First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens.

Antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives to fight against the diverse infections caused by different pathogenic microorganisms. In this context, theoretical approaches in bioinformatics have paved the way toward the creation of several in silico models capable of predicting antimicrobial activities of peptides. All current models have several significant handicaps, which prevent the efficient search for highly active AMPs. Here, we introduce the first multitarget (mt) chemo-bioinformatic model devoted to performing alignment-free prediction of antibacterial activity of peptides against multiple Gram-positive bacterial strains. The model was constructed from a data set containing 2488 cases of AMPs sequences assayed against at least 1 out of 50 Gram-positive bacterial strains. This mt-chemo-bioinformatic model displayed percentages of correct classification higher than 90.00% in both training and prediction (test) sets. For the first time, two computational approaches derived from basic concepts in genetics and molecular biology were applied, allowing the calculations of the relative contributions of any amino acid (in a defined position) to the antibacterial activity of an AMP and depending on the bacterial strain used in the biological assay. The present mt-chemo-bioinformatic model constitutes a powerful tool to enable the discovery of potent and versatile AMPs.

Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals.

Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.

Structural behavior of monomer of SARS-CoV-2 spike protein during initial stage of adsorption on graphene.

Spike glycoprotein of the SARS-CoV-2 virus and its structure play a crucial role in the infections of cells containing angiotensin-converting enzyme 2 (ACE2) as well as in the interactions of this virus with surfaces. Protection against viruses and often even their deactivation is one of the great varieties of graphene applications. The structural changes of the non-glycosylated monomer of the spike glycoprotein trimer (denoted as S-protein in this work) triggered by its adsorption onto graphene at the initial stage are investigated by means of atomistic molecular dynamics simulations. The adsorption of the S-protein happens readily during the first 10 ns. The shape of the S-protein becomes more prolate during the adsorption, but this trend, albeit less pronounced, is observed also for the freely relaxing S-protein in water. The receptor-binding domain (RBD) of the free and adsorbed S-protein manifests itself as the most rigid fragment of the whole S-protein. The adsorption even enhances the rigidity of the whole S-protein as well as its subunits. Only one residue of the RBD involved in the specific interactions with ACE2 during the cell infection is involved in the direct contact of the adsorbed S-protein with the graphene. The new intramolecular hydrogen bonds formed during the S-protein adsorption replace the S-protein-water hydrogen bonds; this trend, although less apparent, is observed also during the relaxation of the free S-protein in water. In the initial phase, the secondary structure of the RBD fragment specifically interacting with ACE2 receptor is not affected during the S-protein adsorption onto the graphene.

A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents.

Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.

Multi-Target QSAR Approaches for Modeling Protein Inhibitors. Simultaneous Prediction of Activities Against Biomacromolecules Present in Gram-Negative Bacteria.

Drug discovery is aimed at finding therapeutic agents for the treatment of many diverse diseases and infections. However, this is a very slow an expensive process, and for this reason, in silico approaches are needed to rationalize the search for new molecular entities with desired biological profiles. Models focused on quantitative structure-activity relationships (QSAR) have constituted useful complementary tools in medicinal chemistry, allowing the virtual predictions of dissimilar pharmacological activities of compounds. In the last 10 years, multi-target (mt) QSAR models have been reported, representing great advances with respect to those models generated from classical approaches. Thus, mt- QSAR models can simultaneously predict activities against different biological targets (proteins, microorganisms, cell lines, etc.) by using large and heterogeneous datasets of chemicals. The present review is devoted to discuss the most promising mt-QSAR models, particularly those developed for the prediction of protein inhibitors. We also report the first multi-tasking QSAR (mtk-QSAR) model for simultaneous prediction of inhibitors against biomacromolecules (specifically proteins) present in Gram-negative bacteria. This model allowed us to consider both different proteins and multiple experimental conditions under which the inhibitory activities of the chemicals were determined. The mtk-QSAR model exhibited accuracies higher than 98% in both training and prediction sets, also displaying a very good performance in the classification of active and inactive cases that depended on the specific elements of the experimental conditions. The physicochemical interpretations of the molecular descriptors were also analyzed, providing important insights regarding the molecular patterns associated with the appearance/enhancement of the inhibitory potency.

Enabling virtual screening of potent and safer antimicrobial agents against noma: mtk-QSBER model for simultaneous prediction of antibacterial activities and ADMET properties.

Neglected diseases are infections that thrive mainly among underdeveloped countries, particularly those belonging to regions found in Asia, Africa, and America. One of the most complex diseases is noma, a dangerous health condition characterized by a polymicrobial and opportunistic nature. The search for potent and safer antibacterial agents against this disease is therefore a goal of particular interest. Chemoinformatics can be used to rationalize the discovery of drug candidates, diminishing time and financial resources. However, in the case of noma, there is no in silico model available for its use in the discovery of efficacious antibacterial agents. This work is devoted to report the first mtk-QSBER model, which integrates dissimilar kinds of chemical and biological data. The model was generated with the aim of simultaneously predicting activity against bacteria present in noma, and ADMET (absorption, distribution, metabolism, elimination, toxicity) parameters. The mtk-QSBER model was constructed by employing a large and heterogeneous dataset of chemicals and displayed accuracies higher than 90% in both training and prediction sets. We confirmed the practical applicability of the model by predicting multiple profiles of the investigational antibacterial drug delafloxacin, and the predictions converged with the experimental reports. To date, this is the first model focused on the virtual search for desirable anti-noma agents.

Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening.

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry.

Mycobacteria represent a group of pathogens which cause serious diseases in mammals, including the lethal tuberculosis (Mycobacterium tuberculosis). Despite the mortality of this community-acquired and nosocomial disease mentioned above, other mycobacteria may cause similar infections, acting as dangerous opportunistic pathogens. Additionally, resistant strains belonging to Mycobacterium spp. have emerged. Thus, the design of novel antimycobacterial agents is a challenge for the scientific community. In this sense, chemoinformatics has played a vital role in drug discovery, helping to rationalize chemical synthesis, as well as the evaluation of pharmacological and ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles in both medicinal and pharmaceutical chemistry. Until now, there is no in silico methodology able to assess antimycobacterial activity and ADMET properties at the same time. This work introduces the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous prediction of antimycobacterial activities and ADMET profiles of drugs/chemicals under diverse experimental conditions. The mtk-QSBER model was constructed by using a large and heterogeneous dataset of compounds (more than 34600 cases), displaying accuracies higher than 90% in both, training and prediction sets. To illustrate the utility of the present model, several molecular fragments were selected and their contributions to different biological effects were calculated and analyzed. Also, many properties of the investigational drug TMC-207 were predicted. Results confirmed that, from one side, TMC-207 can be a promising antimycobacterial drug, and on the other hand, this study demonstrates that the present mtk-QSBER model can be used for virtual screening of safer antimycobacterial agents.

Evolution of graph theory-based QSAR methods and their applications to the search for new antibacterial agents.

Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenicity factors, confirming the need to discover of multi-target antibacterial agents. Drug discovery is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches has played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections.

Unified multi-target approach for the rational in silico design of anti-bladder cancer agents.

Bladder cancer (BLC) is a very dangerous and common disease which is characterized by an uncontrolled growth of the urinary bladder cells. In the field of chemotherapy, many compounds have been synthesized and evaluated as anti-BLC agents. The future design of more potent anti-BLC drugs depends on a rigorous and rational discovery, where the computer-aided design (CADD) methodologies should play a very important role. However, until now, there is no CADD methodology able to predict anti-BLC activity of compounds versus different BLC cell lines. We report in this work the first unified approach by exploring Quantitative- Structure Activity Relationship (QSAR) studies using a large and heterogeneous database of compounds. Here, we constructed two multi-target (mt) QSAR models for the classification of compounds as anti-BLC agents against four BLC cell lines. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. We also extracted different substructural patterns which could be responsible for the activity/inactivity of molecules against BLC and we suggested new molecular entities as possible potent and versatile anti-BLC agents.

Role of ligand-based drug design methodologies toward the discovery of new anti- Alzheimer agents: futures perspectives in Fragment-Based Ligand Design.

Alzheimer's disease (AD), a degenerative disease affecting the brain, is the single most common source of dementia in adults. The cause and the progression of AD still remains a mystery among medical experts. As a result, a cure has not yet been discovered, even after decade's worth of research that started since 1906, when the disease was first identified. Despite the efforts of the scientific community, several of the biological receptors associated with AD have not been sufficiently studied to date, limiting in turn the design of new and more potent anti-AD agents. Thus, the search for new drug candidates as inhibitors of different targets associated with AD constitutes an essential part towards the discovery of new and more efficient anti-AD therapies. The present work is focused on the role of the Ligand-Based Drug Design (LBDD) methodologies which have been applied for the elucidation of new molecular entities with high inhibitory activity against targets related with AD. Particular emphasis is given also to the current state of fragment-based ligand approaches as alternatives of the Fragment-Based Drug Discovery (FBDD) methodologies. Finally, several guidelines are offered to show how the use of fragment-based descriptors can be determinant for the design of multi-target inhibitors of proteins associated with AD.

Abelson tyrosine-protein kinase 1 as principal target for drug discovery against leukemias. Role of the current computer-aided drug design methodologies.

The discovery of anti-cancer agents is an area which continues in accelerated expansion. Leukemias (Lkms) are among the most investigated cancers due to its high and dominant prevalence in children. Computer-aided drug design (CADD) methodologies have been extremely important for the discovery of potent anti-Lkms agents, providing essential insights about the molecular patterns which could be involved in the appearance and development of anti-Lkms activity. The present review is focused on the role of the current CADD methodologies for the discovery of anti-Lkms agents with strong emphasis on the in silico prediction of inhibitors against the primary protein associated with the appearance of Lkms: Abelson tyrosine-protein kinase 1 (TPK-ABL1). In order to make a contribution to the field, we also developed a unified ligand-based approach by exploring Quantitative-Structure Activity Relationships (QSAR) studies. Here, we focused on the construction of two multi-targets (mt) QSAR models by employing a large and heterogeneous database of compounds. These models exhibited excellent statistical quality and predictive power to classifying more than 92% of inhibitors/ no inhibitors against seven proteins associated with Lkms, in both training and prediction sets. By using our unified ligand-based approach we identified several fragments as responsible for the anti-Lkms activity through inhibition of proteins, and new molecules were suggested as versatile inhibitors of the seven proteins under study.

Discovery of anti-Alzheimer agents: current ligand-based approaches toward the design of acetylcholinesterase inhibitors.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive dementia and loss of cognitive abilities. Until now, AD remains incurable. The principal biological target for AD therapy is acetylcholinesterase (AChE). Thus, the search for new drug candidates like AChE inhibitors constitutes an essential part for the discovery of more potent anti-AD agents. In general terms, rational drug design methodologies have played a decisive role. The present work is focused on the current state of the Ligand-Based Drug Design (LBDD) methods which have been applied to the elucidation of new molecular entities with high anti-AChE activity. Also, as a contribution to this field, we suggest a promising fragment-based approach for the search and prediction of new AChE inhibitors and for the fast and efficient extraction of substructural alerts which are responsible for the anti-AChE activity.

Discovery of MAO-B inhibitors - present status and future directions part I: oxygen heterocycles and analogs.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders. The role of monoamine oxidase (MAO) inhibitors has expanded in the PD treatment. The present review will summarize the current structureactivity relationship information available on MAOs inhibitors of unrelated families of compounds of oxygen heterocyclic type based on coumarin, chromone and chalcone scaffolds. As the current hitting-one-target therapeutic strategy has been proved to be quite inefficient in PD, this review will also discuss about the development of multi-target drugs, in which MAO inhibition plays a counter-part, as a novel and promising treatment approach for PD.

QSAR studies of PTP1B inhibitors: recent advances and perspectives.

Diabetes mellitus, a chronic condition caused by defects in insulin secretion, or action, or both, is a group of metabolic disorders, complications of which can contribute significantly to ill health, disability, poor quality of life and premature death. From the three main types of diabetes, Type 2 is by far the most common, accounting for about 90% of cases worldwide. Studies on the role of protein tyrosine phosphatase 1B (PTP1B) have clearly shown that it serves as a key negative regulator of insulin signaling and is involved in the insulin resistance associated with Type 2 diabetes. The present work aims to survey information related to PTP1B research published in the last decade. Emphasis is laid particularly on Quantitative Structure-Activity Relationships (QSAR) based studies that supported so far the design of new, potent and selective PTP1B inhibitors. Finally, the challenges and perspectives of QSAR studies in this field are discussed to show how these method can be used to design new chemical entities with enhanced PTP1B inhibition activity.

Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach.

Worldwide, legislative and governmental efforts are focusing on establishing simple screening tools for identifying those chemicals most likely to cause adverse effects without experimentally testing all chemicals of regulatory concern. This is because even the most basic biological testing of compounds of concern, apart from requiring a huge number of test animals, would be neither resource nor time effective. Thus, alternative approaches such as the one proposed here, quantitative structure-activity relationship (QSAR) modelling, are increasingly being used for identifying the potential health hazards and subsequent regulation of new industrial chemicals. This paper follows up on our earlier work that demonstrated the use of the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach to QSAR modelling for predictions of the carcinogenic potency of nitroso compounds. The data set comprises 56 nitroso compounds which have been bio-assayed in female rats and administered by the oral water route. The QSAR model was able to account for about 81% of the variance in the experimental activity and exhibited good cross-validation statistics. A reasonable interpretation of the TOPS-MODE descriptors was achieved by means of bond contributions, which in turn afforded the recognition of structural alerts (SAs) regarding carcinogenicity. A comparison of the SAs obtained from different data sets showed that experimental factors, such as the sex and the oral administration route, exert a major influence on the carcinogenicity of nitroso compounds. The present and previous QSAR models combined together provide a reliable tool for estimating the carcinogenic potency of yet untested nitroso compounds and they should allow the identification of SAs, which can be used as the basis of prediction systems for the rodent carcinogenicity of these compounds.

Synthesis and QSAR study of the anticancer activity of some novel indane carbocyclic nucleosides.

A set of 14 indane carbocyclic nucleosides were synthesized and experimentally assayed for their inhibitory effects in the proliferation of murine leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. The compounds have promising inhibitory activity judging from the IC(50) values obtained for all these cellular lines. Multiple linear regression analysis was then applied to build up consistent QSAR models based on quantum mechanics-derived molecular descriptors. The derived models reproduce well the experimental data of both three cells (r(2) >/=0.90), display a good predictive power and are, above all, easily interpretable. They show that frontier-orbital energies and hydrophobicity are mainly responsible for the activity of the synthesized compounds and also, suggest similar mechanisms of action. The final QSAR-models involve only two descriptors: the lowest unoccupied molecular orbital energy and the solvent accessible-hydrophobic surface area, but describe a sound correlation between predicted and experimental activity data (r(2)=0.931, r(2)=0.936 and r(2)=0.931 for the cells L1210/0, Molt4/C8 and CEM/0, respectively).

Phenolic acid derivatives with potential anticancer properties--a structure-activity relationship study. Part 1: methyl, propyl and octyl esters of caffeic and gallic acids.

The antiproliferative and cytotoxic properties of polyphenolic acid derivatives, structurally related with the natural models caffeic and gallic acids, have been tested in human cervix adenocarcinoma cells (HeLa). Simultaneous structural information was obtained for these compounds through theoretical ab initio methods. This study was conducted for the following esters: methyl caffeate (MC, 1), propyl caffeate (PC, 2), octyl caffeate (OC, 3), methyl gallate (MG, 4), propyl gallate (PG, 5) and octyl gallate (OG, 6). A significant growth-inhibition effect was assessed for some of these compounds, clearly dependent on their structural characteristics. Marked structure-activity relationships (SARs)--namely the number of hydroxyl ring substituents--were found to rule the biological effect of such systems.