Why OppA protein can bind sequence-independent peptides? A combination of QM/MM, PB/SA, and structure-based QSAR analyses

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

Identification of residues for chaperone-like activity of OppA protein in Yersinia pseudotuberculosis

Periplasmic oligopeptide binding protein (OppA) is part of a multimeric cytoplasmic membrane protein complex, whose function is known as peptide transporters found in Gram-negative bacteria. A chaperone-like activity has been found for the OppA from Yersinia pseudotuberculosis, using biochemical experiments. Through computational analysis, we selected two amino acid residues (R41 and D42) that probably are involved in the chaperone-like activity. Our results to corroborate how OppA assists refolding and renaturation of lactate dehydrogenase and alpha-glucosidase denatured enzymes.

Water in protein hydration and ligand recognition

This review describes selected basics of water in biomolecular recognition. We focus on a qualitative understanding of the most important physical aspects, how these change in magnitude between bulk water and protein environment, and how the roles that water plays for proteins arise from them. These roles include mechanical support, thermal coupling, dielectric screening, mass and charge transport, and the competition with a ligand for the occupation of a binding site. The presence or absence of water has ramifications that range from the thermodynamic binding signature of a single ligand up to cellular survival. The large inhomogeneity in water density, polarity and mobility around a solute is hard to assess in experiment. This is a source of many difficulties in the solvation of protein models and computational studies that attempt to elucidate or predict ligand recognition. The influence of water in a protein binding site on the experimental enthalpic and entropic signature of ligand binding is still a point of much debate. The strong water‐water interaction in enthalpic terms is counteracted by a water molecule's high mobility in entropic terms. The complete arrest of a water molecule's mobility sets a limit on the entropic contribution of a water displacement process, while the solvent environment sets limits on ligand reactivity.

Structure and energy based quantitative missense variant effect analysis provides insights into drug resistance mechanisms of anaplastic lymphoma kinase mutations

Anaplastic lymphoma kinase (ALK) is considered as a validated molecular target in multiple malignancies, such as non-small cell lung cancer (NSCLC). However, the effectiveness of molecularly targeted therapies using ALK inhibitors is almost universally limited by drug resistance. Drug resistance to molecularly targeted therapies has now become a major obstacle to effective cancer treatment and personalized medicine. It is of particular importance to provide an improved understanding on the mechanisms of resistance of ALK inhibitors, thus rational new therapeutic strategies can be developed to combat resistance. We used state-of-the-art computational approaches to systematically explore the mutational effects of ALK mutations on drug resistance properties. We found the activation of ALK was increased by substitution with destabilizing mutations, creating the capacity to confer drug resistance to inhibitors. In addition, results implied that evolutionary constraints might affect the drug resistance properties. Moreover, an extensive profile of drugs against ALK mutations was constructed to give better understanding of the mechanism of drug resistance based on structural transitions and energetic variation. Our work hopes to provide an up-to-date mechanistic framework for understanding the mechanisms of drug resistance induced by ALK mutations, thus tailor treatment decisions after the emergence of resistance in ALK-dependent diseases.

Computer-aided design of amino acid-based therapeutics: a review

During the last two decades, the pharmaceutical industry has progressed from detecting small molecules to designing biologic-based therapeutics. Amino acid-based drugs are a group of biologic-based therapeutics that can effectively combat the diseases caused by drug resistance or molecular deficiency. Computational techniques play a key role to design and develop the amino acid-based therapeutics such as proteins, peptides and peptidomimetics. In this study, it was attempted to discuss the various elements for computational design of amino acid-based therapeutics. Protein design seeks to identify the properties of amino acid sequences that fold to predetermined structures with desirable structural and functional characteristics. Peptide drugs occupy a middle space between proteins and small molecules and it is hoped that they can target "undruggable" intracellular protein-protein interactions. Peptidomimetics, the compounds that mimic the biologic characteristics of peptides, present refined pharmacokinetic properties compared to the original peptides. Here, the elaborated techniques that are developed to characterize the amino acid sequences consistent with a specific structure and allow protein design are discussed. Moreover, the key principles and recent advances in currently introduced computational techniques for rational peptide design are spotlighted. The most advanced computational techniques developed to design novel peptidomimetics are also summarized.

An unexpected way forward: towards a more accurate and rigorous protein-protein binding affinity scoring function by eliminating terms from an already simple scoring function

A fundamental and unsolved problem in biophysical chemistry is the development of a computationally simple, physically intuitive, and generally applicable method for accurately predicting and physically explaining protein-protein binding affinities from protein-protein interaction (PPI) complex coordinates. Here, we propose that the simplification of a previously described six-term PPI scoring function to a four term function results in a simple expression of all physically and statistically meaningful terms that can be used to accurately predict and explain binding affinities for a well-defined subset of PPIs that are characterized by (1) crystallographic coordinates, (2) rigid-body association, (3) normal interface size, and hydrophobicity and hydrophilicity, and (4) high quality experimental binding affinity measurements. We further propose that the four-term scoring function could be regarded as a core expression for future development into a more general PPI scoring function. Our work has clear implications for PPI modeling and structure-based drug design.

Staurosporine scaffold-based rational discovery of the wild-type sparing reversible inhibitors of EGFR T790M gatekeeper mutant in lung cancer with analog-sensitive kinase technology

The human epidermal growth factor receptor (EGFR) has been established as an attractive target for lung cancer therapy. However, an acquired EGFR T790M gatekeeper mutation is frequently observed in patients treated with first-line anticancer agents such as gefitinib and erlotinib to cause drug resistance, largely limiting the application of small-molecule kinase inhibitors in EGFR-targeted chemotherapy. Previously, the reversible pan-kinase inhibitor staurosporine and its several analogs such as Gö6976 and K252a have been reported to selectively inhibit the EGFR T790M mutant (EGFR^T790M ) over wild-type kinase (EGFR^WT ), suggesting that the staurosporine scaffold is potentially to develop the wild-type sparing reversible inhibitors of EGFR^T790M . Here, we systematically evaluated the inhibitor response of 28 staurosporine scaffold-based compounds to EGFR T790M mutation at structural, energetic, and molecular levels by using an integrated in silico-in vitro analog-sensitive (AS) kinase technology. With the strategy, we were able to identify 4 novel wild-type sparing inhibitors UCN-01, UCN-02, AFN941, and SB-218078 with high or moderate selectivity of 30-, 45-, 5-, and 8-fold for EGFR^T790M over EGFR^WT , respectively, which are comparable with or even better than that of the parent compound staurosporine (24-fold). Molecular modeling and structural analysis revealed that van der Waals contacts and hydrophobic forces can form between the side chain of mutated residue Met790 and the pyrrolidinone moiety of inhibitor ligand UCN-02, which may simultaneously improve the favorable interaction energy between the kinase and inhibitor, and reduce the unfavorable desolvation penalty upon the kinase-inhibitor binding. A hydroxyl group of UCN-02 additional to staurosporine locates at the pyrrolidinone moiety, which can largely alter the electronic distribution of pyrrolidinone moiety and thus promote the intermolecular interaction with Met790 residue. This can well explain the measured higher selectivity of UCN-02 than staurosporine for mutant over wild-type kinase.

Calculation of Relative Binding Free Energy in the Water-Filled Active Site of Oligopeptide-Binding Protein A

The periplasmic oligopeptide binding protein A (OppA) represents a well-known example of water-mediated protein-ligand interactions. Here, we perform free-energy calculations for three different ligands binding to OppA, using a thermodynamic integration approach. The tripeptide ligands share a high structural similarity (all have the sequence KXK), but their experimentally-determined binding free energies differ remarkably. Thermodynamic cycles were constructed for the ligands, and simulations conducted in the bound and (freely solvated) unbound states. In the unbound state, it was observed that the difference in conformational freedom between alanine and glycine leads to a surprisingly slow convergence, despite their chemical similarity. This could be overcome by increasing the softness parameter during alchemical transformations. Discrepancies remained in the bound state however, when comparing independent simulations of the three ligands. These difficulties could be traced to a slow relaxation of the water network within the active site. Fluctuations in the number of water molecules residing in the binding cavity occur mostly on a timescale larger than the simulation time along the alchemical path. After extensive simulations, relative binding free energies that were converged to within thermal noise could be obtained, which agree well with available experimental data.

Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II

Human Golgi α-mannosidase II (GMII), a zinc ion co-factor dependent glycoside hydrolase (E.C.3.2.1.114), is a pharmaceutical target for the design of inhibitors with anti-cancer activity. The discovery of an effective inhibitor is complicated by the fact that all known potent inhibitors of GMII are involved in unwanted co-inhibition with lysosomal α-mannosidase (LMan, E.C.3.2.1.24), a relative to GMII. Routine empirical QSAR models for both GMII and LMan did not work with a required accuracy. Therefore, we have developed a fast computational protocol to build predictive models combining interaction energy descriptors from an empirical docking scoring function (Glide-Schrödinger), Linear Interaction Energy (LIE) method, and quantum mechanical density functional theory (QM-DFT) calculations. The QSAR models were built and validated with a library of structurally diverse GMII and LMan inhibitors and non-active compounds. A critical role of QM-DFT descriptors for the more accurate prediction abilities of the models is demonstrated. The predictive ability of the models was significantly improved when going from the empirical docking scoring function to mixed empirical-QM-DFT QSAR models (Q(2)=0.78-0.86 when cross-validation procedures were carried out; and R(2)=0.81-0.83 for a testing set). The average error for the predicted ΔGbind decreased to 0.8-1.1kcalmol(-1). Also, 76-80% of non-active compounds were successfully filtered out from GMII and LMan inhibitors. The QSAR models with the fragmented QM-DFT descriptors may find a useful application in structure-based drug design where pure empirical and force field methods reached their limits and where quantum mechanics effects are critical for ligand-receptor interactions. The optimized models will apply in lead optimization processes for GMII drug developments.

Targeting druggable enzymome by exploiting natural medicines: An in silico-in vitro integrated approach to combating multidrug resistance in bacterial infection

CONTEXT

Antibiotic resistance is a major clinical and public health problem. Development of new therapeutic approaches to prevent bacterial multidrug resistance during antimicrobial chemotherapy has thus been becoming a primary consideration in the medicinal chemistry community.

OBJECTIVE

We described a new strategy that combats multidrug resistance by using natural medicines to target the druggable enzymome (i.e., enzymatic proteome) of Staphylococcus aureus.

MATERIALS AND METHODS

A pipeline of integrating in silico analysis and in vitro assay was purposed to identify antibacterial agents from a large library of natural products with diverse structures, high drug-likeness, and relatively low flexibility, with which a systematic interactome of 826 natural product candidates with 125 functionally essential S. aureus enzymes was constructed via a high-throughput cross-docking approach. The obtained docking score matrix was then converted into an array of synthetic scores; each corresponds to a natural product candidate. By systematically examining the docking results, a number of highly promising candidates with potent antibacterial activity were suggested.

RESULTS

Three natural products, i.e., radicicol, jorumycin, and amygdalin, have been determined to possess strong broad-spectrum potency combating both the drug-resistant and drug-sensitive strains (MIC value <10 μg/ml). In addition, some natural products such as tetrandrine, bilobalide, and arbutin exhibited selective inhibition on different strains.

DISCUSSION AND CONCLUSION

Combined quantum mechanics/molecular mechanics analysis revealed diverse non-bonded interactions across the complex interfaces of newly identified antibacterial agents with their putative targets GyrB ATPase and tyrosyl-tRNA synthetase.

An interaction map of small-molecule kinase inhibitors with anaplastic lymphoma kinase (ALK) mutants in ALK-positive non-small cell lung cancer

Human anaplastic lymphoma kinase (ALK) has become a well-established target for the treatment of ALK-positive non-small cell lung cancer (NSCLC). Here, we have profiled seven small-molecule inhibitors, including 2 that are approved drugs, against a panel of clinically relevant mutations in ALK tyrosine kinase (TK) domain, aiming at a comprehensive understanding of molecular mechanism and biological implication underlying inhibitor response to ALK TK mutation. We find that (i) the gatekeeper mutation L1196M causes crizotinib resistance by simultaneously increasing and decreasing the binding affinities of, respectively, ATP and inhibitor to ALK, whereas the secondary mutation C1156Y, which is located far away from the ATP-binding site of ALK TK domain, causes the resistance by inducing marked allosteric effect on the site, (ii) the 2nd and 3rd generation kinase inhibitors exhibit relatively high sensitivity towards ALK mutants as compared to 1st generation inhibitors, (iii) the pan-kinase inhibitor staurosporine is insensitive for most mutations due to its high structural compatibility, and (iv) ATP affinity to ALK is generally reduced upon most clinically relevant mutations. Furthermore, we also identify six novel mutation-inhibitor pairs that are potentially associated with drug resistance. In addition, the G1202R and C1156Y mutations are expected to generally cause resistance for many existing inhibitors, since they can address significant effect on the geometric shape and physicochemical property of ALK active pocket.

The small-molecule inhibitor selectivity between IKKα and IKKβ kinases in NF-κB signaling pathway

The enzyme complex IκB kinase (IKK) is an essential activator of NF-κB signaling pathway involved in propagating the cellular response to inflammation. The complex contains two functional subunits IKKα and IKKβ, which are structurally conserved kinases and selective inhibition of them would result in distinct biological effects. However, most existing IKK inhibitors show moderate or high promiscuity for the two homologous kinases. Understanding of the molecular mechanism and biological implication underlying the specific interactions in IKK-ligand recognition is thus fundamentally important for the rational design of selective IKK inhibitors. In the current work, we integrated molecular docking, quantum mechanics/molecular mechanics calculation and Poisson-Boltzmann/surface area analysis to investigate the structural basis and energetic property of the selective binding of small-molecule ligands to IKKα and IKKβ. It was found that the selectivity is primarily determined by the size and topology difference in ATP-binding pocket of IKKα and IKKβ kinase domains; bulky inhibitor molecules commonly have, respectively, low and appropriate affinities towards IKKα and IKKβ, and thus exhibit relatively high selectivity for IKKβ over IKKα, whereas small ligands can only bind weakly to both the two kinases with low selectivity. In addition, the conformation, arrangement and distribution of residues in IKK pockets are also responsible for constituting the exquisite specificity of ligand binding to KKα and IKKβ. Next, a novel quantitative structure-selectivity relationship model was developed to characterize the relative contribution of each kinase residue to inhibitor selectivity and to predict the selectivity and specificity for a number of known IKK inhibitors. Results showed that the active-site residues contribute significantly to the selectivity by directly interacting with inhibitor ligands, while those protein portions far away from the kinase active sites may also play an important role in determining the selectivity through long-range non-bonded forces and indirect allosteric effect.

Three dimensional quantitative structure-toxicity relationship modeling and prediction of acute toxicity for organic contaminants to algae

Although numerous chemicals have been identified to have significant toxicological effect on aquatic organisms, there is still lack of a reliable, high-throughput approach to evaluate, screen and monitor the presence of organic contaminants in aquatic system. In the current study, we proposed a synthetic pipeline to automatically model and predict the acute toxicity of chemicals to algae. In the procedure, a new alignment-free three dimensional (3D) structure characterization method was described and, with this method, several 3D-quantitative structure-toxicity relationship (3D-QSTR) models were developed, from which two were found to exhibit strong internal fitting ability and high external predictive power. The best model was established by Gaussian process (GP), which was further employed to perform extrapolation on a random compound library consisting of 1014 virtually generated substituted benzenes. It was found that (i) substitution number can only exert slight influence on chemical׳s toxicity, but low-substituted benzenes seem to have higher toxicity than those of high-substituted entities, and (ii) benzenes substituted by nitro group and halogens exhibit high acute toxicity as compared to other substituents such as methyl and carboxyl groups. Subsequently, several promising candidates suggested by computational prediction were assayed by using a standard algal growth inhibition test. Consequently, four substituted benzenes, namely 2,3-dinitrophenol, 2-chloro-4-nitroaniline, 1,2,3-trinitrobenzene and 3-bromophenol, were determined to have high acute toxicity to Scenedesmus obliquus, with their EC50 values of 2.5±0.8, 10.5±2.1, 1.4±0.2 and 42.7±5.4μmol/L, respectively.

The critical aggregation concentration of peptide surfactants is predictable from dynamic hydrophobic property

Peptide surfactants are a kind of newly emerged functional materials, which have a variety of applications such as building nanoarchitecture, stabilizing membrane proteins and controlling drug release. In the present study, we report the modelling and prediction of critical aggregation concentration (CAC), an important parameter that characterizes the self-assembling behaviour of peptide surfactants through the use of statistical modelling and quantitative structure-property relationship (QSPR) approaches. In order to accurately describe the structural and physicochemical properties of the highly flexible peptide molecules, a new method called molecular dynamics-based hydrophobic cross-field (MD-HCF) is proposed to capture both the hydrophobic profile and dynamic feature of 32 surface-activity, structure-known peptides. A number of statistical models are then developed using partial least squares (PLS) regression with or without improvement by genetic algorithm (GA). We demonstrate that MD-HCF performs much better than the widely used CODESSA method in both its predictability and interpretability. We also highlight the importance of dynamic hydrophobic property in accurate prediction and reasonable explanation of peptide self-assembling behaviour in solution, albeit which is exhaustive to compute compared with those derived directly from peptide static structure. To the best of our knowledge, this study is the first to computationally model and predict the self-assembling behaviour of peptide surfactants.

Purification and crystallization of the ABC-type transport substrate-binding protein OppA from Thermoanaerobacter tengcongensis

Di- and oligopeptide- binding protein OppAs play important roles in solute and nutrient uptake, sporulation, biofilm formation, cell wall muropeptides recycling, peptide-dependent quorum-sensing responses, adherence to host cells, and a variety of other biological processes. Soluble OppA from Thermoanaerobacter tengcongensis was expressed in Escherichia coli. The protein was found to be >95% pure with SDS-PAGE after a series of purification steps and the purity was further verified by mass spectrometry. The protein was crystallized using the sitting-drop vapour-diffusion method with PEG 400 as the precipitant. Crystal diffraction extended to 2.25 Å. The crystal belonged to space group C222(1), with unit-cell parameters of a=69.395, b=199.572, c=131.673 Å, and α=β=γ=90°.