Scientifically, the Gene Function Prediction group is focused on the prediction of molecular and cellular functions of genes and proteins based on the theoretical analysis of biomolecular sequences, expression profiles, and other omics high-throughput data. Most of the work is with internal and/or external collaborators, also including partners in clinics and biotech/pharma industry. Besides that, this group also provides support for teams in BII, an organizational fallback for staff involved in various collaborations, software development and incubation activities that do not readily fit into other existing PI-led teams.
Dramatic recent improvements of nucleic acid sequencing technologies enhance the prospect of general availability of genomes from patients, patient-specific pathogens and of gene expression data. This development has profound implications for life science research and biomedical applications. Sequencing is becoming one of the most informative research technologies in life sciences; consequently, sequence analysis and sequence based structure and function prediction will be more important than ever.
However, at the same time, researchers in the field of genome annotation see that there is a persisting, substantial body of functionally insufficiently or completely not characterized genes (for example, ~10,000 protein-coding genes in the human genome) despite the availability of full genome sequences. A survey of the biomedical literature  shows that the number of reported new protein functions had been steadily growing until 2000 but the trend reversed to a dramatic decline thereafter. The fastest-growing set of genes in the last decade is the set of genes that is well characterized anyhow. At the same time, the annual amount of life science publications doubled between 2000 and 2017.
With regard to new gene functions, we discovered a new membrane-embedded protein domain evolutionarily multiplied in the GPI lipid anchor pathway proteins, BindGPILA. It functions as the unit for recognizing, binding and stabilizing the GPI lipid anchor in a modification-competent form . Recently, we discovered that the mitochondrial gene SUGCT contributes to gut microbiota dysbiosis, leading to age-dependent pathological changes in kidney, liver, and adipose tissue . We contributed to the development of AllerCatPro, a tool that predicts the allergenic potential of proteins based on the similarity of their 3D structure as well as their amino acid sequence to a data set of known protein allergens . Both projects,  and , were carried out in collaboration with S. Maurer-Stroh.
The collaboration with the G. Grüber crystallography lab (NTU, Singapore) resulted in a string of discoveries with regard to the structure, catalytic mechanism and sequence architecture significance of the AhpF/AhpC alkyl hydroperoxide reductase complex by studies of mutated versions of AhpF/AhpC [5-7].
The team is involved in both academic and industry-funded projects in collaboration with the BII Natural Organism Library (teams of Ng Siew Bee, Y. Kanagasundaram, and P. Arumugam) . Recently, an analog of Anthracimycin, an antibiotic that, so far, is only known to be produced by Streptomyces species, was predicted and verified to be produced by Nocardiopsis kunsanensis, a non-Streptomyces actinobacterial microorganism . Together with the BII NOL team, we discovered a new cyclic lipodepsipeptide, BII-Rafflesfungin, possessing antifungal activity that is produced by fungus Phoma sp. F3723 . We identified a biosynthetic gene cluster compatible with the production of this new compound (Figure 1) and proposed a mechanism for its biosynthesis (Figure 2).
The team continues to be successful in attracting grants (BE: NRF-CRP17-2017-03 (green and sustainable pharmaceutical manufacturing via biocatalysis, FE: CITI – Cancer Immunotherapy Imaging). The development of the data management system TIMS (Translational Informatics Management System)  has led to multiple collaborations/grants with both academic and commercial entities. Currently, the group manages the data for joint BII-PRISM projects (GEMINI – gastric cancer dataset, and ATTRaCT - clinical data analysis for heart failure patients), the metadata for the SG10K project (National Precision Medicine Programme), and the data for the CaLiBRe (Cancer Liquid Biopsy for Real-Time Diagnostics and Early Intervention) project. The work with clinical data motivated us to discuss the problem of access to patient data for biomedical research from three different perspectives: patients’, clinicians’ and researchers’ .
Frank Eisenhaber studied mathematics at the Humboldt-University in Berlin and biophysics and medicine at the Pirogov Medical University in Moscow. He received the PhD in molecular biology from the Engelhardt Institute of Molecular Biology in Moscow 1988. After postdoctoral work at the Institute of Molecular Biology in Berlin-Buch and at the EMBL in Heidelberg, he worked as bioinformatics teamleader and head of the general IT department at the Institute of Molecular Pathology (IMP) in Vienna (1999-2007). He joined the Bioinformatics Institute A*STAR Singapore in August 2007 and was its Executive Director from 2007 to 2020.
Frank Eisenhaber’s research interest is focused on the discovery of new biomolecular mechanisms from biological and medical data and the functional characterization of yet uncharacterized genes and pathways. As mechanistic insight is the driver for biotechnology, biomedical and clinical applications, this work has catalyzed various lines of applied research. Frank Eisenhaber is one of the scientists credited with the discovery of the SET domain methyltransferases, ATGL, kleisins, many new protein domain functions (for example in the GPI lipid anchor biosynthsis pathway), with the development of accurate prediction tools for posttranslational modifications and subcellular localizations and with algorithms for omics data analysis.
Birgit Eisenhaber’s research interest is focused on the discovery of molecular functions of previously uncharacterized protein coding genes. She is interested in proteins’ posttranslational modifications with a special focus on lipid anchor modifications. Birgit Eisenhaber studied biocybernetics and medicine at the Pirogov Medical University in Moscow. After working as a professional software developer, she restarted her scientific career at the EMBL Heidelberg. She obtained her Ph.D. from Humboldt University Berlin. Subsequently, she worked as a postdoctoral research fellow at the Institute of Molecular Pathology (IMP) Vienna and at the Experimental Therapeutics Centre (ETC) Singapore. In December 2010, Birgit Eisenhaber was appointed as a Principal Investigator at the Bioinformatics Institute (BII) Singapore.
Birgit is part of the Gene Function Prediction group that is focused on the prediction of molecular and cellular functions of genes and proteins based on the theoretical analysis of biomolecular sequences, expression profiles and other omics high-throughput data.
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