Artem Cherkasov

QSAR Modeling: Where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the external validation data sets with the highest space coverage as compared to individual constituent models. Our studies prove the power of a collaborative and consensual approach to QSAR model development. The best validated models of aquatic toxicity developed by our collaboratory (both individual and consensus) can be used as reliable computational predictors of aquatic toxicity and are available from any of the participating laboratories.

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning

The rise of antibiotic resistant pathogens is one of the most pressing global health issues. Discovery of new classes of antibiotics has not kept pace; new agents often suffer from cross-resistance to existing agents of similar structure. Short, cationic peptides with antimicrobial activity are essential to the host defenses of many organisms and represent a promising new class of antimicrobials. This paper reports the successful in silico screening for potent antibiotic peptides using a combination of QSAR and machine learning techniques. On the basis of initial high-throughput measurements of activity of over 1400 random peptides, artificial neural network models were built using QSAR descriptors and subsequently used to screen an in silico library of approximately 100,000 peptides. In vitro validation of the modeling showed 94% accuracy in identifying highly active peptides. The best peptides identified through screening were found to have activities comparable or superior to those of four conventional antibiotics and superior to the peptide most advanced in clinical development against a broad array of multiresistant human pathogens.

AMPer: a database and an automated discovery tool for antimicrobial peptides

MOTIVATION Increasing antibiotics resistance in human pathogens represents a pressing public health issue worldwide for which novel antibiotic therapies based on antimicrobial peptides (AMPs) may offer one possible solution. In the current study, we utilized publicly available data on AMPs to construct hidden Markov models (HMMs) that enable recognition of individual classes of antimicrobials peptides (such as defensins, cathelicidins, cecropins, etc.) with up to 99% accuracy and can be used for discovering novel AMP candidates. RESULTS HMM models for both mature peptides and propeptides were constructed. A total of 146 models for mature peptides and 40 for propeptides have been developed for individual AMP classes. These were created by clustering and analyzing AMP sequences available in the public sources and by consequent iterative scanning of the Swiss-Prot database for previously unknown gene-coded AMPs. As a result, an additional 229 additional AMPs have been identified from Swiss-Prot, and all but 34 could be associated with known antimicrobial activities according to the literature. The final set of 1045 mature peptides and 253 propeptides have been organized into the open-source AMPer database. AVAILABILITY The developed HMM-based tools and AMP sequences can be accessed through the AMPer resource at http://www.cnbi2.com/cgi-bin/amp.pl

Targeting the Binding Function 3 (BF3) Site of the Human Androgen Receptor Through Virtual Screening

The androgen receptor (AR) is the best studied drug target for the treatment of prostate cancer. While there are a number of drugs that target the AR, they all work through the same mechanism of action and are prone to the development of drug resistance. There is a large unmet need for novel AR inhibitors which work through alternative mechanism(s). Recent studies have identified a novel site on the AR called binding function 3 (BF3) that is involved into AR transcriptional activity. In order to identify inhibitors that target the BF3 site, we have conducted a large-scale in silico screen followed by experimental evaluation. A number of compounds were identified that effectively inhibited the AR transcriptional activity with no obvious cytotoxicity. The mechanism of action of these compounds was validated by biochemical assays and X-ray crystallography. These findings lay a foundation for the development of alternative or supplementary therapies capable of combating prostate cancer even in its antiandrogen resistant forms.

Molecular Analysis of the Multiple GroEL Proteins of Chlamydiae

Genome sequencing revealed that all six chlamydiae genomes contain three groEL-like genes (groEL1, groEL2, and groEL3). Phylogenetic analysis of groEL1, groEL2, and groEL3 indicates that these genes are likely to have been present in chlamydiae since the beginning of the lineage. Comparison of deduced amino acid sequences of the three groEL genes with those of other organisms showed high homology only for groEL1, although comparison of critical amino acid residues that are required for polypeptide binding of the Escherichia coli chaperonin GroEL revealed substantial conservation in all three chlamydial GroELs. This was further supported by three-dimensional structural predictions. All three genes are expressed constitutively throughout the developmental cycle of Chlamydia trachomatis, although groEL1 is expressed at much higher levels than are groEL2 and groEL3. Transcription of groEL1, but not groEL2 and groEL3, was elevated when HeLa cells infected with C. trachomatis were subjected to heat shock. Western blot analysis with polyclonal antibodies raised against recombinant GroEL1, GroEL2, and GroEL3 demonstrated the presence of the three proteins in C. trachomatis elementary bodies, with GroEL1 being present in the largest amount. Only C. trachomatis groEL1 and groES together complemented a temperature-sensitive E. coli groEL mutant. Complementation did not occur with groEL2 or groEL3 alone or together with groES. The role for each of the three GroELs in the chlamydial developmental cycle and in disease pathogenesis requires further study.

Evaluating Different Descriptors for Model Design of Antimicrobial Peptides with Enhanced Activity Toward P. aeruginosa

The number of isolated drug‐resistant pathogenic microbes has increased drastically over the past decades, demonstrating an urgent need for new therapeutic interventions. Antimicrobial peptides have for a long time been looked upon as an interesting template for drug optimization. However, the process of optimizing peptide antimicrobial activity and specificity, using large peptide libraries is both tedious and expensive. Here, we describe the construction of a mathematical model for prediction, prior to synthesis, of peptide antibacterial activity toward Pseudomonas aeruginosa. By use of novel descriptors quantifying the contact energy between neighboring amino acids in addition to a set of inductive and conventional quantitative structure–activity relationship descriptors, we are able to model the peptides antibacterial activity. Cross‐correlation and optimization of the implemented descriptor values have enabled us to build a model (Bac2a‐ #2) that was able to correctly predict the activity of 84% of the tested peptides, within a twofold deviation window of the corresponding IC50 values, measured earlier. The predictive power, is an average of 10 submodels, each predicting the activity of 20 randomly excluded peptides, with a predictive success of 16.7 ± 1.6 peptides. The model has also been proven significantly more accurate than a simpler model (Bac2a‐ #1), where the inductive and conventional quantitative structure–activity relationship descriptors were excluded.

Selectively Targeting the DNA-binding Domain of the Androgen Receptor as a Prospective Therapy for Prostate Cancer

Background: The androgen receptor (AR) is a transcription factor regulating progression of prostate cancer. Results: Developed compounds inhibit AR transcriptional activity in vitro and in vivo by selective targeting of the AR-DNA-binding domain (DBD). Conclusion: By targeting the DBD, the compounds differ from conventional anti-androgens. Significance: Anti-androgens with a novel mechanism of action have the potential to treat recurrent prostate cancer. The androgen receptor (AR) is a transcription factor that has a pivotal role in the occurrence and progression of prostate cancer. The AR is activated by androgens that bind to its ligand-binding domain (LBD), causing the transcription factor to enter the nucleus and interact with genes via its conserved DNA-binding domain (DBD). Treatment for prostate cancer involves reducing androgen production or using anti-androgen drugs to block the interaction of hormones with the AR-LBD. Eventually the disease changes into a castration-resistant form of PCa where LBD mutations render anti-androgens ineffective or where constitutively active AR splice variants, lacking the LBD, become overexpressed. Recently, we identified a surfaced exposed pocket on the AR-DBD as an alternative drug-target site for AR inhibition. Here, we demonstrate that small molecules designed to selectively bind the pocket effectively block transcriptional activity of full-length and splice variant AR forms at low to sub-micromolar concentrations. The inhibition is lost when residues involved in drug interactions are mutated. Furthermore, the compounds did not impede nuclear localization of the AR and blocked interactions with chromatin, indicating the interference of DNA binding with the nuclear form of the transcription factor. Finally, we demonstrate the inhibition of gene expression and tumor volume in mouse xenografts. Our results indicate that the AR-DBD has a surface site that can be targeted to inhibit all forms of the AR, including enzalutamide-resistant and constitutively active splice variants and thus may serve as a potential avenue for the treatment of recurrent and metastatic prostate cancer.

Functional analysis of androgen receptor mutations that confer anti-androgen resistance identified in circulating cell-free DNA from prostate cancer patients

BackgroundThe androgen receptor (AR) is a pivotal drug target for the treatment of prostate cancer, including its lethal castration-resistant (CRPC) form. All current non-steroidal AR antagonists, such as hydroxyflutamide, bicalutamide, and enzalutamide, target the androgen binding site of the receptor, competing with endogenous androgenic steroids. Several AR mutations in this binding site have been associated with poor prognosis and resistance to conventional prostate cancer drugs. In order to develop an effective CRPC therapy, it is crucial to understand the effects of these mutations on the functionality of the AR and its ability to interact with endogenous steroids and conventional AR inhibitors.ResultsWe previously utilized circulating cell-free DNA (cfDNA) sequencing technology to examine the AR gene for the presence of mutations in CRPC patients. By modifying our sequencing and data analysis approaches, we identify four additional single AR mutations and five mutation combinations associated with CRPC. Importantly, we conduct experimental functionalization of all the AR mutations identified by the current and previous cfDNA sequencing to reveal novel gain-of-function scenarios. Finally, we evaluate the effect of a novel class of AR inhibitors targeting the binding function 3 (BF3) site on the activity of CRPC-associated AR mutants.ConclusionsThis work demonstrates the feasibility of a prognostic and/or diagnostic platform combining the direct identification of AR mutants from patients’ serum, and the functional characterization of these mutants in order to provide personalized recommendations regarding the best future therapy.