a promising source of allosteric targets. Recently, we introduced molecular dynamics (MD)-based sectors; MD sectors capitalize on covariance of motion, in place of evolutionary covariance. By focusing on motional covariance, MD sectors tap into the framework of statistical mechanics afforded by the Boltzmann ensemble of structural conformations comprising the underlying data set. We hypothesized that the method of MD sectors can be used to identify a cohesive network of motionally covarying residues capable of transmitting an allosteric signal in a protein. While our initial qualitative results showed promise for the method to predict sectors, that a network of cohesively covarying residues had been produced remained an untested assumption. In this work, we apply network theory to rigorously analyze MD sectors, allowing us to quantitatively assess the biologically relevant property of network cohesiveness of sectors in the context of the tumor suppressor protein, p53. We revised the methodology for assessing and improving MD sectors. Specifically, we introduce a metric to calculate the cohesive properties of the network. Our new approach separates residues into two categories: sector residues and non-sector residues. The relatedness within each respective group is computed with a distance metric. Cohesive sector networks are identified as those that have high relatedness among the sector residues which exceeds the relatedness of the residues to the non-sector residues in terms of the correlation of motions. Our major finding was that the revised means of obtaining sectors was more efficacious than previous iterations, as evidenced by the greater cohesion of the networks. These results are discussed in the context of the development of allosteric regulators of p53 in particular and the expected applicability of the method to the drug design field in general.

Proteins turn "Proteans" - The over 40-year delayed paradigm shift in structural biology: From "native proteins in uniquely defined configurations" to "intrinsically disordered proteins"

The current millennium brought up a revolutionary paradigm shift in molecular biology: many operative proteins, rather than being quasi-rigid polypeptide chains folded into unique configurations - as believed throughout most of the past century - are now known to be intrinsically disordered, dynamic, pleomorphic, and multifunctional structures with stochastic behaviors. Yet, part of this knowledge, including suggestions about possible mechanisms and plenty of evidence for the same, became available by the 1950s and 1960s to remain then nearly forgotten for over 40 years. Here, we review the main steps toward the classic notions about protein structures, as well as the neglected precedents of present views, discuss possible explanations for such long oblivion, and offer a sketch of the current panorama in this field.

Dodecenylsuccinic anhydride-modified oxalate decarboxylase loaded with magnetic nano-Fe3O4@SiO2 for demulsification of oil-in-water emulsions

The inability to demulsify oil-in-water emulsions via green and efficient processes is a challenging problem in many industrial processes. As a novel biodemulsifier, protein demulsifiers display excellent dispersibility and stability, but their demulsification mechanisms are not clear, which severely restricts their large-scale production and application. In this study, the demulsification mechanism of the high-efficiency protein biodemulsifier oxalate decarboxylase (Bacm OxdC), which is secreted by the Bacillus mojavensis XH1 strain, for an oil-in-water emulsion was analyzed. The results showed that Bacm OxdC was spontaneously adsorbed at the oil-water interface and turned its hydrophobic amino acids outward to increase its hydrophobicity and break the emulsified system. Furthermore, it effectively reduced the oil-water interfacial tension and interfacial film strength, thereby reducing the oil-water interfacial energy and finally enabling demulsification. To further improve the demulsification efficiency and reusability, Fe₃O₄@SiO₂@OxdC-DDSA was prepared. This method provided a magnetic response for Bacm OxdC and enabled efficient demulsification. The demulsification rate of Fe₃O₄@SiO₂@OxdC-DDSA reached 98.1% at 24 h, which was 30.7% higher than that of the original Bacm OxdC. After three cycles, the demulsification rate still reached 89.3%, proving it has excellent recyclability. This work is the first study on the demulsification mechanism of protein biodemulsifiers and provides useful insights into the demulsification mechanism of biodemulsifiers for oil-in-water emulsions. In addition, a promising high-efficiency modification technique for protein biodemulsifiers was proposed, which provided information for the development of biodemulsifiers for oil-water separation.

An understanding of coronavirus and exploring the molecular dynamics simulations to find promising candidates against the Mpro of nCoV to combat the COVID-19: A systematic review

The first infection case of new coronavirus was reported at the end of 2019 and after then, the cases are reported in all nations across the world in a very short period. Further, the regular news of mutations in the virus has made life restricted with appropriate behavior. To date, a new strain (Omicron and its new subvariant Omicron XE) has brought fear amongst us due to a higher trajectory of increase in the number of cases. The researchers thus started giving attention to this viral infection and discovering drug-like candidates to cure the infections. Finding a drug for any viral infection is not an easy task and takes plenty of time. Therefore, computational chemistry/bioinformatics is followed to get promising molecules against viral infection. Molecular dynamics (MD) simulations are being explored to get drug candidates in a short period. The molecules are screened via molecular docking, which provides preliminary information which can be further verified by molecular dynamics (MD) simulations. To understand the change in structure, MD simulations generated several trajectories such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonding, and radius of gyration for the main protease (Mpro) of the new coronavirus (nCoV) in the presence of small molecules. Additionally, change in free energy for the formation of complex of Mpro of nCoV with the small molecule can be determined by applying molecular mechanics with generalized born and surface area solvation (MM-GBSA). Thus, the promising molecules can be further explored for clinical trials to combat coronavirus disease-19 (COVID-19).

Mutagenic Activation of Glutathione Peroxidase-4: Approaches toward Rational Design of Allosteric Drugs

Glutathione peroxidase 4 (GPX4) reduces lipid hydroperoxides in lipid membranes, effectively inhibiting iron-dependent cell death or ferroptosis. The upregulation of the enzyme by the mutations at residues D21 and D23 has been suggested to be associated with higher protein activity, which confers more protection against neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's diseases. Therefore, it has become an attractive target for treating and preventing neurodegenerative diseases. However, identifying means of mimicking the beneficial effects of these mutations distant from the active site constitutes a formidable challenge in moving toward therapeutics. In this study, we explore using molecular dynamics simulations to computationally map the conformational and energetic landscape of the wild-type GPX4 protein and three mutant variants to identify the allosteric networks of the enzyme. We present the conformational dynamic profile providing the desired signature behavior of the enzyme. We also discuss the implications of these findings for drug design efforts.

High Energy Channeling and Malleable Transition States: Molecular Dynamics Simulations and Free Energy Landscapes for the Thermal Unfolding of Protein U1A and 13 Mutants

The spliceosome protein U1A is a prototype case of the RNA recognition motif (RRM) ubiquitous in biological systems. The in vitro kinetics of the chemical denaturation of U1A indicate that the unfolding of U1A is a two-state process but takes place via high energy channeling and a malleable transition state, an interesting variation of typical two-state behavior. Molecular dynamics (MD) simulations have been applied extensively to the study of two-state unfolding and folding of proteins and provide an opportunity to obtain a theoretical account of the experimental results and a molecular model for the transition state ensemble. We describe herein all-atom MD studies including explicit solvent of up to 100 ns on the thermal unfolding (UF) of U1A and 13 mutants. Multiple MD UF trajectories are carried out to ensure accuracy and reproducibility. A vector representation of the MD unfolding process in RMSD space is obtained and used to calculate a free energy landscape for U1A unfolding. A corresponding MD simulation and free energy landscape for the protein CI2, well known to follow a simple two state folding/unfolding model, is provided as a control. The results indicate that the unfolding pathway on the MD calculated free energy landscape of U1A shows a markedly extended transition state compared with that of CI2. The MD results support the interpretation of the observed chevron plots for U1A in terms of a high energy, channel-like transition state. Analysis of the MDUF structures shows that the transition state ensemble involves microstates with most of the RRM secondary structure intact but expanded by ~14% with respect to the radius of gyration. Comparison with results on a prototype system indicates that the transition state involves an ensemble of molten globule structures and extends over the region of ~1-35 ns in the trajectories. Additional MDUF simulations were carried out for 13 U1A mutants, and the calculated φ-values show close accord with observed results and serve to validate our methodology.

Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case

Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics. This article is categorized under:Data Science > Computer Algorithms and ProgrammingData Science > Databases and Expert SystemsMolecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods.

Molecular modeling of lactoferrin for food and nutraceutical applications: insights from in silico techniques

Lactoferrin is a protein, primarily found in milk that has attracted the interest of the food industries due to its health properties. Nevertheless, the instability of lactoferrin has limited its commercial application. Recent studies have focused on encapsulation to enhance the stability of lactoferrin. However, the molecular insights underlying the changes of structural properties of lactoferrin and the interaction with protectants remain poorly understood. Computational approaches have proven useful in understanding the structural properties of molecules and the key binding with other constituents. In this review, comprehensive information on the structure and function of lactoferrin and the binding with various molecules for food purposes are reviewed, with a special emphasis on the use of molecular dynamics simulations. The results demonstrate the application of modeling and simulations to determine key residues of lactoferrin responsible for its stability and interactions with other biomolecular components under various conditions, which are also associated with its functional benefits. These have also been extended into the potential creation of enhanced lactoferrin for commercial purposes. This review provides valuable strategies in designing novel nutraceuticals for food science practitioners and those who have interests in acquiring familiarity with the application of computational modeling for food and health purposes.

Computation of the Protein Conformational Transition Pathway on Ligand Binding by Linear Response-Driven Molecular Dynamics

While extremely important for relating the protein structure to its biological function, determination of the protein conformational transition pathway upon ligand binding is made difficult due to the transient nature of intermediates, a large and rugged conformational space, and coupling between protein dynamics and ligand-protein interactions. Existing methods that rely on prior knowledge of the bound (holo) state structure are restrictive. A second concern relates to the correspondence of intermediates obtained to the metastable states on the apo → holo transition pathway. Here, we have taken the protein apo structure and ligand-binding site as only inputs and combined an elastic network model (ENM) representation of the protein Hamiltonian with linear response theory (LRT) for protein-ligand interactions to identify the set of slow normal modes of protein vibrations that have a high overlap with the direction of the protein conformational change. The structural displacement along the chosen direction was performed using excited normal modes molecular dynamics (MDeNM) simulations rather than by the direct use of LRT. Herein, the MDeNM excitation velocity was optimized on-the-fly on the basis of its coupling to protein dynamics and ligand-protein interactions. Thus, a determined set of structures was validated against crystallographic and simulation data on four protein-ligand systems, namely, adenylate kinase-di(adenosine-5')pentaphosphate, ribose binding protein-β-d-ribopyranose, DNA β-glucosyltransferase-uridine-5'-diphosphate, and G-protein α subunit-guanosine-5'-triphosphate, which present important differences in protein conformational heterogeneity, ligand binding mechanism, viz. induced-fit or conformational selection, extent, and nonlinearity in protein conformational changes upon ligand binding, and presence of allosteric effects. The obtained set of intermediates was used as an input to path metadynamics simulations to obtain the free energy profile for the apo → holo transition.

Review on the QM/MM Methodologies and Their Application to Metalloproteins

The multiscaling quantum mechanics/molecular mechanics (QM/MM) approach was introduced in 1976, while the extensive acceptance of this methodology started in the 1990s. The combination of QM/MM approach with molecular dynamics (MD) simulation, otherwise known as the QM/MM/MD approach, is a powerful and promising tool for the investigation of chemical reactions’ mechanism of complex molecular systems, drug delivery, properties of molecular devices, organic electronics, etc. In the present review, the main methodologies in the multiscaling approaches, i.e., density functional theory (DFT), semiempirical methodologies (SE), MD simulations, MM, and their new advances are discussed in short. Then, a review on calculations and reactions on metalloproteins is presented, where particular attention is given to nitrogenase that catalyzes the conversion of atmospheric nitrogen molecules N₂ into NH₃ through the process known as nitrogen fixation and the FeMo-cofactor.

Optimized Elastic Network Models With Direct Characterization of Inter-Residue Cooperativity for Protein Dynamics

The elastic network models (ENMs)are known as representative coarse-grained models to capture essential dynamics of proteins. Due to simple designs of the force constants as a decay with spatial distances of residue pairs in many previous studies, there is still much room for the improvement of ENMs. In this article, we directly computed the force constants with the inverse covariance estimation using a ridge-type operater for the precision matrix estimation (ROPE)on a large-scale set of NMR ensembles. Distance-dependent statistical analyses on the force constants were further comprehensively performed in terms of several paired types of sequence and structural information, including secondary structure, relative solvent accessibility, sequence distance and terminal. Various distinguished distributions of the mean force constants highlight the structural and sequential characteristics coupled with the inter-residue cooperativity beyond the spatial distances. We finally integrated these structural and sequential characteristics to build novel ENM variations using the particle swarm optimization for the parameter estimation. The considerable improvements on the correlation coefficient of the mean-square fluctuation and the mode overlap were achieved by the proposed variations when compared with traditional ENMs. This study opens a novel way to develop more accurate elastic network models for protein dynamics.

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

Interpretation of Single-Molecule Force Experiments on Proteins Using Normal Mode Analysis

Single-molecule force spectroscopy experiments allow protein folding and unfolding to be explored using mechanical force. Probably the most informative technique for interpreting the results of these experiments at the structural level makes use of steered molecular dynamics (MD) simulations, which can explicitly model the protein under load. Unfortunately, this technique is computationally expensive for many of the most interesting biological molecules. Here, we find that normal mode analysis (NMA), a significantly cheaper technique from a computational perspective, allows at least some of the insights provided by MD simulation to be gathered. We apply this technique to three non-homologous proteins that were previously studied by force spectroscopy: T4 lysozyme (T4L), Hsp70 and the glucocorticoid receptor domain (GCR). The NMA results for T4L and Hsp70 are compared with steered MD simulations conducted previously, and we find that we can recover the main results. For the GCR, which did not undergo MD simulation, our approach identifies substructures that correlate with experimentally identified unfolding intermediates. Overall, we find that NMA can make a valuable addition to the analysis toolkit for the structural analysis of single-molecule force experiments on proteins.

Unveiling the "invisible" druggable conformations of GDP-bound inactive Ras

The prevalent view on whether Ras is druggable has gradually changed in the recent decade with the discovery of effective inhibitors binding to cryptic sites unseen in the native structures. Despite the promising advances, therapeutics development toward higher potency and specificity is challenged by the elusive nature of these binding pockets. Here we derive a conformational ensemble of guanosine diphosphate (GDP)-bound inactive Ras by integrating spin relaxation-validated atomistic simulation with NMR chemical shifts and residual dipolar couplings, which provides a quantitative delineation of the intrinsic dynamics up to the microsecond timescale. The experimentally informed ensemble unequivocally demonstrates the preformation of both surface-exposed and buried cryptic sites in Ras•GDP, advocating design of inhibition by targeting the transient druggable conformers that are invisible to conventional experimental methods. The viability of the ensemble-based rational design has been established by retrospective testing of the ability of the Ras•GDP ensemble to identify known ligands from decoys in virtual screening.

Computational Design of Oligosaccharide-Producing Levansucrase from Bacillus licheniformis RN-01 to Increase Its Stability at High Temperature

Levan-type fructooligosaccharides (LFOs) and levan can potentially be used as ingredients in prebiotics, skincare products, and antitumor agents. The Y246S mutant of Bacillus licheniformis RN-01 levansucrase (oligosaccharide-producing levansucrase, OPL) was reported to productively synthesize LFOs; however, OPL's thermostability is low at high temperatures. To enhance OPL structural stability, this study employed molecular dynamics (AMBER) to identify a highly flexible region, as measured by its average root-mean-square fluctuation (RMSF) value, on the OPL surface and computational protein design (Rosetta) to rigidify and increase favorable interactions to increase its structural stability. AMBER identified region nine (residues 277-317) as a highly flexible region that was selected for design because it has the highest number of residues and the second-highest average RMSF, and it is farthest from the active site. Rosetta designed 14 mutants with the best ΔΔG value in each position, where three mutants have better ΔG than OPL. To determine whether their flexibilities and stabilities are lower than those of OPL, all 14 designed mutants were simulated at high temperature (500 K), and we found that K296E, G309S, and A310W mutants were predicted to be more stable and could retain their native structures better than OPL. Our results suggest that enhanced structural stabilities of these mutants are caused by increased hydrogen bond strengths of the designed residues and their neighboring residues. This study designed K296E, G309S, and A310W mutants of OPL with high potential for stability improvement, and they could potentially be used for the effective production of LFOs.

Energetics and structure of alanine-rich α-helices via adaptive steered molecular dynamics

The energetics and hydrogen bonding profiles of the helix-to-coil transition were found to be an additive property and to increase linearly with chain length, respectively, in alanine-rich α-helical peptides. A model system of polyalanine repeats was used to establish this hypothesis for the energetic trends and hydrogen bonding profiles. Numerical measurements of a synthesized polypeptide Ac-Y(AEAAKA)kF-NH2 and a natural α-helical peptide a2N (1-17) provide evidence of the hypothesis's generality. Adaptive steered molecular dynamics was employed to investigate the mechanical unfolding of all of these alanine-rich polypeptides. We found that the helix-to-coil transition is primarily dependent on the breaking of the intramolecular backbone hydrogen bonds and independent of specific side-chain interactions and chain length. The mechanical unfolding of the α-helical peptides results in a turnover mechanism in which a 310-helical structure forms during the unfolding, remaining at a near constant population and thereby maintaining additivity in the free energy. The intermediate partially unfolded structures exhibited polyproline II helical structure as previously seen by others. In summary, we found that the average force required to pull alanine-rich α-helical peptides in between the endpoints-namely the native structure and free coil-is nearly independent of the length or the specific primary structure.

Patterns in Protein Flexibility: A Comparison of NMR "Ensembles", MD Trajectories, and Crystallographic B-Factors

Proteins are molecular machines requiring flexibility to function. Crystallographic B-factors and Molecular Dynamics (MD) simulations both provide insights into protein flexibility on an atomic scale. Nuclear Magnetic Resonance (NMR) lacks a universally accepted analog of the B-factor. However, a lack of convergence in atomic coordinates in an NMR-based structure calculation also suggests atomic mobility. This paper describes a pattern in the coordinate uncertainties of backbone heavy atoms in NMR-derived structural “ensembles” first noted in the development of FindCore2 (previously called Expanded FindCore: DA Snyder, J Grullon, YJ Huang, R Tejero, GT Montelione, Proteins: Structure, Function, and Bioinformatics 82 (S2), 219–230) and demonstrates that this pattern exists in coordinate variances across MD trajectories but not in crystallographic B-factors. This either suggests that MD trajectories and NMR “ensembles” capture motional behavior of peptide bond units not captured by B-factors or indicates a deficiency common to force fields used in both NMR and MD calculations.

Water-Mediated Interactions Determine Helix Formation of Peptides in Open Nanotubes

Water-mediated interactions (WMIs) play diverse roles in molecular biology. They are particularly relevant in geometrically confined spaces such as the interior of the chaperonin, at the interface between ligands and their binding partners, and in the ribosome tunnel. Inspired in part by the geometry of the ribosome tunnel, we consider confinement effects on the stability of peptides. We describe results from replica exchange molecular dynamics simulations of a system containing a 23-alanine or 23-serine polypeptide confined to nonpolar and polar nanotubes in the gas phase and when open to a water reservoir. We quantify the effect of water in determining the preferred conformational states of these polypeptides by calculating the difference in the solvation free energy for the helix and coil states in the open nanotube in the two phases. Our simulations reveal several possibilities. We find that nanoscopic confinement preferentially stabilizes the helical state of polypeptides with hydrophobic side chains, which is explained by the entropic stabilization mechanism proposed on the basis of polymer physics. Polypeptide chains with hydrophilic side chains can adopt helical structures within nanotubes, but helix formation is sensitive to the nature of the nanotube due to WMIs. We elaborate on the potential implications of our findings to the stability of peptides in the ribosome tunnel.

Capturing the Flexibility of a Protein-Ligand Complex: Binding Free Energies from Different Enhanced Sampling Techniques

Enhanced sampling techniques are a promising approach to obtain reliable binding free-energy profiles for flexible protein-ligand complexes from molecular dynamics (MD) simulations. To put four popular enhanced sampling techniques to a biologically relevant and challenging test, we studied the partial dissociation of an antigenic peptide from the Major Histocompatibility Complex I (MHC I) HLA-B*35:01 to systematically investigate the performance of umbrella sampling (US), replica exchange with solute tempering 2 (REST2), bias exchange umbrella sampling (BEUS, or replica-exchange umbrella sampling), and well-tempered metadynamics (MTD). With regard to the speed of sampling and convergence, the peptide-MHC I complex (pMHC I) under study showcases intrinsic strengths and weaknesses of the four enhanced sampling techniques used. We found that BEUS can best handle the sampling challenges that arise from the coexistence of an enthalpically and an entropically stabilized free-energy minimum in the pMHC I under study. These findings might also be relevant for other flexible biomolecular systems with competing enthalpically and entropically stabilized minima.

Computational design of oligosaccharide producing levansucrase from Bacillus licheniformis RN-01 to improve its thermostability for production of levan-type fructooligosaccharides from sucrose

Levansucrase catalyzes production of levan and levan-type fructooligosaccharides (LFOs) with potential applications in food and pharmaceutical industries such as prebiotics and anti-tumor agents. Previous study found that Y246S mutant of Bacillus licheniformis RN-01 levansucrase (oligosaccharide producing levansucrase, OPL) could effectively produce LFOs but its thermostability is limited at high temperature. In this study, molecular dynamics (MD) and computational protein design were used to create mutants with higher thermostability than OPL by rigidifying highly flexible residues on enzyme surface. MD results show that highly flexible residues suitable for design are K82, N83, D179, and Q308. Two approaches were employed to improve their interactions by allowing them to be amino acids that could potentially form favorable interactions with their neighboring residues or natural amino acids except G, P and C. Flexibilities of designed residues of K82H, N83R, Q308S and K82H/N83R mutants are lower than those of OPL. Experimental results show that characteristics and product patterns of designed mutants are relatively similar to those of OPL. K82H/N83R mutant has higher thermostability than OPL with 1.7-fold increase in t_1/2. Circular dichroism result suggests that designed mutations do not drastically affect secondary structures. This study shows how computational technique can engineer enzyme for thermostability improvement.

NMR-Derived Conformational Ensemble of State 1 of Activated Ras Reveals Insights into a Druggable Pocket

The lack of apparent pockets in the ground conformation of Ras has long challenged the rational design of inhibitors against this oncogenic protein. The sparsely populated, transiently formed state 1 of activated Ras, on the other hand, shows appreciable surface roughness and is increasingly recognized as a potential target for drug discovery. State 1, however, is extremely flexible, and a static structure cannot fully unveil its conformational space that can be exploited for drug design. Here, we present a conformational ensemble of state 1 that was derived using chemical shift-based modeling. The ensemble reveals the intrinsic plasticity of a druggable pocket in state 1 and demonstrates the mechanism of conformational selection for inhibitor recognition. The large set of structural templates in the ensemble, providing a comprehensive description of thermally accessible pocket conformations, is expected to significantly aid the rational design of anti-Ras drugs.

Homologs of the Tumor Suppressor Protein p53: A Bioinformatics Study for Drug Design

Sequence and structure of proteins related to the tumor suppressor protein p53 were studied from the perspective of gaining insight for the development of therapeutic drugs. Our study addresses two major issues encumber bringing novel drugs to market: side effects and artifacts from animal models. In the first phase of our study, we performed a genome-wide search to identify potentially similar proteins to p53 that may be susceptible to off target effects. In the second phase, we chose a selection of common model organisms that could potentially be available to undergraduate researchers in the university setting to assess which ones utilize p53 most similar to humans on the basis of sequence homology and structural similarity from predicted structures. Our results confirm the proteins in the humans significantly similar to p53 are known paralogs within the p53 family. In considering model organisms, murine p53 bore great similarity to human p53 in terms of both sequence and structure, but others performed similarly well. We discuss the findings against the background of other structural benchmarks and point out potential benefits and drawbacks of various alternatives for use in future drug design pilot studies.

Spectral analysis of molecular dynamics simulations on PDZ: MD sectors

The idea of protein "sectors" posits that sparse subsets of amino acid residues form cooperative networks that are key elements of protein stability, ligand binding, and allosterism. To date, protein sectors have been calculated by the statistical coupling analysis (SCA) method of Ranganathan and co-workers via the spectral analysis of conservation-weighted evolutionary covariance matrices obtained from a multiple sequence alignments of homologous families of proteins. SCA sectors, a knowledge-based protocol, have been indentified with functional properties and allosterism for a number of systems. In this study, we investigate the utility of the sector idea for the analysis of physics-based molecular dynamics (MD) trajectories of proteins. Our test case for this procedure is PSD95- PDZ3, one of the smallest proteins for which allosterism has been observed. It has served previously as a model system for a number of prediction algorithms, and is well characterized by X-ray crystallography, NMR spectroscopy and site specific mutagenisis. All-atom MD simulations were performed for a total of 500 nanoseconds using AMBER, and MD-calculated covariance matrices for the fluctuations of residue displacements and non-bonded interaction energies were subjected to spectral analysis in a manner analogous to that of SCA. The composition of MD sectors was compared with results from SCA, site specific mutagenesis, and allosterism. The concordance indicates that MD sectors are a viable protocol for analyzing MD trajectories and provide insight into the physical origin of the phenomenon.Communicated by Ramaswamy H. Sarma.

Exploring the different states of wild-type T-cell receptor and mutant conformational changes towards understanding the antigen recognition

Recognition of proteolytic peptide fragments presented by major histocompatibility complex (MHC) on target cells by T-cell receptor (TCR) is among the most important interactions in the adaptive immune system. Several computational studies have been performed to investigate conformational and dynamical properties of TCRs for enhanced immunogenicity. Here, we present the large-scale molecular dynamics (MD) simulation studies of the two comprehensive systems consisting of the wild-type and mutant IG4 TCR in complex with the tumor epitope NY-ESO peptide (SLLMWITQC) and analyzed for mapping conformational changes of TCR in the states prior to antigen binding, upon antigen binding and after the antigen was released. All of the simulations were performed with different states of TCRs for each 1000 ns of simulation time, providing six simulations for time duration of 6000 ns (6µs). We show that rather than undergoing most critical conformational changes upon antigen binding, the high proportion of complementarity-determining region (CDR) loops change by comparatively small amount. The hypervariable CDRα3 and CDRβ3 loops showed significant structural changes. Interestingly, the TCR β chain loops showed the least changes, which is reliable with recent implications that β domain of TCR may propel antigen interaction. The mutant shows higher rigidity than wild-type even in released state; expose an induced fit mechanism occurring from the re-structuring of CDRα3 loop and can allow enhanced binding affinity of the peptide antigen. Additionally, we show that CDRα3 loop and peptide contacts are an adaptive feature of affinity enhanced mutant TCR.Communicated by Ramaswamy H. Sarma.

Order–disorder skewness in alpha-synuclein: a key mechanism to recognize membrane curvature

Currently, membrane curvature is understood as an active mechanism to control cells spatial organization and activity.

BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows

In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.

Normal Mode Analysis as a Routine Part of a Structural Investigation

Normal mode analysis (NMA) is a technique that can be used to describe the flexible states accessible to a protein about an equilibrium position. These states have been shown repeatedly to have functional significance. NMA is probably the least computationally expensive method for studying the dynamics of macromolecules, and advances in computer technology and algorithms for calculating normal modes over the last 20 years have made it nearly trivial for all but the largest systems. Despite this, it is still uncommon for NMA to be used as a component of the analysis of a structural study. In this review, we will describe NMA, outline its advantages and limitations, explain what can and cannot be learned from it, and address some criticisms and concerns that have been voiced about it. We will then review the most commonly used techniques for reducing the computational cost of this method and identify the web services making use of these methods. We will illustrate several of their possible uses with recent examples from the literature. We conclude by recommending that NMA become one of the standard tools employed in any structural study.

Is the cell really a machine?

It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, the recent introduction of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, intracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes.

The Structural Dynamics of Engineered β-Lactamases Vary Broadly on Three Timescales yet Sustain Native Function

Understanding the principles of protein dynamics will help guide engineering of protein function: altering protein motions may be a barrier to success or may be an enabling tool for protein engineering. The impact of dynamics on protein function is typically reported over a fraction of the full scope of motional timescales. If motional patterns vary significantly at different timescales, then only by monitoring motions broadly will we understand the impact of protein dynamics on engineering functional proteins. Using an integrative approach combining experimental and in silico methodologies, we elucidate protein dynamics over the entire span of fast to slow timescales (ps to ms) for a laboratory-engineered system composed of five interrelated β-lactamases: two natural homologs and three laboratory-recombined variants. Fast (ps-ns) and intermediate (ns-µs) dynamics were mostly conserved. However, slow motions (µs-ms) were few and conserved in the natural homologs yet were numerous and widely dispersed in their recombinants. Nonetheless, modified slow dynamics were functionally tolerated. Crystallographic B-factors from high-resolution X-ray structures were partly predictive of the conserved motions but not of the new slow motions captured in our solution studies. Our inspection of protein dynamics over a continuous range of timescales vividly illustrates the complexity of dynamic impacts of protein engineering as well as the functional tolerance of an engineered enzyme system to new slow motions.

Design of a structure-based model for protein folding from flexible conformations

We introduce a coarse-grained, structure-based model for protein folding that considers the flexibility of the native state in the definition of the model interactions.

Enhanced trypsin thermostability in Pichia pastoris through truncating the flexible region

Background

High thermostability is required for trypsin to have wider industrial applications. Target mutagenesis at flexible regions has been proved to be an efficient protein engineering method to enhance the protein thermostability.

Results

The flexible regions in porcine trypsin were predicted using the methods including molecular dynamic simulation, FlexPred, and FoldUnfold. The amino acids 78–90 was predicted to be the highly flexible region simultaneously by the three methods and hence selected to be the mutation target. We constructed five variants (D3, D5, D7, D9, and D11) by truncating the region. And the variant D9 showed higher thermostability, with a 5 °C increase in T_opt, 5.8 °C rise in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{50}^{10}$$\end{document}T5010, and a 4.5 °C rise in T_m, compared to the wild-type. Moreover, the half-life value of the variant D9 was also found to be dramatically improved by 46 min. Circular dichroism and intrinsic fluorescence indicated that the structures had no significant change between the variant D9 and the wild-type. The surface hydrophobicity of D9 was measured to be lower than that of wild-type, indicating the increased hydrophobic interaction, which could have contributed to the improved thermostability of D9.

Conclusions

These results showed that the thermostability of variant D9 was increased. The variant D9 could be expected to be a promising tool enzyme for its wider industrial applications. The method of truncating the flexible region used in our study has the potential to be used for enhancing the thermostability of other proteins.

Electronic supplementary material

The online version of this article (10.1186/s12934-018-1012-x) contains supplementary material, which is available to authorized users.

Immunosilencing peptides by stereochemical inversion and sequence reversal: retro-D-peptides

Peptides are experiencing a new era in medical research, finding applications ranging from therapeutics to vaccines. In spite of the promising properties of peptide pharmaceuticals, their development continues to be hindered by three weaknesses intrinsic to their structure, namely protease sensitivity, clearance through the kidneys, and immune system activation. Here we report on two retro-D-peptides (H2N-hrpyiah-CONH2 and H2N-pwvpswmpprht-CONH2), which are protease-resistant and retain the original BBB shuttle activity of the parent peptide but are much less immunogenic than the parent peptide. Hence, we envisage that retro-D-peptides, which display a similar topological arrangement as their parent peptides, will expand drug design and help to overcome factors that lead to the failure of peptide pharmaceuticals in pre- and clinical trials. Furthermore, we reveal requirements to avoid or elicit specific humoral responses to therapeutic peptides, which might have a strong impact in both vaccine design and peptide therapeutic agents.

Two microcephaly-associated novel missense mutations in CASK specifically disrupt the CASK-neurexin interaction

Deletion and truncation mutations in the X-linked gene CASK are associated with severe intellectual disability (ID), microcephaly and pontine and cerebellar hypoplasia in girls (MICPCH). The molecular origin of CASK-linked MICPCH is presumed to be due to disruption of the CASK-Tbr-1 interaction. This hypothesis, however, has not been directly tested. Missense variants in CASK are typically asymptomatic in girls. We report three severely affected girls with heterozygous CASK missense mutations (M519T (2), G659D (1)) who exhibit ID, microcephaly, and hindbrain hypoplasia. The mutation M519T results in the replacement of an evolutionarily invariant methionine located in the PDZ signaling domain known to be critical for the CASK-neurexin interaction. CASK^M519T is incapable of binding to neurexin, suggesting a critically important role for the CASK-neurexin interaction. The mutation G659D is in the SH3 (Src homology 3) domain of CASK, replacing a semi-conserved glycine with aspartate. We demonstrate that the CASK^G659D mutation affects the CASK protein in two independent ways: (1) it increases the protein's propensity to aggregate; and (2) it disrupts the interface between CASK's PDZ (PSD95, Dlg, ZO-1) and SH3 domains, inhibiting the CASK-neurexin interaction despite residing outside of the domain deemed critical for neurexin interaction. Since heterozygosity of other aggregation-inducing mutations (e.g., CASK^W919R) does not produce MICPCH, we suggest that the G659D mutation produces microcephaly by disrupting the CASK-neurexin interaction. Our results suggest that disruption of the CASK-neurexin interaction, not the CASK-Tbr-1 interaction, produces microcephaly and cerebellar hypoplasia. These findings underscore the importance of functional validation for variant classification.

Genetic determinants restricting the reassortment of heterologous NSP2 genes into the simian rotavirus SA11 genome

Rotaviruses (RVs) can evolve through the process of reassortment, whereby the 11 double-stranded RNA genome segments are exchanged among strains during co-infection. However, reassortment is limited in cases where the genes or encoded proteins of co-infecting strains are functionally incompatible. In this study, we employed a helper virus-based reverse genetics system to identify NSP2 gene regions that correlate with restricted reassortment into simian RV strain SA11. We show that SA11 reassortants with NSP2 genes from human RV strains Wa or DS-1 were efficiently rescued and exhibit no detectable replication defects. However, we could not rescue an SA11 reassortant with a human RV strain AU-1 NSP2 gene, which differs from that of SA11 by 186 nucleotides (36 amino acids). To map restriction determinants, we engineered viruses to contain chimeric NSP2 genes in which specific regions of AU-1 sequence were substituted with SA11 sequence. We show that a region spanning AU-1 NSP2 gene nucleotides 784–820 is critical for the observed restriction; yet additional determinants reside in other gene regions. In silico and in vitro analyses were used to predict how the 784–820 region may impact NSP2 gene/protein function, thereby informing an understanding of the reassortment restriction mechanism.

Gaussian network model can be enhanced by combining solvent accessibility in proteins

Gaussian network model (GNM), regarded as the simplest and most representative coarse-grained model, has been widely adopted to analyze and reveal protein dynamics and functions. Designing a variation of the classical GNM, by defining a new Kirchhoff matrix, is the way to improve the residue flexibility modeling. We combined information arising from local relative solvent accessibility (RSA) between two residues into the Kirchhoff matrix of the parameter-free GNM. The undetermined parameters in the new Kirchhoff matrix were estimated by using particle swarm optimization. The usage of RSA was motivated by the fact that our previous work using RSA based linear regression model resulted out higher prediction quality of the residue flexibility when compared with the classical GNM and the parameter free GNM. Computational experiments, conducted based on one training dataset, two independent datasets and one additional small set derived by molecular dynamics simulations, demonstrated that the average correlation coefficients of the proposed RSA based parameter-free GNM, called RpfGNM, were significantly increased when compared with the parameter-free GNM. Our empirical results indicated that a variation of the classical GNMs by combining other protein structural properties is an attractive way to improve the quality of flexibility modeling.

Slow conformational exchange and overall rocking motion in ubiquitin protein crystals

Proteins perform their functions in solution but their structures are most frequently studied inside crystals. Here we probe how the crystal packing alters microsecond dynamics, using solid-state NMR measurements and multi-microsecond MD simulations of different crystal forms of ubiquitin. In particular, near-rotary-resonance relaxation dispersion (NERRD) experiments probe angular backbone motion, while Bloch–McConnell relaxation dispersion data report on fluctuations of the local electronic environment. These experiments and simulations reveal that the packing of the protein can significantly alter the thermodynamics and kinetics of local conformational exchange. Moreover, we report small-amplitude reorientational motion of protein molecules in the crystal lattice with an ~3–5° amplitude on a tens-of-microseconds time scale in one of the crystals, but not in others. An intriguing possibility arises that overall motion is to some extent coupled to local dynamics. Our study highlights the importance of considering the packing when analyzing dynamics of crystalline proteins.

X-ray crystallography is the main method for protein structure determination. Here the authors combine solid-state NMR measurements and molecular dynamics simulations and show that crystal packing alters the thermodynamics and kinetics of local conformational exchange as well as overall rocking motion of protein molecules in the crystal lattice.

De novo active sites for resurrected Precambrian enzymes

Protein engineering studies often suggest the emergence of completely new enzyme functionalities to be highly improbable. However, enzymes likely catalysed many different reactions already in the last universal common ancestor. Mechanisms for the emergence of completely new active sites must therefore either plausibly exist or at least have existed at the primordial protein stage. Here, we use resurrected Precambrian proteins as scaffolds for protein engineering and demonstrate that a new active site can be generated through a single hydrophobic-to-ionizable amino acid replacement that generates a partially buried group with perturbed physico-chemical properties. We provide experimental and computational evidence that conformational flexibility can assist the emergence and subsequent evolution of new active sites by improving substrate and transition-state binding, through the sampling of many potentially productive conformations. Our results suggest a mechanism for the emergence of primordial enzymes and highlight the potential of ancestral reconstruction as a tool for protein engineering.

Molecular Dynamics-Markov State Model of Protein Ligand Binding and Allostery in CRIB-PDZ: Conformational Selection and Induced Fit

Conformational selection and induced fit are well-known contributors to ligand binding and allosteric effects in proteins. Molecular dynamics (MD) simulations now enable the theoretical study of protein-ligand binding in terms of ensembles of interconverting microstates and the population shifts characteristic of "dynamical allostery." Here we investigate protein-ligand binding and allostery based on a Markov state model (MSM) with states and rates obtained from all-atom MD simulations. As an exemplary case, we consider the single domain protein par-6 PDZ with and without ligand and allosteric effector. This is one of the smallest proteins in which allostery has been experimentally observed. In spite of the increased complexity intrinsic to a statistical ensemble perspective, we find that conformational selection and induced fit mechanisms can be readily identified in the analysis. In the nonallosteric pathway, MD-MSM shows that PDZ binds ligand via conformational selection. However, the allosteric pathway requires an activation step that involves a conformational change induced by the allosteric effector Cdc42. Once in the allosterically activated state, we find that ligand binding can proceed by conformational selection. Our MD-MSM model predicts that allostery in this and possibly other systems involves both induced fit and conformational selection, not just one or the other.

Accelerating physical simulations of proteins by leveraging external knowledge

It is challenging to compute structure-function relationships of proteins using molecular physics. The problem arises from the exponential scaling of the computational searching and sampling of large conformational spaces. This scaling challenge is not met by today's methods, such as Monte Carlo, simulated annealing, genetic algorithms, or molecular dynamics (MD) or its variants such as replica exchange. Such methods of searching for optimal states on complex probabalistic landscapes are referred to more broadly as Explore-and-Exploit (EE), including in contexts such as computational learning, games, industrial planning and modeling military strategies. Here we describe a Bayesian method, called MELD, that 'melds' together explore-and-exploit approaches with externally added information that can be vague, combinatoric, noisy, intuitive, heuristic, or from experimental data. MELD is shown to accelerate physical MD simulations when using experimental data to determine protein structures; for predicting protein structures by using heuristic directives; and when predicting binding affinities of proteins from limited information about the binding site. Such Guided Explore-and-Exploit approaches might also be useful beyond proteins and beyond molecular science.

Discrete Molecular Dynamics Approach to the Study of Disordered and Aggregating Proteins

We present a refinement of the Coarse Grained PACSAB force field for Discrete Molecular Dynamics (DMD) simulations of proteins in aqueous conditions. As the original version, the refined method provides good representation of the structure and dynamics of folded proteins but provides much better representations of a variety of unfolded proteins, including some very large, impossible to analyze by atomistic simulation methods. The PACSAB/DMD method also reproduces accurately aggregation properties, providing good pictures of the structural ensembles of proteins showing a folded core and an intrinsically disordered region. The combination of accuracy and speed makes the method presented here a good alternative for the exploration of unstructured protein systems.

A possible S-glutathionylation of specific proteins by glyoxalase II: An in vitro and in silico study

Glyoxalase II, the second of 2 enzymes in the glyoxalase system, is a hydroxyacylglutathione hydrolase that catalyses the hydrolysis of S-d-lactoylglutathione to form d-lactic acid and glutathione, which is released from the active site. The tripeptide glutathione is the major sulfhydryl antioxidant and has been shown to control several functions, including S-glutathionylation of proteins. S-Glutathionylation is a way for the cells to store reduced glutathione during oxidative stress, or to protect protein thiol groups from irreversible oxidation, and few enzymes involved in protein S-glutathionylation have been found to date. In this work, the enzyme glyoxalase II and its substrate S-d-lactoylglutathione were incubated with malate dehydrogenase or with actin, resulting in a glutathionylation reaction. Glyoxalase II was also submitted to docking studies. Computational data presented a high propensity of the enzyme to interact with malate dehydrogenase or actin through its catalytic site and further in silico investigation showed a high folding stability of glyoxalase II toward its own reaction product glutathione both protonated and unprotonated. This study suggests that glyoxalase II, through a specific interaction of its catalytic site with target proteins, could be able to perform a rapid and specific protein S-glutathionylation using its natural substrate S-d-lactoylglutathione.

SIGNIFICANCE

This article reports for the first time a possible additional role of Glo2 that, after interacting with a target protein, is able to promote S-glutathionylation using its natural substrate SLG, a glutathione derived compound. In this perspective, Glo2 can play a new important regulatory role inS-glutathionylation, acquiring further significance in cellular post-translational modifications of proteins.