Parameterization and Application of the General Amber Force Field to Model Fluoro Substituted Furanose Moieties and Nucleosides.

Molecular mechanics force field calculations have historically shown significant limitations in modeling the energetic and conformational interconversions of highly substituted furanose rings. This is primarily due to the gauche effect that is not easily captured using pairwise energy potentials. In this study, we present a refinement to the set of torsional parameters in the General Amber Force Field (gaff) used to calculate the potential energy of mono, di-, and gem-fluorinated nucleosides. The parameters were optimized to reproduce the pseudorotation phase angle and relative energies of a diverse set of mono- and difluoro substituted furanose ring systems using quantum mechanics umbrella sampling techniques available in the IpolQ engine in the Amber suite of programs. The parameters were developed to be internally consistent with the gaff force field and the TIP3P water model. The new set of angle and dihedral parameters and partial charges were validated by comparing the calculated phase angle probability to those obtained from experimental nuclear magnetic resonance experiments.

Structural modeling and analysis of the SARS-CoV-2 cell entry inhibitor camostat bound to the trypsin-like protease TMPRSS2.

The type II transmembrane serine protease TMPRSS2 facilitates the entry of coronaviruses, such as SARS-CoV-2, into host cells by cleaving the S1/S2 interface of the viral spike protein. Based on structural data derived from X-ray crystallographic data of related trypsin-like proteases, a homology model of TMPRSS2 is described and validated using the broad spectrum COVID-19 drug candidate camostat as a probe. Both active site recognition and catalytic function are examined using quantum mechanics/molecular mechanics molecular dynamic (QM/MM MD) simulations of camostat and its active metabolite, 4-(4-guanidinobenzoyloxy) phenylacetate (GBPA). Substrate binding is shown to be primarily stabilized through salt bridge formation between the shared guanidino pharmacophore and D435 in pocket A (flanking the catalytic S441). Based on the binding mode of GBPA, residues K342 and W461 have been identified as potential contacts involved in TMPRSS2 selective binding and activity. Additional data is reported that indicates the transition state structure is stabilized through H-bonding interactions with the backbone N-H groups within an oxyanion hole following bottom-side attack of the carbonyl by S441. This is supported by prior work on related serine proteases suggesting further strategies to exploit in the design of more potent inhibitors. Taken overall, the proposed structure along with the key contact sites and mechanistic features identified should prove highly advantageous to the design and rational development of safe and effective therapeutics that target TMPRSS2 and avoid inhibition of other trypsin-dependent processes.

Simulation of the Bottleneck Controlling Access into a Rieske Active Site: Predicting Substrates of Naphthalene 1,2-Dioxygenase.

Naphthalene 1,2-dioxygenase (NDO) has been computationally understudied despite the extensive experimental knowledge obtained for this enzyme, including numerous crystal structures and over 100 demonstrated substrates. In this study, we have developed a substrate prediction model that moves away from the traditional active-site-centric approach to include the energetics of substrate entry into the active site. By comparison with experimental data, the accuracy of the model for predicting substrate oxidation is 92%, with a positive predictive value of 93% and a negative predictive value of 98%. Also, the present analysis has revealed that the amino acid residues that provided the largest energetic barrier for compounds entering the active site are residues F224, L227, P234, and L235. In addition, F224 is proposed to play a role in controlling ligand entrance via π-π stacking stabilization as well as providing stabilization via T-shaped π-π interactions once the ligand has reached the active-site cavity. Overall, we present a method capable of being scaled to computationally discover thousands of substrates of NDO, and we present parameters to be used for expanding the prediction method to other members of the Rieske non-heme iron oxygenase family.

In Silico Identification of Bioremediation Potential: Carbamazepine and Other Recalcitrant Personal Care Products.

Emerging contaminants are principally personal care products not readily removed by conventional wastewater treatment and, with an increasing reliance on water recycling, become disseminated in drinking water supplies. Carbamazepine, a widely used neuroactive pharmaceutical, increasingly escapes wastewater treatment and is found in potable water. In this study, a mechanism is proposed by which carbamazepine resists biodegradation, and a previously unknown microbial biodegradation was predicted computationally. The prediction identified biphenyl dioxygenase from Paraburkholderia xenovorans LB400 as the best candidate enzyme for metabolizing carbamazepine. The rate of degradation described here is 40 times greater than the best reported rates. The metabolites cis-10,11-dihydroxy-10,11-dihydrocarbamazepine and cis-2,3-dihydroxy-2,3-dihydrocarbamazepine were demonstrated with the native organism and a recombinant host. The metabolites are considered nonharmful and mitigate the generation of carcinogenic acridine products known to form when advanced oxidation methods are used in water treatment. Other recalcitrant personal care products were subjected to prediction by the Pathway Prediction System and tested experimentally with P. xenovorans LB400. It was shown to biodegrade structurally diverse compounds. Predictions indicated hydrolase or oxygenase enzymes catalyzed the initial reactions. This study highlights the potential for using the growing body of enzyme-structural and genomic information with computational methods to rapidly identify enzymes and microorganisms that biodegrade emerging contaminants.

Prediction of Ligand Transport along Hydrophobic Enzyme Nanochannels.

Buried active sites of enzymes are connected to the bulk solvent through a network of hydrophobic channels. We developed a discretized model that can accurately predict ligand transport along hydrophobic channels up to six orders of magnitude faster than any other existing method. The non-dimensional nature of the model makes it applicable to any hydrophobic channel/ligand combination.

Role of Water Hydrogen Bonding on Transport of Small Molecules inside Hydrophobic Channels.

We conducted a systematic analysis of water networking inside smooth hyperboloid hydrophobic structures (cylindrical, barrel, and hourglass shapes) to elucidate the role of water hydrogen bonding on the transport of small hydrophobic molecules (ligands). Through a series of molecular dynamics simulations, we established that a hydrogen-bonded network forming along the centerline resulted in a water exclusion zone adjacent to the walls. The size of the exclusion zone is a function of the geometry and the nonbonded interaction strength, defining the effective hydrophobicity of the structure. Exclusion of water molecules from this zone results in lower apparent viscosity, leading to acceleration of ligand transport up to 7 times faster than that measured in the bulk. Transport of ligands into and out of the hydrophobic structures was shown to be controlled by a single water molecule that capped the narrow regions in the structure. This mechanism provides physical insights into the behavior and role of water in the bottleneck regions of hydrophobic enzyme channels. These findings were then used in a sister publication [ Escalante , D. E. , Comput. Struct. Biotechnol. J. 2019 17 757 760 ] to develop a model that can accurately predict the transport of ligands along nanochannels of broad-substrate specificity enzymes.