Replication arrest is a major threat to growth at low temperature in Antarctic Pseudomonas syringae Lz4W

Chromosomal damage was detected previously in the recBCD mutants of the Antarctic bacterium Pseudomonas syringae Lz4W, which accumulated linear chromosomal DNA leading to cell death and growth inhibition at 4°C. RecBCD protein generally repairs DNA double‐strand breaks by RecA‐dependent homologous recombination pathway. Here we show that ΔrecA mutant of P. syringae is not cold‐sensitive. Significantly, inactivation of additional DNA repair genes ruvAB rescued the cold‐sensitive phenotype of ΔrecBCD mutant. The ΔrecA and ΔruvAB mutants were UV‐sensitive as expected. We propose that, at low temperature DNA replication encounters barriers leading to frequent replication fork (RF) arrest and fork reversal. RuvAB binds to the reversed RFs (RRFs) having Holliday junction‐like structures and resolves them upon association with RuvC nuclease to cause linearization of the chromosome, a threat to cell survival. RecBCD prevents this by degrading the RRFs, and facilitates replication re‐initiation. This model is consistent with our observation that low temperature‐induced DNA lesions do not evoke SOS response in P. syringae. Additional studies show that two other repair genes, radA (encoding a RecA paralogue) and recF are not involved in providing cold resistance to the Antarctic bacterium.     

Biophysical stability and enzymatic recognition of oxanine in DNA

Oxanine (Oxa), which is one of the major products generated from guanine by nitrosative oxidation and is as long-lived as Gua in DNA, has been thought to be one of the major causes for NO-induced DNA damage. In the present study, using several synthetic Oxa-containing oligodeoxynucleotides, biophysical stability and enzymatic recognition of Oxa was investigated in DNA strands. It was found that Oxa did not mediate marked distortion in the whole DNA structure although Oxa pairing with 4 normal bases decreased thermal stability of the DNA duplexes compared to Gua:Cyt base pair. Regarding the responses of the DNA-relevant enzymes to Oxa, it was determined that Oxa was recognized as Gua except that DNA polymerases incorporated Thy as well as Cyt opposite Oxa. These results imply that Oxa tends to behave as a kind of naturally occurring base, Gua and therefore, would be involved in the genotoxic and cytotoxic threats of NO in cellular system.     

Learning generative models for protein fold families

We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e.g., Hidden Markov Models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov Models in terms of predictive accuracy.     

An efficient randomized algorithm for contact-based NMR backbone resonance assignment

MOTIVATION: Backbone resonance assignment is a critical bottleneck in studies of protein structure, dynamics and interactions by nuclear magnetic resonance (NMR) spectroscopy. A minimalist approach to assignment, which we call 'contact-based', seeks to dramatically reduce experimental time and expense by replacing the standard suite of through-bond experiments with the through-space (nuclear Overhauser enhancement spectroscopy, NOESY) experiment. In the contact-based approach, spectral data are represented in a graph with vertices for putative residues (of unknown relation to the primary sequence) and edges for hypothesized NOESY interactions, such that observed spectral peaks could be explained if the residues were 'close enough'. Due to experimental ambiguity, several incorrect edges can be hypothesized for each spectral peak. An assignment is derived by identifying consistent patterns of edges (e.g. for alpha-helices and beta-sheets) within a graph and by mapping the vertices to the primary sequence. The key algorithmic challenge is to be able to uncover these patterns even when they are obscured by significant noise. RESULTS: This paper develops, analyzes and applies a novel algorithm for the identification of polytopes representing consistent patterns of edges in a corrupted NOESY graph. Our randomized algorithm aggregates simplices into polytopes and fixes inconsistencies with simple local modifications, called rotations, that maintain most of the structure already uncovered. In characterizing the effects of experimental noise, we employ an NMR-specific random graph model in proving that our algorithm gives optimal performance in expected polynomial time, even when the input graph is significantly corrupted. We confirm this analysis in simulation studies with graphs corrupted by up to 500% noise. Finally, we demonstrate the practical application of the algorithm on several experimental beta-sheet datasets. Our approach is able to eliminate a large majority of noise edges and to uncover large consistent sets of interactions. AVAILABILITY: Our algorithm has been implemented in the platform-independent Python code. The software can be freely obtained for academic use by request from the authors.     

Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing

We show that comprehensive sequence-function maps obtained by deep sequencing can be used to reprogram interaction specificity and to leapfrog over bottlenecks in affinity maturation by combining many individually small contributions not detectable in conventional approaches. We use this approach to optimize two computationally designed inhibitors against H1N1 influenza hemagglutinin and, in both cases, obtain variants with subnanomolar binding affinity. The most potent of these, a 51-residue protein, is broadly cross-reactive against all influenza group 1 hemagglutinins, including human H2, and neutralizes H1N1 viruses with a potency that rivals that of several human monoclonal antibodies, demonstrating that computational design followed by comprehensive energy landscape mapping can generate proteins with potential therapeutic utility.     

Learning generative models of molecular dynamics

We introduce three algorithms for learning generative models of molecular structures from molecular dynamics simulations. The first algorithm learns a Bayesian-optimal undirected probabilistic model over user-specified covariates (e.g., fluctuations, distances, angles, etc). L1 regularization is used to ensure sparse models and thus reduce the risk of over-fitting the data. The topology of the resulting model reveals important couplings between different parts of the protein, thus aiding in the analysis of molecular motions. The generative nature of the model makes it well-suited to making predictions about the global effects of local structural changes (e.g., the binding of an allosteric regulator). Additionally, the model can be used to sample new conformations. The second algorithm learns a time-varying graphical model where the topology and parameters change smoothly along the trajectory, revealing the conformational sub-states. The last algorithm learns a Markov Chain over undirected graphical models which can be used to study and simulate kinetics. We demonstrate our algorithms on multiple molecular dynamics trajectories.     

Stabilization of the immobilized linkers and DNA probes for DNA microarray fabrication by end-capping of the remaining unreacted silanol on the glass

High stability of the oligonucleotides immobilized on the glass is essential for the reliable DNA microarray analysis. In the present study, effect of end-capping of the unreacted silanol, remaining after the surface amine-functionalization, was explored: (1) Cy3-NHS (N-hydorxysuccinincimide) dye was spotted on the surface and change in the fluorescent intensity was measured. (2) DNA probes were immobilized by the reactivity of oxanine linked at the 5’-end, the complementary oligonucleotides with Cy5-fluorescence at the 5’-end was hybridized, and the time-dependence of the fluorescence intensity was observed. Both the systems showed improved stability of the immobilized molecules, indicative of the stabilization by end-capping.     

Computation and Functional Studies Provide a Model for the Structure of the Zinc Transporter hZIP4

Members of the Zrt and Irt protein (ZIP) family are a central participant in transition metal homeostasis as they function to increase the cytosolic concentration of zinc and/or iron. However, the lack of a crystal structure hinders elucidation of the molecular mechanism of ZIP proteins. Here, we employed GREMLIN, a co-evolution-based contact prediction approach in conjunction with the Rosetta structure prediction program to construct a structural model of the human (h) ZIP4 transporter. The predicted contact data are best fit by modeling hZIP4 as a dimer. Mutagenesis of residues that comprise a central putative hZIP4 transmembrane transition metal coordination site in the structural model alter the kinetics and specificity of hZIP4. Comparison of the hZIP4 dimer model to all known membrane protein structures identifies the 12-transmembrane monomeric Piriformospora indica phosphate transporter (PiPT), a member of the major facilitator superfamily (MFS), as a likely structural homolog.