Complete genome sequences of an Escherichia coli laboratory strain and trimethoprim-resistant (TMP32XR) mutant strains

We report the whole-genome sequences of an Escherichia coli laboratory wild-type strain and trimethoprim-resistant strains (two biological replicates, TMP32XR1 and TMP32XR2). Compared to the U00096.3 strain, a widely used strain in laboratory experiments, the laboratory wild-type strain and the drug-resistant strains evolved from this (TMP32XR1 and TMP32XR2) are 13, 24, and 37 bp longer, respectively

MS3ALIGN: an efficient molecular surface aligner using the topology of surface curvature

Background: Aligning similar molecular structures is an important step in the process of bio-molecular structure and function analysis. Molecular surfaces are simple representations of molecular structure that are easily constructed from various forms of molecular data such as 3D atomic coordinates (PDB) and Electron Microscopy (EM) data. Methods: We present a Multi-Scale Morse-Smale Molecular-Surface Alignment tool, MS3ALIGN, which aligns molecular surfaces based on significant protrusions on the molecular surface. The input is a pair of molecular surfaces represented as triangle meshes. A key advantage of MS3ALIGN is computational efficiency that is achieved because it processes only a few carefully chosen protrusions on the molecular surface. Furthermore, the alignments are partial in nature and therefore allows for inexact surfaces to be aligned. Results: The method is evaluated in four settings. First, we establish performance using known alignments with varying overlap and noise values. Second, we compare the method with SurfComp, an existing surface alignment method. We show that we are able to determine alignments reported by SurfComp, as well as report relevant alignments not found by SurfComp. Third, we validate the ability of MS3ALIGN to determine alignments in the case of structurally dissimilar binding sites. Fourth, we demonstrate the ability of MS3ALIGN to align iso-surfaces derived from cryo-electron microscopy scans. Conclusions: We have presented an algorithm that aligns Molecular Surfaces based on the topology of surface curvature

Structural analysis of dihydrofolate reductases enables rationalization of antifolate binding affinities and suggests repurposing possibilities

Antifolates are competitive inhibitors of dihydrofolate reductase ( DHFR), a conserved enzyme that is central to metabolism and widely targeted in pathogenic diseases, cancer and autoimmune disorders. Although most clinically used antifolates are known to be target specific, some display a fair degree of cross-reactivity with DHFRs from other species. A method that enables identification of determinants of affinity and specificity in target DHFRs from different species and provides guidelines for the design of antifolates is currently lacking. To address this, we first captured the potential druggable space of a DHFR in a substructure called the `supersite' and classified supersites of DHFRs from 56 species into 16 `site-types' based on pairwise structural similarity. Analysis of supersites across these site-types revealed that DHFRs exhibit varying extents of dissimilarity at structurally equivalent positions in and around the binding site. We were able to explain the pattern of affinities towards chemically diverse antifolates exhibited by DHFRs of different site-types based on these structural differences. We then generated an antifolate-DHFR network by mapping known high-affinity antifolates to their respective supersites and used this to identify antifolates that can be repurposed based on similarity between supersites or antifolates. Thus, we identified 177 human-specific and 458 pathogen-specific antifolates, a large number of which are supported by available experimental data. Thus, in the light of the clinical importance of DHFR, we present a novel approach to identifying differences in the druggable space of DHFRs that can be utilized for rational design of antifolates

Different cancer cell lines resistant to the same drug exhibit differences in folate pathway dynamics

Use of methotrexate (MTX), a widely used anti-cancer drug which targets primarily dihydrofolate reductase (DHFR) in the folate pathway is being limited by the emergence of resistance. Despite a large number of studies, a quantitative understanding of target pathway dynamics in resistant cancers is majorly lacking. In this work, we integrated gene expression data from different MTX-resistant cancer cell lines into kinetic models to study dynamics of the folate metabolic pathway. Given that all cell lines are derived from human cancers, the pathway is essentially the same consisting of 11 reactions catalyzed by the same set of enzymes and 1 non-enzymatic reaction. Kinetic models emulating pathway dynamics in MTX-untreated, MTX-treated-sensitive and MTX-treated-resistant conditions were generated and model behaviour at steady state was analysed with respect to concentrations of six folate metabolites and fluxes through the 12 reactions. We observed differences in steady-state properties across these cell lines even in the absence of MTX inhibition. More interestingly, the response of sensitive and resistant variants of each cancer type was also seen to vary in simulations of MTX-inhibition. However, accumulation of dihydrofolate at steady state for all sensitive cell lines along and a decrease towards normal levels for their resistant counterparts remained a common feature in most cases. Metabolic control analysis performed to identify crucial flux controlling elements in the pathway indicated that the enzymes methenyltetrahydrofolate cyclohydrolase (MTCH) and phosphoribosylglycinamide formyltransferase (PGT) could be targeted in combination with DHFR in MTX-resistant cancers for improved therapy