Computer prediction of drug resistance mutations in proteins

Prediction of Amino Acid Substitutions in ABL1 Protein Leading to Tumor Drug Resistance Based on "Structure-Property" Relationship Classification Models

Drug resistance to anticancer drugs is a serious complication in patients with cancer. Typically, drug resistance occurs due to amino acid substitutions (AAS) in drug target proteins. The study aimed at developing and validating a new approach to the creation of structure-property relationships (SPR) classification models to predict AASs leading to drug resistance to inhibitors of tyrosine-protein kinase ABL1. The approach was based on the representation of AASs as peptides described in terms of structural formulas. The data on drug-resistant and non-resistant variants of AAS for two isoforms of ABL1 were extracted from the COSMIC database. The given training sets (approximately 700 missense variants) were used for the creation of SPR models in MultiPASS software based on substructural atom-centric multiple neighborhoods of atom (MNA) descriptors for the description of the structural formula of protein fragments and a Bayesian-like algorithm for revealing structure-property relationships. It was found that MNA descriptors of the 6th level and peptides from 11 amino acid residues were the best combination for ABL1 isoform 1 with the prediction accuracy (AUC) of resistance to imatinib (0.897) and dasatinib (0.996). For ABL1 isoform 2 (resistance to imatinib), the best combination was MNA descriptors of the 6th level, peptides form 15 amino acids (AUC value was 0.909). The prediction of possible drug-resistant AASs was made for dbSNP and gnomAD data. The six selected most probable imatinib-resistant AASs were additionally validated by molecular modeling and docking, which confirmed the possibility of resistance for the E334V and T392I variants.

Assessment of a Computational Approach to Predict Drug Resistance Mutations for HIV, HBV and SARS-CoV-2

Viral resistance is a worldwide problem mitigating the effectiveness of antiviral drugs. Mutations in the drug-targeting proteins are the primary mechanism for the emergence of drug resistance. It is essential to identify the drug resistance mutations to elucidate the mechanism of resistance and to suggest promising treatment strategies to counter the drug resistance. However, experimental identification of drug resistance mutations is challenging, laborious and time-consuming. Hence, effective and time-saving computational structure-based approaches for predicting drug resistance mutations are essential and are of high interest in drug discovery research. However, these approaches are dependent on accurate estimation of binding free energies which indirectly correlate to the computational cost. Towards this goal, we developed a computational workflow to predict drug resistance mutations for any viral proteins where the structure is known. This approach can qualitatively predict the change in binding free energies due to mutations through residue scanning and Prime MM-GBSA calculations. To test the approach, we predicted resistance mutations in HIV-RT selected by (-)-FTC and demonstrated accurate identification of the clinical mutations. Furthermore, we predicted resistance mutations in HBV core protein for GLP-26 and in SARS-CoV-2 3CLpro for nirmatrelvir. Mutagenesis experiments were performed on two predicted resistance and three predicted sensitivity mutations in HBV core protein for GLP-26, corroborating the accuracy of the predictions.

Prediction and molecular field view of drug resistance in HIV-1 protease mutants

Conquering the mutational drug resistance is a great challenge in anti-HIV drug development and therapy. Quantitatively predicting the mutational drug resistance in molecular level and elucidating the three dimensional structure-resistance relationships for anti-HIV drug targets will help to improve the understanding of the drug resistance mechanism and aid the design of resistance evading inhibitors. Here the MB-QSAR (Mutation-dependent Biomacromolecular Quantitative Structure Activity Relationship) method was employed to predict the molecular drug resistance of HIV-1 protease mutants towards six drugs, and to depict the structure resistance relationships in HIV-1 protease mutants. MB-QSAR models were constructed based on a published data set of K_i values for HIV-1 protease mutants against drugs. Reliable MB-QSAR models were achieved and these models display both well internal and external prediction abilities. Interpreting the MB-QSAR models supplied structural information related to the drug resistance as well as the guidance for the design of resistance evading drugs. This work showed that MB-QSAR method can be employed to predict the resistance of HIV-1 protease caused by polymorphic mutations, which offer a fast and accurate method for the prediction of other drug target within the context of 3D structures.

High-throughput rational design of the remdesivir binding site in the RdRp of SARS-CoV-2: implications for potential resistance

The use of remdesivir to treat COVID-19 will likely continue before clinical trials are completed. Due to the lengthening pandemic and evolving nature of the virus, predicting potential residues prone to mutation is crucial for the management of remdesivir resistance. Using a rational ligand-based interface design complemented with mutational mapping, we generated a total of 100,000 mutations and provided insight into the functional outcomes of mutations in the remdesivir-binding site in nsp12 subunit of RdRp. After designing 46 residues in the remdesivir-binding site of nsp12, the designs retained 97%–98% sequence identity, suggesting that very few mutations in nsp12 are required for SARS-CoV-2 to attain remdesivir resistance. Several mutants displayed decreased binding affinity to remdesivir, suggesting drug resistance. These hotspot residues had a higher probability of undergoing selective mutation and thus conferring remdesivir resistance. Identifying the potential residues prone to mutation improves our understanding of SARS-CoV-2 drug resistance and COVID-19 pathogenesis.

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SARS-CoV-2 may acquire mutations in nsp12 to develop remdesivir resistance

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Hotspot residues that exhibited the highest potential for mutation were identified

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Virus can undergo positive selection and attain resistance with very few mutations

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Data is crucial for the understanding and management of drug resistance

An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.

A combined computational and experimental strategy identifies mutations conferring resistance to drugs targeting the BCR-ABL fusion protein

Drug resistance is of increasing concern, especially during the treatments of infectious diseases and cancer. To accelerate the drug discovery process in combating issues of drug resistance, here we developed a computational and experimental strategy to predict drug resistance mutations. Using BCR-ABL as a case study, we successfully recaptured the clinically observed mutations that confer resistance imatinib, nilotinib, dasatinib, bosutinib, and ponatinib. We then experimentally tested the predicted mutants in vitro. We found that although all mutants showed weakened binding strength as expected, the binding constants alone were not a good indicator of drug resistance. Instead, the half-maximal inhibitory concentration (IC₅₀) was shown to be a good indicator of the incidence of the predicted mutations, together with change in catalytic efficacy. Our suggested strategy for predicting drug-resistance mutations includes the computational prediction and in vitro selection of mutants with increased IC₅₀ values beyond the drug safety window.

Liu et al. develop a computational algorithm they name EVER, to predict drug-resistance mutations in advance of clinical usage. The authors show that EVER can correctly predict mutations that mediate resistance to BCR-ABL-targeting drugs in chronic myeloid leukemia and, through in vitro experiments, provide insights into the mechanism of resistance.

Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics

Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Here, we describe an update of the Therapeutic target database (TTD) previously featured in NAR. This update includes: (i) 2000 drug resistance mutations in 83 targets and 104 target/drug regulatory genes, which are resistant to 228 drugs targeting 63 diseases (49 targets of 61 drugs with patient prevalence data); (ii) differential expression profiles of 758 targets in the disease-relevant drug-targeted tissue of 12 615 patients of 70 diseases; (iii) expression profiles of 629 targets in the non-targeted tissues of 2565 healthy individuals; (iv) 1008 target combinations of 1764 drugs and the 1604 target combination of 664 multi-target drugs; (v) additional 48 successful, 398 clinical trial and 21 research targets, 473 approved, 812 clinical trial and 1120 experimental drugs, and (vi) ICD-10-CM and ICD-9-CM codes for additional 482 targets and 262 drugs against 98 disease conditions. This update makes TTD more useful for facilitating the patient focused research, discovery and clinical investigations of the targeted therapeutics. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.

Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics

The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class. It is known that resistance against protease inhibitors is associated with a wider active site, but results from our large scale molecular dynamics simulations combined with statistical tests and network analysis further show, for the first time, that there are regions of local expansions and compactions associated with high levels of resistance conserved across eight different protease inhibitors visible in their complexed form within closed receptor conformations. The observed conserved expansion sites may provide an alternative drug-targeting site. Further, the method developed here is novel, supplementary to methods of variation analysis at sequence level, and should be applicable in analysing the structural consequences of mutations in other contexts using molecular ensembles.

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/.

Exploring the Drug Resistance of HCV Protease

Hepatitis C virus (HCV) currently affects several million people across the globe. One of the major classes of drugs against HCV inhibits the NS3/4A protease of the polyprotein chain. Efficacy of these drugs is severely limited due to the high mutation rate that results in several genetically related quasispecies. The molecular mechanism of drug resistance is frequently deduced from structural studies and binding free energies. However, prediction of new mutations requires the evaluation of both binding free energy of the drug as well as the parameters (k_cat and K_M) for the natural substrate. The vitality values offer a good approach to investigate and predict mutations that render resistance to the inhibitor. A successful mutation should only affect the binding of the drug and not the catalytic activity and binding of the natural substrate. In this article, we have calculated the vitality values for four known drug inhibitors that are either currently in use or in clinical trials, evaluating binding free energies by the relevant PDLD/S-LRA method and activation barriers by the EVB method. The molecular details pertaining to resistance are also discussed. We show that our calculations are able to reproduce the catalytic effects and binding free energies in a good agreement with the corresponding observed values. Importantly, previous computational approaches have not been able to achieve this task. The trend for the vitality values is in accordance with experimental findings. Finally, we calculate the vitality values for mutations that have either not been studied experimentally or reported for some inhibitors.

Molecular Dynamics Validation of Crizotinib Resistance to ALK Mutations (L1196M and G1269A) and Identification of Specific Inhibitors

Anaplastic lymphoma kinase (ALK) positive non-small cell lung cancer (NSCLC) patients are mostly treated with ALK tyrosine kinase inhibitors (TKIs). Crizotinib is the first generation ALK inhibitor practiced as a primary chemo to combat cancer cells followed by second generation inhibitor ceritinib which are effective against crizotinib resistant ALK mutations. However, patients treated with these drugs invariably relapsed because of the development of new drug resistance mutations. In this study we explored the crizotinib resistance in the presence of ALK mutations L1196M and G1269A through molecular dynamics simulation studies. Further mutation specific inhibitors CID 71748211 and CID 71728095 were identified to potentially inhibit ALK with mutations L1196M and G1269A, respectively. This computational investigation in-sighted the molecular factors involved in crizotinib resistance which enhanced in the identification of new ALK drugs that brings individualized medicine to treat ALK positive NSCLC patients with specific mutations. J. Cell. Biochem. 118: 3462-3471, 2017. © 2017 Wiley Periodicals, Inc.

Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics

Background

Epidermal growth factor receptor (EGFR) mutation-induced drug resistance is a difficult problem in lung cancer treatment. Studying the molecular mechanisms of drug resistance can help to develop corresponding treatment strategies and benefit new drug design.

Methods

In this study, Rosetta was employed to model the EGFR mutant structures. Then Amber was carried out to conduct molecular dynamics (MD) simulation. Afterwards, we used Computational Geometry Algorithms Library (CGAL) to compute the alpha shape model of the mutants.

Results

We analyzed the EGFR mutation-induced drug resistance based on the motion trajectories obtained from MD simulation. We computed alpha shape model of all the trajectory frames for each mutation type. Solid angle was used to characterize the curvature of the atoms at the drug binding site. We measured the knob level of the drug binding pocket of each mutant from two ways and analyzed its relationship with the drug response level. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold.

Conclusions

There is a strong correlation between the geometric properties of the drug binding pocket of the EGFR mutants and the corresponding drug responses, which can be used to predict the response of a new EGFR mutant to a drug molecule.

The prediction of virus mutation using neural networks and rough set techniques

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.

Identification of drug resistance mutations in HIV from constraints on natural evolution

Human immunodeficiency virus (HIV) evolves with extraordinary rapidity. However, its evolution is constrained by interactions between mutations in its fitness landscape. Here we show that an Ising model describing these interactions, inferred from sequence data obtained prior to the use of antiretroviral drugs, can be used to identify clinically significant sites of resistance mutations. Successful predictions of the resistance sites indicate progress in the development of successful models of real viral evolution at the single residue level and suggest that our approach may be applied to help design new therapies that are less prone to failure even where resistance data are not yet available.

Current insights into computer-aided immunotherapeutic design strategies

Drug designing costs as well as design of immunotherapeutic agents could be nearly halved through the involvement of computer-aided drug designing methods in discovery and research. The inter-disciplinary, time-, and money-consuming process of drug discovery is amended by the development of drug designing, the technique of creating or finding a molecule that can render stimulatory or inhibitory activity upon various biological organisms. Meanwhile, the advancements made within this scientific domain in the last couple of decades have significantly modified and affected the way new bioactive molecules have been produced by the pharmaceutical industry. In this regard, improvements made in hardware solutions and computational techniques along with their efficient integration with biological processes have revolutionized the in silico methods in speeding up the lead identification and optimization processes. In this review, we will discuss various methods of recent computer-aided drug designing techniques that forms the basis of modern day drug discovery projects.

EGFR Mutant Structural Database: computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib

Background

Epidermal growth factor receptor (EGFR) mutation-induced drug resistance has caused great difficulties in the treatment of non-small-cell lung cancer (NSCLC). However, structural information is available for just a few EGFR mutants. In this study, we created an EGFR Mutant Structural Database (freely available at http://bcc.ee.cityu.edu.hk/data/EGFR.html), including the 3D EGFR mutant structures and their corresponding binding free energies with two commonly used inhibitors (gefitinib and erlotinib).

Results

We collected the information of 942 NSCLC patients belonging to 112 mutation types. These mutation types are divided into five groups (insertion, deletion, duplication, modification and substitution), and substitution accounts for 61.61% of the mutation types and 54.14% of all the patients. Among all the 942 patients, 388 cases experienced a mutation at residue site 858 with leucine replaced by arginine (L858R), making it the most common mutation type. Moreover, 36 (32.14%) mutation types occur at exon 19, and 419 (44.48%) patients carried a mutation at exon 21. In this study, we predicted the EGFR mutant structures using Rosetta with the collected mutation types. In addition, Amber was employed to refine the structures followed by calculating the binding free energies of mutant-drug complexes.

Conclusions

The EGFR Mutant Structural Database provides resources of 3D structures and the binding affinity with inhibitors, which can be used by other researchers to study NSCLC further and by medical doctors as reference for NSCLC treatment.

Predicting virus mutations through statistical relational learning

BACKGROUND

Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants.

RESULTS

We propose a simple statistical relational learning approach for mutant prediction where the input consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance and uses them to generate a set of potentially resistant mutants. Learning a weighted combination of rules allows to attach generated mutants with a resistance score as predicted by the statistical relational model and select only the highest scoring ones.

CONCLUSIONS

Promising results were obtained in generating resistant mutations for both nucleoside and non-nucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations.

Decoding the EGFR mutation-induced drug resistance in lung cancer treatment by local surface geometric properties

Epidermal growth factor receptor (EGFR) mutation-induced drug resistance leads to a limited efficacy of tyrosine kinase inhibitors during lung cancer treatments. In this study, we explore the correlations between the local surface geometric properties of EGFR mutants and the progression-free survival (PFS). The geometric properties include local surface changes (four types) of the EGFR mutants compared with the wild-type EGFR, and the convex degrees of these local surfaces. Our analysis results show that the Spearman׳s rank correlation coefficients between the PFS and three types of local surface properties are all greater than 0.6 with small P-values, implying a high significance. Moreover, the number of atoms with solid angles in the ranges of [0.71, 1], [0.61, 1] or [0.5, 1], indicating the convex degree of a local EGFR surface, also shows a strong correlation with the PFS. Overall, these characteristics can be efficiently applied to the prediction of drug resistance in lung cancer treatments, and easily extended to other cancer treatments.

Personalized prediction of EGFR mutation-induced drug resistance in lung cancer

EGFR mutation-induced drug resistance has significantly impaired the potency of small molecule tyrosine kinase inhibitors in lung cancer treatment. Computational approaches can provide powerful and efficient techniques in the investigation of drug resistance. In our work, the EGFR mutation feature is characterized by the energy components of binding free energy (concerning the mutant-inhibitor complex), and we combine it with specific personal features for 168 clinical subjects to construct a personalized drug resistance prediction model. The 3D structure of an EGFR mutant is computationally predicted from its protein sequence, after which the dynamics of the bound mutant-inhibitor complex is simulated via AMBER and the binding free energy of the complex is calculated based on the dynamics. The utilization of extreme learning machines and leave-one-out cross-validation promises a successful identification of resistant subjects with high accuracy. Overall, our study demonstrates advantages in the development of personalized medicine/therapy design and innovative drug discovery.

Structure-based methods for predicting target mutation-induced drug resistance and rational drug design to overcome the problem

Drug resistance has become one of the biggest challenges in drug discovery and/or development and has attracted great research interests worldwide. During the past decade, computational strategies have been developed to predict target mutation-induced drug resistance. Meanwhile, various molecular design strategies, including targeting protein backbone, targeting highly conserved residues and dual/multiple targeting, have been used to design novel inhibitors for combating the drug resistance. In this article we review recent advances in development of computational methods for target mutation-induced drug resistance prediction and strategies for rational design of novel inhibitors that could be effective against the possible drug-resistant mutants of the target.

Do we have the tools to manage resistance in the future?

Pesticide resistance is a major factor affecting world food and fibre production, but that has been contained so far by the availability of diverse modes of action. Overcoming resistance by switching to a new mode of action is a concept easily grasped by growers but threatened by losses through resistance and new registration requirements. Opportunities for innovation and development of a diversity of novel modes of action exist through harnessing recent advances, fundamental to all eukaryotes and largely funded for medical rather than agricultural objectives, in understanding cell biology and development. The cystoskeleton, cell wall synthesis, signal transduction and RNAi are discussed as examples where new targets are now exposed. However, new modes of action will be delivered not only by sprayer or seed treatment but also through transgenic crops, although these still need to be transferred from experiment to practice. Improvements in modelling protein structures and target-site changes, supplemented by rapid diagnostics to detect resistance early, will improve resistance risk management and integrate chemical, biopesticide, transgenic and conventional breeding around the concept of diversity in modes of action. However, before agronomy can translate this into practical antiresistance strategies, there is a need to direct more resources to the biochemistry and cell biology of pests, diseases and weeds to translate these new discoveries into key tools needed to manage resistance in the future.

[Bioinformatics studies on drug resistance against anti-HIV-1 drugs]

More than 20 drugs have been available for anti-HIV-1 treatment in Japan. Combination therapy with these drugs dramatically decreases in morbidity and mortality of AIDS. However, due to high mutation rate of HIV-1, treatment with ineffective drugs toward patients infected with HIV-1 causes accumulation of mutations in the virus, and emergence of drug resistant viruses. Thus, to achieve appropriate application of the drugs toward the respective patients living with HIV-1, methods for predicting the level of drug-resistance using viral sequence information has been developed on the basis of bioinformatics. Furthermore, ultra-deep sequencing by next-generation sequencer whose data analysis is also based on bioinformatics, or in silico structural modeling have been achieved to understand drug resistant mechanisms. In this review, I overview the bioinformatics studies about drug resistance against anti-HIV-1 drugs.

Dynamical basis for drug resistance of HIV-1 protease

Background

Protease inhibitors designed to bind to protease have become major anti-AIDS drugs. Unfortunately, the emergence of viral mutations severely limits the long-term efficiency of the inhibitors. The resistance mechanism of these diversely located mutations remains unclear.

Results

Here I use an elastic network model to probe the connection between the global dynamics of HIV-1 protease and the structural distribution of drug-resistance mutations. The models for study are the crystal structures of unbounded and bound (with the substrate and nine FDA approved inhibitors) forms of HIV-1 protease. Coarse-grained modeling uncovers two groups that couple either with the active site or the flap. These two groups constitute a majority of the drug-resistance residues. In addition, the significance of residues is found to be correlated with their dynamical changes in binding and the results agree well with the complete mutagenesis experiment of HIV-1 protease.

Conclusions

The dynamic study of HIV-1 protease elucidates the functional importance of common drug-resistance mutations and suggests a unifying mechanism for drug-resistance residues based on their dynamical properties. The results support the robustness of the elastic network model as a potential predictive tool for drug resistance.

Prediction of zanamivir efficiency over the possible 2009 influenza A (H1N1) mutants by multiple molecular dynamics simulations and free energy calculations

As one of the most important antiviral drugs against 2009 influenza A (H1N1), will zanamivir be effective for the possible drug resistant mutants? To answer this question, we combined multiple molecular dynamics simulations and molecular mechanics generalized Born surface area (MM-GBSA) calculations to study the efficiency of zanamivir over the most frequent drug-resistant strains of neuraminidase including R293K, R152K, E119A/D and H275Y mutants. The calculated results indicate that the modeled mutants of the 2009-H1N1 strains except H275Y will be significantly resistant to zanamivir. The resistance to zanamivir is mainly caused by the loss of polar interactions. The identified potential resistance sites in this study will be useful for the development of new effective anti-influenza drugs and to avoid the occurrence of the state without effective drugs to new mutant influenza strains.

A combined sequence-structure approach for predicting resistance to the non-nucleoside HIV-1 reverse transcriptase inhibitor Nevirapine

The development of drug resistance to antiretroviral medications used to treat infection with HIV-1 is a major concern. Given the cost and time constraints associated with phenotypic resistance testing, computational approaches leading to accurate predictive models of resistance based on a patient's mutational patterns in the target protein would provide a welcome alternative. A combined sequence-structure computational mutagenesis procedure is used to generate attribute vectors for each of 222 mutational patterns of HIV-1 reverse transcriptase that were isolated and sequenced from patients. Phenotypic fold-levels of resistance to the non-nucleoside inhibitor Nevirapine are known for over 25% of these mutants, whose values are used to assign each assayed mutant to a drug susceptibility class, either sensitive or resistant. Support vector machine and random forest supervised learning algorithms applied to this subset respectively classify mutants based on drug susceptibility with 85% and 92% cross-validation accuracy. The trained models are used to predict susceptibility to Nevirapine for all remaining mutant isolates, and predictions are in agreement for 90% of the test cases.

In silico identification of the potential drug resistance sites over 2009 influenza A (H1N1) virus neuraminidase

The outbreak and high speed global spread of the new strain of influenza A (H1N1) virus in 2009 poses a serious threat to the general population and governments. At present, the most effective drugs for the treatment of 2009 influenza A (H1N1) virus are neuraminidase inhibitors: mainly oseltamivir and zanamivir. The use of these two inhibitors will undoubtedly increase, and therefore it is more likely that drug-resistant influenza strains will arise. The identification of the potential resistance sites for these drugs in advance and the understanding of corresponding molecular basis to cause drug resistance are no doubt very important to fight against the new resistant influenza strains. In this study, first, the complexes of neuraminidase with the substrate sialic acid and two inhibitors oseltamivir and zanamivir were obtained by fitting them to the 3D structure of 2009 influenza A (H1N1) neuraminidase obtained by homology modeling. By using these complexes as the initial structures, molecular dynamics simulation and molecular mechanics generalized Born surface area (MM-GBSA) calculations were performed to identify the residues with significant contribution to the binding of substrate and inhibitors. By analyzing the difference of interaction profiles of substrate and inhibitors, the potential drug resistance sites for two inhibitors were identified. Parts of the identified sites have been verified to confer resistance to oseltamivir and zanamivir for influenza virus of the past flu epidemic. The identified potential resistance sites in this study will be useful for the development of new effective drugs against the drug resistance and avoid the situation of having no effective drugs to treat new mutant influenza strains.

Predicting resistance mutations using protein design algorithms

Drug resistance resulting from mutations to the target is an unfortunate common phenomenon that limits the lifetime of many of the most successful drugs. In contrast to the investigation of mutations after clinical exposure, it would be powerful to be able to incorporate strategies early in the development process to predict and overcome the effects of possible resistance mutations. Here we present a unique prospective application of an ensemble-based protein design algorithm, K*, to predict potential resistance mutations in dihydrofolate reductase from Staphylococcus aureus using positive design to maintain catalytic function and negative design to interfere with binding of a lead inhibitor. Enzyme inhibition assays show that three of the four highly-ranked predicted mutants are active yet display lower affinity (18-, 9-, and 13-fold) for the inhibitor. A crystal structure of the top-ranked mutant enzyme validates the predicted conformations of the mutated residues and the structural basis of the loss of potency. The use of protein design algorithms to predict resistance mutations could be incorporated in a lead design strategy against any target that is susceptible to mutational resistance.

Molecular surface mesh generation by filtering electron density map

Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.

Predicting drug resistance of the HIV-1 protease using molecular interaction energy components

Drug resistance significantly impairs the efficacy of AIDS therapy. Therefore, precise prediction of resistant viral mutants is particularly useful for developing effective drugs and designing therapeutic regimen. In this study, we applied a structure-based computational approach to predict mutants of the HIV-1 protease resistant to the seven FDA approved drugs. We analyzed the energetic pattern of the protease-drug interaction by calculating the molecular interaction energy components (MIECs) between the drug and the protease residues. Support vector machines (SVMs) were trained on MIECs to classify protease mutants into resistant and nonresistant categories. The high prediction accuracies for the test sets of cross-validations suggested that the MIECs successfully characterized the interaction interface between drugs and the HIV-1 protease. We conducted a proof-of-concept study on a newly approved drug, darunavir (TMC114), on which no drug resistance data were available in the public domain. Compared with amprenavir, our analysis suggested that darunavir might be more potent to combat drug resistance. To quantitatively estimate binding affinities of drugs and study the contributions of protease residues to causing resistance, linear regression models were trained on MIECs using partial least squares (PLS). The MIEC-PLS models also achieved satisfactory prediction accuracy. Analysis of the fitting coefficients of MIECs in the regression model revealed the important resistance mutations and shed light into understanding the mechanisms of these mutations to cause resistance. Our study demonstrated the advantages of characterizing the protease-drug interaction using MIECs. We believe that MIEC-SVM and MIEC-PLS can help design new agents or combination of therapeutic regimens to counter HIV-1 protease resistant strains.

Virtual Physiological Human research initiative: the future for rational HIV treatment design?

The clinical management of HIV-positive patients receiving existing drug regimens is performed largely as a clinician’s best guess, based on available viral and patient data. The development of new antiretroviral therapies for HIV is at an impasse. How can we progress beyond this? ViroLab, an example of a new ‘style’ of research project, is attempting to answer this question by stepping into the realm of prediction. A method is in development that can model and predict the efficacy of antiretroviral treatment and may enhance clinical decision support. If extended, this method can potentially target any other pathology where a ligand–substrate interaction is concerned. This type of research falls within the remit of a new initiative: the Virtual Physiological Human (VPH). The potential impact of VPH on current and future rational treatment design is discussed.

Evaluating the potency of HIV-1 protease drugs to combat resistance

HIV-1 protease has been an important drug target for the antiretroviral treatment of HIV infection. The efficacy of protease drugs is impaired by the rapid emergence of resistant virus strains. Understanding the molecular basis and evaluating the potency of an inhibitor to combat resistance are no doubt important in AIDS therapy. In this study, we first identified residues that have significant contributions to binding with six substrates using molecular dynamics simulations and Molecular Mechanics Generalized Born Surface Area calculations. Among the critical residues, Asp25, Gly27, Ala28, Asp29, and Gly49 are well conserved, with which the potent drugs should form strong interactions. We then calculated the contribution of each residue to binding with eight FDA approved drugs. We analyzed the conservation of each protease residue and also compared the interaction between the HIV protease and individual residues of the drugs and substrates. Our analyses showed that resistant mutations usually occur at less conserved residues forming more favorable interactions with drugs than with substrates. To quantitatively integrate the binding free energy and conservation information, we defined an empirical parameter called free energy/variability (FV) value, which is the product of the contribution of a single residue to the binding free energy and the sequence variability at that position. As a validation, the FV value was shown to identify single resistant mutations with an accuracy of 88%. Finally, we evaluated the potency of a newly approved drug, darunavir, to combat resistance and predicted that darunavir is more potent than amprenavir but may be susceptible to mutations on Val32 and Ile84.

Current topics in computer-aided drug design

The addition of computer-aided drug design (CADD) technologies to the research and drug discovery approaches could lead to a reduction of up to 50% in the cost of drug design. Designing a drug is the process of finding or creating a molecule which has a specific activity on a biological organism. Development and drug discovery is a time-consuming, expensive, and interdisciplinary process whereas scientific advancements during the past two decades have altered the way pharmaceutical research produces new bioactive molecules. Advances in computational techniques and hardware solutions have enabled in silico methods to speed up lead optimization and identification. We will review current topics in computer-aided molecular design underscoring some of the most recent approaches and interdisciplinary processes. We will discuss some of the most efficient pathways and design.

Bioinformatics-assisted anti-HIV therapy

Highly active antiretroviral therapy (HAART), in which three or more drugs are given in combination, has substantially improved the clinical management of HIV-1 infection. Still, the emergence of drug-resistant variants eventually leads to therapy failure in most patients. In such a scenario, the high diversity of resistance-associated mutational patterns complicates the choice of an optimal follow-up regimen. To support physicians in this task, a range of bioinformatics tools for predicting drug resistance or response to combination therapy from the viral genotype have been developed. With several free and commercial software services available, computational advice is rapidly gaining acceptance as an important element of rational decision-making in the treatment of HIV infection.

Beyond annotation transfer by homology: novel protein-function prediction methods to assist drug discovery

Every entirely sequenced genome reveals 100 s to 1000 s of protein sequences for which the only annotation available is 'hypothetical protein'. Thus, in the human genome and in the genomes of pathogenic agents there could be 1000 s of potential, unexplored drug targets. Computational prediction of protein function can play a role in studying these targets. We shall review the challenges, research approaches and recently developed tools in the field of computational function-prediction and we will discuss the ways these issues can change the process of drug discovery.

Structural analysis of an HIV-1 protease I47A mutant resistant to the protease inhibitor lopinavir

We have identified a rare HIV-1 protease (PR) mutation, I47A, associated with a high level of resistance to the protease inhibitor lopinavir (LPV) and with hypersusceptibility to the protease inhibitor saquinavir (SQV). The I47A mutation was found in 99 of 112,198 clinical specimens genotyped after LPV became available in late 2000, but in none of 24,426 clinical samples genotyped from 1998 to October 2000. Phenotypic data obtained for five I47A mutants showed unexpected resistance to LPV (86- to >110-fold) and hypersusceptibility to SQV (0.1- to 0.7-fold). Molecular modeling and energy calculations for these mutants using our structural phenotyping methodology showed an increase in the binding energy of LPV by 1.9-3.1 kcal/mol with respect to the wild type complex, corresponding to a 20- to >100-fold decrease in binding affinity, consistent with the observed high levels of LPV resistance. In the WT PR-LPV complex, the Ile 47 side chain is positioned close to the phenoxyacetyl moiety of LPV and its van der Waals interactions contribute significantly to the ligand binding. These interactions are lost for the smaller Ala 47 residue. Calculated binding energy changes for SQV ranged from -0.4 to -1.2 kcal/mol. In the mutant I47A PR-SQV complexes, the PR flaps are packed more tightly around SQV than in the WT complex, resulting in the formation of additional hydrogen bonds that increase binding affinity of SQV consistent with phenotypic hypersusceptibility. The emergence of mutations at PR residue 47 strongly correlates with increasing prescriptions of LPV (Spearman correlation r(s) = 0.96, P < .0001).