The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.
The genetic architecture of many phenotypic traits is such that genes often contribute to multiple traits, and mutations in these genes can therefore affect multiple phenotypes. These pleiotropic interactions often manifest as tradeoffs between traits where improvement in one property entails a cost in another. The life cycles of many pathogens include periods of growth within a host punctuated with transmission events, such as passage through a digestive tract or a passive stage of exposure in the environment. Populations exposed to such fluctuating selective pressures are expected to acquire mutations showing tradeoffs between reproduction within and survival outside of a host. We selected for individual mutations under fluctuating selective pressures for a ssDNA microvirid bacteriophage by alternating selection for increased growth rate with selection on biophysical properties of the phage capsid in high-temperature or low-pH conditions. Surprisingly, none of the seven unique mutations identified showed a pleiotropic cost; they all improved both growth rate and pH or temperature stability, suggesting that single mutations even in a simple genetic system can simultaneously improve two distinct traits. Selection on growth rate alone revealed tradeoffs, but some mutations still benefited both traits. Tradeoffs were therefore prevalent when selection acted on a single trait, but payoffs resulted when multiple traits were selected for simultaneously. We employed a molecular-dynamics simulation method to determine the mechanisms underlying beneficial effects for three heat-shock mutations. All three mutations significantly enhanced the affinities of protein-protein interfacial bindings, thereby improving capsid stability. The ancestral residues at the mutation sites did not contribute to protein-protein interfacial binding, indicating that these sites acquired a new function. Computational models, such as those used here, may be used in future work not only as predictive tools for mutational effects on protein stability but, ultimately, for evolution.
Accurate prediction of binding free energies is critical to streamlining the drug development and protein design process. With the advent of GPU acceleration, absolute alchemical methods, which simulate the removal of ligand electrostatics and van der Waals interactions with the protein, have become routinely accessible and provide a physically rigorous approach that enables full consideration of flexibility and solvent interaction. However, standard explicit solvent simulations are unable to model protonation or electronic polarization changes upon ligand transfer from water to the protein interior, leading to inaccurate prediction of binding affinities for charged molecules. Here, we perform extensive simulation totaling ~540 µs to benchmark the impact of modeling conditions on predictive accuracy for absolute alchemical simulations. Binding to urokinase plasminogen activator (UPA), a protein frequently overexpressed in metastatic tumors, is evaluated for a set of ten inhibitors with extended flexibility, highly charged character, and titratable properties. We demonstrate that the alchemical simulations can be adapted to utilize the MBAR/PBSA method to improve the accuracy upon incorporating electronic polarization, highlighting the importance of polarization in alchemical simulations of binding affinities. Comparison of binding energy prediction at various protonation states indicates that proper electrostatic setup is also crucial in binding affinity prediction of charged systems, prompting us to propose an alternative binding mode with protonated ligand phenol and Hid-46 at the binding site, a testable hypothesis for future experimental validation.
Hydrogen sulfide (H2S), a commonly known toxic gas compound, possesses unique chemical features that allow this small solute molecule to quickly diffuse through cell membranes. Taking advantage of the recent orthogonal space tempering (OST) method, we comparatively mapped the trans-membrane (POPC) free energy landscapes of H2S and its structural analogue, water (H2O), seeking to decipher the molecular determinants that govern their drastically different permeabilities. As revealed by our OST sampling results, in contrast to the highly polar water solute, hydrogen sulfide is evidently amphipathic, and thus inside membrane is favorably localized at the interfacial region, i.e. the interface between the polar head-group and non-polar acyl chain regions. Because the membrane binding affinity of H2S is mainly governed by its small hydrophobic moiety and the barrier height in between the interfacial region and the membrane center is largely determined by its moderate polarity, the trans-membrane free energy barriers to encounter by this toxic molecule are very small. Moreover when H2S diffuses from the bulk solution to the membrane center, the above two effects nearly cancel each other, so as to lead to a negligible free energy difference. This study not only explains why H2S can quickly pass through cell membranes but also provides a practical illustration on how to employ the OST free energy sampling method to conveniently analyze complex molecular processes.
Noncanonical cofactors such as nicotinamide mononucleotide (NMN+) supplant the electron-transfer functionality of the natural cofactors, NAD(P)+, at a lower cost in cell-free biomanufacturing and enable orthogonal electron delivery in whole-cell metabolic engineering. Here, we redesign the high-flux Embden–Meyerhof–Parnas (EMP) glycolytic pathway to generate NMN+-based reducing power, by engineering Streptococcus mutans glyceraldehyde-3-phosphate dehydrogenase (Sm GapN) to utilize NMN+. Through iterative rounds of rational design, we discover the variant GapN Penta (P179K-F153S-S330R-I234E-G210Q) with high NMN+-dependent activity and GapN Ortho (P179K-F153S-S330R-I234E-G214E) with ∼3.4 × 106-fold switch in cofactor specificity from its native cofactor NADP+ to NMN+. GapN Ortho is further demonstrated to function in Escherichia coli only in the presence of NMN+, enabling orthogonal control of glucose utilization. Molecular dynamics simulation and residue network connectivity analysis indicate that mutations altering cofactor specificity must be coordinated to maintain the appropriate degree of backbone flexibility to position the catalytic cysteine. These results provide a strategy to guide future designs of NMN+-dependent enzymes and establish the initial steps toward an orthogonal EMP pathway with biomanufacturing potential.
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