Extreme entropy-enthalpy compensation in a drug-resistant variant of HIV-1 protease

Strong Ligand-Protein Interactions Derived from Diffuse Ligand Interactions with Loose Binding Sites

Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorable ΔG. Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorable ΔG because of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total binding ΔG arising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo (MCMC) approach is presented. This method yields a metric that quantifies the influence on overall ligand affinity of ligand binding to multiple, distinct sites within a protein binding region. This metric is essentially a measure of dispersion in equilibrium ligand binding and depends on both the number of potential sites of interaction and the distribution of their individual predicted affinities. Analysis of test cases indicates that, for some ligand/protein pairs involving transporters and carrier proteins, diffuse binding contributes greatly to total affinity, whereas in other cases the influence is modest. This approach may be useful for studying situations where “nonspecific” interactions contribute to biological function.

Computational challenges of structure-based approaches applied to HIV

Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.

Drug Resistance Mutations Alter Dynamics of Inhibitor-Bound HIV-1 Protease

Under the selective pressure of therapy, HIV-1 protease mutants resistant to inhibitors evolve to confer drug resistance. Such mutations can impact both the dynamics and structures of the bound and unbound forms of the enzyme. Flap+ is a multidrug-resistant variant of HIV-1 protease with a combination of primary and secondary resistance mutations (L10I, G48V, I54V, V82A) and a strikingly altered thermodynamic profile for darunavir (DRV) binding relative to the wild-type protease. We elucidated the impact of these mutations on protein dynamics in the DRV-bound state using molecular dynamics simulations and NMR relaxation experiments. Both methods concur in that the conformational ensemble and dynamics of protease are impacted by the drug resistance mutations in Flap+ variant. Surprisingly this change in ensemble dynamics is different from that observed in the unliganded form of the same variant (Cai, Y. et al. J. Chem. Theory Comput.2012, 8, 3452–3462). Our comparative analysis of both inhibitor-free and bound states presents a comprehensive picture of the altered dynamics in drug-resistant mutant HIV-1 protease and underlies the importance of incorporating dynamic analysis of the whole system, including the unliganded state, into revealing drug resistance mechanisms.

Computation of binding energies including their enthalpy and entropy components for protein-ligand complexes using support vector machines

Computing binding energies of protein-ligand complexes including their enthalpy and entropy terms by means of computational methods is an appealing approach for selecting initial hits and for further optimization in early stages of drug discovery. Despite the importance, computational predictions of thermodynamic components have evaded attention and reasonable solutions. In this study, support vector machines are used for developing scoring functions to compute binding energies and their enthalpy and entropy components of protein-ligand complexes. The binding energies computed from our newly derived scoring functions have better Pearson's correlation coefficients with experimental data than previously reported scoring functions in benchmarks for protein-ligand complexes from the PDBBind database. The protein-ligand complexes with binding energies dominated by enthalpy or entropy term could be qualitatively classified by the newly derived scoring functions with high accuracy. Furthermore, it is found that the inclusion of comprehensive descriptors based on ligand properties in the scoring functions improved the accuracy of classification as well as the prediction of binding energies including their thermodynamic components. The prediction of binding energies including the enthalpy and entropy components using the support vector machine based scoring functions should be of value in the drug discovery process.

A combined 3D-QSAR and docking studies for the In-silico prediction of HIV-protease inhibitors

BACKGROUND

Tremendous research from last twenty years has been pursued to cure human life against HIV virus. A large number of HIV protease inhibitors are in clinical trials but still it is an interesting target for researchers due to the viral ability to get mutated. Mutated viral strains led the drug ineffective but still used to increase the life span of HIV patients.

RESULTS

In the present work, 3D-QSAR and docking studies were performed on a series of Danuravir derivatives, the most potent HIV- protease inhibitor known so far. Combined study of 3D-QSAR was applied for Danuravir derivatives using ligand-based and receptor-based protocols and generated models were compared. The results were in good agreement with the experimental results. Additionally, docking analysis of most active 32 and least active 46 compounds into wild type and mutated protein structures further verified our results. The 3D-QSAR and docking results revealed that compound 32 bind efficiently to the wild and mutated protein whereas, sufficient interactions were lost in compound 46.

CONCLUSION

The combination of two computational techniques would helped to make a clear decision that compound 32 with well inhibitory activity bind more efficiently within the binding pocket even in case of mutant virus whereas compound 46 lost its interactions on mutation and marked as least active compound of the series. This is all due to the presence or absence of substituents on core structure, evaluated by 3D-QSAR studies. This set of information could be used to design highly potent drug candidates for both wild and mutated form of viruses.

Extreme multidrug resistant HIV-1 protease with 20 mutations is resistant to novel protease inhibitors with P1'-pyrrolidinone or P2-tris-tetrahydrofuran

Extreme drug resistant mutant of HIV-1 protease (PR) bearing 20 mutations (PR20) has been studied with the clinical inhibitor amprenavir (1) and two potent antiviral investigational inhibitors GRL-02031 (2) and GRL-0519 (3). Clinical inhibitors are >1000-fold less active on PR20 than on wild-type enzyme, which is consistent with dissociation constants (KL) from isothermal titration calorimetry of 40 nM for 3, 178 nM for amprenavir, and 960 nM for 2. High resolution crystal structures of PR20-inhibitor complexes revealed altered interactions compared with the corresponding wild-type PR complexes in agreement with relative inhibition. Amprenavir lacks interactions due to PR20 mutations in the S2/S2' subsites relative to PR. Inhibitors 2 and 3 lose interactions with Arg8' in PR20 relative to the wild-type enzyme because Arg8' shifts to interact with mutated L10F side chain. Overall, inhibitor 3 compares favorably with darunavir in affinity for PR20 and shows promise for further development.

Cooperative effects of drug-resistance mutations in the flap region of HIV-1 protease

Understanding the interdependence of multiple mutations in conferring drug resistance is crucial to the development of novel and robust inhibitors. As HIV-1 protease continues to adapt and evade inhibitors while still maintaining the ability to specifically recognize and efficiently cleave its substrates, the problem of drug resistance has become more complicated. Under the selective pressure of therapy, correlated mutations accumulate throughout the enzyme to compromise inhibitor binding, but characterizing their energetic interdependency is not straightforward. A particular drug resistant variant (L10I/G48V/I54V/V82A) displays extreme entropy-enthalpy compensation relative to wild-type enzyme but a similar variant (L10I/G48V/I54A/V82A) does not. Individual mutations of sites in the flaps (residues 48 and 54) of the enzyme reveal that the thermodynamic effects are not additive. Rather, the thermodynamic profile of the variants is interdependent on the cooperative effects exerted by a particular combination of mutations simultaneously present.

Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design

Recent calorimetric studies of interactions between small molecules and biomolecular targets have generated renewed interest in the phenomenon of entropy-enthalpy compensation. In these studies, entropic and enthalpic contributions to binding are observed to vary substantially and in an opposing manner as the ligand or protein is modified, whereas the binding free energy varies little. In severe examples, engineered enthalpic gains can lead to completely compensating entropic penalties, frustrating ligand design. Here, we examine the evidence for compensation, as well as its potential origins, prevalence, severity, and ramifications for ligand engineering. We find the evidence for severe compensation to be weak in light of the large magnitude of and correlation between errors in experimental measurements of entropic and enthalpic contributions to binding, though a limited form of compensation may be common. Given the difficulty of predicting or measuring entropic and enthalpic changes to useful precision, or using this information in design, we recommend ligand engineering efforts instead focus on computational and experimental methodologies to directly assess changes in binding free energy.

Differential Flap Dynamics in Wild-type and a Drug Resistant Variant of HIV-1 Protease Revealed by Molecular Dynamics and NMR Relaxation

In the rapidly evolving disease of HIV drug resistance readily emerges, nullifying the effectiveness of therapy. Drug resistance has been extensively studied in HIV-1 protease where resistance occurs when the balance between enzyme inhibition and substrate recognition and turn-over is perturbed to favor catalytic activity. Mutations which confer drug resistance can impact the dynamics and structure of both the bound and unbound forms of the enzyme. Flap+ is a multi-drug-resistant variant of HIV-1 protease with a combination of mutations at the edge of the active site, within the active site, and in the flaps (L10I, G48V, I54V, V82A). The impact of these mutations on the dynamics in the unliganded form in comparison with the wild-type protease was elucidated with Molecular Dynamic simulations and NMR relaxation experiments. The comparative analyses from both methods concur in showing that the enzyme's dynamics are impacted by the drug resistance mutations in Flap+ protease. These alterations in the enzyme dynamics, particularly within the flaps, likely modulate the balance between substrate turn-over and drug binding, thereby conferring drug resistance.