Thermodynamics of Protein Folding

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In the previous chapter, "Introduction to Protein Folding", we introduced the concept of free energy and the protein folding landscape. Here, we provide a deeper, more formal underpinning of free energy in terms of the entropy and enthalpy; to this end, we will first need to better define the meaning of equilibrium, entropy and enthalpy. When we understand these concepts, we will come back for a more quantitative explanation of protein folding and dynamics. We will discuss the influence of temperature on the free energy landscape, and the difference between microstates and macrostates.Comment: editorial responsability: Juami H. M. van Gils, K. Anton Feenstra, Sanne Abeln. This chapter is part of the book "Introduction to Protein Structural Bioinformatics". The Preface arXiv:1801.09442 contains links to all the (published) chapters. The update adds available arxiv hyperlinks for the chapter

Thermodynamics of Protein Folding

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. In the previous chapter, "Introduction to Protein Folding", we introduced the concept of free energy and the protein folding landscape. Here, we provide a deeper, more formal underpinning of free energy in terms of the entropy and enthalpy; to this end, we will first need to better define the meaning of equilibrium, entropy and enthalpy. When we understand these concepts, we will come back for a more quantitative explanation of protein folding and dynamics. We will discuss the influence of temperature on the free energy landscape, and the difference between microstates and macrostates

Anti-Tumor Necrosis Factor α Therapeutics Differentially Affect Leishmania Infection of Human Macrophages

Tumor necrosis factor α (TNFα) drives the pathophysiology of human autoimmune diseases and consequently, neutralizing antibodies (Abs) or Ab-derived molecules directed against TNFα are essential therapeutics. As treatment with several TNFα blockers has been reported to entail a higher risk of infectious diseases such as leishmaniasis, we established an in vitro model based on Leishmania-infected human macrophages, co-cultured with autologous T-cells, for the analysis and comparison of anti-TNFα therapeutics. We demonstrate that neutralization of soluble TNFα (sTNFα) by the anti-TNFα Abs Humira®, Remicade®, and its biosimilar Remsima® negatively affects infection as treatment with these agents significantly reduces Leishmania-induced T-cell proliferation and increases the number of infected macrophages. By contrast, we show that blockade of sTNFα by Cimzia® does not affect T-cell proliferation and infection rates. Moreover, compared to Remicade®, treatment with Cimzia® does not impair the expression of cytolytic effector proteins in proliferating T-cells. Our data demonstrate that Cimzia® supports parasite control through its conjugated polyethylene glycol (PEG) moiety as PEGylation of Remicade® improves the clearance of intracellular Leishmania. This effect can be linked to complement activation, with levels of complement component C5a being increased upon treatment with Cimzia® or a PEGylated form of Remicade®. Taken together, we provide an in vitro model of human leishmaniasis that allows direct comparison of different anti-TNFα agents. Our results enhance the understanding of the efficacy and adverse effects of TNFα blockers and they contribute to evaluate anti-TNFα therapy for patients living in countries with a high prevalence of leishmaniasis

Data_Sheet_1_Anti-Tumor Necrosis Factor α Therapeutics Differentially Affect Leishmania Infection of Human Macrophages.PDF

<p>Tumor necrosis factor α (TNFα) drives the pathophysiology of human autoimmune diseases and consequently, neutralizing antibodies (Abs) or Ab-derived molecules directed against TNFα are essential therapeutics. As treatment with several TNFα blockers has been reported to entail a higher risk of infectious diseases such as leishmaniasis, we established an in vitro model based on Leishmania-infected human macrophages, co-cultured with autologous T-cells, for the analysis and comparison of anti-TNFα therapeutics. We demonstrate that neutralization of soluble TNFα (sTNFα) by the anti-TNFα Abs Humira^®, Remicade^®, and its biosimilar Remsima^® negatively affects infection as treatment with these agents significantly reduces Leishmania-induced T-cell proliferation and increases the number of infected macrophages. By contrast, we show that blockade of sTNFα by Cimzia^® does not affect T-cell proliferation and infection rates. Moreover, compared to Remicade^®, treatment with Cimzia^® does not impair the expression of cytolytic effector proteins in proliferating T-cells. Our data demonstrate that Cimzia^® supports parasite control through its conjugated polyethylene glycol (PEG) moiety as PEGylation of Remicade^® improves the clearance of intracellular Leishmania. This effect can be linked to complement activation, with levels of complement component C5a being increased upon treatment with Cimzia^® or a PEGylated form of Remicade^®. Taken together, we provide an in vitro model of human leishmaniasis that allows direct comparison of different anti-TNFα agents. Our results enhance the understanding of the efficacy and adverse effects of TNFα blockers and they contribute to evaluate anti-TNFα therapy for patients living in countries with a high prevalence of leishmaniasis.</p