Investigation of Structural Dynamics of Enzymes and Protonation States of Substrates Using Computational Tools

FMO-guided design of darunavir analogs as HIV-1 protease inhibitors

The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.

Modulation of α-Chymotrypsin Conjugated to Magnetic Nanoparticles by the Non-Heating Low-Frequency Magnetic Field: Molecular Dynamics, Reaction Kinetics, and Spectroscopy Analysis

Enzymes conjugated to magnetic nanoparticles (MNPs) undergo changes in the catalytic activity of the non-heating low-frequency magnetic field (LFMF). We apply in silico simulations by molecular dynamics (MD) and in vitro spectroscopic analysis of the enzyme kinetics and secondary structure to study α-chymotrypsin (CT) conjugated to gold-coated iron oxide MNPs. The latter are functionalized by either carboxylic or amino group moieties to vary the points of enzyme attachment. The MD simulation suggests that application of the stretching force to the CT globule by its amino or carboxylic groups causes shrinkage of the substrate-binding site but little if any changes in the catalytic triad. Consistent with this, in CT conjugated to MNPs by either amino or carboxylic groups, LFMF alters the Michaelis-Menten constant but not the apparent catalytic constant k _cat (= V _max/[E]_o). Irrespective of the point of conjugation to MNPs, the CT secondary structure was affected with nearly complete loss of α-helices and increase in the random structures in LFMF, as shown by attenuated total reflection Fourier transformed infrared spectroscopy. Both the catalytic activity and the protein structure of MNP-CT conjugates restored 3 h after the field exposure. We believe that such remotely actuated systems can find applications in advanced manufacturing, nanomedicine, and other areas.

Protein conformational switch discerned via network centrality properties

Network analysis has emerged as a powerful tool for examining structural biology systems. The spatial organization of the components of a biomolecular structure has been rendered as a graph representation and analyses have been performed to deduce the biophysical and mechanistic properties of these components. For proteins, the analysis of protein structure networks (PSNs), especially via network centrality measurements and cluster coefficients, has led to identifying amino acid residues that play key functional roles and classifying amino acid residues in general. Whether these network properties examined in various studies are sensitive to subtle (yet biologically significant) conformational changes remained to be addressed. Here, we focused on four types of network centrality properties (betweenness, closeness, degree, and eigenvector centralities) for conformational changes upon ligand binding of a sensor protein (constitutive androstane receptor) and an allosteric enzyme (ribonucleotide reductase). We found that eigenvector centrality is sensitive and can distinguish salient structural features between protein conformational states while other centrality measures, especially closeness centrality, are less sensitive and rather generic with respect to the structural specificity. We also demonstrated that an ensemble-informed, modified PSN with static edges removed (which we term PSN*) has enhanced sensitivity at discerning structural changes.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Molecular docking and molecular dynamics simulation of Bacillus thuringiensis dehalogenase against haloacids, haloacetates and chlorpyrifos

The high dependency and surplus use of agrochemical products have liberated enormous quantities of toxic halogenated pollutants into the environment and threaten the well-being of humankind. Herein, this study performed molecular docking, molecular dynamic (MD) simulations, molecular mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculations on the DehH2 from Bacillus thuringiensis, to identify the order of which the enzyme degrades different substrates, haloacids, haloacetate and chlorpyrifos. The study discovered that the DehH2 favored the degradation of haloacids and haloacetates (-3.3 - 4.6 kcal/mol) and formed three hydrogen bonds with Asp125, Arg201 and Lys202. Despite the inconclusive molecular docking result, chlorpyrifos was consistently shown to be the least favored substrate of the DehH2 in MD simulations and MM-PBSA calculations. Results of MD simulations revealed the DehH2-haloacid- (RMSD 0.15 - 0.25 nm) and DehH2-haloacetates (RMSF 0.05 - 0.25 nm) were more stable, with the DehH2-L-2CP complex being the most stable while the least was the DehH2-chlorpyrifos (RMSD 0.295 nm; RMSF 0.05 - 0.59 nm). The Molecular Mechanics Poisson-Boltzmann Surface Area calculations showed the DehH2-L-2CP complex (-24.27 kcal/mol) having the lowest binding energy followed by DehH2-MCA (-22.78 kcal/mol), DehH2-D-2CP (-21.82 kcal/mol), DehH2-3CP (-21.11 kcal/mol), DehH2-2,2-DCP (-18.34 kcal/mol), DehH2-2,3-DCP (-8.34 kcal/mol), DehH2-TCA (-7.62 kcal/mol), while chlorpyrifos was unable to spontaneously bind to DehH2 (+127.16 kcal/mol). In a nutshell, the findings of this study offer valuable insights into the rational tailoring of the DehH2 for expanding its substrate specificity and catalytic activity in the near future.Communicated by Ramaswamy H. Sarma.

Molecular Modelling of the Ni(II)-Responsive Synechocystis PCC 6803 Transcriptional Regulator InrS in the Metal Bound Form

InrS (internal nickel-responsive sensor) is a transcriptional regulator found in cyanobacteria that represses the transcription of the nickel exporter NrsD in the apo form and de-represses expression of the exporter upon Ni(II) binding. Although a crystal structure of apo-InrS from Synechocystis PCC 6803 has been reported, no structure of the protein with metal ions bound is available. Here we report the results of a computational study aimed to reconstruct the metal binding site by taking advantage of recent X-ray absorption spectroscopy (XAS) data and to envisage the structural rearrangements occurring upon Ni(II) binding. The modelled Ni(II) binding site shows a square planar geometry consistent with experimental data. The structural details of the conformational changes occurring upon metal binding are also discussed in the framework of trying to rationalize the different affinity of the apo- and holo-forms of the protein for DNA.

G-score: A function to solve the puzzle of modeling the protonation states of β-secretase binding pocket

The population density concept has emerged as a proposal for the analysis of molecular dynamics results, the key characteristic of population density is the evaluation of the simultaneous occurrence of a set of relevant parameters for a system. However, despite its statistical strength, selection of the tolerance level for the comparison of different models may appear as arbitrary. This work introduces the G-score, a function which summarizes and categorizes the results of population density analysis. Additionally, it incorporates parameters based on rmsd and dihedral angles, besides the protein-protein and protein-ligand interatomic distances conventionally used, which complement each other to provide a better description of the behavior of the system. These newly-proposed tools were applied to determine the most probable protonation state of the aspartic dyad of BACE1, Asp93 and Asp289, in the presence of three types of transition state inhibitors namely: reduced amides, tertiary carbinamines and hydroxyethylamines. The results show a full agreement between G-score values and population density charts, with the advantage of allowing a quick and direct comparison among all the considered models. We anticipate that the simplicity of calculating the parameters employed in this study will permit the extensive use of population density and the G-score for other molecular systems.

Directed Evolution Mimics Allosteric Activation by Stepwise Tuning of the Conformational Ensemble

Allosteric enzymes contain a wealth of catalytic diversity that remains distinctly underutilized for biocatalysis. Tryptophan synthase is a model allosteric system and a valuable enzyme for the synthesis of noncanonical amino acids (ncAA). Previously, we evolved the β-subunit from Pyrococcus furiosus, PfTrpB, for ncAA synthase activity in the absence of its native partner protein PfTrpA. However, the precise mechanism by which mutation activated TrpB to afford a stand-alone catalyst remained enigmatic. Here, we show that directed evolution caused a gradual change in the rate-limiting step of the catalytic cycle. Concomitantly, the steady-state distribution of the intermediates shifts to favor covalently bound Trp adducts, which have increased thermodynamic stability. The biochemical properties of these evolved, stand-alone TrpBs converge on those induced in the native system by allosteric activation. High-resolution crystal structures of the wild-type enzyme, an intermediate in the lineage, and the final variant, encompassing five distinct chemical states, show that activating mutations have only minor structural effects on their immediate environment. Instead, mutation stabilizes the large-scale motion of a subdomain to favor an otherwise transiently populated closed conformational state. This increase in stability enabled the first structural description of Trp covalently bound in a catalytically active TrpB, confirming key features of catalysis. These data combine to show that sophisticated models of allostery are not a prerequisite to recapitulating its complex effects via directed evolution, opening the way to engineering stand-alone versions of diverse allosteric enzymes.