Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein-protein interactions via affinity-based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.

Mass spectrometry-based proteomics

Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery. This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation. Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE
 .

/pdf/an-automated-method-for-finding-molecular-complexes-in-large-1xr24lgr4a.pdf

An automated method for finding molecular complexes in large protein interaction networks.

We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences.

/pdf/the-psychological-meaning-of-words-liwc-and-computerized-4sd1bn03dk.pdf

The psychological meaning of words: LIWC and computerized text analysis methods

A fundamental goal of cell biology is to define the functions of proteins in the context of compartments that organize them in the cellular environment. Here we describe the construction and analysis of a collection of yeast strains expressing full-length, chromosomally tagged green fluorescent protein fusion proteins. We classify these proteins, representing 75% of the yeast proteome, into 22 distinct subcellular localization categories, and provide localization information for 70% of previously unlocalized proteins. Analysis of this high-resolution, high-coverage localization data set in the context of transcriptional, genetic, and protein-protein interaction data helps reveal the logic of transcriptional co-regulation, and provides a comprehensive view of interactions within and between organelles in eukaryotic cells.

/pdf/global-analysis-of-protein-localization-in-budding-yeast-4y1j25x0if.pdf

Global analysis of protein localization in budding yeast

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Network Medicine: A Network-Based Approach to Human Disease

Enthalpies of hydrolysis or of oxidative hydrolysis are reported for the hexahalogenometallates(IV) K2[WCl6], K2[WBr6], Rb2[WBr6], Cs2[WBr6], K2[ReCl6], and K2[ReBr6], and for ReCl4 and ReBr4. Enthalpies of formation of these compounds have been calculated from these hydrolysis results. Halide-ion affinities of WCl4, WBr4, ReCl4, and ReBr4, and two-electron affinities of WCl6 and WBr6, have been estimated from these and other thermochemical results. These estimates have been made using the new direct-minimisation method of Jenkins and Pratt based on the Huggins and Mayer potential to assign the lattice energies of the hexahalogenometallate salts involved.

Thermochemistry of alkali-metal hexachloro- and hexabromo-tungstates(IV) and -rhenates(IV)

Purpose. This article considers whether the modular facet of popular ‘radex’ models of offender behaviour is falsifiable or a statistical inevitability when using Jaccard coefficient, as evidence from other domains suggests. Method. Data equivalent to that examined in previous papers, and artificial data varying on four parameters, were examined using the conventional procedure of deriving Jaccard coefficients and submitting these to a smallest space analyses (SSA-I). The parameters were number of variables, number of cases, highest frequency of variable occurrence, and distribution of occurrences. Evidence of a modular pattern in each SSA-I solution was assessed using one qualitative and two quantitative measures. Results. When variables were free to occur in more than 50% of cases, none of the Jaccard-based SSA-I solutions supported the null hypothesis of no modular facet. This contrasts equivalent analyses using Yules Q, where 95.7% of the solutions supported the null hypothesis. When variables were restricted to occur in less than 50% of cases, the number of solutions supporting the null hypothesis changes to .003 and 78%, respectively. Analyses of the artificial data found that reducing the number of variables in a Jaccard-based solution increased the likelihood of supporting the null hypothesis, which suggests that these solutions are structured by variable occurrence (i.e., frequency) rather than variable co-occurrence. Implications. Research using Jaccard coefficient to measure co-occurrences among behaviours should not claim that the modular facet of their radex model is an empirical finding. Unfortunately, this is many of the existing publications.

Jaccard's heel: Radex models of criminal behaviour are rarely falsifiable when derived using Jaccard coefficient

PP118 Detection and quantification of EGF receptor phosphorylation in formalin-fixed tumor sections by selected/multiple reaction monitoring mass spectrometry

In “The Criminal Profiling Illusion: What’s Behind the Smoke and Mirrors?” (Snook, Cullen, Bennell, Taylor, & Gendreau, 2008), we questioned the evidence base for criminal profiling (CP) and offered an explanation regarding how people have been misled into thinking that it is more effective than what research suggests. In their reply, Dern, Dern, Horn, and Horn (2009 [this issue]) challenged some of our provisional conclusions and outlined a “highly complex and scientifically well-founded practice of criminal profiling” (p. 1086) known as behavioral case analysis. We are pleased to be part of a dialogue that forms the basis for collaborations that promote understanding and enhance scientific contributions to CP. Here we respond to Dern et al. by highlighting some of the areas in which discussion, collaboration, and scientific experimentation are needed.

https://pauljtaylor.com/wp-content/uploads/2015/09/2009-CJB-Snook-et-al.pdf

On the Need for Scientific Experimentation in the Criminal Profiling Field: A Reply to Dern and Colleagues

Protein phosphorylation is a major post-translational modification involved in most biological processes and numerous diseases. Much biological relevance lies in the knowledge of the extent of phosphorylation at any given protein phosphorylation site, i.e., protein phosphorylation stoichiometry. Major progress in quantitative mass spectrometry has allowed the establishment of protocols that permit accurate determination of protein phosphorylation stoichiometry. The Basic Protocol presented herein describes a label-free mass spectrometry approach for determining phosphorylation stoichiometry using electrospray nano-liquid chromatography coupled to selected reaction monitoring (SRM). This entails the calculation of the respective response rates of the phosphorylated and unphosphorylated cognate peptide species using a peptide dephosphorylation reaction. The ratio of these response rates is then applied as a correction factor to the LC-SRM-MS signal intensity ratio of the corresponding endogenous phosphopeptide/nonphosphopeptide pair in a biological sample of interest. An Alternate Protocol is also presented that describes how heavy isotope-labeled synthetic peptides can be used as internal standards to determine the stoichiometry of phosphorylation. Curr. Protoc. Chem. Biol. 4:65-81 © 2012 by John Wiley & Sons, Inc.


Keywords:

mass spectrometry;
SRM;
MRM;
phosphorylation;
AQUA;
stoichiometry;
label-free

Paul J. Taylor

Papers

Thermochemistry of alkali-metal hexachloro- and hexabromo-tungstates(IV) and -rhenates(IV)

Jaccard's heel: Radex models of criminal behaviour are rarely falsifiable when derived using Jaccard coefficient

PP118 Detection and quantification of EGF receptor phosphorylation in formalin-fixed tumor sections by selected/multiple reaction monitoring mass spectrometry

On the Need for Scientific Experimentation in the Criminal Profiling Field: A Reply to Dern and Colleagues

Measurement of Protein Phosphorylation Stoichiometry by SRM-MS