Advances in chemical protein modification.

Chemical protein modification has emerged as an invaluable tool for the development of modified proteins. The complementary use of both genetic and chemical methods has provided a large toolbox that allows the preparation of almost unlimited protein constructs with either natural or synthetically modified residues. Such a protein chemodiversity, usually achieved after translation and commonly referred to as post-translational protein modifications (PTMs), is often responsible for the vast biodiversity found in nature. These modifications include acylation, methylation, phosphorylation, sulfation, farnesylation, ubiquitination, and glycosylation, among others, and play a pivotal role in important cellular processes including trafficking, differentiation, migration, and signaling. Consequently, reproducing in a highly efficient and controlled way such natural modifications of proteins (by introducing natural PTMs) would provide an invaluable tool to study their precise function. Additionally, the possibility offered by the introduction and (bio)orthogonal modification of unnatural moieties/amino acids (usually improving the properties of natural PTMs during isolation, analysis, and processing) makes site-selective modification of proteins a key tool for interrogating and intervening biological systems both in vitro and in vivo. Given the range of chemical modification methods available, it is now possible to decide which residue to target and which modification to attach in order to confer the desired property/function (affinity probes, fluorophores, reactive tags, etc.). For example, increasing the circulation half-life of a therapeutic protein may be achieved by the addition of polyethylene glycol (PEG). On the other hand, the use of a spectroscopic label to monitor biomolecule distribution in vivo enables the construction of highly selective imaging agents. Despite the vast progress in the field of bioconjugation chemistry, scientists still face many challenges, not only synthetically but also from a processing, manufacturing, safety, and stability perspective. A number of methods have been developed and applied for the modification of particular proteins and therefore may not be applicable to any protein of interest. Thus, there remains a need for the development of complementary reactions for the site-selective chemical modification of proteins that are mild, efficient, and robust. Several reviews covering different aspects of the chemical synthesis of proteins, from general native chemical ligation strategies and the modification of endogenous amino acids to more specialized topics such as click modification protocols, the introduction of particular PTMs including glycosylation, PEGylation, and polymerization of protein-based initiators, and the challenging labeling of a specific protein of interest in a complex cellular mixture using the so-called “bioorthogonal” reactions, have been published during the past decade. While the later publications describe different protein syntheses/modifications in detail, the aim of the present review is not to be an exhaustive survey of all available bioconjugation methodologies but to discuss recent chemical strategies for the site-selective modification of proteins such as fast sulfur exchange or stable thioether formation, photo and metal-free cycloadditions, and other particularly challenging metal-mediated protocols. This review will be divided into two sections: transition metal-free and transition metal-mediated approaches. For clarity, we will use the following terminology throughout this manuscript: residue/amino acid/site-selective (or simply site-selective) reactions are those transformations that preferentially modify one amino acid residue over the others (e.g., cysteine versus lysine) and, thus, can be considered examples of chemoselective reactions; on the other hand transformations described as regioselective preferentially modify only one of a set of the same amino acid, in particular when more than one is present in the same molecule (e.g., solvent-exposed lysine versus internal lysine).O.B. thanks the European Commission (Marie Curie CIG) and Ministerio de Ciencia e Innovación, Spain (Juan de la Cierva Fellowship). G.J.L.B. thanks his generous sources of funding: Royal Society, FCT Portugal (FCT Investigator), European Commission (Marie Curie CIG), and the EPSRC. G.J.L.B. is a Royal Society University Research Fellow. The authors thank Paula Boutureira Regla and Francisco Pinteus da Cruz Lopes Bernardes for inspiration.This article was originally published in Chemical Reviews, 2015, 115 (5), pp 2174–2195 DOI: 10.1021/cr500399p. This is the final published version

A Sweet Galactose Transfer: Metabolic Oligosaccharide Engineering as a Tool To Study Glycans in Plasmodium Infection.

The introduction of chemical reporter groups into glycan structures through metabolic oligosaccharide engineering (MOE) followed by bio-orthogonal ligation is an important tool to study glycosylation. We show the incorporation of synthetic galactose derivatives that bear terminal alkene groups in hepatic cells, with and without infection by Plasmodium berghei parasites, the causative agent of malaria. Additionally, we demonstrated the contribution of GLUT1 to the transport of these galactose derivatives, and observed a consistent increase in the uptake of these compounds going from naïve to P. berghei-infected cells. Finally, we used MOE to study the interplay between Plasmodium parasites and their mosquito hosts, to reveal a possible transfer of galactose building blocks from the latter to the former. This strategy has the potential to provide new insights into Plasmodium glycobiology as well as for the identification and characterization of key glycan structures for further vaccine development

Machine learning for target discovery in drug development.

The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.T.R. is an Investigador Auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant 807281) and FCT/FEDER (02/SAICT/2017, Grant 28333) for funding. G.J.L.B. is a Royal Society University Research Fellow (URF\R\180019) and a FCT Investigator (IF/00624/2015)

Protein-Protein Conjugates: Tyrosine Delivers.

The residues cysteine and lysine are the undisputed champions of bioconjugation chemistry. Targeting other amino acids has been touted as a potential way to improve the synthesis of protein–peptide and protein–protein conjugates that are widely studied for their potential therapeutic ability and used as tools for understanding biological function. Now, a team of researchers at the University of California, Berkeley, have targeted solvent-exposed tyrosine residues to develop a method of preparing such conjugates

Bases granulares de agregados reciclados

Mestrado em Engenharia CivilO sector da construção civil incluindo a manutenção dos pavimentos rodoviá-rios consome enormes quantidades de agregados anualmente. A utilização de materiais reciclados contribui para a diminuição da demanda de exploração destes recursos naturais. Seguindo esta perspectiva, o objectivo principal do presente trabalho foi ava-liar a aplicação de agregados obtidos pela reciclagem de pavimentos betumi-nosos degradados para a construção de camadas de base de novos pavimen-tos em obras de reabilitação rodoviária. Este trabalho também faz referência à constituição e comportamento dos vários tipos de pavimentos, dando ênfase aos pavimentos rodoviários flexíveis, tal como às suas principais formas de degradação. Descrevem-se, tam-bém, as técnicas de reciclagem mais usuais. Na parte experimental, descrevem-se os trabalhos laboratoriais realizados para a caracterização dos materiais e para o estudo das misturas recicladas, avaliando o seu comportamento. No final, apresentam-se as principais conclusões e perspectivas futuras.The civil construction including the roads pavements maintenance consumes large amounts of aggregates annually. The utilization of recycled materials contributes for reducing natural resources extraction demand. Following this perspective, the aim of the present work was to evaluate the aggregates implementation obtained by bituminous pavement recycling for new pavement base layers construction in rehabilitation roads works. This work also refers the several pavements types, constitution and behavior giving emphasis to flexible road pavements as well as their main degradation forms. The recycling techniques are also described. Laboratorial tests are described for the materials characterization and for the study of mixed recycled mixtures, in order to assess their behavior. Main conclusions and future prospects are also presented

Urban Endocrine Disruptors Targeting Breast Cancer Proteins.

Humans are exposed to a huge amount of environmental pollutants called endocrine disrupting chemicals (EDCs). These molecules interfere with the homeostasis of the body, usually through mimicking natural hormones leading to activation or blocking of their receptors. Many of these compounds have been associated with a broad range of diseases including the development or increased susceptibility to breast cancer, the most prevalent cancer in women worldwide, according to the World Health Organization. Thus, this article presents a virtual high-throughput screening (vHTS) to evaluate the affinity of proteins related to breast cancer, such as ESR1, ERBB2, PGR, BCRA1, and SHBG, among others, with EDCs from urban sources. A blind docking strategy was employed to screen each protein-ligand pair in triplicate in AutoDock Vina 2.0, using the computed binding affinities as ranking criteria. The three-dimensional structures were previously obtained from EDCs DataBank and Protein Data Bank, prepared and optimized by SYBYL X-2.0. Some of the chemicals that exhibited the best affinity scores for breast cancer proteins in each category were 1,3,7,8-tetrachlorodibenzo-p-dioxin, bisphenol A derivatives, perfluorooctanesulfonic acid, and benzo(a)pyrene, for catalase, several proteins, sex hormone-binding globulin, and cytochrome P450 1A2, respectively. An experimental validation of this approach was performed with a complex that gave a moderate binding affinity in silico, the sex hormone binding globulin (SHBG), and bisphenol A (BPA) complex. The protein was obtained using DNA recombinant technology and the physical interaction with BPA assessed through spectroscopic techniques. BPA binds on the recombinant SHBG, and this results in an increase of its α helix content. In short, this work shows the potential of several EDCs to bind breast cancer associated proteins as a tool to prioritize compounds to perform in vitro analysis to benefit the regulation or exposure prevention by the general population.This work was supported by Colciencias (567-2012 to D.M-G., 1107-519-29058, 1107-459- 21616), the University of Cartagena (0342010) and the EPSRC. G.J.L.B. is a Royal Society University Research Fellow.This is the author accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acs.chemrestox.5b0034

Tetrazine Carbon Nanotubes for Pretargeted In Vivo "Click-to-Release" Bioorthogonal Tumour Imaging.

The bioorthogonal inverse-electron-demand Diels-Alder (IEDDA) cleavage reaction between tetrazine and trans-cyclooctene (TCO) is a powerful way to control the release of bioactive agents and imaging probes. In this study, a pretargeted activation strategy using single-walled carbon nanotubes (SWCNTs) that bear tetrazines (TZ@SWCNTs) and a TCO-caged molecule was used to deliver active effector molecules. To optimize a turn-on signal by using in vivo fluorescence imaging, we developed a new fluorogenic near-infrared probe that can be activated by bioorthogonal chemistry and image tumours in mice by caging hemicyanine with TCO (tHCA). With our pretargeting strategy, we have shown selective doxorubicin prodrug activation and instantaneous fluorescence imaging in living cells. By combining a tHCA probe and a pretargeted bioorthogonal approach, real-time, non-invasive tumour visualization with a high target-to-background ratio was achieved in a xenograft mice tumour model. The combined advantages of enhanced stability, kinetics and biocompatibility, and the superior pharmacokinetics of tetrazine-functionalised SWCNTs could allow application of targeted bioorthogonal decaging approaches with minimal off-site activation of fluorophore/drug

Adaptive Optimization of Chemical Reactions with Minimal Experimental Information

Optimizing reaction conditions depends on expert chemistry knowledge and laborious exploration of reaction parameters. To automate this task and augment chemical intuition, we here report a computational tool to navigate search spaces. Our approach (LabMate.ML) integrates random sampling of 0.03%–0.04% of all search space as input data with an interpretable, adaptive machine-learning algorithm. LabMate.ML can optimize many real-valued and categorical reaction parameters simultaneously, with minimal computational resources and time. In nine prospective proof-of-concept studies pursuing distinctive objectives, we demonstrate how LabMate.ML can identify optimal goal-oriented conditions for several different chemistries and substrates. Double-blind competitions and the conducted expert surveys reveal that its performance is competitive with that of human experts. LabMate.ML does not require specialized hardware, affords quantitative and interpretable reactivity insights, and autonomously formalizes chemical intuition, thereby providing an innovative framework for informed, automated experiment selection toward the democratization of synthetic chemistry.D.R. is a Swiss National Science Foundation Fellow (grant nos. P2EZP3_168827 and P300P2_177833). E.A.H. is supported by the Herchel Smith Fellowship awarded by Williams College. G.J.L.B. is a Royal Society URF (URF\R\180019). T.R. is an Investigador Auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges the H2020 (TWINN-2017 ACORN, grant no. 807281), FCT/FEDER (02/SAICT/2017, grant no. 28333). D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding. The authors are extremely grateful to several colleagues for suggesting Ugi reaction conditions, and to Prof. R. Langer and Prof. G. Traverso, who provided invaluable comments on the research and manuscript. The authors are indebted to Prof. R. Moreira for access to the CEM microwave reactor; Dr. F. Corzana for technical assistance with HRMS; and the 13 graduate students, 17 postdoctoral researchers, and eight principal investigators across Austria, Denmark, Portugal, Spain, the United Kingdom, and the United States who took part in the survey. We thank R. Rodrigues for help in producing Figure 1. The survey was approved by the iMM and MIT (COUHES protocol 1809514426). The authors also thank the four anonymous reviewers for their most insightful comments.info:eu-repo/semantics/publishedVersio

Protein micro- and nano-capsules for biomedical applications

Micro- and nano-scale systems have emerged as important tools for developing clinically useful drug delivery systems. In this tutorial review, we discuss the exploitation of biomacromolecules for this purpose, focusing on proteins, polypeptides, nucleic acids and polysaccharides and mixtures thereof as potential building blocks for novel drug delivery systems. We focus on the mechanisms of formation of micro- and nano-scale protein-based capsules and shells, as well as on the functionalization of such structures for use in targeted delivery of bioactive materials. We summarise existing methods for protein-based capsule synthesis and functionalization and highlight future challenges and opportunities for delivery strategies based on biomacromolecules.U.S. is grateful to Professor Aharon Gedanken, Chemistry Department, Bar-Ilan University, Israel, for his support and supervision during her PhD research work. G.J.L.B. is a Royal Society University Research Fellow at the Department of Chemistry, University of Cambridge and an Investigador FCT at the Instituto de Medicina Molecular, Lisboa. We thank Nuno Azoia assistance with the preparation of the figures. The authors thank the European Union Seventh Framework Programme (FP7/20072013) under grant agreement NMP4-LA-2009-228827 NANOFOL

Computational advances in combating colloidal aggregation in drug discovery.

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.D.R. is a Swiss National Science Foundation Fellow (Grants P2EZP3_168827 and P300P2_177833). G.J.L.B. is a Royal Society URF (UF110046 and URF/R/180019), an iFCT Investigator (IF/00624/2015), and the recipient of an ERC StG (TagIt, Grant Agreement 676832). T.R. and G.J.L.B. acknowledge Marie Sklodowska-Curie ITN Protein Conjugates (Grant Agreement 675007) for funding. T.R. is a Marie Curie Fellow (Grant Agreement 743640). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant Agreement 807281) and POR Lisboa 2020/FEDER (02/SAICT/2017, Grant Agreement Lisboa-01-0145-FEDER-028333) for funding. D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding