Molecular surface generation using a variable-radius solvent probe

Unveiling Allosteric Regulation and Binding Mechanism of BRD9 through Molecular Dynamics Simulations and Markov Modeling

Bromodomain-containing protein 9 (BRD9) is a key player in chromatin remodeling and gene expression regulation, and it is closely associated with the development of various diseases, including cancers. Recent studies have indicated that inhibition of BRD9 may have potential value in the treatment of certain cancers. Molecular dynamics (MD) simulations, Markov modeling and principal component analysis were performed to investigate the binding mechanisms of allosteric inhibitor POJ and orthosteric inhibitor 82I to BRD9 and its allosteric regulation. Our results indicate that binding of these two types of inhibitors induces significant structural changes in the protein, particularly in the formation and dissolution of α-helical regions. Markov flux analysis reveals notable changes occurring in the α-helicity near the ZA loop during the inhibitor binding process. Calculations of binding free energies reveal that the cooperation of orthosteric and allosteric inhibitors affects binding ability of inhibitors to BRD9 and modifies the active sites of orthosteric and allosteric positions. This research is expected to provide new insights into the inhibitory mechanism of 82I and POJ on BRD9 and offers a theoretical foundation for development of cancer treatment strategies targeting BRD9.

Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning

Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and binding free energy calculations were combined together to probe the binding mechanism of non-peptide inhibitors K23 and 0Y7 and peptide ones PDI6W and PDI to MDM2. The GaMD trajectory-based DL approach successfully identified significant functional domains, predominantly located at the helixes α2 and α2', as well as the β-strands and loops between α2 and α2'. The post-processing analysis of the GaMD simulations indicated that inhibitor binding highly influences the structural flexibility and collective motions of MDM2. Calculations of molecular mechanics-generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) not only suggest that the ranking of the calculated binding free energies is in agreement with that of the experimental results, but also verify that van der Walls interactions are the primary forces responsible for inhibitor-MDM2 binding. Our findings also indicate that peptide inhibitors yield more interaction contacts with MDM2 compared to non-peptide inhibitors. Principal component analysis (PCA) and free energy landscape (FEL) analysis indicated that the piperidinone inhibitor 0Y7 shows the most pronounced impact on the free energy profiles of MDM2, with the piperidinone inhibitor demonstrating higher fluctuation amplitudes along primary eigenvectors. The hot spots of MDM2 revealed by residue-based free energy estimation provide target sites for drug design toward MDM2. This study is expected to provide useful theoretical aid for the development of selective inhibitors of MDM2 family members.

S288T mutation altering MmpL3 periplasmic domain channel and H-bond network: a novel dual drug resistance mechanism

CONTEXT

Mycobacterial membrane proteins Large 3 (MmpL3) is responsible for the transport of mycobacterial acids out of cell membrane to form cell wall, which is essential for the survival of Mycobacterium tuberculosis (Mtb) and has become a potent anti-tuberculosis target. SQ109 is an ethambutol (EMB) analogue, as a novel anti-tuberculosis drug, can effectively inhibit MmpL3, and has completed phase 2b-3 clinical trials. Drug resistance has always been the bottleneck problem in clinical treatment of tuberculosis. The S288T mutant of MmpL3 shows significant resistance to the inhibitor SQ109, while the specific action mechanism remains unclear. The results show that MmpL3 S288T mutation causes local conformational change with little effect on the global structure. With MmpL3 bound by SQ109 inhibitor, the distance between D710 and R715 increases resulting in H-bond destruction, but their interactions and proton transfer function are still restored. In addition, the rotation of Y44 in the S288T mutant leads to an obvious bend in the periplasmic domain channel and an increased number of contact residues, reducing substrate transport efficiency. This work not only provides a possible dual drug resistance mechanism of MmpL3 S288T mutant but also aids the development of novel anti-tuberculosis inhibitors.

METHODS

In this work, molecular dynamics (MD) and quantum mechanics (QM) simulations both were performed to compare inhibitor (i.e., SQ109) recognition, motion characteristics, and H-bond energy change of MmpL3 after S288T mutation. In addition, the WT_SQ109 complex structure was obtained by molecular docking program (Autodock 4.2); Molecular Mechanics/ Poisson Boltzmann Surface Area (MM-PBSA) and Solvated Interaction Energy (SIE) methods were used to calculate the binding free energies (∆Gbind); Geometric criteria were used to analyze the changes of hydrogen bond networks.

Identification Mechanism of BACE1 on Inhibitors Probed by Using Multiple Separate Molecular Dynamics Simulations and Comparative Calculations of Binding Free Energies

β-amyloid cleaving enzyme 1 (BACE1) is regarded as an important target of drug design toward the treatment of Alzheimer's disease (AD). In this study, three separate molecular dynamics (MD) simulations and calculations of binding free energies were carried out to comparatively determine the identification mechanism of BACE1 for three inhibitors, 60W, 954 and 60X. The analyses of MD trajectories indicated that the presence of three inhibitors influences the structural stability, flexibility and internal dynamics of BACE1. Binding free energies calculated by using solvated interaction energy (SIE) and molecular mechanics generalized Born surface area (MM-GBSA) methods reveal that the hydrophobic interactions provide decisive forces for inhibitor-BACE1 binding. The calculations of residue-based free energy decomposition suggest that the sidechains of residues L91, D93, S96, V130, Q134, W137, F169 and I179 play key roles in inhibitor-BACE1 binding, which provides a direction for future drug design toward the treatment of AD.

Macromolecular crowding impact on anti-CRISPR AcrIIC3/NmeCas9 complex: Insights from scaled particle theory, molecular dynamics, and elastic networks models

The coupling of Cas9 and its inhibitor AcrIIC3, both from the bacterium Neisseria meningitidis (Nme), form a homodimer of the (NmeCas9/AcrIIC3)₂ type. This coupling was studied to assess the impact of their interaction with the crowders in the following environments: (1) homogeneous crowded, (2) heterogeneous, and (3) microheterogeneous cytoplasmic. For this, statistical thermodynamic models based on the scaled particle theory (SPT) were used, considering the attractive and repulsive protein-crowders contributions and the stability of the formation of spherocylindrical homodimers and the effects of changes in the size of spherical dimers were estimated. Studies based on models of dynamics, elastic networks, and statistical potentials to the formation of complexes NmeCas9/AcrIIC3 using PEG as the crowding agent support the predictions from SPT. Macromolecular crowding stabilizes the formation of the dimers, being more significant when the attractive protein-crowder interactions are weaker and the crowders are smaller. The coupling is favored towards the formation of spherical and compact dimers due to crowding addition (excluded-volume effects) and the thermodynamic stability of the dimers is markedly dependent on the size of the crowders. These results support the experimental mechanistic proposal of inhibition of NmeCas9 mediated by AcrIIC3.

A data-driven and topological mapping approach for the a priori prediction of stable molecular crystalline hydrates

Predictions of the structures of stoichiometric, fractional, or nonstoichiometric hydrates of organic molecular crystals are immensely challenging due to the extensive search space of different water contents, host molecular placements throughout the crystal, and internal molecular conformations. However, the dry frameworks of these hydrates, especially for nonstoichiometric or isostructural dehydrates, can often be predicted from a standard anhydrous crystal structure prediction (CSP) protocol. Inspired by developments in the field of drug binding, we introduce an efficient data-driven and topologically aware approach for predicting organic molecular crystal hydrate structures through a mapping of water positions within the crystal structure. The method does not require a priori specification of water content and can, therefore, predict stoichiometric, fractional, and nonstoichiometric hydrate structures. This approach, which we term a mapping approach for crystal hydrates (MACH), establishes a set of rules for systematic determination of favorable positions for water insertion within predicted or experimental crystal structures based on considerations of the chemical features of local environments and void regions. The proposed approach is tested on hydrates of three pharmaceutically relevant compounds that exhibit diverse crystal packing motifs and void environments characteristic of hydrate structures. Overall, we show that our mapping approach introduces an advance in the efficient performance of hydrate CSP through generation of stable hydrate stoichiometries at low cost and should be considered an integral component for CSP workflows.

Guest-occupiable space in the crystalline solid state: a simple rule-of-thumb for predicting occupancy

The generally greater degree of thermal motion of guest molecule(s) relative to the host often impedes their accurate modelling in crystal structures. We propose a 'rule-of-thumb' for estimating the maximum number of guest molecules that can be accommodated in a given amount of accessible space in an adequately modelled host structure. A survey of the Cambridge Structural Database was carried out to evaluate the fractional occupancy θ of the accessible space for almost 40 000 solvates involving 20 common solvents. Using widely accessible software tools, the volume of a guest is estimated as its van der Waals surface, while the guest-occupiable space of a potentially porous host is determined as that available to a virtual spherical probe. We propose terminology more appropriate to the supramolecular interpretation of surface typology: the probe-traversable and probe-accessible boundaries as traced out by the locus and surface of a spherical probe, respectively. High-throughput analysis using commercial and free software packages yielded a mean θ = 51.1(4)%, ranging from 45.3(6)% for hexane to 60(1)% for acetic acid.

Cyclic Peptide Inhibitors of the Tsg101 UEV Protein Interactions Refined through Global Docking and Gaussian Accelerated Molecular Dynamics Simulations

Tsg101 UEV domain proteins are potential targets for virus infection therapy, especially for HIV and Ebola viruses. Peptides are key in curbing virus transmission, and cyclic peptides have a greater survival time than their linear peptides. To date, the accurate prediction of cyclic peptide-protein receptors binding conformations still is challenging because of high peptide flexibility. Here, a useful approach combined the global peptide docking, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF), normal molecular dynamics (cMD), and solvated interaction energy (SIE) techniques. Then we used this approach to investigate the binding conformations of UEV domain proteins with three cyclic peptides inhibitors. We reported the possible cyclic peptide-UEV domain protein binding conformations via 2D PMF free energy profiles and SIE free energy calculations. The residues Trp145, Tyr147, and Trp148 of the native cyclic peptide (CP1) indeed play essential roles in the cyclic peptides-UEV domain proteins interactions. Our findings might increase the accuracy of cyclic peptide-protein conformational prediction, which may facilitate cyclic peptide inhibitor design. Our approach is expected to further aid in addressing the challenges in cyclic peptide inhibitor design.

Gene Cloning, Characterization, and Molecular Simulations of a Novel Recombinant Chitinase from Chitinibacter Tainanensis CT01 Appropriate for Chitin Enzymatic Hydrolysis

Chitin, a polymer of N-acetyl-d-glucosamine (GlcNAc), can be degraded by chitinase, which is produced by higher plants, vertebrates, and bacteria. Chitinases are characterized by the ability to hydrolyze the beta-1,4-linkages in the chitin chain by either an endolytic or an exolytic mechanism. Chitinase 1198 is a novel endochitinase from the genome sequence of Chitinibacter tainanensis CT01. Herein, we report the findings of molecular simulations and bioassays for chitinase 1198. Our experimental results suggest that chitinase 1198 can recognize the nonreducing end of chitin and cleave the second or third glycosidic linkage from the nonreducing end of chitin oligomers. Furthermore, our simulations results revealed that chitinase 1198 is more likely to bind chitin oligomers with the main hydrogen bonds of the Asp440, the second GlcNAc unit of chitin oligomers, and degrade chitin oligomers to (GlcNAc)₂ molecules. Moreover, chitinase 1198 is less likely to bind chitin oligomers with the main hydrogen bonds of the Asp440, the third GlcNAc unit of chitin oligomers, and degrade chitin oligomers to (GlcNAc)₃ molecules. Lastly, chitinase 1198 can bind (GlcNAc)₃ molecules with the main hydrogen bonds of the Asp440, the second GlcNAc of the (GlcNAc)₃ molecules, and degrade chitin oligomers to GlcNAc and (GlcNAc)₂ molecules.

Computational modeling of cyclic peptide inhibitor-MDM2/MDMX binding through global docking and Gaussian accelerated molecular dynamics simulations

MDM2 and MDMX are potential targets for p53-dependent cancer therapy. Peptides are key in cellular immunology and oncology, and cyclic peptides generally have higher half-life than their linear counterparts. However, prediction of cyclic peptide-protein binding is challenging with normal molecular simulation approaches because of high peptide flexibility. Here, we used global peptide docking, normal molecular dynamics, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF) profiles, and solvated interaction energy (SIE) techniques to investigate the interactions of MDM2/MDMX with three N-to-C-terminal cyclic peptide-based inhibitors. We determined the possible cyclic peptide-MDM2/MDMX complex structures via 2D PMF profiles and SIE calculations. Our findings increase the accuracy of peptide-protein structural prediction, which may facilitate cyclic peptide drug design. Advancements in the computational methods and computing power may further aid in addressing the challenges in cyclic peptide drug design. Communicated by Ramaswamy H. Sarma.

Systematic approach for wettability prediction using molecular dynamics simulations

We present a fast and efficient approach to predict the wettability and spreading of liquids on polymeric substrates. First, a molecular dynamics parameterization is proposed for the calculation of the solubility parameter for 74 compounds including surfactants typically used in inkjet printing. Then, we introduce a molecular geometrical factor to relate the solubility parameter to the surface tension, obtaining estimates in remarkable agreement with experiments. By using a modified Young-Fowkes equation, the contact angles of liquids on various polymeric substrates are determined and their dependence on the hydrogen bonding, dispersion and polar contribution of the solubility parameter are investigated. We find that wetting properties are obtained with a good accuracy when taking into account the hydrogen-bonding and polar interactions in the geometric sum of the solubility parameter. Based on these findings, a 3D wetting space is proposed to evaluate liquids wettability and judge their suitability for specific substrates. This will enable easy formulation of liquids with wettability tailored for a particular surface and application.

Improving the accuracy of predicting protein-ligand binding-free energy with semiempirical quantum chemistry charge

Aim: It is a challenge to predict binding-free energy (ΔG) accurately. Methodology/results: For accurate ΔG prediction, a new strategy combining solvated interaction energy (SIE) or molecular mechanics/generalized Born surface area (MM/GBSA) approach with the Coulson charge of both protein and ligand calculated by semiempirical quantum mechanics (SQM), named SIE-SQMPC or MM/GBSA-SQMPC approach, was developed and tested on 50 protein-ligand complexes. Both approaches achieved higher correlation (R ²) between experimental and predicted ΔG than that with Amber-ff03 charge, even for ligands with highly different scaffolds. But, SIE-SQMPC is computationally much faster than MM/GBSA-SQMPC. Conclusion: SIE-SQMPC provided an effective alternative to predict ΔG of protein-ligand binding (R ² = 0.66-0.94 for SIE-AM1; R ² = 0.59-0.98 for SIE-PM7), which has the potential of high-throughput processing for molecular docking and drug design.

Chemical Structure-Biological Activity Models for Pharmacophores' 3D-Interactions

Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding) and quantitative (for predicting) mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR) offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD) as the revived precursor for comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine congeners’ (HEPT ligands) antiviral activity against Human Immunodeficiency Virus of first type (HIV-1) and new pharmacophores in treating severe genetic disorders (like depression and psychosis), respectively, all involving 3D pharmacophore interactions.

Drug resistance mechanisms of three mutations V32I, I47V and V82I in HIV-1 protease toward inhibitors probed by molecular dynamics simulations and binding free energy predictions

Molecular dynamics simulation and binding free energy calculations were used to probe drug resistance of HIV-1 protease mutations toward inhibitors.

Catalytic mechanism of a retinoid isomerase essential for vertebrate vision

Visual function in vertebrates is dependent on the membrane-bound retinoid isomerase RPE65, an essential component of the retinoid cycle pathway that regenerates 11-cis-retinal for rod and cone opsins. The mechanism by which RPE65 catalyzes stereoselective retinoid isomerization has remained elusive because of uncertainty about how retinoids bind to its active site. Here we present crystal structures of RPE65 in complex with retinoid-mimetic compounds, one of which is in clinical trials for the treatment of age-related macular degeneration. The structures reveal the active site retinoid-binding cavity located near the membrane-interacting surface of the enzyme as well as an Fe-bound palmitate ligand positioned in an adjacent pocket. With the geometry of the RPE65-substrate complex clarified, we delineate a mechanism of catalysis that reconciles the extensive biochemical and structural research on this enzyme. These data provide molecular foundations for understanding a key process in vision and pharmacological inhibition of RPE65 with small molecules.

Molecular basis of R294K mutation effects of H7N9 neuraminidases with drugs and cyclic peptides: an in silico and experimental study

An overview of Shanghai N9/cyclic peptide I complex structure.

Insight into the modified Ibalizumab-human CD4 receptor interactions: using a computational binding free energy approach

Antibody drugs are very useful tools for the treatment of many chronic diseases. Recently, however, patients and doctors have encountered the problem of drug resistance. How to improve the affinity of antibody drugs has therefore become a pressing issue. Ibalizumab is a humanized monoclonal antibody that binds human CD4, the primary receptor for human immunodeficiency virus type 1. This study investigates the mutation residues of the complementarity determining regions of Ibalizumab. We propose using the wild and mutations of Ibalizumab-human CD4 receptor complex structures, molecular dynamics techniques, alanine-scanning mutagenesis calculations and solvated interaction energies methods to predict the binding free energy of the Ibalizumab-human CD4 receptor complex structures. This work found that revealed three key positions (31th, 32th and 33th in HCDR-1) of the residues may play an important role in Ibalizumab-human CD4 receptor complex interactions. Therefore, bioengineering substitutions of the three key positions and increasing number of intermolecular interactions (HCDR-1 of Ibalizumab/human CD4 receptor) might improve the binding affinities of this complex structure.

Binding modes of three inhibitors 8CA, F8A and I4A to A-FABP studied based on molecular dynamics simulation

Adipocyte fatty-acid binding protein (A-FABP) is an important target of drug designs treating some diseases related to lipid-mediated biology. Molecular dynamics (MD) simulations coupled with solvated interaction energy method (SIE) were carried out to study the binding modes of three inhibitors 8CA, F8A and I4A to A-FABP. The rank of our predicted binding affinities is in accordance with experimental data. The results show that the substitution in the position 5 of N-benzyl and the seven-membered ring of N-benzyl-indole carboxylic acids strengthen the I4A binding, while the substitution in the position 2 of N-benzyl weakens the F8A binding. Computational alanine scanning and dynamics analyses were performed and the results suggest that the polar interactions of the positively charged residue R126 with the three inhibitors provide a significant contribution to inhibitor bindings. This polar interaction induces the disappearance of the correlated motion of the C terminus of A-FABP relative to the N terminus and favors the stability of the binding complex. This study is helpful for the rational design of potent inhibitors within the fields of metabolic disease, inflammation and atherosclerosis.

The effect of macromolecular crowding on the electrostatic component of barnase-barstar binding: a computational, implicit solvent-based study

Macromolecular crowding within the cell can impact both protein folding and binding. Earlier models of cellular crowding focused on the excluded volume, entropic effect of crowding agents, which generally favors compact protein states. Recently, other effects of crowding have been explored, including enthalpically-related crowder–protein interactions and changes in solvation properties. In this work, we explore the effects of macromolecular crowding on the electrostatic desolvation and solvent-screened interaction components of protein–protein binding. Our simple model enables us to focus exclusively on the electrostatic effects of water depletion on protein binding due to crowding, providing us with the ability to systematically analyze and quantify these potentially intuitive effects. We use the barnase–barstar complex as a model system and randomly placed, uncharged spheres within implicit solvent to model crowding in an aqueous environment. On average, we find that the desolvation free energy penalties incurred by partners upon binding are lowered in a crowded environment and solvent-screened interactions are amplified. At a constant crowder density (fraction of total available volume occupied by crowders), this effect generally increases as the radius of model crowders decreases, but the strength and nature of this trend can depend on the water probe radius used to generate the molecular surface in the continuum model. In general, there is huge variation in desolvation penalties as a function of the random crowder positions. Results with explicit model crowders can be qualitatively similar to those using a lowered “effective” solvent dielectric to account for crowding, although the “best” effective dielectric constant will likely depend on multiple system properties. Taken together, this work systematically demonstrates, quantifies, and analyzes qualitative intuition-based insights into the effects of water depletion due to crowding on the electrostatic component of protein binding, and it provides an initial framework for future analyses.

Chondroitin sulfate proteoglycans: structure-function relationship with implication in neural development and brain disorders

Chondroitin sulfate proteoglycans (CSPGs) are extracellular matrix components that contain two structural parts with distinct functions: a protein core and glycosaminoglycan (GAG) side chains. CSPGs are known to be involved in important cell processes like cell adhesion and growth, receptor binding, or cell migration. It is recognized that the presence of CSPGs is critical in neuronal growth mechanisms including axon guidance following injury of nervous system components such as spinal cord and brain. CSPGs are upregulated in the central nervous system after injury and participate in the inhibition of axon regeneration mainly through their GAG side chains. Recently, it was shown that some CSPGs members like aggrecan, versican, and neurocan were strongly involved in brain disorders like bipolar disorder (BD), schizophrenia, and ADHD. In this paper, we present the chemical structure-biological functions relationship of CSPGs, both in health state and in genetic disorders, addressing methods represented by genome-wide and crystallographic data as well as molecular modeling and quantitative structure-activity relationship.

Exhaustive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge

We continued prospective assessments of the Wilma-solvated interaction energy (SIE) platform for pose prediction, binding affinity prediction, and virtual screening on the challenging SAMPL4 data sets including the HIV-integrase inhibitor and two host-guest systems. New features of the docking algorithm and scoring function are tested here prospectively for the first time. Wilma-SIE provides good correlations with actual binding affinities over a wide range of binding affinities that includes strong binders as in the case of SAMPL4 host-guest systems. Absolute binding affinities are also reproduced with appropriate training of the scoring function on available data sets or from comparative estimation of the change in target's vibrational entropy. Even when binding modes are known, SIE predictions lack correlation with experimental affinities within dynamic ranges below 2 kcal/mol as in the case of HIV-integrase ligands, but they correctly signaled the narrowness of the dynamic range. Using a common protein structure for all ligands can reduce the noise, while incorporating a more sophisticated solvation treatment improves absolute predictions. The HIV-integrase virtual screening data set consists of promiscuous weak binders with relatively high flexibility and thus it falls outside of the applicability domain of the Wilma-SIE docking platform. Despite these difficulties, unbiased docking around three known binding sites of the enzyme resulted in over a third of ligands being docked within 2 Å from their actual poses and over half of the ligands docked in the correct site, leading to better-than-random virtual screening results.

Allosteric noncompetitive small molecule selective inhibitors of CD45 tyrosine phosphatase suppress T-cell receptor signals and inflammation in vivo

CD45 is a receptor-like member of the protein tyrosine phosphatase (PTP) family. We screened in silico for small molecules binding at a predicted allosteric pocket unique to the CD45 intracellular domain, and validated inhibitors by in vitro phosphatase assays. Compound 211 exhibited a CD45 IC50 value of 200 nM and had >100-fold selectivity over six related PTPs. The relevance of the allosteric pocket was verified through site-directed mutagenesis. Compound 211 has a noncompetitive mechanism of action, and it is extremely effective at preventing dephosphorylation of substrate Lck phosphotyrosine (pY)-505 versus preventing dephosphorylation of Lck pY-393. In cultured primary T cells, compound 211 prevents T-cell receptor-mediated activation of Lck, Zap-70, and mitogen-activated protein kinase, and interleukin-2 production. In a delayed-type hypersensitivity reaction in vivo, compound 211 abolished inflammation. This work demonstrates a novel approach to develop effective allosteric inhibitors that can be expanded to target the corresponding allosteric domains of other receptor PTPs.

Protein-ligand binding free energies from exhaustive docking

We explore the use of exhaustive docking as an alternative to Monte Carlo and molecular dynamics sampling for the direct integration of the partition function for protein-ligand binding. We enumerate feasible poses for the ligand and calculate the Boltzmann factor contribution of each pose to the partition function. From the partition function, the free energy, enthalpy, and entropy can be derived. All our calculations are done with a continuum solvation model that includes solving the Poisson equation. In contrast to Monte Carlo and molecular dynamics simulations, exhaustive docking avoids (within the limitations of a discrete sampling) the question of "Have we run long enough?" due to its deterministic complete enumeration of states. We tested the method on the T4 lysozyme L99A mutant, which has a nonpolar cavity that can accommodate a number of small molecules. We tested two electrostatic models. Model 1 used a solute dielectric of 2.25 for the complex apoprotein and free ligand and 78.5 for the solvent. Model 2 used a solute dielectric of 2.25 for the complex and apoprotein but 1.0 for the free ligand. For our test set of eight molecules, we obtain a reasonable correlation with a Pearson r(2) = 0.66 using model 1. The trend in binding affinity ranking is generally preserved with a Kendall τ = 0.64 and Spearman ρ = 0.83. With model 2, the correlation is improved with a Pearson r(2) = 0.83, Kendall τ = 0.93, and Spearman ρ = 0.98. This suggests that the energy function and sampling method adequately captured most of the thermodynamics of binding of the nonpolar ligands to T4 lysozyme L99A.

Biomolecular surface construction by PDE transform

This work proposes a new framework for the surface generation based on the partial differential equation (PDE) transform. The PDE transform has recently been introduced as a general approach for the mode decomposition of images, signals, and data. It relies on the use of arbitrarily high-order PDEs to achieve the time-frequency localization, control the spectral distribution, and regulate the spatial resolution. The present work provides a new variational derivation of high-order PDE transforms. The fast Fourier transform is utilized to accomplish the PDE transform so as to avoid stringent stability constraints in solving high-order PDEs. As a consequence, the time integration of high-order PDEs can be done efficiently with the fast Fourier transform. The present approach is validated with a variety of test examples in two-dimensional and three-dimensional settings. We explore the impact of the PDE transform parameters, such as the PDE order and propagation time, on the quality of resulting surfaces. Additionally, we utilize a set of 10 proteins to compare the computational efficiency of the present surface generation method and a standard approach in Cartesian meshes. Moreover, we analyze the present method by examining some benchmark indicators of biomolecular surface, that is, surface area, surface-enclosed volume, solvation free energy, and surface electrostatic potential. A test set of 13 protein molecules is used in the present investigation. The electrostatic analysis is carried out via the Poisson-Boltzmann equation model. To further demonstrate the utility of the present PDE transform-based surface method, we solve the Poisson-Nernst-Planck equations with a PDE transform surface of a protein. Second-order convergence is observed for the electrostatic potential and concentrations. Finally, to test the capability and efficiency of the present PDE transform-based surface generation method, we apply it to the construction of an excessively large biomolecule, a virus surface capsid. Virus surface morphologies of different resolutions are attained by adjusting the propagation time. Therefore, the present PDE transform provides a multiresolution analysis in the surface visualization. Extensive numerical experiment and comparison with an established surface model indicate that the present PDE transform is a robust, stable, and efficient approach for biomolecular surface generation in Cartesian meshes.

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

We carried out a prospective evaluation of the utility of the SIE (solvation interaction energy) scoring function for virtual screening and binding affinity prediction. Since experimental structures of the complexes were not provided, this was an exercise in virtual docking as well. We used our exhaustive docking program, Wilma, to provide high-quality poses that were rescored using SIE to provide binding affinity predictions. We also tested the combination of SIE with our latest solvation model, first shell of hydration (FiSH), which captures some of the discrete properties of water within a continuum model. We achieved good enrichment in virtual screening of fragments against trypsin, with an area under the curve of about 0.7 for the receiver operating characteristic curve. Moreover, the early enrichment performance was quite good with 50% of true actives recovered with a 15% false positive rate in a prospective calculation and with a 3% false positive rate in a retrospective application of SIE with FiSH. Binding affinity predictions for both trypsin and host-guest complexes were generally within 2 kcal/mol of the experimental values. However, the rank ordering of affinities differing by 2 kcal/mol or less was not well predicted. On the other hand, it was encouraging that the incorporation of a more sophisticated solvation model into SIE resulted in better discrimination of true binders from binders. This suggests that the inclusion of proper Physics in our models is a fruitful strategy for improving the reliability of our binding affinity predictions.

Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 2. Benchmark in the CSAR-2010 scoring exercise

Solvated interaction energy (SIE) is an end-point physics-based scoring function for predicting binding affinities from force-field nonbonded interaction terms, continuum solvation, and configurational entropy linear compensation. We tested the SIE function in the Community Structure-Activity Resource (CSAR) scoring challenge consisting of high-resolution cocrystal structures for 343 protein-ligand complexes with high-quality binding affinity data and high diversity with respect to protein targets. Particular emphasis was placed on the sensitivity of SIE predictions to the assignment of protonation and tautomeric states in the complex and the treatment of metal ions near the protein-ligand interface. These were manually curated from an originally distributed CSAR-HiQ data set version, leading to the currently distributed CSAR-NRC-HiQ version. We found that this manual curation was a critical step for accurately testing the performance of the SIE function. The standard SIE parametrization, previously calibrated on an independent data set, predicted absolute binding affinities with a mean-unsigned-error (MUE) of 2.41 kcal/mol for the CSAR-HiQ version, which improved to 1.98 kcal/mol for the upgraded CSAR-NRC-HiQ version. Half-half retraining-testing of SIE parameters on two predefined subsets of CSAR-NRC-HiQ led to only marginal further improvements to an MUE of 1.83 kcal/mol. Hence, we do not recommend altering the current default parameters of SIE at this time. For a sample of SIE outliers, additional calculations by molecular dynamics-based SIE averaging with or without incorporation of ligand strain, by MM-PB(GB)/SA methods with or without entropic estimates, or even by the linear interaction energy (LIE) formalism with an explicit solvent model, did not further improve predictions.

Naphthalene-based RNA editing inhibitor blocks RNA editing activities and editosome assembly in Trypanosoma brucei

RNA editing, catalyzed by the multiprotein editosome complex, is an essential step for the expression of most mitochondrial genes in trypanosomatid pathogens. It has been shown previously that Trypanosoma brucei RNA editing ligase 1 (TbREL1), a core catalytic component of the editosome, is essential in the mammalian life stage of these parasitic pathogens. Because of the availability of its crystal structure and absence from human, the adenylylation domain of TbREL1 has recently become the focus of several studies for designing inhibitors that target its adenylylation pocket. Here, we have studied new and existing inhibitors of TbREL1 to better understand their mechanism of action. We found that these compounds are moderate to weak inhibitors of adenylylation of TbREL1 and in fact enhance adenylylation at higher concentrations of protein. Nevertheless, they can efficiently block deadenylylation of TbREL1 in the editosome and, consequently, result in inhibition of the ligation step of RNA editing. Further experiments directly showed that the studied compounds inhibit the interaction of the editosome with substrate RNA. This was supported by the observation that not only the ligation activity of TbREL1 but also the activities of other editosome proteins such as endoribonuclease, terminal RNA uridylyltransferase, and uridylate-specific exoribonuclease, all of which require the interaction of the editosome with the substrate RNA, are efficiently inhibited by these compounds. In addition, we found that these compounds can interfere with the integrity and/or assembly of the editosome complex, opening the exciting possibility of using them to study the mechanism of assembly of the editosome components.

Rapid Prediction of Solvation Free Energy. 2. The First-Shell Hydration (FiSH) Continuum Model

Local ordering of water in the first hydration shell around a solute is different from isotropic bulk water. This leads to effects that are captured by explicit solvation models and missed by continuum solvation models which replace the explicit waters with a continuous medium. In this paper, we introduce the First-Shell Hydration (FiSH) model as a first attempt to introduce first-shell effects within a continuum solvation framework. One such effect is charge asymmetry, which is captured by a modified electrostatic term within the FiSH model by introducing a nonlinear correction of atomic Born radii based on the induced surface charge density. A hybrid van der Waals formulation consisting of two continuum zones has been implemented. A shell of water restricted to and uniformly distributed over the solvent-accessible surface (SAS) represents the first solvation shell. A second region starting one solvent diameter away from the SAS is treated as bulk water with a uniform density function. Both the electrostatic and van der Waals terms of the FiSH model have been calibrated against linear interaction energy (LIE) data from molecular dynamics simulations. Extensive testing of the FiSH model was carried out on large hydration data sets including both simple compounds and drug-like molecules. The FiSH model accurately reproduces contributing terms, absolute predictions relative to experimental hydration free energies, and functional class trends of LIE MD simulations. Overall, the implementation of the FiSH model achieves a very acceptable performance and transferability improving over previously developed solvation models, while being complemented by a sound physical foundation.

Bounding the electrostatic free energies associated with linear continuum models of molecular solvation

The importance of electrostatic interactions in molecular biology has driven extensive research toward the development of accurate and efficient theoretical and computational models. Linear continuum electrostatic theory has been surprisingly successful, but the computational costs associated with solving the associated partial differential equations (PDEs) preclude the theory's use in most dynamical simulations. Modern generalized-Born models for electrostatics can reproduce PDE-based calculations to within a few percent and are extremely computationally efficient but do not always faithfully reproduce interactions between chemical groups. Recent work has shown that a boundary-integral-equation formulation of the PDE problem leads naturally to a new approach called boundary-integral-based electrostatics estimation (BIBEE) to approximate electrostatic interactions. In the present paper, we prove that the BIBEE method can be used to rigorously bound the actual continuum-theory electrostatic free energy. The bounds are validated using a set of more than 600 proteins. Detailed numerical results are presented for structures of the peptide met-enkephalin taken from a molecular-dynamics simulation. These bounds, in combination with our demonstration that the BIBEE methods accurately reproduce pairwise interactions, suggest a new approach toward building a highly accurate yet computationally tractable electrostatic model.

Molecular dynamics-solvated interaction energy studies of protein-protein interactions: the MP1-p14 scaffolding complex

Using the MP1-p14 scaffolding complex from the mitogen-activated protein kinase signaling pathway as model system, we explored a structure-based computational protocol to probe and characterize binding affinity hot spots at protein-protein interfaces. Hot spots are located by virtual alanine-scanning consensus predictions over three different energy functions and two different single-structure representations of the complex. Refined binding affinity predictions for select hot-spot mutations are carried out by applying first-principle methods such as the molecular mechanics generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) to the molecular dynamics (MD) trajectories for mutated and wild-type complexes. Here, predicted hot-spot residues were actually mutated to alanine, and crystal structures of the mutated complexes were determined. Two mutated MP1-p14 complexes were investigated, the p14(Y56A)-mutated complex and the MP1(L63A,L65A)-mutated complex. Alternative ways to generate MD ensembles for mutant complexes, not relying on crystal structures for mutated complexes, were also investigated. The SIE function, fitted on protein-ligand binding affinities, gave absolute binding affinity predictions in excellent agreement with experiment and outperformed standard MM-GBSA predictions when tested on the MD ensembles of Ras-Raf and Ras-RalGDS protein-protein complexes. For wild-type and mutant MP1-p14 complexes, SIE predictions of relative binding affinities were supported by a yeast two-hybrid assay that provided semiquantitative relative interaction strengths. Results on the MP1-mutated complex suggested that SIE predictions deteriorate if mutant MD ensembles are approximated by just mutating the wild-type MD trajectory. The SIE data on the p14-mutated complex indicated feasibility for generating mutant MD ensembles from mutated wild-type crystal structure, despite local structural differences observed upon mutation. For energetic considerations, this would circumvent costly needs to produce and crystallize mutated complexes. The sensitized protein-protein interface afforded by the p14(Y56A) mutation identified here has practical applications in screening-based discovery of first-generation small-molecule hits for further development into specific modulators of the mitogen-activated protein kinase signaling pathway.

Minimal molecular surfaces and their applications

This article presents a novel concept, the minimal molecular surface (MMS), for the theoretical modeling of biomolecules. The MMS can be viewed as a result of the surface free energy minimization when an apolar molecule, such as protein, DNA or RNA is immersed in a polar solvent. Based on the theory of differential geometry, the MMS is created via the mean curvature minimization of molecular hypersurface functions. A detailed numerical algorithm is presented for the practical generation of MMSs. Extensive numerical experiments, including those with internal and open cavities, are carried out to demonstrated the proposed concept and algorithms. The proposed MMS is typically free of geometric singularities. Application of the MMS to the electrostatic analysis is considered for a set of twenty six proteins.

I-SOLV: A new surface-based empirical model for computing solvation free energies

We have developed a new empirical model, I-SOLV, for computing solvation free energies of neutral organic molecules. It computes the solvation free energy of a solute molecule by summing up the contributions from its component atoms. The contribution from a certain atom is determined by the solvent-accessible surface area as well as the surface tension of this atom. A total of 49 atom types are implemented in our model for classifying C, N, O, S, P, F, Cl, Br and I in common organic molecules. Their surface tensions are parameterized by using a data set of 532 neutral organic molecules with experimentally measured solvation free energies. A head-to-head comparison of our model with several other solvation models was performed on a test set of 82 molecules. Our model outperformed other solvation models, including widely used PB/SA and GB/SA models, with a mean unsigned error as low as 0.39 kcal/mol. Our study has demonstrated again that well-developed empirical solvation models are not necessarily less accurate than more sophisticated theoretical models. Empirical models may serve as appealing alternatives due to their simplicity and accuracy.

Maintaining solvent accessible surface area under rotamer substitution for protein design

Although quantities derived from solvent accessible surface areas (SASA) are useful in many applications in protein design and structural biology, the computational cost of accurate SASA calculation makes SASA-based scores difficult to integrate into commonly used protein design methodologies. We demonstrate a method for maintaining accurate SASA during a Monte Carlo search of sequence and rotamer space for a fixed protein backbone. We extend the fast Le Grand and Merz algorithm (Le Grand and Merz, J Comput Chem, 14, 349), which discretizes the solvent accessible surface for each atom by placing dots on a sphere and combines Boolean masks to determine which dots are exposed. By replacing semigroup operations with group operations (from Boolean logic to counting dot coverage) we support SASA updates. Our algorithm takes time proportional to the number of atoms affected by rotamer substitution, rather than the number of atoms in the protein. For design simulations with a one hundred residue protein our approach is approximately 145 times faster than performing a Le Grand and Merz SASA calculation from scratch following each rotamer substitution. To demonstrate practical effectiveness, we optimize a SASA-based measure of protein packing in the complete redesign of a large set of proteins and protein-protein interfaces.

Crystal structure of StaL, a glycopeptide antibiotic sulfotransferase from Streptomyces toyocaensis

Over the past decade, antimicrobial resistance has emerged as a major public health crisis. Glycopeptide antibiotics such as vancomycin and teicoplanin are clinically important for the treatment of Gram-positive bacterial infections. StaL is a 3'-phosphoadenosine 5'-phosphosulfate-dependent sulfotransferase capable of sulfating the cross-linked heptapeptide substrate both in vivo and in vitro, yielding the product A47934, a unique teicoplanin-class glycopeptide antibiotic. The sulfonation reaction catalyzed by StaL constitutes the final step in A47934 biosynthesis. Here we report the crystal structure of StaL and its complex with the cofactor product 3'-phosphoadenosine 5'-phosphate. This is only the second prokaryotic sulfotransferase to be structurally characterized. StaL belongs to the large sulfotransferase family and shows higher similarity to cytosolic sulfotransferases (ST) than to the bacterial ST (Stf0). StaL has a novel dimerization motif, different from any other STs that have been structurally characterized. We have also applied molecular modeling to investigate the binding mode of the unique substrate, desulfo-A47934. Based on the structural analysis and modeling results, a series of residues was mutated and kinetically characterized. In addition to the conserved residues (Lys(12), His(67), and Ser(98)), molecular modeling, fluorescence quenching experiments, and mutagenesis studies identified several other residues essential for substrate binding and/or activity, including Trp(34), His(43), Phe(77), Trp(132), and Glu(205).

Travel Depth, a New Shape Descriptor for Macromolecules: Application to Ligand Binding

Depth is a term frequently applied to the shape and surface of macromolecules, describing for example the grooves in DNA, the shape of an enzyme active site, or the binding site for a small molecule in a protein. Yet depth is a difficult property to define rigorously in a macromolecule, and few computational tools exist to quantify this notion, to visualize it, or analyze the results. We present our notion of travel depth, simply put the physical distance a solvent molecule would have to travel from a surface point to a suitably defined reference surface. To define the reference surface, we use the limiting form of the molecular surface with increasing probe size: the convex hull. We then present a fast, robust approximation algorithm to compute travel depth to every surface point. The travel depth is useful because it works for pockets of any size and complexity. It also works for two interesting special cases. First, it works on the grooves in DNA, which are unbounded in one direction. Second, it works on the case of tunnels, that is pockets that have no "bottom", but go through the entire macromolecule. Our algorithm makes it straightforward to quantify discussions of depth when analyzing structures. High-throughput analysis of macromolecule depth is also enabled by our algorithm. This is demonstrated by analyzing a database of protein-small molecule binding pockets, and the distribution of bound magnesium ions in RNA structures. These analyses show significant, but subtle effects of depth on ligand binding localization and strength.