Incorporating post-translational modifications and unnatural amino acids into high-throughput modeling of protein structures

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine

The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubility. Although previous efforts to identify features which accurately capture these site-specific effects have been unsuccessful, we have developed a set of novel Rosetta Custom Score Functions and alternative Empirical Score Functions that accurately predict the effects of acridon-2-yl-alanine (Acd) incorporation on protein yield and solubility. Acd-containing mutants were simulated in PyRosetta, and machine learning (ML) was performed using either the decomposed values of the Rosetta energy function, or changes in residue contacts and bioinformatics. Using these feature sets, which represent Rosetta score function specific and bioinformatics-derived terms, ML models were trained to predict highly abstract experimental parameters such as mutant protein yield and solubility and displayed robust performance on well-balanced holdouts. Model feature importance analyses demonstrated that terms corresponding to hydrophobic interactions, desolvation, and amino acid angle preferences played a pivotal role in predicting tolerance of mutation to Acd. Overall, this work provides evidence that the application of ML to features extracted from simulated structural models allow for the accurate prediction of diverse and abstract biological phenomena, beyond the predictivity of traditional modeling and simulation approaches.

Ten quick tips for homology modeling of high-resolution protein 3D structures

The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

In silico prediction and characterization of protein post-translational modifications

This review outlines the computational approaches and procedures for predicting post translational modification (PTM)-induced changes in protein conformation and their influence on protein function(s), the latter being assessed as differential affinity in interaction with either low (ligands for receptors or transporters, substrates for enzymes) or high molecular mass molecules (proteins or nucleic acids in supramolecular assemblies). The scope for an in silico approach is discussed against a summary of the in vitro evidence on the structural and functional outcome of protein PTM.

Phosphorylation of FE65 Ser610 by serum- and glucocorticoid-induced kinase 1 modulates Alzheimer's disease amyloid precursor protein processing

Phosphorylation of FE65 Ser⁶¹⁰ by serum- and glucocorticoid-induced kinase 1 (SGK1) attenuates amyloid precursor protein (APP) processing via regulation of FE65–APP interaction.

Alzheimer's disease (AD) is a fatal neurodegenerative disease affecting 36 million people worldwide. Genetic and biochemical research indicate that the excessive generation of amyloid-β peptide (Aβ) from amyloid precursor protein (APP), is a major part of AD pathogenesis. FE65 is a brain-enriched adaptor protein that binds to APP. However, the role of FE65 in APP processing and the mechanisms that regulate binding of FE65 to APP are not fully understood. In the present study, we show that serum- and glucocorticoid-induced kinase 1 (SGK1) phosphorylates FE65 on Ser⁶¹⁰ and that this phosphorylation attenuates FE65 binding to APP. We also show that FE65 promotes amyloidogenic processing of APP and that FE65 Ser⁶¹⁰ phosphorylation inhibits this effect. Furthermore, we found that the effect of FE65 Ser⁶¹⁰ phosphorylation on APP processing is linked to a role of FE65 in metabolic turnover of APP via the proteasome. Thus FE65 influences APP degradation via the proteasome and phosphorylation of FE65 Ser⁶¹⁰ by SGK1 regulates binding of FE65 to APP, APP turnover and processing.

Accelerated formulation development of monoclonal antibodies (mAbs) and mAb-based modalities: review of methods and tools

More therapeutic monoclonal antibodies and antibody-based modalities are in development today than ever before, and a faster and more accurate drug discovery process will ensure that the number of candidates coming to the biopharmaceutical pipeline will increase in the future. The process of drug product development and, specifically, formulation development is a critical bottleneck on the way from candidate selection to fully commercialized medicines. This article reviews the latest advances in methods of formulation screening, which allow not only the high-throughput selection of the most suitable formulation but also the prediction of stability properties under manufacturing and long-term storage conditions. We describe how the combination of automation technologies and high-throughput assays creates the opportunity to streamline the formulation development process starting from early preformulation screening through to commercial formulation development. The application of quality by design (QbD) concepts and modern statistical tools are also shown here to be very effective in accelerated formulation development of both typical antibodies and complex modalities derived from them.

PyTMs: a useful PyMOL plugin for modeling common post-translational modifications

BACKGROUND

Post-translational modifications (PTMs) constitute a major aspect of protein biology, particularly signaling events. Conversely, several different pathophysiological PTMs are hallmarks of oxidative imbalance or inflammatory states and are strongly associated with pathogenesis of autoimmune diseases or cancers. Accordingly, it is of interest to assess both the biological and structural effects of modification. For the latter, computer-based modeling offers an attractive option. We thus identified the need for easily applicable modeling options for PTMs.

RESULTS

We developed PyTMs, a plugin implemented with the commonly used visualization software PyMOL. PyTMs enables users to introduce a set of common PTMs into protein/peptide models and can be used to address research questions related to PTMs. Ten types of modification are currently supported, including acetylation, carbamylation, citrullination, cysteine oxidation, malondialdehyde adducts, methionine oxidation, methylation, nitration, proline hydroxylation and phosphorylation. Furthermore, advanced settings integrate the pre-selection of surface-exposed atoms, define stereochemical alternatives and allow for basic structure optimization of the newly modified residues.

CONCLUSION

PyTMs is a useful, user-friendly modelling plugin for PyMOL. Advantages of PyTMs include standardized generation of PTMs, rapid time-to-result and facilitated user control. Although modeling cannot substitute for conventional structure determination it constitutes a convenient tool that allows uncomplicated exploration of potential implications prior to experimental investments and basic explanation of experimental data. PyTMs is freely available as part of the PyMOL script repository project on GitHub and will further evolve. Graphical Abstract PyTMs is a useful PyMOL plugin for modeling common post-translational modifications.