Conotoxin Prediction: New Features to Increase Prediction Accuracy.

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

Integrative structural studies of the SARS-CoV-2 spike protein during the fusion process (2022).

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic and catastrophic, worldwide health and economic impacts. The spike protein on the viral surface is responsible for viral entry into the host cell. The binding of spike protein to the host cell receptor ACE2 is the first step leading to fusion of the host and viral membranes. Despite the vast amount of structure data that has been generated for the spike protein of SARS-CoV-2, many of the detailed structures of the spike protein in different stages of the fusion pathway are unknown, leaving a wealth of potential drug-target space unexplored. The atomic-scale structure of the complete S2 segment, as well as the complete fusion intermediate are also unknown and represent major gaps in our knowledge of the infectious pathway of SAR-CoV-2. The conformational changes of the spike protein during this process are similarly not well understood. Here we present structures of the spike protein at different stages of the fusion process. With the transitions being a necessary step before the receptor binding, we propose sites along the transition pathways as potential targets for drug development.

Modeling the Influenza A NP-vRNA-Polymerase Complex in Atomic Detail.

Seasonal flu is an acute respiratory disease that exacts a massive toll on human populations, healthcare systems and economies. The disease is caused by an enveloped Influenza virus containing eight ribonucleoprotein (RNP) complexes. Each RNP incorporates multiple copies of nucleoprotein (NP), a fragment of the viral genome (vRNA), and a viral RNA-dependent RNA polymerase (POL), and is responsible for packaging the viral genome and performing critical functions including replication and transcription. A complete model of an Influenza RNP in atomic detail can elucidate the structural basis for viral genome functions, and identify potential targets for viral therapeutics. In this work we construct a model of a complete Influenza A RNP complex in atomic detail using multiple sources of structural and sequence information and a series of homology-modeling techniques, including a motif-matching fragment assembly method. Our final model provides a rationale for experimentally-observed changes to viral polymerase activity in numerous mutational assays. Further, our model reveals specific interactions between the three primary structural components of the RNP, including potential targets for blocking POL-binding to the NP-vRNA complex. The methods developed in this work open the possibility of elucidating other functionally-relevant atomic-scale interactions in additional RNP structures and other biomolecular complexes.

Cyclooxygenase-2 haplotypes influence the longitudinal risk of malaria and severe malarial anemia in Kenyan children from a holoendemic transmission region.

Cyclooxygenase-2 [(COX-2) or prostaglandin endoperoxide H2 synthase-2 (PTGS-2)] induces the production of prostaglandins as part of the host-immune response to infections. Although a number of studies have demonstrated the effects of COX-2 promoter variants on autoimmune and inflammatory diseases, their role in malaria remains undefined. As such, we investigated the relationship between four COX-2 promoter variants (COX-2 -512 C > T, -608 T > C, -765 G > C, and -1195 A > G) and susceptibility to malaria and severe malarial anemia (SMA) upon enrollment and longitudinally over a 36-month follow-up period. All-cause mortality was also explored. The investigation was carried out in children (n = 1081, age; 2-70 months) residing in a holoendemic Plasmodium falciparum transmission region of western Kenya. At enrollment, genotypes/haplotypes (controlling for anemia-promoting covariates) did not reveal any strong effects on susceptibility to either malaria or SMA. Longitudinal analyses showed decreased malaria episodes in children who inherited the -608 CC mutant allele (RR = 0.746, P = 1.811 × 10-4) and -512C/-608T/-765G/-1195G (CTGG) haplotype (RR = 0.856, P = 0.011), and increased risk in TTCA haplotype carriers (RR = 1.115, P = 0.026). Over the follow-up period, inheritance of the rare TTCG haplotype was associated with enhanced susceptibility to both malaria (RR = 1.608, P = 0.016) and SMA (RR = 5.714, P = 0.004), while carriage of the rare TTGG haplotype increased the risk of malaria (RR = 1.755, P = 0.007), SMA (RR = 8.706, P = 3.97 × 10-4), and all-cause mortality (HR = 110.000, P = 0.001). Collectively, these results show that SNP variations in the COX-2 promoter, and their inherited combinations, are associated with the longitudinal risk of malaria, SMA, and all-cause mortality among children living in a high transmission area for P. falciparum.

Connectivity and free-surface effects in polymer glasses.

The glass transition is one of the few unsolved problems in condensed matter physics: agreement on the cause of the slowing down of structural relaxation in glass-forming liquids is lacking. Glasses are amorphous solids, which do not possess the long-range crystalline order, yet display arrested dynamics and the shear elastic modulus characteristic of equilibrium elasticity. It has been suggested that due to the influence of intramolecular interactions and chain connectivity, the nature of the glass transition in polymers and in standard glass-formers is fundamentally different. Here, we discuss the role of connectivity in polymer glasses, demonstrating that although covalent bonding promotes glass formation, bonding sequentiality that defines a polymer chain is not critical in the bulk: glassy dynamics is purely a result of the number of connections per particle, independently of how these connections are formed, agreeing with the classical Phillips-Thorpe topological constraint theory. We show that bonding sequentiality does play an important role in the surface effects of the glass, highlighting a major difference between polymeric and colloidal glasses. Further, we identify the heterogenous dynamics of model coarse-grained polymer chains both in 'bulk' and near the free surface, and demonstrate characteristic domain patterns in local displacement and connectivity.

Decision Support for Mitigation of Livestock Disease: Rinderpest as a Case Study.

A versatile, interactive model to predict geographically resolved epidemic progression after pathogen introduction into a population is presented. Deterministic simulations incorporating a compartmental disease model run rapidly, facilitating the analysis of mitigations such as vaccination and transmission reduction on epidemic spread and progression. We demonstrate the simulation model using rinderpest infection of cattle, a devastating livestock disease. Rinderpest has been extinguished in the wild, but it is still a threat due to stored virus in some laboratories. Comparison of simulations to historical outbreaks provides some validation of the model. Simulations of potential outbreaks demonstrate potential consequences of rinderpest virus release for a variety of possible disease parameters and mitigations. Our results indicate that a rinderpest outbreak could result in severe social and economic consequences.

The role of momentum transfer during incoherent neutron scattering is explained by the energy landscape model.

We recently introduced a model of incoherent quasielastic neutron scattering (QENS) that treats the neutrons as wave packets of finite length and the protein as a random walker in the free energy landscape. We call the model ELM for "energy landscape model." In ELM, the interaction of the wave packet with a proton in a protein provides the dynamic information. During the scattering event, the momentum [Formula: see text] is transferred by the wave packet to the struck proton and its moiety, exerting the force [Formula: see text] The resultant energy [Formula: see text] is stored elastically and returned to the neutron as it exits. The energy is given by [Formula: see text], where [Formula: see text] is the ambient temperature and [Formula: see text] ([Formula: see text] 91 K Å) is a new elastobaric coefficient. Experiments yield the scattering intensity (dynamic structure factor) [Formula: see text] as a function of [Formula: see text] and [Formula: see text] To test our model, we use published data on proteins where only thermal vibrations are active. ELM competes with the currently accepted theory, here called the spatial motion model (SMM), which explains [Formula: see text] by motions in real space. ELM is superior to SMM: It can explain the experimental angular and temperature dependence, whereas SMM cannot do so.

Reduced Parasite Burden in Children with Falciparum Malaria and Bacteremia Coinfections: Role of Mediators of Inflammation.

Bacteremia and malaria coinfection is a common and life-threatening condition in children residing in sub-Saharan Africa. We previously showed that coinfection with Gram negative (G[-]) enteric Bacilli and Plasmodium falciparum (Pf[+]) was associated with reduced high-density parasitemia (HDP, >10,000 parasites/µL), enhanced respiratory distress, and severe anemia. Since inflammatory mediators are largely unexplored in such coinfections, circulating cytokines were determined in four groups of children (n = 206, aged <3 yrs): healthy; Pf[+] alone; G[-] coinfected; and G[+] coinfected. Staphylococcus aureus and non-Typhi Salmonella were the most frequently isolated G[+] and G[-] organisms, respectively. Coinfected children, particularly those with G[-] pathogens, had lower parasite burden (peripheral and geometric mean parasitemia and HDP). In addition, both coinfected groups had increased IL-4, IL-5, IL-7, IL-12, IL-15, IL-17, IFN-γ, and IFN-α and decreased TNF-α relative to malaria alone. Children with G[-] coinfection had higher IL-1ß and IL-1Ra and lower IL-10 than the Pf[+] group and higher IFN-γ than the G[+] group. To determine how the immune response to malaria regulates parasitemia, cytokine production was investigated with a multiple mediation model. Cytokines with the greatest mediational impact on parasitemia were IL-4, IL-10, IL-12, and IFN-γ. Results here suggest that enhanced immune activation, especially in G[-] coinfected children, acts to reduce malaria parasite burden.

Filovirus RefSeq entries: evaluation and selection of filovirus type variants, type sequences, and names.

Sequence determination of complete or coding-complete genomes of viruses is becoming common practice for supporting the work of epidemiologists, ecologists, virologists, and taxonomists. Sequencing duration and costs are rapidly decreasing, sequencing hardware is under modification for use by non-experts, and software is constantly being improved to simplify sequence data management and analysis. Thus, analysis of virus disease outbreaks on the molecular level is now feasible, including characterization of the evolution of individual virus populations in single patients over time. The increasing accumulation of sequencing data creates a management problem for the curators of commonly used sequence databases and an entry retrieval problem for end users. Therefore, utilizing the data to their fullest potential will require setting nomenclature and annotation standards for virus isolates and associated genomic sequences. The National Center for Biotechnology Information's (NCBI's) RefSeq is a non-redundant, curated database for reference (or type) nucleotide sequence records that supplies source data to numerous other databases. Building on recently proposed templates for filovirus variant naming [ ()////-], we report consensus decisions from a majority of past and currently active filovirus experts on the eight filovirus type variants and isolates to be represented in RefSeq, their final designations, and their associated sequences.

A wave-mechanical model of incoherent quasielastic scattering in complex systems.

Quasielastic incoherent neutron scattering (QENS) is an important tool for the exploration of the dynamics of complex systems such as biomolecules, liquids, and glasses. The dynamics is reflected in the energy spectra of the scattered neutrons. Conventionally these spectra are decomposed into a narrow elastic line and a broad quasielastic band. The band is interpreted as being caused by Doppler broadening due to spatial motion of the target molecules. We propose a quantum-mechanical model in which there is no separate elastic line. The quasielastic band is composed of sharp lines with twice the natural line width, shifted from the center by a random walk of the protein in the free-energy landscape of the target molecule. The walk is driven by vibrations and by external fluctuations. We first explore the model with the Mössbauer effect. In the subsequent application to QENS we treat the incoming neutron as a de Broglie wave packet. While the wave packet passes the protons in the protein and the hydration shell it exchanges energy with the protein during the passage time of about 100 ns. The energy exchange broadens the ensemble spectrum. Because the exchange involves the free-energy landscape of the protein, the QENS not only provides insight into the protein dynamics, but it may also illuminate the free-energy landscape of the protein-solvent system.

Dynamics and the free-energy landscape of proteins, explored with the Mössbauer effect and quasi-elastic neutron scattering.

The Mössbauer effect and quasi-elastic neutron scattering (QENS) from hydrated proteins yield sharp elastic lines that are accompanied by broad wings. Conventionally, the elastic line and the broad wings are treated as separate phenomena. We show that there is no separation; the entire spectrum consists of Lorentzians with the natural line width. In protein crystals, the shifts of the individual lines from the elastic center above about 150 K are caused by beta fluctuations in the hydration shell. Vibrations cause shifts in the entire temperature range but are best seen below about 150 K. We construct a microscopic model for the dynamics that is based on a random walk of the proteins in their free-energy landscape. The model yields approximate values for the steps in the energy landscape. Remarkably, the quantum electrodynamic concept of gamma rays is needed to justify the model.

Designing and testing broadly-protective filoviral vaccines optimized for cytotoxic T-lymphocyte epitope coverage.

We report the rational design and in vivo testing of mosaic proteins for a polyvalent pan-filoviral vaccine using a computational strategy designed for the Human Immunodeficiency Virus type 1 (HIV-1) but also appropriate for Hepatitis C virus (HCV) and potentially other diverse viruses. Mosaics are sets of artificial recombinant proteins that are based on natural proteins. The recombinants are computationally selected using a genetic algorithm to optimize the coverage of potential cytotoxic T lymphocyte (CTL) epitopes. Because evolutionary history differs markedly between HIV-1 and filoviruses, we devised an adapted computational technique that is effective for sparsely sampled taxa; our first significant result is that the mosaic technique is effective in creating high-quality mosaic filovirus proteins. The resulting coverage of potential epitopes across filovirus species is superior to coverage by any natural variants, including current vaccine strains with demonstrated cross-reactivity. The mosaic cocktails are also robust: mosaics substantially outperformed natural strains when computationally tested against poorly sampled species and more variable genes. Furthermore, in a computational comparison of cross-reactive potential a design constructed prior to the Bundibugyo outbreak performed nearly as well against all species as an updated design that included Bundibugyo. These points suggest that the mosaic designs would be more resilient than natural-variant vaccines against future Ebola outbreaks dominated by novel viral variants. We demonstrate in vivo immunogenicity and protection against a heterologous challenge in a mouse model. This design work delineates the likely requirements and limitations on broadly-protective filoviral CTL vaccines.

Mössbauer effect in proteins.

In proteins, the Mössbauer effect and neutron scattering show a broad line and a rapid increase of the conformational mean-square displacement above about 180 K. The increase, dubbed the "dynamical transition," is controversial. We introduce a new interpretation of the Mössbauer effect in proteins and demonstrate that no dynamical transition is required. The increase in the mean-square displacement and the broad line are caused by fluctuations in the protein's hydration shell. Using the dielectric spectrum of these fluctuations, we predict the shape of the Mössbauer spectrum from 80 to 295 K with one dimensionless coefficient.

Genotype 1 and global hepatitis C T-cell vaccines designed to optimize coverage of genetic diversity.

Immunological control of hepatitis C virus (HCV) is possible and is probably mediated by host T-cell responses, but the genetic diversity of the virus poses a major challenge to vaccine development. We considered monovalent and polyvalent candidates for an HCV vaccine, including natural, consensus and synthetic 'mosaic' sequence cocktails. Mosaic vaccine reagents were designed using a computational approach first applied to and demonstrated experimentally for human immunodeficiency virus type 1 (HIV-Delta). Mosaic proteins resemble natural proteins, but are assembled from fragments of natural sequences via a genetic algorithm and optimized to maximize the coverage of potential T-cell epitopes (all 9-mers) found in natural sequences and to minimize the inclusion of rare 9-mers to avoid vaccine-specific responses. Genotype 1-specific and global vaccine cocktails were evaluated. Among vaccine candidates considered, polyvalent mosaic sequences provided the best coverage of both known and potential epitopes and had the fewest rare epitopes. A global vaccine based on conserved proteins across genotypes may be feasible, as a five-antigen mosaic cocktail provided 90, 77 and 70% coverage of the Core, NS3 and NS4 proteins, respectively; protein coverage diminished with increased protein variability, dropping to 38% for NS2. For the genotype 1-specific vaccine, the H77 prototype vaccine sequence matched only 50% of the potential epitopes in the population, whilst a polyprotein three-antigen mosaic cocktail increased potential epitope coverage to 83%. More than 75% coverage of all HCV proteins was achieved with a three-antigen mosaic cocktail, suggesting that genotype-specific vaccines could also include the more variable proteins.

A unified model of protein dynamics.

Protein functions require conformational motions. We show here that the dominant conformational motions are slaved by the hydration shell and the bulk solvent. The protein contributes the structure necessary for function. We formulate a model that is based on experiments, insights from the physics of glass-forming liquids, and the concepts of a hierarchically organized energy landscape. To explore the effect of external fluctuations on protein dynamics, we measure the fluctuations in the bulk solvent and the hydration shell with broadband dielectric spectroscopy and compare them with internal fluctuations measured with the Mössbauer effect and neutron scattering. The result is clear. Large-scale protein motions are slaved to the fluctuations in the bulk solvent. They are controlled by the solvent viscosity, and are absent in a solid environment. Internal protein motions are slaved to the beta fluctuations of the hydration shell, are controlled by hydration, and are absent in a dehydrated protein. The model quantitatively predicts the rapid increase of the mean-square displacement above approximately 200 K, shows that the external beta fluctuations determine the temperature- and time-dependence of the passage of carbon monoxide through myoglobin, and explains the nonexponential time dependence of the protein relaxation after photodissociation.

Protein dynamics and function: insights from the energy landscape and solvent slaving.

Protein motions are complex and a good way to describe them is in terms of a very high-dimensional conformation space. We give here a simple explanation of the conformation space and the energy landscape, the conformational motions and protein reactions, based on an analogy to a traffic problem. The analogy provides insight into the slaving of protein processes to bulk solvent fluctuations, in both the native and unfolded states.

Model parameters and outbreak control for SARS.

Control of the 2002-2003 severe acute respiratory syndrome (SARS) outbreak was based on rapid diagnosis coupled with effective patient isolation. We used uncertainty and sensitivity analysis of the basic reproductive number R0 to assess the role that model parameters play in outbreak control. The transmission rate and isolation effectiveness have the largest fractional effect on R0. We estimated the distribution of the reproductive number R0 under perfect isolation conditions. The distribution lies in the interquartile range 0.19-1.08, with a median of 0.49. Even though the median of R0 is <1, we found that 25% of our R0 distribution lies at R0 > 1, even with perfect isolation. This implies the need to simultaneously apply more than one method of control.