Application of the frozen atom approximation to the GB/SA continuum model for solvation free energy

QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs

S2R overexpression is associated with various forms of cancer as well as both neuropsychiatric disorders (e.g., schizophrenia) and neurodegenerative diseases (Alzheimer’s disease: AD). In the present study, three ligand-based methods (QSAR modeling, pharmacophore mapping, and shape-based screening) were implemented to select putative S2R ligands from the DrugBank library comprising 2000+ entries. Four separate optimization algorithms (i.e., stepwise regression, Lasso, genetic algorithm (GA), and a customized extension of GA called GreedGene) were adapted to select descriptors for the QSAR models. The subsequent biological evaluation of selected compounds revealed that three FDA-approved drugs for unrelated therapeutic indications exhibited sub-1 uM binding affinity for S2R. In particular, the antidepressant drug nefazodone elicited a S2R binding affinity Ki = 140 nM. A total of 159 unique S2R ligands were retrieved from 16 publications for model building, validation, and testing. To our best knowledge, the present report represents the first case to develop comprehensive QSAR models sourced by pooling and curating a large assemblage of structurally diverse S2R ligands, which should prove useful for identifying new drug leads and predicting their S2R binding affinity prior to the resource-demanding tasks of chemical synthesis and biological evaluation.

Structural analysis of experimental drugs binding to the SARS-CoV-2 target TMPRSS2

The emergence of SARS-CoV-2 has prompted a worldwide health emergency. There is an urgent need for therapeutics, both through the repurposing of approved drugs and the development of new treatments. In addition to the viral drug targets, a number of human drug targets have been suggested. In theory, targeting human proteins should provide an advantage over targeting viral proteins in terms of drug resistance, which is commonly a problem in treating RNA viruses. This paper focuses on the human protein TMPRSS2, which supports coronavirus life cycles by cleaving viral spike proteins. The three-dimensional structure of TMPRSS2 is not known and so we have generated models of the TMPRSS2 in the apo state as well as in complex with a peptide substrate and putative inhibitors to aid future work. Importantly, many related human proteases have 80% or higher identity with TMPRSS2 in the S1-S1' subsites, with plasminogen and urokinase-type plasminogen activator (uPA) having 95% identity. We highlight 376 approved, investigational or experimental drugs targeting S1A serine proteases that may also inhibit TMPRSS2. Whilst the presence of a relatively uncommon lysine residue in the S2/S3 subsites means that some serine protease inhibitors will not inhibit TMPRSS2, this residue is likely to provide a handle for selective targeting in a focused drug discovery project. We discuss how experimental drugs targeting related serine proteases might be repurposed as TMPRSS2 inhibitors to treat coronaviruses.

Synthesis and biological evaluation of a MraY selective analogue of tunicamycins

Tunicamycins, which are nucleoside natural products, inhibit both bacterial phospho-N-acetylmuraminic acid (MurNAc)-pentapeptide translocase (MraY) and human UDP-N-acetylglucosamine (GlcNAc): polyprenol phosphate translocase (GPT). The improved synthesis and detailed biological evaluation of an MraY-selective inhibitor, 2, where the GlcNAc moiety was modified to a MurNAc amide, has been described.

Characterization of unfolding mechanism of human lamin A Ig fold by single-molecule force spectroscopy-implications in EDMD

A- and B-type lamins are intermediate filament proteins constituting the nuclear lamina underneath the nuclear envelope thereby conferring proper shape and mechanical rigidity to the nucleus. Lamin proteins are also shown to be related diversely to basic nuclear processes. More than 400 mutations in human lamin A protein alone have been reported to produce at least 11 different disease conditions jointly termed as laminopathies. These mutations in lamin A are scattered throughout its helical rod domain, as well as the C-terminal domain containing the conserved Ig-fold region. The commonality of phenotypes in all these diseases is characterized by misshapen nuclei of the affected tissues which might stem from altered rigidity of the supporting lamina hence lamins. Here we have focused on autosomal dominant Emery-Dreifuss Muscular Dystrophy, one such laminopathy where R453W is the causative mutation located in the Ig domain of lamin A. We have investigated by single-molecule force spectroscopy how a stretching mechanical perturbation senses the destabilizing effect of the mutation in the lamin A Ig domain and compared the mechanoelastic properties of the mutant R453W with that of the wild-type in conjunction with steered molecular dynamics. Furthermore, we have shown the interaction of Ig domain with emerin, another key player and interacting partner in the pathogenesis of EDMD, is disrupted in the R453W mutant. This altered mechanoresistance of Ig domain itself and consequent uncoupling of lamin A-emerin interaction might underlie the altered mechanotransduction properties of EDMD affected nuclei.

Implicit ligand theory: rigorous binding free energies and thermodynamic expectations from molecular docking

A rigorous formalism for estimating noncovalent binding free energies and thermodynamic expectations from calculations in which receptor configurations are sampled independently from the ligand is derived. Due to this separation, receptor configurations only need to be sampled once, facilitating the use of binding free energy calculations in virtual screening. Demonstrative calculations on a host-guest system yield good agreement with previous free energy calculations and isothermal titration calorimetry measurements. Implicit ligand theory provides guidance on how to improve existing molecular docking algorithms and insight into the concepts of induced fit and conformational selection in noncovalent macromolecular recognition.

Comparison of protein force fields for molecular dynamics simulations

In the context of molecular dynamics simulations of proteins, the term "force field" refers to the combination of a mathematical formula and associated parameters that are used to describe the energy of the protein as a function of its atomic coordinates. In this review, we describe the functional forms and parameterization protocols of the widely used biomolecular force fields Amber, CHARMM, GROMOS, and OPLS-AA. We also summarize the ability of various readily available noncommercial molecular dynamics packages to perform simulations using these force fields, as well as to use modern methods for the generation of constant-temperature, constant-pressure ensembles and to treat long-range interactions. Finally, we finish with a discussion of the ability of these force fields to support the modeling of proteins in conjunction with nucleic acids, lipids, carbohydrates, and/or small molecules.

Analogue inhibitors by modifying oseltamivir based on the crystal neuraminidase structure for treating drug-resistant H5N1 virus

The worldwide spread of H5N1 avian influenza and the increasing reports about its resistance to the existing drugs have made a priority for the development of the new anti-influenza molecules. The crystal structure of H5N1 avian influenza neuraminidase reported recently by Russell et al. [R.J. Russell, L.F. Haire, D.J. Stevens, P.J. Collins, Y. P. Lin, G.M. Blackburn, A.J. Hay, S.J. Gamblin, J.J. Skehel, The structure of H5N1 avian influenza neuraminidase suggests new opportunities for drug design, Nature 443 (2006) 45-49] have provided new opportunities for drug design in this regard. It is revealed through the structure that the active sites of the group-1 neuraminidases, which contain the N1 subtype, have a very different three-dimensional structure from those of group-2 neuraminidases. The key difference is in the 150-loop cavity adjacent to the conserved active site in neuraminidase. Based on these findings and by modifying oseltamivir, six analog inhibitors were proposed as candidates for developing inhibitors against H5N1 virus, particularly against the oseltamivir-resistant H5N1 virus strain.

Mutagenesis and modeling of the peroxiredoxin (Prx) complex with the NMR structure of ATP-bound human sulfiredoxin implicate aspartate 187 of Prx I as the catalytic residue in ATP hydrolysis

The catalytic cysteine of certain members of the peroxiredoxin (Prx) family can be hyperoxidized to cysteinesulfinic acid during reduction of peroxides. Sulfiredoxin is responsible for the ATP-dependent reduction of cysteinesulfinic acid (SO2H) of hyperoxidized Prx. Here we report the NMR solution structure of human sulfiredoxin (hSrx), both with and without bound ATP, and we model the complex of ATP-bound hSrx with Prx. Binding ATP causes only small changes in the NMR structure of hSrx, and the bound ATP conformation is quite similar to that seen for the previously reported X-ray structure of the ADP-hSrx complex. Although hSrx binds ATP, it does not catalyze hydrolysis by itself and has no catalytic acid residue typical of most ATPase and kinase family proteins. For modeling the complex, the ATP-bound hSrx was docked to hyperoxidized Prx II using EMAP of CHARMM. In the model complex, Asn186 of Prx II (Asp187 of Prx I) is in contact with the hSrx-bound ATP beta- and gamma-phosphate groups. Asp187 of Prx I was mutated to alanine and asparagine, and binding and activity of the mutants with hSrx were compared to those of the wild type. For the D187N mutant, both binding and hydrolysis and reduction activities were comparable to those of the wild type, whereas for D187A, binding was unimpaired but ATP hydrolysis and reduction did not occur. The modeling and mutagenesis analyses strongly implicate Asp187 of Prx I as the catalytic residue responsible for ATP hydrolysis in the cysteinesulfinic acid reduction of Prx by hSrx.

Hierarchical Database Screenings for HIV-1 Reverse Transcriptase Using a Pharmacophore Model, Rigid Docking, Solvation Docking, and MM−PB/SA

In this work, an efficient strategy was presented to search drug leads for human immunodeficiency virus type 1 reverse transcriptase (HIV-1 RT) using hierarchical database screenings, which included a pharmacophore model, multiple-conformation rigid docking, solvation docking, and molecular mechanics-Poisson-Boltzmann/surface area (MM-PB/SA) sequentially. Encouraging results were achieved in searching a refined available chemical directory (ACD) database: the enrichment factor after the first three filters was estimated to be 25-fold; the hit rate for all the four filters was predicted to be 41% in a control test using 37 known HIV-1 non-nucleoside reverse transcriptase inhibitors; 10 out of 30 promising solvation-docking hits had MM-PB/SA binding free energies better than -6.8 kcal/mol and the best one, HIT15, had -17.0 kcal/mol. In conclusion, the hierarchical multiple-filter database searching strategy is an attractive strategy in drug lead exploration.

FDS: flexible ligand and receptor docking with a continuum solvent model and soft-core energy function

The docking of flexible small molecule ligands to large flexible protein targets is addressed in this article using a two-stage simulation-based method. The methodology presented is a hybrid approach where the first component is a dock of the ligand to the protein binding site, based on deriving sets of simultaneously satisfied intermolecular hydrogen bonds using graph theory and a recursive distance geometry algorithm. The output structures are reduced in number by cluster analysis based on distance similarities. These structures are submitted to a modified Monte Carlo algorithm using the AMBER-AA molecular mechanics force field with the Generalized Born/Surface Area (GB/SA) continuum model. This solvent model is not only less expensive than an explicit representation, but also yields increased sampling. Sampling is also increased using a rotamer library to direct some of the protein side-chain movements along with large dihedral moves. Finally, a softening function for the nonbonded force field terms is used, enabling the potential energy function to be slowly turned on throughout the course of the simulation. The docking procedure is optimized, and the results are presented for a single complex of the arabinose binding protein. It was found that for a rigid receptor model, the X-ray binding geometry was reproduced and uniquely identified based on the associated potential energy. However, when side-chain flexibility was included, although the X-ray structure was identified, it was one of three possible binding geometries that were energetically indistinguishable. These results suggest that on relaxing the constraint on receptor flexibility, the docking energy hypersurface changes from being funnel-like to rugged. A further 14 complexes were then examined using the optimized protocol. For each complex the docking methodology was tested for a fully flexible ligand, both with and without protein side-chain flexibility. For the rigid protein docking, 13 out of the 15 test cases were able to find the experimental binding mode; this number was reduced to 11 for the flexible protein docking. However, of these 11, in the majority of cases the experimental binding mode was not uniquely identified, but was present in a cluster of low energy structures that were energetically indistinguishable. These results not only support the presence of a rugged docking energy hypersurface, but also suggest that it may be necessary to consider the possibility of more than one binding conformation during ligand optimization.

Ab initio construction of polypeptide fragments: Accuracy of loop decoy discrimination by an all-atom statistical potential and the AMBER force field with the Generalized Born solvation model

The accuracy of model selection from decoy ensembles of protein loop conformations was explored by comparing the performance of the Samudrala-Moult all-atom statistical potential (RAPDF) and the AMBER molecular mechanics force field, including the Generalized Born/surface area solvation model. Large ensembles of consistent loop conformations, represented at atomic detail with idealized geometry, were generated for a large test set of protein loops of 2 to 12 residues long by a novel ab initio method called RAPPER that relies on fine-grained residue-specific phi/psi propensity tables for conformational sampling. Ranking the conformers on the basis of RAPDF scores resulted in selected conformers that had an average global, non-superimposed RMSD for all heavy mainchain atoms ranging from 1.2 A for 4-mers to 2.9 A for 8-mers to 6.2 A for 12-mers. After filtering on the basis of anchor geometry and RAPDF scores, ranking by energy minimization of the AMBER/GBSA potential energy function selected conformers that had global RMSD values of 0.5 A for 4-mers, 2.3 A for 8-mers, and 5.0 A for 12-mers. Minimized fragments had, on average, consistently lower RMSD values (by 0.1 A) than their initial conformations. The importance of the Generalized Born solvation energy term is reflected by the observation that the average RMSD accuracy for all loop lengths was worse when this term is omitted. There are, however, still many cases where the AMBER gas-phase minimization selected conformers of lower RMSD than the AMBER/GBSA minimization. The AMBER/GBSA energy function had better correlation with RMSD to native than the RAPDF. When the ensembles were supplemented with conformations extracted from experimental structures, a dramatic improvement in selection accuracy was observed at longer lengths (average RMSD of 1.3 A for 8-mers) when scoring with the AMBER/GBSA force field. This work provides the basis for a promising hybrid approach of ab initio and knowledge-based methods for loop modeling.