Mass spectrometry-based methods for characterizing transient protein–protein interactions

The dynamic associations of transient protein–protein interactions (PPIs) are critical mediators of myriad biochemical processes. These specific, low-affinity interactions are often mediated by conserved amino acid sequences or short linear motifs (SLiMs) that interact with corresponding binding domains. The short-lived and dynamic nature of these interactions make their biophysical characterization a significant challenge. This review focuses on the development and future directions of mass spectrometry (MS)-based techniques for elucidating and characterizing SLiM-mediated PPIs. This includes the application of protein footprinting techniques to infer the location of SLiM binding sites and the growing role of native MS for direct observation of protein–SLiM interactions, highlighting their potential for the assessment of small molecule modulation of transient PPIs and the identification of interfacial SLiMs.</p

Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models

Background: QSAR is among the most extensively used computational methodology for analogue-based design. The application of various descriptor classes like quantum chemical, molecular mechanics, conceptual density functional theory (DFT)- and docking-based descriptors for predicting anti-cancer activity is well known. Although in vitro assay for anti-cancer activity is available against many different cell lines, most of the computational studies are carried out targeting insufficient number of cell lines. Hence, statistically robust and extensive QSAR studies against 29 different cancer cell lines and its comparative account, has been carried out. Results: The predictive models were built for 266 compounds with experimental data against 29 different cancer cell lines, employing independent and least number of descriptors. Robust statistical analysis shows a high correlation, cross-validation coefficient values, and provides a range of QSAR equations. Comparative performance of each class of descriptors was carried out and the effect of number of descriptors (1-10) on statistical parameters was tested. Charge-based descriptors were found in 20 out of 39 models (approx. 50%), valency-based descriptor in 14 (approx. 36%) and bond order-based descriptor in 11 (approx. 28%) in comparison to other descriptors. The use of conceptual DFT descriptors does not improve the statistical quality of the models in most cases. Conclusion: Analysis is done with various models where the number of descriptors is increased from 1 to 10; it is interesting to note that in most cases 3 descriptor-based models are adequate. The study reveals that quantum chemical descriptors are the most important class of descriptors in modelling these series of compounds followed by electrostatic, constitutional, geometrical, topological and conceptual DFT descriptors. Cell lines in nasopharyngeal (2) cancer average R2 = 0.90 followed by cell lines in melanoma cancer (4) with average R2 = 0.81 gave the best statistical values

Ligand binding site structure influences the evolution of protein complex function and topology

Summary: It has been suggested that the evolution of protein complexes is significantly influenced by stochastic, non-adaptive processes. Using ligand binding as a proxy of function, we show that the structure of ligand-binding sites significantly influences the evolution of protein complexes. We show that homomers with multi-chain binding sites (MBSs) evolve new functions slower than monomers or other homomers, and those binding cofactors and metals have more conserved quaternary structure than other homomers. Moreover, the ligands and ligand-binding pockets of homologous MBS homomers are more similar than monomers and other homomers. Our results suggest strong evolutionary selection for quaternary structure in cofactor-binding MBS homomers, whereas neutral processes are more important in complexes with single-chain binding sites. They also have pharmacological implications, suggesting that complexes with single-chain binding sites are better targets for selective drugs, whereas MBS homomers are good candidates for broad-spectrum antibiotic and multitarget drug design. : Homomers with ligand binding sites involving multiple protein chains (MBS homomers) evolve new functions slower than other homomers and monomers, and the ones binding cofactors/metals also have more conserved quaternary structure (QS). These complexes are likely to be promising targets for antibiotics and multitarget drugs. Keywords: protein complex evolution, neutral evolution, heteromers, drug design, polypharmacology, homomers, ligand bindin

Hybrid micro-/nanogels for optical sensing and intracellular imaging

Hybrid micro-/nanogels are playing an increasing important part in a diverse range of applications, due to their tunable dimensions, large surface area, stable interior network structure, and a very short response time. We review recent advances and challenges in the developments of hybrid micro-/nanogels toward applications for optical sensing of pH, temperature, glucose, ions, and other species as well as for intracellular imaging. Due to their unique advantages, hybrid micro-/nanogels as optical probes are attracting substantial interests for continuous monitoring of chemical parameters in complex samples such as blood and bioreactor fluids, in chemical research and industry, and in food quality control. In particular, their intracellular probing ability enables the monitoring of the biochemistry and biophysics of live cells over time and space, thus contributing to the explanation of intricate biological processes and the development of novel diagnoses. Unlike most other probes, hybrid micro-/nanogels could also combine other multiple functions into a single probe. The rational design of hybrid micro-/nanogels will not only improve the probing applications as desirable, but also implement their applications in new arenas. With ongoing rapid advances in bionanotechnology, the well-designed hybrid micro-/nanogel probes will be able to provide simultaneous sensing, imaging diagnosis, and therapy toward clinical applications

Solvation Thermodynamic Mapping in Computer Aided Drug Design

The displacement of water from surfaces upon biomolecular recognition and association makes a significant contribution to the free energy changes of these processes. We therefore posit that accurate characterization of local structural and thermodynamic molecular water properties can improve computational model accuracy and predictivity of recognition and association processes. In this thesis, we discuss Solvation Thermodynamic Mapping (STM) methods that we have developed using inhomogeneous fluid solvation theory (IST) to better characterize active site water structural and thermodynamic properties on protein surfaces and the open source tools that we have developed, GIST-CPPTRAJ and SSTMap, which implement these methods which we have distributed to both the academic and industrial scientific community. These methods include a nearest neighbor approximation for water entropies, a significant improvement over previous entropy formulations. We then discuss our application of these tools to the rational modification of (-)-stepholidine, a lead compound for human dopamine receptor 3 (D3R), which led to a handful of promising analogues. Finally, we describe a new method of creating pharmacophores from solvation thermodynamic maps applied retrospectively to 7 protein targets. The results documented here demonstrate promising applications of STM methods for prospective drug design. In our conclusions, we discuss potential improvements to the molecular modeling work with the goal of improving accuracy of predictions in prospective drug design projects

Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions