Hybrid schemes based on quantum mechanics/molecular mechanics simulations goals to success, problems, and perspectives

Molecular mechanics approaches for rational drug design: forcefields and solvation models

The use of molecular mechanics (MM) in understanding the energy and target of a drug, its structures, and properties has increased recently. This is achieved by the formulation of a simple MM energy equation, which represents the sum of the different energy interactions, often referred to as “forcefields” (FFs). The concept of FFs is now widely used as one of the fundamental tools for the in silico prediction of drug-target interactions. To generate more accurate predictions in the in silico drug discovery projects, the solvent effects are often taken into account. This review seeks to present an introductory guide for the reader on the fundamentals of MM with special emphasis on the role of FFs and the solvation models.

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.

Adaptive QM/MM for Molecular Dynamics Simulations: 5. On the Energy-Conserved Permuted Adaptive-Partitioning Schemes

In combined quantum-mechanical/molecular-mechanical (QM/MM) dynamics simulations, the adaptive-partitioning (AP) schemes reclassify atoms on-the-fly as QM or MM in a smooth manner. This yields a mobile QM subsystem with contents that are continuously updated as needed. Here, we tailor the Hamiltonian adaptive many-body correction (HAMBC) proposed by Boreboom et al. [J. Chem. Theory Comput.2016, 12, 3441] to the permuted AP (PAP) scheme. The treatments lead to the HAMBC-PAP method (HPAP), which both conserves energy and produces accurate solvation structures in the test of "water-in-water" model system.

In Silico Studies of Small Molecule Interactions with Enzymes Reveal Aspects of Catalytic Function

Small molecules, such as solvent, substrate, and cofactor molecules, are key players in enzyme catalysis. Computational methods are powerful tools for exploring the dynamics and thermodynamics of these small molecules as they participate in or contribute to enzymatic processes. In-depth knowledge of how small molecule interactions and dynamics influence protein conformational dynamics and function is critical for progress in the field of enzyme catalysis. Although numerous computational studies have focused on enzyme-substrate complexes to gain insight into catalytic mechanisms, transition states and reaction rates, the dynamics of solvents, substrates, and cofactors are generally less well studied. Also, solvent dynamics within the biomolecular solvation layer play an important part in enzyme catalysis, but a full understanding of its role is hampered by its complexity. Moreover, passive substrate transport has been identified in certain enzymes, and the underlying principles of molecular recognition are an area of active investigation. Enzymes are highly dynamic entities that undergo different conformational changes, which range from side chain rearrangement of a residue to larger-scale conformational dynamics involving domains. These events may happen nearby or far away from the catalytic site, and may occur on different time scales, yet many are related to biological and catalytic function. Computational studies, primarily molecular dynamics (MD) simulations, provide atomistic-level insight and site-specific information on small molecule interactions, and their role in conformational pre-reorganization and dynamics in enzyme catalysis. The review is focused on MD simulation studies of small molecule interactions and dynamics to characterize and comprehend protein dynamics and function in catalyzed reactions. Experimental and theoretical methods available to complement and expand insight from MD simulations are discussed briefly.

Mechanistic insights on the reduction of glutathione disulfide by protein disulfide isomerase

We explore the enzymatic mechanism of the reduction of glutathione disulfide (GSSG) by the reduced a domain of human protein disulfide isomerase (hPDI) with atomistic resolution. We use classical molecular dynamics and hybrid quantum mechanics/molecular mechanics calculations at the mPW1N/6-311+G(2d,2p):FF99SB//mPW1N/6-31G(d):FF99SB level. The reaction proceeds in two stages: (i) a thiol-disulfide exchange through nucleophilic attack of the Cys53-thiolate to the GSSG-disulfide followed by the deprotonation of Cys56-thiol by Glu47-carboxylate and (ii) a second thiol-disulfide exchange between the Cys56-thiolate and the mixed disulfide intermediate formed in the first step. The Gibbs activation energy for the first stage was 18.7 kcal·mol^-1, and for the second stage, it was 7.2 kcal·mol^-1, in excellent agreement with the experimental barrier (17.6 kcal·mol^-1). Our results also suggest that the catalysis by protein disulfide isomerase (PDI) and thiol-disulfide exchange is mostly enthalpy-driven (entropy changes below 2 kcal·mol^-1 at all stages of the reaction). Hydrogen bonds formed between the backbone of His55 and Cys56 and the Cys56-thiol result in an increase in the Gibbs energy barrier of the first thiol-disulfide exchange. The solvent plays a key role in stabilizing the leaving glutathione thiolate formed. This role is not exclusively electrostatic, because an explicit inclusion of several water molecules at the density-functional theory level is a requisite to form the mixed disulfide intermediate. In the intramolecular oxidation of PDI, a transition state is only observed if hydrogen bond donors are nearby the mixed disulfide intermediate, which emphasizes that the thermochemistry of thiol-disulfide exchange in PDI is influenced by the presence of hydrogen bond donors.

Efficient Calculation of Enzyme Reaction Free Energy Profiles Using a Hybrid Differential Relaxation Algorithm: Application to Mycobacterial Zinc Hydrolases

Determination of the free energy profile for an enzyme reaction mechanism is of primordial relevance, paving the way for our understanding of the enzyme's catalytic power at the molecular level. Although hybrid, mostly DFT-based, QM/MM methods have been extensively applied to this type of studies, achieving accurate and statistically converged results at a moderate computational cost is still an open challenge. Recently, we have shown that accurate results can be achieved in less computational time, combining Jarzynski's relationship with a hybrid differential relaxation algorithm (HyDRA), which allows partial relaxation of the solvent during the nonequilibrium steering of the reaction. In this work, we have applied this strategy to study two mycobacterial zinc hydrolases. Mycobacterium tuberculosis infections are still a worldwide problem and thus characterization and validation of new drug targets is an intense field of research. Among possible drug targets, recently two essential zinc hydrolases, MshB (Rv1170) and MA-amidase (Rv3717), have been proposed and structurally characterized. Although possible mechanisms have been proposed by analogy to the widely studied human Zn hydrolases, several key issues, particularly those related to Zn coordination sphere and its role in catalysis, remained unanswered. Our results show that mycobacterial Zn hydrolases share a basic two-step mechanism. First, the attacking water becomes deprotonated by the conserved base and establishes the new C-O bond leading to a tetrahedral intermediate. The intermediate requires moderate reorganization to allow for proton transfer to the amide N and C-N bond breaking to occur in the second step. Zn ion plays a key role in stabilizing the tetrahedral intermediate and balancing the negative charge of the substrate during hydroxide ion attack. Finally, comparative analysis of other Zn hydrolases points to a convergent mechanistic evolution.

Quantum Calculations in Solution for Large to Very Large Molecules: A New Linear Scaling QM/Continuum Approach

We present a new implementation of continuum solvation models for semiempirical Hamiltonians that allows the description of environmental effects on very large molecular systems. In this approach based on a domain decomposition strategy of the COSMO model (ddCOSMO), the solution to the COSMO equations is no longer the computational bottleneck but becomes a negligible part of the overall computation time. In this Letter, we analyze the computational impact of COSMO on the solution of the SCF equations for large to very large molecules, using semiempirical Hamiltonians, for both the new ddCOSMO implementation and the most recent, linear scaling one, based on the fast multipole method. A further analysis is on the simulation of the UV/visible spectrum of a light-harvesting pigment-protein complex. All of the results show how the new ddCOSMO algorithm paves the way to routine computations for large molecular systems in the condensed phase.

Transition States, analogues, and drug development

Enzymes achieve their transition states by dynamic conformational searches on the femtosecond to picosecond time scale. Mimics of reactants at enzymatic transition states bind tightly to enzymes by stabilizing the conformation optimized through evolution for transition state formation. Instead of forming the transient transition state geometry, transition state analogues convert the short-lived transition state to a stable thermodynamic state. Enzymatic transition states are understood by combining kinetic isotope effects and computational chemistry. Analogues of the transition state can bind millions of times more tightly than substrates and show promise for drug development for several targets.

The increasing role of QM/MM in drug discovery

Since its first appearance in 1976, the quantum mechanics/molecular mechanics (QM/MM) approach has mostly been used to study the chemical reactions of enzymes, which are frequently the target of drug discovery programs. In principle, a detailed understanding of the enzymatic mechanism should help researchers to design a potent enzyme inhibitor or new drug. However, QM/MM has not yet had a widespread impact on structure-based drug design. This is mostly due to its high computational cost. We expect this to change with the recent and extraordinary increases in computational power, and with the availability of more efficient algorithms for QM/MM calculations. Here, we report on some representative examples of QM/MM studies, including our own research, of pharmaceutically relevant enzymes, such as ribonuclease H and fatty acid amide hydrolase (FAAH). We aim to show how QM/MM has traditionally been used to study enzymatic catalysis. In this regard, we discuss its potential to become a routinely used drug design tool. To support this, we also discuss selected computational studies where QM/MM insights have been helpful in improving the potency of covalent inhibitors of FAAH.