Comparison between Generalized-Born and Poisson-Boltzmann methods in physics-based scoring functions for protein structure prediction

In Silico Investigations into the Selectivity of Psychoactive and New Psychoactive Substances in Monoamine Transporters

New psychoactive substances (NPS) are a group of compounds that mimic the effects of illicit substances. A range of NPS have been shown to interact with the three main classes of monoamine transporters (DAT, NET, and SERT) to differing extents, but it is unclear why these differences arise. To aid in understanding the differences in affinity between the classes of monoamine transporters, several in silico experiments were conducted. Docking experiments showed there was no direct correlation between a range of scoring functions and experimental activity, but Spearman ranking analysis showed a significant correlation (α = 0.1) for DAT, with the affinity ΔG (0.42), αHB (0.40), GoldScore (0.40), and PLP (0.41) scoring functions, and for DAT (0.38) and SERT (0.40) using a consensus scoring approach. Qualitative structure-activity relationship (QSAR) experiments resulted in the generation of robust and predictive three-descriptor models for SERT (r ² = 0.87, q ² = 0.8, and test set r ² = 0.74) and DAT (r ² = 0.68, q ² = 0.51, test set r ² = 0.63). Both QSAR models described similar characteristics for binding, i.e., rigid hydrophobic molecules with a biogenic amine moiety, and were not sufficient to facilitate a deeper understanding of differences in affinity between the monoamine transporters. This contextualizes the observed promiscuity for NPS between the isoforms and highlights the difficulty in the design and development of compounds that are isoform-selective.

Cytotoxic Effects on Breast Cancer Cell Lines of Chalcones Derived from a Natural Precursor and Their Molecular Docking Analysis

This study aimed to determine the in vitro cytotoxicity and understand possible cytotoxic mechanisms via an in silico study of eleven chalcones synthesized from two acetophenones. Five were synthesized from a prenylacetophenone isolated from a plant that grows in the Andean region of the Atacama Desert. The cytotoxic activity of all the synthesized chalcones was tested against breast cancer cell lines using an MTT cell proliferation assay. The results suggest that the prenyl group in the A-ring of the methoxy and hydroxyl substituents of the B-ring appear to be crucial for the cytotoxicity of these compounds. The chalcones 12 and 13 showed significant inhibitory effects against growth in MCF-7 cells (IC₅₀ 4.19 ± 1.04 µM and IC₅₀ 3.30 ± 0.92 µM), ZR-75-1 cells (IC₅₀ 9.40 ± 1.74 µM and IC₅₀ 8.75 ± 2.01µM), and MDA-MB-231 cells (IC₅₀ 6.12 ± 0.84 µM and IC₅₀ 18.10 ± 1.65 µM). Moreover, these chalcones showed differential activity between MCF-10F (IC₅₀ 95.76 ± 1.52 µM and IC₅₀ 95.11 ± 1.97 µM, respectively) and the tumor lines. The in vitro results agree with molecular coupling results, whose affinity energies and binding mode agree with the most active compounds. Thus, compounds 12 and 13 can be considered for further studies and are candidates for developing new antitumor agents. In conclusion, these observations give rise to a new hypothesis for designing chalcones with potential cytotoxicity with high potential for the pharmaceutical industry.

Computational studies with flavonoids and terpenoids as BRPF1 inhibitors: in silico biological activity prediction, molecular docking, molecular dynamics simulations, MM/PBSA calculations

The BRPF1 protein is encoded by the BRPF1 gene. In addition, the BRPF1 gene is known to be upregulated in leukaemia. Recent studies have shown that it is also overexpressed in hepatocellular carcinoma (HCC) as well. Therefore, BRPF1 is a significant target for anti-cancer drug development studies, especially on HCC. 40 terpenoids and flavonoids were chosen because of their anticancer properties given in the literature. In this study, the biological activity of molecules was also investigated with in silico structure-activity relationship analysis. In addition, interactions between a series of terpenoids and flavonoids and the BRPF1 protein were investigated by molecular docking and molecular dynamics simulations. The energy change caused by the interactions of BRPF1 with different compounds was also evaluated by MM/PBSA calculations. It has been revealed that compound 5 (-9.2 kcal/mol), a kind of secoclerodane type diterpenoid, has a higher affinity both compared to other flavonoids and terpenoids, and 9F9 (-7.9 kcal/mol), a selective BRPF1 inhibitor. The study presented in this article demonstrates that compound 5, as a natural product, could form a chemical scaffold for the development of selective BRPF1 bromodomain inhibitors.

Distinct binding kinetics of E-, P- and L-selectins to CD44

Molecular-level selectin-cluster of differentiation 44 (CD44) interactions are far from clear because of the complexity and diversity of CD44 glycosylation and isoforms expressed on various types of cells. By combining experimental measurements and simulation predictions, the binding kinetics of three selectin members to the recombinant CD44 were quantified and the corresponding microstructural mechanisms were explored, respectively. Experimental results showed that the E-selectin-CD44 interactions mainly mediated the firm adhesion of microbeads under shear flow with the strongest rupture force. P- and L-selectins had similar interaction strength but different association and dissociation rates by mediating stable rolling and transient adhesions of microbeads, respectively. Molecular docking and molecular dynamics (MD) simulations predicted that the binding epitopes of CD44 to selectins are all located at the side face of each selectin, although the interfaces denoted as the hinge region are between lectin and epidermal growth factor domains of E-selectin, Lectin domain side of P-selectin and epidermal growth factor domain side of L-selectin, respectively. The lowest binding free energy, the largest rupture force and the longest lifetime for E-selectin, as well as the comparable values for P- and L-selectins, demonstrated in both equilibration and steered MD simulations, supported the above experimental results. These results offer basic data for understanding the functional differences of selectin-CD44 interactions.

Analysis of natural compounds against the activity of SARS-CoV-2 NSP15 protein towards an effective treatment against COVID-19: a theoretical and computational biology approach

Coronavirus infectious disease 2019 (COVID-19), a viral infection caused by a novel coronavirus (nCoV), continues to emerge as a serious threat to public health. This pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2) has infected globally with 1,550,000 plus deaths to date, representing a high risk to public health. No effective drug or vaccine is available to curb down this deadly virus. The expedition for searching for a potential drug or vaccine against COVID-19 is of massive potential and favour to the community. This study is focused on finding an effective natural compound that can be processed further into a potential inhibitor to check the activity of SARS-CoV-2 with minimal side effects targeting NSP15 protein, which belongs to the EndoU enzyme family. The natural screening suggested two efficient compounds (PubChem ID: 95372568 and 1776037) with dihydroxyphenyl region of the compound, found to be important in the interaction with the viral protein showing promising activity which may act as a potent lead inhibitory molecule against the virus. In combination with virtual screening, modelling, drug likeliness, molecular docking, and 500 ns cumulative molecular dynamics simulations (100 ns for each complex) along with the decomposition analysis to calculate and confirm the stability and fold, we propose 95372568 and 1776037 as novel compounds of natural origin capable of getting developed into potent lead molecules against SARS-CoV-2 target protein NSP15.

SAMPL7: Host-guest binding prediction by molecular dynamics and quantum mechanics

Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges provide routes to compare chemical quantities determined using computational chemistry approaches to experimental measurements that are shared after the competition. For this effort, several computational methods have been used to calculate the binding energies of Octa Acid (OA) and exo-Octa Acid (exoOA) host-guest systems for SAMPL7. The initial poses for molecular dynamics (MD) were generated by molecular docking. Binding free energy calculations were performed using molecular mechanics combined with Poisson-Boltzmann or generalized Born surface area solvation (MMPBSA/MMGBSA) approaches. The factors that affect the utility of the MMPBSA/MMGBSA approaches including solvation, partial charge, and solute entropy models were also analyzed. In addition to MD calculations, quantum mechanics (QM) calculations were performed using several different density functional theory (DFT) approaches. From SAMPL6 results, B3PW91-D3 was found to overestimate binding energies though it was effective for geometry optimizations, so it was considered for the DFT geometry optimizations in the current study, with single-point energy calculations carried out with B2PLYP-D3 with double-, triple-, and quadruple-ζ level basis sets. Accounting for dispersion effects, and solvation models was deemed essential for the predictions. MMGBSA and MMPBSA correlated better to experiment when used in conjunction with an empirical/linear correction.

In Silico Screening of Aptamers Configuration against Hepatitis B Surface Antigen

Aptamer has been long studied as a substitute of antibodies for many purposes. However, due to the exceeded length of the aptamers obtained in vitro, difficulties arise in its manipulation during its molecular conjugation on the matrix surfaces. Current study focuses on computational improvement for aptamers screening of hepatitis B surface antigen (HBsAg) through optimization of the length sequences obtained from SELEX. Three original aptamers with affinity against HBsAg were truncated into five short hairpin structured aptamers and their affinity against HBsAg was thoroughly studied by molecular docking, molecular dynamics (MD) simulation, and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method. The result shows that truncated aptamers binding on HBsAg “a” determinant region are stabilized by the dynamic H-bond formation between the active binding residues and nucleotides. Amino acids residues with the highest hydrogen bonds hydrogen bond interactions with all five aptamers were determined as the active binding residues and further characterized. The computational prediction of complexes binding will include validations through experimental assays in future studies. Current study will improve the current in vitro aptamers by minimizing the aptamer length for its easy manipulation.

Salt bridge interactions within the β2 integrin α7 helix mediate force-induced binding and shear resistance ability

The functional performance of the αI domain α₇ helix in β₂ integrin activation depends on the allostery of the α₇ helix, which axially slides down; therefore, it is critical to elucidate what factors regulate the allostery. In this study, we determined that there were two conservative salt bridge interaction pairs that constrain both the upper and bottom ends of the α₇ helix. Molecular dynamics (MD) simulations for three β₂ integrin members, lymphocyte function-associated antigen-1 (LFA-1; α_L β₂ ), macrophage-1 antigen (Mac-1; α_M β₂ ) and α_x β₂ , indicated that the magnitude of the salt bridge interaction is related to the stability of the αI domain and the strength of the corresponding force-induced allostery. The disruption of the salt bridge interaction, especially with double mutations in both salt bridges, significantly reduced the force-induced allostery time for all three members. The effects of salt bridge interactions of the αI domain α₇ helix on β₂ integrin conformational stability and allostery were experimentally validated using Mac-1 constructs. The results demonstrated that salt bridge mutations did not alter the conformational state of Mac-1, but they did increase the force-induced ligand binding and shear resistance ability, which was consistent with MD simulations. This study offers new insight into the importance of salt bridge interaction constraints of the αI domain α₇ helix and external force for β₂ integrin function.

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

DelPhi Web Server: A comprehensive online suite for electrostatic calculations of biological macromolecules and their complexes

Here we report a web server, the DelPhi web server, which utilizes DelPhi program to calculate electrostatic energies and the corresponding electrostatic potential and ionic distributions, and dielectric map. The server provides extra services to fix structural defects, as missing atoms in the structural file and allows for generation of missing hydrogen atoms. The hydrogen placement and the corresponding DelPhi calculations can be done with user selected force field parameters being either Charmm22, Amber98 or OPLS. Upon completion of the calculations, the user is given option to download fixed and protonated structural file, together with the parameter and Delphi output files for further analysis. Utilizing Jmol viewer, the user can see the corresponding structural file, to manipulate it and to change the presentation. In addition, if the potential map is requested to be calculated, the potential can be mapped onto the molecule surface. The DelPhi web server is available from http://compbio.clemson.edu/delphi_webserver.

DelPhi: a comprehensive suite for DelPhi software and associated resources

Background

Accurate modeling of electrostatic potential and corresponding energies becomes increasingly important for understanding properties of biological macromolecules and their complexes. However, this is not an easy task due to the irregular shape of biological entities and the presence of water and mobile ions.

Results

Here we report a comprehensive suite for the well-known Poisson-Boltzmann solver, DelPhi, enriched with additional features to facilitate DelPhi usage. The suite allows for easy download of both DelPhi executable files and source code along with a makefile for local installations. The users can obtain the DelPhi manual and parameter files required for the corresponding investigation. Non-experienced researchers can download examples containing all necessary data to carry out DelPhi runs on a set of selected examples illustrating various DelPhi features and demonstrating DelPhi’s accuracy against analytical solutions.

Conclusions

DelPhi suite offers not only the DelPhi executable and sources files, examples and parameter files, but also provides links to third party developed resources either utilizing DelPhi or providing plugins for DelPhi. In addition, the users and developers are offered a forum to share ideas, resolve issues, report bugs and seek help with respect to the DelPhi package. The resource is available free of charge for academic users from URL: http://compbio.clemson.edu/DelPhi.php.

Quantum mechanics/molecular mechanics strategies for docking pose refinement: distinguishing between binders and decoys in cytochrome C peroxidase

We investigate the effect of systematically applying molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) to docked poses in an attempt to improve the correspondence between theoretical prediction and experimental observation. The proposed scheme involves running a short time scale MD simulation on a docked ligand pose (and any known structurally important crystal structure waters in the active site), followed by QM/MM minimization. Both of these steps are relatively fast for moderately sized ligands; longer time scale MD involving the protein is not found to improve the results. The final binding energy is given in terms of the QM/MM total energy, a van der Waals correction, and a term to account for desolvation effects. This methodology is first tested with a trypsin inhibitor, for which we establish the importance of running MD before reoptimizing with QM/MM. The method is then applied to cytochrome c peroxidase using a set of binders and decoys. In this example, the proposed methodology affords much better discrimination between binders and decoys than the traditional docking approach used. For both systems presented, application of this protocol results in a significantly better energetic ranking and a smaller root mean squared deviation from known crystallographic ligand poses. This work highlights the importance of including polarization effects through QM/MM and of sampling with MD to refine a set of initial docked poses.

A coarse-grained potential for fold recognition and molecular dynamics simulations of proteins

A coarse-grained potential for protein simulations and fold ranking is presented. The potential is based on a two-point model of individual amino acids and a specific implementation of hydrogen bonding. Parameters are determined for distance dependent pair interactions, pseudo bonds, angles, and torsions. A scaling factor for a hydrogen bonding term is also determined. Iterative sampling for 4867 proteins reproduces distributions of internal coordinates and distances observed in the Protein Data Bank. The adjustment of the potential and resampling are in the spirit of the generalized ensemble approach. No native structure information (e.g., secondary structure) is used in the calculation of the potential or in the simulation of a particular protein. The potential is subject to two tests as follows: (i) simulations of 956 globular proteins in the neighborhood of their native folds (these proteins were not used in the training set) and (ii) discrimination between native and decoy structures for 2470 proteins with 305,000 decoys and the "Decoys 'R' Us" dataset. In the first test, 58% of tested proteins stay within 5 A from the native fold in Molecular Dynamics simulations of more than 20 nanoseconds using the new potential. The potential is also useful in differentiating between correct and approximate folds providing significant signal for structure prediction algorithms. Sampling with the potential consistently regenerates the distribution of distances and internal coordinates it learned. Nevertheless, during Molecular Dynamics simulations structures are found that reproduce the learned distributions but are far from the native fold.

Identifying native-like protein structures with scoring functions based on all-atom ECEPP force fields, implicit solvent models and structure relaxation

Availability of energy functions which can discriminate native-like from non-native protein conformations is crucial for theoretical protein structure prediction and refinement of low-resolution protein models. This article reports the results of benchmark tests for scoring functions based on two all-atom ECEPP force fields, that is, ECEPP/3 and ECEPP05, and two implicit solvent models for a large set of protein decoys. The following three scoring functions are considered: (i) ECEPP05 plus a solvent-accessible surface area model with the parameters optimized with a set of protein decoys (ECEPP05/SA); (ii) ECEPP/3 plus the solvent-accessible surface area model of Ooi et al. (Proc Natl Acad Sci USA 1987;84:3086-3090) (ECEPP3/OONS); and (iii) ECEPP05 plus an implicit solvent model based on a solution of the Poisson equation with an optimized Fast Adaptive Multigrid Boundary Element (FAMBEpH) method (ECEPP05/FAMBEpH). Short Monte Carlo-with-Minimization (MCM) simulations, following local energy minimization, are used as a scoring method with ECEPP05/SA and ECEPP3/OONS potentials, whereas energy calculation is used with ECEPP05/FAMBEpH. The performance of each scoring function is evaluated by examining its ability to distinguish between native-like and non-native protein structures. The results of the tests show that the new ECEPP05/SA scoring function represents a significant improvement over the earlier ECEPP3/OONS version of the force field. Thus, it is able to rank native-like structures with C(alpha) root-mean-square-deviations below 3.5 A as lowest-energy conformations for 76% and within the top 10 for 87% of the proteins tested, compared with 69 and 80%, respectively, for ECEPP3/OONS. The use of the FAMBEpH solvation model, which provides a more accurate description of the protein-solvent interactions, improves the discriminative ability of the scoring function to 89%. All failed tests in which the native-like structures cannot be discriminated as those with low energy, are due to omission of protein-protein interactions. The results of this study represent a benchmark in force-field development, and may be useful for evaluation of the performance of different force fields.

Explicit orientation dependence in empirical potentials and its significance to side-chain modeling

Protein structure modeling and prediction have important applications throughout the biological sciences, from the design of pharmaceuticals to the elucidation of enzyme mechanisms. At the core of most protein modeling is an energy function, the minimum of which represents the free energy "cost" for forming a correct protein structure. The most commonly used energy functions are knowledge-based statistical potential functions; that is, they are empirically derived from statistical analysis of a set of high-resolution protein structures. When that kind of potential function is constructed, the anisotropic orientation dependence between the interacting groups is a critical component for accurately representing key molecular interactions, such as those involved in protein side-chain packing. In the literature, however, many potential functions are limited in their ability to describe orientation dependence. In all-atom potentials, they typically ignore heterogeneous chemical-bond connectivity. In coarse-grained potentials, such as (semi)-residue-based potentials, the simplified representation of residues often reduces the sensitivity of the potential to side-chain orientation. Recently, in an effort to maximally capture the orientation dependence in side-chain interactions, a new type of all-atom statistical potential was developed: OPUS-PSP (potential derived from side-chain packing). The key feature of this potential is its explicit description of orientation dependence in molecular interactions, which is achieved with a basis set of 19 rigid-body blocks extracted from the chemical structures of 20 amino acid residues. This basis set is specifically designed to maximally capture the essential elements of orientation dependence in molecular packing interactions. The potential is constructed from the orientation-specific packing statistics of pairs of those blocks in a nonredundant structural database. On decoy set tests, OPUS-PSP significantly outperforms most of the existing knowledge-based potentials in terms of both its ability to recognize native structures and its consistency in achieving high Z scores across decoy sets. The application of OPUS-PSP to conformational modeling of side chains has led to another method, called OPUS-Rota. In terms of combined speed and accuracy, OPUS-Rota outperforms all of the other methods in modeling side-chain conformation. In this Account, we briefly outline the basic scheme of the OPUS-PSP potential and its application to side-chain modeling via OPUS-Rota. Future perspectives on the modeling of orientation dependence are also discussed. The computer programs for OPUS-PSP and OPUS-Rota can be downloaded at http://sigler.bioch.bcm.tmc.edu/MaLab . They are free for academic users.

Technique for energy decomposition in the study of "receptor-ligand" complexes

A new methodology to describe the interactions in "receptor-ligand" complexes is presented. The methodology is based on a combination of the 3D/4D QSAR BiS/MC and CoCon algorithms. The first algorithm performs the restricted docking of compounds to receptor pockets. The second determines the relationships between the bioactivity and the parameters of interactions in the "receptor-ligand" complexes, including a new formalism for estimating hydrogen bond energies.

Use of decoys to optimize an all-atom force field including hydration

A novel method of parameter optimization is proposed. It makes use of large sets of decoys generated for six nonhomologous proteins with different architecture. Parameter optimization is achieved by creating a free energy gap between sets of nativelike and nonnative conformations. The method is applied to optimize the parameters of a physics-based scoring function consisting of the all-atom ECEPP05 force field coupled with an implicit solvent model (a solvent-accessible surface area model). The optimized force field is able to discriminate near-native from nonnative conformations of the six training proteins when used either for local energy minimization or for short Monte Carlo simulated annealing runs after local energy minimization. The resulting force field is validated with an independent set of six nonhomologous proteins, and appears to be transferable to proteins not included in the optimization; i.e., for five out of the six test proteins, decoys with 1.7- to 4.0-A all-heavy-atom root mean-square deviations emerge as those with the lowest energy. In addition, we examined the set of misfolded structures created by Park and Levitt using a four-state reduced model. The results from these additional calculations confirm the good discriminative ability of the optimized force field obtained with our decoy sets.

OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing

Here we report an orientation-dependent statistical all-atom potential derived from side-chain packing, named OPUS-PSP. It features a basis set of 19 rigid-body blocks extracted from the chemical structures of all 20 amino acid residues. The potential is generated from the orientation-specific packing statistics of pairs of those blocks in a non-redundant structural database. The purpose of such an approach is to capture the essential elements of orientation dependence in molecular packing interactions. Tests of OPUS-PSP on commonly used decoy sets demonstrate that it significantly outperforms most of the existing knowledge-based potentials in terms of both its ability to recognize native structures and consistency in achieving high Z-scores across decoy sets. As OPUS-PSP excludes interactions among main-chain atoms, its success highlights the crucial importance of side-chain packing in forming native protein structures. Moreover, OPUS-PSP does not explicitly include solvation terms, and thus the potential should perform well when the solvation effect is difficult to determine, such as in membrane proteins. Overall, OPUS-PSP is a generally applicable potential for protein structure modeling, especially for handling side-chain conformations, one of the most difficult steps in high-accuracy protein structure prediction and refinement.

Sampling of near-native protein conformations during protein structure refinement using a coarse-grained model, normal modes, and molecular dynamics simulations

Protein structure refinement from comparative models with the goal of predicting structures at near-experimental accuracy remains an unsolved problem. Structure refinement might be achieved with an iterative protocol where the most native-like structure from a set of decoys generated from an initial model in one cycle is used as the starting structure for the next cycle. Conformational sampling based on the coarse-grained SICHO model, atomic level of detail molecular dynamics simulations, and normal-mode analysis is compared in the context of such a protocol. All of the sampling methods can achieve significant refinement close to experimental structures, although the distribution of structures and the ability to reach native-like structures differs greatly. Implications for the practical application of such sampling methods and the requirements for scoring functions in an iterative refinement protocol are analyzed in the context of theoretical predictions for the distribution of protein-like conformations with a random sampling protocol.

Dynamics and assembly of the cytolethal distending toxin

The cytolethal distending toxin (CDT) is a widespread bacterial toxin that consists of an active subunit CdtB with nuclease activity and two ricin-like lectin domains, CdtA and CdtC, that are involved in the delivery of CdtB into the host cell. The three subunits form a tripartite complex that is required to achieve the fully active holotoxin. In the present study we investigate the assembly and dynamic properties of the CDT holotoxin using molecular dynamics simulations and binding free energy calculations. The results have revealed that CdtB likely adopts a different conformation in the unbound state with a closed DNA binding site. The two characterized structural elements of the aromatic patch and groove on the CdtA and CdtC protein surfaces exhibit high mobility, and free energy calculations show that the heterodimeric complex CdtA-CdtC, as well as the CdtA-CdtB and CdtB-CdtC sub-complexes are less energetically stable as compared to the binding in the tripartite complex. Analysis of the dynamical cross-correlation map reveals information on the correlated motions and long-range interplay among the CDT subunits associated with complex formation. Finally, the estimated binding free energies of subunit interactions are presented, together with the free energy decomposition to determine the contributions of residues for both binding partners, providing insight into the protein-protein interactions in the CDT holotoxin.