An all-atom, active site exploration of antiviral drugs that target Flaviviridae polymerases.

Natural 2'-modified nucleosides are the most widely used antiviral therapy. In their triphosphorylated form, also known as nucleotide analogues, they target the active site of viral polymerases. Viral polymerases have an overall right-handed structure that includes the palm, fingers and thumb domains. These domains are further subdivided into structurally conserved motifs A-G, common to all viral polymerases. The structural motifs encapsulate the allosteric/initiation (N1) and orthosteric/catalytic (N2) nucleotide-binding sites. The current study investigated how nucleotide analogues explore the N2 site of viral polymerases from three genera of the family Flaviviridae using a stochastic, biophysical, Metropolis Monte Carlo-based software. The biophysical simulations showed a statistical distinction in nucleotide-binding energy and exploration between phylogenetically related viral polymerases. This distinction is clearly demonstrated by the respective analogue contacts made with conserved viral polymerase residues, the heterogeneous dynamics of structural motifs, and the orientation of the nucleotide analogues within the N2 site. Being able to simulate what occurs within viral-polymerase-binding sites can prove useful in rational drug designs against viruses.

Enhancing backbone sampling in Monte Carlo simulations using internal coordinates normal mode analysis.

Normal mode methods are becoming a popular alternative to sample the conformational landscape of proteins. In this study, we describe the implementation of an internal coordinate normal mode analysis method and its application in exploring protein flexibility by using the Monte Carlo method PELE. This new method alternates two different stages, a perturbation of the backbone through the application of torsional normal modes, and a resampling of the side chains. We have evaluated the new approach using two test systems, ubiquitin and c-Src kinase, and the differences to the original ANM method are assessed by comparing both results to reference molecular dynamics simulations. The results suggest that the sampled phase space in the internal coordinate approach is closer to the molecular dynamics phase space than the one coming from a Cartesian coordinate anisotropic network model. In addition, the new method shows a great speedup (∼5-7×), making it a good candidate for future normal mode implementations in Monte Carlo methods.

Nucleoside inhibitors of tick-borne encephalitis virus.

Tick-borne encephalitis virus (TBEV) is a leading cause of human neuroinfections in Europe and Northeast Asia. There are no antiviral therapies for treating TBEV infection. A series of nucleoside analogues was tested for the ability to inhibit the replication of TBEV in porcine kidney cells and human neuroblastoma cells. The interactions of three nucleoside analogues with viral polymerase were simulated using advanced computational methods. The nucleoside analogues 7-deaza-2'-C-methyladenosine (7-deaza-2'-CMA), 2'-C-methyladenosine (2'-CMA), and 2'-C-methylcytidine (2'-CMC) inhibited TBEV replication. These compounds showed dose-dependent inhibition of TBEV-induced cytopathic effects, TBEV replication (50% effective concentrations [EC50]of 5.1 ± 0.4 µM for 7-deaza-2'-CMA, 7.1 ± 1.2 µM for 2'-CMA, and 14.2 ± 1.9 µM for 2'-CMC) and viral antigen production. Notably, 2'-CMC was relatively cytotoxic to porcine kidney cells (50% cytotoxic concentration [CC50] of ∼50 µM). The anti-TBEV effect of 2'-CMA in cell culture diminished gradually after day 3 posttreatment. 7-Deaza-2'-CMA showed no detectable cellular toxicity (CC50 > 50 µM), and the antiviral effect in culture was stable for >6 days posttreatment. Computational molecular analyses revealed that compared to the other two compounds, 7-deaza-2'-CMA formed a large cluster near the active site of the TBEV polymerase. High antiviral activity and low cytotoxicity suggest that 7-deaza-2'-CMA is a promising candidate for further investigation as a potential therapeutic agent in treating TBEV infection.

Monte Carlo Free Ligand Diffusion with Markov State Model Analysis and Absolute Binding Free Energy Calculations.

Obtaining absolute binding free energies from unbiased ligand diffusion has attracted a significant amount of attention due to its implications in drug design. Several studies have used special purpose computers and software to achieve microsecond molecular dynamics which, combined with a Markov state model analysis, are capable of providing absolute binding free energies. We have recently developed a Monte Carlo based technique, PELE, capable of performing a dynamical exploration of the protein-ligand energy landscape including free ligand diffusion into the active site, at a fraction of the computational cost of molecular dynamics techniques. We demonstrate here the capabilities of our Monte Carlo technique in obtaining absolute binding free energies for a series of benzamidine like inhibitors into trypsin. Our results are in good agreement with experimental data and other molecular dynamics simulations, indicating that PELE can be a useful tool for quick estimates of binding free energies and mechanisms.

pyProCT: Automated Cluster Analysis for Structural Bioinformatics.

Cluster analysis is becoming a relevant tool in structural bioinformatics. It allows analyzing large conformational ensembles in order to extract features or diminish redundancy, or just as a first step for other methods. Unfortunately, the successfulness of this analysis strongly depends on the data set traits, the chosen algorithm, and its parameters, which can lead to poor or even erroneous results not easily detected. In order to overcome this problem, we have developed pyProCT, a Python open source cluster analysis toolkit specially designed to be used with ensembles of biomolecule conformations. pyProCT implements an automated protocol to choose the clustering algorithm and parameters that produce the best results for a particular data set. It offers different levels of customization according to users' expertise. Moreover, pyProCT has been designed as a collection of interchangeable libraries, making it easier to reuse it as part of other programs.

pyRMSD: a Python package for efficient pairwise RMSD matrix calculation and handling.

SUMMARY: We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms. AVAILABILITY: The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section. CONTACT: victor.guallar@bsc.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.