A blinded study to assess the state of the art in three-dimensional structure modeling of the variable region (Fv) of antibodies was conducted. Nine unpublished high-resolution x-ray Fab crystal structures covering a wide range of antigen-binding site conformations were used as benchmark to compare Fv models generated by four structure prediction methodologies. The methodologies included two homology modeling strategies independently developed by CCG (Chemical Computer Group) and Accerlys Inc, and two fully automated antibody modeling servers: PIGS (Prediction of ImmunoGlobulin Structure), based on the canonical structure model, and Rosetta Antibody Modeling, based on homology modeling and Rosetta structure prediction methodology. The benchmark structure sequences were submitted to Accelrys and CCG and a set of models for each of the nine antibody structures were generated. PIGS and Rosetta models were obtained using the default parameters of the servers. In most cases, we found good agreement between the models and x-ray structures. The average rmsd (root mean square deviation) values calculated over the backbone atoms between the models and structures were fairly consistent, around 1.2 Å. Average rmsd values of the framework and hypervariable loops with canonical structures (L1, L2, L3, H1, and H2) were close to 1.0 Å. H3 prediction yielded rmsd values around 3.0 Å for most of the models. Quality assessment of the models and the relative strengths and weaknesses of the methods are discussed. We hope this initiative will serve as a model of scientific partnership and look forward to future antibody modeling assessments.

The neuronal apoptosis inhibitory protein (NAIP) was identified as a candidate gene for the inherited neurodegenerative disorder spinal muscular atrophy. NAIP is the founding member of a human protein family that is characterized by highly conserved N-terminal motifs called baculovirus inhibitor of apoptosis repeats (BIR). Five members of the human family of inhibitor of apoptosis proteins including NAIP have been shown to be antiapoptotic in various systems. To date, a mechanism for the antiapoptotic effect of NAIP has not been elucidated. To investigate NAIP function, we found cytoprotection of NAIP-expressing primary cortical neurons treated to undergo caspase-3-dependent apoptosis. The additional treatment of these neurons with the pancaspase inhibitor boc-aspartyl(OMe)-fluoromethylketone did not result in increased survival. Similar cytoprotective effects were obtained using HeLa cells transiently transfected with a NAIP N-terminal construct and treated to undergo a caspase-3-dependent cell death. To examine whether NAIP inhibits caspases directly, recombinant N-terminal NAIP protein containing BIR domains was overexpressed, purified, and tested for caspase inhibition potential. Our results demonstrate that inhibition of caspases is selective and restricted to the effector group of caspases, with K i values as low as ϳ14 nM for caspase-3 and ϳ45 nM for caspase-7. Additional investigations with NAIP fragments containing either one or two NAIP BIRs revealed that the second BIR and to a lesser extent the third BIR alone are sufficient to mediate full caspase inhibition.

Protein aggregation is a phenomenon that has attracted considerable attention within the pharmaceutical industry from both a developability standpoint (to ensure stability of protein formulations) and from a research perspective for neurodegenerative diseases. Experimental identification of aggregation behavior in proteins can be expensive; and hence, the development of accurate computational approaches is crucial. The existing methods for predicting protein aggregation rely mostly on the primary sequence and are typically trained on amyloid-like proteins. However, the training bias toward beta amyloid peptides may worsen prediction accuracy of such models when applied to larger protein systems. Here, we present a novel algorithm to identify aggregation-prone regions in proteins termed "AggScore" that is based entirely on three-dimensional structure input. The method uses the distribution of hydrophobic and electrostatic patches on the surface of the protein, factoring in the intensity and relative orientation of the respective surface patches into an aggregation propensity function that has been trained on a benchmark set of 31 adnectin proteins. AggScore can accurately identify aggregation-prone regions in several well-studied proteins and also reliably predict changes in aggregation behavior upon residue mutation. The method is agnostic to an amyloid-specific aggregation context and thus may be applied to globular proteins, small peptides and antibodies.

The success of antibody-based drugs has led to an increased demand for predictive computational tools to assist antibody engineering efforts surrounding the six hypervariable loop regions making up the antigen binding site. Accurate computational modeling of isolated protein loop regions can be quite difficult; consequently, modeling an antigen binding site that includes six loops is particularly challenging. In this work, we present a method for automatic modeling of the FV region of an immunoglobulin based upon the use of a precompiled antibody x-ray structure database, which serves as a source of framework and hypervariable region structural templates that are grafted together. We applied this method (on common desktop hardware) to the Second Antibody Modeling Assessment (AMA-II) target structures as well as an experimental specialized CDR-H3 loop modeling method. The results of the computational structure predictions will be presented and discussed.