Why Regulate Guns?

Courts reviewing gun laws that burden Second Amendment rights ask how effectively the laws serve public safety - yet typically discuss public safety narrowly, without considering the many dimensions of that interest gun laws serve. "Public safety" is a social good: it includes the public's interest in physical safety as a good in itself, and as a foundation for community and for the exercise of constitutional liberties. Gun laws protect bodies from bullets - and Americans' freedom and confidence to participate in every domain of our shared life, whether to attend school, to shop, to listen to a concert, to gather for prayer, or to assemble in peaceable debate. Courts must enforce the Second Amendment in ways that respect the public health and constitutional reasons a democracy seeks to protect public safety. Lawyers and citizen advocates can help, by creating a richer record of their reasons in seeking to enact laws regulating guns.This inquiry is urgent at a time when the Supreme Court's new conservative majority may expand restrictions on gun laws beyond the right to keep arms for self-defense in the home first recognized in District of Columbia v. Heller in 2008.

Immunoglobulin G subclass switching impacts sensitivity of an immunoassay targeting Francisella tularensis lipopolysaccharide.

The CDC Tier 1 select agent Francisella tularensis is a small, Gram-negative bacterium and the causative agent of tularemia, a potentially life-threatening infection endemic in the United States, Europe and Asia. Currently, there is no licensed vaccine or rapid point-of-care diagnostic test for tularemia. The purpose of this research was to develop monoclonal antibodies (mAbs) specific to the F. tularensis surface-expressed lipopolysaccharide (LPS) for a potential use in a rapid diagnostic test. Our initial antigen capture ELISA was developed using murine IgG3 mAb 1A4. Due to the low sensitivity of the initial assay, IgG subclass switching, which is known to have an effect on the functional affinity of a mAb, was exploited for the purpose of enhancing assay sensitivity. The ELISA developed using the IgG1 or IgG2b mAbs from the subclass-switch family of 1A4 IgG3 yielded improved assay sensitivity. However, surface plasmon resonance (SPR) demonstrated that the functional affinity was decreased as a result of subclass switching. Further investigation using direct ELISA revealed the potential self-association of 1A4 IgG3, which could explain the higher functional affinity and higher assay background seen with this mAb. Additionally, the higher assay background was found to negatively affect assay sensitivity. Thus, enhancement of the assay sensitivity by subclass switching is likely due to the decrease in assay background, simply by avoiding the self-association of IgG3.

Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers.

Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial diversity of potential cancer-driving events limits the applicability of statistical methods to determine tumor-specific "driver" alterations among an overwhelming majority of "passengers". An alternative approach to determining driver mutations is to assess the functional impact of mutations in a given tumor and predict drivers based on a numerical value of the mutation impact in a particular context of genomic alterations.Recently, we introduced a functional impact score, which assesses the mutation impact by the value of entropic disordering of the evolutionary conservation patterns in proteins. The functional impact score separates disease-associated variants from benign polymorphisms with an accuracy of ~80%. Can the score be used to identify functionally important non-recurrent cancer-driver mutations? Assuming that cancer-drivers are positively selected in tumor evolution, we investigated how the functional impact score correlates with key features of natural selection in cancer, such as the non-uniformity of distribution of mutations, the frequency of affected tumor suppressors and oncogenes, the frequency of concurrent alterations in regions of heterozygous deletions and copy gain; as a control, we used presumably non-selected silent mutations. Using mutations of six cancers studied in TCGA projects, we found that predicted high-scoring functional mutations as well as truncating mutations tend to be evolutionarily selected as compared to low-scoring and silent mutations. This result justifies prediction of mutations-drivers using a shorter list of predicted high-scoring functional mutations, rather than the "long tail" of all mutations.

Identification of circulating bacterial antigens by in vivo microbial antigen discovery.

UNLABELLED: Detection of microbial antigens in clinical samples can lead to rapid diagnosis of an infection and administration of appropriate therapeutics. A major barrier in diagnostics development is determining which of the potentially hundreds or thousands of antigens produced by a microbe are actually present in patient samples in detectable amounts against a background of innumerable host proteins. In this report, we describe a strategy, termed in vivo microbial antigen discovery (InMAD), that we used to identify circulating bacterial antigens. This technique starts with "InMAD serum," which is filtered serum that has been harvested from BALB/c mice infected with a bacterial pathogen. The InMAD serum, which is free of whole bacterial cells, is used to immunize syngeneic BALB/c mice. The resulting "InMAD immune serum" contains antibodies specific for the soluble microbial antigens present in sera from the infected mice. The InMAD immune serum is then used to probe blots of bacterial lysates or bacterial proteome arrays. Bacterial antigens that are reactive with the InMAD immune serum are precisely the antigens to target in an antigen immunoassay. By employing InMAD, we identified multiple circulating antigens that are secreted or shed during infection using Burkholderia pseudomallei and Francisella tularensis as model organisms. Potential diagnostic targets identified by the InMAD approach included bacterial proteins, capsular polysaccharide, and lipopolysaccharide. The InMAD technique makes no assumptions other than immunogenicity and has the potential to be a broad discovery platform to identify diagnostic targets from microbial pathogens. IMPORTANCE: Effective treatment of microbial infection is critically dependent on early diagnosis and identification of the etiological agent. One means for rapid diagnosis is immunoassay for antigens that are shed into body fluids during infection. Immunoassays can be inexpensive, rapid, and adaptable to a point-of-care format. A major impediment to immunoassay for diagnosis of infectious disease is identification of appropriate antigen targets. This report describes a strategy that can be used for identification of microbial antigens that are shed into serum during infection by the biothreats Burkholderia pseudomallei and Francisella tularensis. Termed InMAD (in vivo microbial antigen discovery), the strategy has the potential for application to a broad spectrum of microbial pathogens.

Identification of Burkholderia cepacia complex bacteria with a lipopolysaccharide-specific monoclonal antibody.

The genus Burkholderia includes many bacteria that cause serious human infections. As is the case with other Gram-negative bacteria, Burkholderia species produce LPS, which is an abundant component of the bacterial cell surface. Burkholderia cepacia complex (Bcc) bacteria (which include at least 17 separate species) produce LPS structures that are quite different. In an attempt to determine the degree of LPS epitope variation among Bcc species, a mAb was produced, designated 5D8, specific for the LPS of B. cepacia. Western blot analysis determined that mAb 5D8 was able to produce the classic 'ladder pattern' when used to probe B. cepacia and Burkholderia anthina lysates, although 5D8 did not produce this pattern with the other seven Bcc species tested. mAb 5D8 reacted with varying intensity to most but not all of the additional B. cepacia and B. anthina strains tested. Therefore, there seems to be significant epitope variation among Bcc LPS both between and within species. Additionally, mAb 5D8 reacted with a proteinase-K-sensitive 22 kDa antigen in all Bcc strains and also in a strain of Burkholderia pseudomallei.

Recovery of ambulation after traumatic brain injury.

OBJECTIVES: To identify variables that are predictive of independent ambulation after traumatic brain injury (TBI) and to define the time course of recovery. DESIGN: Retrospective review of consecutive admissions of patients with severe TBI over a 32-month period. SETTING: Brain injury unit in an acute, inpatient rehabilitation hospital. PARTICIPANTS: Of 264 patients screened, 116 met criteria that included the ability to participate in motor and functional evaluation on admission to acute rehabilitation, and the absence of other neurologic disorders or fractures that affect one's ability to ambulate. INTERVENTION: Inpatient rehabilitation on a specialized TBI unit by an interdisciplinary team.Main outcome measures Recovery of independent ambulation and time to recover independent ambulation. RESULTS: Of eligible patients, 73.3% achieved independent ambulation by latest follow-up (up to 5.1 mo). Patients who achieved independent ambulation were significantly younger (P<.05), had better gait scores on admission (P<.05), and tended to be less severely injured-based on duration of posttraumatic amnesia (PTA; P=.058)-than those who did not ambulate independently. There were no differences in recovery based on neuropathologic profile. Mean time to independent ambulation +/- standard deviation was 5.7+/-4.3 weeks; of those achieving independent ambulation, 82.4% did so by 2 months and 94.1% by 3 months. If not independent by 3 months postinjury, patients had a 13.9% chance of recovery. Multivariate regression analysis generated prediction models for time to independent ambulation, using admission FIM instrument scores and age (38% of variance); initial gait score, loss of consciousness, and age (40% of variance); or initial gait score and PTA (58% of variance), when restricted to just those patients with diffuse axonal injury. CONCLUSIONS: Most patients with severe TBI achieved independent ambulation; the vast majority did so within 3 months postinjury. Functional measures, injury severity measures, and age can help guide prognosis and expectations for time to recover.

Can drug therapies improve language functions of individuals with aphasia? A review of the evidence.

The neurochemistry of language and the neuropharmacology of aphasia are two domains of cognitive neuroscience still in their infancy. In this article we review what is known about these two domains, especially with regard to treating aphasia with drugs. Selected neurotransmitters can improve language function in certain patients with aphasia. We discuss which neurotransmitters work for which language functions in which patients, and why.

Determining the roles of different chain fragments in recognition of immunoglobulin fold.

We examine sequence-to-structure specificity of beta-structural fragments of immunoglobulin domains. The structure specificity of separate chain fragments is estimated by computing the Z-score values in recognition of the native structure in gapless threading tests. To improve the accuracy of our calculations we use energy averaging over diverse homologs of immunoglobulin domains. We show that the interactions between residues of beta-structure are more determinant in recognition of the native structure than the interactions within the whole chain molecule. This result distinguishes immunoglobulins from more typical proteins where the interactions between residues of the whole chain normally recognize the native fold more accurately than interactions between the residues of the secondary structure residues alone [Reva,B. and Topiol,S. (2000) BIOCOMPUTING: Proceedings of the Pacific Symposium. World Scientific Publishing Co., pp. 168-178]. We also find that the predominant contributions of the secondary structure are produced by the four central beta-strands that form the core of the molecule. The results of this study allow us through quantitative means to understand the architecture of immunoglobulin molecules. Comparing the fold recognition data for different chain fragments one can say that beta-strands form a rigid frame for immunoglobulin molecules, whereas loops, with no structural role, can develop a broad variety of binding specificities. It is well known that protein function is determined by specific portions of a protein chain. This study suggests that the whole protein structure can be predominantly determined by a few fragments of chain which form the structural framework of the molecule. This idea may help in better understanding the mechanisms of protein evolution: strengthening a protein structure in the key framework-forming regions allows mutations and flexibility in other chain regions.

Recognition of protein structure: determining the relative energetic contributions of beta-strands, alpha-helices and loops.

We examine the role of residues of secondary structure in recognition of the native structure of proteins. The accuracy of recognition was estimated by computing the Z-score values for fragments of protein chains in threading tests. By testing different combinations of secondary structure fragments of 240 non-homologous proteins we show that the overwhelming majority of proteins can be successfully recognized by the energies of interaction between residues of secondary structure. We also found that beta-structures contribute more significantly to fold recognition than alpha-helices or loops. To validate the Z-score calculations in measuring the accuracy of recognition we evaluated the deviation of the energy distribution from the normal law. The normal law satisfactory approximates the shape of the energy distribution for the majority of proteins and chain fragments; however, deviations are often observed for short fragments and for fragments with relatively high Z-score values. The results of the study justify recognition of remote homologs by threading methods based on a backbone of secondary structure rather than of a whole chain because loops of homologs differ more significantly than strands and helices, and the contribution of loops in structure recognition is relatively small.

Averaging interaction energies over homologs improves protein fold recognition in gapless threading.

Protein structure prediction is limited by the inaccuracy of the simplified energy functions necessary for efficient sorting over many conformations. It was recently suggested (Finkelstein, Phys Rev Lett 1998;80:4823-4825) that these errors can be reduced by energy averaging over a set of homologous sequences. This conclusion is confirmed in this study by testing protein structure recognition in gapless threading. The accuracy of recognition was estimated by the Z-score values obtained in gapless threading tests. For threading, we used 20 target proteins, each having from 20 to 70 homologs taken from the HSSP sequence base. The energy of the native structures was compared with the energy from 34 to 75 thousand of alternative structures generated by threading. The energy calculations were done with our recently developed Calpha atom-based phenomenological potentials. We show that averaging of protein energies over homologs reduces the Z-score from approximately -6.1 (average Z-score for individual chains) to approximately -8.1. This means that a correct fold can be found among 3 x 10(9) random folds in the first case and among 3 x 10(15) in the second. Such increase in selectivity is important for recognition of protein folds.

[When and how can homologs overcome errors in the energy estimates and make the 3D structure prediction possible]. / Kak i kogda otdalennye gomologi mogut prevozmoch' pogreshnosti énergeticheskikh otsenok i sdelat' vozmozhnym predskazaniie prostranstvennykh struktur belkov.

One still cannot predict the 3D fold of a protein from its amino acid sequence, mainly because of errors in the energy estimates underlying the prediction. However, a recently developed theory [1] shows that having a set of homologs (i.e., the chains with equal, in despite of numerous mutations, 3D folds) one can average the potential of each interaction over the homologs and thus predict the common 3D fold of protein family even when a correct fold prediction for an individual sequence is impossible because the energies are known only approximately. This theoretical conclusion has been verified by simulation of the energy spectra of simplified models of protein chains [2], and the further investigation of these simplified models shows that their true "native" fold can be found by folding of the chain where each interaction potential is averaged over the homologs. In conclusion, the applicability of the "homolog-averaging" approach is tested by recognition of real protein 3D structures. Both the gapless threading of sequences onto the known protein folds [3] and the more practically important gapped threading (which allows to consider not only the known 3D structures, but the more or less similar to them folds as well) shows a significant increase in selectivity of the native chain fold recognition.

Optimization of protein structure on lattices using a self-consistent field approach.

Lattice modeling of proteins is commonly used to study the protein folding problem. The reduced number of possible conformations of lattice models enormously facilitates exploration of the conformational space. In this work, we suggest a method to search for the optimal lattice models that reproduced the off-lattice structures with minimal errors in geometry and energetics. The method is based on the self-consistent field optimization of a combined pseudoenergy function that includes two force fields: an "interaction field," that drives the residues to optimize the chain energy, and a "geometrical field," that attracts the residues towards their native positions. By varying the contributions of these force fields in the combined pseudoenergy, one can also test the accuracy of potentials: the better the potentials, i.e., the more accurate the "interaction field," and the smaller the contribution of the "geometrical field" required for building accurate lattice models.

What is the probability of a chance prediction of a protein structure with an rmsd of 6 A?

BACKGROUND: The root mean square deviation (rmsd) between corresponding atoms of two protein chains is a commonly used measure of similarity between two protein structures. The smaller the rmsd is between two structures, the more similar are these two structures. In protein structure prediction, one needs the rmsd between predicted and experimental structures for which a prediction can be considered to be successful. Success is obvious only when the rmsd is as small as that for closely homologous proteins (< 3 A). To estimate the quality of the prediction in the more general case, one has to compare the native structure not only with the predicted one but also with randomly chosen protein-like folds. One can ask: how many such structures must be considered to find a structure with a given rmsd from the native structure? RESULTS: We calculated the rmsd values between native structures of 142 proteins and all compact structures obtained in the threading of these protein chains over 364 non-homologous structures. The rmsd distributions have a Gaussian form, with the average rmsd approximately proportional to the radius of gyration. CONCLUSIONS: We estimated the number of protein-like structures required to obtain a structure within an rmsd of 6 A to be 10(4)-10(5) for chains of 60-80 residues and 10(11)-10(12) structures for chains of 160-200 residues. The probability of obtaining a 6 A rmsd by chance is so remote that when such structures are obtained from a prediction algorithm, it should be considered quite successful.

Residue-residue mean-force potentials for protein structure recognition.

We present two new sets of energy functions for protein structure recognition, given the primary sequence of amino acids along the polypeptide chain. The first set of potentials is based on the positions of alpha- and the second on positions of beta- and alpha-carbon atoms of amino acid residues. The potentials are derived using a theory of Boltzmann-like statistics of protein structure. The energy terms incorporate both long-range interactions between residues remote along a chain and short-range interactions between near neighbors. Distance dependence is approximated by a piecewise constant function defined on intervals of equal size. The size of the interval is optimized to preserve as much detail as possible without introducing excessive error due to limited statistics. A database of 214 non-homologous proteins was used both for the derivation of the potentials, and for the 'threading' test originally suggested by Hendlich et al. (1990) J. Mol. Biol., 216, 167-180. Special care is taken to avoid systematic error in this test. For threading, we used 100 non-homologous protein chains of 60-205 residues. The energy of each of the native structures was compared with the energy of 43,000 to 19,000 alternative structures generated by threading. Of these 100 native structures, 92 have the lowest energy with alpha-carbon-based potentials and, even more, 98 of these 100 structures, have the lowest energy with the beta- and alpha-carbon based potentials.

Accurate mean-force pairwise-residue potentials for discrimination of protein folds.

We present two new sets of energy functions for protein structure recognition. The first set of potentials is based on the positions of alpha- and the second on positions of beta-carbon atoms of amino acid residues. The potentials are derived using a theory of Boltzmann-like statistics of protein structure by Finkelstein et al. The energy terms incorporate both long-range interactions between residues remote along a chain and short-range interactions between near neighbors. Distance-dependence is approximated by a piecewise constant function defined on intervals of equal size. The size of this interval is optimized. A database of 222 non-homologous proteins was used both for the derivation of the potentials, and for the "threading" test originally suggested by Hendlich et al. For threading, we used 102 non-homologous protein chains of 60 to 200 residues. The energy of each of the native structures was compared with the energy of 45 to 20 thousand alternative structures generated by threading. Of these 102 native structures 94 have the lowest energy with alpha-carbon-based potentials, and even more, 100 of these 102 structures, have the lowest energy with the beta-carbon-based potentials.

Recognition of protein structure on coarse lattices with residue-residue energy functions.

We suggest and test potentials for the modeling of protein structure on coarse lattices. The coarser the lattice, the more complete and faster is the exploration of the conformational space of a molecule. However, there are inevitable energy errors in lattice modeling caused by distortions in distances between interacting residues; the coarser the lattice, the larger are the energy errors. It is generally believed that an improvement in the accuracy of lattice modelling can be achieved only by reducing the lattice spacing. We reduce the errors on coarse lattices with lattice-adapted potentials. Two methods are used: in the first approach, 'lattice-derived' potentials are obtained directly from a database of lattice models of protein structure; in the second approach, we derive 'lattice-adjusted' potentials using our previously developed method of statistical adjustment of the 'off-lattice' energy functions for lattices. The derivation of off-lattice Calpha atom-based distance-dependent pairwise potentials has been reported previously. The accuracy of 'lattice-derived', 'lattice-adjusted' and 'off-lattice' potentials is estimated in threading tests. It is shown that 'lattice-derived' and 'lattice-adjusted' potentials give virtually the same accuracy and ensure reasonable protein fold recognition on the coarsest considered lattice (spacing 3.8 A), however, the 'off-lattice' potentials, which efficiently recognize off-lattice folds, do not work on this lattice, mainly because of the errors in short-range interactions between neighboring residues.

Building self-avoiding lattice models of proteins using a self-consistent field optimization.

We present an algorithm to build self-avoiding lattice models of chain molecules with low RMS deviation from their actual 3D structures. To find the optimal coordinates for the lattice chain model, we minimize a function that consists of three terms: (1) the sum of squared deviations of link coordinates on a lattice from their off-lattice values, (2) the sum of "short-range" terms, penalizing violation of chain connectivity, and (3) the sum of "long-range" repulsive terms, penalizing chain self-intersections. We treat this function as a chain molecule "energy" and minimize it using self-consistent field (SCF) theory to represent the pairwise link repulsions as 3D fields acting on the links. The statistical mechanics of chain molecules enables computation of the chain distribution in this field on the lattice. The field is refined by iteration to become self-consistent with the chain distribution, then dynamic programming is used to find the optimal lattice model as the "lowest-energy" chain pathway in this SCF. We have tested the method on one of the coarsest (and most difficult) lattices used for model building on proteins of all structural types and show that the method is adequate for building self-avoiding models of proteins with low RMS deviations from the actual structures.

Adjusting potential energy functions for lattice models of chain molecules.

Lattice models of proteins can approximate off-lattice structure to arbitrary precision with RMS (root mean squared) deviations roughly equal to half the lattice spacing (Rykunov et al., Proteins 22:100-109, 1995; Reva et al., J. Comp. Biol., 1996). However, even small distortions in the positions of chain links lead to significant errors in lattice-based energy calculations (Reva et al., J. Comp. Chem., 1996). These errors arise mainly from rigid interactions (such as steric repulsion) which change their energies considerably at a range which is much smaller than the usual accuracy of lattice modeling (> 1.0 A). To reduce this error, we suggest a procedure of adjusting energy functions to a given lattice. The general approach is illustrated with energy calculations based on pairwise potentials by Kolinski et al. (J. Chem. Phys. 98:1-14, 1993). At all the lattice spacings, from 0.5-3.8 A, the lattice-adjusted potentials improve the accuracy of lattice-based energy calculations and increase the correlations between off-lattice and lattice energies.