38 research outputs found
Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development
In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied ‘proteochemometric’ modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 untested compound – mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62). Furthermore, dependent on the similarity of a new mutant to the training set, we could predict with high accuracy which compound will be most effective on a sequence with a previously unknown genotype. Hence, our models allow the evaluation of compound performance on untested sequences and the selection of the most promising leads for further preclinical research. The modeling concept is likely to be applicable also to other target families with genetic variability like other viruses or bacteria, or with similar orthologs like GPCRs
The development of the advanced web shop based on purchase history
The goal of thesis is to develop a typical web shop application with some additional functionality. This functionality enables web shop customers to browse products in a more efficient way and thus makes shop more profitable. For this purpose, we developed a specific mechanism that handles product presentation in customer adapted way.
First we describe technologies used for development. Programing language C# is presented shortly as well as some other frameworks (ASP.net, Entity framework,), libraries (LINQ) and other web technologies (HTML, CSS, AJAX). For storing and manipulating data a database with tables in MS SQL database is created.
Furthermore we take a look at requirements, idea and logic of solution. We present solution design and present how specific functionality behaves in case of different user types. We present a solution analysis where a comparison with other similar solutions and user tests are shown. Finally we discuss problems during the development and possibilities about the future improvements
The Suzuki–Miyaura Cross-Coupling as a Versatile Tool for Peptide Diversification and Cyclization
The (site-selective) derivatization of amino acids and peptides represents an attractive field with potential applications in the establishment of structure–activity relationships and labeling of bioactive compounds. In this respect, bioorthogonal cross-coupling reactions provide valuable means for ready access to peptide analogues with diversified structure and function. Due to the complex and chiral nature of peptides, mild reaction conditions are preferred; hence, a suitable cross-coupling reaction is required for the chemical modification of these challenging substrates. The Suzuki reaction, involving organoboron species, is appropriate given the stability and environmentally benign nature of these reactants and their amenability to be applied in (partial) aqueous reaction conditions, an expected requirement upon the derivatization of peptides. Concerning the halogenated reaction partner, residues bearing halogen moieties can either be introduced directly as halogenated amino acids during solid-phase peptide synthesis (SPPS) or genetically encoded into larger proteins. A reversed approach building in boron in the peptidic backbone is also possible. Furthermore, based on this complementarity, cyclic peptides can be prepared by halogenation, and borylation of two amino acid side chains present within the same peptidic substrate. Here, the Suzuki–Miyaura reaction is a tool to induce the desired cyclization. In this review, we discuss diverse amino acid and peptide-based applications explored by means of this extremely versatile cross-coupling reaction. With the advent of peptide-based drugs, versatile bioorthogonal conversions on these substrates have become highly valuable
DrugEx v3: Scaffold-Constrained Drug Design with Graph Transformer-based Reinforcement Learning
Due to the large drug-like chemical space available to search for feasible drug-like molecules, rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified. With the rapid growth of the application of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work, we proposed a method named DrugEx, which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives similar to other known methods and does not allow users to input any prior information (i.e. a desired scaffold). In order to improve the general applicability, we updated DrugEx to design drug molecules based on scaffolds which consist of multiple fragments provided by users. In this work, the Transformer model was employed to generate molecular structures. The Transformer is a multi-head self-attention deep learning model containing an encoder to receive scaffolds as input and a decoder to generate molecules as output. In order to deal with the graph representation of molecules we proposed a novel positional encoding for each atom and bond based on an adjacency matrix to extend the architecture of the Transformer. Each molecule was generated by growing and connecting procedures for the fragments in the given scaffold that were unified into one model. Moreover, we trained this generator under a reinforcement learning framework to increase the number of desired ligands. As a proof of concept, our proposed method was applied to design ligands for the adenosine A2A receptor (A2AAR) and compared with SMILES-based methods. The results demonstrated the effectiveness of our method in that 100% of the generated molecules are valid and most of them had a high predicted affinity value towards A2AAR with given scaffolds
Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data.
Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions
Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling
Abstract Sodium-dependent glucose co-transporter 1 (SGLT1) is a solute carrier responsible for active glucose absorption. SGLT1 is present in both the renal tubules and small intestine. In contrast, the closely related sodium-dependent glucose co-transporter 2 (SGLT2), a protein that is targeted in the treatment of diabetes type II, is only expressed in the renal tubules. Although dual inhibitors for both SGLT1 and SGLT2 have been developed, no drugs on the market are targeted at decreasing dietary glucose uptake by SGLT1 in the gastrointestinal tract. Here we aim at identifying SGLT1 inhibitors in silico by applying a machine learning approach that does not require structural information, which is absent for SGLT1. We applied proteochemometrics by implementation of compound- and protein-based information into random forest models. We obtained a predictive model with a sensitivity of 0.64 ± 0.06, specificity of 0.93 ± 0.01, positive predictive value of 0.47 ± 0.07, negative predictive value of 0.96 ± 0.01, and Matthews correlation coefficient of 0.49 ± 0.05. Subsequent to model training, we applied our model in virtual screening to identify novel SGLT1 inhibitors. Of the 77 tested compounds, 30 were experimentally confirmed for SGLT1-inhibiting activity in vitro, leading to a hit rate of 39% with activities in the low micromolar range. Moreover, the hit compounds included novel molecules, which is reflected by the low similarity of these compounds with the training set (< 0.3). Conclusively, proteochemometric modeling of SGLT1 is a viable strategy for identifying active small molecules. Therefore, this method may also be applied in detection of novel small molecules for other transporter proteins
Selecting an Optimal Number of Binding Site Waters To Improve Virtual Screening Enrichments Against the Adenosine A<sub>2A</sub> Receptor
A major
challenge in structure-based virtual screening (VS) involves
the treatment of explicit water molecules during docking in order
to improve the enrichment of active compounds over decoys. Here we
have investigated this in the context of the adenosine A<sub>2A</sub> receptor, where water molecules have previously been shown to be
important for achieving high enrichment rates with docking, and where
the positions of some binding site waters are known from a high-resolution
crystal structure. The effect of these waters (both their presence
and orientations) on VS enrichment was assessed using a carefully
curated set of 299 high affinity A<sub>2A</sub> antagonists and 17,337
decoys. We show that including certain crystal waters greatly improves
VS enrichment and that optimization of water hydrogen positions is
needed in order to achieve the best results. We also show that waters
derived from a molecular dynamics simulation î—¸ without any
knowledge of crystallographic waters î—¸ can improve enrichments
to a similar degree as the crystallographic waters, which makes this
strategy applicable to structures without experimental knowledge of
water positions. Finally, we used decision trees to select an ensemble
of structures with different water molecule positions and orientations
that outperforms any single structure with water molecules. The approach
presented here is validated against independent test sets of A<sub>2A</sub> receptor antagonists and decoys from the literature. In
general, this water optimization strategy could be applied to any
target with waters-mediated protein–ligand interactions