Effects of Xylanase A double mutation on substrate specificity and structural dynamics

While protein activity is traditionally studied with a major focus on the active site, the activity of enzymes has been hypothesized to be linked to the flexibility of adjacent regions, warranting more exploration into how the dynamics in these regions affects catalytic turnover. One such enzyme is Xylanase A (XylA), which cleaves hemicellulose xylan polymers by hydrolysis at internal β-1,4-xylosidic linkages. It contains a "thumb" region whose flexibility has been suggested to affect the activity. The double mutation D11F/R122D was previously found to affect activity and potentially bias the thumb region to a more open conformation. We find that the D11F/R122D double mutation shows substrate-dependent effects, increasing activity on the non-native substrate ONPX2 but decreasing activity on its native xylan substrate. To characterize how the double mutant causes these kinetics changes, nuclear magnetic resonance (NMR) and molecular dynamics (MD) simulations were used to probe structural and flexibility changes. NMR chemical shift perturbations revealed structural changes in the double mutant relative to the wild-type, specifically in the thumb and fingers regions. Increased slow-timescale dynamics in the fingers region was observed as intermediate-exchange line broadening. Lipari-Szabo order parameters show negligible changes in flexibility in the thumb region in the presence of the double mutation. To help understand if there is increased energetic accessibility to the open state upon mutation, alchemical free energy simulations were employed that indicated thumb opening is more favorable in the double mutant. These studies aid in further characterizing how flexibility in adjacent regions affects the function of XylA.

Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential

To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. bioRxiv 2020, 10.1101/2020.07.29.227959.] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement.

In Vitro and In Silico Explorations of the Protein Conformational Changes of Corynebacterium glutamicum MshA, a Model Retaining GT-B Glycosyltransferase

MshA is a GT-B glycosyltransferase catalyzing the first step in the biosynthesis of mycothiol. While many GT-B enzymes undergo an open-to-closed transition, MshA is unique because its 97° rotation is beyond the usual range of 10-25°. Molecular dynamics (MD) simulations were carried out for MshA in both ligand bound and unbound states to investigate the effect of ligand binding on localized protein dynamics and its conformational free energy landscape. Simulations showed that both the unliganded "opened" and liganded "closed" forms of the enzyme sample a wide degree of dihedral angles and interdomain distances with relatively low overlapping populations. Calculation of the free energy surface using replica exchange MD for the apo "opened" and an artificial generated apo "closed" structure revealed overlaps in the geometries sampled, allowing calculation of a barrier of 2 kcal/mol for the open-to-closed transition in the absence of ligands. MD simulations of fully liganded MshA revealed a smaller sampling of the dihedral angles. The localized protein fluctuation changes suggest that UDP-GlcNAc binding activates the motions of loops in the 1-l-myo-inositol-1-phosphate (I1P)-binding site despite little change in the interactions with UDP-GlcNAc. Circular dichroism, intrinsic fluorescence spectroscopy, and mutagenesis studies were used to confirm the ligand-induced structural changes in MshA. The results support a proposed mechanism where UDP-GlcNAc binds with rigid interactions to the C-terminal domain of MshA and activates flexible loops in the N-terminal domain for binding and positioning of I1P. This model can be used for future structure-based drug development of inhibitors of the mycothiol biosynthetic pathway.

Computational Design of Potent and Selective d-Peptide Agonists of the Glucagon-like Peptide-2 Receptor

Here, we designed three d-GLP-2 agonists that activated the glucagon-like peptide-2 receptor (GLP-2R) cyclic adenosine monophosphate (cAMP) accumulation without stimulating the glucagon-like peptide-1 receptor (GLP-1R). All the d-GLP-2 agonists increased the protein kinase B phosphorylated (p-AKT) expression levels in a time- and concentration-dependent manner in vitro. The most effective d-GLP-2 analogue boosted the AKT phosphorylation 2.28 times more effectively compared to the native l-GLP-2. The enhancement in the p-AKT levels induced by the d-GLP-2 analogues could be explained by GLP-2R's more prolonged activation, given that the d-GLP-2 analogues induce a lower β-arrestin recruitment. The higher stability to protease degradation of our d-GLP-2 agonists helps us envision their potential applications in enhancing intestinal absorption and treating inflammatory bowel illness while lowering the high dosage required by the current treatments.

High-Throughput Molecular Dynamics-Based Alchemical Free Energy Calculations for Predicting the Binding Free Energy Change Associated with the Selected Omicron Mutations in the Spike Receptor-Binding Domain of SARS-CoV-2

The ongoing pandemic caused by SARS-CoV-2 has gone through various phases. Since the initial outbreak, the virus has mutated several times, with some lineages showing even stronger infectivity and faster spread than the original virus. Among all the variants, omicron is currently classified as a variant of concern (VOC) by the World Health Organization, as the previously circulating variants have been replaced by it. In this work, we have focused on the mutations observed in omicron sub lineages BA.1, BA.2, BA.4 and BA.5, particularly at the receptor-binding domain (RBD) of the spike protein that is responsible for the interactions with the host ACE2 receptor and binding of antibodies. Studying such mutations is particularly important for understanding the viral infectivity, spread of the disease and for tracking the escape routes of this virus from antibodies. Molecular dynamics (MD) based alchemical free energy calculations have been shown to be very accurate in predicting the free energy change, due to a mutation that could have a deleterious or a stabilizing effect on either the protein itself or its binding affinity to another protein. Here, we investigated the significance of five spike RBD mutations on the stability of the spike protein binding to ACE2 by free energy calculations using high throughput MD simulations. For comparison, we also used conventional MD simulations combined with a Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) based approach, and compared our results with the available experimental data. Overall, the alchemical free energy calculations performed far better than the MM-GBSA approach in predicting the individual impact of the mutations. When considering the experimental variation, the alchemical free energy method was able to produce a relatively accurate prediction for N501Y, the mutant that has previously been reported to increase the binding affinity to hACE2. On the other hand, the other individual mutations seem not to have a significant effect on the spike RBD binding affinity towards hACE2.

Identification of Activating Mutations in the Transmembrane and Extracellular Domains of EGFR

The epidermal growth factor receptor (EGFR) is frequently mutated in human cancer, most notably non-small-cell lung cancer and glioblastoma. While many frequently occurring EGFR mutations are known to confer constitutive EGFR activation, the situation is less clear for rarely detected variants. In fact, more than 1000 distinct EGFR mutations are listed in the Catalogue of Somatic Mutations in Cancer (COSMIC), but for most of them, the functional consequence is unknown. To identify additional, previously unknown activating mutations in EGFR, we screened a randomly mutated EGFR library for constitutive EGFR phosphorylation using a recently developed high-throughput approach termed PhosphoFlowSeq. Enrichment of the well-known activating mutations S768I, T790M, and L858R validated the experimental approach. Importantly, we also identified the activating mutations S442I and L658Q located in the extracellular and transmembrane domains of EGFR, respectively. To the best of our knowledge, neither S442I nor L658Q has been associated with an activating phenotype before. However, both have been detected in cancer samples. Interestingly, molecular dynamics (MD) simulations suggest that the L658Q mutation located in the hydrophobic transmembrane region forms intermolecular hydrogen bonds, thereby promoting EGFR dimerization and activation. Based on these findings, we screened the COSMIC database for additional hydrophilic mutations in the EGFR transmembrane region and indeed detected moderate constitutive activation of EGFR-G652R. Together, this study demonstrates that unbiased screening for activating mutations in EGFR not only yields well-established substitutions located in the kinase domain but also activating mutations in other regions of EGFR, including the extracellular and transmembrane domains.

Molecular basis of Toxoplasma gondii oryzalin resistance from a novel α-tubulin binding site model

Oryzalin (ORY) is a dinitroaniline derivative that inhibits the microtubule polymerization in plants and parasitic protozoa by selectively binding to the α-tubulin subunit. This herbicidal agent exhibits good antiprotozoal activity against major human parasites, such as Toxoplasma gondii (toxoplasmosis), Leishmania mexicana (leishmaniasis), and Plasmodium falciparum (malaria). Previous chemical mutagenesis assays on T. gondii α-tubulin (TgAT) have identified key mutations that lead to ORY resistance. Herein, we employed alchemical free energy methods and molecular dynamics simulations to determine if the ORY resistance mutations either decrease the TgAT's affinity of the compound or increase the protein stability. Our results here suggest that L136F and V202F mutations significantly decrease the affinity of ORY to TgAT, while T239I and V252L mutations diminish TgAT's flexibility. On the other hand, protein stability predictors determined that R243S mutation reduces TgAT stability due to the loss of its salt bridge interaction with E27. Interestingly, molecular dynamics simulations confirm that the loss of this key interaction leads to ORY binding site closure. Our study provides a better insight into the TgAT-ORY interaction, further supporting our recently proposed ORY-binding site.

Targeting the Receptor-Binding Motif of SARS-CoV-2 with D-Peptides Mimicking the ACE2 Binding Helix: Lessons for Inhibiting Omicron and Future Variants of Concern

The COVID-19 pandemic continues to spread around the world, with several new variants emerging, particularly those of concern (VOCs). Omicron (B.1.1.529), a recent VOC with many mutations in the spike protein's receptor-binding domain (RBD), has attracted a great deal of scientific and public interest. We previously developed two D-peptide inhibitors for the infection of the original SARS-CoV-2 and its VOCs, alpha and beta, in vitro. Here, we demonstrated that Covid3 and Covid_extended_1 maintained their high-affinity binding (29.4-31.3 nM) to the omicron RBD. Both D-peptides blocked the omicron variant in vitro infection with IC50s of 3.13 and 5.56 μM, respectively. We predicted that Covid3 shares a larger overlapping binding region with the ACE2 binding motif than different classes of neutralizing monoclonal antibodies. We envisioned the design of D-peptide inhibitors targeting the receptor-binding motif as the most promising approach for inhibiting current and future VOCs of SARS-CoV-2, given that the ACE2 binding interface is more limited to tolerate mutations than most of the RBD's surface.

How Peptides Bind to PSD-95/Discs-Large/ZO-1 Domains

PSD-95/discs-large/ZO-1 (PDZ) domains form a large family of adaptor proteins that bind to the C-terminal tails of their binding partner proteins. Via extensive molecular dynamics simulations and alchemical free energy calculations, we characterized the binding modi of phosphorylated and unphosphorylated EQVSAV peptides and of a EQVEAV phosphate mimic to the hPTP1E PDZ2 and MAGI1 PDZ1 domains. The simulations reproduced the well-known binding characteristics such as tight coordination of the peptidic carboxyl tail and pronounced hydrogen bonding between the peptide backbone and the backbone atoms of a β-sheet in PDZ. Overall, coordination by hPTP1E PDZ2 appeared tighter than by MAGI1 PDZ1. Simulations of wild-type PDZ and arginine mutants suggest that contacts with Arg79/85 in hPTP1E/MAGI1 are more important for the EQVEAV peptide than for EQVSAV. Alchemical free energy calculations and PaCS-MD simulations could well reproduce the difference in binding free energy between unphosphorylated EQVSAV and EQVEAV peptides and the absolute binding free energy of EQVSAV. However, likely due to small force field inaccuracies, the simulations erroneously favored binding of the phosphorylated peptide instead of its unphosphorylated counterpart, which is in contrast to the experiment.

Concerted Rolling and Penetration of Peptides during Membrane Binding

Peptide binding to membranes is common and fundamental in biochemistry and biophysics and critical for applications ranging from drug delivery to the treatment of bacterial infections. However, it is largely unclear, from a theoretical point of view, what peptides of different sequences and structures share in the membrane-binding and insertion process. In this work, we analyze three prototypical membrane-binding peptides (α-helical magainin, PGLa, and β-hairpin tachyplesin) during membrane binding, using molecular details provided by Markov state modeling and microsecond-long molecular dynamics simulations. By leveraging both geometric and data-driven collective variables that capture the essential physics of the amphiphilic and cationic peptide-membrane interactions, we reveal how the slowest kinetic process of membrane binding is the dynamic rolling of the peptide from an attached to a fully bound state. These results not only add fundamental knowledge of the theory of how peptides bind to biological membranes but also open new avenues to study general peptides in more complex environments for further applications.

Addressing Intersite Coupling Unlocks Large Combinatorial Chemical Spaces for Alchemical Free Energy Methods

Alchemical free energy methods are playing a growing role in molecular design, both for computer-aided drug design of small molecules and for computational protein design. Multisite λ dynamics (MSλD) is a uniquely scalable alchemical free energy method that enables more efficient exploration of combinatorial alchemical spaces encountered in molecular design, but simulations have typically been limited to a few hundred ligands or sequences. Here, we focus on coupling between sites to enable scaling to larger alchemical spaces. We first discuss updates to the biasing potentials that facilitate MSλD sampling to include coupling terms and show that this can provide more thorough sampling of alchemical states. We then harness coupling between sites by developing a new free energy estimator based on the Potts models underlying direct coupling analysis, a method for predicting contacts from sequence coevolution, and find it yields more accurate free energies than previous estimators. The sampling requirements of the Potts model estimator scale with the square of the number of sites, a substantial improvement over the exponential scaling of the standard estimator. This opens up exploration of much larger alchemical spaces with MSλD for molecular design.

BLaDE: A Basic Lambda Dynamics Engine for GPU-Accelerated Molecular Dynamics Free Energy Calculations

There is an accelerating interest in practical applications of alchemical free energy methods to problems in protein design, constant pH simulations, and especially computer-aided drug design. In the present paper, we describe a basic lambda dynamics engine (BLaDE) that enables alchemical free energy simulations, including multisite λ dynamics (MSλD) simulations, on graphical processor units (GPUs). We find that BLaDE is 5 to 8 times faster than the current GPU implementation of MSλD-based free energy calculations in CHARMM. We also demonstrate that BLaDE running standard molecular dynamics attains a performance competitive with and sometimes exceeding that of the highly optimized OpenMM GPU code. BLaDE is available as a standalone program and through an API in CHARMM.

Computational Design of Potent D-Peptide Inhibitors of SARS-CoV-2

Blocking the association between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) is an attractive therapeutic approach to prevent the virus from entering human cells. While antibodies and other modalities have been developed to this end, d-amino acid peptides offer unique advantages, including serum stability, low immunogenicity, and low cost of production. Here, we designed potent novel D-peptide inhibitors that mimic the ACE2 α1-binding helix by searching a mirror-image version of the PDB. The two best designs bound the RBD with affinities of 29 and 31 nM and blocked the infection of Vero cells by SARS-CoV-2 with IC50 values of 5.76 and 6.56 μM, respectively. Notably, both D-peptides neutralized with a similar potency the infection of two variants of concern: B.1.1.7 and B.1.351 in vitro. These potent D-peptide inhibitors are promising lead candidates for developing SARS-CoV-2 prophylactic or therapeutic treatments.

Perturbation Free-Energy Toolkit: An Automated Alchemical Topology Builder

Free-energy calculations play an important role in the application of computational chemistry to a range of fields, including protein biochemistry, rational drug design, or materials science. Importantly, the free-energy difference is directly related to experimentally measurable quantities such as partition and adsorption coefficients, water activity, and binding affinities. Among several techniques aimed at predicting free-energy differences, perturbation approaches, involving the alchemical transformation of one molecule into another through intermediate states, stand out as rigorous methods based on statistical mechanics. However, despite the importance of free-energy calculations, the applicability of the perturbation approaches is still largely impeded by a number of challenges, including the definition of the perturbation path, i.e., alchemical changes leading to the transformation of one molecule to the other. To address this, an automatic perturbation topology builder based on a graph-matching algorithm is developed, which can identify the maximum common substructure (MCS) of two or multiple molecules and provide the perturbation topologies suitable for free-energy calculations using the GROMOS and the GROMACS simulation packages. Various MCS search options are presented leading to alternative definitions of the perturbation pathway. Moreover, perturbation topologies generated using the default multistate MCS search are used to calculate the changes in free energy between lysine and its two post-translational modifications, 3-methyllysine and acetyllysine. The pairwise free-energy calculations performed on this test system led to a cycle closure of 0.5 ± 0.3 and 0.2 ± 0.2 kJ mol-1, with GROMOS and GROMACS simulation packages, respectively. The same relative free energies between the three states are obtained by employing the enveloping distribution sampling (EDS) approach when compared to the pairwise perturbations. Importantly, this toolkit is made available online as an open-source Python package (https://github.com/drazen-petrov/SMArt).

How phosphorylation of peptides affects their interaction with 14-3-3η domains

Members of the 14-3-3 domain family have important functions as adapter domains. Via an amphipathic groove on their protein surface they typically bind to disordered C-terminals of other proteins. Importantly, binding partners of 14-3-3 domains usually contain a phosphorylated serine or threonine residue at their binding interface and possess one of three different sequence motifs. Binding of the respective unphosphorylated versions of the peptides is typically strongly disfavored. There is a wealth of structural and thermodynamic data available for the phosphorylated forms but not for the unphosphorylated forms as the binding affinities seem to be too weak to be measurable experimentally. Here, we characterized the mechanistic details that govern the preference for the binding of phosphorylated peptides to 14-3-3η domains by means of molecular dynamics (MD) simulations. We found that the phosphate group is ideally coordinated in the binding pocket whereas the respective unphosphorylated side-chain counterpart is not. Thus, the binding preference results from the tight coordination of the phosphorylated residue at the center of the binding interface. Furthermore, MD simulations of 14-3-3η dimers showed a preference for the simultaneous binding of two phosphorylated peptides in agreement with their experimentally observed cooperativity.

A strategy for proline and glycine mutations to proteins with alchemical free energy calculations

Computation of the thermodynamic consequences of protein mutations holds great promise in protein biophysics and design. Alchemical free energy methods can give improved estimates of mutational free energies, and are already widely used in calculations of relative and absolute binding free energies in small molecule design problems. In principle, alchemical methods can address any amino acid mutation with an appropriate alchemical pathway, but identifying a strategy that produces such a path for proline and glycine mutations is an ongoing challenge. Most current strategies perturb only side chain atoms, while proline and glycine mutations also alter the backbone parameters and backbone ring topology. Some strategies also perturb backbone parameters and enable glycine mutations. This work presents a strategy that enables both proline and glycine mutations and comprises two key elements: a dual backbone with restraints and scaling of bonded terms, facilitating backbone parameter changes, and a soft bond in the proline ring, enabling ring topology changes in proline mutations. These elements also have utility for core hopping and macrocycle studies in computer-aided drug design. This new strategy shows slight improvements over an alternative side chain perturbation strategy for a set T4 lysozyme mutations lacking proline and glycine, and yields good agreement with experiment for a set of T4 lysozyme proline and glycine mutations not previously studied. To our knowledge this is the first report comparing alchemical predictions of proline mutations with experiment. With this strategy in hand, alchemical methods now have access to the full palette of amino acid mutations.

Accurate absolute free energies for ligand-protein binding based on non-equilibrium approaches

The accurate calculation of the binding free energy for arbitrary ligand-protein pairs is a considerable challenge in computer-aided drug discovery. Recently, it has been demonstrated that current state-of-the-art molecular dynamics (MD) based methods are capable of making highly accurate predictions. Conventional MD-based approaches rely on the first principles of statistical mechanics and assume equilibrium sampling of the phase space. In the current work we demonstrate that accurate absolute binding free energies (ABFE) can also be obtained via theoretically rigorous non-equilibrium approaches. Our investigation of ligands binding to bromodomains and T4 lysozyme reveals that both equilibrium and non-equilibrium approaches converge to the same results. The non-equilibrium approach achieves the same level of accuracy and convergence as an equilibrium free energy perturbation (FEP) method enhanced by Hamiltonian replica exchange. We also compare uni- and bi-directional non-equilibrium approaches and demonstrate that considering the work distributions from both forward and reverse directions provides substantial accuracy gains. In summary, non-equilibrium ABFE calculations are shown to yield reliable and well-converged estimates of protein-ligand binding affinity.

Implementing and Assessing an Alchemical Method for Calculating Protein-Protein Binding Free Energy

Protein-protein binding is fundamental to most biological processes. It is important to be able to use computation to accurately estimate the change in protein-protein binding free energy due to mutations in order to answer biological questions that would be experimentally challenging, laborious, or time-consuming. Although nonrigorous free-energy methods are faster, rigorous alchemical molecular dynamics-based methods are considerably more accurate and are becoming more feasible with the advancement of computer hardware and molecular simulation software. Even with sufficient computational resources, there are still major challenges to using alchemical free-energy methods for protein-protein complexes, such as generating hybrid structures and topologies, maintaining a neutral net charge of the system when there is a charge-changing mutation, and setting up the simulation. In the current study, we have used the pmx package to generate hybrid structures and topologies, and a double-system/single-box approach to maintain the net charge of the system. To test the approach, we predicted relative binding affinities for two protein-protein complexes using a nonequilibrium alchemical method based on the Crooks fluctuation theorem and compared the results with experimental values. The method correctly identified stabilizing from destabilizing mutations for a small protein-protein complex, and a larger, more challenging antibody complex. Strong correlations were obtained between predicted and experimental relative binding affinities for both protein-protein systems.

A Benchmark of Electrostatic Method Performance in Relative Binding Free Energy Calculations

Relative free energy calculations are fast becoming a critical part of early stage pharmaceutical design, making it important to know how to obtain the best performance with these calculations in applications that could span hundreds of calculations and molecules. In this work, we compared two different treatments of long-range electrostatics, Particle Mesh Ewald (PME) and Reaction Field (RF), in relative binding free energy calculations using a nonequilibrium switching protocol. We found simulations using RF achieve comparable results to those using PME but gain more efficiency when using CPU and similar performance using GPU. The results from this work encourage more use of RF in molecular simulations.

Redox Potentials of Disulfide Bonds in LOXL2 Studied by Nonequilibrium Alchemical Simulation

Lysyl oxidase-like 2 (LOXL2) is a metalloenzyme that catalyzes the oxidative deamination ε-amino group of lysine. It is found that LOXL2 is a promotor for the metastasis and invasion of cancer cells. Disulfide bonds are important components in LOXL2, and they play a stabilizing role for protein structure or a functional role for regulating protein bioactivity. The redox potential of disulfide bond is one important property to determine the functional role of disulfide bond. In this study, we have calculated the reduction potential of all the disulfide bonds in LOXL2 by non-equilibrium alchemical simulations. Our results show that seven of seventeen disulfide bonds have high redox potentials between -182 and -298 mV and could have a functional role, viz., Cys573-Cys625, Cys579-Cys695, Cys657-Cys673, and Cys663-Cys685 in the catalytic domain, Cys351-Cys414, Cys464-Cys530, and Cys477-Cys543 in the scavenger receptor cysteine-rich (SRCR) domains. The disulfide bond of Cys351-Cys414 is predicted to play an allosteric function role, which could affect the metastasis and invasion of cancer cells. Other functional bonds have a catalytic role related to enzyme activity. The rest of disulfide bonds are predicted to play a structural role. Our study provides an important insight for the classification of disulfide bonds in LOXL2 and can be utilized for the drug design that targets the cysteine residues in LOXL2.

From adaptive resolution to molecular dynamics of open systems

ABSTRACT

We provide an overview of the Adaptive Resolution Simulation method (AdResS) based on discussing its basic principles and presenting its current numerical and theoretical developments. Examples of applications to systems of interest to soft matter, chemical physics, and condensed matter illustrate the method's advantages and limitations in its practical use and thus settle the challenge for further future numerical and theoretical developments.

Somatic mutational landscapes of adherens junctions and their functional consequences in cutaneous melanoma development

Cell-cell interaction in skin homeostasis is tightly controlled by adherens junctions (AJs). Alterations in such regulation lead to melanoma development. However, mutations in AJs and their functional consequences are still largely unknown.

Methods: Cadherin mutations in skin cutaneous melanoma were identified using sequencing data from TCGA dataset, followed by cross-validation with data from non-TCGA cohorts. Mutations with significant occurrence were subjected to structural prediction using MODELLER and functional protein simulation using GROMACS software. Neo-antigen prediction was carried out using NetMHCpan tool. Cell-based fluorescence reporter assay was used to validate β-catenin activity in the presence of cadherin mutations. Clinical significance was analyzed using datasets from TCGA and other non-TCGA cohorts. Targeted gene exon sequencing and immunofluorescence staining on melanoma tissues were performed to confirm the in silico findings.

Results: Highly frequent mutations in type-II classical cadherins were found in melanoma with one unique recurrent mutation (S524L) in the fifth domain of CDH6, which potentially destabilizes Ca²⁺-binding and cell-cell contacts. Mutational co-occurrence and physical dynamics analyses placed CDH6 at the center of the top-four mutated cadherins (core CDHs; all type-II), suggesting altered heterophilic interactions in melanoma development. Mutations in the intracellular domains significantly disturbed CDH6/β-catenin complex formation, resulting in β-catenin translocation into cytosol or nucleus and dysregulation of canonical Wnt/β-catenin signaling. Although mutations in core CDH genes correlated with advanced cancer stages and lymph node invasion, the overall and disease-free survival times in those patients were longer in patients with wild-type. Peptide/MHC-I binding affinity predictions confirmed overall increased neo-antigen potentials of mutated cadherins, which associated with T-lymphocyte infiltration and better clinical outcomes after immunotherapy.

Conclusion: Changes in cell-cell communications by somatic mutations in AJ cadherins function as one of mechanisms to trigger melanoma development. Certain mutations in AJs may serve as potential neo-antigens which conversely benefit patients for longer survival times.

KRAS K104 modification affects the KRASG12D-GEF interaction and mediates cell growth and motility

Mutant RAS genes play an important role in regulating tumors through lysine residue 104 to impair GEF-induced nucleotide exchange, but the regulatory role of KRAS K104 modification on the KRAS^G12D mutant remains unclear. Therefore, we simulated the acetylation site on the KRAS^G12D three-dimensional protein structure, including KRAS^G12D, KRAS^G12D/K104A and KRAS^G12D/K104Q, and determined their trajectories and binding free energy with GEF. KRAS^G12D/K104Q induced structural changes in the α2- and α3-helices, promoted KRAS instability and hampered GEF binding (ΔΔG = 6.14 kJ/mol). We found decreased binding to the Raf1 RBD by KRAS^G12D/K104Q and reduced cell growth, invasion and migration. Based on whole-genome cDNA microarray analysis, KRAS^G12D/K104Q decreased expression of NPIPA2, DUSP1 and IL6 in lung and ovarian cancer cells. This study reports computational and experimental analyses of Lys104 of KRAS^G12D and GEF, and the findings provide a target for exploration for future treatment.

Molecular modelling of sorption processes of a range of diverse small organic molecules in Leonardite humic acid

Soil organic matter (SOM) is abundant in the environment and plays an important role in several biogeochemical processes, including microbial activity, soil aggregation, plant growth and carbon storage. One of its key functions is the retention and release of various chemical compounds, primarily governed by the sorption process, which strongly affects the environmental fate of nutrients and pollutants. Sorption largely depends on the composition of SOM, as well as its structure, dynamics and the thermodynamic conditions. Although several approaches are available, experimental characterization of sorption mechanisms is not easy. Computational models for predicting sorption coefficients often require a wealth of experimental data for training and are only applicable to compounds and conditions related to the training dataset. Here, we use molecular dynamics (MD) simulations to study the sorption of a range of small organic compounds. As a model SOM system we use the standard Leonardite humic acid (LHA) sample, which physicochemical properties have recently been characterized computationally in detail. This model allowed us to estimate sorption propensities of the systems at two different hydration levels (water activities close to 0 and 1), showing a remarkable correlation with experimental data. Importantly, this molecular modelling approach based on perturbation free-energy calculations is rigorously derived from statistical thermodynamics and requires no experimental sorption data for training. It is therefore in principle applicable to any SOM model or thermodynamic condition. Moreover, the power of MD simulations to provide high-resolution insight into atomistic and molecular interactions was employed to explore how sorbate molecules associate with the LHA matrix and which contacts they form. The heteroatoms of both sorbate and sorbent play an important role and water molecules are identified as further key players in facilitating the sorption process.

HIGHLIGHTS

Modelling of the sorption processes in soil organic matter at atomistic level.Rigorous, physics-based approach applicable to a range of SOM systems and conditions.Remarkable level of matching with experimental data with additional insight into the molecular mechanism.Interactions between the sorbate and local environment strongly affects the sorption process.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

Efficient Estimation of Absolute Binding Free Energy for a Homeodomain-DNA Complex from Nonequilibrium Pulling Simulations

Estimation of binding free energies is one of the central aims of simulations of biomolecular complexes. We explore the accuracy and efficiency of setups based on nonequilibrium pulling simulations applied to the estimation of binding affinities of DNA-binding proteins. Absolute binding free energies are calculated over a range of temperatures and compared to results obtained previously using an equilibrium method. We show that realistic binding affinities can be obtained with the presented nonequilibrium approach, which also entails lower computational requirements. Errors of the binding free energy estimates are investigated and are shown to be comparable to those observed previously. Bounds are provided on the convergence of the errors with respect to the number of pulling simulations performed and with respect to the applied pull rate.

Effects of Mutating Trp42 Residue on γD-Crystallin Stability

Oligomerization and aggregation of γD-crystallins (HγDC) in the eye lens is one of the main causes of cataract development. To date, several congenital mutations related to this protein are known to promote the formation of aggregates. Previous studies have demonstrated that mutations in W42 residue of HγDC lead to the generation of partially unfolded intermediates that are more prone to aggregate. To understand the role of W42 in the stability of HγDC, we performed alchemical free-energy calculations and all-atom molecular dynamics simulations of different W42 mutant models. Our results suggest that substitution of W42 by small size and/or polar residues promotes HγDC denaturation due to the entry of water molecules into the hydrophobic core of the N-terminal domain. Similar behavior was observed in the C-terminal domain of HγDC when mutating the W130 residue located in a homologous position. Moreover, the exposure of the hydrophobic core residues could lead to the formation of aggregation-prone partially unfolded species. Overall, this study takes a step toward understanding the role of HγDC in cataract development.

Thermostability improvement of Aspergillus awamori glucoamylase via directed evolution of its gene located on episomal expression vector in Pichia pastoris cells

Novel thermostable variants of glucoamylase (GA) from filamentous fungus Aspergillus awamori X100 were constructed using the directed evolution approach based on random mutagenesis by error-prone PCR of the catalytic domain region of glucoamylase gene located on a new episomal expression vector pPEHα in Pichia pastoris cells. Out of 3000 yeast transformants screened, six new thermostable GA variants with amino acid substitutions Val301Asp, Thr390Ala, Thr390Ala/Ser436Pro, Leu7Met/His391Tyr, Asn9His/Ile82Phe and Ser8Arg/Gln338Leu were identified and studied. To estimate the effect of each substitution in the double mutants, we have constructed the relevant single mutants of GA by site-directed mutagenesis and analyzed their thermal properties. Results of the analysis showed that only Ile82Phe and Ser8Arg substitutions by themselves increased enzyme thermostability. While the substitutions Leu7Met, Asn9His and Gln338Leu decreased the thermal stability of GA, the synergistic effect of double mutant variants Leu7Met/His391Tyr, Asn9His/Ile82Phe and Ser8Arg/Gln338Leu resulted in significant thermostability improvement as compared to the wild type GA. Thr390Ala and Thr390Ala/Ser436Pro mutant variants revealed the highest thermostability with free activation energy changes ΔΔG of 2.99 and 3.1 kJ/mol at 80°C, respectively.

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol-1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol-1. For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software.

Steering a solute between coexisting solvation states: Revisiting nonequilibrium work relations and the calculation of free energy differences

By analogy with single-molecule pulling experiments, we present a computational framework to obtain free energy differences between complex solvation states. To illustrate our approach, we focus on the calculation of solvation free energies (SFEs). However, the method can be readily extended to cases involving more complex solutes and solvation conditions as well as to the calculation of binding free energies. The main idea is to drag the solute across the simulation box where atomistic and ideal gas representations of the solvent coexist at constant temperature and chemical potential. At finite pulling speeds, the resulting work allows one to extract SFEs via nonequilibrium relations, whereas at infinitely slow pulling speeds, this process becomes equivalent to the thermodynamic integration method. Results for small molecules well agree with literature data and pave the way to systematic studies of arbitrarily large and complex molecules.

Allosteric gate modulation confers K+ coupling in glutamate transporters

Excitatory amino acid transporters (EAATs) mediate glial and neuronal glutamate uptake to terminate synaptic transmission and to ensure low resting glutamate concentrations. Effective glutamate uptake is achieved by cotransport with 3 Na⁺ and 1 H⁺, in exchange with 1 K⁺. The underlying principles of this complex transport stoichiometry remain poorly understood. We use molecular dynamics simulations and electrophysiological experiments to elucidate how mammalian EAATs harness K⁺ gradients, unlike their K⁺‐independent prokaryotic homologues. Glutamate transport is achieved via elevator‐like translocation of the transport domain. In EAATs, glutamate‐free re‐translocation is prevented by an external gate remaining open until K⁺ binding closes and locks the gate. Prokaryotic Glt_Ph contains the same K⁺‐binding site, but the gate can close without K⁺. Our study provides a comprehensive description of K⁺‐dependent glutamate transport and reveals a hitherto unknown allosteric coupling mechanism that permits adaptions of the transport stoichiometry without affecting ion or substrate binding.

Thermodynamic Stability of Human γD-Crystallin Mutants Using Alchemical Free-Energy Calculations

γD-Crystallin (HγDC) is a key structural protein in the human lens, whose aggregation has been associated with the development of cataracts. Single-point mutations and post-translational modifications destabilize HγDC interactions, forming partially folded intermediates, where hydrophobic residues are exposed and thus triggering its aggregation. In this work, we used alchemical free-energy calculations to predict changes in thermodynamic stability (ΔΔG) of 10 alanine-scanning variants and 12 HγDC mutations associated with the development of congenital cataract. Our results show that W42R is the most destabilizing mutation in HγDC. This has been corroborated through experimental determination of ΔΔG employing differential scanning calorimetry. Calculations of hydration free energies from the HγDC WT and the W42R mutant suggested that the mutant has a higher aggregation propensity. Our combined theoretical and experimental results contribute to understand HγDC destabilization and aggregation mechanisms in age-onset cataracts.

Accurate Calculation of Free Energy Changes upon Amino Acid Mutation

Molecular dynamics based free energy calculations allow for a robust and accurate evaluation of free energy changes upon amino acid mutation in proteins. In this chapter we cover the basic theoretical concepts important for the use of calculations utilizing the non-equilibrium alchemical switching methodology. We further provide a detailed step-by-step protocol for estimating the effect of a single amino acid mutation on protein thermostability. In addition, the potential caveats and solutions to some frequently encountered issues concerning the non-equilibrium alchemical free energy calculations are discussed. The protocol comprises details for the hybrid structure/topology generation required for alchemical transitions, equilibrium simulation setup, and description of the fast non-equilibrium switching. Subsequently, the analysis of the obtained results is described. The steps in the protocol are complemented with an illustrative practical application: a destabilizing mutation in the Trp cage mini protein. The concepts that are described are generally applicable. The shown example makes use of the pmx software package for the free energy calculations using Gromacs as a molecular dynamics engine. Finally, we discuss how the current protocol can readily be adapted to carry out charge-changing or multiple mutations at once, as well as large-scale mutational scans.

Studying Functional Disulphide Bonds by Computer Simulations

Biochemical and structural data reveal important aspects of the properties and function of a protein disulphide bond. Molecular dynamics simulations can complement this experimental data and can yield valuable insights into the dynamical behavior of the disulphide bond within the protein environment. Due to the increasing accuracy of the underlying energetic description and the increasing computational power at hand, such simulations have now reached a level, at which they can also make quantitative and experimentally testable predictions. We here give an overview of the computational methods used to predict functional aspects of protein disulphides, including the prestress, protein allosteric effects upon thiol/disulphide exchange, and disulphide redox potentials. We then outline in detail the use of free-energy perturbation methods to calculate the redox potential of a protein disulphide bond of interest. In a step-by-step protocol, we describe the workflow within the MD suite Gromacs, including practical advice on the simulation setup and choice of parameters. For other disulphide-related simulation methods, we refer to resources available online.

Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study

The RNA recognition motif (RRM) is the most common RNA binding domain across eukaryotic proteins. It is therefore of great value to engineer its specificity to target RNAs of arbitrary sequence. This was recently achieved for the RRM in Rbfox protein, where four mutations R118D, E147R, N151S, and E152T were designed to target the precursor to the oncogenic miRNA 21. Here, we used a variety of molecular dynamics-based approaches to predict specific interactions at the binding interface. Overall, we have run approximately 50 microseconds of enhanced sampling and plain molecular dynamics simulations on the engineered complex as well as on the wild-type Rbfox·pre-miRNA 20b from which the mutated systems were designed. Comparison with the available NMR data on the wild type molecules (protein, RNA, and their complex) served to establish the accuracy of the calculations.

Free energy calculations suggest that further improvements in affinity and selectivity are achieved by the S151T replacement.

RNA is an outstanding target for oncological intervention. Engineering the most common RNA binding motif in human proteins (called RRM) so as to bind to a specific RNA has an enormous pharmacological potential. Yet, it is highly non trivial to design RRM-bearing protein variants with RNA selectivity and affinity sufficiently high for clinical applications. Here we present an extensive molecular simulation study which shed light on the exquisite molecular recognition of the empirically-engineered complex between the RRM-bearing protein Rbfox and its RNA target pre-miR21. The simulations allow predicting a variant, the S151T, which may lead to further enhancement of selectivity and affinity for pre-miR21.

Consistent Prediction of Mutation Effect on Drug Binding in HIV-1 Protease Using Alchemical Calculations

Despite a large number of antiretroviral drugs targeting HIV-1 protease for inhibition, mutations in this protein during the course of patient treatment can render them inefficient. This emerging resistance inspired numerous computational studies of the HIV-1 protease aimed at predicting the effect of mutations on drug binding in terms of free binding energy Δ G, as well as in mechanistic terms. In this study, we analyze ten different protease-inhibitor complexes carrying major resistance-associated mutations (RAMs) G48V, I50V, and L90M using molecular dynamics simulations. We demonstrate that alchemical free energy calculations can consistently predict the effect of mutations on drug binding. By explicitly probing different protonation states of the catalytic aspartic dyad, we reveal the importance of the correct choice of protonation state for the accuracy of the result. We also provide insight into how different mutations affect drug binding in their specific ways, with the unifying theme of how all of them affect the crucial drug binding regions of the protease.

Making sense of the past: hyperstability of ancestral thioredoxins explained by free energy simulations

Thioredoxin (Trx), a small and globular protein, is present in all kinds of organisms, from Archea to higher mammals. Throughout evolution, the Trx sequence has undergone subtle modifications to adapt to varying environmental conditions. The high degree of sequence conservation makes Trx very amenable to ancestral protein reconstruction techniques. In this work, we address the study of the structural and energetic determinants of thermostability in E. coli Trx using a dataset of mutations inspired by ancestral reconstruction. We compute, from first principles, the expected contribution of 19 different amino acid substitutions to the stability (ΔΔG) and the melting temperature (ΔT_m) of the protein. We also describe the specific changes in structure and protein dynamics responsible for the stabilizing or destabilizing effects of these mutations. Our results point to local and independent changes for most of the variants. Our predictions are accurate enough to substantiate the proposal of new hypotheses regarding evolutionary relationships between mutations, as in the case of T89R, P68A and G74S or K90L and F102A, and reach beyond the initial set to suggest improved variants, such as K90I or K90Y.

Computational Analysis of the Molecular Mechanism of RamR Mutations Contributing to Antimicrobial Resistance in Salmonella enterica

Antimicrobial resistance (AMR) in pathogenic microorganisms with multidrug resistance (MDR) constitutes a severe threat to human health. A major causative mechanism of AMR is mediated through the multidrug efflux pump (MEP). The resistance-nodulation-division superfamily (RND family) of Gram-negative bacteria is usually the major cause of MDR in clinical studies. In Salmonella enterica, the RND pump is translated from the acrAB gene, which is regulated by the activator RamA. Many MEP-caused AMR strains have high ramA gene expression due to mutations in RamR, which has a homodimeric structure comprising the dimerization domain and DNA-binding domain (DBD). Three mutations on the dimerization domain, namely Y59H, M84I, and E160D, are far from the DBD; the molecular mechanism through which they influence RamR’s binding affinity to the ramA gene promoter and consequently disrupt RamA remains unclear. The present study conducted molecular dynamics simulations, binding free energy calculations, and normal mode analysis to investigate the mechanism through which Y59H, M84I, and E160D mutations on the dimerization domain influence the binding affinity of RamR to the ramA promoter. The present results suggest that the three mutations alter the RamR structure, resulting in decreased DNA-binding affinity.

Deficiencies in Molecular Dynamics Simulation-Based Prediction of Protein-DNA Binding Free Energy Landscapes

Transcriptional regulation allows cells to match their gene expression profiles to their current requirements based on environment, cellular physiological state, and extracellular signals. DNA binding transcription factors are major agents of transcriptional regulation, and bind to DNA with a factor-specific sequence preference to exert regulatory effects. A crucial step in unraveling the logic of a regulatory network is determining the sequence-specific binding affinity landscapes for the transcription factors in it. While such landscapes can be measured experimentally, the ability to predict them computationally would both reduce the effort required to obtain the needed data and provide additional insight into the key interactions shaping protein-DNA interactions. Here we apply free energy calculations based on all-atom molecular dynamics simulations to predict the changes in binding free energy for all single base pair perturbations of the binding sites for four eukaryotic transcription factors for which high-quality experimental data exist. We find that the simulated results both vastly overestimate the magnitude of changes in binding free energy, and frequently predict the incorrect signs. These simulations will nevertheless serve as a jumping-off point for refining our current representation of protein-DNA interactions to allow quantitative reproduction of experimental data on such systems in the future.

Molecular Dynamics Simulations of the Standard Leonardite Humic Acid: Microscopic Analysis of the Structure and Dynamics

Humic substances (HS) are abundant in the environment and play an important role in a number of biogeochemical processes including microbial activity, soil aggregation, plant growth, the retention and release of nutrients, the environmental fate of pollutants, and carbon storage. They are flexible, relatively small molecules forming supramolecular structures through weak interactions. Despite the great importance of understanding their behavior at the atomic level, computational modeling, a premier high-resolution technique providing great level of detail, has been surprisingly little-employed to study humic substances. Here, we use the recently developed Vienna Soil Organic-Matter Modeler to create representative models of a real HS sample, the standard Leonardite humic acid. Molecular dynamics simulations were used to probe the structure and dynamics of the system at a range of hydration levels. The studied systems were characterized in terms of their physicochemical properties, including density, dielectric properties, hydrogen bonding, etc. Moreover, the strength of sorption was estimated for three small organic compounds: benzaldehyde, propan-2-ol, and acetone. Strikingly, the HS models were validated against experimental data showing a remarkable agreement with calculated properties. Finally, we make the equilibrated models of the standard Leonardite humic acid, together with corresponding force-field parameters, available at the Vienna Soil Organic-Matter Modeler.

Mutation G1629E Increases von Willebrand Factor Cleavage via a Cooperative Destabilization Mechanism

The large multimeric glycoprotein von Willebrand Factor (VWF) plays a pivotal adhesive role during primary hemostasis. VWF is cleaved by the protease ADAMTS13 as a down-regulatory mechanism to prevent excessive VWF-mediated platelet aggregation. For each VWF monomer, the ADAMTS13 cleavage site is located deeply buried inside the VWF A2 domain. External forces in vivo or denaturants in vitro trigger the unfolding of this domain, thereby leaving the cleavage site solvent-exposed and ready for cleavage. Mutations in the VWF A2 domain, facilitating the cleavage process, cause a distinct form of von Willebrand disease (VWD), VWD type 2A. In particular, the VWD type 2A Gly1629Glu mutation drastically accelerates the proteolytic cleavage activity, even in the absence of forces or denaturants. However, the effect of this mutation has not yet been quantified, in terms of kinetics or thermodynamics, nor has the underlying molecular mechanism been revealed. In this study, we addressed these questions by using fluorescence correlation spectroscopy, molecular dynamics simulations, and free energy calculations. The measured enzyme kinetics revealed a 20-fold increase in the cleavage rate for the Gly1629Glu mutant compared with the wild-type VWF. Cleavage was found cooperative with a cooperativity coefficient n = 2.3, suggesting that the mutant VWF gives access to multiple cleavage sites of the VWF multimer at the same time. According to our simulations and free energy calculations, the Gly1629Glu mutation causes structural perturbation in the A2 domain and thereby destabilizes the domain by ∼10 kJ/mol, promoting its unfolding. Taken together, the enhanced proteolytic activity of Gly1629Glu can be readily explained by an increased availability of the ADAMTS13 cleavage site through A2-domain-fold thermodynamic destabilization. Our study puts forward the Gly1629Glu mutant as a very efficient enzyme substrate for ADAMTS13 activity assays.

Microscopic Characterization of Membrane Transporter Function by In Silico Modeling and Simulation

Membrane transporters mediate one of the most fundamental processes in biology. They are the main gatekeepers controlling active traffic of materials in a highly selective and regulated manner between different cellular compartments demarcated by biological membranes. At the heart of the mechanism of membrane transporters lie protein conformational changes of diverse forms and magnitudes, which closely mediate critical aspects of the transport process, most importantly the coordinated motions of remotely located gating elements and their tight coupling to chemical processes such as binding, unbinding and translocation of transported substrate and cotransported ions, ATP binding and hydrolysis, and other molecular events fueling uphill transport of the cargo. An increasing number of functional studies have established the active participation of lipids and other components of biological membranes in the function of transporters and other membrane proteins, often acting as major signaling and regulating elements. Understanding the mechanistic details of these molecular processes require methods that offer high spatial and temporal resolutions. Computational modeling and simulations technologies empowered by advanced sampling and free energy calculations have reached a sufficiently mature state to become an indispensable component of mechanistic studies of membrane transporters in their natural environment of the membrane. In this article, we provide an overview of a number of major computational protocols and techniques commonly used in membrane transporter modeling and simulation studies. The article also includes practical hints on effective use of these methods, critical perspectives on their strengths and weak points, and examples of their successful applications to membrane transporters, selected from the research performed in our own laboratory.

II. Dissociation free energies in drug–receptor systems via nonequilibrium alchemical simulations: application to the FK506-related immunophilin ligands

The fast switch double annihilation method (FS-DAM) provides an effective mean to the compute the binding free energies in drug-receptor systems. Here we present an application to the FK506-related ligands of the FKBP12 protein.