Thermodynamic stability of water molecules in the bacteriorhodopsin proton channel: a molecular dynamics free energy perturbation study

Anti-Leishmania activity and molecular docking of unusual flavonoids-rich fraction from Arrabidaea brachypoda (Bignoniaceae)

Leishmaniases comprise a group of infectious parasitic diseases caused by various species of Leishmania and are considered a significant public health problem worldwide. Only a few medications, including miltefosine, amphotericin B, and meglumine antimonate, are used in current therapy. These medications are associated with severe side effects, low efficacy, high cost, and the need for hospital support. Additionally, there have been occurrences of drug resistance. Additionally, only a limited number of drugs, such as meglumine antimonate, amphotericin B, and miltefosine, are available, all of which are associated with severe side effects. In this context, the need for new effective drugs with fewer adverse effects is evident. Therefore, this study investigated the anti-Leishmania activity of a dichloromethane fraction (DCMF) extracted from Arrabidaea brachypoda roots. This fraction inhibited the viability of L. infantum, L. braziliensis, and L. Mexicana promastigotes, with IC50 values of 10.13, 11.44, and 11.16 µg/mL, respectively, and against L. infantum amastigotes (IC50 = 4.81 µg/mL). Moreover, the DCMF exhibited moderate cytotoxicity (CC50 = 25.15) towards RAW264.7 macrophages, with a selectivity index (SI) of 5.2. Notably, the DCMF caused damage to the macrophage genome only at 40 µg/mL, which is greater than the IC50 found for all Leishmania species. The results suggest that DCMF demonstrates similar antileishmanial effectiveness to isolated brachydin B, without causing genotoxic effects on mammalian cells. This finding is crucial because the isolation of the compounds relies on several steps and is very costly while obtaining the DCMF fraction is a simple and cost-effective process. Furthermore, In addition, the potential mechanisms of action of brachydins were also investigated. The computational analysis indicates that brachydin compounds bind to the Triosephosphate isomerase (TIM) enzyme via two main mechanisms: destabilizing the interface between the homodimers and interacting with catalytic residues situated at the site of binding. Based on all the results, DCMF exhibits promise as a therapeutic agent for leishmaniasis due to its significantly reduced toxicity in comparison to the adverse effects associated with current reference treatments.

Accelerating and Automating the Free Energy Perturbation Absolute Binding Free Energy Calculation with the RED-E Function

The accurate prediction of the binding affinities between small molecules and biological macromolecules plays a fundamental role in structure-based drug design, which is still challenging. The free energy perturbation-based absolute binding free energy (FEP-ABFE) approach has shown potential in its reliability. To correctly calculate the energy related to the ligand being restrained by the receptor, additional restraints between the ligand and the receptor are needed. However, determining the restraint parameters for individual ligands empirically is too trivial to be automated, and usually gives rise to numerical instabilities, which set back the applications of FEP-ABFE. To address these issues, we derived the analytical expression for the probability distribution of energy differences, P(ΔU), during the process of restraint addition, which is called the RED-E (restraint energy distribution at equilibrium position) function. Simulations indicated that the RED-E function can accurately describe P(ΔU) when restraints are added at the equilibrium position. Based on the RED-E function, an automatic restraint selection method was proposed to select the best restraint. With this method, there is a high phase-space overlap between the free and restrained states, such that using a 2-λ perturbation can accurately calculate the free energy of the restraint addition, which is a nearly 6 times acceleration compared with current widely used 12-λ perturbation method. The RED-E function gives insight into the non-Gaussian behavior of the sampled P(ΔU) in certain FEP processes in an analytical way. The highly automated and accelerated restraint selection also makes it possible for the large-scale application of FEP-ABFE in real drug discovery practices.

Voltage Imaging with Engineered Proton-Pumping Rhodopsins: Insights from the Proton Transfer Pathway

Voltage imaging using genetically encoded voltage indicators (GEVIs) has taken the field of neuroscience by storm in the past decade. Its ability to create subcellular and network level readouts of electrical dynamics depends critically on the kinetics of the response to voltage of the indicator used. Engineered microbial rhodopsins form a GEVI subclass known for their high voltage sensitivity and fast response kinetics. Here we review the essential aspects of microbial rhodopsin photocycles that are critical to understanding the mechanisms of voltage sensitivity in these proteins and link them to insights from efforts to create faster, brighter and more sensitive microbial rhodopsin-based GEVIs.

Comparison of Receptor-Ligand Restraint Schemes for Alchemical Absolute Binding Free Energy Calculations

Alchemical absolute binding free energy calculations are of increasing interest in drug discovery. These calculations require restraints between the receptor and ligand to restrict their relative positions and, optionally, orientations. Boresch restraints are commonly used, but they must be carefully selected in order to sufficiently restrain the ligand and to avoid inherent instabilities. Applying multiple distance restraints between anchor points in the receptor and ligand provides an alternative framework without inherent instabilities which may provide convergence benefits by more strongly restricting the relative movements of the receptor and ligand. However, there is no simple method to calculate the free energy of releasing these restraints due to the coupling of the internal and external degrees of freedom of the receptor and ligand. Here, a method to rigorously calculate free energies of binding with multiple distance restraints by imposing intramolecular restraints on the anchor points is proposed. Absolute binding free energies for the human macrophage migration inhibitory factor/MIF180, system obtained using a variety of Boresch restraints and rigorous and nonrigorous implementations of multiple distance restraints are compared. It is shown that several multiple distance restraint schemes produce estimates in good agreement with Boresch restraints. In contrast, calculations without orientational restraints produce erroneously favorable free energies of binding by up to approximately 4 kcal mol-1. These approaches offer new options for the deployment of alchemical absolute binding free energy calculations.

Cumulative Millisecond-Long Sampling for a Comprehensive Energetic Evaluation of Aqueous Ionic Liquid Effects on Amino Acid Interactions

The interactions of amino acid side-chains confer diverse energetic contributions and physical properties to a protein's stability and function. Various computational tools estimate the effect of changing a given amino acid on the protein's stability based on parametrized (free) energy functions. When parametrized for the prediction of protein stability in water, such energy functions can lead to suboptimal results for other solvents, such as ionic liquids (IL), aqueous ionic liquids (aIL), or salt solutions. However, to our knowledge, no comprehensive data are available describing the energetic effects of aIL on intramolecular protein interactions. Here, we present the most comprehensive set of potential of mean force (PMF) profiles of pairwise protein-residue interactions to date, covering 50 relevant interactions in water, the two biotechnologically relevant aIL [BMIM/Cl] and [BMIM/TfO], and [Na/Cl]. These results are based on a cumulated simulation time of >1 ms. aIL and salt ions can weaken, but also strengthen, specific residue interactions by more than 3 kcal mol^-1, depending on the residue pair, residue-residue configuration, participating ions, and concentration, necessitating considering such interactions specifically. These changes originate from a complex interplay of competitive or cooperative noncovalent ion-residue interactions, changes in solvent structural dynamics, or unspecific charge screening effects and occur at the contact distance but also at larger, solvent-separated distances. This data provide explanations at the atomistic and energetic levels for complex IL effects on protein stability and should help improve the prediction accuracies of computational tools that estimate protein stability based on (free) energy functions.

Binding affinity estimation from restrained umbrella sampling simulations

The protein-ligand binding affinity quantifies the binding strength between a protein and its ligand. Computer modeling and simulations can be used to estimate the binding affinity or binding free energy using data- or physics-driven methods or a combination thereof. Here we discuss a purely physics-based sampling approach based on biased molecular dynamics simulations. Our proposed method generalizes and simplifies previously suggested stratification strategies that use umbrella sampling or other enhanced sampling simulations with additional collective-variable-based restraints. The approach presented here uses a flexible scheme that can be easily tailored for any system of interest. We estimate the binding affinity of human fibroblast growth factor 1 to heparin hexasaccharide based on the available crystal structure of the complex as the initial model and four different variations of the proposed method to compare against the experimentally determined binding affinity obtained from isothermal titration calorimetry experiments.

Finding potential inhibitors against RNA-dependent RNA polymerase (RdRp) of bovine ephemeral fever virus (BEFV): an in-silico study

The bovine ephemeral fever virus (BEFV) is an enzootic agent that affects millions of bovines and causes major economic losses. Though the virus is seasonally reported with a very high morbidity rate (80-100%) from African, Australian, and Asiatic continents, it remains a neglected pathogen in many of its endemic areas, with no proper therapeutic drugs or vaccines presently available for treatment. The RNA-dependent RNA polymerase (RdRp) catalyzes the viral RNA synthesis and is an appropriate candidate for antiviral drug developments. We utilized integrated computational tools to build the 3D model of BEFV-RdRp and then predicted its probable active binding sites. The virtual screening and optimization against these active sites, using several small-molecule inhibitors from a different category of Life Chemical database and FDA-approved drugs from the ZINC database, was performed. We found nine molecules that have docking scores varying between -6.84 to -10.43 kcal/mol. Furthermore, these complexes were analyzed for their conformational dynamics and thermodynamic stability using molecular dynamics simulations in conjunction with the molecular mechanics generalized Born surface area (MM-GBSA) scheme. The binding free energy calculations depict that the electrostatic interactions play a dominant role in the RdRp-inhibitor binding. The hot spot residues, such as Arg565, Asp631, Glu633, Asp740, and Glu707, were found to control the RdRp-inhibitor interaction. The ADMET analysis strongly suggests favorable pharmacokinetics of these compounds that may prove useful for treating the BEFV ailment. Overall, we anticipate that these findings would help explore and develop a wide range of anti-BEFV therapy.Communicated by Ramaswamy H. Sarma.

Absolute Binding Free Energy Calculations for Buried Water Molecules

Water often plays a key role in mediating protein-ligand interactions. Understanding contributions from active-site water molecules to binding thermodynamics of a ligand is important in predicting binding free energies for ligand optimization. In this work, we tested a non-equilibrium switching method for absolute binding free energy calculations on water molecules in binding sites of 13 systems. We discuss the lessons we learned about identified issues that affected our calculations and ways to address them. This work fits with our larger focus on how to do accurate ligand binding free energy calculations when water rearrangements are very slow, such as rearrangements due to ligand modification (as in relative free energy calculations) or ligand binding (as in absolute free energy calculations). The method studied in this work can potentially be used to account for limited water sampling via providing endpoint corrections to free energy calculations using our calculated binding free energy of water.

Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy

Systematic and quantitative analysis of the reliability of formally exact methods that in silico calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.

Concentration-Dependent Thermodynamic Analysis of the Partition Process of Small Ligands into Proteins

In the category of functional low-affinity interactions, small ligands may interact with multiple protein sites in a highly degenerate manner. Better conceived as a partition phenomenon at the molecular interface of proteins, such low-affinity interactions appear to be hidden to our current experimental resolution making their structural and functional characterization difficult in the low concentration regime of physiological processes. Characterization of the partition phenomenon under higher chemical forces could be a relevant strategy to tackle the problem provided the results can be scaled back to the low concentration range. Far from being trivial, such scaling demands a concentration-dependent understanding of self-interactions of the ligands, structural perturbations of the protein, among other molecular effects. Accordingly, we elaborate a novel and detailed concentration-dependent thermodynamic analysis of the partition process of small ligands aiming at characterizing the stability and structure of the dilute phenomenon from high concentrations. In analogy to an “aggregate” binding constant of a small molecule over multiple sites of a protein receptor, the model defines the stability of the process as a macroscopic equilibrium constant for the partition number of ligands that can be used to analyze biochemical and functional data of two-component systems driven by low-affinity interactions. Acquisition of such modeling-based structural information is expected to be highly welcome by revealing more traceable protein-binding spots for non-specific ligands.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

On Computing Equilibrium Binding Constants for Protein-Protein Association in Membranes

Protein association in lipid membranes is fundamental to membrane protein function and of great biomedical relevance. All-atom and coarse-grained models have been extensively used to understand the protein-protein interactions in the membrane and to compute equilibrium association constants. However, slow translational and rotational diffusion of protein in membrane presents challenges to the effective sampling of conformations defining the ensembles of free and bound states contributing to the association equilibrium and the free energy of dimerization. We revisit the homodimerization equilibrium of the TM region of glycophorin A. Conformational sampling is performed using umbrella sampling along previously proposed one-dimensional collective variables and compared with sampling over a two-dimensional collective variable space using the MARTINI v2.2 force field. We demonstrate that the one-dimensional collective variables suffer from restricted sampling of the native homodimer conformations leading to a biased free energy landscape. Conversely, simulations along the two-dimensional collective variable effectively characterize the thermodynamically relevant native and non-native interactions contributing to the association equilibrium. These results demonstrate the challenges associated with accurately characterizing binding equilibria when multiple poses contribute to the bound state ensemble.

Comparison of Grand Canonical and Conventional Molecular Dynamics Simulation Methods for Protein-Bound Water Networks

Water molecules play important roles in all biochemical processes. Therefore, it is of key importance to obtain information of the structure, dynamics, and thermodynamics of water molecules around proteins. Numerous computational methods have been suggested with this aim. In this study, we compare the performance of conventional and grand-canonical Monte Carlo (GCMC) molecular dynamics (MD) simulations to sample the water structure, as well GCMC and grid-based inhomogeneous solvation theory (GIST) to describe the energetics of the water network. They are evaluated on two proteins: the buried ligand-binding site of a ferritin dimer and the solvent-exposed binding site of galectin-3. We show that GCMC/MD simulations significantly speed up the sampling and equilibration of water molecules in the buried binding site, thereby making the results more similar for simulations started from different states. Both GCMC/MD and conventional MD reproduce crystal-water molecules reasonably for the buried binding site. GIST analyses are normally based on restrained MD simulations. This improves the precision of the calculated energies, but the restraints also significantly affect both absolute and relative energies. Solvation free energies for individual water molecules calculated with and without restraints show a good correlation, but with large quantitative differences. Finally, we note that the solvation free energies calculated with GIST are ∼5 times larger than those estimated by GCMC owing to differences in the reference state.

Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations

Designing a reliable computational methodology to calculate protein:ligand standard binding free energies is extremely challenging. The large change in configurational enthalpy and entropy that accompanies the association of ligand and protein is notoriously difficult to capture in naive brute-force simulations. Addressing this issue, the present protocol rests upon a rigorous statistical mechanical framework for the determination of protein:ligand binding affinities together with the comprehensive Binding Free-Energy Estimator 2 (BFEE2) application software. With the knowledge of the bound state, available from experiments or docking, application of the BFEE2 protocol with a reliable force field supplies in a matter of days standard binding free energies within chemical accuracy, for a broad range of protein:ligand complexes. Limiting undesirable human intervention, BFEE2 assists the end user in preparing all the necessary input files and performing the post-treatment of the simulations towards the final estimate of the binding affinity.

Mechanisms of long-distance allosteric couplings in proton-binding membrane transporters

Membrane transporters that use proton binding and proton transfer for function couple local protonation change with changes in protein conformation and water dynamics. Changes of protein conformation might be required to allow transient formation of hydrogen-bond networks that bridge proton donor and acceptor pairs separated by long distances. Inter-helical hydrogen-bond networks adjust rapidly to protonation change, and ensure rapid response of the protein structure and dynamics. Membrane transporters with known three-dimensional structures and proton-binding groups inform on general principles of protonation-coupled protein conformational dynamics. Inter-helical hydrogen bond motifs between proton-binding carboxylate groups and a polar sidechain are observed in unrelated membrane transporters, suggesting common principles of coupling protonation change with protein conformational dynamics.

Computational Tools for Accurate Binding Free-Energy Prediction

A quantitative thermodynamic understanding of the noncovalent association of (bio)molecules is of central importance in molecular life sciences. An important quantity characterizing (bio)molecular association is the binding affinity or absolute binding free energy. In recent years, the computational prediction of absolute binding free energies has evolved considerably in terms of accuracy, computational speed, and user-friendliness. In this chapter, we first give an overview of how absolute free energies are defined and how they can be determined with computational means. We proceed with an outline of the theoretical basis of the two most reliable methods, potential of mean force, and double decoupling calculations. In particular, we describe how the sampling problem can be alleviated by application of restraints. Finally, we provide step-by-step instructions of how to set up corresponding molecular simulations with a commonly employed molecular dynamics simulation engine.

Water molecules at protein-drug interfaces: computational prediction and analysis methods

The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.

Recent Developments in Free Energy Calculations for Drug Discovery

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.

Building intuition for binding free energy calculations: Bound state definition, restraints, and symmetry

The theory behind computation of absolute binding free energies using explicit-solvent molecular simulations is well-established, yet somewhat complex, with counter-intuitive aspects. This leads to frequent frustration, common misconceptions, and sometimes erroneous numerical treatment. To improve this, we present the main practically relevant segments of the theory with constant reference to physical intuition. We pinpoint the role of the implicit or explicit definition of the bound state (or the binding site) to make a robust link between an experimental measurement and a computational result. We clarify the role of symmetry and discuss cases where symmetry number corrections have been misinterpreted. In particular, we argue that symmetry corrections as classically presented are a source of confusion and could be advantageously replaced by restraint free energy contributions. We establish that contrary to a common intuition, partial or missing sampling of some modes of symmetric bound states does not affect the calculated decoupling free energies. Finally, we review these questions and pitfalls in the context of a few common practical situations: binding to a symmetric receptor (equivalent binding sites), binding of a symmetric ligand (equivalent poses), and formation of a symmetric complex, in the case of homodimerization.

Assessment of the PETase conformational changes induced by poly(ethylene terephthalate) binding

Recently, a bacterium strain of Ideonella sakaiensis was identified with the uncommon ability to degrade the poly(ethylene terephthalate) (PET). The PETase from I. sakaiensis strain 201-F6 (IsPETase) catalyzes the hydrolysis of PET converting it to mono(2-hydroxyethyl) terephthalic acid (MHET), bis(2-hydroxyethyl)-TPA (BHET), and terephthalic acid (TPA). Despite the potential of this enzyme for mitigation or elimination of environmental contaminants, one of the limitations of the use of IsPETase for PET degradation is the fact that it acts only at moderate temperature due to its low thermal stability. Besides, molecular details of the main interactions of PET in the active site of IsPETase remain unclear. Herein, molecular docking and molecular dynamics (MD) simulations were applied to analyze structural changes of IsPETase induced by PET binding. Results from the essential dynamics revealed that the β1-β2 connecting loop is very flexible. This loop is located far from the active site of IsPETase and we suggest that it can be considered for mutagenesis to increase the thermal stability of IsPETase. The free energy landscape (FEL) demonstrates that the main change in the transition between the unbound to the bound state is associated with the β7-α5 connecting loop, where the catalytic residue Asp206 is located. Overall, the present study provides insights into the molecular binding mechanism of PET into the IsPETase structure and a computational strategy for mapping flexible regions of this enzyme, which can be useful for the engineering of more efficient enzymes for recycling plastic polymers using biological systems.

Estimating the Roles of Protonation and Electronic Polarization in Absolute Binding Affinity Simulations

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 10 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.

Exploring the potency of currently used drugs against HIV-1 protease of subtype D variant by using multiscale simulations

Acquired immune deficiency syndrome (AIDS) is caused by the human immunodeficiency virus (HIV), type 1 and 2. Further, the diversity in HIV-1 has given rise to many serotypes and recombinant strains. The currently used protease inhibitors have been developed for subtype B, although non-B subtype strains account for ∼ 90% of the global HIV infections. Subtype D is spreading rapidly and infecting a large population in North Africa and the Middle East. In the current study, molecular dynamics simulations in conjunction with the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) scheme was used to investigate the potency of four drugs, namely atazanavir (ATV), darunavir (DRV), lopinavir (LPV) and tipranavir (TPV) against the subtype D variant. Our calculations predicted that the potency of the inhibitors decreased in the order TPV > ATV > DRV > LPV. TPV was found to be the most potent against subtype D due to an increase in van der Waals and electrostatic interactions and reduction in the desolvation energy compared to other inhibitors. This result is further supported by the hydrogen bond interactions between inhibitors and protease. Furthermore, our analyses suggested that the binding of TPV induced a more closed conformation of the flap compared to apo or other complexes. It was observed that TPV/PR^D has a lower cavity volume relative to the other three complexes leading to a tighter binding. The open conformation of the flap was observed for LPV/PR^D. We expect that this study might be useful for designing more potent inhibitors against HIV-1 subtype D. Communicated by Ramaswamy H. Sarma.

Binding of divalent cations to acetate: molecular simulations guided by Raman spectroscopy

We combine Raman-MCR vibrational spectroscopy experiments with ab initio and classical MD simulations to gain molecular insights into carboxylate–cation binding.

Quantum Mechanical and Molecular Mechanics Modeling of Membrane-Embedded Rhodopsins

Computational chemistry provides versatile methods for studying the properties and functioning of biological systems at different levels of precision and at different time scales. The aim of this article is to review the computational methodologies that are applicable to rhodopsins as archetypes for photoactive membrane proteins that are of great importance both in nature and in modern technologies. For each class of computational techniques, from methods that use quantum mechanics for simulating rhodopsin photophysics to less-accurate coarse-grained methodologies used for long-scale protein dynamics, we consider possible applications and the main directions for improvement.

Atomistic Model for Simulations of the Sedative Hypnotic Drug 2,2,2-Trichloroethanol

2,2,2-Trichloroethanol (TCE) is the active form of the sedative hypnotic drug chloral hydrate, one of the oldest sleep medications in the market. Understanding of TCE's action mechanisms to its many targets, particularly within the ion channel family, could benefit from the state-of-the-art computational molecular studies. In this direction, we employed de novo modeling aided by the force field toolkit to develop CHARMM36-compatible TCE parameters. The classical potential energy function was calibrated targeting molecular conformations, local interactions with water molecules, and liquid bulk properties. Reference data comes from both tabulated thermodynamic properties and ab initio calculations at the MP2 level. TCE solvation free energy calculations in water and oil reproduce a lipophilic, yet nonhydrophobic, behavior. Indeed, the potential mean force profile for TCE partition through the phospholipid bilayer reveals the sedative's preference for the interfacial region. The calculated partition coefficient also matches experimental measures. Further validation of the proposed parameters is supported by the model's ability to recapitulate quenching experiments demonstrating TCE binding to bovine serum albumin.

Binding of the general anesthetic sevoflurane to ion channels

The direct-site hypothesis assumes general anesthetics bind ion channels to impact protein equilibrium and function, inducing anesthesia. Despite advancements in the field, a first principle all-atom demonstration of this structure-function premise is still missing. We focus on the clinically used sevoflurane interaction to anesthetic-sensitive Kv1.2 mammalian channel to resolve if sevoflurane binds protein’s well-characterized open and closed structures in a conformation-dependent manner to shift channel equilibrium. We employ an innovative approach relying on extensive docking calculations and free-energy perturbation of all potential binding sites revealed by the latter, and find sevoflurane binds open and closed structures at multiple sites under complex saturation and concentration effects. Results point to a non-trivial interplay of site and conformation-dependent modes of action involving distinct binding sites that increase channel open-probability at diluted ligand concentrations. Given the challenge in exploring more complex processes potentially impacting channel-anesthetic interaction, the result is revealing as it demonstrates the process of multiple anesthetic binding events alone may account for open-probability shifts recorded in measurements.

General anesthetics are central to modern medicine, yet their microscopic mechanism of action is still unknown. Here, we demonstrate that a clinically used anesthetic, sevoflurane, binds the mammalian voltage-gated potassium channel Kv1.2 effecting a shift in its open probability, even at low concentrations. The results, supported by recent experimental measurements, are promising as they demonstrate that the molecular process of direct binding of anesthetic to ion channels play a relevant role in anesthesia.

Detailed potential of mean force studies on host-guest systems from the SAMPL6 challenge

Accurately predicting receptor-ligand binding free energies is one of the holy grails of computational chemistry with many applications in chemistry and biology. Many successes have been reported, but issues relating to sampling and force field accuracy remain significant issues affecting our ability to reliably calculate binding free energies. In order to explore these issues in more detail we have examined a series of small host-guest complexes from the SAMPL6 blind challenge, namely octa-acids (OAs)-guest complexes and Curcurbit[8]uril (CB8)-guest complexes. Specifically, potential of mean force studies using umbrella sampling combined with the weighted histogram method were carried out on both systems with both known and unknown binding affinities. We find that using standard force fields and straightforward simulation protocols we are able to obtain satisfactory results, but that simply scaling our results allows us to significantly improve our predictive ability for the unknown test sets: the overall RMSD of the binding free energy versus experiment is reduced from 5.59 to 2.36 kcal/mol; for the CB8 test system, the RMSD goes from 8.04 to 3.51 kcal/mol, while for the OAs test system, the RSMD goes from 2.89 to 0.95 kcal/mol. The scaling approach was inspired by studies on structurally related known benchmark sets: by simply scaling, the RMSD was reduced from 6.23 to 1.19 kcal/mol and from 2.96 to 0.62 kcal/mol for the CB8 benchmark system and the OA benchmark system, respectively. We find this scaling procedure to correct absolute binding affinities to be highly effective especially when working across a "congeneric" series with similar charge states. It is less successful when applied to mixed ligands with varied charges and chemical characteristics, but improvement is still realized in the present case. This approach suggests that there are large systematic errors in absolute binding free energy calculations that can be straightforwardly accounted for using a scaling procedure. Random errors are still an issue, but near chemical accuracy can be obtained using the present strategy in select cases.

Ensemble- and Rigidity Theory-Based Perturbation Approach To Analyze Dynamic Allostery

Allostery describes the functional coupling between sites in biomolecules. Recently, the role of changes in protein dynamics for allosteric communication has been highlighted. A quantitative and predictive description of allostery is fundamental for understanding biological processes. Here, we integrate an ensemble-based perturbation approach with the analysis of biomolecular rigidity and flexibility to construct a model of dynamic allostery. Our model, by definition, excludes the possibility of conformational changes, evaluates static, not dynamic, properties of molecular systems, and describes allosteric effects due to ligand binding in terms of a novel free-energy measure. We validated our model on three distinct biomolecular systems: eglin c, protein tyrosine phosphatase 1B, and the lymphocyte function-associated antigen 1 domain. In all cases, it successfully identified key residues for signal transmission in very good agreement with the experiment. It correctly and quantitatively discriminated between positively or negatively cooperative effects for one of the systems. Our model should be a promising tool for the rational discovery of novel allosteric drugs.

Concentration-Dependent Binding of Small Ligands to Multiple Saturable Sites in Membrane Proteins

Membrane proteins are primary targets for most therapeutic indications in cancer and neurological diseases, binding over 50% of all known small molecule drugs. Understanding how such ligands impact membrane proteins requires knowledge on the molecular structure of ligand binding, a reasoning that has driven relentless efforts in drug discovery and translational research. Binding of small ligands appears however highly complex involving interaction to multiple transmembrane protein sites featuring single or multiple occupancy states. Within this scenario, looking for new developments in the field, we investigate the concentration-dependent binding of ligands to multiple saturable sites in membrane proteins. The study relying on docking and free-energy perturbation provides us with an extensive description of the probability density of protein-ligand states that allows for computation of thermodynamic properties of interest. It also provides one- and three-dimensional spatial descriptions for the ligand density across the protein-membrane system which can be of interest for structural purposes. Illustration and discussion of the results are shown for binding of the general anesthetic sevoflurane against Kv1.2, a mammalian ion channel for which experimental data are available.

Tinker-OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs

The capabilities of the polarizable force fields for alchemical free energy calculations have been limited by the high computational cost and complexity of the underlying potential energy functions. In this work, we present a GPU-based general alchemical free energy simulation platform for polarizable potential AMOEBA. Tinker-OpenMM, the OpenMM implementation of the AMOEBA simulation engine has been modified to enable both absolute and relative alchemical simulations on GPUs, which leads to a ∼200-fold improvement in simulation speed over a single CPU core. We show that free energy values calculated using this platform agree with the results of Tinker simulations for the hydration of organic compounds and binding of host-guest systems within the statistical errors. In addition to absolute binding, we designed a relative alchemical approach for computing relative binding affinities of ligands to the same host, where a special path was applied to avoid numerical instability due to polarization between the different ligands that bind to the same site. This scheme is general and does not require ligands to have similar scaffolds. We show that relative hydration and binding free energy calculated using this approach match those computed from the absolute free energy approach. © 2017 Wiley Periodicals, Inc.

Ligand Selectivity Mechanism and Conformational Changes in Guanine Riboswitch by Molecular Dynamics Simulations and Free Energy Calculations

Riboswitches regulate gene expression through direct and specific interactions with small metabolite molecules. Binding of a ligand to its RNA target is high selectivity and affinity and induces conformational changes of the RNA's secondary and tertiary structure. The structural difference of two purine riboswitches aptamers is caused by only one single mutation, where cytosine 74 in the guanine riboswitch is corresponding to a uracil 74 in adenine riboswitch. Here we employed molecular dynamics (MD) simulation, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and thermodynamic integration computational methodologies to evaluate the energetic and conformational changes of ligands binding to purine riboswitches. The snapshots used in MM-PBSA calculation were extracted from ten 50 ns MD simulation trajectories for each complex. These free energy results are in consistent with the experimental data and rationalize the selectivity of the riboswitches for different ligands. In particular, it is found that the loss in binding free energy upon mutation is mainly electrostatic in guanine (GUA) and riboswitch complex. Furthermore, new hydrogen bonds are found in mutated complexes. To reveal the conformational properties of guanine riboswitch, we performed a total of 6 μs MD simulations in both the presence and the absence of the ligand GUA. The MD simulations suggest that the conformation of guanine riboswitch depends on the distance of two groups in the binding pocket of ligand. The conformation is in a close conformation when U51-A52 is close to C74-U75.

Allosteric modulation model of the mu opioid receptor by herkinorin, a potent not alkaloidal agonist

Modulation of opioid receptors is the primary choice for pain management and structural information studies have gained new horizons with the recently available X-ray crystal structures. Herkinorin is one of the most remarkable salvinorin A derivative with high affinity for the mu opioid receptor, moderate selectivity and lack of nitrogen atoms on its structure. Surprisingly, binding models for herkinorin are lacking. In this work, we explore binding models of herkinorin using automated docking, molecular dynamics simulations, free energy calculations and available experimental information. Our herkinorin D-ICM-1 binding model predicted a binding free energy of -11.52 ± 1.14 kcal mol^-1 by alchemical free energy estimations, which is close to the experimental values -10.91 ± 0.2 and -10.80 ± 0.05 kcal mol^-1 and is in agreement with experimental structural information. Specifically, D-ICM-1 molecular dynamics simulations showed a water-mediated interaction between D-ICM-1 and the amino acid H297^6.52, this interaction coincides with the co-crystallized ligands. Another relevant interaction, with N127^2.63, allowed to rationalize herkinorin's selectivity to mu over delta opioid receptors. Our suggested binding model for herkinorin is in agreement with this and additional experimental data. The most remarkable observation derived from our D-ICM-1 model is that herkinorin reaches an allosteric sodium ion binding site near N150^3.35. Key interactions in that region appear relevant for the lack of β-arrestin recruitment by herkinorin. This interaction is key for downstream signaling pathways involved in the development of side effects, such as tolerance. Future SAR studies and medicinal chemistry efforts will benefit from the structural information presented in this work.

Mechanism of the Association Pathways for a Pair of Fast and Slow Binding Ligands of HIV-1 Protease

Equilibrium constants, together with kinetic rate constants of binding, are key factors in the efficacy and safety of drug compounds, informing drug design. However, the association pathways of protein-ligand binding, which contribute to their kinetic behaviors, are little understood. In this work, we used unbiased all-atom molecular dynamics (MD) simulations with an explicit solvent model to study the association processes of protein-ligand binding. Using the HIV protease (HIVp)-xk263 and HIVp-ritonavir protein-ligand systems as cases, we observed that ligand association is a multistep process involving diffusion, localization, and conformational rearrangements of the protein, ligand, and water molecules. Moreover, these two ligands preferred different routes of binding, which reflect two well-known binding mechanisms: induced-fit and conformation selection models. Our study shows that xk263 has a stronger capacity for desolvating surrounding water molecules, thereby inducing a semiopen conformation of the HIVp flaps (induced-fit model). In contrast, the slow dehydration characteristic of ritonavir allows for gradual association with the binding pocket of HIVp when the protein's flap conformation is fully open (conformation selection model). By studying the mechanism of ligand association and understanding the role of solvent molecules during the binding event, we can obtain a different perspective on the mechanism of macromolecule recognition, providing insights into drug discovery.

Biophysically inspired model for functionalized nanocarrier adhesion to cell surface: roles of protein expression and mechanical factors

In order to achieve selective targeting of affinity-ligand coated nanoparticles to the target tissue, it is essential to understand the key mechanisms that govern their capture by the target cell. Next-generation pharmacokinetic (PK) models that systematically account for proteomic and mechanical factors can accelerate the design, validation and translation of targeted nanocarriers (NCs) in the clinic. Towards this objective, we have developed a computational model to delineate the roles played by target protein expression and mechanical factors of the target cell membrane in determining the avidity of functionalized NCs to live cells. Model results show quantitative agreement with in vivo experiments when specific and non-specific contributions to NC binding are taken into account. The specific contributions are accounted for through extensive simulations of multivalent receptor-ligand interactions, membrane mechanics and entropic factors such as membrane undulations and receptor translation. The computed NC avidity is strongly dependent on ligand density, receptor expression, bending mechanics of the target cell membrane, as well as entropic factors associated with the membrane and the receptor motion. Our computational model can predict the in vivo targeting levels of the intracellular adhesion molecule-1 (ICAM1)-coated NCs targeted to the lung, heart, kidney, liver and spleen of mouse, when the contributions due to endothelial capture are accounted for. The effect of other cells (such as monocytes, etc.) do not improve the model predictions at steady state. We demonstrate the predictive utility of our model by predicting partitioning coefficients of functionalized NCs in mice and human tissues and report the statistical accuracy of our model predictions under different scenarios.

Protein-directed self-assembly of a fullerene crystal

Learning to engineer self-assembly would enable the precise organization of molecules by design to create matter with tailored properties. Here we demonstrate that proteins can direct the self-assembly of buckminsterfullerene (C₆₀) into ordered superstructures. A previously engineered tetrameric helical bundle binds C₆₀ in solution, rendering it water soluble. Two tetramers associate with one C₆₀, promoting further organization revealed in a 1.67-Å crystal structure. Fullerene groups occupy periodic lattice sites, sandwiched between two Tyr residues from adjacent tetramers. Strikingly, the assembly exhibits high charge conductance, whereas both the protein-alone crystal and amorphous C₆₀ are electrically insulating. The affinity of C₆₀ for its crystal-binding site is estimated to be in the nanomolar range, with lattices of known protein crystals geometrically compatible with incorporating the motif. Taken together, these findings suggest a new means of organizing fullerene molecules into a rich variety of lattices to generate new properties by design.

Self-assembly enables complex structures to be fabricated from a few relatively simple components, but requires a detailed understanding of how the constituents may interact. Here, the authors report the rational assembly and crystallographic characterization of a fullerene-protein superstructure.

Molecular simulation and modeling of complex I

Molecular modeling and molecular dynamics simulations play an important role in the functional characterization of complex I. With its large size and complicated function, linking quinone reduction to proton pumping across a membrane, complex I poses unique modeling challenges. Nonetheless, simulations have already helped in the identification of possible proton transfer pathways. Simulations have also shed light on the coupling between electron and proton transfer, thus pointing the way in the search for the mechanistic principles underlying the proton pump. In addition to reviewing what has already been achieved in complex I modeling, we aim here to identify pressing issues and to provide guidance for future research to harness the power of modeling in the functional characterization of complex I. This article is part of a Special Issue entitled Respiratory complex I, edited by Volker Zickermann and Ulrich Brandt.

Prediction of Water Binding to Protein Hydration Sites with a Discrete, Semiexplicit Solvent Model

Buried water molecules are ubiquitous in protein structures and are found at the interface of most protein-ligand complexes. Determining their distribution and thermodynamic effect is a challenging yet important task, of great of practical value for the modeling of biomolecular structures and their interactions. In this study, we present a novel method aimed at the prediction of buried water molecules in protein structures and estimation of their binding free energies. It is based on a semiexplicit, discrete solvation model, which we previously introduced in the context of small molecule hydration. The method is applicable to all macromolecular structures described by a standard all-atom force field, and predicts complete solvent distribution within a single run with modest computational cost. We demonstrate that it indicates positions of buried hydration sites, including those filled by more than one water molecule, and accurately differentiates them from sterically accessible to water but void regions. The obtained estimates of water binding free energies are in fair agreement with reference results determined with the double decoupling method.

Quantifying the entropy of binding for water molecules in protein cavities by computing correlations

Protein structural analysis demonstrates that water molecules are commonly found in the internal cavities of proteins. Analysis of experimental data on the entropies of inorganic crystals suggests that the entropic cost of transferring such a water molecule to a protein cavity will not typically be greater than 7.0 cal/mol/K per water molecule, corresponding to a contribution of approximately +2.0 kcal/mol to the free energy. In this study, we employ the statistical mechanical method of inhomogeneous fluid solvation theory to quantify the enthalpic and entropic contributions of individual water molecules in 19 protein cavities across five different proteins. We utilize information theory to develop a rigorous estimate of the total two-particle entropy, yielding a complete framework to calculate hydration free energies. We show that predictions from inhomogeneous fluid solvation theory are in excellent agreement with predictions from free energy perturbation (FEP) and that these predictions are consistent with experimental estimates. However, the results suggest that water molecules in protein cavities containing charged residues may be subject to entropy changes that contribute more than +2.0 kcal/mol to the free energy. In all cases, these unfavorable entropy changes are predicted to be dominated by highly favorable enthalpy changes. These findings are relevant to the study of bridging water molecules at protein-protein interfaces as well as in complexes with cognate ligands and small-molecule inhibitors.

Stay Wet, Stay Stable? How Internal Water Helps the Stability of Thermophilic Proteins

We present a systematic computational investigation of the internal hydration of a set of homologous proteins of different stability content and molecular complexities. The goal of the study is to verify whether structural water can be part of the molecular mechanisms ensuring enhanced stability in thermophilic enzymes. Our free-energy calculations show that internal hydration in the thermophilic variants is generally more favorable, and that the cumulated effect of wetting multiple sites results in a meaningful contribution to stability. Moreover, thanks to a more effective capability to retain internal water, some thermophilic proteins benefit by a systematic gain from internal wetting up to their optimal working temperature. Our work supports the idea that internal wetting can be viewed as an alternative molecular variable to be tuned for increasing protein stability.

Statistical survey of the buried waters in the Protein Data Bank

The structures of buried water molecules were studied in an ensemble of high-quality and non-redundant protein crystal structures. Buried water molecules were clustered and classified in lake-like clusters, which are completely isolated from the bulk solvent, and bay-like clusters, which are in contact with the bulk solvent through a surface water molecule. Buried water molecules are extremely common: lake-like clusters are found in 89 % of the protein crystal structures and bay-like clusters in 93 %. Clusters with only one water molecule are much more common than larger clusters. Both cluster types incline to be surrounded by loop residues, and to a minor extent by residues in extended secondary structure. Helical residues on the contrary do not tend to surround clusters of buried water molecules. One buried water molecule is found every 30-50 amino acid residues, depending on the secondary structures that are more abundant in the protein. Both main- and side-chain atoms are in contact with buried waters; they form four hydrogen bonds with the first water and 1-1.5 additional hydrogen bond for each additional water in the cluster. Consequently, buried water molecules appear to be firmly packed and rigid like the protein atoms. In this regard, it is remarkable to observe that prolines often surround water molecules buried in the protein interior. Interestingly, clusters of buried water molecules tend to be just beneath the protein surface. Moreover, water molecules tend to form a one-dimensional wire rather than more compact arrangements. This agrees with recent evidence of the mechanisms of solvent exchange between internal cavities and bulk solvent.

Hydrated Excess Protons Can Create Their Own Water Wires

Grotthuss shuttling of an excess proton charge defect through hydrogen bonded water networks has long been the focus of theoretical and experimental studies. In this work we show that there is a related process in which water molecules move ("shuttle") through a hydrated excess proton charge defect in order to wet the path ahead for subsequent proton charge migration. This process is illustrated through reactive molecular dynamics simulations of proton transport through a hydrophobic nanotube, which penetrates through a hydrophobic region. Surprisingly, before the proton enters the nanotube, it starts "shooting" water molecules into the otherwise dry space via Grotthuss shuttling, effectively creating its own water wire where none existed before. As the proton enters the nanotube (by 2-3 Å), it completes the solvation process, transitioning the nanotube to the fully wet state. By contrast, other monatomic cations (e.g., K(+)) have just the opposite effect, by blocking the wetting process and making the nanotube even drier. As the dry nanotube gradually becomes wet when the proton charge defect enters it, the free energy barrier of proton permeation through the tube via Grotthuss shuttling drops significantly. This finding suggests that an important wetting mechanism may influence proton translocation in biological systems, i.e., one in which protons "create" their own water structures (water "wires") in hydrophobic spaces (e.g., protein pores) before migrating through them. An existing water wire, e.g., one seen in an X-ray crystal structure or MD simulations without an explicit excess proton, is therefore not a requirement for protons to transport through hydrophobic spaces.

Computational study of Gleevec and G6G reveals molecular determinants of kinase inhibitor selectivity

Gleevec is a potent inhibitor of Abl tyrosine kinase but not of the highly homologous c-Src kinase. Because the ligand binds to an inactive form of the protein in which an Asp-Phe-Gly structural motif along the activation loop adopts a so-called DFG-out conformation, it was suggested that binding specificity was controlled by a "conformational selection" mechanism. In this context, the binding affinity displayed by the kinase inhibitor G6G poses an intriguing challenge. Although it possesses a chemical core very similar to that of Gleevec, G6G is a potent inhibitor of both Abl and c-Src kinases. Both inhibitors bind to the DFG-out conformation of the kinases, which seems to be in contradiction with the conformational selection mechanism. To address this issue and display the hidden thermodynamic contributions affecting the binding selectivity, molecular dynamics free energy simulations with explicit solvent molecules were carried out. Relative to Gleevec, G6G forms highly favorable van der Waals dispersive interactions upon binding to the kinases via its triazine functional group, which is considerably larger than the corresponding pyridine moiety in Gleevec. Upon binding of G6G to c-Src, these interactions offset the unfavorable free energy cost of the DFG-out conformation. When binding to Abl, however, G6G experiences an unfavorable free energy penalty due to steric clashes with the phosphate-binding loop, yielding an overall binding affinity that is similar to that of Gleevec. Such steric clashes are absent when G6G binds to c-Src, due to the extended conformation of the phosphate-binding loop.