A Point of Inflection and Reflection on Systems Chemical Biology

Perturbation-specific transcriptional mapping for unbiased target elucidation of antibiotics

The rising prevalence of antibiotic resistance threatens human health. While more sophisticated strategies for antibiotic discovery are being developed, target elucidation of new chemical entities remains challenging. In the postgenomic era, expression profiling can play an important role in mechanism-of-action (MOA) prediction by reporting on the cellular response to perturbation. However, the broad application of transcriptomics has yet to fulfill its promise of transforming target elucidation due to challenges in identifying the most relevant, direct responses to target inhibition. We developed an unbiased strategy for MOA prediction, called perturbation-specific transcriptional mapping (PerSpecTM), in which large-throughput expression profiling of wild-type or hypomorphic mutants, depleted for essential targets, enables a computational strategy to address this challenge. We applied PerSpecTM to perform reference-based MOA prediction based on the principle that similar perturbations, whether chemical or genetic, will elicit similar transcriptional responses. Using this approach, we elucidated the MOAs of three molecules with activity against Pseudomonas aeruginosa by comparing their expression profiles to those of a reference set of antimicrobial compounds with known MOAs. We also show that transcriptional responses to small-molecule inhibition resemble those resulting from genetic depletion of essential targets by clustered regularly interspaced short palindromic repeats interference (CRISPRi) by PerSpecTM, demonstrating proof of concept that correlations between expression profiles of small-molecule and genetic perturbations can facilitate MOA prediction when no chemical entities exist to serve as a reference. Empowered by PerSpecTM, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts.

Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions

In the field of chemical synthesis planning, the accurate recommendation of reaction conditions is essential for achieving successful outcomes. This work introduces an innovative deep learning approach designed to address the complex task of predicting appropriate reagents, solvents, and reaction temperatures for chemical reactions. Our proposed methodology combines a multi-label classification model with a ranking model to offer tailored reaction condition recommendations based on relevance scores derived from anticipated product yields. To tackle the challenge of limited data for unfavorable reaction contexts, we employed the technique of hard negative sampling to generate reaction conditions that might be mistakenly classified as suitable, forcing the model to refine its decision boundaries, especially in challenging cases. Our developed model excels in proposing conditions where an exact match to the recorded solvents and reagents is found within the top-10 predictions 73% of the time. It also predicts temperatures within ± 20 [Formula: see text] of the recorded temperature in 89% of test cases. Notably, the model demonstrates its capacity to recommend multiple viable reaction conditions, with accuracy varying based on the availability of condition records associated with each reaction. What sets this model apart is its ability to suggest alternative reaction conditions beyond the constraints of the dataset. This underscores its potential to inspire innovative approaches in chemical research, presenting a compelling opportunity for advancing chemical synthesis planning and elevating the field of reaction engineering. Scientific contribution: The combination of multi-label classification and ranking models provides tailored recommendations for reaction conditions based on the reaction yields. A novel approach is presented to address the issue of data scarcity in negative reaction conditions through data augmentation.

Kinetic Modeling and Parameter Estimation of a Prebiotic Peptide Reaction Network

Although our understanding of how life emerged on Earth from simple organic precursors is speculative, early precursors likely included amino acids. The polymerization of amino acids into peptides and interactions between peptides are of interest because peptides and proteins participate in complex interaction networks in extant biology. However, peptide reaction networks can be challenging to study because of the potential for multiple species and systems-level interactions between species. We developed and employed a computational network model to describe reactions between amino acids to form di-, tri-, and tetra-peptides. Our experiments were initiated with two of the simplest amino acids, glycine and alanine, mediated by trimetaphosphate-activation and drying to promote peptide bond formation. The parameter estimates for bond formation and hydrolysis reactions in the system were found to be poorly constrained due to a network property known as sloppiness. In a sloppy model, the behavior mostly depends on only a subset of parameter combinations, but there is no straightforward way to determine which parameters should be included or excluded. Despite our inability to determine the exact values of specific kinetic parameters, we could make reasonably accurate predictions of model behavior. In short, our modeling has highlighted challenges and opportunities toward understanding the behaviors of complex prebiotic chemical experiments.

Protein Modification at Tyrosine with Iminoxyl Radicals

Post-translational modifications (PTMs) of proteins are a biological mechanism for reversibly controlling protein function. Synthetic protein modifications (SPMs) at specific canonical amino acids can mimic PTMs. However, reversible SPMs at hydrophobic amino acid residues in proteins are especially limited. Here, we report a tyrosine (Tyr)-selective SPM utilizing persistent iminoxyl radicals, which are readily generated from sterically hindered oximes via single-electron oxidation. The reactivity of iminoxyl radicals with Tyr was dependent on the steric and electronic demands of oximes; isopropyl methyl piperidinium oxime 1f formed stable adducts, whereas the reaction of tert-butyl methyl piperidinium oxime 1o was reversible. The difference in reversibility between 1f and 1o, differentiated only by one methyl group, is due to the stability of iminoxyl radicals, which is partly dictated by the bond dissociation energy of oxime O-H groups. The Tyr-selective modifications with 1f and 1o proceeded under physiologically relevant, mild conditions. Specifically, the stable Tyr-modification with 1f introduced functional small molecules, including an azobenzene photoswitch, to proteins. Moreover, masking critical Tyr residues by SPM with 1o, and subsequent deconjugation triggered by the treatment with a thiol, enabled on-demand control of protein functions. We applied this reversible Tyr modification with 1o to alter an enzymatic activity and the binding affinity of a monoclonal antibody with an antigen upon modification/deconjugation. The on-demand ON/OFF switch of protein functions through Tyr-selective and reversible covalent-bond formation will provide unique opportunities in biological research and therapeutics.

Aggregation induced emission based active conjugated imidazole luminogens for visualization of latent fingerprints and multiple anticounterfeiting applications

Aggregation-induced emission based organic heterocyclic luminogens bearing conjugated electronic structures showed much attention due to its excellent fluorescence in aggregation state. In this communication, a novel conjugated blue light emitting imidazole molecule is synthesized by one pot multicomponent reaction route is reported for the first time. The prepared molecule exhibits a strong fluorescence in aggregation state with exceptional properties, such as high purity, inexpensive, eco-friendly, large scale production, high photostability, etc. By considering these advantages, a new fluorescence based platform has been setup for in-situ visualization of latent fingerprints and its preservation by spray method followed by Poly(vinyl alcohol) masking. A clear and well defined fluorescence fingerprint images are noticed on variety of surfaces by revealing level 1-3 ridge features upon ultraviolet 365 nm light exposure. The dual nature of binding specificity as well as excellent fluorescence properties permits the visualization of latent fingerprints for longer durations (up to 365 days) with superior contrast, high sensitivity, efficiency, selectivity and minimal background hindrance. We further fabricated unclonable invisible security ink for various printing modes on valuable goods for protection against forging. The developed labels are displaying uniform distribution of ink and exceptional stability under various atmospheric environments. The development of long preservative information using aggregation-induced emission based luminogen opens up a new avenue in advanced forensic and data security applications.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

High-Throughput Screening Platforms in the Discovery of Novel Drugs for Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) are incurable and debilitating conditions that result in progressive degeneration and/or death of nerve cells in the central nervous system (CNS). Identification of viable therapeutic targets and new treatments for CNS disorders and in particular, for NDDs is a major challenge in the field of drug discovery. These difficulties can be attributed to the diversity of cells involved, extreme complexity of the neural circuits, the limited capacity for tissue regeneration, and our incomplete understanding of the underlying pathological processes. Drug discovery is a complex and multidisciplinary process. The screening attrition rate in current drug discovery protocols mean that only one viable drug may arise from millions of screened compounds resulting in the need to improve discovery technologies and protocols to address the multiple causes of attrition. This has identified the need to screen larger libraries where the use of efficient high-throughput screening (HTS) becomes key in the discovery process. HTS can investigate hundreds of thousands of compounds per day. However, if fewer compounds could be screened without compromising the probability of success, the cost and time would be largely reduced. To that end, recent advances in computer-aided design, in silico libraries, and molecular docking software combined with the upscaling of cell-based platforms have evolved to improve screening efficiency with higher predictability and clinical applicability. We review, here, the increasing role of HTS in contemporary drug discovery processes, in particular for NDDs, and evaluate the criteria underlying its successful application. We also discuss the requirement of HTS for novel NDD therapies and examine the major current challenges in validating new drug targets and developing new treatments for NDDs.