A Descriptor Set for Quantitative Structure-property Relationship Prediction in Biologics.

There has been a remarkable increase in the number of biologics, especially monoclonal antibodies, in the market over the last decade. In addition to attaining the desired binding to their targets, a crucial aspect is the 'developability' of these drugs, which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability, low immunogenicity and low poly-specificity. The lack of any of these desirable properties can lead to significant hurdles in advancing them to the clinic and are often discovered only during late stages of drug development. Hence, in silico methods for early detection of these properties, particularly the ones that affect aggregation and solubility in the earlier stages can be highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors that is ideal for making liability predictions using a QSPR (quantitative structure-property relationship) approach. This set offers a high degree of feature diversity that may coarsely be classified based on (1) sequence (2) structure and (3) surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process of a biologics drug candidate. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results show that the descriptors are well suited to assist in the improvement of protein properties of systems that exhibit poor solubility or aggregation.

Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation.

This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R2 of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures.

AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches.

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.

Assessment of fully automated antibody homology modeling protocols in molecular operating environment.

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.

Distribution of neuronal apoptosis inhibitory protein in human tissues.

The neuronal apoptosis inhibitory protein (NAIP) gene, also known as the baculovirus inhibitor of apoptosis repeat-containing protein 1 (BIRC1) gene, is a member of the inhibitors of apoptosis (IAP) family and was first characterized as a candidate gene for spinal muscular atrophy (SMA). The expression of NAIP has been thoroughly studied in the central nervous system and overlaps the pattern of neurodegeneration in SMA. Recent studies have pointed to a role for NAIP in non-neuronal cells. We report here the production of a specific anti-NAIP antibody and the profile of NAIP expression in human adult tissues by Western blot and immunohistochemical detection methods. NAIP was detected in a number of tissues by Western blot analysis, but immunohistochemistry revealed that NAIP's presence in certain tissues, such as liver, lung, and spleen, is most likely due to macrophage infiltration. In the small intestine, the expression of NAIP coincides with the expression of p21(WAF1). This observation, coupled with findings from other groups, suggests a role for NAIP in increasing the survival of cells undergoing terminal differentiation as well as the possibility that the protein serves as an intestinal pathogen recognition protein. This manuscript contains online supplemental material at http://www.jhc.org. Please visit this article online to view these materials.

The neuronal apoptosis inhibitory protein is a direct inhibitor of caspases 3 and 7.

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 approximately 14 nm for caspase-3 and approximately 45 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.