Essential dynamics: foundation and applications

Conformational plasticity of SpyCas9 induced by AcrIIA4 and AcrIIA2: Insights from molecular dynamics simulation

CRISPR-Cas9 systems constitute bacterial adaptive immune systems that protect against phage infections. Bacteriophages encode anti-CRISPR proteins (Acrs) that mitigate the bacterial immune response. However, the structural basis for their inhibitory actions from a molecular perspective remains elusive. In this study, through microsecond atomistic molecular dynamics simulations, we demonstrated the remarkable flexibility of Streptococcus pyogenes Cas9 (SpyCas9) and its conformational adaptability during interactions with AcrIIA4 and AcrIIA2. Specifically, we demonstrated that the binding of AcrIIA4 and AcrIIA2 to SpyCas9 induces a conformational rearrangement that causes spatial separation between the nuclease and cleavage sites, thus making the endonuclease inactive. This separation disrupts the transmission of signals between the protospacer adjacent motif recognition and nuclease domains, thereby impeding the efficient processing of double-stranded DNA. The simulation also reveals that AcrIIA4 and AcrIIA2 cause different structural variations of SpyCas9. Our research illuminates the precise mechanisms underlying the suppression of SpyCas9 by AcrIIA4 and AcrIIA2, thus presenting new possibilities for controlling genome editing with higher accuracy.

Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

Exploring the constitutive activation mechanism of the class A orphan GPR20

GPR20, an orphan G protein-coupled receptor (GPCR), shows significant expression in intestinal tissue and represents a potential therapeutic target to treat gastrointestinal stromal tumors. GPR20 performs high constitutive activity when coupling with Gi. Despite the pharmacological importance of GPCR constitutive activation, determining the mechanism has long remained unclear. In this study, we explored the constitutive activation mechanism of GPR20 through large-scale unbiased molecular dynamics simulations. Our results unveil the allosteric nature of constitutively activated GPCR signal transduction involving extracellular and intracellular domains. Moreover, the constitutively active state of the GPR20 requires both the N-terminal cap and Gi protein. The N-terminal cap of GPR20 functions like an agonist and mediates long-range activated conformational shift. Together with the previous study, this study enhances our knowledge of the self-activation mechanism of the orphan receptor, facilitates the drug discovery efforts that target GPR20.

Investigating the structural impact of Omicron RBD mutation on antibody escape and receptor management

The SARS-CoV-2 Variant B.1.1.5291 evolved rapidly in late November 2021 from the existing mutants sparking fear worldwide owing to its infamous immune escape from a varied class of neutralising antibodies. To assess the structural behaviour of Omicron-Receptor Binding Domain (RBD) upon interacting with cross-reactive CR3022 antibody, we investigated the computational approach of structural engagement in B.1.1529 RBD and wild-type RBD with CR3022 antibody. The current study investigates the interacting interface between the RBDs and CR3022 to decipher the key residues accompanying the potential mutational landscape of SARS-CoV-2 variants. We conducted in-silico docking followed by molecular dynamics simulation analysis to examine the dynamic behaviour of protein-protein interactions. Furthermore, the study unleashed possible interactions post energy decomposition analysis via MM-GBSA. Conclusively, the mutational landscape of RBD eases in designing and discovering the effective neutralisation accompanied by development of a universal vaccine.Communicated by Ramaswamy H. Sarma.

Conformational Dynamics in Corynebacterium glutamicum Diaminopimelate Epimerase: Insights from Ligand Parameterization, Atomistic Simulation, and Markov State Modeling

The microbial enzyme diaminopimelate epimerase (DapF), a vital enzyme in the lysine biosynthetic pathway, catalyzes the conversion of L, L-diaminopimelate (L, L-DAP) to D, L-diaminopimelate (D, L-DAP) using a catalytic cysteine dyad with one cysteine in thiol state and another in thiolate. Under oxidizing conditions, the catalytic cysteines of apo DapF form a disulfide bond that alters the structure and function of DapF. Given its potential as a target for antimicrobial resistance treatments, understanding DapF's functional dynamics is imperative. In the present work, we employ microsecond-scale all-atom molecular dynamics simulations of product-bound DapF and apo-DapF under oxidized and reduced conditions. We employ a polarized charge model for the ligand and the active site residues, which was necessary to preserve the electrostatic environment in the active site and retain the ligand in the active site. The product-bound DapF and apo-DapF in oxidized and reduced conditions exhibit a closed, semi-open, and open conformation, respectively, as identified using the internal coordinates of the dimeric enzyme and the principal component analysis. The conformational switch is guided by the dynamic catalytic (DC) loop, loop II, and loop III movements in the active site. The time scale of the close-to-open conformational transition is estimated to be 0.8 μs through Markov state modeling (MSM) and transition path theory (TPT). The present study explains the role of various active site residues and loops in ligand binding and protein dynamics in the DapF enzyme under different redox conditions. Such information will be helpful in future inhibitor design studies targeting the DapF enzyme.

tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation

Molecular simulations are commonly used to understand the mechanism of membrane permeation of small molecules, particularly for biomedical and pharmaceutical applications. However, despite significant advances in computing power and algorithms, calculating an accurate permeation free energy profile remains elusive for many drug molecules because it can require identifying the rate-limiting degrees of freedom (i.e., appropriate reaction coordinates). To resolve this issue, researchers have developed machine learning approaches to identify slow system dynamics. In this work, we apply time-lagged independent component analysis (tICA), an unsupervised dimensionality reduction algorithm, to molecular dynamics simulations with well-tempered metadynamics to find the slowest collective degrees of freedom of the permeation process of trimethoprim through a multicomponent membrane. We show that tICA-metadynamics yields translational and orientational collective variables (CVs) that increase convergence efficiency ∼1.5 times. However, crossing the periodic boundary is shown to introduce artifacts in the translational CV that can be corrected by taking absolute values of molecular features. Additionally, we find that the convergence of the tICA CVs is reached with approximately five membrane crossings and that data reweighting is required to avoid deviations in the translational CV.

Anti-breast cancer potential of a new xanthine derivative: In silico, antiproliferative, selectivity, VEGFR-2 inhibition, apoptosis induction and migration inhibition studies

BACKGROUND

The overexpression of VEGFR-2 receptors in breast cancer provides a valuable approach to anticancer strategies. Targeting VEGFR-2, a new semisynthetic compound (T-1-MCPAB) has been designed.

METHODS

Computational methods (ADMET, toxicity, DFT, Molecular Docking, Molecular Dynamics Simulations, MM-GBSA, PLIP, and PCAT) were conducted. In addition to the semi-synthesis, in vitro studies (anti-VEGFR-2, anti-proliferative, flow cytometry, and wound scratch assay) were employed.

RESULTS

ADME and toxicity profiles of T-1-MCPAB studies indicated its overall drug-likeness showing results much better than Sorafenib. Then, T-1-MCPAB's exact 3D structure, stability, and reactivity were evoked by the DFT calculations. Molecular docking, molecular dynamics simulations, MM-GPSA, PLIP, and PCAT studies denoted the correct binding and inhibiting potential of T-1-MCPAB, towards VEGFR-2 protein. After the semisynthesis, T-1-MCPAB inhibited VEGFR-2 with an IC50 of 0.135 µM, which was comparable to sorafenib's IC50 of 0.0591 µM. T-1-MCPAB also showed a notable performance against MCF7 and T47D breast cancer cell lines with IC50 values of 30.95 µM and 63.64 µM, respectively, and had high selectivity index values of 3.7 and 1.8, respectively. Furthermore, T-1-MCPAB influenced early and late apoptosis and significantly decreased the potential of MCF7 cells to heal and migrate.

CONCLUSION

T-1-MCPAB is a promising VEGFR-2 inhibitor with potential for breast cancer treatment. Further chemical and biological studies are needed to explore its potential as a therapeutic agent.

tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation

Molecular simulations are commonly used to understand the mechanism of membrane permeation of small molecules, particularly for biomedical and pharmaceutical applications. However, despite significant advances in computing power and algorithms, calculating an accurate permeation free energy profile remains elusive for many drug molecules because it can require identifying the rate-limiting degrees of freedom (i.e., appropriate reaction coordinates). To resolve this issue, researchers have developed machine learning approaches to identify slow system dynamics. In this work, we apply time-lagged independent component analysis (tICA), an unsupervised dimensionality reduction algorithm, to molecular dynamics simulations with well-tempered metadynamics to find the slowest collective degrees of freedom of the permeation process of trimethoprim through a multicomponent membrane. We show that tICA-metadynamics yields translational and orientational collective variables (CVs) that increase convergence efficiency ∼1.5 times. However, crossing the periodic boundary is shown to introduce artefacts in the translational CV that can be corrected by taking absolute values of molecular features. Additionally, we find that the convergence of the tICA CVs is reached with approximately five membrane crossings, and that data reweighting is required to avoid deviations in the translational CV.

Unveiling the interdomain dynamics of type II DNA topoisomerase through all-atom simulations: Implications for understanding its catalytic cycle

Type IIA DNA topoisomerases are complex molecular nanomachines that manage topological states of the DNA molecule in the cell and play a crucial role in cellular processes such as cell division and transcription. They are also established targets of cancer chemotherapy. Starting from the available crystal structure of a fully catalytic topoisomerase IIA homodimer from Saccharomyces cerevisiae, we constructed three states of this molecular motor primarily changing the configurations of the DNA segment bound in the DNA gate and performed μs-long all-atom molecular simulations. A comprehensive analysis revealed a sliding motion within the DNA gate and a teamwork between the N-gate and DNA gate that may be associated with the necessary molecular events that allow passage of the T-segment of DNA. The observed movement of the ATPase dimer relative to the DNA domain was reflected in different interaction patterns between the K-loops of the transducer domain and the B-A-B form of the bound DNA. Based on the obtained results, we mapped simulated configurations to the structures in the proposed catalytic cycle through which type IIA topoisomerases exert their function and discussed the possible transition events. The results extend our understanding of the mechanism of action of type IIA topoisomerases and provide an atomistic interpretation of some of the observed features of these molecular motors.

Thermodynamic Evolution of a Metamorphic Protein: A Theoretical-Computational Study of Human Lymphotactin

Metamorphic, or fold-switching, proteins feature different folds that are physiologically relevant. The human chemokine XCL1 (or Lymphotactin) is a metamorphic protein that features two native states, an [Formula: see text] and an all[Formula: see text] fold, which have similar stability at physiological condition. Here, extended molecular dynamics (MD) simulations, principal component analysis of atomic fluctuations and thermodynamic modeling based on both the configurational volume and free energy landscape, are used to obtain a detailed characterization of the conformational thermodynamics of human Lymphotactin and of one of its ancestors (as was previously obtained by genetic reconstruction). Comparison of our computational results with the available experimental data show that the MD-based thermodynamics can explain the experimentally observed variation of the conformational equilibrium between the two proteins. In particular, our computational data provide an interpretation of the thermodynamic evolution in this protein, revealing the relevance of the configurational entropy and of the shape of the free energy landscape within the essential space (i.e., the space defined by the generalized internal coordinates providing the largest, typically non-Gaussian, structural fluctuations).

All-Atom Simulations Reveal the Intricacies of Signal Transduction upon Binding of the HLA-E Ligand to the Transmembrane Inhibitory CD94/NKG2A Receptor

Natural killer (NK) cells play an important role in the innate immune response against tumors and various pathogens such as viruses and bacteria. Their function is controlled by a wide array of activating and inhibitory receptors, which are expressed on their cell surface. Among them is a dimeric NKG2A/CD94 inhibitory transmembrane (TM) receptor which specifically binds to the non-classical MHC I molecule HLA-E, which is often overexpressed on the surface of senescent and tumor cells. Using the Alphafold 2 artificial intelligence system, we constructed the missing segments of the NKG2A/CD94 receptor and generated its complete 3D structure comprising extracellular (EC), TM, and intracellular regions, which served as a starting point for the multi-microsecond all-atom molecular dynamics simulations of the receptor with and without the bound HLA-E ligand and its nonameric peptide. The simulated models revealed that an intricate interplay of events is taking place between the EC and TM regions ultimately affecting the intracellular immunoreceptor tyrosine-based inhibition motif (ITIM) regions that host the point at which the signal is transmitted further down the inhibitory signaling cascade. Signal transduction through the lipid bilayer was also coupled with the changes in the relative orientation of the NKG2A/CD94 TM helices in response to linker reorganization, mediated by fine-tuned interactions in the EC region of the receptor, taking place after HLA-E binding. This research provides atomistic details of the cells' protection mechanism against NK cells and broadens the knowledge regarding the TM signaling of ITIM-bearing receptors.

Theoretical-Computational Modeling of CD Spectra of Aqueous Monosaccharides by Means of Molecular Dynamics Simulations and Perturbed Matrix Method

The electronic circular dichroism (ECD) spectra of aqueous d-glucose and d-galactose were modeled using a theoretical-computational approach combining molecular dynamics (MD) simulations and perturbed matrix method (PMM) calculations, hereafter termed MD-PMM. The experimental spectra were reproduced with a satisfactory accuracy, confirming the good performances of MD-PMM in modeling different spectral features in complex atomic-molecular systems, as already reported in previous studies. The underlying strategy of the method was to perform a preliminary long timescale MD simulation of the chromophore followed by the extraction of the relevant conformations through essential dynamics analysis. On this (limited) number of relevant conformations, the ECD spectrum was calculated via the PMM approach. This study showed that MD-PMM was able to reproduce the essential features of the ECD spectrum (i.e., the position, the intensity, and the shape of the bands) of d-glucose and d-galactose while avoiding the rather computationally expensive aspects, which were demonstrated to be important for the final outcome, such as (i) the use of a large number of chromophore conformations; (ii) the inclusion of quantum vibronic coupling; and (iii) the inclusion of explicit solvent molecules interacting with the chromophore atoms within the chromophore itself (e.g., via hydrogen bonds).

The Conformational Dynamics of the Ligands Determines the Electronic Circular Dichroism of the Chiral Au38(SC2H4Ph)24 Cluster

Effects of the conformational dynamics of 2-PET protective ligands on the electronic circular dichroism (ECD) of the chiral Au38(SC2H4Ph)24 cluster are investigated. We adopt a computational protocol in which ECD spectra are calculated via the first principle polTDDFT approach on a series of conformations extracted from MD simulations by using Essential Dynamics (ED) analysis, and then properly weighted to predict the final spectrum. We find that the experimental spectral features are well reproduced, whereas significant discrepancies arise when the spectrum is calculated using the experimental X-ray structure. This result unambiguously demonstrates the need to account for the conformational effects in the ECD modeling of chiral nanoclusters. The present procedure proved to be able of capturing the essential conformational features of the dynamic Au38(SC2H4Ph)24 system, opening the possibility to model the ECD of soluble chiral nanoclusters in a realistic way.

Effect of Salts on the Conformational Dynamics of the Cytochrome P450 OleP

Cytochrome P450 OleP catalytic activity is strongly influenced by its structural dynamic conformational behavior. Here, we combine equilibrium-binding experiments with all-atom molecular dynamics simulations to clarify how different environments affect OleP conformational equilibrium between the open and the closed-catalytic competent-forms. Our data clearly show that at high-ionic strength conditions, the closed form is favored, and, very interestingly, different mechanisms, depending on the chemistry of the cations, can be used to rationalize such an effect.

PyMM: An Open-Source Python Program for QM/MM Simulations Based on the Perturbed Matrix Method

Quantum mechanical/molecular mechanics (QM/MM) methods are important tools in molecular modeling as they are able to couple an extended phase space sampling with an accurate description of the electronic properties of the system. Here, we describe a Python software package, called PyMM, which has been developed to apply a QM/MM approach, the perturbed matrix method, in a simple and efficient way. PyMM requires a classical atomic trajectory of the whole system and a set of unperturbed electronic properties of the ground and electronic excited states. The software output includes a set of the most common perturbed properties, such as the electronic excitation energies and the transitions dipole moments, as well as the eigenvectors describing the perturbed electronic states, which can be then used to estimate whatever electronic property. The software is composed of a simple and complete command-line interface, a set of internal input validation, and three main analyses focusing on (i) the perturbed eigenvector behavior, (ii) the calculation of the electronic absorption spectrum, and (iii) the estimation of the free energy differences along a reaction coordinate.

A computational approach for modeling electronic circular dichroism of solvated chromophores

The present study consists in a novel computational protocol to model the UV-circular dichroism spectra of solvated species. It makes use of quantum-chemical calculations on a series of conformations of a flexible chromophore or on a series of chromophore/solvent clusters extracted from molecular dynamic simulations. The protocol is described and applied to the aqueous cationic tripeptide GAG⁺ and to the aqueous neutral decapeptide (GVGVP)₂ . The protocol has proven able to: (i) properly consider the conformational motion of solute in the given environment; (ii) give the actual statistical weight of each conformational state; (iii) provide a reliable quantum mechanical method able to reproduce the spectral features. Temperature effects on conformations and spectral properties are properly taken into account. The role of explicit solvent on the conformational analysis and the spectra calculation is discussed. The comparison of the calculated circular dichroism spectra with experimental ones recorded at different temperatures represents a strict validation test of the method.

Theoretical-Computational Modeling of Gas-State Thermodynamics in Flexible Molecular Systems: Ionic Liquids in the Gas Phase as a Case Study

A theoretical-computational procedure based on the quasi-Gaussian entropy (QGE) theory and molecular dynamics (MD) simulations is proposed for the calculation of thermodynamic properties for molecular and supra-molecular species in the gas phase. The peculiarity of the methodology reported in this study is its ability to construct an analytical model of all the most relevant thermodynamic properties, even within a wide temperature range, based on a practically automatic sampling of the entire conformational repertoire of highly flexible systems, thereby bypassing the need for an explicit search for all possible conformers/rotamers deemed relevant. In this respect, the reliability of the presented method mainly depends on the quality of the force field used in the MD simulations and on the ability to discriminate in a physically coherent way between semi-classical and quantum degrees of freedom. The method was tested on six model systems (n-butane, n-butane, n-octanol, octadecane, 1-butyl-3-methylimidazolium hexafluorophosphate and 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ionic pairs), which, being experimentally characterized and already addressed by other theoretical-computational methods, were considered as particularly suitable to allow us to evaluate the method's accuracy and efficiency, bringing out advantages and possible drawbacks. The results demonstrate that such a physically coherent yet relatively simple method can represent a further valid computational tool that is alternative and complementary to other extremely efficient computational methods, as it is particularly suited for addressing the thermodynamics of gaseous systems with a high conformational complexity over a large range of temperature.

Mechanistic Insights into the Protection Effect of Argonaute-RNA Complex on the HCV Genome

While host miRNA usually plays an antiviral role, the relentless tides of viral evolution have carved out a mechanism to recruit host miRNA as a viral protector. By complementing miR-122 at the 5' end of the genome, the hepatitis C virus (HCV) gene can form a complex with Argonaute 2 (Ago2) protein to protect the 5' end of HCV RNA from exonucleolytic attacks. Experiments showed that the disruption of the stem-loop 1(SL1) structure and the 9th nucleotide (T9) of HCV site 1 RNA could enhance the affinity of the Ago2 protein to the HCV site 1 RNA (target RNA). However, the underlying mechanism of how the conformation and dynamics of the Ago2: miRNA: target RNA complex is affected by the SL1 and T9 remains unclear. To address this, we performed large-scale molecular dynamics simulations on the AGO2-miRNA complex binding with the WT target, T9-abasic target and SL1-disruption target, respectively. The results revealed that the T9 and SL1 structures could induce the departing motion of the PAZ, PIWI and N domains, propping up the mouth of the central groove which accommodates the target RNA, causing the instability of the target RNA and disrupting the Ago2 binding. The coordinated motion among the PAZ, PIWI and N domains were also weakened by the T9 and SL1 structures. Moreover, we proposed a new model wherein the Ago2 protein could adopt a more constraint conformation with the proximity and more correlated motions of the PAZ, N and PIWI domains to protect the target RNA from dissociation. These findings reveal the mechanism of the Ago2-miRNA complex's protective effect on the HCV genome at the atomic level, which will offer guidance for the design of drugs to confront the protection effect and engineering of Ago2 as a gene-regulation tool.

Conformational Heterogeneity and Frustration of the Tumor Suppressor p53 as Tuned by Punctual Mutations

The conformational heterogeneity of the p53 tumor suppressor, the wild-type (p53wt) and mutated forms, was investigated by a computational approach, including the modeling and all atoms of the molecular dynamics (MD) simulations. Four different punctual mutations (p53R175H, p53R248Q, p53R273H, and p53R282W) which are known to affect the DNA binding and belong to the most frequent hot-spot mutations in human cancers, were taken into consideration. The MD trajectories of the wild-type and mutated p53 forms were analyzed by essential dynamics to extract the relevant collective motions and by the frustration method to evaluate the degeneracy of the energy landscape. We found that p53 is characterized by wide collective motions and its energy landscape exhibits a rather high frustration level, especially in the regions involved in the binding to physiological ligands. Punctual mutations give rise to a modulation of both the collective motions and the frustration of p53, with different effects depending on the mutation. The regions of p53wt and of the mutated forms characterized by a high frustration level are also largely involved in the collective motions. Such a correlation is discussed also in connection with the intrinsic disordered character of p53 and with its central functional role.

Effects of Environmental and Electric Perturbations on the pKa of Thioredoxin Cysteine 35: A Computational Study

Here we present a theoretical-computational study dealing with the evaluation of the pKa of the Cysteine residues in Thioredoxin (TRX) and in its complex with the Thioredoxin-interacting protein (TXNIP). The free energy differences between the anionic and neutral form of the Cysteine 32 and 35 have been evaluated by means of the Perturbed Matrix Method with classical perturbations due to both the environment and an exogenous electric field as provided by Molecular Dynamics (MD) simulations. The evaluation of the free energies allowed us to show that the effect of the perturbing terms is to lower the pKa of Cysteine 32 and Cysteine 35 with respect to the free amino-acid. On the other hand, in the complex TRX-TXNIP, our data show an enhanced stabilization of the neutral reduced form of Cys 35. These results suggest that external electric stimuli higher than 0.02 V/nm can modulate the Cysteine pKa, which can be connected to the tight regulation of the TRX acting as an antioxidant agent.

Mechanistic insights into the clinical Y96D mutation with acquired resistance to AMG510 in the KRASG12C

Special oncogenic mutations in the RAS proteins lead to the aberrant activation of RAS and its downstream signaling pathways. AMG510, the first approval drug for KRAS, covalently binds to the mutated cysteine 12 of KRASG12C protein and has shown promising antitumor activity in clinical trials. Recent studies have reported that the clinically acquired Y96D mutation could severely affect the effectiveness of AMG510. However, the underlying mechanism of the drug-resistance remains unclear. To address this, we performed multiple microsecond molecular dynamics simulations on the KRASG12C−AMG510 and KRASG12C/Y96D−AMG510 complexes at the atomic level. The direct interaction between the residue 96 and AMG510 was impaired owing to the Y96D mutation. Moreover, the mutation yielded higher flexibility and more coupled motion of the switch II and α3-helix, which led to the departing motion of the switch II and α3-helix. The resulting departing motion impaired the interaction between the switch II and α3-helix and subsequently induced the opening and loosening of the AMG510 binding pocket, which further disrupted the interaction between the key residues in the pocket and AMG510 and induced an increased solvent exposure of AMG510. These findings reveal the resistance mechanism of AMG510 to KRASG12C/Y96D, which will help to offer guidance for the development of KRAS targeted drugs to overcome acquired resistance.

Exploring the Conformational Impact of Glycine Receptor TM1-2 Mutations Through Coarse-Grained Analysis and Atomistic Simulations

Pentameric ligand-gated ion channels (PLGICs) are a family of proteins that convert chemical signals into ion fluxes through cellular membranes. Their structures are highly conserved across all kingdoms from bacteria to eukaryotes. Beyond their classical roles in neurotransmission and neurological disorders, PLGICs have been recently related to cell proliferation and cancer. Here, we focus on the best characterized eukaryotic channel, the glycine receptor (GlyR), to investigate its mutational patterns in genomic-wide tumor screens and compare them with mutations linked to hyperekplexia (HPX), a Mendelian neuromotor disease that disrupts glycinergic currents. Our analysis highlights that cancer mutations significantly accumulate across TM1 and TM2, partially overlapping with HPX changes. Based on 3D-clustering, conservation, and phenotypic data, we select three mutations near the pore, expected to impact GlyR conformation, for further study by molecular dynamics (MD). Using principal components from experimental GlyR ensembles as framework, we explore the motions involved in transitions from the human closed and desensitized structures and how they are perturbed by mutations. Our MD simulations show that WT GlyR spontaneously explores opening and re-sensitization transitions that are significantly impaired by mutations, resulting in receptors with altered permeability and desensitization properties in agreement with HPX functional data.

Allosteric Binding Sites of Ab Peptides on the Acetylcholine Synthesizing Enzyme ChAT as Deduced by In Silico Molecular Modeling

The native function of amyloid-β (Aβ) peptides is still unexplored. However, several recent reports suggest a prominent role of Aβ peptides in acetylcholine homeostasis. To clarify this role of Aβ, we have reported that Aβ peptides at physiological concentrations can directly enhance the catalytic efficiency of the key cholinergic enzyme, choline acetyltransferase (ChAT), via an allosteric interaction. In the current study, we further aimed to elucidate the underlying ChAT-Aβ interaction mechanism using in silico molecular docking and dynamics analysis. Docking analysis suggested two most probable binding clusters on ChAT for Aβ40 and three for Aβ42. Most importantly, the docking results were challenged with molecular dynamic studies of 100 ns long simulation in triplicates (100 ns × 3 = 300 ns) and were analyzed for RMSD, RMSF, RoG, H-bond number and distance, SASA, and secondary structure assessment performed together with principal component analysis and the free-energy landscape diagram, which indicated that the ChAT-Aβ complex system was stable throughout the simulation time period with no abrupt motion during the evolution of the simulation across the triplicates, which also validated the robustness of the simulation study. Finally, the free-energy landscape analysis confirmed the docking results and demonstrated that the ChAT-Aβ complexes were energetically stable despite the unstructured nature of C- and N-terminals in Aβ peptides. Overall, this study supports the reported in vitro findings that Aβ peptides, particularly Aβ42, act as endogenous ChAT-Potentiating-Ligand (CPL), and thereby supports the hypothesis that one of the native biological functions of Aβ peptides is the regulation of acetylcholine homeostasis.

Estimating ruggedness of free-energy landscapes of small globular proteins from principal component analysis of molecular dynamics trajectories

The internal dynamics of biomolecules, and hence their function, is governed by the structure of their free-energy landscape. Early flash-photolysis experiments on myoglobin suggested that the free-energy landscapes of proteins are hierarchically structured, with a characteristic distribution of free-energy barriers which gives rise to anomalous diffusion. Analytical results have been derived for one-dimensional or high-dimensional hierarchical free-energy landscapes. Recent improvements in methods and computer performance enable generating sufficiently long molecular dynamics (MD) trajectories to extract dynamics information covering many orders of magnitude, such that the broad distributions of energy barriers of proteins become accessible to quantitative studies of intermediate dimensions. In this work, we present a nonequilibrium method to estimate barrier height distributions from microsecond-long MD simulations. It infers barrier height distributions from anomalous diffusion exponents derived from principal component analysis and by comparison to simple hierarchical lattice models. These models are d-dimensional lattices of states separated by free-energy barriers, the heights of which are distributed as p(ΔG)=1/γexp(-ΔG/γ). The parameter γ quantifies the "ruggedness" of the free-energy landscape in such models. We show that both parameters, i.e., ruggedness and effective dimensionality d, can be inferred from anomalous diffusion exponents. Assuming a similar dependency of anomalous diffusion exponents on γ and d for proteins, we estimate the ruggedness of the free-energy landscapes of 500 small, single-domain globular proteins between 15 and 20 kT per dimension with an estimated accuracy of 4.2 kT and dimensionality between 40 and 60 with an estimated accuracy of 10 dimensions. Remarkably, neither effective dimensionality nor the ruggedness correlates with protein size and both ruggedness and effective dimensionality are much smaller than the scatter of protein sizes. From this finding, we conclude that these two properties of the free-energy landscape of a protein are rather adapted to the particular function of each single protein and that, quite generally, are functionally relevant for globular proteins.

Cryptic Pockets Repository through Pocket Dynamics Tracking and Metadynamics on Essential Dynamics Space: Applications to Mcl-1

Detection of cryptic pockets (hidden protein pockets) is a hot topic in structure-based drug discovery, especially for drugging the yet undruggable proteome. The experimental detection of cryptic pockets is still considered an expensive endeavor. Thus, computational methods, such as atomistic simulations, are used instead. These simulation methods can provide a perspective on protein dynamics that overpasses the experimental X-ray structures' static and average view. Nonetheless, unbiased molecular dynamics (MD) simulations fall short to detect transient and cryptic pockets requiring the crossing of high-energy barriers. Enhanced sampling methods, such as Metadynamics, provide a solution to overcome the time-scale problem faced by unbiased MD simulations. However, these methods are still limited by the availability of collective variable space to capture the intricate parameters, leading to the opening of cryptic pockets. Unfortunately, the design of such collective variables requires a priori knowledge of the binding site, information that is by definition lacking for cryptic pockets. In this work, we evaluated the use of the Metadynamics biasing scheme on essential coordinates space as a general method for cryptic pocket detection. This approach was applied to an antiapoptotic protein: Mcl-1 as a test model. In addition to providing a broader characterization of Mcl-1's conformational space, we show the effectiveness of this method in drawing the full repository of Mcl-1's known and novel cryptic pockets in an unsupervised manner.

Activation of Abl1 Kinase Explored Using Well-Tempered Metadynamics Simulations on an Essential Dynamics Sampled Path

Well-tempered metadynamics (wT-metaD) simulations using path collective variables (CVs) have been successfully applied in recent years to explore conformational transitions in protein kinases and other biomolecular systems. While this methodology has the advantage of describing the transitions with a limited number of predefined path CVs, it requires as an input a reference path connecting the initial and target states of the system. It is desirable to automate the path generation using approaches that do not rely on the choice of geometric CVs to describe the transition of interest. To this end, we developed an approach that couples essential dynamics sampling with wT-metaD simulations. We used this newly developed procedure to explore the activation mechanism of Abl1 kinase and compute the associated free energy barriers. Through these simulations, we identified a three-step mechanism for the activation that involved two metastable intermediates that possessed a partially open activation loop and differed primarily in the “in” or “out” conformation of the aspartate residue of the DFG motif. One of these states is similar to a conformation that was detected in previous spectroscopic studies of Abl1 kinase, albeit its mechanistic role in the activation was hitherto not well understood. The present study establishes its intermediary role in the activation and predicts a rate-determining free energy barrier of 13.8 kcal/mol that is in good agreement with previous experimental and computational estimates. Overall, our study demonstrates the usability of essential dynamics sampling as a path CV in wT-metaD to conveniently study conformational transitions and accurately calculate the associated barriers.

Drug repurposing for ligand-induced rearrangement of Sirt2 active site-based inhibitors via molecular modeling and quantum mechanics calculations

Sirtuin 2 (Sirt2) nicotinamide adenine dinucleotide-dependent deacetylase enzyme has been reported to alter diverse biological functions in the cells and onset of diseases, including cancer, aging, and neurodegenerative diseases, which implicate the regulation of Sirt2 function as a potential drug target. Available Sirt2 inhibitors or modulators exhibit insufficient specificity and potency, and even partially contradictory Sirt2 effects were described for the available inhibitors. Herein, we applied computational screening and evaluation of FDA-approved drugs for highly selective modulation of Sirt2 activity via a unique inhibitory mechanism as reported earlier for SirReal2 inhibitor. Application of stringent molecular docking results in the identification of 48 FDA-approved drugs as selective putative inhibitors of Sirt2, but only top 10 drugs with docking scores > - 11 kcal/mol were considered in reference to SirReal2 inhibitor for computational analysis. The molecular dynamics simulations and post-simulation analysis of Sirt2-drug complexes revealed substantial stability for Fluphenazine and Nintedanib with Sirt2. Additionally, developed 3D-QSAR-models also support the inhibitory potential of drugs, which exclusively revealed highest activities for Nintedanib (pIC50 ≥ 5.90 µM). Conclusively, screened FDA-approved drugs were advocated as promising agents for Sirt2 inhibition and required in vitro investigation for Sirt2 targeted drug development.

Establishing the allosteric mechanism in CRISPR-Cas9

Allostery is a fundamental property of proteins, which regulates biochemical information transfer between spatially distant sites. Here, we report on the critical role of molecular dynamics (MD) simulations in discovering the mechanism of allosteric communication within CRISPR-Cas9, a leading genome editing machinery with enormous promises for medicine and biotechnology. MD revealed how allostery intervenes during at least three steps of the CRISPR-Cas9 function: affecting DNA recognition, mediating the cleavage and interfering with the off-target activity. An allosteric communication that activates concerted DNA cleavages was found to led through the L1/L2 loops, which connect the HNH and RuvC catalytic domains. The identification of these "allosteric transducers" inspired the development of novel variants of the Cas9 protein with improved specificity, opening a new avenue for controlling the CRISPR-Cas9 activity. Discussed studies also highlight the critical role of the recognition lobe in the conformational activation of the catalytic HNH domain. Specifically, the REC3 region was found to modulate the dynamics of HNH by sensing the formation of the RNA:DNA hybrid. The role of REC3 was revealed to be particularly relevant in the presence of DNA mismatches. Indeed, interference of REC3 with the RNA:DNA hybrid containing mismatched pairs at specific positions resulted in locking HNH in an inactive "conformational checkpoint" conformation, thereby hampering off-target cleavages. Overall, MD simulations established the fundamental mechanisms underlying the allosterism of CRISPR-Cas9, aiding engineering strategies to develop new CRISPR-Cas9 variants for improved genome editing.

Estimating the Directional Flexibility of Proteins from Equilibrium Thermal Fluctuations

The directional flexibility of proteins is an equilibrium molecular property which is accessible to both experiment and computation. Single molecule force spectroscopy (SMFS) experiments report effective directional spring constants to describe the collective anisotropic response of a protein structure to mechanical pulling forces applied along selected axes. On the other hand, computational methods have thus far employed either indirect force based nonequilibrium simulations or coarse-grained elastic network models (ENM) to predict protein directional spring constants. Here, we examine the ability of equilibrium atomistic Molecular Dynamics (MD) simulations to estimate the directional flexibility and mechanical anisotropy of proteins. MD-derived effective directional spring constants are found to correlate well with SMFS spring constants (ρ² = 0.97-0.99; Adj R² = 0.92-0.99) and unfolding forces (ρ² = 0.85-0.97; Adj R² = 0.63-0.91) for five different globular proteins. Specifically, the computed spring constants reproduce the mechanical anisotropy reported by SMFS along five different directions of green fluorescence protein (GFP) and six directions of the immunoglobulin-binding B1 domain of streptococcal protein G (GB1). Further, protein dynamics as captured in MD can be translated into spring constants which can distinguish the N-C directional flexibility of ubiquitin (Ub) from two structurally homologous small ubiquitin-like modifier (SUMO1 and SUMO2) isoforms. We apply our computational framework to study the mechanical anisotropy of Ub along the seven lysine-C-term directions which are functionally relevant. We show that Ub possesses two distinct flexibility scales along these directions which roughly differ by an order of magnitude. Further, our studies reveal that the mechanical anisotropy of Ub is modified in contrasting ways by the binding of two partner proteins (UBCH5A and UEV) which attach and recognize these biomolecular tag proteins. On the basis of equilibrium MD benchmarks for flexibility along 2485 bond vectors in Ub, we propose and validate a new covariance-propagation scheme to extract spring constants from ENM normal modes. We also critically examine the ability of ENM to predict directional flexibility of proteins and suggest modifications to improve these intuitive and scalable descriptions.

Molecular dynamics and in silico mutagenesis on the reversible inhibitor-bound SARS-CoV-2 main protease complexes reveal the role of lateral pocket in enhancing the ligand affinity

The 2019 novel coronavirus pandemic caused by SARS-CoV-2 remains a serious health threat to humans and there is an urgent need to develop therapeutics against this deadly virus. Recent scientific evidences have suggested that the main protease (Mpro) enzyme in SARS-CoV-2 can be an ideal drug target due to its crucial role in the viral replication and transcription processes. Therefore, there are ongoing research efforts to identify drug candidates against SARS-CoV-2 Mpro that resulted in hundreds of X-ray crystal structures of ligand-bound Mpro complexes in the Protein Data Bank (PDB) describing the interactions of different fragment chemotypes within different sites of the Mpro. In this work, we performed rigorous molecular dynamics (MD) simulation of 62 reversible ligand-Mpro complexes in the PDB to gain mechanistic insights about their interactions at the atomic level. Using a total of over 3 µs long MD trajectories, we characterized different pockets in the apo Mpro structure, and analyzed the dynamic interactions and binding affinity of ligands within those pockets. Our results identified the key residues that stabilize the ligands in the catalytic sites and other pockets of Mpro. Our analyses unraveled the role of a lateral pocket in the catalytic site in Mpro that is critical for enhancing the ligand binding to the enzyme. We also highlighted the important contribution from HIS163 in the lateral pocket towards ligand binding and affinity against Mpro through computational mutation analyses. Further, we revealed the effects of explicit water molecules and Mpro dimerization in the ligand association with the target. Thus, comprehensive molecular-level insights gained from this work can be useful to identify or design potent small molecule inhibitors against SARS-CoV-2 Mpro.

Unraveling the internal conversion process within the Q-bands of a chlorophyll-like-system through surface-hopping molecular dynamics simulations

The non-radiative relaxation process within the Q-bands of chlorophylls represents a crucial preliminary step during the photosynthetic mechanism. Despite several experimental and theoretical efforts performed in order to clarify the complex dynamics characterizing this stage, a complete understanding of this mechanism is still far to be reached. In this study, non-adiabatic excited-state molecular dynamic simulations have been performed to model the non-radiative process within the Q-bands for a model system of chlorophylls. This system has been considered in the gas phase and then, to have a more representative picture of the environment, with implicit and mixed implicit-explicit solvation models. In the first part of this analysis, absorption spectra have been simulated for each model in order to guide the setup for the non-adiabatic excited-state molecular dynamic simulations. Then, non-adiabatic excited-state molecular dynamic simulations have been performed on a large set of independent trajectories and the population of the Q_x and Q_y states has been computed as the average of all the trajectories, estimating the rate constant for the process. Finally, with the aim of investigating the possible role played by the solvent in the Q_x-Q_y crossing mechanism, an essential dynamic analysis has been performed on the generated data, allowing one to find the most important motions during the simulated dynamics.

Structure-Based Identification of Natural Products as SARS-CoV-2 Mpro Antagonist from Echinacea angustifolia Using Computational Approaches

Coronavirus disease-19 (COVID-19) pandemic, caused by the novel SARS-CoV-2 virus, continues to be a global threat. The number of cases and deaths will remain escalating due to the lack of effective therapeutic agents. Several studies have established the importance of the viral main protease (Mpro) in the replication of SARS-CoV-2 which makes it an attractive target for antiviral drug development, including pharmaceutical repurposing and other medicinal chemistry approaches. Identification of natural products with considerable inhibitory potential against SARS-CoV-2 could be beneficial as a rapid and potent alternative with drug-likeness by comparison to de novo antiviral drug discovery approaches. Thereof, we carried out the structure-based screening of natural products from Echinacea-angustifolia, commonly used to prevent cold and other microbial respiratory infections, targeting SARS-CoV-2 Mpro. Four natural products namely, Echinacoside, Quercetagetin 7-glucoside, Levan N, Inulin from chicory, and 1,3-Dicaffeoylquinic acid, revealed significant docking energy (>-10 kcal/mol) in the SARS-CoV-2 Mpro catalytic pocket via substantial intermolecular contacts formation against co-crystallized ligand (<-4 kcal/mol). Furthermore, the docked poses of SARS-CoV-2 Mpro with selected natural products showed conformational stability through molecular dynamics. Exploring the end-point net binding energy exhibited substantial contribution of Coulomb and van der Waals interactions to the stability of respective docked conformations. These results advocated the natural products from Echinacea angustifolia for further experimental studies with an elevated probability to discover the potent SARS-CoV-2 Mpro antagonist with higher affinity and drug-likeness.

Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations

The p53 tumor suppressor is a multifaceted context-dependent protein, which is involved in multiple cellular pathways, with the ability to either keep the cells alive or to kill them through mechanisms such as apoptosis. To complicate this picture, cancer cells that express mutant p53 becomes addicted to the mutant activity, so that the mutant variant features a myriad of gain-of-function activities, opening different venues for therapy. This makes essential to think outside the box and apply new approaches to the study of p53 structure-(mis)function relationship to find new critical components of its pathway or to understand how known parts are interconnected, compete, or cooperate. In this context, I will here illustrate how to integrate different computational methods to the identification of possible allosteric effects transmitted from the DNA binding interface of p53 to regions for cofactor recruitment. The protocol can be extended to any other cases of study. Indeed, it does not necessarily apply only to the study of DNA-induced effects, but more broadly to the investigation of long-range effects induced by a biological partner that binds to a biomolecule of interest.

Molecular Dynamics Reveals a DNA-Induced Dynamic Switch Triggering Activation of CRISPR-Cas12a

CRISPR-Cas12a is a genome-editing system, recently also harnessed for nucleic acid detection, which is promising for the diagnosis of the SARS-CoV-2 coronavirus through the DETECTR technology. Here, a collective ensemble of multimicrosecond molecular dynamics characterizes the key dynamic determinants allowing nucleic acid processing in CRISPR-Cas12a. We show that DNA binding induces a switch in the conformational dynamics of Cas12a, which results in the activation of the peripheral REC2 and Nuc domains to enable cleavage of nucleic acids. The simulations reveal that large-amplitude motions of the Nuc domain could favor the conformational activation of the system toward DNA cleavages. In this process, the REC lobe plays a critical role. Accordingly, the joint dynamics of REC and Nuc shows the tendency to prime the conformational transition of the DNA target strand toward the catalytic site. Most notably, the highly coupled dynamics of the REC2 region and Nuc domain suggests that REC2 could act as a regulator of the Nuc function, similar to what was observed previously for the HNH domain in the CRISPR-associated nuclease Cas9. These mutual domain dynamics could be critical for the nonspecific binding of DNA and thereby for the underlying mechanistic functioning of the DETECTR technology. Considering that REC is a key determinant in the system's specificity, our findings provide a rational basis for future biophysical studies aimed at characterizing its function in CRISPR-Cas12a. Overall, our outcomes advance our mechanistic understanding of CRISPR-Cas12a and provide grounds for novel engineering efforts to improve genome editing and viral detection.

Data-Driven Molecular Dynamics: A Multifaceted Challenge

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

A pan-cancer assessment of alterations of the kinase domain of ULK1, an upstream regulator of autophagy

Autophagy is a key clearance process to recycle damaged cellular components. One important upstream regulator of autophagy is ULK1 kinase. Several three-dimensional structures of the ULK1 catalytic domain are available, but a comprehensive study, including molecular dynamics, is missing. Also, an exhaustive description of ULK1 alterations found in cancer samples is presently lacking. We here applied a framework which links -omics data to structural protein ensembles to study ULK1 alterations from genomics data available for more than 30 cancer types. We predicted the effects of mutations on ULK1 function and structural stability, accounting for protein dynamics, and the different layers of changes that a mutation can induce in a protein at the functional and structural level. ULK1 is down-regulated in gynecological tumors. In other cancer types, ULK2 could compensate for ULK1 downregulation and, in the majority of the cases, no marked changes in expression have been found. 36 missense mutations of ULK1, not limited to the catalytic domain, are co-occurring with mutations in a large number of ULK1 interactors or substrates, suggesting a pronounced effect of the upstream steps of autophagy in many cancer types. Moreover, our results pinpoint that more than 50% of the mutations in the kinase domain of ULK1, here investigated, are predicted to affect protein stability. Three mutations (S184F, D102N, and A28V) are predicted with only impact on kinase activity, either modifying the functional dynamics or the capability to exert effects from distal sites to the functional and catalytic regions. The framework here applied could be extended to other protein targets to aid the classification of missense mutations from cancer genomics studies, as well as to prioritize variants for experimental validation, or to select the appropriate biological readouts for experiments.

Variance of Atomic Coordinates as a Dynamical Metric to Distinguish Proteins and Protein-Protein Interactions in Molecular Dynamics Simulations

Protein dynamics is a manifestation of the complex trajectories of these biomolecules on a multidimensional rugged potential energy surface (PES) driven by thermal energy. At present, computational methods such as atomistic molecular dynamics (MD) simulations can describe thermal protein conformational changes in fully solvated environments over millisecond timescales. Despite these advances, a quantitative assessment of protein dynamics remains a complicated topic, intricately linked to issues such as sampling convergence and the identification of appropriate reaction coordinates/structural features to describe protein conformational states and motions. Here, we present the cumulative variance of atomic coordinate fluctuations (CVCF) along trajectories as an intuitive PES sensitive metric to assess both the extent of sampling and protein dynamics captured in MD simulations. We first examine the sampling problem in model one- (1D) and two-dimensional (2D) PES to demonstrate that the CVCF when traced as a function of the sampling variable (time in MD simulations) can identify local and global equilibria. Further, even far from global equilibrium, a situation representative of standard MD trajectories of proteins, the CVCF can distinguish different PES and therefore resolve the resultant protein dynamics. We demonstrate the utility of our CVCF analysis by applying it to distinguish the dynamics of structurally homologous proteins from the ubiquitin family (ubiquitin, SUMO1, SUMO2) and ubiquitin protein-protein interactions. Our CVCF analysis reveals that differential side-chain dynamics from the structured part of the protein (the conserved β-grasp fold) present distinct protein PES to distinguish ubiquitin from SUMO isoforms. Upon binding to two functionally distinct protein partners (UBCH5A and UEV), intrinsic ubiquitin dynamics changes to reflect the binding context even though the two proteins have similar binding modes, which lead to negligible (sub-angstrom scale) structural changes.

Molecular dynamics simulation of the heart type fatty acid binding protein in a crystal environment

Crystallographic data comes from a space-time average over all the unit cells within the crystal, so dynamic phenomena do not contribute significantly to the diffraction data. Many efforts have been made to reconstitute the movement of the macromolecules and explore the microstates that the confined proteins can adopt in the crystalline network. We explored different strategies to simulate a heart fatty acid binding protein (H-FABP) crystal by means of Molecular Dynamics (MD) simulations. We evaluate the effect of introducing restraints according to experimental isotropic B-factors and we analyzed the H-FABP motions in the crystal using Principal Component Analysis (PCA), isotropic and anisotropic B-factors. We compared the behavior of the protein simulated in the crystal confinement versus in solution, and we observed the effect of that confinement in the mobility of the protein residues. Restraining one-third of Cα atoms based on experimental B-factors produce lower B-factors than simulations without restraints, showing that the position restraint of the atoms with the lowest experimental B-factor is a good strategy to maintain the geometry of the crystal with an obvious decrease in the degrees of motion of the protein. PCA shows that, as position restraint reduces the conformational space explored by the system, the motion of the crystal is better recovered, for an essential subspace of the same size, in the simulations without restraints. Restraining only one Cα seems to be a good balance between giving flexibility to the system and preserving its structure. Communicated by Ramaswamy H. Sarma.

Conformational gating in ammonia lyases

BACKGROUND

Ammonia lyases are enzymes of industrial and biomedical interest. Knowledge of structure-dynamics-function relationship in ammonia lyases is instrumental for exploiting the potential of these enzymes in industrial or biomedical applications.

METHODS

We investigated the conformational changes in the proximity of the catalytic pocket of a 3-methylaspartate ammonia lyase (MAL) as a model system. At this scope, we used microsecond all-atom molecular dynamics simulations, analyzed with dimensionality reduction techniques, as well as in terms of contact networks and correlated motions.

RESULTS

We identify two regulatory elements in the MAL structure, i.e., the β5-α2 loop and the helix-hairpin-loop subdomain. These regulatory elements undergo conformational changes switching from 'occluded' to 'open' states. The rearrangements are coupled to changes in the accessibility of the active site. The β5-α2 loop and the helix-hairpin-loop subdomain modulate the formation of tunnels from the protein surface to the catalytic site, making the active site more accessible to the substrate when they are in an open state.

CONCLUSIONS

Our work pinpoints a sequential mechanism, in which the helix-hairpin-loop subdomain of MAL needs to break a subset of intramolecular interactions first to favor the displacement of the β5-α2 loop. The coupled conformational changes of these two elements contribute to modulate the accessibility of the catalytic site.

GENERAL SIGNIFICANCE

Similar molecular mechanisms can have broad relevance in other ammonia lyases with similar regulatory loops. Our results also imply that it is important to account for protein dynamics in the design of variants of ammonia lyases for industrial and biomedical applications.

Assessment of the conformational profile of bombesin by computational methods

In the present work, the results of a computational study aimed at assessing the conformational profile of bombesin are reported. The conformational space of the peptide was sampled by means of a 4 μs accelerated molecular dynamics simulation in water, using an explicit solvent model. The results were analyzed using Principal Component Analysis to get essential information on peptide fluctuations, along with cluster analysis to characterize different conformations in the sample. Analysis of the results suggests that the peptide adopts helical structures at the C-terminus that tend to unwind at the end of the peptide chain, since there are many structures exhibiting only two turns of a helix at the central segment of the peptide. In addition, the peptide also adopts hairpin turn structures at the N-terminus. Results of the simulation were confronted with available NMR results in a 2,2,2-trifluoroethanol/water (30% v/v) solution. Distances deduced form NOEs experiments only provide support to the presence of helical conformations that represent the most populated structures in the simulation. The absence of other conformations in the NMR experiments can be explained to be due to the α-helix enhancing nature of the solvent used in the experiments.

Chemical promenades: Exploring potential-energy surfaces with immersive virtual reality

The virtual-reality framework AVATAR (Advanced Virtual Approach to Topological Analysis of Reactivity) for the immersive exploration of potential-energy landscapes is presented. AVATAR is based on modern consumer-grade virtual-reality technology and builds on two key concepts: (a) the reduction of the dimensionality of the potential-energy surface to two process-tailored, physically meaningful generalized coordinates, and (b) the analogy between the evolution of a chemical process and a pathway through valleys (potential wells) and mountain passes (saddle points) of the associated potential energy landscape. Examples including the discovery of competitive reaction paths in simple A + BC collisional systems and the interconversion between conformers in ring-puckering motions of flexible rings highlight the innovation potential that augmented and virtual reality convey for teaching, training, and supporting research in chemistry.

A new analogue of islet neogenesis associated protein with higher structural and plasma stability

Islet Neogenesis Associated Protein pentadecapeptide (INGAP-PP) increases β-cell mass and function in experimental animals. A short clinical trial also yielded promising results. However, HTD4010, a new peptide derived from INGAP-PP, was developed in order to optimize its specific effects by minimizing its side effects. To study and compare the tertiary structure, stability dynamics, and plasma stability of HTD4010, an INGAP-PP analogue. Both peptides were pre-incubated in human, rat and mouse plasma at 37 °C, and their presence was identified and quantified by high performance liquid chromatography at different time-points. GROMACS 2019 package and the Gromos 54A7 force field were used to evaluate overall correlated motion of the peptide molecule during molecular dynamics simulation by essential dynamics. HTD4010 exhibited significantly larger plasma stability than INGAP-PP, and its structural stability was almost 3.36-fold higher than INGAP-PP. These results suggest that HTD4010 may facilitate longer tissue interaction, thereby developing higher potential biological effects. If so, HTD4010 may become a promising therapeutic agent to treat people with diabetes. Communicated by Ramaswamy H. Sarma.

Ligand-Induced Conformational Dynamics of A Tyramine Receptor from Sitophilus oryzae

Tyramine receptor (TyrR) is a biogenic amine G protein-coupled receptor (GPCR) associated with many important physiological functions in insect locomotion, reproduction, and pheromone response. Binding of specific ligands to the TyrR triggers conformational changes, relays the signal to G proteins, and initiates an appropriate cellular response. Here, we monitor the binding effect of agonist compounds, tyramine and amitraz, to a Sitophilus oryzae tyramine receptor (SoTyrR) homology model and their elicited conformational changes. All-atom molecular dynamics (MD) simulations of SoTyrR-ligand complexes have shown varying dynamic behavior, especially at the intracellular loop 3 (IL3) region. Moreover, in contrast to SoTyrR-tyramine, SoTyrR-amitraz and non-liganded SoTyrR shows greater flexibility at IL3 residues and were found to be coupled to the most dominant motion in the receptor. Our results suggest that the conformational changes induced by amitraz are different from the natural ligand tyramine, albeit being both agonists of SoTyrR. This is the first attempt to understand the biophysical implication of amitraz and tyramine binding to the intracellular domains of TyrR. Our data may provide insights into the early effects of ligand binding to the activation process of SoTyrR.

Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier

Large-scale conformational changes are essential to link protein structures with their function at the cell and organism scale, but have been elusive both experimentally and computationally. Over the past few years developments in cryo-electron microscopy and crystallography techniques have started to reveal multiple snapshots of increasingly large and flexible systems, deemed impossible only short time ago. As structural information accumulates, theoretical methods become central to understand how different conformers interconvert to mediate biological function. Here we briefly survey current in silico methods to tackle large conformational changes, reviewing recent examples of cross-validation of experiments and computational predictions, which show how the integration of different scale simulations with biological information is already starting to break the barriers between the in silico, in vitro, and in vivo worlds, shedding new light onto complex biological problems inaccessible so far.

ANO5 mutations in the Polish limb girdle muscular dystrophy patients: Effects on the protein structure

LGMD2L is a subtype of limb-girdle muscular dystrophy (LGMD), caused by recessive mutations in ANO5, encoding anoctamin-5 (ANO5). We present the analysis of five patients with skeletal muscle weakness for whom heterozygous mutations within ANO5 were identified by whole exome sequencing (WES). Patients varied in the age of the disease onset (from 22 to 38 years) and severity of the morphological and clinical phenotypes. Out of the nine detected mutations one was novel (missense p.Lys132Met, accompanied by p.His841Asp) and one was not yet characterized in the literature (nonsense, p.Trp401Ter, accompanied by p.Asp81Gly). The p.Asp81Gly mutation was also identified in another patient carrying a p.Arg758Cys mutation as well. Also, a c.191dupA frameshift (p.Asn64LysfsTer15), the first described and common mutation was identified. Mutations were predicted by in silico tools to have damaging effects and are likely pathogenic according to criteria of the American College of Medical Genetics and Genomics (ACMG). Indeed, molecular modeling of mutations revealed substantial changes in ANO5 conformation that could affect the protein structure and function. In addition, variants in other genes associated with muscle pathology were identified, possibly affecting the disease progress. The presented data indicate that the identified ANO5 mutations contribute to the observed muscle pathology and broaden the genetic spectrum of LGMD myopathies.

Activation and Inactivation of the FLT3 Kinase: Pathway Intermediates and the Free Energy of Transition

The aberrant expression of kinases is often associated with pathologies such as cancer and autoimmune diseases. Like other types of enzymes, kinases can adopt active and inactive states, where a shift toward more stable active state often leads to disease. Dozens of kinase inhibitors are, therefore, used as drugs. Most of these bind to either the inactive or active state. In this work, we study the transitions between these two states in FLT3, an important drug target in leukemias. Kinases are composed of two lobes (N- and C-terminal lobes) with the catalytic site in-between. Through projection of the largest motions obtained through molecular dynamics (MD) simulations, we show that each of the end-states (active or inactive) already possess the ability for transition as the two lobes rotate which initiates the transition. A targeted simulation approach known as essential dynamics sampling (EDS) was used to speed up the transition between the two protein states. Coupling the EDS to implicit-solvent MD was performed to estimate the free energy barriers of the transitions. The activation energies were found in good agreement with previous estimates obtained for other kinases. Finally, we identified FLT3 intermediates that assumed configurations that resemble that of the c-Src nonreceptor tyrosine kinase. The intermediates show better binding to the drug ponatinib than c-Src and the inactive state of FLT3. This suggests that targeting intermediate states can be used to explain the drug-binding patterns of kinases and for rational drug design.

A deep learning approach to the structural analysis of proteins

Deep learning (DL) algorithms hold great promise for applications in the field of computational biophysics. In fact, the vast amount of available molecular structures, as well as their notable complexity, constitutes an ideal context in which DL-based approaches can be profitably employed. To express the full potential of these techniques, though, it is a prerequisite to express the information contained in a molecule’s atomic positions and distances in a set of input quantities that the network can process. Many of the molecular descriptors devised so far are effective and manageable for relatively small structures, but become complex and cumbersome for larger ones. Furthermore, most of them are defined locally, a feature that could represent a limit for those applications where global properties are of interest. Here, we build a DL architecture capable of predicting non-trivial and intrinsically global quantities, that is, the eigenvalues of a protein’s lowest-energy fluctuation modes. This application represents a first, relatively simple test bed for the development of a neural network approach to the quantitative analysis of protein structures, and demonstrates unexpected use in the identification of mechanically relevant regions of the molecule.

Understanding the Mechanism of Direct Activation of AMP-Kinase: Toward a Fine Allosteric Tuning of the Kinase Activity

Mammalian AMP-activated protein kinase (AMPK) is a Ser/Thr protein kinase with a key role as a sensor in cellular energy homeostasis. It has a major role in numerous metabolic disorders, such as type 2 diabetes, obesity, and cancer, and hence it has gained progressive interest as a potential therapeutic target. AMPK is a heterotrimeric enzyme composed by an α-catalytic subunit and two regulatory subunits, β and γ. It is regulated by several mechanisms, including indirect activators such as metformin and direct activators such as compound A-769662. The crystal structure of AMPK bound to A-769662 has been recently reported, suggesting a hypothetical allosteric mechanism of AMPK activation assisted by phosphorylated Ser108 at the β-subunit. Here, we have studied the direct activation mechanism of A-769662 by means of molecular dynamics simulations, suggesting that the activator may act as a glue, coupling the dynamical motion of the β-subunit and the N-terminal domain of the α-subunit, and assisting the preorganization of the ATP-binding site. This is achieved through the formation of an allosteric network that connects the activator and ATP-binding sites, particularly through key interactions formed between αAsp88 and βArg83 and between βpSer108 and αLys29. Overall, these studies shed light into key mechanistic determinants of the allosteric regulation of this cellular energy sensor, and pave the way for the fine-tuning of the rational design of direct activators of this cellular energy sensor.