Cytochromes P450 (P450s) are a large superfamily of heme-containing monooxygenases. P450s are found in all Kingdoms of life and exhibit incredible diversity, both at sequence level and also on a biochemical basis. In the majority of cases, P450s can be assigned into one of ten classes based on their associated redox partners, domain architecture and cellular localization. Prokaryotic P450s now represent a large diverse collection of annotated/known enzymes, of which many have great potential biocatalytic potential. The self-sufficient P450 classes (Class VII/VIII) have been explored significantly over the past decade, with many annotated and biochemically characterized members. It is clear that the prokaryotic P450 world is expanding rapidly, as the number of published genomes and metagenome studies increases, and more P450 families are identified and annotated (CYP families).Protein phosphatase 1 is a major Ser/Thr protein phosphatase activity in eukaryotic cells. It is composed of a catalytic polypeptide (PP1C), with little substrate specificity, that interacts with a large variety of proteins of diverse structure (regulatory subunits). The diversity of holoenzymes that can be formed explain the multiplicity of cellular functions under the control of this phosphatase. In quite a few cases, regulatory subunits have an inhibitory role, downregulating the activity of the phosphatase. In this chapter we shall introduce PP1C and review the most relevant families of PP1C regulatory subunits, with particular emphasis in describing the structural basis for their interaction.There is a growing interest to study and address neglected tropical diseases (NTD). To this end, in silico methods can serve as the bridge that connects academy and industry, encouraging the development of future treatments against these diseases. This chapter discusses current challenges in the development of new therapies, available computational methods and successful cases in computer-aided design with particular focus on human trypanosomiasis. Novel targets are also discussed. As a case study, we identify amentoflavone as a potential inhibitor of TcSir2rp3 (sirtuine) from Trypanosoma cruzi (20.03 μM) with a workflow that integrates chemoinformatic approaches, molecular modeling, and theoretical affinity calculations, as well as in vitro assays.Significant advances have been made toward discovering allosteric inhibitors for challenging drug targets such as the Ras family of membrane-associated signaling proteins. Malfunction of Ras proteins due to somatic mutations is associated with up to a quarter of all human cancers. Computational techniques have played critical roles in identifying and characterizing allosteric ligand-binding sites on these proteins, and to screen ligand libraries against those sites. These efforts, combined with a wide range of biophysical, structural, biochemical and cell biological experiments, are beginning to yield promising inhibitors to treat malignancies associated with mutated Ras proteins. In this chapter, we discuss some of these developments and how the lessons learned from Ras might be applied to similar other challenging drug targets.Epigenetics was coined almost 70 years ago for the description of heritable phenotype without altering DNA sequences. Research on the field has uncovered significant roles of such mechanisms, that account for the biogenesis of several diseases. Further studies have led the way for drug development which targets epi-enzymes, mainly for cancer treatment. Of the numerous epi-targets involved with histone acetylation, bromodomains have captured the spotlight of drug discovery focused on novel therapies. However, due to high sequence identity, the development of potent and selective inhibitors poses a significant challenge. Herein, we discuss recent computational developments on BET inhibitors and other methods that may be applied for drug discovery in general. As a proof-of-concept, we discuss a virtual screening to identify novel BET inhibitors based on coumarin derivatives. From public data, we identified putative structure-activity relationships of coumarin scaffold and propose R-group modifications for BET selectivity. Results showed that the optimization and design of novel coumarins could be further explored.With the increase of the need to use more sustainable processes for the industry in our society, the modeling of enzymes has become crucial to fully comprehend their mechanism of action and use this knowledge to enhance and design their properties. A lot of methods to study enzymes computationally exist and they have been classified on sequence-based, structure-based, and the more new artificial intelligence-based ones. Albeit the abundance of methods to help predict the function of an enzyme, molecular modeling is crucial when trying to understand the enzyme mechanism, as they aim to correlate atomistic information with experimental data. Among them, methods that simulate the system dynamics at a molecular mechanics level of theory (classical force fields) have shown to offer a comprehensive study. In this book chapter, we will analyze these techniques, emphasizing the importance of precise modeling of enzyme-substrate interactions. In the end, a brief explanation of the transference of the information from research studies to the industry is given accompanied with two examples of family enzymes where their modeling has helped their exploitation.

Nondaily smoking is increasing in the United States and common among Hispanic/Latino smokers. We characterized factors related to longitudinal smoking transitions in Hispanic/Latino nondaily smokers.

The Hispanic Community Health Study/Study of Latinos is a population-based cohort study of Hispanics/Latinos aged 18-74 years. Multinomial logistic regression assessed the baseline factors (2008-2011) associated with follow-up smoking status (2014-2017) in nondaily smokers (n= 573), accounting for complex survey design.

After ∼6 years, 41% of nondaily smokers became former smokers, 22% became daily smokers, and 37% remained nondaily smokers. Factors related to follow-up smoking status were number of days smoked in the previous month, household smokers, education, income, and insurance. Selleck Pamiparib Those smoking 16 or more of the last 30days had increased risk of becoming a daily smoker [vs.<4days; relative risk ratio (RRR)= 5.65, 95% confidence interval (95% CI)= 1.96-16.33]. Greater education was inversely associated with transitioning to daily smoking [>high school vs.