Stabilization of the integrase-DNA complex by Mg2+ions and prediction of key residues for binding HIV-1 integrase inhibitors

Low-resolution description of the conformational space for intrinsically disordered proteins

Intrinsically disordered proteins (IDP) are at the center of numerous biological processes, and attract consequently extreme interest in structural biology. Numerous approaches have been developed for generating sets of IDP conformations verifying a given set of experimental measurements. We propose here to perform a systematic enumeration of protein conformations, carried out using the TAiBP approach based on distance geometry. This enumeration was performed on two proteins, Sic1 and pSic1, corresponding to unphosphorylated and phosphorylated states of an IDP. The relative populations of the obtained conformations were then obtained by fitting SAXS curves as well as Ramachandran probability maps, the original finite mixture approach RamaMix being developed for this second task. The similarity between profiles of local gyration radii provides to a certain extent a converged view of the Sic1 and pSic1 conformational space. Profiles and populations are thus proposed for describing IDP conformations. Different variations of the resulting gyration radius between phosphorylated and unphosphorylated states are observed, depending on the set of enumerated conformations as well as on the methods used for obtaining the populations.

Repurposing FDA-approved drugs as HIV-1 integrase inhibitors: an in silico investigation

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to-date. One of the most efficacious treatments for naïve or pretreated HIV patients is the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is life-long, the emergence of HIV strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r² = 0.998, q²_10CV = 0.721, q²_{external_test} = 0.754) and a boosted K* algorithm (r² = 0.987, q²_10CV = 0.721, q²_{external_test} = 0.758) to predict the pIC₅₀ values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top-ranked compounds were further evaluated for their target engagement activity using molecular docking studies and accelerated Molecular Dynamics simulation. Lastly, their potential as INSTIs were also evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.Communicated by Ramaswamy H. Sarma.

HIV-1 Reverse Transcriptase/Integrase Dual Inhibitors: A Review of Recent Advances and Structure-activity Relationship Studies

The significant threat to humanity is HIV infection, and it is uncertain whether a definitive treatment or a safe HIV vaccine is. HIV-1 is continually evolving and resistant to commonly used HIV-resistant medications, presenting significant obstacles to HIV infection management. The drug resistance adds to the need for new anti-HIV drugs; it chooses ingenious approaches to fight the emerging virus. Highly Active Antiretroviral Therapy (HAART), a multi-target approach for specific therapies, has proved effective in AIDS treatment. Therefore, it is a dynamic system with high prescription tension, increased risk of medication reactions, and adverse effects, leading to poor compliance with patients. In the HIV-1 lifecycle, two critical enzymes with high structural and functional analogies are reverse transcriptase (RT) and integrase (IN), which can be interpreted as druggable targets for modern dual-purpose inhibitors. Designed multifunctional ligand (DML) is a new technique that recruited many targets to be achieved by one chemical individual. A single chemical entity that acts for multiple purposes can be much more successful than a complex multidrug program. The production of these multifunctional ligands as antiretroviral drugs is valued with the advantage that the viral-replication process may end in two or more phases. This analysis will discuss the RT-IN dual-inhibitory scaffolds' developments documented so far.

Tandem domain structure determination based on a systematic enumeration of conformations

Protein structure determination is undergoing a change of perspective due to the larger importance taken in biology by the disordered regions of biomolecules. In such cases, the convergence criterion is more difficult to set up and the size of the conformational space is a obstacle to exhaustive exploration. A pipeline is proposed here to exhaustively sample protein conformations using backbone angle limits obtained by nuclear magnetic resonance (NMR), and then to determine the populations of conformations. The pipeline is applied to a tandem domain of the protein whirlin. An original approach, derived from a reformulation of the Distance Geometry Problem is used to enumerate the conformations of the linker connecting the two domains. Specifically designed procedure then permit to assemble the domains to the linker conformations and to optimize the tandem domain conformations with respect to two sets of NMR measurements: residual dipolar couplings and paramagnetic resonance enhancements. The relative populations of optimized conformations are finally determined by fitting small angle X-ray scattering (SAXS) data. The most populated conformation of the tandem domain is a semi-closed one, fully closed and more extended conformations being in minority, in agreement with previous observations. The SAXS and NMR data show different influences on the determination of populations.

Systematic Exploration of Protein Conformational Space Using a Distance Geometry Approach

The optimization approaches classically used during the determination of protein structure encounter various difficulties, especially when the size of the conformational space is large. Indeed, in such a case, algorithmic convergence criteria are more difficult to set up. Moreover, the size of the search space makes it difficult to achieve a complete exploration. The interval branch-and-prune (iBP) approach, based on the reformulation of the distance geometry problem (DGP) provides a theoretical frame for the generation of protein conformations, by systematically sampling the conformational space. When an appropriate subset of interatomic distances is known exactly, this worst-case exponential-time algorithm is provably complete and fixed-parameter tractable. These guarantees, however, immediately disappear as distance measurement errors are introduced. Here we propose an improvement of this approach: threading-augmented interval branch-and-prune (TAiBP), where the combinatorial explosion of the original iBP approach arising from its exponential complexity is alleviated by partitioning the input instances into consecutive peptide fragments and by using self-organizing maps (SOMs) to obtain clusters of similar solutions. A validation of the TAiBP approach is presented here on a set of proteins of various sizes and structures. The calculation inputs are a uniform covalent geometry extracted from force field covalent terms, the backbone dihedral angles with error intervals, and a few long-range distances. For most of the proteins smaller than 50 residues and interval widths of 20°, the TAiBP approach yielded solutions with RMSD values smaller than 3 Å with respect to the initial protein conformation. The efficiency of the TAiBP approach for proteins larger than 50 residues will require the use of nonuniform covalent geometry and may have benefits from the recent development of residue-specific force-fields.

An effective HIV-1 integrase inhibitor screening platform: Rationality validation of drug screening, conformational mobility and molecular recognition analysis for PFV integrase complex with viral DNA

As an important target for the development of novel anti-AIDS drugs, HIV-1 integrase (IN) has been widely concerned. However, the lack of a complete accurate crystal structure of HIV-1 IN greatly blocks the discovery of novel inhibitors. In this work, an effective HIV-1 IN inhibitor screening platform, namely PFV IN, was filtered from all species of INs. Next, the 40.8% similarity with HIV-1 IN, as well as the high efficiency of virtual screening and the good agreement between calculated binding free energies and experimental ones all proved PFV IN is a promising screening platform for HIV-1 IN inhibitors. Then, the molecular recognition mechanism of PFV IN by its substrate viral DNA and six naphthyridine derivatives (NRDs) inhibitors was investigated through molecular docking, molecular dynamics simulations and water-mediated interactions analyses. The functional partition of NRDs IN inhibitors could be divided into hydrophobic and hydrophilic ones, and the Mg²⁺ ions, water molecules and conserved DDE motif residues all interacted with the hydrophilic partition, while the bases in viral DNA and residues like Tyr212, Pro214 interacted with the hydrophobic one. Finally, the free energy landscape (FEL) and cluster analyses were performed to explore the molecular motion of PFV IN-DNA system. It is found that the association with NRDs inhibitors would obviously decrease the motion amplitude of PFV IN-DNA, which may be one of the most potential mechanisms of IN inhibitors. This work will provide a theoretical basis for the inhibitor design based on the structure of HIV-1 IN.

Development of HIV-1 integrase inhibitors: recent molecular modeling perspectives

Of the three viral enzymes essential to HIV replication, HIV-1 integrase (IN) is gaining popularity as a target for the antiviral therapy of AIDS. Substantial work focusing on IN has been done over the past three decades, which has facilitated and led to the approval of three drugs. Here, we discuss in detail the development of IN inhibitors between January 2012 and May 2014, with a particular focus on molecular simulation. We highlight controversial aspects of computational drug design and refer to alternative practices where appropriate. The analysis of these computational approaches provides some useful clues to the possible future discovery of novel IN inhibitors.

Temperature Accelerated Molecular Dynamics with Soft-Ratcheting Criterion Orients Enhanced Sampling by Low-Resolution Information

Many proteins exhibit an equilibrium between multiple conformations, some of them being characterized only by low-resolution information. Visiting all conformations is a demanding task for computational techniques performing enhanced but unfocused exploration of collective variable (CV) space. Otherwise, pulling a structure toward a target condition biases the exploration in a way difficult to assess. To address this problem, we introduce here the soft-ratcheting temperature-accelerated molecular dynamics (sr-TAMD), where the exploration of CV space by TAMD is coupled to a soft-ratcheting algorithm that filters the evolving CV values according to a predefined criterion. Any low resolution or even qualitative information can be used to orient the exploration. We validate this technique by exploring the conformational space of the inactive state of the catalytic domain of the adenyl cyclase AC from Bordetella pertussis. The domain AC gets activated by association with calmodulin (CaM), and the available crystal structure shows that in the complex the protein has an elongated shape. High-resolution data are not available for the inactive, CaM-free protein state, but hydrodynamic measurements have shown that the inactive AC displays a more globular conformation. Here, using as CVs several geometric centers, we use sr-TAMD to enhance CV space sampling while filtering for CV values that correspond to centers moving close to each other, and we thus rapidly visit regions of conformational space that correspond to globular structures. The set of conformations sampled using sr-TAMD provides the most extensive description of the inactive state of AC up to now, consistent with available experimental information.

Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis

Background

Identifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands.

Results

We present a novel cavity prediction method that analyzes results of virtual screening of specific ligands or fragment libraries by means of Self-Organizing Maps. We demonstrate the method with two thoroughly studied proteins where it successfully identified their active sites (AS) and relevant secondary binding sites (BS). Moreover, known active ligands mapped the AS better than inactive ones. Interestingly, docking a naive fragment library brought even more insight. We then systematically applied the method to the 102 targets from the DUD-E database, where it showed a 90% identification rate of the AS among the first three consensual clusters of the SOM, and in 82% of the cases as the first one. Further analysis by chemical decomposition of the fragments improved BS prediction. Chemical substructures that are representative of the active ligands preferentially mapped in the AS.

Conclusion

The new approach provides valuable information both on relevant BSs and on chemical features promoting bioactivity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0518-z) contains supplementary material, which is available to authorized users.

An algorithm to enumerate all possible protein conformations verifying a set of distance constraints

BACKGROUND

The determination of protein structures satisfying distance constraints is an important problem in structural biology. Whereas the most common method currently employed is simulated annealing, there have been other methods previously proposed in the literature. Most of them, however, are designed to find one solution only.

RESULTS

In order to explore exhaustively the feasible conformational space, we propose here an interval Branch-and-Prune algorithm (iBP) to solve the Distance Geometry Problem (DGP) associated to protein structure determination. This algorithm is based on a discretization of the problem obtained by recursively constructing a search space having the structure of a tree, and by verifying whether the generated atomic positions are feasible or not by making use of pruning devices. The pruning devices used here are directly related to features of protein conformations.

CONCLUSIONS

We described the new algorithm iBP to generate protein conformations satisfying distance constraints, that would potentially allows a systematic exploration of the conformational space. The algorithm iBP has been applied on three α-helical peptides.

An automatic tool to analyze and cluster macromolecular conformations based on self-organizing maps

MOTIVATION

Sampling the conformational space of biological macromolecules generates large sets of data with considerable complexity. Data-mining techniques, such as clustering, can extract meaningful information. Among them, the self-organizing maps (SOMs) algorithm has shown great promise; in particular since its computation time rises only linearly with the size of the data set. Whereas SOMs are generally used with few neurons, we investigate here their behavior with large numbers of neurons.

RESULTS

We present here a python library implementing the full SOM analysis workflow. Large SOMs can readily be applied on heavy data sets. Coupled with visualization tools they have very interesting properties. Descriptors for each conformation of a trajectory are calculated and mapped onto a 3D landscape, the U-matrix, reporting the distance between neighboring neurons. To delineate clusters, we developed the flooding algorithm, which hierarchically identifies local basins of the U-matrix from the global minimum to the maximum.

AVAILABILITY AND IMPLEMENTATION

The python implementation of the SOM library is freely available on github: https://github.com/bougui505/SOM.

CONTACT

michael.nilges@pasteur.fr or guillaume.bouvier@pasteur.fr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.