Protein Structure and
Bioinformatics Group

Prof. Mauno Vihinen

Protein Structure and Bioinformatics Group

The Protein Structure and Bioinformatics (LU PSB) develops methods and performs analyses to understand biological and medical phenomena at genetic, functional, mechanical and systems level.

The group has two major research areas.
One is related to collection, interpretation and distribution of information related to variations and their effects in multiple levels.
The major development areas are

• identification and development of prediction tools for pathogenic variations
• studies of structural bases of diseases
• phenotype-genotype -correlations in diseases
• protein structure-function relationships
• development of systematic representations and standards for variations, their effects, and data systems to store them, including Variation Ontology (VariO)
  

The other research line in highly related and investigates the effects of the variations and perturbations as systems biological and medical level. These studies are done especially in relation to primary immunodeficiencies and cancers.
Developmental areas include

• definition and studies of immunome
• systems biology in relation to human diseases
• modeling and simulation of biological systems
• analysis of perturbations, including variations, on biological systems
• large scale data analysis

Latest news

• 2017-06-22 New publication: Daneshjou et al., 2017. Working towards precision medicine: predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges. Hum Mutat doi/10.1002/humu.23280
• 2017-06-14 New publication: Schaafsma G, Vihinen M. 2017. Large differences in proportions of harmful and benign amino acid substitutions between proteins and diseases. Hum Mutat 38: 839-848 doi: 10.1002/humu.23236
• 2017-04-05 New publication: Niroula A, Vihinen M. 2017. Predicting severity of disease-causing variants. Hum Mutat 38: 357-364 doi: 10.1002/humu.23173
• 2017-02-23 New publication: Niroula A, Vihinen M. 2017. PON-P and PON-P2 predictor performance in CAGI challenges: Lessons learned. Hum Mutat doi: 10.1002/humu.23199
• 2017-02-16 VariSNP datasets update: The VariSNP benchmark datasets were updated using dbSNP build 149 (GRCh38p7).

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