pyProCT: Automated Cluster Analysis for Structural Bioinformatics

pyDockDNA: A new web server for energy-based protein-DNA docking and scoring

Proteins and nucleic acids are essential biological macromolecules for cell life. Indeed, interactions between proteins and DNA regulate many biological processes such as protein synthesis, signal transduction, DNA storage, or DNA replication and repair. Despite their importance, less than 4% of total structures deposited in the Protein Data Bank (PDB) correspond to protein-DNA complexes, and very few computational methods are available to model their structure. We present here the pyDockDNA web server, which can successfully model a protein-DNA complex with a reasonable predictive success rate (as benchmarked on a standard dataset of protein-DNA complex structures, where DNA is in B-DNA conformation). The server implements the pyDockDNA program, as a module of pyDock suite, thus including third-party programs, modules, and previously developed tools, as well as new modules and parameters to handle the DNA properly. The user is asked to enter Protein Data Bank files for protein and DNA input structures (or suitable models) and select the chains to be docked. The server calculations are mainly divided into three steps: sampling by FTDOCK, scoring with new energy-based parameters and the possibility of applying external restraints. The user can select different options for these steps. The final output screen shows a 3D representation of the top 10 models and a table sorting the model according to the scoring function selected previously. All these output files can be downloaded, including the top 100 models predicted by pyDockDNA. The server can be freely accessed for academic use (https://model3dbio.csic.es/pydockdna).

RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules

Motivation

The root mean square deviation (RMSD) is one of the most used similarity criteria in structural biology and bioinformatics. Standard computation of the RMSD has a linear complexity with respect to the number of atoms in a molecule, making RMSD calculations time-consuming for the large-scale modeling applications, such as assessment of molecular docking predictions or clustering of spatially proximate molecular conformations. Previously, we introduced the RigidRMSD algorithm to compute the RMSD corresponding to the rigid-body motion of a molecule. In this study, we go beyond the limits of the rigid-body approximation by taking into account conformational flexibility of the molecule. We model the flexibility with a reduced set of collective motions computed with e.g. normal modes or principal component analysis.

Results

The initialization of our algorithm is linear in the number of atoms and all the subsequent evaluations of RMSD values between flexible molecular conformations depend only on the number of collective motions that are selected to model the flexibility. Therefore, our algorithm is much faster compared to the standard RMSD computation for large-scale modeling applications. We demonstrate the efficiency of our method on several clustering examples, including clustering of flexible docking results and molecular dynamics (MD) trajectories. We also demonstrate how to use the presented formalism to generate pseudo-random constant-RMSD structural molecular ensembles and how to use these in cross-docking.

Availability and implementation

We provide the algorithm written in C++ as the open-source RapidRMSD library governed by the BSD-compatible license, which is available at http://team.inria.fr/nano-d/software/RapidRMSD/. The constant-RMSD structural ensemble application and clustering of MD trajectories is available at http://team.inria.fr/nano-d/software/nolb-normal-modes/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Adaptive simulations, towards interactive protein-ligand modeling

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.

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.

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.

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.