Average information content maximization : a new approach for fingerprint hybridization and reduction

Fingerprints, bit representations of compound chemical structure, have been widely used in cheminformatics for many years. Although fingerprints with the highest resolution display satisfactory performance in virtual screening campaigns, the presence of a relatively high number of irrelevant bits introduces noise into data and makes their application more time-consuming. In this study, we present a new method of hybrid reduced fingerprint construction, the Average Information Content Maximization algorithm (AIC-Max algorithm), which selects the most informative bits from a collection of fingerprints. This methodology, applied to the ligands of five cognate serotonin receptors (5-HT2A, 5-HT2B, 5-HT2C, 5-HT5A, 5-HT6), proved that 100 bits selected from four non-hashed fingerprints reflect almost all structural information required for a successful in silico discrimination test. A classification experiment indicated that a reduced representation is able to achieve even slightly better performance than the state-of-the-art 10-times-longer fingerprints and in a significantly shorter time

SVM with a neutral class

In many real binary classification problems, in addition to the presence of positive and negative classes, we are also given the examples of third neutral class, i.e., the examples with uncertain or intermediate state between positive and negative. Although it is a common practice to ignore the neutral class in a learning process, its appropriate use can lead to the improvement in classification accuracy. In this paper, to include neutral examples in a training stage, we adapt two variants of Tri-Class SVM (proposed by Angulo et al. in Neural Process Lett 23(1):89–101, 2006), the method designed to solve three-class problems with a use of single learning model. In analogy to classical SVM, we look for such a hyperplane, which maximizes the margin between positive and negative instances and which is localized as close to the neutral class as possible. In addition to original Angulo’s paper, we give a new interpretation of the model and show that it can be easily implemented in the primal. Our experiments demonstrate that considered methods obtain better results in binary classification problems than classical SVM and semi-supervised SVM

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Modelling of serotonergic receptors and molecular optimization of X-ray crystal structures of serotonin transporter and their interactions with exogenous compounds

The serotonin (5-hydroxytryptamine, 5-HT) receptors and transporter are in the serotonergic neurotransmission system, and believed to have a major role in pathology of depression. They are of pharmacological importance, being targeted by many nowadays antidepressants. It is therefore of great interest to understand their structural and functional properties for development of future drugs. There is generally little knowledge today about the effects of environmental toxicants on the human brain. If the exogenous compounds interact with the serotonin receptors and transporter, they may interfere with the serotonergic neurotransmission in the brain and interfere with the effects of the CNS drugs. Homology modelling is an in silico method used for prediction of the 3D structure of structurally unknown proteins. Models of serotonergic receptors (5-HT1A, 5-HT2A, 5-HT2C) were constructed by the homology approach with known structures in the PDB. The newly released X-ray crystal structures of the human serotonin transporter (SERT) were also imported from the PDB and optimized with molecular modelling techniques. Molecular docking was utilized to predict putative harmful effects and drug interactions of the toxicants in the Tox21 database with these protein targets. Many toxic compounds were predicted to interact with serotonergic receptors and the SERT and many of these had physiochemical properties that suggest that they may act in the CNS. Detailed interaction analysis of the selected compounds of serotonergic receptors and the SERT indicated that besides the crucial interaction with an aspartic acid, aromatic interactions with phenylalanine residues are also very important. The obtained high CNS MPO scores and similar Glide scores between the known high affinity binders and toxicants could suggest harmful effects and drug interactions in serotonergic system of the CNS

Binding mode of novel multimodal serotonin transporter compounds in 5-hydroxytryptamine receptors

Antidepressants are the most common treatment of depression, one of the leading causes of suicide and disability worldwide. Currently marketed antidepressants have certain limitations; they have a delayed response time, only about 1/3 of the patients respond to the first agent prescribed, and many of them produce side effects that reduce the quality of life. The need for more efficacious and faster-acting antidepressants with fewer side effects is thus apparent. Studies have shown that 5-HT receptors (5-HTRs) are involved in many of the adverse effects of antidepressants, and may be responsible for efficacy issues and the delayed onset of therapeutic action. Some novel multimodal (two or more pharmacological actions) antidepressants combine inhibition of the serotonin transporter (SERT) with agonist or antagonist activity at 5-HTRs, to counteract the activity responsible for the aforementioned problems with the present antidepressants. This study continues a previous virtual screening study, where we identified new compounds for SERT. Several of the compounds also showed affinity for one or more 5-HTRs. Although affinities are known, their ligand – 5-HTRs binding modes and their mode of action (agonist or antagonist action) for the target 5-HTRs have not been established. The aim of this study was to predict their mode of action, and to identify binding modes important for high affinity, by the use of computational methods. Homology modeling was used to construct models of 5-HT1AR, 5-HT2AR, 5-HT6R and 5-HT7R. The models were used for molecular docking and calculations of structural interaction fingerprints. Several residues important for affinity to the target receptors were identified, and preferable binding modes were determined. The mode of action of the compounds was predicted based on their preferences for agonist/antagonist-selective models, and on previous studies of agonists and antagonists showing that agonists form strong polar interactions transmembrane helix 5 (TM5). The results indicated that several of the compounds might have potential to be developed into new antidepressant drugs

Modeling the Binding of Neurotransmitter Transporter Inhibitors with Molecular Dynamics and Free Energy Calculations

The monoamine transporter (MAT) proteins responsible for the reuptake of the neurotransmitter substrates, dopamine, serotonin, and norepinephrine, are drug targets for the treatment of psychiatric disorders including depression, anxiety, and attention deficit hyperactivity disorder. Small molecules that inhibit these proteins can serve as useful therapeutic agents. However, some dopamine transporter (DAT) inhibitors, such as cocaine and methamphetamine, are highly addictive and abusable. Efforts have been made to develop small molecules that will inhibit the transporters and elucidate specific binding site interactions. This work provides knowledge of molecular interactions associated with MAT inhibitors by offering an atomistic perspective that can guide designs of new pharmacotherapeutics with enhanced activity. The work described herein evaluates intermolecular interactions using computational methods to reveal the mechanistic detail of inhibitors binding in the DAT. Because cocaine recognizes the extracellular-facing or outward-facing (OF) DAT conformation and benztropine recognizes the intracellular-facing or inward-facing (IF) conformation, it was postulated that behaviorally “typical” (abusable, locomotor psychostimulant) inhibitors stabilize the OF DAT and “atypical” (little or no abuse potential) inhibitors favor IF DAT. Indeed, behaviorally-atypical cocaine analogs have now been shown to prefer the OF DAT conformation. Specifically, the binding interactions of two cocaine analogs, LX10 and LX11, were studied in the OF DAT using molecular dynamics simulations. LX11 was able to interact with residues of transmembrane helix 8 and bind in a fashion that allowed for hydration of the primary binding site (S1) from the intracellular space, thus impacting the intracellular interaction network capable of regulating conformational transitions in DAT. Additionally, a novel serotonin transporter (SERT) inhibitor previously discovered through virtual screening at the SERT secondary binding site (S2) was studied. Intermolecular interactions between SM11 and SERT have been assessed using binding free energy calculations to predict the ligand-binding site and optimize ligand-binding interactions. Results indicate the addition of atoms to the 4-chlorobenzyl moiety were most energetically favorable. The simulations carried out in DAT and SERT were supported by experimental results. Furthermore, the co-crystal structures of DAT and SERT share similar ligand-binding interactions with the homology models used in this study

The search of new negative allosteric GABAB receptor modulators using in silico and in vitro approaches

γ-aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in the CNS. GABA exerts its function on both ionotropic ligand-gated GABAA receptors and metabotropic GABAB G-protein coupled receptors (GPCRs). Disruption in the GABAergic system has been associated with numerous neurological and psychiatric disorders in humans. These include developmental dysfunctions, epilepsy, sleep disorders, drug and alcohol dependence, schizophrenia, motor coordination disorders, anxiety, autism, inability to regulate emotions, Huntington's disease, and Parkinson's disease. Hence, developing drugs to act on such a remarkable system can attract much attention and be beneficial. In recent years, there has been colossal attention toward development of allosteric modulators of GPCRs. These compounds provide high selectivity, novel modes of action and may lead to unique therapeutic agents for the treatment of many neurological and psychiatric human disorders. Baclofen, a GABAB receptor agonist, is still the only GABAB receptor approved drug, and is used for the treatment of muscle spasticity associated with spinal cord injury and multiple sclerosis; however, numerous side effects hamper its clinical use. Allosteric modulators, on the other hand, are expected to have a much better side-effect profile than traditional orthosteric drugs. In the current study, in silico and in vitro methods were adopted to screen for potential negative allosteric modulators within the MolPort database. A sequential combination of ligand- and structure-based virtual screening was first performed to reduce the significant number of chemical compounds followed by the in vitro experimental testing. The virtual screening procedure facilitated the selection of 16 hit compounds that were purchased and tested experimentally using an in vitro functional assay. Only one compound, A-8, was tested in a dose-response cAMP assay, and results indicate that it is a negative allosteric modulator. In addition, analysis of the initial test results suggests that A-9 might be a negative allosteric modulator and that A-20 might be a positive allosteric modulator. Further accurate experimental tests are required for these compounds

Identification of Novel Serotonin Transporter Compounds by Virtual Screening

The serotonin (5-hydroxytryptamine, 5-HT) transporter (SERT) plays an essential role in the termination of serotonergic neurotransmission by removing 5-HT from the synaptic cleft into the presynaptic neuron. It is also of pharmacological importance being targeted by antidepressants and psychostimulant drugs. Here, five commercial databases containing approximately 3.24 million drug-like compounds have been screened using a combination of two-dimensional (2D) fingerprint-based and three-dimensional (3D) pharmacophore-based screening and flexible docking into multiple conformations of the binding pocket detected in an outward-open SERT homology model. Following virtual screening (VS), selected compounds were evaluated using in vitro screening and full binding assays and an in silico hit-to-lead (H2L) screening was performed to obtain analogues of the identified compounds. Using this multistep VS/H2L approach, 74 active compounds, 46 of which had <i>K</i>_i values of ≤1000 nM, belonging to 16 structural classes, have been identified, and multiple compounds share no structural resemblance with known SERT binders