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Structural variation (SV) is typically defined as variation within the human genome that exceeds 50 base pairs (bp). SV may be copy number neutral or it may involve duplications, deletions, and complex rearrangements. Recent studies have shown SV to be associated with many human diseases. However, studies of SV have been challenging due to technological constraints. With the advent of third generation (long-read) sequencing technology, exploration of longer stretches of DNA not easily examined previously has been made possible. In the present study, we utilized third generation (long-read) sequencing techniques to examine SV in the EGFR landscape of four haplotypes derived from two human samples. We analyzed the EGFR gene and its landscape (+/- 500,000 base pairs) using this approach and were able to identify a region of non-coding DNA with over 90% similarity to the most common activating EGFR mutation in non-small cell lung cancer. Based on previously published Alu-element genome instability algorithms, we propose a molecular mechanism to explain how this non-coding region of DNA may be interacting with and impacting the stability of the EGFR gene and potentially generating this cancer-driver gene. By these techniques, we were also able to identify previously hidden structural variation in the four haplotypes and in the human reference genome (hg38). We applied previously published algorithms to compare the relative stabilities of these five different EGFR gene landscape haplotypes to estimate their relative potentials to generate the EGFR exon 19, 15 bp canonical deletion. To our knowledge, the present study is the first to use the differences in genomic architecture between targeted cancer-linked phased haplotypes to estimate their relative potentials to form a common cancer-linked driver mutation.

Pim-2 kinase is one of the three highly conserved Pim family members which are known to be involved in cell survival and cell proliferation. Here we demonstrate that like Pim-1, Pim-2 also phosphorylates the cell cycle inhibitor p21Cip1/WAF1 (p21) on Thr145 in vitro and in vivo. Overexpression of Pim-2 in HCT116 cells leads to the increased stability of p21 and results in enhanced levels of both exogenous and endogenous p21 proteins. Knockdown of Pim-2 expression via siRNA results in reduced level of endogenous p21, indicating that like Pim-1, Pim-2 is another legitimate p21 kinase. However, Pim-2 has no influence on the nuclear localization of p21 in HCT116 cells. In addition, Pim-2 is able to arrest the cell cycle at G1/S phase and inhibit cell proliferation through phosphorylation of p21 in HCT116 cells. These data suggest that Pim-2 phosphorylation of p21 enhances p21's stability and inhibits cell proliferation in HCT116 cells.

Thermal adaptation of individual proteins is often achieved through modulating protein stability, with proteins that are adapted to extreme cold environments having increased conformational flexibility when brought to mesophilic conditions. Conversely, proteins adapted to higher temperatures appear less dynamic and are found to be much more stable against thermal denaturation than their mesophilic counterparts. According to the current paradigm, the adaptation of an organism for survival at higher or lower temperatures is facilitated by the adaptation of the component proteins. We note, however, that these observations have been carried out on relatively few proteins. The extent to which the conformational stabilities of all members of the proteome have been modulated for thermal adaptation remains unclear, with no direct experimental strategies to address this issue. Adapted extremophilies are likely to use a multitude of molecular and biophysical strategies for survival and, therefore, evolution of specific biophysical properties of proteins for optimal function may not be necessary for all proteins in the proteome. Using a sequence-based predictor of protein stability, eScape, an in silico examination of several extremophilic proteomes shows a correlation between the collective stability of the proteins and the thermal range of survival for the organism as expected. Unexpectedly, however, the analysis shows that protein thermostability is modified to different extents across the proteome and depends on the functional role for which the protein is involved. Identification of these differences provides unique opportunities to study interdependence within the proteome as well as the role that the proteome plays in the process of evolutionary thermal adaptation.

Experimental behavioral assays and animal models are essential to elucidate the mechanisms that underlie behavioral phenotypes associated with disorders such as Autism Spectrum Disorders and Fragil...

The hemagglutinating protein HA33 from Clostridium botulinum is associated with the large botulinum neurotoxin secreted complexes and is critical in toxin protection, internalization, and possibly activation. We report the crystal structure of serotype A HA33 (HA33/A) at 1.5 A resolution that contains a unique domain organization and a carbohydrate recognition site. In addition, sequence alignments of the other toxin complex components, including the neurotoxin BoNT/A, hemagglutinating protein HA17/A, and non-toxic non-hemagglutinating protein NTNHA/A, suggests that most of the toxin complex consists of a reoccurring beta-trefoil fold.

In contrast to other viral infections such as CMV, circulating frequencies of HIV-1-specific CD4+ T cells in peripheral blood are quantitatively diminished in the majority of HIV-1-infected individuals. One mechanism for this quantitative defect is preferential infection of HIV-1-specific CD4+ T cells, although

The serine/threonine kinase, PIM1, is involved in promoting cell survival in part by phosphorylation and inhibition of proapoptotic proteins. Apoptosis signaling kinase 1 (ASK1), a mitogen-activated protein kinase kinase kinase, is involved in the so-called stress-activated pathways that contribute to apoptotic cell death. Here we show that PIM1 phosphorylates ASK1 specifically on serine residue 83 (Ser83) both in vitro and in vivo and that PIM1 binds to ASK1 in cells by co-immunoprecipitation. Using H1299 cells, our results further demonstrate that PIM1 phosphorylation of ASK1 decreases its kinase activity induced by oxidative stress. PIM1 phosphorylation of ASK1 on Ser83 inhibited ASK1-mediated c-Jun N-terminal kinase phosphorylation as well as p38 kinase phosphorylation. Under H(2)O(2)-induced stress conditions that normally lead to apoptosis, these phosphorylation events were associated with inhibition of caspase-3 activation and resulted in reduced cell death. Moreover, knockdown of PIM1 in H1299 cells decreased phosphorylation of endogenous Ser83 of ASK1 and was associated with a decrease in cell viability after H(2)O(2) treatment. Taken together, these data reveal a novel mechanism by which PIM1 promotes cell survival that involves negative regulation of the stress-activated kinase, ASK1.

Position-specific denatured-state thermodynamics were determined for a database of human proteins by use of an ensemble-based model of protein structure. The results of modeling denatured protein in this manner reveal important sequence-dependent thermodynamic properties in the denatured ensembles as well as fundamental differences between the denatured and native ensembles in overall thermodynamic character. The generality and robustness of these results were validated by performing fold-recognition experiments, whereby sequences were matched with their respective folds based on amino acid propensities for the different energetic environments in the protein, as determined through cluster analysis. Correlation analysis between structure and energetic information revealed that sequence segments destined for beta-sheet in the final native fold are energetically more predisposed to a broader repertoire of states than are sequence segments destined for alpha-helix. These results suggest that within the subensemble of mostly unstructured states, the energy landscapes are dominated by states in which parts of helices adopt structure, whereas structure formation for sequences destined for beta-strand is far less probable. These results support a framework model of folding, which suggests that, in general, the denatured state has evolutionarily evolved to avoid low-energy conformations in sequences that ultimately adopt beta-strand. Instead, the denatured state evolved so that sequence segments that ultimately adopt alpha-helix and coil will have a high intrinsic structure formation capability, thus serving as potential nucleation sites.

Choosing to go to graduate school is a major life decision. Whether you have already made that decision or are about to, now it is time to consider how best to be a successful graduate student. Here are some thoughts from someone who holds these memories fresh in her mind (JG) and from someone who has had a whole career to reflect back on the decisions made in graduate school, both good and bad (PEB). These thoughts taken together, from former student and mentor, represent experiences spanning some 25 or more years. For ease, these experiences are presented as ten simple rules, in approximate order of priority as defined by a number of graduate students we have consulted here in the US; but we hope the rules are more globally applicable, even though length, method of evaluation, and institutional structure of graduate education varies widely. These rules are intended as a companion to earlier editorials covering other areas of professional development [1–7].

Background The mechanisms underlying protein function and associated conformational change are dominated by a series of local entropy fluctuations affecting the global structure yet are mediated by only a few key residues. Transitional Dynamic Analysis (TDA) is a new method to detect these changes in local protein flexibility between different conformations arising from, for example, ligand binding. Additionally, Positional Impact Vertex for Entropy Transfer (PIVET) uses TDA to identify important residue contact changes that have a large impact on global fluctuation. We demonstrate the utility of these methods for Cyclin-dependent kinase 2 (CDK2), a system with crystal structures of this protein in multiple functionally relevant conformations and experimental data revealing the importance of local fluctuation changes for protein function. Results TDA and PIVET successfully identified select residues that are responsible for conformation specific regional fluctuation in the activation cycle of Cyclin Dependent Kinase 2 (CDK2). The detected local changes in protein flexibility have been experimentally confirmed to be essential for the regulation and function of the kinase. The methodologies also highlighted possible errors in previous molecular dynamic simulations that need to be resolved in order to understand this key player in cell cycle regulation. Finally, the use of entropy compensation as a possible allosteric mechanism for protein function is reported for CDK2. Conclusion The methodologies embodied in TDA and PIVET provide a quick approach to identify local fluctuation change important for protein function and residue contacts that contributes to these changes. Further, these approaches can be used to check for possible errors in protein dynamic simulations and have the potential to facilitate a better understanding of the contribution of entropy to protein allostery and function.

The Wiggle series are support vector machine–based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility. A coarse-grained protein dynamic modeling approach was used to generate the dataset required for support vector machine training. We define our regions of interest based on the participation of residues in correlated large-scale fluctuations. Even with this structure-based approach to computationally define regions of functional flexibility, predictors successfully extract sequence-flexibility relationships that have been experimentally confirmed to be functionally important. Thus, a sequence-based tool to identify flexible regions important for protein function has been created. The ability to identify functional flexibility using a sequence based approach complements structure-based definitions and will be especially useful for the large majority of proteins with unknown structures. The methodology offers promise to identify structural genomics targets amenable to crystallization and the possibility to engineer more flexible or rigid regions within proteins to modify their bioactivity., Synopsis Proteins are not static entities in biology and are constantly changing their shape and form to perform their necessary biological roles. While we are intuitively aware of their constantly changing nature, we have little understanding of how their flexibility is encoded in the protein sequence. To address this knowledge gap, predictors were created to identify sequence patterns that dictate local regions to be flexible and serve a functional purpose. By combining protein dynamic modeling and machine learning techniques, the Wiggle predictor series were able to generalize the sequence-flexibility relationship for all proteins. With these predictors we are able to identify flexible regions of functional importance such as hinges, recognition loops, and catalytic loops using only sequence information. This work has important contributions to our understanding of the sequence-flexibility relationship and paves the road to identifying local sequence modulations that impact protein function without necessarily changing the structure.

SummaryThe abundance of dynamic and disordered regions in proteins suggests that structural determinants alone may not be sufficient to describe function. Instead, descriptors that account for the dynamic features of the energy landscape populated by the protein ensemble may be required. Here, we show that the thermodynamics of the dynamical complexity that imparts biological function can be largely reconstructed using sequence information alone, allowing thermodynamic characterization of entire proteomes without the need for structural analysis. We show that this tool can be used to analyze conserved energetic signatures within classes of proteins, as well as to compare the thermodynamic character of different proteomes.