We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the logexpectation score, and refinement using treedependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

MUSCLE: multiple sequence alignment with high accuracy and high throughput

CHARMM (Chemistry at HARvard Macromolecular Mechanics) is a highly flexible computer program which uses empirical energy functions to model macromolecular systems. The program can read or model build structures, energy minimize them by first- or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations. The operations that CHARMM can perform are described, and some implementation details are given. A set of parameters for the empirical energy function and a sample run are included.

CHARMM: A program for macromolecular energy, minimization, and dynamics calculations

For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-recognition process of hydrogen-bonded and geometrical features extracted from x-ray coordinates. Cooperative secondary structure is recognized as repeats of the elementary hydrogen-bonding patterns “turn” and “bridge.” Repeating turns are “helices,” repeating bridges are “ladders,” connected ladders are “sheets.” Geometric structure is defined in terms of the concepts torsion and curvature of differential geometry. Local chain “chirality” is the torsional handedness of four consecutive Cα positions and is positive for right-handed helices and negative for ideal twisted β-sheets. Curved pieces are defined as “bends.” Solvent “exposure” is given as the number of water molecules in possible contact with a residue. The end result is a compilation of the primary structure, including SS bonds, secondary structure, and solvent exposure of 62 different globular proteins. The presentation is in linear form: strip graphs for an overall view and strip tables for the details of each of 10.925 residues. The dictionary is also available in computer-readable form for protein structure prediction work.

Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features

6.2.2. Definition of Effective Properties 3064 6.3. Response Properties to Magnetic Fields 3066 6.3.1. Nuclear Shielding 3066 6.3.2. Indirect Spin−Spin Coupling 3067 6.3.3. EPR Parameters 3068 6.4. Properties of Chiral Systems 3069 6.4.1. Electronic Circular Dichroism (ECD) 3069 6.4.2. Optical Rotation (OR) 3069 6.4.3. VCD and VROA 3070 7. Continuum and Discrete Models 3071 7.1. Continuum Methods within MD and MC Simulations 3072

/pdf/quantum-mechanical-continuum-solvation-models-3edv963g14.pdf

Quantum mechanical continuum solvation models.

This article describes the software suite GROMACS (Groningen MAchine for Chemical Simulation) that was developed at the University of Groningen, The Netherlands, in the early 1990s. The software, written in ANSI C, originates from a parallel hardware project, and is well suited for parallelization on processor clusters. By careful optimization of neighbor searching and of inner loop performance, GROMACS is a very fast program for molecular dynamics simulation. It does not have a force field of its own, but is compatible with GROMOS, OPLS, AMBER, and ENCAD force fields. In addition, it can handle polarizable shell models and flexible constraints. The program is versatile, as force routines can be added by the user, tabulated functions can be specified, and analyses can be easily customized. Nonequilibrium dynamics and free energy determinations are incorporated. Interfaces with popular quantum-chemical packages (MOPAC, GAMES-UK, GAUSSIAN) are provided to perform mixed MM/QM simulations. The package includes about 100 utility and analysis programs. GROMACS is in the public domain and distributed (with source code and documentation) under the GNU General Public License. It is maintained by a group of developers from the Universities of Groningen, Uppsala, and Stockholm, and the Max Planck Institute for Polymer Research in Mainz. Its Web site is http://www.gromacs.org.

/pdf/gromacs-fast-flexible-and-free-15aazpel5u.pdf

GROMACS: Fast, flexible, and free

Use of a Two-Compartment Model to Predict Ethanol Metabolism

Between 2009 and 2016 the number of protein sequences from known species increased 10-fold from 8 million to 85 million. About 80% of these sequences contain at least one region recognized by the conserved domain architecture retrieval tool (CDART) as a sequence motif. Motifs provide clues to biological function but CDART often matches the same region of a protein by two or more profiles. Such synonyms complicate estimates of functional complexity. We do full-linkage clustering of redundant profiles by finding maximum disjoint cliques: Each cluster is replaced by a single representative profile to give what we term a unique function word (UFW). From 2009 to 2016, the number of sequence profiles used by CDART increased by 80%; the number of UFWs increased more slowly by 30%, indicating that the number of UFWs may be saturating. The number of sequences matched by a single UFW (sequences with single domain architectures) increased as slowly as the number of different words, whereas the number of sequences matched by a combination of two or more UFWs in sequences with multiple domain architectures (MDAs) increased at the same rate as the total number of sequences. This combinatorial arrangement of a limited number of UFWs in MDAs accounts for the genomic diversity of protein sequences. Although eukaryotes and prokaryotes use very similar sets of "words" or UFWs (57% shared), the "sentences" (MDAs) are different (1.3% shared).

Unique function words characterize genomic proteins.

It has been claimed that ingested ethanol is metabolized during absorption through the gastric mucosa, and inhibition of this metabolism by certain histamine-2 (H-2) receptor antagonists enhances the blood alcohol response to small doses of ethanol. In this issue of the Annals, however, Raufman and coworkers convincingly demonstrate that none of the commercially available H-2 antagonists had a perceptible effect on blood ethanol concentrations after ingestion of 0.3 g/kg of body weight of ethanol, roughly the quantity in a bottle of 6% beer. This editorial reviews how and if ethanol metabolism might be influenced by H-2 antagonists and concludes that the stomach probably is not an important site of ethanol metabolism and that the interaction between H-2 antagonists and ethanol is clinically insignificant. Although the liver metabolizes virtually all ethanol reaching the peripheral circulation, recent studies have suggested that an appreciable quantity of ingested ethanol may be oxidized during absorption through the gastric mucosa [1-3]. Of particular importance are claims that certain histamine-2 (H-2) antagonists inhibit this gastric metabolism and hence cause unexpectedly high blood ethanol concentrations in the imbiber [4, 5]. If correct, such findings have obvious medical, legal, and commercial implications. However, in this issue of the Annals, Raufman and coworkers [6] convincingly demonstrate that none of the commercially available H-2 antagonists influenced the blood ethanol response to ingestion of 0.3 g/kg of body weight of ethanol. This article adds fuel to an ongoing controversy regarding if and how H-2 antagonists influence alcohol metabolism. Appreciation of the controversy concerning the interaction of H-2 antagonists and ethanol requires an understanding of first-pass metabolism. An ingested compound undergoes first-pass metabolism when an appreciable fraction is metabolized before it reaches the peripheral circulation. Such metabolism may occur in the gastrointestinal mucosa or the liver or both and results from the higher concentration of compound to which these two organs are exposed when delivery is via the gut as opposed to intravenous administration. Comparison of peripheral ethanol levels after oral and intravenous administration of low doses of alcohol clearly demonstrate a first-pass effect [1, 2], and, judging from recently published textbooks [7, 8], the scientific world is embracing the concept that this metabolism takes place in the stomach. In this editorial, I first will present data to support the iconoclastic notion that first-pass metabolism of ethanol occurs in the liver, not the gastric mucosa, and then, with this concept in mind, summarize the many studies that have investigated the influence of H-2 antagonists on ethanol metabolism. Two types of data have been used to show that the gastric mucosa is the site of first-pass metabolism of ethanol. First, bypass of the stomach via either an intraportal infusion of ethanol in rats [3] or by direct intraduodenal instillation in humans [9] eliminated first-pass metabolism observed with oral dosing. Second, the gastric mucosa has been shown to contain alcohol dehydrogenase (ADH) [1, 2], the rate-limiting step in ethanol metabolism; ADH activity correlates with alterations in first-pass metabolism. Strong evidence can be marshaled, however, to argue that the stomach is not responsible for the first-pass effect. First, up to 50% of a small ethanol dose may undergo first-pass metabolism in rats [1]. Because only about 20% of ingested ethanol is absorbed from the rat stomach [10], the stomach cannot account for metabolism of 50% of the dose. In contrast, the liver has a first-pass exposure to the entire dose of ingested ethanol and thus could carry out efficient metabolism. Second, ADH activity in the gastric mucosa seemingly is insufficient to oxidize the grams of ethanol required for the first-pass effect. For example, Moreno and Pares conclude that the human stomach could metabolize about 1.2 mmol of ethanol per L in 1 hour [11], a negligible fraction of the approximately 500 mmol/L in a 0.3 g/kg dose. Finally, in vivo assessment of gastric ADH function determined from measurements of the venous-arterial difference across the stomach for ethanol and its metabolites showed negligible gastric oxidation of ethanol [10]. In contrast, the liver, which has about 100 times the ethanol-oxidizing capacity of the stomach [11], could account for the observed first-pass effect (Levitt MD, Levitt DG. Unpublished data). If, as argued above, first-pass metabolism occurs in the liver rather than in the stomach, how can one account for the disappearance of this metabolism when the stomach was by-passed by direct portal vein or duodenal instillation of ethanol [3, 9]? The explanation is that first-pass metabolism is extremely sensitive to the rate of hepatic delivery of alcohol; the faster the delivery, the more inefficient is the metabolism. Ingestion of a large dose of ethanol or rapid gastric emptying of a smaller dose saturates hepatic removal and eliminates the first-pass effect. In the experiments in which the stomach was by-passed, the rate of hepatic delivery of ethanol was so rapid that little or no first-pass effect would have been expected. If gastric metabolism of ethanol is negligible, inhibition of gastric ADH by H-2 antagonists is clinically irrelevant. The possibility remains, however, that these agents could influence ethanol metabolism by altering the gastric emptying rate, hence, the hepatic delivery rate, or by interacting with hepatic ADH. This possibility has been investigated in at least 20 studies by comparing a person's blood ethanol response to a given dose of ethanol, with and without treatment with H-2 antagonists. There is general agreement that treatment with these agents has no appreciable effect on blood ethanol concentrations with doses of ethanol that could lead to legally defined inebriation [12-15]. Thus, there are no legal implications to the concurrent ingestion of H-2 blockers and ethanol. Controversy does exist, however, concerning a possible interaction with small doses of alcohol. As pointed out by Raufman and colleagues [6], interindividual variability in blood alcohol is marked following ingestion of low doses of ethanol. This variability, in part, probably reflects differences in gastric emptying rate that alter the efficient first-pass metabolism that occurs with slow delivery of ethanol to the liver. As a result, reliable conclusions concerning interactions between H-2 antagonists and low doses of ethanol require large, meticulously controlled trials. A highly publicized study involving a small number of persons initially suggested that ranitidine and cimetidine, but not famotidine, modestly enhanced the blood ethanol response to 0.3 g/kg of ethanol [5]. However, the two largest and best controlled studies [6, 16] of this dose of ethanol in nonalcoholic men (allegedly the most susceptible group) showed that no commercially available H-2 antagonist had an appreciable effect on blood alcohol concentrations. The possibility still remains that there could be an interaction with even smaller doses of ethanol, and several studies [17, 18] have suggested that H-2 antagonists influence the blood alcohol response to ingestion of 0.15 g/kg of ethanol, the equivalent of about one-half bottle of 6% beer. A carefully controlled trial at this dose indicated that ranitidine increased the peak blood concentration from 4.9 mg/dL to 6.5 mg/dL [17]. Although this increase was statistically significant, it should be stressed that these peak levels only represent an increase from 4.9% to 6.5% of the legal inebriation concentration of 100 mg/dL and that these trivial peak values declined rapidly over 30 minutes. In addition, this difference in peak values is minor relative to the differences that would be observed if this dose of ethanol were taken while fasting (with rapid gastric emptying) compared with ingestion with food. The purist probably will argue that any increase in blood ethanol is bad. However, in light of the enormous problems represented by the abuse of alcohol and the overuse of H-2 antagonists, it seems safe to conclude that the possible harmful interaction of these agents should be relegated to the status of a nonproblem.

Do histamine-2 receptor antagonists influence the metabolism of ethanol?

https://www.gastrojournal.org/article/0016-5085(86)90694-3/pdf

Michael Levitt

Papers

Use of a Two-Compartment Model to Predict Ethanol Metabolism

Unique function words characterize genomic proteins.

Do histamine-2 receptor antagonists influence the metabolism of ethanol?

Role of the unstirred layer in protecting the murine gastric mucosa from bile salt

Intrinsic hepatic clearance in cirrhosis