"Redirecting an anti-IL-1β antibody to bind a new, unrelated and computationally predicted epitope on hIL-17A".

Antibody engineering technology is at the forefront of therapeutic antibody development. The primary goal for engineering a therapeutic antibody is the generation of an antibody with a desired specificity, affinity, function, and developability profile. Mature antibodies are considered antigen specific, which may preclude their use as a starting point for antibody engineering.

Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads.

Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusing on specific CDRs or resorting to sequencing VH and VL variable domains separately, thus limiting its utility in comprehensive monitoring of selection dynamics. Here we present a simple and robust method for deep sequencing repertoires of full length scFv, Fab and Fv antibody sequences.

Computational Design of Epitope-Specific Functional Antibodies.

The ultimate goal of protein design is to introduce new biological activity. We propose a computational approach for designing functional antibodies by focusing on functional epitopes, integrating large-scale statistical analysis with multiple structural models. Machine learning is used to analyze these models and predict specific residue-residue contacts.

Using a combined computational-experimental approach to predict antibody-specific B cell epitopes.

Antibody epitope mapping is crucial for understanding B cell-mediated immunity and required for characterizing therapeutic antibodies. In contrast to T cell epitope mapping, no computational tools are in widespread use for prediction of B cell epitopes. Here, we show that, utilizing the sequence of an antibody, it is possible to identify discontinuous epitopes on its cognate antigen.

The α-helical structure of prodomains promotes translocation of intrinsically disordered neuropeptide hormones into the endoplasmic reticulum.

Different neuropeptide hormones, which are either too small to adopt a stable conformation or are predicted to be intrinsically disordered, are synthesized as larger precursors containing a prodomain in addition to an N-terminal signal peptide. We analyzed the biogenesis of three unstructured neuropeptide hormones and observed that translocation of these precursors into the lumen of the endoplasmic reticulum (ER) is critically dependent on the presence of the prodomain. The hormone domains could be deleted from the precursors without interfering with ER import and secretion, whereas constructs lacking the prodomain remained in the cytosol.

iDBPs: a web server for the identification of DNA binding proteins.

Summary: The iDBPs server uses the three-dimensional (3D) structure of a query protein to predict whether it binds DNA. First, the algorithm predicts the functional region of the protein based on its evolutionary profile; the assumption is that large clusters of conserved residues are good markers of functional regions. Next, various characteristics of the predicted functional region as well as global features of the protein are calculated, such as the average surface electrostatic potential, the dipole moment and cluster-based amino acid conservation patterns.

Identification of DNA-binding proteins using structural, electrostatic and evolutionary features.

DNA-binding proteins (DBPs) participate in various crucial processes in the life-cycle of the cells, and the identification and characterization of these proteins is of great importance. We present here a random forests classifier for identifying DBPs among proteins with known 3D structures. First, clusters of evolutionarily conserved regions (patches) on the surface of proteins were detected using the PatchFinder algorithm; earlier studies showed that these regions are typically the functionally important regions of proteins.

Detection of functionally important regions in "hypothetical proteins" of known structure.

Structural genomics initiatives provide ample structures of "hypothetical proteins" (i.e., proteins of unknown function) at an ever increasing rate.

The ConSurf-DB: pre-calculated evolutionary conservation profiles of protein structures.

ConSurf-DB is a repository for evolutionary conservation analysis of the proteins of known structures in the Protein Data Bank (PDB). Sequence homologues of each of the PDB entries were collected and aligned using standard methods. The evolutionary conservation of each amino acid position in the alignment was calculated using the Rate4Site algorithm, implemented in the ConSurf web server.

Disruption of the Aspergillus fumigatus ECM33 homologue results in rapid conidial germination, antifungal resistance and hypervirulence.

The ECM33/SPS2 family of glycosylphosphatidylinositol-anchored proteins plays an important role in maintaining fungal cell wall integrity and virulence. However, the precise molecular role of these proteins is unknown. In this work, AfuEcm33, the gene encoding the ECM33 homologue in the important pathogenic fungus Aspergillus fumigatus, has been cloned and its function analysed.

In silico identification of functional regions in proteins.

Motivation: In silico prediction of functional regions on protein surfaces, i.e. sites of interaction with DNA, ligands, substrates and other proteins, is of utmost importance in various applications in the emerging fields of proteomics and structural genomics.