Efficient a Priori Identification of Drug Resistant Mutations Using Dead-End Elimination and MM-PBSA

Computational study of SENP1 in cancer by novel natural compounds and ZINC database screening

Introduction: Sentrin-specific protease 1 (SENP1) is a protein whose main function is deSUMOylation. SENP1 inhibits apoptosis, and increases angiogenesis, estrogen and androgen receptor transcription and c-Jun transcription factor, proliferation, growth, cell migration, and invasion of cancer. The in vivo and in vitro studies also demonstrated which natural compounds, especially phytochemicals, minerals, and vitamins, prevent cancer. More than 3,000 plant species have been reported in modern medicine. Natural compounds have many anti-cancerous andanti-turmeric properties such as antioxidative, antiangiogenic, antiproliferative, and pro-apoptotic properties. Methods: In this study, we investigated the interaction of some natural compounds with SENP1 to inhibit its activity. We also screened the ZINC database including natural compounds. Molecular docking was performed, and toxicity of compounds was determined; then, molecular dynamics simulation (MDS) and essential dynamics (ED) were performed on natural compounds with higher free binding energies and minimal side effects. By searching in a large library, virtual screening of the ZINC database was performed using LibDock and CDOCKER, and the final top 20 compounds were allowed for docking against SENP1. According to the docking study, the top three leading molecules were selected and further analyzed by MDS and ED. Results: The results suggest that resveratrol (from the selected compounds) and ZINC33916875 (from the ZINC database) could be more promising SENP1 inhibitory ligands. Discussion: Because these compounds can inhibit SENP1 activity, then they can be novel candidates for cancer treatment. However, wet laboratory experiments are needed to validate their efficacy as SENP1 inhibitors.

Identification of novel anti-cancer agents, applying in silico method for SENP1 protease inhibition

The SENP1 (Sentrin-Specific Protease1) is essential for desumoylation. SENP1 plays an essential role in many diseases such as cardiovascular disease, diabetes and cancer via targeting GATA2, NEMO, Pin1, SMAD4 and HIF-1α for deSUMOylation. Considering that, over expression of SENP1 was reported in cancer, thus an optional inhibitor of SENP1 can restitute the balance to the skewed system of SUMO and act as an effective therapeutic agent. The purpose of this study was to select and to sort inhibitors with a stronger binding affinity with SENP1. Molecular docking of SENP1 with natural compounds including Gallic acid, Caffeic acid, Thymoquinone, Thymol, Betaine, Alkannin, Ellagic acid, Betanin, Shikonin, Betanidin and Momordin IC was performed using AutoDock 4, then docking complexes for molecular dynamics (MD) simulation with GROMACS 4.6.5 were applied. Results with RMSD, RMSF, SASA, DSSP, gyrate, H-bond, ADMET and TOPKAT measurements, binding energy and structural features were surveyed. Among those, Gallic acid has shown the most significant results including RMSD and RMSF plots with high stability, high hydrogen bonds, high binding energy and the highest intermolecular bonds with SENP1. Gallic acid demonstrated strong connections and results of toxicity better than Momordin as control. Gallic acid is a phenolic compound which affects several pharmacological and biochemical pathways and has strong antioxidant, anti-inflammatory, antimutagenic and anticancer properties. Further research can improve the appropriate use of plant products drastically. Basic, pre-clinical and clinical research on Gallic acid may provide a roadmap for its ultimate application in the field of cancer prevention.Communicated by Ramaswamy H. Sarma.

In silico identification of genetic mutations conferring resistance to acetohydroxyacid synthase inhibitors: A case study of Kochia scoparia

Mutations that confer herbicide resistance are a primary concern for herbicide-based chemical control of invasive plants and are often under-characterized structurally and functionally. As the outcome of selection pressure, resistance mutations usually result from repeated long-term applications of herbicides with the same mode of action and are discovered through extensive field trials. Here we used acetohydroxyacid synthase (AHAS) of Kochia scoparia (KsAHAS) as an example to demonstrate that, given the sequence of a target protein, the impact of genetic mutations on ligand binding could be evaluated and resistance mutations could be identified using a biophysics-based computational approach. Briefly, the 3D structures of wild-type (WT) and mutated KsAHAS-herbicide complexes were constructed by homology modeling, docking and molecular dynamics simulation. The resistance profile of two AHAS-inhibiting herbicides, tribenuron methyl and thifensulfuron methyl, was obtained by estimating their binding affinity with 29 KsAHAS (1 WT and 28 mutated) using 6 molecular mechanical (MM) and 18 hybrid quantum mechanical/molecular mechanical (QM/MM) methods in combination with three structure sampling strategies. By comparing predicted resistance with experimentally determined resistance in the 29 biotypes of K. scoparia field populations, we identified the best method (i.e., MM-PBSA with single structure) out of all tested methods for the herbicide-KsAHAS system, which exhibited the highest accuracy (up to 100%) in discerning mutations conferring resistance or susceptibility to the two AHAS inhibitors. Our results suggest that the in silico approach has the potential to be widely adopted for assessing mutation-endowed herbicide resistance on a case-by-case basis.

AIMMS suite: a web server dedicated for prediction of drug resistance on protein mutation

Drug resistance is one of the most intractable issues for successful treatment in current clinical practice. Although many mutations contributing to drug resistance have been identified, the relationship between the mutations and the related pharmacological profile of drug candidates has yet to be fully elucidated, which is valuable both for the molecular dissection of drug resistance mechanisms and for suggestion of promising treatment strategies to counter resistant. Hence, effective prediction approach for estimating the sensitivity of mutations to agents is a new opportunity that counters drug resistance and creates a high interest in pharmaceutical research. However, this task is always hampered by limited known resistance training samples and accurately estimation of binding affinity. Upon this challenge, we successfully developed Auto In Silico Macromolecular Mutation Scanning (AIMMS), a web server for computer-aided de novo drug resistance prediction for any ligand-protein systems. AIMMS can qualitatively estimate the free energy consequences of any mutations through a fast mutagenesis scanning calculation based on a single molecular dynamics trajectory, which is differentiated with other web services by a statistical learning system. AIMMS suite is available at http://chemyang.ccnu.edu.cn/ccb/server/AIMMS/.

Understanding molecular consequences of putative drug resistant mutations in Mycobacterium tuberculosis

Genomic studies of Mycobacterium tuberculosis bacteria have revealed loci associated with resistance to anti-tuberculosis drugs. However, the molecular consequences of polymorphism within these candidate loci remain poorly understood. To address this, we have used computational tools to quantify the effects of point mutations conferring resistance to three major anti-tuberculosis drugs, isoniazid (n = 189), rifampicin (n = 201) and D-cycloserine (n = 48), within their primary targets, katG, rpoB, and alr. Notably, mild biophysical effects brought about by high incidence mutations were considered more tolerable, while different structural effects brought about by haplotype combinations reflected differences in their functional importance. Additionally, highly destabilising mutations such as alr Y388, highlighted a functional importance of the wildtype residue. Our qualitative analysis enabled us to relate resistance mutations onto a theoretical landscape linking enthalpic changes with phenotype. Such insights will aid the development of new resistance-resistant drugs and, via an integration into predictive tools, in pathogen surveillance.

Binding mechanism of CDK5 with roscovitine derivatives based on molecular dynamics simulations and MM/PBSA methods

Roscovitine derivatives are potent inhibitors of cyclin-dependent kinase 5 (CDK5), but they exhibit different activities, which has not been understood clearly up to now. On the other hand, the task of drug design is difficult because of the fuzzy binding mechanism. In this context, the methods of molecular docking, molecular dynamics (MD) simulation, and binding free energy analysis are applied to investigate and reveal the detailed binding mechanism of four roscovitine derivatives with CDK5. The electrostatic and van der Waals interactions of the four inhibitors with CDK5 are analyzed and discussed. The calculated binding free energies in terms of MM-PBSA method are consistent with experimental ranking of inhibitor effectiveness for the four inhibitors. The hydrogen bonds of the inhibitors with Cys83 and Lys33 can stabilize the inhibitors in binding sites. The van der Waals interactions, especially the pivotal contacts with Ile10 and Leu133 have larger contributions to the binding free energy and play critical roles in distinguishing the variant bioactivity of four inhibitors. In terms of binding mechanism of the four inhibitors with CDK5 and energy contribution of fragments of each inhibitor, two new CDK5 inhibitors are designed and have stronger inhibitory potency.

Protein design algorithms predict viable resistance to an experimental antifolate

Methods to accurately predict potential drug target mutations in response to early-stage leads could drive the design of more resilient first generation drug candidates. In this study, a structure-based protein design algorithm (K* in the OSPREY suite) was used to prospectively identify single-nucleotide polymorphisms that confer resistance to an experimental inhibitor effective against dihydrofolate reductase (DHFR) from Staphylococcus aureus. Four of the top-ranked mutations in DHFR were found to be catalytically competent and resistant to the inhibitor. Selection of resistant bacteria in vitro reveals that two of the predicted mutations arise in the background of a compensatory mutation. Using enzyme kinetics, microbiology, and crystal structures of the complexes, we determined the fitness of the mutant enzymes and strains, the structural basis of resistance, and the compensatory relationship of the mutations. To our knowledge, this work illustrates the first application of protein design algorithms to prospectively predict viable resistance mutations that arise in bacteria under antibiotic pressure.

Protein modeling: what happened to the "protein structure gap"?

Computational modeling of three-dimensional macromolecular structures and complexes from their sequence has been a long-standing vision in structural biology. Over the last 2 decades, a paradigm shift has occurred: starting from a large "structure knowledge gap" between the huge number of protein sequences and small number of known structures, today, some form of structural information, either experimental or template-based models, is available for the majority of amino acids encoded by common model organism genomes. With the scientific focus of interest moving toward larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows the study of large and complex molecular machines. One of the open challenges for computational modeling and prediction techniques is to convey the underlying assumptions, as well as the expected accuracy and structural variability of a specific model, which is crucial to understanding its limitations.

Exploring the desumoylation process of SENP1: a study combined MD simulations with QM/MM calculations on SENP1-SUMO1-RanGAP1

The small ubiquitin-related modifier (SUMO)-specific protease (SENP) processes SUMOs to mature forms and deconjugates them from various modified substrates. Loss of the equilibrium from desumoylation catalyzed by abnormal SENP1 is associated with cancers and transcription factor activity. In spite of the significant role of SENP1, the molecular basis of its desumoylation remains unclear. Here, MD simulations and QM/MM methods are combined to investigate the catalytic mechanism of desumoylation. The results showed that substrate SUMO1-RanGAP1 fitted into the catalytic pocket of SENP1 by the break of internal hydrophobic interactions and the isomerization of isopeptide from trans to cis. After that, the nucleophilic sulfur anion of Cys603 in SENP1 attacked the carbonyl carbon of Gly97 of SUMO1 to trigger the reaction, and then a tetrahedral intermediate and an acyl-enzyme intermediate were generated in turn, leading to the final release of enzyme SENP1 and two products, free SUMO1 and RanGAP1. In the process, nucleophilic attack was identified as the rate-determining step with a potential energy barrier of 20.2 kcal/mol. These results are in agreement with experimental data from mutagenesis and other experiments. Our findings elucidate the catalytic mechanism of SENP1 with its substrate and may provide a better understanding of SENP desumoylation. In particular, we have identified key residues in SENP1 needed for desumoylation that might be beneficial for the design of novel inhibitors of SENP1-related diseases.

Correlated electrostatic mutations provide a reservoir of stability in HIV protease

HIV protease, an aspartyl protease crucial to the life cycle of HIV, is the target of many drug development programs. Though many protease inhibitors are on the market, protease eventually evades these drugs by mutating at a rapid pace and building drug resistance. The drug resistance mutations, called primary mutations, are often destabilizing to the enzyme and this loss of stability has to be compensated for. Using a coarse-grained biophysical energy model together with statistical inference methods, we observe that accessory mutations of charged residues increase protein stability, playing a key role in compensating for destabilizing primary drug resistance mutations. Increased stability is intimately related to correlations between electrostatic mutations – uncorrelated mutations would strongly destabilize the enzyme. Additionally, statistical modeling indicates that the network of correlated electrostatic mutations has a simple topology and has evolved to minimize frustrated interactions. The model's statistical coupling parameters reflect this lack of frustration and strongly distinguish like-charge electrostatic interactions from unlike-charge interactions for of the most significantly correlated double mutants. Finally, we demonstrate that our model has considerable predictive power and can be used to predict complex mutation patterns, that have not yet been observed due to finite sample size effects, and which are likely to exist within the larger patient population whose virus has not yet been sequenced.

HIV is incurable because its enzymes evolve rapidly by developing resistance mutations to retroviral inhibitors. Most of these mutations work synergistically, but the biophysical basis behind their cooperation is not well understood. Our work addresses these important issues by bridging the gap between the statistical modeling of HIV protease subtype B sequences with the energetics of mutations involving charged amino acids by showing that electrostatic stability is intimately related to correlations. Moreover, we demonstrate that our statistical model has considerable predictive power and can be used to predict complex mutation patterns that have not yet been observed due to the finite sizes of the current sequence databases. In other words, as the database size increases, our model has the ability to predict the identities of the high probability mutations patterns, which are more likely to be observed. Knowing which currently unobserved mutations are more likely to be observed can be very advantageous in combating the disease.