A solvated ligand rotamer approach and its application in computational protein design

Phytochemical study: molecular docking of eugenol derivatives as antioxidant and antimicrobial agents

Abstract:

Eugenol (4-allyl-2-methoxyphenol) is a natural phenolic compound present in certain aromatic plants; however, it is generally extracted from essential oil of Eugenia caryophyllata (Syzygiumaromaticum) (L.) Merr. and L.M. Perry. This bioactive natural compound has generated considerable biological interest with well-known antimicrobial and antioxidant actions. The authors have aimed to the evaluations of eugenol derivatives and their as antimicrobial and antioxidant agent with the aid of molecular dynamic simulation. The starting material was extracted from cloves using hydrodistillation. Two eugenol derivatives, acetyleugenol (4-allyl-2-methoxyphenylacetate) and epoxyeugenol (4-allyl-2-methoxyphenol) were prepared and tested against two strains Escherichia coli (E. Coli) and Staphylococcus aureus (S. Aureus). The results have revealed that the three compounds (Eugenol, acetyleugenol and epoxyeugenol) possess important potentials of inhibition against E. coli and S. Aureus. The antioxidant activity of eugenol derivatives was evaluated by the reaction with DPPH (1,1-diphenyl-2-picrylhydrazyl), showed that the epoxyeugenol was the most active compound. The molecular docking scores of three compounds and the amino acids in the active site pockets of the selected proteins of the two bacteria have approved and explain the biological experimental outcomes.

An Engineered Glutamate in Biosynthetic Models of Heme-Copper Oxidases Drives Complete Product Selectivity by Tuning the Hydrogen-Bonding Network

Efficiently carrying out the oxygen reduction reaction (ORR) is critical for many applications in biology and chemistry, such as bioenergetics and fuel cells, respectively. In biology, this reaction is carried out by large, transmembrane oxidases such as heme-copper oxidases (HCOs) and cytochrome bd oxidases. Common to these oxidases is the presence of a glutamate residue next to the active site, but its precise role in regulating the oxidase activity remains unclear. To gain insight into its role, we herein report that incorporation of glutamate next to a designed heme-copper center in two biosynthetic models of HCOs improves O2 binding affinity, facilitates protonation of reaction intermediates, and eliminates release of reactive oxygen species. High-resolution crystal structures of the models revealed extended, water-mediated hydrogen-bonding networks involving the glutamate. Electron paramagnetic resonance of the cryoreduced oxy-ferrous centers at cryogenic temperature followed by thermal annealing allowed observation of the key hydroperoxo intermediate that can be attributed to the hydrogen-bonding network. By demonstrating these important roles of glutamate in oxygen reduction biochemistry, this work offers deeper insights into its role in native oxidases, which may guide the design of more efficient artificial ORR enzymes or catalysts for applications such as fuel cells.

FASPR: an open-source tool for fast and accurate protein side-chain packing

MOTIVATION

Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory.

RESULTS

We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20°, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies.

AVAILABILITY AND IMPLEMENTATION

The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Co-evolution of β-glucosidase activity and product tolerance for increasing cellulosic ethanol yield

β-Glucosidase (BGL) plays a key role in cellulose hydrolysis. However, it is still a great challenge to enhance product tolerance and enzyme activity of BGL simultaneously. Here, we utilized one round error-prone PCR to engineer the Penicillium oxalicum 16 BGL (16BGL) for improving the cellulosic ethanol yield. We identified a new variant (L-6C), a triple mutant (M280T/V484L/D589E), with enhanced catalytic efficiency ([Formula: see text]) for hydrolyzing pNPG and reduced strength of inhibition ([Formula: see text]) by glucose. To be specific, L-6C achieved a [Formula: see text] of 0.35 at a glucose concentration of 20 mM, which was 3.63 times lower than that attained by 16BGL. The catalytic efficiency for L-6C to hydrolyze pNPG was determined to be 983.68 mM^-1 s^-1, which was 22% higher than that for 16BGL. However, experiments showed that L-6C had reduced binding affinity (2.88 mM) to pNGP compared with 16BGL (1.69 mM). L-6C produced 6.15 g/L ethanol whose yield increased by about 10% than 16BGL. We performed molecular docking and molecular dynamics (MD) simulation, and binding free energy calculation using the Molecular Mechanics/Poisson Boltzmann surface area (MM/PBSA) method. MD simulation together with the MM/PBSA calculation suggested that L-6C had reduced binding free energy to pNPG, which was consistent with the experimental data.

Toward the Accuracy and Speed of Protein Side-Chain Packing: A Systematic Study on Rotamer Libraries

Protein rotamers refer to the conformational isomers taken by the side-chains of amino acids to accommodate specific structural folding environments. Since accurate modeling of atomic interactions is difficult, rotamer information collected from experimentally solved protein structures is often used to guide side-chain packing in protein folding and sequence design studies. Many rotamer libraries have been built in the literature but there is little quantitative guidance on which libraries should be chosen for different structural modeling studies. Here, we performed a comparative study of six widely used rotamer libraries and systematically examined their suitability for protein folding and sequence design in four aspects: (1) side-chain match accuracy, (2) side-chain conformation prediction, (3) de novo protein sequence design, and (4) computational time cost. We demonstrated that, compared to the backbone-dependent rotamer libraries (BBDRLs), the backbone-independent rotamer libraries (BBIRLs) generated conformations that more closely matched the native conformations due to the larger number of rotamers in the local rotamer search spaces. However, more practically, using an optimized physical energy function incorporated into a simulated annealing Monte Carlo searching scheme, we showed that utilization of the BBDRLs could result in higher accuracies in side-chain prediction and higher sequence recapitulation rates in protein design experiments. Detailed data analyses showed that the major advantage of BBDRLs lies in the energy term derived from the rotamer probabilities that are associated with the individual backbone torsion angle subspaces. This term is important for distinguishing between amino acid identities as well as the rotamer conformations of an amino acid. Meanwhile, the backbone torsion angle subspace-specific rotamer search drastically speeds up the searching time, despite the significantly larger number of total rotamers in the BBDRLs. These results should provide important guidance for the development and selection of rotamer libraries for practical protein design and structure prediction studies.

Computational design of enzyme-ligand binding using a combined energy function and deterministic sequence optimization algorithm

Enzyme amino-acid sequences at ligand-binding interfaces are evolutionarily optimized for reactions, and the natural conformation of an enzyme-ligand complex must have a low free energy relative to alternative conformations in native-like or non-native sequences. Based on this assumption, a combined energy function was developed for enzyme design and then evaluated by recapitulating native enzyme sequences at ligand-binding interfaces for 10 enzyme-ligand complexes. In this energy function, the electrostatic interaction between polar or charged atoms at buried interfaces is described by an explicitly orientation-dependent hydrogen-bonding potential and a pairwise-decomposable generalized Born model based on the general side chain in the protein design framework. The energy function is augmented with a pairwise surface-area based hydrophobic contribution for nonpolar atom burial. Using this function, on average, 78% of the amino acids at ligand-binding sites were predicted correctly in the minimum-energy sequences, whereas 84% were predicted correctly in the most-similar sequences, which were selected from the top 20 sequences for each enzyme-ligand complex. Hydrogen bonds at the enzyme-ligand binding interfaces in the 10 complexes were usually recovered with the correct geometries. The binding energies calculated using the combined energy function helped to discriminate the active sequences from a pool of alternative sequences that were generated by repeatedly solving a series of mixed-integer linear programming problems for sequence selection with increasing integer cuts.

Evaluation of active designs of cephalosporin C acylase by molecular dynamics simulation and molecular docking

Optimization to identify the global minimum energy conformation sequence in in silico enzyme design is computationally non-deterministic polynomial-time (NP)-hard, with the search time growing exponentially as the number of design sites increases. This drawback forces the modeling of protein-ligand systems to adopt discrete amino acid rotamers and ligand conformers, as well as continuum solvent treatment of the environment; however, such compromises produce large numbers of false positives in sequence selection. In this report, cephalosporin acylase, which catalyzes the hydrolytic reaction of cephalosporin C to 7-aminocephalosporanic acid, was used to investigate the dynamic features of active-site-transition-state complex structures using molecular dynamics (MD) simulations to potentially eliminate false positives. The molecular docking between cephalosporin C and wild type acylase N176 and its eight mutants showed that the rate-limiting step in the hydrolytic reaction of cephalosporin C is the acylation process. MD simulations of the active-site-transition-state complex structures of the acylation processes for N176 and its eight mutants showed that the geometrical constraints between catalytic residues and small molecule transition states are always well maintained during the 20 ns simulation for mutants with higher activities, and more hydrogen bonds between binding residues and functional groups of the ligand side chain in the active pocket are formed for mutants with higher activities. The conformations of the ligand transition states were changed greatly after the simulation. This indicates that the hydrogen bond network between the ligand and protein could be improved to enhance the activity of cephalosporin C acylase in subsequent design.

Systematic optimization model and algorithm for binding sequence selection in computational enzyme design

A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph-theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions.