Examining the acceptability and feasibility of the Compassionate Mindful Resilience (CMR) programme in adults living with chronic kidney disease: the COSMIC study findings

BACKGROUND

Individuals with chronic kidney disease experience difficult physical and psychological symptoms, that impact quality of life, and are at increased risk of anxiety and depression. Access to specialist psychological support is limited. This study aimed to support a new service development project, in collaboration with Kidney Care UK, to implement the Compassionate Mindful Resilience (CMR) programme, developed by MindfulnessUK, which provides accessible mindfulness techniques and practices to enhance compassion and resilience, and explore its feasibility for people living with stage 4 or 5 kidney disease and transplant.

METHODS

A multi-method feasibility design was utilised. Participants over 18 years, from the UK, with stage 4 or 5 kidney disease or post-transplant, and who were not currently undergoing psychotherapy, were recruited to the four-week CMR programme. Data was collected at baseline, post-intervention and three-months post to measure anxiety, depression, self-compassion, mental wellbeing, resilience, and mindfulness. The acceptability of the intervention for a kidney disease population was explored through qualitative interviews with participants, and the Mindfulness Teacher.

RESULTS

In total, 75 participants were recruited to the study, with 65 completing the CMR programme. The majority were female (66.2%) and post-transplant (63.1%). Analysis of completed outcome measures at baseline and post-intervention timepoints (n = 61), and three-months post intervention (n = 45) revealed significant improvements in participant's levels of anxiety (p < .001) and depression (p < .001), self-compassion (p = .005), mental wellbeing (p < .001), resilience (p.001), and mindfulness (p < .001). Thematic analysis of interviews with participants (n = 19) and Mindfulness Teacher (n = 1) generated three themes (and nine-subthemes); experiences of the CMR programme that facilitated subjective benefit, participants lived and shared experiences, and practicalities of programme participation. All participants interviewed reported that they found programme participation to be beneficial.

CONCLUSION

The findings suggest that the CMR programme has the potential to improve psychological outcomes among people with chronic kidney disease. Future randomized controlled trials are required to further test its effectiveness.

Experiences of People with Kidney Disease Following the Implementation of the Compassionate Mindful Resilience Programme: Qualitative Findings from the COSMIC Study

BACKGROUND

Kidney disease is a progressive, debilitating condition. Patients experience challenging physical and psychological symptoms and are at increased risk of anxiety, depression, and poor mental wellbeing. Access to specialist psychological or social support is limited, with inadequate provision of psychosocial support available across UK renal units. The COSMIC study (examining the acceptability and feasibility of the Compassionate Mindful Resilience programme for adult patients with chronic kidney disease) aimed to support a new service development project, in partnership with Kidney Care UK, by implementing the Compassionate Mindful Resilience (CMR) programme, developed by MindfulnessUK, and explore its feasibility for patients with stage 4 or 5 kidney disease and kidney transplant recipients. This paper reports on the qualitative exploratory work which examined the experiences of study participants, their adherence to practice, and the acceptability of the intervention.

METHOD

Participants (n = 19) took part in semi-structured interviews, which were transcribed, coded, and thematically analysed.

RESULTS

Three themes (and nine subthemes) were reported: experiences of the CMR programme that facilitated subjective benefit, participants' lived and shared experiences, and the practicalities of CMR programme participation. All participants reported that they found taking part in the CMR programme to be a beneficial experience.

CONCLUSION

The CMR programme was found to be an acceptable intervention for people living with kidney disease and provided tools and techniques that support the mental health and wellbeing of this patient group. Further qualitative exploration into participant experience should be integrated within future trials of this intervention.

Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

Optimizing effector functions of monoclonal antibodies via tailored N-glycan engineering using a dual landing pad CHO targeted integration platform

Monoclonal antibodies (mAbs) eliminate cancer cells via various effector mechanisms including antibody-dependent cell-mediated cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC), which are influenced by the N-glycan structures on the Fc region of mAbs. Manipulating these glycan structures on mAbs allows for optimization of therapeutic benefits associated with effector functions. Traditional approaches such as gene deletion or overexpression often lead to only all-or-nothing changes in gene expression and fail to modulate the expression of multiple genes at defined ratios and levels. In this work, we have developed a CHO cell engineering platform enabling modulation of multiple gene expression to tailor the N-glycan profiles of mAbs for enhanced effector functions. Our platform involves a CHO targeted integration platform with two independent landing pads, allowing expression of multiple genes at two pre-determined genomic sites. By combining with internal ribosome entry site (IRES)-based polycistronic vectors, we simultaneously modulated the expression of α-mannosidase II (MANII) and chimeric β-1,4-N-acetylglucosaminyl-transferase III (cGNTIII) genes in CHO cells. This strategy enabled the production of mAbs carrying N-glycans with various levels of bisecting and non-fucosylated structures. Importantly, these engineered mAbs exhibited different degrees of effector cell activation and CDC, facilitating the identification of mAbs with optimal effector functions. This platform was demonstrated as a powerful tool for producing antibody therapeutics with tailored effector functions via precise engineering of N-glycan profiles. It holds promise for advancing the field of metabolic engineering in mammalian cells.

Insight into muscle stem cell regeneration and mechanobiology

Stem cells possess the unique ability to differentiate into specialized cell types. These specialized cell types can be used for regenerative medicine purposes such as cell therapy. Myosatellite cells, also known as skeletal muscle stem cells (MuSCs), play important roles in the growth, repair, and regeneration of skeletal muscle tissues. However, despite its therapeutic potential, the successful differentiation, proliferation, and expansion processes of MuSCs remain a significant challenge due to a variety of factors. For example, the growth and differentiation of MuSCs can be greatly influenced by actively replicating the MuSCs microenvironment (known as the niche) using mechanical forces. However, the molecular role of mechanobiology in MuSC growth, proliferation, and differentiation for regenerative medicine is still poorly understood. In this present review, we comprehensively summarize, compare, and critically analyze how different mechanical cues shape stem cell growth, proliferation, differentiation, and their potential role in disease development (Fig. 1). The insights developed from the mechanobiology of stem cells will also contribute to how these applications can be used for regenerative purposes using MuSCs.

Enhancing pharmacokinetic and pharmacodynamic properties of recombinant therapeutic proteins by manipulation of sialic acid content

The circulatory half-life of recombinant therapeutic proteins is an important pharmacokinetic attribute because it determines the dosing frequency of these drugs, translating directly to treatment cost. Thus, recombinant therapeutic glycoproteins such as monoclonal antibodies have been chemically modified by various means to enhance their circulatory half-life. One approach is to manipulate the N-glycan composition of these agents. Among the many glycan constituents, sialic acid (specifically, N-acetylneuraminic acid) plays a critical role in extending circulatory half-life by masking the terminal galactose that would otherwise be recognised by the hepatic asialoglycoprotein receptor (ASGPR), resulting in clearance of the biotherapeutic from the circulation. This review aims to provide an illustrative overview of various strategies to enhance the pharmacokinetic/pharmacodynamic properties of recombinant therapeutic proteins through manipulation of their sialic acid content.

Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland

BACKGROUND

The potential for digital health technologies, including machine learning (ML)-enabled tools, to disrupt the medical profession is the subject of ongoing debate within biomedical informatics.

OBJECTIVE

We aimed to describe the opinions of final-year medical students in Ireland regarding the potential of future technology to replace or work alongside general practitioners (GPs) in performing key tasks.

METHODS

Between March 2019 and April 2020, using a convenience sample, we conducted a mixed methods paper-based survey of final-year medical students. The survey was administered at 4 out of 7 medical schools in Ireland across each of the 4 provinces in the country. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses.

RESULTS

In total, 43.1% (252/585) of the final-year students at 3 medical schools responded, and data collection at 1 medical school was terminated due to disruptions associated with the COVID-19 pandemic. With regard to forecasting the potential impact of artificial intelligence (AI)/ML on primary care 25 years from now, around half (127/246, 51.6%) of all surveyed students believed the work of GPs will change minimally or not at all. Notably, students who did not intend to enter primary care predicted that AI/ML will have a great impact on the work of GPs.

CONCLUSIONS

We caution that without a firm curricular foundation on advances in AI/ML, students may rely on extreme perspectives involving self-preserving optimism biases that demote the impact of advances in technology on primary care on the one hand and technohype on the other. Ultimately, these biases may lead to negative consequences in health care. Improvements in medical education could help prepare tomorrow's doctors to optimize and lead the ethical and evidence-based implementation of AI/ML-enabled tools in medicine for enhancing the care of tomorrow's patients.

The renal-bone axis in older people living with HIV on stable antiretroviral therapy: A sub-analysis of the GS-US-104-0423 study

BACKGROUND

Data on low bone mineral density (BMD) in people living with HIV (PLWH) are mainly derived from younger adults; little is known about how antiretroviral therapy (ART) and alterations in the renal-bone axis relate to BMD in older PLWH.

METHODS

Cross-sectional study of men > 50 years and post-menopausal women with HIV. Antiretroviral therapy exposure was stratified into four groups based on use of tenofovir disoproxil fumarate (TDF) and protease inhibitors (PI): non-TDF/non-PI, non-TDF/PI, TDF/non-PI, and TDF/PI. Bone mineral density was measured by dual X-ray absorptiometry (DXA). Bone turnover/regulatory markers and renal tubular function were analysed in stored plasma and urine samples. The association of ART exposure and bone/renal biomarkers on BMD was explored using logistic regression models.

RESULTS

247 individuals (median [IQR] age 57 [53, 65] years; 47% female; 13% of Black ethnicity; CD4 count 643 [473, 811] cells/mm³; and 98% with HIV RNA < 200 copies/mL) were included. Bone turnover and renal tubular function differed significantly by ART exposure. In analyses adjusted for demographic and traditional renal/bone risk factors, exposure to TDF and PI was associated with a fourfold greater risk of low BMD at the femoral neck and exposure to TDF and/or PI with a threefold greater risk of low BMD at the lumbar spine. The relationship between ART and low BMD was not altered by further adjustment for bone turnover or renal tubular function markers.

CONCLUSIONS

The associations between low BMD and ART exposure (TDF vs. non-TDF and boosted vs. unboosted third agents) were minimally affected by adjustments for bone and kidney biomarkers.

Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.

Mindfulness as a well-being initiative for future nurses: a survey with undergraduate nursing students

BACKGROUND

Mindfulness can potentially positively impact well-being and resilience in undergraduate nursing students. The psychological well-being of such students undertaking clinical training is paramount to ensure optimal learning, and to equip them with skills to manage their wellbeing in future clinical practice. The aim of our study was to explore the views of undergraduate nursing students in relation to understanding and engaging with mindfulness, and how mindfulness could best be delivered within their university programme.

METHODS

An online survey was administered via a cloud-based student response system to a convenience sample of first year undergraduate nursing students completing a Bachelor of Science (BSc) Honours (Hons) degree in nursing at a University in the United Kingdom. Data were analysed using descriptive statistics and thematic analysis.

RESULTS

The survey achieved a response rate of 78% (n = 208). Seventy-nine percent of participants had heard of mindfulness and were interested in taking part in a mindfulness programme. Respondents reported that the ideal delivery of the programme would consist of weekly 45-min, in person group sessions, over a 6-week period. Respondents also indicated that a mobile application could potentially facilitate participation in the programme. Thematic analysis of open-ended comments, and free text, within the survey indicated 4 overarching themes: 1) Perceptions of what mindfulness is; 2) Previous mindfulness practice experiences; 3) Impact of mindfulness in nursing; 4) The need for a future well-being initiative for undergraduate nursing students.

CONCLUSIONS

Undergraduate nursing students perceived that a mindfulness programme has the potential to enhance well-being and future clinical practice. This student cohort are familiar with mindfulness and want more integrated within their undergraduate curriculum. Further research is required to examine the effectiveness of a tailored mindfulness intervention for this population that incorporates the use of both face-to-face and mobile delivery.

Exploration of Caregiver Experiences of Conservatively Managed End-Stage Kidney Disease to Inform Development of a Psychosocial Intervention: The ACORN Study Protocol

Background: End-stage kidney disease (ESKD) is an overwhelming illness that impacts not just patients, but also their informal carers. Patients who opt for conservative management rather than dialysis experience difficult symptoms and the psychosocial consequences of their condition. Informal carers of patients who choose conservative management can also experience high levels of psychosocial burden, yet there is little guidance on how best to support informal carers, and no evidence on psychosocial interventions to address unmet needs. Aim: The aim of this study is to explore the experiences and unmet needs of informal carers of patients with ESKD receiving conservative management in order to inform the development of a psychosocial intervention. Methods: This qualitative study will consist of three stages: (I) semi-structured interviews with informal carers in England and Northern Ireland, (II) focus groups with healthcare professionals and informal carers, and (III) national workshops to refine the components of a psychosocial intervention. Discussion: Informal carers of patients with ESKD who are receiving conservative management experience a high psychosocial burden, but there is limited evidence on how best to provide support, particularly as the patient nears the end of life. To our knowledge this study will be the first to address this gap by exploring the experiences and unmet needs of informal carers, with the aim of informing the development of a psychosocial intervention to support the health and wellbeing of informal carers.

The Experiences of a Complex Arts-Based Intervention for Patients with End-Stage Kidney Disease Whilst Receiving Haemodialysis: A Qualitative Process Evaluation

The global prevalence and burden of end-stage kidney disease (ESKD) is increasing, partially as a result of an aging population. Patients with ESKD who receive haemodialysis experience a difficult, protracted treatment regimen that can negatively impact mental health and wellbeing. One way of addressing this issue could be through the provision of arts-based interventions during haemodialysis treatment. A process evaluation was conducted as part of a larger feasibility study, to explore experiences and acceptability of an intra-dialytic (during haemodialysis) arts-based intervention. Thirteen patients and nine healthcare professionals were interviewed. The interviews were transcribed verbatim and thematically analysed. Three themes related to the experience of art on dialysis included: the perception of art participation, as patients described initial anxiety around participating in art, which reduced after they experienced the intervention; the benefits of art participation on both patients and healthcare professionals; the benefits including improved self-esteem, motivation, increased social interaction and an overall improved haemodialysis experience; and the acceptability of the arts-based intervention, as the intervention was highly acceptable, even when delivered by a facilitator who was not a professional artist. This study highlights that arts-based interventions could be used to improve the mental health and wellbeing of patients with ESKD receiving haemodialysis.

Experiences of renal healthcare practitioners during the COVID-19 pandemic: a multi-methods approach

BACKGROUND

Globally, renal healthcare practitioners provide intensive and protracted support to a highly complex multi-morbid patient population however knowledge about the impact of COVID-19 on these practitioners is extremely limited.

OBJECTIVE

This study aimed to explore the experiences of COVID-19 with renal healthcare practitioners during the first global lockdown between June 2020 and September 2020.

METHODS

A multi-methods approach was carried out including a quantitative survey and qualitative interviews. This was a multinational study of renal healthcare practitioners from 29 countries. Quantitative: A self-designed survey on COVID-19 experiences and standardised questionnaires (General Health Questionnaire-12; Maslach Burnout Inventory). Descriptive statistics were generated for numerical data. Qualitative: Online semi-structured interviews were conducted. Data was subjected to thematic analysis. Renal healthcare practitioners (n = 251) completed an online survey. Thirteen renal healthcare practitioners took part in semi-structured interviews (12 nurses and 1 dietician).

RESULTS

The majority of participants surveyed were female (86.9 %; n = 218), nurses (86.9 %; n = 218) with an average 21.5 (SD = 11.1) years' experience since professional qualification, and 16.3 years (SD = 9.3) working in renal healthcare. Survey responses indicated a level of preparedness, training and satisfactory personal protective equipment during the pandemic however approximately 40.3 % experienced fear about attending work, and 49.8 % experienced mental health distress. The highest prevalence of burnout was emotional exhaustion (35.9 %). Three themes emerged from the qualitative analysis highlighting the holistic complexities in managing renal healthcare, a neglected specialist workforce, and the need for appropriate support at work during a pandemic.

CONCLUSIONS

Results have highlighted the psychological impact, in terms of emotional exhaustion and mental health distress in our sample of renal healthcare practitioners. As the pandemic has continued, it is important to consider the long-term impact on an already stretched workforce including the risk of developing mental health disorders. Future research and interventions are required to understand and improve the provision of psychological support for specialist medical and nursing personnel.

Development of a complex arts-based intervention for patients with end-stage kidney disease whilst receiving haemodialysis

BACKGROUND

Patients with end-stage kidney disease who receive haemodialysis experience a protracted treatment regimen that can result in an increased risk of depression and anxiety. Arts-based interventions could address this unique issue; however, no arts-based interventions have been developed for delivery within a haemodialysis unit and evaluation within a randomised controlled trials (RCTs).

AIM

To develop a complex arts-based intervention for patients with end-stage kidney disease whilst receiving haemodialysis.

METHODS

The development process utilised the Arts in Health framework (Fancourt, 2017). The framework was addressed through the establishment of an interdisciplinary advisory group, collaboration and consultation with stakeholders, a scoping and realist review, shadowing of artists-in-residence, personal arts practice and logic modelling.

RESULTS

The intervention involved six 1-h long, one-to-one facilitated sessions focused on creative writing and visual art. Patients could choose between art form and self-select a subject matter. The sessions had a primary focus on skill development and were delivered using principles derived from the psychological theory of flow.

CONCLUSION

The Arts in Health framework provided an appropriate and pragmatic approach to intervention development. Complex arts-based interventions can be developed for the purpose of evaluation within a trial framework. This intervention was designed to strike a balance between standardised components, and a person-centred approach necessary to address existential boredom.

Semi-Automated Glycoproteomic Data Analysis of LC-MS Data Using GlycopeptideGraphMS in Process Development of Monoclonal Antibody Biologics

The glycosylation of antibody-based proteins is vital in translating the right therapeutic outcomes of the patient. Despite this, significant infrastructure is required to analyse biologic glycosylation in various unit operations from biologic development, process development to QA/QC in bio-manufacturing. Simplified mass spectrometers offer ease of operation as well as the portability of method development across various operations. Furthermore, data analysis would need to have a degree of automation to relay information back to the manufacturing line. We set out to investigate the applicability of using a semiautomated data analysis workflow to investigate glycosylation in different biologic development test cases. The workflow involves data acquisition using a BioAccord LC-MS system with a data-analytical tool called GlycopeptideGraphMS along with Progenesis QI to semi-automate glycoproteomic characterisation and quantitation with a LC-MS1 dataset of a glycopeptides and peptides. Data analysis which involved identifying glycopeptides and their quantitative glycosylation was performed in 30 min with minimal user intervention. To demonstrate the effectiveness of the antibody and biologic glycopeptide assignment in various scenarios akin to biologic development activities, we demonstrate the effectiveness in the filtering of IgG1 and IgG2 subclasses from human serum IgG as well as innovator drugs trastuzumab and adalimumab and glycoforms by virtue of their glycosylation pattern. We demonstrate a high correlation between conventional released glycan analysis with fluorescent tagging and glycopeptide assignment derived from GraphMS. GraphMS workflow was then used to monitor the glycoform of our in-house trastuzumab biosimilar produced in fed-batch cultures. The demonstrated utility of GraphMS to semi-automate quantitation and qualitative identification of glycopeptides proves to be an easy data analysis method that can complement emerging multi-attribute monitoring (MAM) analytical toolsets in bioprocess environments.

A scoping review of high-fidelity bench models for teaching closed reduction of distal radial fractures

This article was migrated. The article was marked as recommended.

INTRODUCTION

Simulation training can be beneficial for developing clinical skills without risks to patients. This review considers the literature on simulation models used for teaching closed reduction (manipulation) procedures for distal radius (wrist) fractures, particularly high-fidelity models, and the evidence supporting the use of such models.

METHODS

A scoping review of Medline and Embase was performed.

RESULTS

Five articles described low-fidelity models, predominantly focussing on low costs and teaching basic principles. Three articles and two commentary pieces discussing high-fidelity models were identified.

DISCUSSION

Attitudes towards a high-fidelity simulator were assessed by Egan et al. (2013), who found the majority of participants to be in favour of the model's use as a teaching tool, although participant selection may have been subject to bias. Mayne et al. (2016) subsequently used a high-fidelity model including radio-opaque markers and more objective measurements tools to assess orthopaedic trainees' closed reduction technique and adequacy of the achieved fracture position and casting. Seniority correlated with higher scores on objective structured assessment of technical skills (OSATS) and global rating scores (GRS) but not radiological measures of fracture position or cast quality, and over 90% of all participants achieved an adequate reduction. Seeley et al. (2017) used radiological measurements and time to task completion with another high-fidelity reduction model. The two most experienced participant groups could not be differentiated on any radiological measures of fracture reduction or on the time taken for reduction, although these groups were significantly better than the most junior participants.

CONCLUSION

The discussed models appear helpful to teach inexperienced participants the basic principles and steps in a procedures but a plateau effect appears to limit the potential benefit to more experienced learners. The constraints of educators' time and financial costs may influence the usage of such models in this type of training.

A mixed-methods feasibility study of an arts-based intervention for patients receiving maintenance haemodialysis

Background

Haemodialysis can negatively impact quality of life and mental health. Arts-based interventions used successfully in other settings to improve health and well-being, could help address the impact of haemodialysis. This study aimed to evaluate the feasibility and acceptability of conducting a randomised controlled trial (RCT) of an arts-based intervention for patients receiving haemodialysis.

Methods

A parallel convergent mixed-methods design was used, including a pilot cluster RCT and qualitative process evaluation.

Phase 1 evaluated recruitment and retention rates through a pilot cluster RCT at a single haemodialysis unit in Northern Ireland. Participants included patients who received haemodialysis for ESKD, were over the age of 18 and had the capacity to consent. These participants were randomised to the intervention or control group according to their haemodialysis shift. The intervention involved six one-hour, one-to-one facilitated arts sessions during haemodialysis.

Phase 2 explored intervention and trial acceptability through a qualitative process evaluation using semi-structured interviews based on the RE-AIM framework. Participants included 13 patients who participated in phase 1 of the study, including 9 participants from the experimental group and four participants from the control group, and nine healthcare professionals who were present on the unit during implementation.

Results

Out of 122 outpatient haemodialysis patients, 94 were assessed as eligible for participation. Twenty-four participants were randomised, meaning 80% of the target sample size was recruited and the attrition rate at 3 months was 12.5% (n = 3). Participants viewed the arts as more accessible and enjoyable than anticipated following implementation. All participants who started the intervention (n = 11) completed the full six sessions. Qualitative benefits of the intervention suggest improvements in mental well-being. Patient choice and facilitation were important factors for successful implementation.

Conclusion

An arts-based intervention for patients receiving haemodialysis is acceptable for both patients and healthcare professionals, and a definitive trial is feasible. The intervention may help improve mental-wellbeing in patients receiving haemodialysis, but this requires further investigation in a definitive trial.

Trial registration

The trial was prospectively registered on clinicaltrials.gov on 14/8/2018, registration number NCT03629496.

Clustering and curation of electropherograms: an efficient method for analyzing large cohorts of capillary electrophoresis glycomic profiles for bioprocessing operations

The accurate assessment of antibody glycosylation during bioprocessing requires the high-throughput generation of large amounts of glycomics data. This allows bioprocess engineers to identify critical process parameters that control the glycosylation critical quality attributes. The advances made in protocols for capillary electrophoresis-laser-induced fluorescence (CE-LIF) measurements of antibody N-glycans have increased the potential for generating large datasets of N-glycosylation values for assessment. With large cohorts of CE-LIF data, peak picking and peak area calculations still remain a problem for fast and accurate quantitation, despite the presence of internal and external standards to reduce misalignment for the qualitative analysis. The peak picking and area calculation problems are often due to fluctuations introduced by varying process conditions resulting in heterogeneous peak shapes. Additionally, peaks with co-eluting glycans can produce peaks of a non-Gaussian nature in some process conditions and not in others. Here, we describe an approach to quantitatively and qualitatively curate large cohort CE-LIF glycomics data. For glycan identification, a previously reported method based on internal triple standards is used. For determining the glycan relative quantities our method uses a clustering algorithm to ‘divide and conquer’ highly heterogeneous electropherograms into similar groups, making it easier to define peaks manually. Open-source software is then used to determine peak areas of the manually defined peaks. We successfully applied this semi-automated method to a dataset (containing 391 glycoprofiles) of monoclonal antibody biosimilars from a bioreactor optimization study. The key advantage of this computational approach is that all runs can be analyzed simultaneously with high accuracy in glycan identification and quantitation and there is no theoretical limit to the scale of this method.

Experimentally Determined Long Intrinsically Disordered Protein Regions Are Now Abundant in the Protein Data Bank

Intrinsically disordered protein regions are commonly defined from missing electron density in X-ray structures. Experimental evidence for long disorder regions (LDRs) of at least 30 residues was so far limited to manually curated proteins. Here, we describe a comprehensive and large-scale analysis of experimental LDRs for 3133 unique proteins, demonstrating an increasing coverage of intrinsic disorder in the Protein Data Bank (PDB) in the last decade. The results suggest that long missing residue regions are a good quality source to annotate intrinsically disordered regions and perform functional analysis in large data sets. The consensus approach used to define LDRs allows to evaluate context dependent disorder and provide a common definition at the protein level.

Complex arts-based interventions for patients receiving haemodialysis: A realist review

Many patients with end-stage kidney disease require haemodialysis, a treatment that requires attending hospital three times a week for four hours each visit. This treatment impacts profoundly on mental health. Arts-based interventions for patients receiving haemodialysis could address the impact of this treatment; however, there is no consensus on methods of implementation and mechanisms underlying these interventions in specific clinical contexts. Using a realist approach, relevant articles were synthesised to inform theory relating to the mechanism and implementation of complex arts-based interventions for haemodialysis patients. The theoretical framework includes two implementation phases, firstly delivery of person-centred art activities during haemodialysis and secondly, display of completed artwork. This intervention triggers mechanisms including flow and social capital. Implementation is hindered by constraints of the haemodialysis unit and patients' lack of confidence in their artistic skills. These issues can be addressed through aflexible approach to implementation and support from healthcare professionals.

GlycanAnalyzer: software for automated interpretation of N-glycan profiles after exoglycosidase digestions

SUMMARY

Many eukaryotic proteins are modified by N-glycans. Liquid chromatography (ultra-performance -UPLC and high-performance-HPLC) coupled with mass spectrometry (MS) is conventionally used to characterize N-glycan structures. Software can automatically assign glycan structures by matching their observed retention times and masses with standardized values in reference databases. However, more precise confirmation of N-glycan structures can be derived using exoglycosidases, enzymes that remove specific monosaccharides from glycans. Exoglycosidase removal of monosaccharides results in signature peak shifts, in both UPLC and MS1, yielding an effective way to verify N-glycan structure with high detail (down to the position and isomeric linkage of each monosaccharide). Because manual interpretation of exoglycosidase data is complex and time consuming, we developed GlycanAnalyzer, a web application that pattern matches N-glycan peak shifts following exoglycosidase digestion and automates structure assignments. GlycanAnalyzer significantly improves assignment accuracy over other auto-assignment methods on tests with a monoclonal antibody and four glycan standards (100% versus 82% for the next best software). By automating data interpretation, GlycanAnalyzer enables the easier use of exoglycosidases to precisely define N-glycan structure.

AVAILABILITY AND IMPLEMENTATION

http://glycananalyzer.neb.com. Datasets available online.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Robust and Versatile Automated Glycoanalytical Technology for Serum Antibodies and Acute Phase Proteins: Ovarian Cancer Case Study

The direct association of the genome, transcriptome, metabolome, lipidome and proteome with the serum glycome has revealed systems of interconnected cellular pathways. The exact roles of individual glycoproteomes in the context of disease have yet to be elucidated. In a move toward personalized medicine, it is now becoming critical to understand disease pathogenesis, and the traits, stages, phenotypes and molecular features that accompany it, as the disruption of a whole system. To this end, we have developed an innovative technology on an automated platform, "GlycoSeqCap," which combines N-glycosylation data from six glycoproteins using a single source of human serum. Specifically, we multiplexed and optimized a successive serial capture and glycoanalysis of six purified glycoproteins, immunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin A (IgA), transferrin (Trf), haptoglobin (Hpt) and alpha-1-antitrypsin (A1AT), from 50 μl of human serum. We provide the most comprehensive and in-depth glycan analysis of individual glycoproteins in a single source of human serum to date. To demonstrate the technological application in the context of a disease model, we performed a pilot study in an ovarian cancer cohort (n = 34) using discrimination and classification analyses to identify aberrant glycosylation. In our sample cohort, we exhibit improved selectivity and specificity over the currently used biomarker for ovarian cancer, CA125, for early stage ovarian cancer. This technology will establish a new state-of-the-art strategy for the characterization of individual serum glycoproteomes as a diagnostic and monitoring tool which represents a major step toward understanding the changes that take place during disease.

GlycoStore: a database of retention properties for glycan analysis

Summary

GlycoStore is a curated chromatographic, electrophoretic and mass-spectrometry composition database of N-, O-, glycosphingolipid (GSL) glycans and free oligosaccharides associated with a range of glycoproteins, glycolipids and biotherapeutics. The database is built on publicly available experimental datasets from GlycoBase developed in the Oxford Glycobiology Institute and then the National Institute for Bioprocessing Research and Training (NIBRT). It has now been extended to include recently published and in-house data collections from the Bioprocessing Technology Institute (BTI) A*STAR, Macquarie University and Ludger Ltd. GlycoStore provides access to approximately 850 unique glycan structure entries supported by over 8500 retention positions determined by: (i) hydrophilic interaction chromatography (HILIC) ultra-high performance liquid chromatography (U/HPLC) and reversed phase (RP)-U/HPLC with fluorescent detection; (ii) porous graphitized carbon (PGC) chromatography in combination with ESI-MS/MS detection; and (iii) capillary electrophoresis with laser induced fluorescence detection (CE-LIF). GlycoStore enhances many features previously available in GlycoBase while addressing the limitations of the data collections and model of this popular resource. GlycoStore aims to support detailed glycan analysis by providing a resource that underpins current workflows. It will be regularly updated by expert annotation of published data and data obtained from the project partners.

Availability and implementation

http://www.glycostore.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Implementing an arts-based intervention for patients with end-stage kidney disease whilst receiving haemodialysis: a feasibility study protocol

BACKGROUND

End-stage kidney disease is a life-changing illness. Many patients require haemodialysis, a treatment that impacts profoundly on quality of life and mental health. Arts-based interventions have been used in other healthcare settings to improve mental health and quality of life; therefore, they may help address the impact of haemodialysis by improving these outcomes. However, there is a lack of evidence assessing their effectiveness in this population and few randomised controlled trials (RCTs) evaluating the effectiveness of complex arts-based interventions.

METHODS

The aims of this study are to establish the feasibility of a cluster RCT of an arts-based intervention for patients with end-stage kidney disease whilst receiving haemodialysis through a cluster randomised pilot study, explore the acceptability of the intervention with a process evaluation and explore the feasibility of an economic evaluation. The study will have three phases. The first phase consists of a cluster randomised pilot study to establish recruitment, participation and retention rates. This will involve the recruitment of 30 participants who will be randomly allocated through cluster randomisation according to shift pattern to experimental and control group. The second phase will be a qualitative process evaluation to establish the acceptability of the intervention within a clinical setting. This will involve semi-structured interviews with 13 patients and three focus groups with healthcare professionals. The third phase will be a feasibility economic evaluation to establish the best methods for data collection within a future cluster RCT.

DISCUSSION

Arts-based interventions have been shown to improve quality of life in healthcare settings, but there is a lack of evidence evaluating arts-based interventions for patients receiving haemodialysis. This study aims to assess the feasibility of a future cluster RCT assessing the impact of an arts-based intervention on the wellbeing and mental health of patients receiving haemodialysis and identify the key factors leading to successful implementation. The hope is this study will inform a trial that can influence future healthcare policy by providing robust evidence for arts-based interventions within the haemodialysis setting.

TRIAL REGISTRATION

The trial was prospectively registered on clinicaltrials.gov on 14/8/2018, registration number NCT03629496.

AnArtomy: Arts, Anatomy and Medicine - Human Beings Being Human

This article was migrated. The article was marked as recommended. Background The study of anatomy underpins medical education and is an important facet of clinical practice in various diverse disciplines. We explored the dynamic relationship between arts, anatomy and medicine, along the continuum axis of anatomy, medicine, healthcare and art. Aims 1. to foster and gauge artistic and creative expression within the context of medical science and practice. 2. to generate representative artwork examining the relationship between art, medicine and healthcare. Methods Two purposefully open and expressive creative workshops were held within the cadaveric dissection laboratory of the Queen's University Department of Anatomy; with awide variety of artistic substrates available for faculty and student participants. Themes included: the relationship between art and medicine, the impact art and science have upon each other and the effects of creativity on wellbeing. Accompanying questionnaires included a quantification of perceived relationships between art and medicine; with an estimation of connectedness to feelings. Qualitative items within each questionnaire also addressed key humanistic questions. Comparative analysis of quantitative results was by Student's t-testing; statistical significance being p values <0.05. Results, Summary and Conclusions There was a statistically significant increase in "connectedness to feelings" amongst participants over the course of the workshop. There was a trend for participants to agree or strongly agree that art and medicine were important to each other. Qualitative responses changed from specific, task-oriented hopes to responses more aligned with social/gregarious themes and those related to higher order functioning. Humanistic responses changed across the entire group from a largely fixed inclusion of the concept of emotions to broader, more altruistic visions; inclusive of communal, social views. There was a noticeable shift in emphasis from succinctly defined descriptive terms to more expressive terms; reflective and inclusive of caring, holistic practice. The most arresting and compelling results were those of the resulting representative artwork.

Plasma N-glycans in colorectal cancer risk

Aberrant glycosylation has been associated with a number of diseases including cancer. Our aim was to elucidate changes in whole plasma N-glycosylation between colorectal cancer (CRC) cases and controls in one of the largest cohorts of its kind. A set of 633 CRC patients and 478 age and gender matched controls was analysed. Additionally, patients were stratified into four CRC stages. Moreover, N-glycan analysis was carried out in plasma of 40 patients collected prior to the initial diagnosis of CRC. Statistically significant differences were observed in the plasma N-glycome at all stages of CRC, this included a highly significant decrease in relation to the core fucosylated bi-antennary glycans F(6)A2G2 and F(6)A2G2S(6)1 (P < 0.0009). Stage 1 showed a unique biomarker signature compared to stages 2, 3 and 4. There were indications that at risk groups could be identified from the glycome (retrospective AUC = 0.77 and prospective AUC = 0.65). N-glycome biomarkers related to the pathogenic progress of the disease would be a considerable asset in a clinical setting and it could enable novel therapeutics to be developed to target the disease in patients at risk of progression.

FELLS: fast estimator of latent local structure

Motivation

The behavior of a protein is encoded in its sequence, which can be used to predict distinct features such as secondary structure, intrinsic disorder or amphipathicity. Integrating these and other features can help explain the context-dependent behavior of proteins. However, most tools focus on a single aspect, hampering a holistic understanding of protein structure. Here, we present Fast Estimator of Latent Local Structure (FELLS) to visualize structural features from the protein sequence. FELLS provides disorder, aggregation and low complexity predictions as well as estimated local propensities including amphipathicity. A novel fast estimator of secondary structure (FESS) is also trained to provide a fast response. The calculations required for FELLS are extremely fast and suited for large-scale analysis while providing a detailed analysis of difficult cases.

Availability and Implementation

The FELLS web server is available from URL: http://protein.bio.unipd.it/fells/ . The server also exposes RESTful functionality allowing programmatic prediction requests. An executable version of FESS for Linux can be downloaded from URL: protein.bio.unipd.it/download/.

Contact

silvio.tosatto@unipd.it.

Supplementary information

Supplementary data are available at Bioinformatics online.

Serum N-glycome alterations in breast cancer during multimodal treatment and follow-up

Using our recently developed high-throughput automated platform, N-glycans from all serum glycoproteins from patients with breast cancer were analysed at diagnosis, after neoadjuvant chemotherapy, surgery, radiotherapy and up to 3 years after surgery. Surprisingly, alterations in the serum N-glycome after chemotherapy were pro-inflammatory with an increase in glycan structures associated with cancer. Surgery, on the other hand, induced anti-inflammatory changes in the serum N-glycome, towards a noncancerous phenotype. At the time of first follow-up, glycosylation in patients with affected lymph nodes changed towards a malignant phenotype. C-reactive protein showed a different pattern, increasing after first line of neoadjuvant chemotherapy, then decreasing throughout treatment until 1 year after surgery. This may reflect a switch from acute to chronic inflammation, where chronic inflammation is reflected in the serum after the acute phase response subsides. In conclusion, we here present the first time-course serum N-glycome profiling of patients with breast cancer during and after treatment. We identify significant glycosylation changes with chemotherapy, surgery and follow-up, reflecting the host response to therapy and tumour removal.

Kidney stones: pathophysiology, diagnosis and management

The prevalence of kidney stones is increasing, and approximately 12 000 hospital admissions every year are due to this condition. This article will use a case study to focus on a patient diagnosed with a calcium oxalate kidney stone. It will discuss the affected structures in relation to kidney stones and describe the pathology of the condition. Investigations for kidney stones, differential diagnosis and diagnosis, possible complications and prognosis, will be discussed. Finally, a detailed account of management strategies for the patient with kidney stones will be given, looking at pain management, medical procedures and dietary interventions.

Quantitative profiling of glycans and glycopeptides: an informatics' perspective

Experimental techniques to identify and quantify glycan structures in a given sample are continuously improving. However, as they advance data analysis and annotation seems to become more complex. To address this issue, much progress has been made in developing software for interpretation of quantitative glycan profiles. Here, we focus on these informatics tools for high/ultra performance liquid chromatography (H/UPLC), mass spectrometry (MS), tandem mass spectrometry (MSⁿ) and combinations thereof. Software for biomarker discovery, pathway, genomic and disease analysis and a final note on some future prospects for glycoinformatics are also mentioned.

Correct machine learning on protein sequences: a peer-reviewing perspective

Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation of the performance. This article espouses a set of guidelines to allow both peer reviewers and authors to avoid common machine learning pitfalls. Understanding biology is necessary to produce useful data sets, which have to be large and diverse. Separating the training and test process is imperative to avoid over-selling method performance, which is also dependent on several hidden parameters. A novel predictor has always to be compared with several existing methods, including simple baseline strategies. Using the presented guidelines will help nonspecialists to appreciate the critical issues in machine learning.

MobiDB 2.0: an improved database of intrinsically disordered and mobile proteins

MobiDB (http://mobidb.bio.unipd.it/) is a database of intrinsically disordered and mobile proteins. Intrinsically disordered regions are key for the function of numerous proteins. Here we provide a new version of MobiDB, a centralized source aimed at providing the most complete picture on different flavors of disorder in protein structures covering all UniProt sequences (currently over 80 million). The database features three levels of annotation: manually curated, indirect and predicted. Manually curated data is extracted from the DisProt database. Indirect data is inferred from PDB structures that are considered an indication of intrinsic disorder. The 10 predictors currently included (three ESpritz flavors, two IUPred flavors, two DisEMBL flavors, GlobPlot, VSL2b and JRONN) enable MobiDB to provide disorder annotations for every protein in absence of more reliable data. The new version also features a consensus annotation and classification for long disordered regions. In order to complement the disorder annotations, MobiDB features additional annotations from external sources. Annotations from the UniProt database include post-translational modifications and linear motifs. Pfam annotations are displayed in graphical form and are link-enabled, allowing the user to visit the corresponding Pfam page for further information. Experimental protein–protein interactions from STRING are also classified for disorder content.

Comprehensive large-scale assessment of intrinsic protein disorder

MOTIVATION

Intrinsically disordered regions are key for the function of numerous proteins. Due to the difficulties in experimental disorder characterization, many computational predictors have been developed with various disorder flavors. Their performance is generally measured on small sets mainly from experimentally solved structures, e.g. Protein Data Bank (PDB) chains. MobiDB has only recently started to collect disorder annotations from multiple experimental structures.

RESULTS

MobiDB annotates disorder for UniProt sequences, allowing us to conduct the first large-scale assessment of fast disorder predictors on 25 833 different sequences with X-ray crystallographic structures. In addition to a comprehensive ranking of predictors, this analysis produced the following interesting observations. (i) The predictors cluster according to their disorder definition, with a consensus giving more confidence. (ii) Previous assessments appear over-reliant on data annotated at the PDB chain level and performance is lower on entire UniProt sequences. (iii) Long disordered regions are harder to predict. (iv) Depending on the structural and functional types of the proteins, differences in prediction performance of up to 10% are observed.

AVAILABILITY

The datasets are available from Web site at URL: http://mobidb.bio.unipd.it/lsd.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PASTA 2.0: an improved server for protein aggregation prediction

The formation of amyloid aggregates upon protein misfolding is related to several devastating degenerative diseases. The propensities of different protein sequences to aggregate into amyloids, how they are enhanced by pathogenic mutations, the presence of aggregation hot spots stabilizing pathological interactions, the establishing of cross-amyloid interactions between co-aggregating proteins, all rely at the molecular level on the stability of the amyloid cross-beta structure. Our redesigned server, PASTA 2.0, provides a versatile platform where all of these different features can be easily predicted on a genomic scale given input sequences. The server provides other pieces of information, such as intrinsic disorder and secondary structure predictions, that complement the aggregation data. The PASTA 2.0 energy function evaluates the stability of putative cross-beta pairings between different sequence stretches. It was re-derived on a larger dataset of globular protein domains. The resulting algorithm was benchmarked on comprehensive peptide and protein test sets, leading to improved, state-of-the-art results with more amyloid forming regions correctly detected at high specificity. The PASTA 2.0 server can be accessed at http://protein.bio.unipd.it/pasta2/.

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation

BACKGROUND

The rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes.

RESULTS

Here we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/.

CONCLUSIONS

NeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks

Background

Protein inter-residue contact maps provide a translation and rotation invariant topological representation of a protein. They can be used as an intermediary step in protein structure predictions. However, the prediction of contact maps represents an unbalanced problem as far fewer examples of contacts than non-contacts exist in a protein structure.

In this study we explore the possibility of completely eliminating the unbalanced nature of the contact map prediction problem by predicting real-value distances between residues. Predicting full inter-residue distance maps and applying them in protein structure predictions has been relatively unexplored in the past.

Results

We initially demonstrate that the use of native-like distance maps is able to reproduce 3D structures almost identical to the targets, giving an average RMSD of 0.5Å. In addition, the corrupted physical maps with an introduced random error of ±6Å are able to reconstruct the targets within an average RMSD of 2Å.

After demonstrating the reconstruction potential of distance maps, we develop two classes of predictors using two-dimensional recursive neural networks: an ab initio predictor that relies only on the protein sequence and evolutionary information, and a template-based predictor in which additional structural homology information is provided. We find that the ab initio predictor is able to reproduce distances with an RMSD of 6Å, regardless of the evolutionary content provided. Furthermore, we show that the template-based predictor exploits both sequence and structure information even in cases of dubious homology and outperforms the best template hit with a clear margin of up to 3.7Å.

Lastly, we demonstrate the ability of the two predictors to reconstruct the CASP9 targets shorter than 200 residues producing the results similar to the state of the machine learning art approach implemented in the Distill server.

Conclusions

The methodology presented here, if complemented by more complex reconstruction protocols, can represent a possible path to improve machine learning algorithms for 3D protein structure prediction. Moreover, it can be used as an intermediary step in protein structure predictions either on its own or complemented by NMR restraints.

RUBI: rapid proteomic-scale prediction of lysine ubiquitination and factors influencing predictor performance

Post-translational modification of protein lysines was recently shown to be a common feature of eukaryotic organisms. The ubiquitin modification is regarded as a versatile regulatory mechanism with many important cellular roles. Large-scale datasets are becoming available for H. sapiens ubiquitination. However, using current experimental techniques the vast majority of their sites remain unidentified and in silico tools may offer an alternative. Here, we introduce Rapid UBIquitination (RUBI) a sequence-based ubiquitination predictor designed for rapid application on a genome scale. RUBI was constructed using an iterative approach. At each iteration, important factors which influenced performance and its usability were investigated. The final RUBI model has an AUC of 0.868 on a large cross-validation set and is shown to outperform other available methods on independent sets. Predicted intrinsic disorder is shown to be weakly anti-correlated to ubiquitination for the H. sapiens dataset and improves performance slightly. RUBI predicts the number of ubiquitination sites correctly within three sites for ca. 80% of the tested proteins. The average potentially ubiquitinated proteome fraction is predicted to be at least 25% across a variety of model organisms, including several thousand possible H. sapiens proteins awaiting experimental characterization. RUBI can accurately predict ubiquitination on unseen examples and has a signal across different eukaryotic organisms. The factors which influenced the construction of RUBI could also be tested in other post-translational modification predictors. One of the more interesting factors is the influence of intrinsic protein disorder on ubiquitinated lysines where residues with low disorder probability are preferred.

RepeatsDB: a database of tandem repeat protein structures

RepeatsDB (http://repeatsdb.bio.unipd.it/) is a database of annotated tandem repeat protein structures. Tandem repeats pose a difficult problem for the analysis of protein structures, as the underlying sequence can be highly degenerate. Several repeat types haven been studied over the years, but their annotation was done in a case-by-case basis, thus making large-scale analysis difficult. We developed RepeatsDB to fill this gap. Using state-of-the-art repeat detection methods and manual curation, we systematically annotated the Protein Data Bank, predicting 10 745 repeat structures. In all, 2797 structures were classified according to a recently proposed classification schema, which was expanded to accommodate new findings. In addition, detailed annotations were performed in a subset of 321 proteins. These annotations feature information on start and end positions for the repeat regions and units. RepeatsDB is an ongoing effort to systematically classify and annotate structural protein repeats in a consistent way. It provides users with the possibility to access and download high-quality datasets either interactively or programmatically through web services.

PANADA: protein association network annotation, determination and analysis

Increasingly large numbers of proteins require methods for functional annotation. This is typically based on pairwise inference from the homology of either protein sequence or structure. Recently, similarity networks have been presented to leverage both the ability to visualize relationships between proteins and assess the transferability of functional inference. Here we present PANADA, a novel toolkit for the visualization and analysis of protein similarity networks in Cytoscape. Networks can be constructed based on pairwise sequence or structural alignments either on a set of proteins or, alternatively, by database search from a single sequence. The Panada web server, executable for download and examples and extensive help files are available at URL: http://protein.bio.unipd.it/panada/.

Analysis and consensus of currently available intrinsic protein disorder annotation sources in the MobiDB database

BACKGROUND

Intrinsic protein disorder is becoming an increasingly important topic in protein science. During the last few years, intrinsically disordered proteins (IDPs) have been shown to play a role in many important biological processes, e.g. protein signalling and regulation. This has sparked a need to better understand and characterize different types of IDPs, their functions and roles. Our recently published database, MobiDB, provides a centralized resource for accessing and analysing intrinsic protein disorder annotations.

RESULTS

Here, we present a thorough description and analysis of the data made available by MobiDB, providing descriptive statistics on the various available annotation sources. Version 1.2.1 of the database contains annotations for ca. 4,500,000 UniProt sequences, covering all eukaryotic proteomes. In addition, we describe a novel consensus annotation calculation and its related weighting scheme. The comparison between disorder information sources highlights how the MobiDB consensus captures the main features of intrinsic disorder and correlates well with manually curated datasets. Finally, we demonstrate the annotation of 13 eukaryotic model organisms through MobiDB's datasets, and of an example protein through the interactive user interface.

CONCLUSIONS

MobiDB is a central resource for intrinsic disorder research, containing both experimental data and predictions. In the future it will be expanded to include additional information for all known proteins.

RAPHAEL: recognition, periodicity and insertion assignment of solenoid protein structures

MOTIVATION

Repeat proteins form a distinct class of structures where folding is greatly simplified. Several classes have been defined, with solenoid repeats of periodicity between ca. 5 and 40 being the most challenging to detect. Such proteins evolve quickly and their periodicity may be rapidly hidden at sequence level. From a structural point of view, finding solenoids may be complicated by the presence of insertions or multiple domains. To the best of our knowledge, no automated methods are available to characterize solenoid repeats from structure.

RESULTS

Here we introduce RAPHAEL, a novel method for the detection of solenoids in protein structures. It reliably solves three problems of increasing difficulty: (1) recognition of solenoid domains, (2) determination of their periodicity and (3) assignment of insertions. RAPHAEL uses a geometric approach mimicking manual classification, producing several numeric parameters that are optimized for maximum performance. The resulting method is very accurate, with 89.5% of solenoid proteins and 97.2% of non-solenoid proteins correctly classified. RAPHAEL periodicities have a Spearman correlation coefficient of 0.877 against the manually established ones. A baseline algorithm for insertion detection in identified solenoids has a Q(2) value of 79.8%, suggesting room for further improvement. RAPHAEL finds 1931 highly confident repeat structures not previously annotated as solenoids in the Protein Data Bank records.

Bluues server: electrostatic properties of wild-type and mutated protein structures

MOTIVATION

Electrostatic calculations are an important tool for deciphering many functional mechanisms in proteins. Generalized Born (GB) models offer a fast and convenient computational approximation over other implicit solvent-based electrostatic models. Here we present a novel GB-based web server, using the program Bluues, to calculate numerous electrostatic features including pKa-values and surface potentials. The output is organized allowing both experts and beginners to rapidly sift the data. A novel feature of the Bluues server is that it explicitly allows to find electrostatic differences between wild-type and mutant structures.

AVAILABILITY

The Bluues server, examples and extensive help files are available for non-commercial use at URL: http://protein.bio.unipd.it/bluues/.