Structural Optimization and De Novo Design of Dengue Virus Entry Inhibitory Peptides

Viral fusogenic envelope proteins are important targets for the development of inhibitors of viral entry. We report an approach for the computational design of peptide inhibitors of the dengue 2 virus (DENV-2) envelope (E) protein using high-resolution structural data from a pre-entry dimeric form of the protein. By using predictive strategies together with computational optimization of binding “pseudoenergies”, we were able to design multiple peptide sequences that showed low micromolar viral entry inhibitory activity. The two most active peptides, DN57opt and 1OAN1, were designed to displace regions in the domain II hinge, and the first domain I/domain II beta sheet connection, respectively, and show fifty percent inhibitory concentrations of 8 and 7 µM respectively in a focus forming unit assay. The antiviral peptides were shown to interfere with virus:cell binding, interact directly with the E proteins and also cause changes to the viral surface using biolayer interferometry and cryo-electron microscopy, respectively. These peptides may be useful for characterization of intermediate states in the membrane fusion process, investigation of DENV receptor molecules, and as lead compounds for drug discovery

Capturing the essence of folding and functions of biomolecules using Coarse-Grained Models

The distances over which biological molecules and their complexes can function range from a few nanometres, in the case of folded structures, to millimetres, for example during chromosome organization. Describing phenomena that cover such diverse length, and also time scales, requires models that capture the underlying physics for the particular length scale of interest. Theoretical ideas, in particular, concepts from polymer physics, have guided the development of coarse-grained models to study folding of DNA, RNA, and proteins. More recently, such models and their variants have been applied to the functions of biological nanomachines. Simulations using coarse-grained models are now poised to address a wide range of problems in biology.Comment: 37 pages, 8 figure

Fuzzy oil drop model to interpret the structure of antifreeze proteins and their mutants

Mutations in proteins introduce structural changes and influence biological activity: the specific effects depend on the location of the mutation. The simple method proposed in the present paper is based on a two-step model of in silico protein folding. The structure of the first intermediate is assumed to be determined solely by backbone conformation. The structure of the second one is assumed to be determined by the presence of a hydrophobic center. The comparable structural analysis of the set of mutants is performed to identify the mutant-induced structural changes. The changes of the hydrophobic core organization measured by the divergence entropy allows quantitative comparison estimating the relative structural changes upon mutation. The set of antifreeze proteins, which appeared to represent the hydrophobic core structure accordant with “fuzzy oil drop” model was selected for analysis

BioDMET: a physiologically based pharmacokinetic simulation tool for assessing proposed solutions to complex biological problems

We developed a detailed, whole-body physiologically based pharmacokinetic (PBPK) modeling tool for calculating the distribution of pharmaceutical agents in the various tissues and organs of a human or animal as a function of time. Ordinary differential equations (ODEs) represent the circulation of body fluids through organs and tissues at the macroscopic level, and the biological transport mechanisms and biotransformations within cells and their organelles at the molecular scale. Each major organ in the body is modeled as composed of one or more tissues. Tissues are made up of cells and fluid spaces. The model accounts for the circulation of arterial and venous blood as well as lymph. Since its development was fueled by the need to accurately predict the pharmacokinetic properties of imaging agents, BioDMET is more complex than most PBPK models. The anatomical details of the model are important for the imaging simulation endpoints. Model complexity has also been crucial for quickly adapting the tool to different problems without the need to generate a new model for every problem. When simpler models are preferred, the non-critical compartments can be dynamically collapsed to reduce unnecessary complexity. BioDMET has been used for imaging feasibility calculations in oncology, neurology, cardiology, and diabetes. For this purpose, the time concentration data generated by the model is inputted into a physics-based image simulator to establish imageability criteria. These are then used to define agent and physiology property ranges required for successful imaging. BioDMET has lately been adapted to aid the development of antimicrobial therapeutics. Given a range of built-in features and its inherent flexibility to customization, the model can be used to study a variety of pharmacokinetic and pharmacodynamic problems such as the effects of inter-individual differences and disease-states on drug pharmacokinetics and pharmacodynamics, dosing optimization, and inter-species scaling. While developing a tool to aid imaging agent and drug development, we aimed at accelerating the acceptance and broad use of PBPK modeling by providing a free mechanistic PBPK software that is user friendly, easy to adapt to a wide range of problems even by non-programmers, provided with ready-to-use parameterized models and benchmarking data collected from the peer-reviewed literature

Phase II study of second-line therapy with DTIC, BCNU, cisplatin and tamoxifen (Dartmouth regimen) chemotherapy in patients with malignant melanoma previously treated with dacarbazine

This study assessed response rates to combination dacarbazine (DTIC), BCNU (carmustine), cisplatin and tamoxifen (DBPT) chemotherapy in patients with progressive metastatic melanoma previously treated with DTIC, as an evaluation of DBPT as a second-line regimen, and as an indirect comparison of DBPT with DTIC. Thirty-five consecutive patients received DBPT. The patients were divided into two groups. Group 1 comprised 17 patients with progressive disease (PD) on DTIC + tamoxifen therapy who were switched directly to DBPT. Group 2 comprised 18 patients not immediately switched to DBPT and included patients who had either a partial response (PR; one patient) or developed stable disease (SD; four patients) with DTIC, or received adjuvant DTIC (nine patients). All except four patients had received tamoxifen at the time of initial DTIC treatment. Median times since stopping DTIC were 22 days (range 20–41) and 285 days (range 50–1240) in Groups 1 and 2 respectively. In Group 1, one patient developed SD for 5 months and the remainder had PD. In Group 2, there were two PRs, four patients with SD (4, 5, 6, and 6 months), and 11 with PD. These results indicate that the DBPT regimen is not of value in melanoma primarily refractory to DTIC. There were responses in patients not directly switched from DTIC to DBPT, suggesting combination therapy may be of value in a small subgroup of melanoma patients. © 2000 Cancer Research Campaig

Association of limbic system-associated membrane protein (LSAMP) to male completed suicide

<p>Abstract</p> <p>Background</p> <p>Neuroimaging studies have demonstrated volumetric abnormalities in limbic structures of suicide victims. The morphological changes might be caused by some inherited neurodevelopmental defect, such as failure to form proper axonal connections due to genetically determined dysfunction of neurite guidance molecules. Limbic system-associated membrane protein (LSAMP) is a neuronal adhesive molecule, preferentially expressed in developing limbic system neuronal dendrites and somata. Some evidence for the association between LSAMP gene and behavior has come from both animal as well as human studies but further investigation is required. In current study, polymorphic loci in human LSAMP gene were examined in order to reveal any associations between genetic variation in <it>LSAMP </it>and suicidal behaviour.</p> <p>Methods</p> <p>DNA was obtained from 288 male suicide victims and 327 healthy male volunteers. Thirty SNPs from LSAMP gene and adjacent region were selected by Tagger algorithm implemented in Haploview 3.32. Genotyping was performed using the SNPlex™ (Applied Biosystems) platform. Data was analyzed by Genemapper 3.7, Haploview 3.32 and SPSS 13.0.</p> <p>Results</p> <p>Chi square test revealed four allelic variants (rs2918215, rs2918213, rs9874470 and rs4821129) located in the intronic region of the gene to be associated with suicide, major alleles being overrepresented in suicide group. However, the associations did not survive multiple correction test. Defining the haplotype blocks using confidence interval algorithm implemented in Haploview 3.32, we failed to detect any associated haplotypes.</p> <p>Conclusion</p> <p>Despite a considerable amount of investigation on the nature of suicidal behaviour, its aetiology and pathogenesis remain unknown. This study examined the variability in LSAMP gene in relation to completed suicide. Our results indicate that LSAMP might play a role in pathoaetiology of suicidal behaviour but further studies are needed to understand its exact contribution.</p

Mechanical and Assembly Units of Viral Capsids Identified via Quasi-Rigid Domain Decomposition

Key steps in a viral life-cycle, such as self-assembly of a protective protein container or in some cases also subsequent maturation events, are governed by the interplay of physico-chemical mechanisms involving various spatial and temporal scales. These salient aspects of a viral life cycle are hence well described and rationalised from a mesoscopic perspective. Accordingly, various experimental and computational efforts have been directed towards identifying the fundamental building blocks that are instrumental for the mechanical response, or constitute the assembly units, of a few specific viral shells. Motivated by these earlier studies we introduce and apply a general and efficient computational scheme for identifying the stable domains of a given viral capsid. The method is based on elastic network models and quasi-rigid domain decomposition. It is first applied to a heterogeneous set of well-characterized viruses (CCMV, MS2, STNV, STMV) for which the known mechanical or assembly domains are correctly identified. The validated method is next applied to other viral particles such as L-A, Pariacoto and polyoma viruses, whose fundamental functional domains are still unknown or debated and for which we formulate verifiable predictions. The numerical code implementing the domain decomposition strategy is made freely available

Epidemiologic heterogeneity of common mood and anxiety disorders over the lifecourse in the general population: a systematic review

Background Clinical evidence has long suggested there may be heterogeneity in the patterns and predictors of common mood and anxiety disorders; however, epidemiologic studies have generally treated these outcomes as homogenous entities. The objective of this study was to systematically review the epidemiologic evidence for potential patterns of heterogeneity of common mood and anxiety disorders over the lifecourse in the general population. Methods We reviewed epidemiologic studies examining heterogeneity in either the nature of symptoms experienced ( symptom syndromes ) or in patterns of symptoms over time ( symptom trajectories ). To be included, studies of syndromes were required to identify distinct symptom subtypes, and studies of trajectories were required to identify distinct longitudinal patterns of symptoms in at least three waves of follow-up. Studies based on clinical or patient populations were excluded. Results While research in this field is in its infancy, we found growing evidence that, not only can mood and anxiety disorders be differentiated by symptom syndromes and trajectories, but that the factors associated with these disorders may vary between these subtypes. Whether this reflects a causal pathway, where genetic or environmental factors influence the nature of the symptom or trajectory subtype experienced by an individual, or whether individuals with different subtypes differed in their susceptibility to different environmental factors, could not be determined. Few studies addressed issues of comorbidity or transitions in symptoms between common disorders. Conclusion Understanding the diversity of these conditions may help us identify preventable factors that are only associated with some subtypes of these common disorders

How Many Protein-Protein Interactions Types Exist in Nature?

“Protein quaternary structure universe” refers to the ensemble of all protein-protein complexes across all organisms in nature. The number of quaternary folds thus corresponds to the number of ways proteins physically interact with other proteins. This study focuses on answering two basic questions: Whether the number of protein-protein interactions is limited and, if yes, how many different quaternary folds exist in nature. By all-to-all sequence and structure comparisons, we grouped the protein complexes in the protein data bank (PDB) into 3,629 families and 1,761 folds. A statistical model was introduced to obtain the quantitative relation between the numbers of quaternary families and quaternary folds in nature. The total number of possible protein-protein interactions was estimated around 4,000, which indicates that the current protein repository contains only 42% of quaternary folds in nature and a full coverage needs approximately a quarter century of experimental effort. The results have important implications to the protein complex structural modeling and the structure genomics of protein-protein interactions

Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach

The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far