The pH-dependent tertiary structure of a designed helix–loop–helix dimer

Background: De novo designed helix–loop–helix motifs can fold into well-defined tertiary structures if residues or groups of residues are incorporated at the helix–helix boundary to form helix-recognition sites that restrict the conformational degrees of freedom of the helical segments. Understanding the relationship between structure and function of conformational constraints therefore forms the basis for the engineering of non-natural proteins. This paper describes the design of an interhelical HisH+–Asp- hydrogen-bonded ion pair and the conformational stability of the folded helix–loop–helix motif.Results: GTD-C, a polypeptide with 43 amino acid residues, has been designed to fold into a hairpin helix–loop–helix motif that can dimerise to form a four-helix bundle. The folded motif is in slow conformational exchange on the NMR timescale and has a well-dispersed 1H NMR spectrum, a narrow temperature interval for thermal denaturation and a near-UV CD spectrum with some fine structure. The conformational stability is pH dependent with an optimum that corresponds to the pH for maximum formation of a hydrogen-bonded ion pair between HisH17+ in helix I and Asp27- in helix II.Conclusions: The formation of an interhelical salt bridge is strongly suggested by the pH dependence of a number of spectroscopic probes to generate a well-defined tertiary structure in a designed helix–loop–helix motif. The thermodynamic stability of the folded motif is not increased by the formation of the salt bridge, but neighbouring conformations are destabilised. The use of this novel design principle in combination with hydrophobic interactions that provide sufficient binding energy in the folded structure should be of general use in de novo design of native-like proteins

The Future of Generic Biologics: Should the United States “Follow-On” the European Pathway?

The United States is embarking on a biotechnology drug revolution. In the last few decades, biotech drugs have saved millions of lives, and the market for these miracle cures continues to grow at an astronomical rate. Unfortunately, as the market for biotech drugs is skyrocketing, drug prices are following suit. As Congress strives to make these new drugs more affordable, it must not ignore significant safety concerns unique to these revolutionary therapies. Congress should follow the lead of the European Union to create an accessible pathway for generic forms of biotech drugs that includes strict regulatory measures to ensure drug safety and efficacy

R.ROSETTA: an interpretable machine learning framework.

Funder: Uppsala Universitet; doi: http://dx.doi.org/10.13039/501100007051Funder: Polska Akademia Nauk; doi: http://dx.doi.org/10.13039/501100004382Funder: Uppsala UniversityBACKGROUND: Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components. RESULTS: We present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA . To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case-control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes. CONCLUSIONS: R.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables

Crossing borders to bind proteins—a new concept in protein recognition based on the conjugation of small organic molecules or short peptides to polypeptides from a designed set

A new concept for protein recognition and binding is highlighted. The conjugation of small organic molecules or short peptides to polypeptides from a designed set provides binder molecules that bind proteins with high affinities, and with selectivities that are equal to those of antibodies. The small organic molecules or peptides need to bind the protein targets but only with modest affinities and selectivities, because conjugation to the polypeptides results in molecules with dramatically improved binder performance. The polypeptides are selected from a set of only sixteen sequences designed to bind, in principle, any protein. The small number of polypeptides used to prepare high-affinity binders contrasts sharply with the huge libraries used in binder technologies based on selection or immunization. Also, unlike antibodies and engineered proteins, the polypeptides have unordered three-dimensional structures and adapt to the proteins to which they bind. Binder molecules for the C-reactive protein, human carbonic anhydrase II, acetylcholine esterase, thymidine kinase 1, phosphorylated proteins, the D-dimer, and a number of antibodies are used as examples to demonstrate that affinities are achieved that are higher than those of the small molecules or peptides by as much as four orders of magnitude. Evaluation by pull-down experiments and ELISA-based tests in human serum show selectivities to be equal to those of antibodies. Small organic molecules and peptides are readily available from pools of endogenous ligands, enzyme substrates, inhibitors or products, from screened small molecule libraries, from phage display, and from mRNA display. The technology is an alternative to established binder concepts for applications in drug development, diagnostics, medical imaging, and protein separation

Impact of interfractional target motion in locally advanced cervical cancer patients treated with spot scanning proton therapy using an internal target volume strategy

Background and purpose: The more localized dose deposition of proton therapy (PT) compared to photon therapy might allow a reduction in treatment-related side effects but induces additional challenges to address. The aim of this study was to evaluate the impact of interfractional motion on the target and organs at risk (OARs) in cervical cancer patients treated with spot scanning PT using an internal target volume (ITV) strategy. Methods and materials: For ten locally advanced cervical cancer patients, empty and full bladder planning computed tomography (pCT) as well as 25 daily cone beam CTs (CBCTs) were available. The Clinical Target Volume (CTV), the High Risk CTV (CTVHR) (gross tumor volume and whole cervix), the non-involved uterus as well as the OARs (bowel, bladder and rectum) were contoured on the daily CBCTs and transferred to the pCT through rigid bony match. Using synthetic CTs derived from pCTs, four-beam spot scanning PT plans were generated to target the patient-specific ITV with 45 Gy(RBE) in 25 fractions. This structure was defined based on pre-treatment MRI and CT to anticipate potential target motion throughout the treatment. D98% of the targets and V40Gy(RBE) of the OARs were extracted from the daily anatomies, accumulated and analyzed. In addition, the impact of bladder volume deviations from planning values on target and bowel dose was investigated. Results: The ITV strategy ensured a total accumulated dose >42.75 Gy(RBE) to the CTVHR for all ten patients. Two patients with large bladder-related uterus motion had accumulated dose to the non-involved uterus of 35.7 Gy(RBE) and 41.1 Gy(RBE). Variations in bowel V40Gy(RBE) were found to be correlated (Pearson r = −0.55; p-value <0.0001) with changes in bladder volume during treatment. Conclusion: The ITV concept ensured adequate dose to the CTVHR, but was insufficient for the non-involved uterus of patients subject to large target interfractional motion. CBCT monitoring and occasional replanning is recommended along the same lines as with photon radiotherapy in cervical cancer

Liquefaction of Lignosulfonate in Supercritical Ethanol Using Alumina-Supported NiMo Catalyst

Lignosulfonate was subjected to a reductive catalytic degradation in ethanol medium at 310 °C in the presence of alumina supported NiMo catalysts and H2. The liquid and solid products were analyzed with size exclusion chromatography (SEC), gas chromatography mass spectrometry (GC–MS), two-dimensional gas chromatography (GC × GC), heteronuclear single quantum coherence nuclear magnetic resonance (HSQC NMR) and elemental analysis. The highest oil yield and the lowest char yield obtained was 88 and 15 wt %, respectively. The liquefied species were mainly dimers and oligomers with minor yields of monomers. The catalyst was important for stabilization of reactive intermediates either by hydrogenation or coupling with ethanol. Simultaneous deoxygenation and desulfurization reactions took place in the presence of the catalyst; the oxygen and sulfur content in the oil fraction obtained after 4 h reaction time were 11.2 and 0.1 wt %, indicating considerable deoxygenation and desulfurization compared to the lignosulfonate feedstock (O, 30.8 wt %; S, 3.1 wt %). The effect of the reaction parameters such as temperature, reaction time and catalyst mass was studied. It was observed that by increasing the temperature from 260 to 310 °C the degradation increased, however, the SEC analysis showed that the degradation progressed only to a certain size range dimers to oligomers in the reaction temperatures studied. Investigating the effect of reaction time of 1, 2, 3, and 4 h indicated that degradation, deoxygenation, desulfurization and alkylation reactions progressed over time. The reusability of the catalyst without any pretreatment was confirmed by an almost constant oil yield in three repeated experiments with the same catalyst batch. The results show that alumina supported NiMo catalysts are very promising catalysts for conversion of lignosulfonate to liquid products

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives