'Reverse Fingerprinting' is a method that uses feature list fingerprints to detect differentiating structural elements in small molecule and protein datasets. This talk covers the theory of reverse fingerprinting and presents examples of its application to detecting important structural motifs, coloring atoms by activity contribution, generating 3D pharmacophore queries and identifying liability regions in protein structures.

There is considerable enthusiasm in the pharmaceutical industry towards antibody-based biotherapeutics because they can bind diverse receptors highly selectively and often possess desirable pharmacology. Here, we have reviewed all approved antibody-based biotherapeutics from 1986 to Mid-2020 by gathering a wide range of public domain information on 89 marketed antibody-based biotherapeutics. Our analyses has documented ‘coming of age’ for therapeutic antibodies by revealing major trends in their establishment as the best-selling class of pharmaceuticals. For example, all therapeutic antibodies approved recently are either humanized or fully human, while the earlier ones were mostly chimeric. Before 2010, most therapeutic mAbs were developed to treat cancer and focused on merely fifteen receptors, with CD20 being the most common one. Over the next decade (2010-present), thanks to industrialization of antibody manufacturing technologies, their usage has blossomed to 38 additional targets distributed over 15 different therapeutic areas. Drug developers appear to be solidifying their choices in regard to source of antibodies, formulation, and routes of administration. The drivers for these trends are (a) minimize adverse effects such as immunogenicity; (b) improve the drug efficacy and pharmacology; and (c) improve patient convenience for greater compliance. However, humanized or fully human antibodies are merely 3% less likely to generate ADAs than chimeric ones. In spite of high concentration and greater likelihood for aggregation in vivo because of small volumes, the subcutaneous route of administration is ~4% less likely to generate ADAs than the intravenous one. These learnings should help us develop improved novel biotherapeutics.

Monoclonal antibodies play an increasingly important role for the development of new drugs across multiple therapy areas. The term ‘developability’ encompasses the feasibility of molecules to successfully progress from discovery to development via evaluation of their physicochemical properties. These properties include the tendency for self-interaction and aggregation, thermal stability, colloidal stability, and optimization of their properties through sequence engineering. Selection of the best antibody molecule based on biological function, efficacy, safety, and developability allows for a streamlined and successful CMC phase. An efficient and practical high-throughput developability workflow (100 s-1,000 s of molecules) implemented during early antibody generation and screening is crucial to select the best lead candidates. This involves careful assessment of critical developability parameters, combined with binding affinity and biological properties evaluation using small amounts of purified material (<1 mg), as well as an efficient data management and database system. Herein, a panel of 152 various human or humanized monoclonal antibodies was analyzed in biophysical property assays. Correlations between assays for different sets of properties were established. We demonstrated in two case studies that physicochemical properties and key assay endpoints correlate with key downstream process parameters. The workflow allows the elimination of antibodies with suboptimal properties and a rank ordering of molecules for further evaluation early in the candidate selection process. This enables any further engineering for problematic sequence attributes without affecting program timelines.

Studies of protein-protein interactions by molecular docking has been extensively reported. It remains one of the focal points of activity in computational biophysics and structural biology.[1, 2]

In this research, MOE protein design and protein-protein docking protocols were employed to explore potential site-specific mutations on the contact surfaces of two subunits of a ternary complex to enhance the subunit interactions. To do this, we first screened the possible contact residues of each subunit by single mutation, then in addition to those mutations forming more favorable interactions, we generated possible double mutations and then triple mutations of each subunit in MOE. A set of unique novel designs was suggested by the procedure. They were all validated by protein-protein docking; three of the designs showed enhanced interactions in terms of the docking score compared with that of the wild type, and thus were suggested for experimental assay.

The first significance of this work is in the exploration of computational power for thorough screening of single, double, and triple mutations, and prioritizing the list of the novel designs. The second is that the selected designs can be further ranked by protein-protein docking, and that docking scores as well as interaction analysis can be key factors for decision making for experimental assay.

Selected References:
1. Pozzati, G., P. Kundrotas, and A. Elofsson, Scoring of protein-protein docking models utilizing predicted interface residues. Proteins, 2022.
2. Sunny, S. and P.B. Jayaraj, Protein-Protein Docking: Past, Present, and Future. Protein J, 2022. 41(1): p. 1-26.

The MOE Antibody Database contains the three-dimensional (3D) structures of antibodies and antigen-antibody complexes derived from the Protein Data Bank (PDB) and provides powerful custom features enabling structural bioinformatics analysis useful for antibody research. We recently used the MOE Antibody Database to identify and analyze the 3D structures of antibodies and complexes that contain disulfide motifs in their CDR-H3s. We present the discovery of disulfide-bonded motifs (CXnC) in the CDR-H3s from the human antibody repertoire sequence data and compared them with structurally known motifs found in antibody structures. These results provide the fundamental understanding on the role of non-canonical cysteines in shaping the complex paratope diversity in human antibodies with potential applications in antibody design and engineering (Prabakaran and Chowdhury, Cell Reports 2020).

In this work, we present a method for modeling antibodies and performing pH-dependent conformational sampling, which can enhance property calculations. Structure-based charge descriptors are evaluated for their predictive performance on recently published antibody pI, viscosity, and clearance data. From this, we devised four rules for therapeutic antibody profiling which address developability issues arising from hydrophobicity and charged-based solution behavior, PK, and the ability to enrich for those that are approved by the U.S. Food and Drug Administration. Differences in strategy for optimizing the solution behavior of human IgG1 antibodies versus the IgG2 and IgG4 isotypes and the impact of pH alterations in formulation are discussed.

Given the high attrition rate of preclinically potent compounds as they progress through clinical trials, the industry is constantly seeking more relevant and more predictive preclinical indicators to discriminate successful from unsuccessful compounds. This applies to all classes of compounds, yet certain modalities such as protein based therapeutics have additional challenges due to the relative dearth of predictive in vitro and in vivo preclinical systems. The difficulty in obtaining and interpreting benchmark clinical data further complicates matters, as understanding why a compound progresses or fails to do so, and what its specific clinical properties are, is a key element in determining which preclinical parameters are relevant and meaningful.

We curated clinical pharmacokinetic data for 64 monoclonal antibodies to calculate linear clearance values, as low clearance is one property that meaningfully impacts a drug candidate’s probability of approval and commercial success. We used statistical analysis and a Random Forest classifier to identify properties that best enrich for slow clearance, from a set of 40 in silico and in vitro properties, identifying poly-specificity and calculated pI as the most useful discriminators in our dataset. Others have reported results qualitatively similar to and disparate from ours. To resolve these discrepancies, and to extend the approach to additional properties, it is necessary to continually collect and analyze more data, both retrospectively and prospectively. To that end, we have implemented an automated pipeline to calculate 250 different in silico sequence and structure based properties for antibodies and nanobodies from input sequences. These are continually compared to experimental data for correlations; used to generate machine learning models; and visualized by expert users on 3D models with regard to their diversity and developability properties, such as liabilities, PTMs, immunogenicity risks, PK properties and compatibilities with formulations. Over time, this approach should help to systematically identify the most important preclinical indicators for clinical success.

Bispecific antibodies (Bispecifics) demonstrate exceptional clinical potential to address some of the most complex diseases. However, Bispecific production in a single cell often requires the correct pairing of multiple polypeptide chains for desired assembly. This is a considerable hurdle that hinders the development ofmany immunoglobulin G (IgG)-like bispecific formats. Our approach focuses on the rational engineering of charged residues to facilitate the chain pairing of distinct heavy chains (HC). Here, we deploy structure-guided protein design to engineer charge pair mutations (CPMs) placed in the CH3-CH30 interface of the fragment crystallizable (Fc) region of an antibody (Ab) to correctly steer heavy chain pairing. When used in combination with our stable effector functionless 2 (SEFL2.2) technology, we observed high pairing efficiency without significant losses in expression yields. Furthermore, we investigate the relationship between CPMs and the sequence diversity in the parental antibodies, proposing a rational strategy to deploy these engineering technologies.