CUPSAT: prediction of protein stability upon point mutations

CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict ΔΔG, the difference in free energy of unfolding between wild-type and mutant proteins. It requires the protein structure in Protein Data Bank format and the location of the residue to be mutated. The output consists information about mutation site, its structural features (solvent accessibility, secondary structure and torsion angles), and comprehensive information about changes in protein stability for 19 possible substitutions of a specific amino acid mutation. Additionally, it also analyses the ability of the mutated amino acids to adapt the observed torsion angles. Results were tested on 1538 mutations from thermal denaturation and 1603 mutations from chemical denaturation experiments. Several validation tests (split-sample, jack-knife and k-fold) were carried out to ensure the reliability, accuracy and transferability of the prediction method that gives >80% prediction accuracy for most of these validation tests. Thus, the program serves as a valuable tool for the analysis of protein design and stability. The tool is accessible from the link

Computational modeling of protein mutant stability: analysis and optimization of statistical potentials and structural features reveal insights into prediction model development

<p>Abstract</p> <p>Background</p> <p>Understanding and predicting protein stability upon point mutations has wide-spread importance in molecular biology. Several prediction models have been developed in the past with various algorithms. Statistical potentials are one of the widely used algorithms for the prediction of changes in stability upon point mutations. Although the methods provide flexibility and the capability to develop an accurate and reliable prediction model, it can be achieved only by the right selection of the structural factors and optimization of their parameters for the statistical potentials. In this work, we have selected five atom classification systems and compared their efficiency for the development of amino acid atom potentials. Additionally, torsion angle potentials have been optimized to include the orientation of amino acids in such a way that altered backbone conformation in different secondary structural regions can be included for the prediction model. This study also elaborates the importance of classifying the mutations according to their solvent accessibility and secondary structure specificity. The prediction efficiency has been calculated individually for the mutations in different secondary structural regions and compared.</p> <p>Results</p> <p>Results show that, in addition to using an advanced atom description, stepwise regression and selection of atoms are necessary to avoid the redundancy in atom distribution and improve the reliability of the prediction model validation. Comparing to other atom classification models, Melo-Feytmans model shows better prediction efficiency by giving a high correlation of 0.85 between experimental and theoretical ΔΔG with 84.06% of the mutations correctly predicted out of 1538 mutations. The theoretical ΔΔG values for the mutations in partially buried <it>β</it>-strands generated by the structural training dataset from PISCES gave a correlation of 0.84 without performing the Gaussian apodization of the torsion angle distribution. After the Gaussian apodization, the correlation increased to 0.92 and prediction accuracy increased from 80% to 88.89% respectively.</p> <p>Conclusion</p> <p>These findings were useful for the optimization of the Melo-Feytmans atom classification system and implementing them to develop the statistical potentials. It was also significant that the prediction efficiency of mutations in the partially buried <it>β</it>-strands improves with the help of Gaussian apodization of the torsion angle distribution. All these comparisons and optimization techniques demonstrate their advantages as well as the restrictions for the development of the prediction model. These findings will be quite helpful not only for the protein stability prediction, but also for various structure solutions in future.</p

A contemporary approach for treatment planning of horizontally resorbed alveolar ridge: Ridge split technique with simultaneous implant placement using platelet rich fibrin membrane application in mandibular anterior region

Treatment of edentulous sites with horizontal atrophy represents a clinical situation in which the positioning of endosseous implants might be complex or sometimes impossible without a staged regenerative approach. This case report presents management of horizontally deficient mandibular anterior ridge with a contemporary approach to treatment planning and application of platelet-rich fibrin membrane for ridge split technique and simultaneous implant placement. Implants in anterior mandibular area are considered to be most predictable, stable, with high success rate and patients' satisfaction with implant esthetics. In contrast to traditional ridge augmentation techniques, ridge splitting allows for immediate implant placement following surgery and eradicates the possible morbidity from a second surgical site

Separation of sillimanite from beach sands contaminated with shell

Sillimanite is a very important raw material for the manufacture of refractory bricks used in iron and steel, cement and glass industries. In some coastal stretches of south India, the beach sand is contaminated with shell. After recovering heavy minerals like ilmenite, rutile, zircon and garnet by various physical separation techniques, the final sand containing sillimanite, quartz and shell is presently dumped as waste. Separation of sillimanite from these rejects was studied by flotation process using oleic acid and octylhydroxamate as collectors for selective flotation of sillimanite. Starch and tannin were tried as depressants for shell and sodium silicate as dispersant for quartz. Though the quartz could be depressed effectively, the shell was found to float along with sillimanite both in the presence of oleic acid and octylhydroxamate. When starch or tannin was added to suppress the shell flotation, the floatability of sillimanite and in turn its recovery was drastically affected. Alternatively shell was totally removed by treating with dilute hydrochloric acid and sillimanite was separated from quartz using oleic acid and sodium silicate. The concentrate analyzing 97% sillimanite free from shell were achieved from the initial sand assaying 48 % sillimanite, 46 % quartz and 3.3% shell

Exome sequencing improves genetic diagnosis of structural fetal abnormalities revealed by ultrasound

The genetic etiology of non-aneuploid fetal structural abnormalities is typically investigated by karyotyping and array-based detection of microscopically detectable rearrangements, and submicroscopic copy-number variants (CNVs), which collectively yield a pathogenic finding in up to 10% of cases. We propose that exome sequencing may substantially increase the identification of underlying etiologies. We performed exome sequencing on a cohort of 30 non-aneuploid fetuses and neonates (along with their parents) with diverse structural abnormalities first identified by prenatal ultrasound. We identified candidate pathogenic variants with a range of inheritance models, and evaluated these in the context of detailed phenotypic information. We identified 35 de novo single-nucleotide variants (SNVs), small indels, deletions or duplications, of which three (accounting for 10% of the cohort) are highly likely to be causative. These are de novo missense variants in FGFR3 and COL2A1, and a de novo 16.8 kb deletion that includes most of OFD1. In five further cases (17%) we identified de novo or inherited recessive or X-linked variants in plausible candidate genes, which require additional validation to determine pathogenicity. Our diagnostic yield of 10% is comparable to, and supplementary to, the diagnostic yield of existing microarray testing for large chromosomal rearrangements and targeted CNV detection. The de novo nature of these events could enable couples to be counseled as to their low recurrence risk. This study outlines the way for a substantial improvement in the diagnostic yield of prenatal genetic abnormalities through the application of next-generation sequencing

presents Student Council 1st International Student Symposium in Computational Biology

Roderic Guigo and Fernando Moran for letting us have our first International Symposium, a very enjoyable adventure which we hope will help students from around the world to meet, share ideas and grow in passion for this fascinating field. Thank you also for letting have this event free of charge for attendants of the ECCB’05 main conference. On the logistics front we would like to thank Teresa Diez, Marta de la Riva and Mercedes del Portillo for all their patience and assistance. In ISCB we would like to thank BJ Morrison for helping us make the Student Council a reality and for creating the brochures for ECCB’05. Thanks to the Board of Directors of ISCB, especially to Mike Gribskov and Phil Bourne for their enthusiastic commitment to the mission of the Student Council and ISCB as a whole

Genetic diagnosis of developmental disorders in the DDD study:a scalable analysis of genome-wide research data

SummaryBackgroundHuman genome sequencing has transformed our understanding of genomic variation and its relevance to health and disease, and is now starting to enter clinical practice for the diagnosis of rare diseases. The question of whether and how some categories of genomic findings should be shared with individual research participants is currently a topic of international debate, and development of robust analytical workflows to identify and communicate clinically relevant variants is paramount.MethodsThe Deciphering Developmental Disorders (DDD) study has developed a UK-wide patient recruitment network involving over 180 clinicians across all 24 regional genetics services, and has performed genome-wide microarray and whole exome sequencing on children with undiagnosed developmental disorders and their parents. After data analysis, pertinent genomic variants were returned to individual research participants via their local clinical genetics team.FindingsAround 80 000 genomic variants were identified from exome sequencing and microarray analysis in each individual, of which on average 400 were rare and predicted to be protein altering. By focusing only on de novo and segregating variants in known developmental disorder genes, we achieved a diagnostic yield of 27% among 1133 previously investigated yet undiagnosed children with developmental disorders, whilst minimising incidental findings. In families with developmentally normal parents, whole exome sequencing of the child and both parents resulted in a 10-fold reduction in the number of potential causal variants that needed clinical evaluation compared to sequencing only the child. Most diagnostic variants identified in known genes were novel and not present in current databases of known disease variation.InterpretationImplementation of a robust translational genomics workflow is achievable within a large-scale rare disease research study to allow feedback of potentially diagnostic findings to clinicians and research participants. Systematic recording of relevant clinical data, curation of a gene–phenotype knowledge base, and development of clinical decision support software are needed in addition to automated exclusion of almost all variants, which is crucial for scalable prioritisation and review of possible diagnostic variants. However, the resource requirements of development and maintenance of a clinical reporting system within a research setting are substantial.FundingHealth Innovation Challenge Fund, a parallel funding partnership between the Wellcome Trust and the UK Department of Health

INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS FOR MOLECULAR BIOLOGY (ISMB)

The Intelligent Systems for Molecular Biology (ISMB) conference has provided a general forum for disseminating the latest developments in bioinformatics on an annual basis for the past 13 years. ISMB is a multidisciplinary conference that brings together scientists from computer science, molecular biology, mathematics and statistics. The goal of the ISMB meeting is to bring together biologists and computational scientists in a focus on actual biological problems, i.e., not simply theoretical calculations. The combined focus on “intelligent systems” and actual biological data makes ISMB a unique and highly important meeting, and 13 years of experience in holding the conference has resulted in a consistently well organized, well attended, and highly respected annual conference. The ISMB 2005 meeting was held June 25-29, 2005 at the Renaissance Center in Detroit, Michigan. The meeting attracted over 1,730 attendees. The science presented was exceptional, and in the course of the five-day meeting, 56 scientific papers, 710 posters, 47 Oral Abstracts, 76 Software demonstrations, and 14 tutorials were presented. The attendees represented a broad spectrum of backgrounds with 7% from commercial companies, over 28% qualifying for student registration, and 41 countries were represented at the conference, emphasizing its important international aspect. The ISMB conference is especially important because the cultures of computer science and biology are so disparate. ISMB, as a full-scale technical conference with refereed proceedings that have been indexed by both MEDLINE and Current Contents since 1996, bridges this cultural gap