TCR-Pred: A new web-application for prediction of epitope and MHC specificity for CDR3 TCR sequences using molecular fragment descriptors.

The search for the relationships between CDR3 TCR sequences and epitopes or MHC types is a challenging task in modern immunology. We propose a new approach to develop the classification models of structure-activity relationships (SAR) using molecular fragment descriptors MNA (Multilevel Neighbourhoods of Atoms) to represent CDR3 TCR sequences and the naïve Bayes classifier algorithm. We have created the freely available TCR-Pred web application (http://way2drug.com/TCR-pred/) to predict the interactions between α chain CDR3 TCR sequences and 116 epitopes or 25 MHC types, as well as the interactions between ß chain CDR3 TCR sequences and 202 epitopes or 28 MHC types. The TCR-Pred web application is based on the data (more 250 000 unique CDR3 TCR sequences) from VDJdb, McPAS-TCR, and IEDB databases and the proposed approach. The average AUC values of the prediction accuracy calculated using a 20-fold cross-validation procedure varies from 0.857 to 0.884. The created web application may be useful in studies related with T-cell profiling based on CDR3 TCR sequences.

SAV-Pred: A Freely Available Web Application for the Prediction of Pathogenic Amino Acid Substitutions for Monogenic Hereditary Diseases Studied in Newborn Screening.

Next Generation Sequencing (NGS) technologies are rapidly entering clinical practice. A promising area for their use lies in the field of newborn screening. The mass screening of newborns using NGS technology leads to the discovery of a large number of new missense variants that need to be assessed for association with the development of hereditary diseases. Currently, the primary analysis and identification of pathogenic variations is carried out using bioinformatic tools. Although extensive efforts have been made in the computational approach to variant interpretation, there is currently no generally accepted pathogenicity predictor. In this study, we used the sequence-structure-property relationships (SSPR) approach, based on the representation of protein fragments by molecular structural formula. The approach predicts the pathogenic effect of single amino acid substitutions in proteins related with twenty-five monogenic heritable diseases from the Uniform Screening Panel for Major Conditions recommended by the Advisory Committee on Hereditary Disorders in Newborns and Children. In order to create SSPR models of classification, we modified a piece of cheminformatics software, MultiPASS, that was originally developed for the prediction of activity spectra for drug-like substances. The created SSPR models were compared with traditional bioinformatic tools (SIFT 4G, Polyphen-2 HDIV, MutationAssessor, PROVEAN and FATHMM). The average AUC of our approach was 0.804 ± 0.040. Better quality scores were achieved for 15 from 25 proteins with a significantly higher accuracy for some proteins (IVD, HADHB, HBB). The best SSPR models of classification are freely available in the online resource SAV-Pred (Single Amino acid Variants Predictor).

Predicting the Impact of OTOF Gene Missense Variants on Auditory Neuropathy Spectrum Disorder.

Auditory neuropathy spectrum disorder (ANSD) associated with mutations of the OTOF gene is one of the common types of sensorineural hearing loss of a hereditary nature. Due to its high genetic heterogeneity, ANSD is considered one of the most difficult hearing disorders to diagnose. The dataset from 270 known annotated single amino acid substitutions (SAV) related to ANSD was created. It was used to estimate the accuracy of pathogenicity prediction using the known (from dbNSFP4.4) method and a new one. The new method (ConStruct) for the creation of the protein-centric classification model is based on the use of Random Forest for the analysis of missense variants in exons of the OTOF gene. A system of predictor variables was developed based on the modern understanding of the structure and function of the otoferlin protein and reflecting the location of changes in the tertiary structure of the protein due to mutations in the OTOF gene. The conservation values of nucleotide substitutions in genomes of 100 vertebrates and 30 primates were also used as variables. The average prediction of balanced accuracy and the AUC value calculated by the 5-fold cross-validation procedure were 0.866 and 0.903, respectively. The model shows good results for interpreting data from the targeted sequencing of the OTOF gene and can be implemented as an auxiliary tool for the diagnosis of ANSD in the early stages of ontogenesis. The created model, together with the results of the pathogenicity prediction of SAVs via other known accurate methods, were used for the evaluation of a manually created set of 1302 VUS related to ANSD. Based on the analysis of predicted results, 16 SAVs were selected as the new most probable pathogenic variants.

CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds.

In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program's NCI60 data (22,726 structures) with different thresholds of IG50 data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV). Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.

Towards Novel Potential Molecular Targets for Antidepressant and Antipsychotic Pharmacotherapies.

Depression and schizophrenia are two highly prevalent and severely debilitating neuropsychiatric disorders. Both conventional antidepressant and antipsychotic pharmacotherapies are often inefficient clinically, causing multiple side effects and serious patient compliance problems. Collectively, this calls for the development of novel drug targets for treating depressed and schizophrenic patients. Here, we discuss recent translational advances, research tools and approaches, aiming to facilitate innovative drug discovery in this field. Providing a comprehensive overview of current antidepressants and antipsychotic drugs, we also outline potential novel molecular targets for treating depression and schizophrenia. We also critically evaluate multiple translational challenges and summarize various open questions, in order to foster further integrative cross-discipline research into antidepressant and antipsychotic drug development.

Relationships between the Structure and Severe Drug-Induced Liver Injury for Low, Medium, and High Doses of Drugs.

Assessment of structure-activity relationships (SARs) for predicting severe drug-induced liver injury (DILI) is essential since in vivo and in vitro preclinical methods cannot detect many druglike compounds disrupting liver functions. To date, plenty of SAR models for the prediction of DILI have been developed; however, none of them considered the route of drug administration and daily dose, which may introduce significant bias into prediction results. We have created a dataset of 617 drugs with parenteral and oral administration routes and consistent information on DILI severity. We have found a clear relationship between route, dose, and DILI severity. According to SAR, nearly 40% of moderate- and non-DILI-causing drugs would cause severe DILI if they were administered at high oral doses. We have proposed the following approach to predict severe DILI. New compounds recommended to be used at low oral doses (<∼10 mg daily), or parenterally, can be considered not causing severe DILI. DILI for compounds administered at medium oral doses (∼10-100 mg daily; 22.2% of drugs under consideration) can be considered unpredictable because reasonable SAR models were not obtained due to the small size and heterogeneity of the corresponding dataset. The DILI potential of the compounds recommended to be used at high oral doses (more than ∼100 mg daily) can be estimated using SAR modeling. The balanced accuracy of the approach calculated by a 10-fold cross-validation procedure is 0.803. The developed approach can be used to estimate severe DILI for druglike compounds proposed to use at low and high oral doses or parenterally at the early stages of drug development.

Pyridine Carboxamides Based on Sulfobetaines: Design, Reactivity, and Biological Activity.

The synthesis of the products of the 1,3-propanesultone ring opening during its interaction with amides of pyridinecarboxylic acids has been carried out. The dependence of the yield of the reaction products on the position (ortho-, meta-, para-) of the substituent in the heteroaromatic fragment and temperature condition was revealed. In contrast to the meta- and para-substituted substrates, the reaction involving ortho-derivatives at the boiling point of methanol unexpectedly led to the formation of a salt. On the basis of spectroscopic, X-Ray, and quantum-chemical calculation data, a model of the transition-state, as well as a mechanism for this alkylation reaction of pyridine carboxamides with sultone were proposed in order to explain the higher yields obtained with the nicotinamide and its N-methyl analog compared to ortho or meta parents. Based on the analysis of ESP maps, the positions of the binding sites of reagents with a potential complexing agent in space were determined. The in silico evaluation of possible biological activity showed that the synthetized compounds revealed some promising pharmacological effects and low acute toxicity.

A Novel Phenylpyrrolidine Derivative: Synthesis and Effect on Cognitive Functions in Rats with Experimental Ishemic Stroke.

We performed an in silico, in vitro, and in vivo assessment of a potassium 2-[2-(2-oxo-4-phenylpyrrolidin-1-yl) acetamido]ethanesulfonate (compound 1) as a potential prodrug for cognitive function improvement in ischemic brain injury. Using in silico methods, we predicted the pharmacological efficacy and possible safety in rat models. In addition, in silico data showed neuroprotective features of compound 1, which were further supported by in vitro experiments in a glutamate excitotoxicity-induced model in newborn rat cortical neuron cultures. Next, we checked whether compound 1 is capable of crossing the blood-brain barrier in intact and ischemic animals. Compound 1 improved animal behavior both in intact and ischemic rats and, even though the concentration in intact brains was low, we still observed a significant anxiety reduction and activity escalation. We used molecular docking and molecular dynamics to support our hypothesis that compound 1 could affect the AMPA receptor function. In a rat model of acute focal cerebral ischemia, we studied the effects of compound 1 on the behavior and neurological deficit. An in vivo experiment demonstrated that compound 1 significantly reduced the neurological deficit and improved neurological symptom regression, exploratory behavior, and anxiety. Thus, here, for the first time, we show that compound 1 can be considered as an agent for restoring cognitive functions.

Rational Use of Heterogeneous Data in Quantitative Structure-Activity Relationship (QSAR) Modeling of Cyclooxygenase/Lipoxygenase Inhibitors.

Numerous studies have been published in recent years with acceptable quantitative structure-activity relationship (QSAR) modeling based on heterogeneous data. In many cases, the training sets for QSAR modeling were constructed from compounds tested by different biological assays, contradicting the opinion that QSAR modeling should be based on the data measured by a single protocol. We attempted to develop approaches that help to determine how heterogeneous data should be used for the creation of QSAR models on the basis of different sets of compounds tested by different experimental methods for the same target and the same endpoint. To this end, more than 100 QSAR models for the IC50 values of ligands interacting with cyclooxygenase 1,2 (COX) and seed lipoxygenase (LOX), obtained from ChEMBL database were created using the GUSAR software. The QSAR models were tested on the external set, including 26 new thiazolidinone derivatives, which were experimentally tested for COX-1,2/LOX inhibition. The IC50 values of the derivatives varied from 89 µM to 26 µM for LOX, from 200 µM to 0.018 µM for COX-1, and from 210 µM to 1 µM for COX-2. This study showed that the accuracy of the models is dependent on the distribution of IC50 values of low activity compounds in the training sets. In the most cases, QSAR models created based on the combined training sets had advantages in comparison with QSAR models, based on a single publication. We introduced a new method of combination of quantitative data from different experimental studies based on the data of reference compounds, which was called "scaling".

AntiBac-Pred: A Web Application for Predicting Antibacterial Activity of Chemical Compounds.

Discovery of new antibacterial agents is a never-ending task of medicinal chemistry. Every new drug brings significant improvement to patients with bacterial infections, but prolonged usage of antibacterials leads to the emergence of resistant strains. Therefore, novel active structures with new modes of action are required. We describe a web application called AntiBac-Pred aimed to help users in the rational selection of the chemical compounds for experimental studies of antibacterial activity. This application is developed using antibacterial activity data available in ChEMBL and PASS software. It allows users to classify chemical structures of interest into growth inhibitors or noninhibitors of 353 different bacteria strains, including both resistant and nonresistant ones.

ADVERPred-Web Service for Prediction of Adverse Effects of Drugs.

Application of structure-activity relationships (SARs) for the prediction of adverse effects of drugs (ADEs) has been reported in many published studies. Training sets for the creation of SAR models are usually based on drug label information which allows for the generation of data sets for many hundreds of drugs. Since many ADEs may not be related to drug consumption, one of the main problems in such studies is the quality of data on drug-ADE pairs obtained from labels. The information on ADEs may be included in three sections of the drug labels: "Boxed warning," "Warnings and Precautions," and "Adverse reactions." The first two sections, especially Boxed warning, usually contain the most frequent and severe ADEs that have either known or probable relationships to drug consumption. Using this information, we have created manually curated data sets for the five most frequent and severe ADEs: myocardial infarction, arrhythmia, cardiac failure, severe hepatotoxicity, and nephrotoxicity, with more than 850 drugs on average for each effect. The corresponding SARs were built with PASS (Prediction of Activity Spectra for Substances) software and had balanced accuracy values of 0.74, 0.7, 0.77, 0.67, and 0.75, respectively. They were implemented in a freely available ADVERPred web service ( http://www.way2drug.com/adverpred/ ), which enables a user to predict five ADEs based on the structural formula of compound. This web service can be applied for estimation of the corresponding ADEs for hits and lead compounds at the early stages of drug discovery.

MetaTox: Web Application for Predicting Structure and Toxicity of Xenobiotics' Metabolites.

A new freely available web-application MetaTox ( http://www.way2drug.com/mg ) for prediction of xenobiotic's metabolism and calculation toxicity of metabolites based on the structural formula of chemicals has been developed. MetaTox predicts metabolites, which are formed by nine classes of reactions (aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation). The calculation of probability for generated metabolites is based on analyses of "structure-biotransformation reactions" and "structure-modified atoms" relationships using a Bayesian approach. Prediction of LD50 values is performed by GUSAR software for the parent compound and each of the generated metabolites using quantitative structure-activity relationahip (QSAR) models created for acute rat toxicity with the intravenous type of administration.

QSAR Modeling and Prediction of Drug-Drug Interactions.

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review.

In silico approaches have been widely recognised to be useful for drug discovery. Here, we consider the significance of available databases of medicinal plants and chemo- and bioinformatics tools for in silico drug discovery beyond the traditional use of folk medicines. This review contains a practical example of the application of combined chemo- and bioinformatics methods to study pleiotropic therapeutic effects (known and novel) of 50 medicinal plants from Traditional Indian Medicine.

Identification of drug-induced myocardial infarction-related protein targets through the prediction of drug-target interactions and analysis of biological processes.

Drug-induced myocardial infarction (DIMI) is one of the most serious adverse drug effects that often lead to death. Therefore, the identification of DIMI at the early stages of drug development is essential. For this purpose, the in vitro testing and in silico prediction of interactions between drug-like substances and various off-target proteins associated with serious adverse drug reactions are performed. However, only a few DIMI-related protein targets are currently known. We developed a novel in silico approach for the identification of DIMI-related protein targets. This approach is based on the computational prediction of drug-target interaction profiles based on information from approximately 1738 human targets and 828 drugs, including 254 drugs that cause myocardial infarction. Through a statistical analysis, we revealed the 155 most significant associations between protein targets and DIMI. Because not all of the identified associations may lead to DIMI, an analysis of the biological functions of these proteins was performed. The Random Walk with Restart algorithm based on a functional linkage gene network was used to prioritize the revealed DIMI-related protein targets according to the functional similarity between their genes and known genes associated with myocardial infarction. The biological processes associated with the 155 selected protein targets were determined by gene ontology and pathway enrichment analysis. This analysis indicated that most of the processes leading to DIMI are associated with atherosclerosis. The revealed proteins were manually annotated with biological processes using functional and disease-related data extracted from the literature. Finally, the 155 protein targets were classified into three categories of confidence: (1) high (the protein targets are known to be involved in DIMI via atherosclerotic progression; 50 targets), (2) medium (the proteins are known to participate in biological processes related with DIMI; 65 targets), and (3) low (the proteins are indirectly involved in DIMI pathogenesis; 40 proteins).

Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm.

A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6.

DIGEP-Pred 2.0: A web application for predicting drug-induced cell signaling and gene expression changes.

The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred.

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.

MetaTox 2.0: Estimating the Biological Activity Spectra of Drug-like Compounds Taking into Account Probable Biotransformations.

After the biotransformation of xenobiotics in the human body, the biological activity of the metabolites may differ from the activity of parent compounds. Therefore, to assess the overall biological activity of a drug-like compound, it is important to take into account its metabolites and their biological activity. We developed MetaTox 2.0-an updated version of the MetaTox web application that was able to predict the metabolites of xenobiotics. Innovations include estimating the biological activity profile of a compound and taking into account its metabolites. The estimation is based on the PASS (prediction of activity spectra for substances) algorithm and on the latest version of the training set covering over 1900 biological activities predicted with an average accuracy exceeding 0.97. Also, MetaTox 2.0 allows the search for similar substances among more than 2000 drugs with known metabolic networks, which were extracted from the ChEMBL, MetXBIODB, and DrugBank databases. MetaTox 2.0 is freely available on the web at https://www.way2drug.com/metatox.

Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets.

Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR (HIV drug Resistance), for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.