Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations

Optimal COVID-19 therapeutic candidate discovery using the CANDO platform

The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Isoleucine with secondary sulfonamide functionality as anticancer, antibacterial and antifungal agents

Isoleucine substituted analogues with secondary sulfonamide group (I₁-I₆) have been synthesized. Structures of synthesized analogues have been confirmed by Fourier Transform-Infrared Red, Nuclear Magnetic Resonance (¹H and ¹³C) and ESI-MS spectroscopic tools. Cytotoxic screenings of synthesized analogues have been done on MCF-7 (breast), Prostate Cancer-3 (PC-3) and A549 (lung) cancer cell lines. N-(1-isobutyl-2-oxo-2-anilinoethyl) p-toluene sulfonamide (I₅) screened to be better cytotoxic agent on MCF-7 and A549 cell lines whereas N-(1-isobutyl-2-oxo-2-p-chloroanilino ethyl) benzene sulfonamide (I₃) against PC-3 cell line. Cell cycle analysis of N-(1-isobutyl-2-oxo-2-anilinoethyl) p-toluene sulfonamide (I₅) analogue has been carried out on A549 cell line in comparison to control and Vinblastine (standard drug). Complete arrest in G0 and G1 phase along with mild disturbance in S-phase of cell cycle has been observed. The screened analogues (I₁-I₆) also showed good antifungal and antibacterial potential against gram positive as well as gram negative strains. Computer simulation indicated good bioactivity prediction by the 'Lipinski rule' and synthesized analogues did not violate this rule. Docking study of isoleucine sulfonamide analogues (I₁-I₆) were carried out to determine the possible interaction sites of the analogues with p53 tumor suppressor-DNA complex and demonstrate that the analogues confirmed binding and inhibition with the most mutated residues of p53. Density functional theory has been used to correlate the electronic and chemical properties of analogues and they were found to be stable and chemically reactive. Thus the results suggest that isoleucine substituted sulfonamide analogues can serve as a structural model for the design of anticancer agents, antibacterial agents as well as antifungal agents with better inhibitory potential.Communicated by Ramaswamy H. Sarma.

Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.

Sulfonamide phenylalanine (SPA) series of analogues as an antibacterial, antifungal, anticancer agents along with p53 tumor suppressor-DNA complex inhibitor - part 1

A series of N-[1-benzyl-2-oxo-2-substituted(ethyl)] benzene/p-toluene sulfonamide (K1-K12) are synthesized. Structure of the synthesized analogues has been confirmed by FT-IR, ¹H & ¹³C NMR and ESI-MS spectroscopic techniques. All the synthesized analogues (K1-K12) have also been examined for their in-vitro antibacterial and antifungal activities. Compounds showed good antibacterial and antifungal activity against standard drug. Anticancer study has been carried out on three cancer cell lines PC-3, MCF-7 and A549 on two different concentrations (mg/mL and μg/mL). The K4 sulfonamide analogue showed better anticancer activity amongst all analogues against PC-3 and A549 cell lines. K4 inhibit G0/G1 phase in cell-cycle analysis experiment. All synthesized molecules (K1-K12) dock at junction p53-DNA and make hydrogen bonded with residues of p53 protein as per docking study. ADMET predictions of synthesized phenylalanine sulfonamide analogues (K1-K12) has been done using 'Lipinski rule' and it has been observed that all synthesized analogues did not violate the rule. Electronic, chemical properties and mulliken atomic charges of analogues were calculated using density functional theory (DFT). Communicated by Ramaswamy H. Sarma.

Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications

We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

Computational Approaches in Multitarget Drug Discovery

Current therapeutic strategies entail identifying and characterizing a single protein receptor whose inhibition is likely to result in the successful treatment of a disease of interest, and testing experimentally large libraries of small molecule compounds "in vitro" and "in vivo" to identify promising inhibitors in model systems and determine if the findings are extensible to humans. This highly complex process is largely based on tests, errors, risk, time, and intensive costs. The virtual computational study of compounds simulates situations predicting possible drug linkages with multiple protein target atomic structures, taking into account the dynamic protein inhibitor, and can help identify inhibitors efficiently, particularly for complex drug-resistant diseases. Some discussions will relate to the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. Some authors have proposed a virtual drug discovery that not only identifies efficient inhibitors but also helps to minimize side effects and toxicity, thus increasing the likelihood of successful therapies. This chapter discusses concepts and research of bioactive multitargets related to toxicology.

Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform

BACKGROUND

Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery.

METHODS

We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner.

RESULTS

We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform.

CONCLUSION

These results have implications for future drug development and repurposing in the context of polypharmacology.

All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds

Recent advances in understanding protein folding have benefitted from coarse-grained representations of protein structures. Empirical energy functions derived from these techniques occasionally succeed in distinguishing native structures from their corresponding ensembles of nonnative folds or decoys which display varying degrees of structural dissimilarity to the native proteins. Here we utilized atomic coordinates of single protein chains, comprising a large diverse training set, to develop and evaluate twelve all-atom four-body statistical potentials obtained by exploring alternative values for a pair of inherent parameters. Delaunay tessellation was performed on the atomic coordinates of each protein to objectively identify all quadruplets of interacting atoms, and atomic potentials were generated via statistical analysis of the data and implementation of the inverted Boltzmann principle. Our potentials were evaluated using benchmarking datasets from Decoys-‘R'-Us, and comparisons were made with twelve other physics- and knowledge-based potentials. Ranking 3rd, our best potential tied CHARMM19 and surpassed AMBER force field potentials. We illustrate how a generalized version of our potential can be used to empirically calculate binding energies for target-ligand complexes, using HIV-1 protease-inhibitor complexes for a practical application. The combined results suggest an accurate and efficient atomic four-body statistical potential for protein structure prediction and assessment.

Investigation of the binding free energies of FDA approved drugs against subtype B and C-SA HIV PR: ONIOM approach

Human immune virus subtype C is the most widely spread HIV subtype in Sub-Sahara Africa and South Africa. A profound structural insight on finding potential lead compounds is therefore necessary for drug discovery. The focus of this study is to rationalize the nine Food and Drugs Administration (FDA) HIV antiviral drugs complexed to subtype B and C-SA PR using ONIOM approach. To achieve this, an integrated two-layered ONIOM model was used to optimize the geometrics of the FDA approved HIV-1 PR inhibitors for subtype B. In our hybrid ONIOM model, the HIV-1 PR inhibitors as well as the ASP 25/25' catalytic active residues were treated at high level quantum mechanics (QM) theory using B3LYP/6-31G(d), and the remaining HIV PR residues were considered using the AMBER force field. The experimental binding energies of the PR inhibitors were compared to the ONIOM calculated results. The theoretical binding free energies (?G_bind) for subtype B follow a similar trend to the experimental results, with one exemption. The computational model was less suitable for C-SA PR. Analysis of the results provided valuable information about the shortcomings of this approach. Future studies will focus on the improvement of the computational model by considering explicit water molecules in the active pocket. We believe that this approach has the potential to provide much improved binding energies for complex enzyme drug interactions.

Combating Ebola with Repurposed Therapeutics Using the CANDO Platform

Ebola virus disease (EVD) is extremely virulent with an estimated mortality rate of up to 90%. However, the state-of-the-art treatment for EVD is limited to quarantine and supportive care. The 2014 Ebola epidemic in West Africa, the largest in history, is believed to have caused more than 11,000 fatalities. The countries worst affected are also among the poorest in the world. Given the complexities, time, and resources required for a novel drug development, finding efficient drug discovery pathways is going to be crucial in the fight against future outbreaks. We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) platform based on the hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for rapid therapeutic repurposing and discovery. We used the CANDO platform to identify and rank FDA-approved drug candidates that bind and inhibit all proteins encoded by the genomes of five different Ebola virus strains. Top ranking drug candidates for EVD treatment generated by CANDO were compared to in vitro screening studies against Ebola virus-like particles (VLPs) by Kouznetsova et al. and genetically engineered Ebola virus and cell viability studies by Johansen et al. to identify drug overlaps between the in virtuale and in vitro studies as putative treatments for future EVD outbreaks. Our results indicate that integrating computational docking predictions on a proteomic scale with results from in vitro screening studies may be used to select and prioritize compounds for further in vivo and clinical testing. This approach will significantly reduce the lead time, risk, cost, and resources required to determine efficacious therapies against future EVD outbreaks.

CANDO and the infinite drug discovery frontier

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound-proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12-25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 'high value' predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.

ReFlexIn: a flexible receptor protein-ligand docking scheme evaluated on HIV-1 protease

For many targets of pharmaceutical importance conformational changes of the receptor protein are relevant during the ligand binding process. A new docking approach, ReFlexIn (Receptor Flexibility by Interpolation), that combines receptor flexibility with the computationally efficient potential grid representation of receptor molecules has been evaluated on the retroviral HIV-1 (Human Immunodeficiency Virus 1) protease system. An approximate inclusion of receptor flexibility is achieved by using interpolation between grid representations of individual receptor conformations. For the retroviral protease the method was tested on an ensemble of protease structures crystallized in the presence of different ligands and on a set of structures obtained from morphing between the unbound and a ligand-bound protease structure. Docking was performed on ligands known to bind to the protease and several non-binders. For the binders the ReFlexIn method yielded in almost all cases ligand placements in similar or closer agreement with experiment than docking to any of the ensemble members without degrading the discrimination with respect to non-binders. The improved docking performance compared to docking to rigid receptors allows for systematic virtual screening applications at very small additional computational cost.

In silico screening of the juvabione category of juvenile hormone analogues with juvenile hormone binding protein of Galleria mellonella--a docking study

Juvabione, dehydrojuvabione and their aromatic analogues act as juvenile hormone mimics against diverse strains of insect species. Large numbers of modified juvenoids containing the juvabione skeleton, with various structural variations, are synthesized. Some of these compounds exhibit a very high degree of juvenile hormone activity and are presently in use. In this paper we report a comparative molecular docking study of synthesized juvabione, natural juvenile hormone III and synthetic insect growth regulators (fenoxycarb, S-21149, Compound 1, pyriproxyfen) with the juvenile hormone binding protein of Galleria mellonella. The study clearly indicates a higher binding affinity of nitro-substituted juvabione over natural juvenile hormone III and synthetic insect growth regulators such as fenoxycarb and S-21149.

Prediction of HIV-1 protease/inhibitor affinity using RosettaLigand

Predicting HIV-1 protease/inhibitor binding affinity as the difference between the free energy of the inhibitor bound and unbound state remains difficult as the unbound state exists as an ensemble of conformations with various degrees of flap opening. We improve computational prediction of protease/inhibitor affinity by invoking the hypothesis that the free energy of the unbound state while difficult to predict is less sensitive to mutation. Thereby the HIV-1 protease/inhibitor binding affinity can be approximated with the free energy of the bound state alone. Bound state free energy can be predicted from comparative models of HIV-1 protease mutant/inhibitor complexes. Absolute binding energies are predicted with R = 0.71 and SE = 5.91 kJ/mol. Changes in binding free energy upon mutation can be predicted with R = 0.85 and SE = 4.49 kJ/mol. Resistance mutations that lower inhibitor binding affinity can thereby be recognized early in HIV-1 protease inhibitor development.

Predicting inactive conformations of protein kinases using active structures: conformational selection of type-II inhibitors

Protein kinases have been found to possess two characteristic conformations in their activation-loops: the active DFG-in conformation and the inactive DFG-out conformation. Recently, it has been very interesting to develop type-II inhibitors which target the DFG-out conformation and are more specific than the type-I inhibitors binding to the active DFG-in conformation. However, solving crystal structures of kinases with the DFG-out conformation remains a challenge, and this seriously hampers the application of the structure-based approaches in development of novel type-II inhibitors. To overcome this limitation, here we present a computational approach for predicting the DFG-out inactive conformation using the DFG-in active structures, and develop related conformational selection protocols for the uses of the predicted DFG-out models in the binding pose prediction and virtual screening of type-II ligands. With the DFG-out models, we predicted the binding poses for known type-II inhibitors, and the results were found in good agreement with the X-ray crystal structures. We also tested the abilities of the DFG-out models to recognize their specific type-II inhibitors by screening a database of small molecules. The AUC (area under curve) results indicated that the predicted DFG-out models were selective toward their specific type-II inhibitors. Therefore, the computational approach and protocols presented in this study are very promising for the structure-based design and screening of novel type-II kinase inhibitors.

Recent estimates of the structure of the factor VIIa (FVIIa)/tissue factor (TF) and factor Xa (FXa) ternary complex

The putative structure of the Tissue Factor/Factor VIIa/Factor Xa (TF/FVIIa/FXa) ternary complex is reconsidered. Two independently derived docking models proposed in 2003 (one for our laboratory: CHeA and one from the Scripps laboratory: Ss) are dynamically equilibrated for over 10 ns in an electrically neutral solution using all-atom molecular dynamics. Although the dynamical models (CHeB and Se) differ in atomic detail, there are similarities in that TF is found to interact with the gamma-carboxyglutamic acid (Gla) and Epidermal Growth Factor-like 1 (EGF-1) domains of FXa, and FVIIa is found to interact with the Gla, EGF-2 and serine protease (SP) domains of FXa in both models. FVIIa does not interact with the FXa EGF-1 domain in Se and the EGF domains of FVIIa do not interact with FXa in the CHeB. Both models are consistent with experimentally suggested contacts between the SP domain of FVIIa with the EGF-2 and SP domains of FXa.

Targeting STAT1 by myricetin and delphinidin provides efficient protection of the heart from ischemia/reperfusion-induced injury

Flavonoids exhibit a variety of beneficial effects in cardiovascular diseases. Although their therapeutic properties have been attributed mainly to their antioxidant action, they have additional protective mechanisms such as inhibition of signal transducer and activator of transcription 1 (STAT1) activation. Here, we have investigated the cardioprotective mechanisms of strong antioxidant flavonoids such as quercetin, myricetin and delphinidin. Although all of them protect the heart from ischemia/reperfusion-injury, myricetin and delphinidin exert a more pronounced protective action than quercetin by their capacity to inhibit STAT1 activation. Biochemical and computer modeling analysis indicated the direct interaction between STAT1 and flavonoids with anti-STAT1 activity.

Assessment of programs for ligand binding affinity prediction

The prediction of the binding free energy between a ligand and a protein is an important component in the virtual screening and lead optimization of ligands for drug discovery. To determine the quality of current binding free energy estimation programs, we examined FlexX, X-Score, AutoDock, and BLEEP for their performance in binding free energy prediction in various situations including cocrystallized complex structures, cross docking of ligands to their non-cocrystallized receptors, docking of thermally unfolded receptor decoys to their ligands, and complex structures with "randomized" ligand decoys. In no case was there a satisfactory correlation between the experimental and estimated binding free energies over all the datasets tested. Meanwhile, a strong correlation between ligand molecular weight-binding affinity correlation and experimental predicted binding affinity correlation was found. Sometimes the programs also correctly ranked ligands' binding affinities even though native interactions between the ligands and their receptors were essentially lost because of receptor deformation or ligand randomization, and the programs could not decisively discriminate randomized ligand decoys from their native ligands; this suggested that the tested programs miss important components for the accurate capture of specific ligand binding interactions.

Close correspondence between the motions from principal component analysis of multiple HIV-1 protease structures and elastic network modes

The large number of available HIV-1 protease structures provides a remarkable sampling of conformations of the different conformational states, which can be viewed as direct structural information about the dynamics of the HIV-1 protease. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide similar sampling of conformations.

Novel paradigms for drug discovery: computational multitarget screening

An established paradigm in current drug development is (i) to identify a single protein target whose inhibition is likely to result in the successful treatment of a disease of interest; (ii) to assay experimentally large libraries of small-molecule compounds in vitro and in vivo to identify promising inhibitors in model systems; and (iii) to determine whether the findings are extensible to humans. This complex process, which is largely based on trial and error, is risk-, time- and cost-intensive. Computational (virtual) screening of drug-like compounds simultaneously against the atomic structures of multiple protein targets, taking into account protein-inhibitor dynamics, might help to identify lead inhibitors more efficiently, particularly for complex drug-resistant diseases. Here we discuss the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. We propose a virtual drug discovery 'pipeline' that will not only identify lead inhibitors efficiently, but also help minimize side-effects and toxicity, thereby increasing the likelihood of successful therapies.

Identification of potential HIV-1 targets of minocycline

Minocycline, a broad spectrum antibiotic, has been discovered to have inhibitory activity against HIV-1 in vitro, but the targets inhibited are unknown. We used a docking with dynamics protocol developed by us to predict the binding affinities of minocycline against seven active sites of five HIV-1 proteins to putatively identify the potential target(s) of minocycline. The results indicate that minocycline has the highest predicted binding affinity against HIV-1 integrase.

Resistant mechanism against nelfinavir of human immunodeficiency virus type 1 proteases

Inhibitors against human immunodeficiency virus type-1 (HIV-1) proteases are finely effective for anti-HIV-1 treatments. However, the therapeutic efficacy is reduced by the rapid emergence of inhibitor-resistant variants of the protease. Among patients who failed in the inhibitor nelfinavir (NFV) treatment, D30N, N88D, and L90M mutations of HIV-1 protease are often observed. Despite the serious clinical problem, it is not clear how these mutations, especially nonactive site mutations N88D and L90M, affect the affinity of NFV or why they cause the resistance to NFV. In this study, we executed molecular dynamics simulations of the NFV-bound proteases in the wild-type and D30N, N88D, D30N/N88D, and L90M mutants. Our simulations clarified the conformational change at the active site of the protease and the change of the affinity with NFV for all of these mutations, even though the 88th and 90th residues are not located in the NFV-bound cavity and not able to directly interact with NFV. D30N mutation causes the disappearance of the hydrogen bond between the m-phenol group of NFV and the 30th residue. N88D mutation alters the active site conformation slightly and induces a favorable hydrophobic contact. L90M mutation dramatically changes the conformation at the flap region and leads to an unfavorable distortion of the binding pocket of the protease, although 90M is largely far apart from the flap region. Furthermore, the changes of binding energies of the mutants from the wild-type protease are shown to be correlated with the mutant resistivity previously reported by the phenotypic experiments.

A likelihood-based index of protein protein binding affinities with application to influenza HA escape from antibodies

In many biological systems, proteins interact with other organic molecules to produce indispensable functions, in which molecular recognition phenomena are essential. Proteins have kept or gained their functions during molecular evolution. Their functions seem to be flexible, and a few amino acid substitutions sometimes cause drastic changes in function. In order to monitor and predict such drastic changes in the early stages in target populations, we need to identify patterns of structural changes during molecular evolution causing decreases or increases in the binding affinity of protein complexes. In previous work, we developed a likelihood-based index to quantify the degree to which a sequence fits a given structure. This index was named the sequence-structure fitness (SSF) and is calculated empirically based on amino acid preferences and pairwise interactions in the structural environment present in template structures. In the present work, we used the SSF to develop an index to measure the binding affinity of protein-protein complexes defined as the log likelihood ratio, contrasting the fitness of the sequences to the structure of the complex and that of the uncomplexed proteins. We applied the developed index to the complexes formed between influenza A hemagglutinin (HA) and four antibodies. The antibody-antigen binding region of HA is under strong selection pressure by the host immune system. Hence, examination of the long-term adaptation of HA to the four antibodies could reveal the strategy of the molecular evolution of HA. Two antibodies cover the HA receptor-binding region, while the other two bind away from the receptor-binding region. By focusing on branches with a significant decline in binding ability, we could detect key amino acid replacements and investigate the mechanism via conditional probabilities. The contrast between the adaptations to the two types of antibodies suggests that the virus adapts to the immune system at the cost of structural change.

Progress in Anti-SARS Coronavirus Chemistry, Biology and Chemotherapy

Proteolytic processing of the coronavirus replicase polyproteins is essential for ongoing viral ribonucleic acid (RNA) synthesis. Therefore, the severe acute respiratory syndrome (SARS)-coronaviruses (SARS-CoV) proteases are attractive targets for the development of antiviral drugs to reduce viral replication and pathogenicity. The structure and activity of the coronavirus 3C-like protease (3CLpro) has already been elucidated, and the design of inhibitors to 3CLpro as therapeutics has been proposed. The chapter discusses SARS-CoV 3CLpro inhibitors that include covalent inhibitors, noncovalent inhibitors, and inhibitors from screening. SARS-CoV papain-like protease (PLpro) is considered an equally viable target to 3CLpro for drug design because both are essential for viral replication. However, PLpro has likely not been pursued because of the paucity of structural information. Several compounds have been identified that have shown inhibitory activity against SARS-CoV. However, no information regarding their mechanism of action or the corresponding target is known. Glycyrrhizin showed inhibitory activity for SARS-CoV replication with EC50 = 300 mg/L after virus absorption in Vero cells. Some glycyrrhizin acid derivatives were found to inhibit SARS-CoV replication in vitro with EC50 values ranging from 5 to 50 μ M. Unfortunately, these compounds show high cytotoxity.

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.

Molecular modeling of non-covalent binding of homochiral (3S,3′S)-astaxanthin to matrix metalloproteinase-13 (MMP-13)

Inhibitors for matrix metalloproteinases (MMPs) are under investigation for the treatment of various important chronic illnesses, including cancer, arthritis, and cardiovascular disease (CVD). In particular, MMP-13 is currently being probed as a potential key target in CVD and malignant disease due to its documented effects on extracellular matrix (ECM) remodeling, important in the pathophysiology of these diseases. Within the family of related mammalian MMP enzymes, MMP-13 possesses a large hydrophobic binding pocket relative to that of other MMPs. Homochiral astaxanthin (3S,3'S-AST; 3S,3'S-dihydroxy-beta,beta-carotene-4,4'-dione), an important antioxidant and anti-inflammatory xanthophyll carotenoid, is an active metabolite of several novel soft drugs in clinical development; it is also extensively used and tested as a human nutraceutical. In the current study, the prediction of the geometry and energetics of its binding to human MMP-13 was conducted with molecular modeling. The method used was found to predict the energy of binding of known ligands of MMP-13 with great precision. Blind docking using the whole protein target was then used in order to identify the possible binding site(s) of AST. AST was predicted to bind at several sites in close proximity to the active center. Subsequent analyses focused on the binding site at the atomic (i.e., amino acid sequence) level suggested that AST can bind to MMP-13 with high affinity and favorable energetics. Therefore, the modeling study predicts potential direct enzyme-inhibitory activity of AST against MMP-13, a behavior that may be exploited in mammalian systems in which pathological upregulation of MMP activity is paramount.

Metalloantibiotic Mn(II)-bacitracin complex mimicking manganese superoxide dismutase

Superoxide dismutase (SOD) activities of various metallobacitracin complexes were evaluated using the riboflavin-methionine-nitro blue tetrazolium assay. The radical scavenging activity of various metallobacitracin complexes was shown to be higher than those of the negative controls, e.g., free transition metal ions and metal-free bacitracin. The SOD activity of the complex was found to be in the order of Mn(II)>Cu(II)>Co(II)>Ni(II). Furthermore, the effect of bacitracin and their complexation to metals on various microorganisms was assessed by antibiotic susceptibility testing. Moreover, molecular modeling and quantum chemical calculation of the metallobacitracin complex was performed to evaluate the correlation of electrostatic charge of transition metal ions on the SOD activity.

Expanding the frontiers of existing antiviral drugs: possible effects of HIV-1 protease inhibitors against SARS and avian influenza

When unexpected diseases such as the severe acute respiratory syndrome (SARS) and avian influenza become a serious threat to public health, an immediate response is imperative. This should take into consideration existing licensed antiviral drugs against other viral diseases already known to be safe for use in humans. In this report, evidence is presented that HIV-1 protease inhibitors (PIs) currently used in anti-HIV-1 therapies might exert some effects on SARS and perhaps, on avian influenza. Evidence for the potential benefits of PIs against the SARS coronavirus (SARS-CoV) is provided by empirical clinical studies, in vivo viral inhibition assays and computational simulations of the docking of these compounds to the active site of the main SARS-CoV protease. As suggested by in silico docking of these molecules to a theoretical model of a subunit of type A influenza virus RNA-dependent RNA polymerase, there also exists a remote possibility that these PIs may have an effect on avian influenza viruses. Although this evidence is still far from being definitive, the results so far obtained suggest that PIs should be seriously taken into consideration for further testing as potential therapeutic agents for SARS and avian influenza.

Evaluation of a neural networks QSAR method based on ligand representation using substituent descriptors. Application to HIV-1 protease inhibitors

We present here a neural networks method designed to predict biological activity based on a local representation of the ligand. The compounds of the series are represented by a vector mapping for each of four substituent properties: volume, log P, dipole moment and a simple 'steric' parameter relating to its shape. This ligand representation was tested using neural networks on a set of 42 cyclic-urea derivatives, inhibiting HIV-1 protease. The leave-one-out cross-validation using all descriptors in the input gave a correlation factor between prediction and experiment of 0.76 for the overall set and 0.88 when three outliers were left out. To rank the significance of the four descriptors, we further tested all combinations of two and three parameters for each substituent, using two disjunctive testing sets of five inhibitors. In these sets, vectors with extreme descriptor values were used either in the training or the testing set (sets A and B, respectively). The method is a very good interpolator (set A, 95+/-2% accuracy) but a less effective extrapolator (set B, 85+/-2% accuracy). Generally, the combinations including the 'steric' parameter predict better than average, while those containing the volume are less effective. The best prediction, 98.8+/-1.2%, was obtained when log P, the dipole and the steric parameter were used on set A. At the opposite end, the lowest ranked descriptor set was obtained when replacing log P with the volume, giving 92.3+/-6.7% accuracy over the set A.

Virtual screening of HIV-1 protease inhibitors against human cytomegalovirus protease using docking and molecular dynamics

The clearance of cytomegalovirus viraemia in HIV-1-infected patients may partly result from the inhibition of cytomegalovirus protease by HIV-1 protease inhibitors contained in highly active antiretroviral therapy. We used a computational method to calculate the binding affinity of six HIV-1 protease inhibitors to cytomegalovirus protease based on its X-ray crystallography structure. The calculations showed that amprenavir and indinavir occupy the substrate-binding site of the cytomegalovirus protease with high affinity, and may be implicated in alleviating cytomegalovirus infection.

Prediction of HIV-1 Protease Inhibitor Resistance using a Protein–Inhibitor Flexible Docking Approach

Emergence of drug resistance remains one of the most challenging issues in the treatment of HIV-1 infection. Here we focus on resistance to HIV-1 protease inhibitors (PIs) at a molecular level, which can be analysed genotypically or phenotypically. Genotypic assays are based on the analysis of mutations associated with reduced drug susceptibility, but are problematic because of the numerous mutations and mutational patterns that confer drug resistance. Phenotypic resistance or susceptibility can be experimentally evaluated by measuring the amount of free drug bound to HIV-1 protease molecules, but this procedure is expensive and time-consuming. To overcome these problems, we have developed a docking protocol that takes protein–inhibitor flexibility into account to predict phenotypic drug resistance. For six FDA-approved PIs and a total of 1792 HIV-1 protease sequence mutants, we used a combination of inhibitor flexible docking and molecular dynamics (MD) simulations to calculate protein–inhibitor binding energies. Prediction results were expressed as fold changes of the calculated inhibitory constant ( Ki), and the samples predicted to have fold-increase in calculated Ki above the fixed cut-off were defined as drug resistant. Our combined docking and MD protocol achieved accuracies ranging from 72–83% in predicting resistance/susceptibility for five of the six drugs evaluated. Evaluating the method only on samples where our predictions concurred with established knowledge-based methods resulted in increased accuracies of 83–94% for the six drugs. The results suggest that a physics-based approach, which is readily applicable to any novel PI and/or mutant, can be used judiciously with knowledge-based approaches that require experimental training data to devise accurate models of HIV-1 PI resistance prediction.

Old drugs as lead compounds for a new disease? Binding analysis of SARS coronavirus main proteinase with HIV, psychotic and parasite drugs

The SARS-associated coronavirus (SARS-CoV) main proteinase is a key enzyme in viral polyprotein processing. To allow structure-based design of drugs directed at SARS-CoV main proteinase, we predicted its binding pockets and affinities with existing HIV, psychotic and parasite drugs (lopinavir, ritonavir, niclosamide and promazine), which show signs of inhibiting the replication of SARS-CoV. Our results suggest that these drugs and another two HIV inhibitors (PNU and UC2) could be used as templates for designing SARS-CoV proteinase inhibitors.

Molecular dynamics simulations of 14 HIV protease mutants in complexes with indinavir

The molecular mechanisms of HIV drug resistance were studied using molecular dynamics simulations of HIV-1 protease complexes with the clinical inhibitor indinavir. One nanosecond molecular dynamics simulations were run for solvated complexes of indinavir with wild type protease, a control variant and 12 drug resistant mutants. The quality of the simulations was assessed by comparison with crystallographic and inhibition data. Molecular mechanisms that contribute to drug resistance include structural stability and affinity for inhibitor. The mutants showed a range of structural variation from 70 to 140% of the wild type protease. The protease affinity for indinavir was estimated by calculating the averaged molecular mechanics interaction energy. A correlation coefficient of 0.96 was obtained with observed inhibition constants for wild type and four mutants. Based on this good agreement, the trends in binding were predicted for the other mutants and discussed in relation to the clinical data for indinavir resistance. [figure: see text]. Poincare map representation for WT protease-indinavir complex. The side chain of Tyr 59 showing the positions of hydrogen atoms.

Identifying inhibitors of the SARS coronavirus proteinase

The Severe Acute Respiratory Syndrome (SARS) is a serious respiratory illness that has recently been reported in parts of Asia and Canada. In this study, we use molecular dynamics (MD) simulations and docking techniques to screen 29 approved and experimental drugs against the theoretical model of the SARS CoV proteinase as well as the experimental structure of the transmissible gastroenteritis virus (TGEV) proteinase. Our predictions indicate that existing HIV-1 protease inhibitors, l-700,417 for instance, have high binding affinities and may provide good starting points for designing SARS CoV proteinase inhibitors.

Simple Linear Model Provides Highly Accurate Genotypic Predictions of HIV-1 Drug Resistance

Drug resistance is a major obstacle to the successful treatment of HIV-1 infection. Genotypic assays are used widely to provide indirect evidence of drug resistance, but the performance of these assays has been mixed. We used standard stepwise linear regression to construct drug resistance models for seven protease inhibitors and 10 reverse transcriptase inhibitors using data obtained from the Stanford HIV drug resistance database. We evaluated these models by hold-one-out experiments and by tests on an independent dataset. Our linear model out-performed other publicly available genotypic interpretation algorithms, including decision tree, support vector machine and four rules-based algorithms (HIVdb, VGI, ANRS and Rega) under both tests. Interestingly, our model did well despite the absence of any terms for interactions between different residues in protease or reverse transcriptase. The resulting linear models are easy to understand and can potentially assist in choosing combination therapy regimens.

Identification of Endogenous Surrogate Ligands for Human P2Y Receptors Through an In Silico Search

G protein-coupled receptors (GPCRs) are distributed widely throughout the human body, and nearly 50% of current medicines act on a GPCR. GPCRs are considered to consist of seven transmembrane alpha-helices that form an alpha-helical bundle in which agonists and antagonists bind. A 3D structure of the target GPCR is indispensable for designing novel medicines acting on a GPCR. We have previously constructed the 3D structure of human P2Y(1) (hP2Y(1)) receptor, a GPCR, by homology modeling with the 3D structure of bovine rhodopsin as a template. In the present study, we have employed an in silico screening for compounds that could bind to the hP2Y(1)-receptor model using AutoDock 3.0. We selected 21 of the 30 top-ranked compounds, and by measuring intracellular Ca(2+) concentration, we identified 12 compounds that activated or blocked the hP2Y(1) receptor stably expressed in recombinant CHO cells. 5-Phosphoribosyl-1-pyrophosphate (PRPP) was found to activate the hP2Y(1) receptor with a low ED(50) value of 15 nM. The Ca(2+) assays showed it had no significant effect on P2Y(2), P2Y(6), or P2X(2) receptors, but acted as a weak agonist on the P2Y(12) receptor. This is the first study to rationally identify surrogate ligands for the P2Y-receptor family.