Computational design of protein therapeutics

Computational avenues in oral protein and peptide therapeutics

Proteins and peptides are amongst the most sought-after biomolecules because of their exceptional potential to cater to a vast range of diseases. Although widely studied and researched, the oral delivery of these biomolecules remains a challenge. Alongside formulation strategies, approaches to overcome the inherent barriers for peptide absorption are being designed at the molecular level to establish a sound rationale and to achieve higher bioavailability. Computer-aided drug design (CADD) is a modern in silico approach for developing successful bio-formulations. CADD enables intricate study of the biomolecules in conjunction with their target sites or receptors at the molecular level. Knowledge of the molecular interactions of proteins and peptides makes way for the pre-screening of suitable formulation components and facilitates their delivery.

High-Throughput Generation of In Silico Derived Synthetic Antibodies via One-step Enzymatic DNA Assembly of Fragments

Phage-display technology offers robust methods for isolating antibody (Ab) molecules with specificity for different target antigens. Recent advancements couple Ab selections with in silico strategies, such as predictive computational models or next-generation sequencing metadata analysis of Ab selections. These advancements result in enhanced Ab clonal diversities with potential for enlarged epitope coverage of the target antigen. A current limitation however, is that de novo Ab sequences must undergo DNA gene synthesis, and subsequent expression as Ab proteins for downstream validations. Due to the high costs and time for commercially generating large sets of DNA genes, we report a high-throughput platform for the synthesis of in silico derived Ab clones. As a proof of concept we demonstrate the simultaneous synthesis of 96 unique Abs with varied lengths and complementary determining region compositions. Each of the 96 Ab clones undergoes a one-step enzymatic assembly of distinct DNA fragments that combine into a circularized Fab expression plasmid. This strategy allows for the rapid and efficient synthesis of 96 DNA constructs in a 3 day window, and exhibits high percentage fidelity-greater than 93%. Accordingly, the synthesis of Ab DNA constructs as Fab expression plasmids allow for rapid execution of downstream Ab protein validations, with potential for implementation into high-throughput Ab protein characterization pipelines. Altogether, the platform presented here proves rapid and also cost-effective, which is important for labs with limited resources, since it utilizes standard laboratory equipment and molecular reagents.

Characterization and In Silico Analysis of The Structural Features of G-CSF Derived from Lysates of Escherichia coli

Objective

Granulocyte colony-stimulating factor (G-CSF) has a wide variety of functions including stimulation of hematopoiesis and proliferation of granulocyte progenitor cells. Recombinant human G-CSF (rh-G-CSF) is used for treatment of neutropenia in patients receiving chemotherapy. The mature bloodstream neutrophils express G-CSF receptor (G-CSFR), presenting a significant and specific mechanism for circulating G-CSF clearance. Computational studies are essential bioinformatics methods used for characterization of proteins with regard to their physicochemical properties and 3D configuration, as well as protein–ligand interactions for recombinant drugs. We formerly produced rh-G-CSF in E. coli and showed that the isolated protein had unacceptable biological activity in mice. In the present paper, we aimed to characterize the purified rh-G-CSF by analytical tests and developed an in vivo model by computational modelling of G-CSF.

Materials and Methods

In this experimental study, we analyzed the purified G-CSF using the analytical experiments. Then, the crystalline structure was extracted from Protein Data Bank (PDB) and molecular dynamics (MD) simulation was performed using Gromacs 5.1 package under an Amber force field. The importance of amino acid contents of G-CSF, to bind the respective receptor was also detected; moreover, the effect of dithiothreitol (DTT) used in G-CSF purification was studied.

Results

The results revealed that characteristics of the produced recombinant G-CSF were comparable with those of the standard G-CSF and the recombinant G-CSF with the residual amino acid was stable. Also, purification conditions (DTT and existence of extra cysteine) had a significant effect on the stability and functionality of the produced G-CSF.

Conclusion

Experimental and in silico analyses provided good information regarding the function and characteristics of our recombinant G-CSF which could be useful for industrial researches.

A High-Throughput Platform for the Generation of Synthetic Ab Clones by Single-Strand Site-Directed Mutagenesis

Current developments in meta-data analysis and predictive computational models offer alternative routes for the identification of antibodies. In silico-based technologies and NGS data analysis from Ab phage-display selections offer expanded selections of Ab candidates. Accordingly, the identified de novo Abs with predicted selectivity for a target antigen must undergo rapid gene synthesis for downstream Ab characterizations. Here we describe a high-throughput strategy for the generation of synthetic Ab clones for expression as Fab proteins in Escherichia coli. Our approach utilizes simultaneous single-stranded site-directed mutagenesis of diversified Ab regions of a phagemid template with engineered complementary determining regions that contain multiple stop codon and restriction enzyme sites. Subsequently, we perform rapid screening of Ab DNA clones for correct gene assemblies by high-throughput Ab-phage protein expression screens. Identified sequences are corroborated by Sanger DNA sequencing analysis. In summary, our work describes a rapid and cost-effective platform for the high-throughput synthesis of synthetic Ab genes as Fab proteins for implementation into downstream protein validation pipelines.

ProtDataTherm: A database for thermostability analysis and engineering of proteins

Protein thermostability engineering is a powerful tool to improve resistance of proteins against high temperatures and thereafter broaden their applications. For efficient protein thermostability engineering, different thermostability-classified data sources including sequences and 3D structures are needed for different protein families. However, no data source is available providing such data easily. It is the first release of ProtDataTherm database for analysis and engineering of protein thermostability which contains more than 14 million protein sequences categorized based on their thermal stability and protein family. This database contains data needed for better understanding protein thermostability and stability engineering. Providing categorized protein sequences and structures as psychrophilic, mesophilic and thermophilic makes this database useful for the development of new tools in protein stability prediction. This database is available at http://profiles.bs.ipm.ir/softwares/protdatatherm. As a proof of concept, the thermostability that improves mutations were suggested for one sample protein belonging to one of protein families with more than 20 mesophilic and thermophilic sequences and with known experimentally measured ΔT of mutations available within ProTherm database.

The Framework of Computational Protein Design

Computational protein design (CPD) has established itself as a leading field in basic and applied science with a strong coupling between the two. Proteins are computationally designed from the level of amino acids to the level of a functional protein complex. Design targets range from increased thermo- (or other) stability to specific requested reactions such as protein-protein binding, enzymatic reactions, or nanotechnology applications. The design scheme may encompass small regions of the proteins or the entire protein. In either case, the design may aim at the side-chains or at the full backbone conformation. Herein, the main framework for the process is outlined highlighting key elements in the CPD iterative cycle. These include the very definition of CPD, the diverse goals of CPD, components of the CPD protocol, methods for searching sequence and structure space, scoring functions, and augmenting the CPD with other optimization tools. Taken together, this chapter aims to introduce the framework of CPD.

Immunogenicity of Biotherapeutics: Causes and Association with Posttranslational Modifications

Today, potential immunogenicity can be better evaluated during the drug development process, and we have rational approaches to manage the clinical consequences of immunogenicity. The focus of the scientific community should be on developing sensitive diagnostics that can predict immunogenicity-mediated adverse events in the small fraction of subjects that develop clinically relevant anti-drug antibodies. Here, we discuss the causes of immunogenicity which could be product-related (inherent property of the product or might be picked up during the manufacturing process), patient-related (genetic profile or eating habits), or linked to the route of administration. We describe various posttranslational modifications (PTMs) and how they may influence immunogenicity. Over the last three decades, we have significantly improved our understanding about the types of PTMs of biotherapeutic proteins and their association with immunogenicity. It is also now clear that all PTMs do not lead to clinical immunogenicity. We also discuss the mechanisms of immunogenicity (which include T cell-dependent and T cell-independent responses) and immunological tolerance. We further elaborate on the management of immunogenicity in preclinical and clinical setting and the unique challenges raised by biosimilars, which may have different immunogenic potential from their parent biotherapeutics.

Protein thermostability engineering

Using structure and sequence based analysis we can engineer proteins to increase their thermal stability.

Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

The scoring of poses in protein-protein docking: current capabilities and future directions

BACKGROUND

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.

RESULTS

We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically.

CONCLUSIONS

All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

Intermolecular Contact Potentials for Protein-Protein Interactions Extracted from Binding Free Energy Changes upon Mutation

Understanding and predicting the energetics of protein-protein interactions is fundamental to the structural modeling of protein complexes. Binding free energy can be approximated as a sum of pairwise atomic or residue contact energies, which are commonly inferred from contact frequencies observed in experimental protein structures. However, such statistically inferred potentials require certain assumptions and approximation. Here, we explore the possibility of deriving atomic and residue contact potentials directly from experimental binding free energy changes following mutation and present a number of such potentials. The first set of potentials is obtained by unweighted least-squares fitting and bootsrap aggregating. The second set is calculated using a weighting scheme optimized against absolute binding affinity data, so as to account for the over-representation of certain complexes, residues, and families of interactions. The congruence of the potentials with known physical chemistry is investigated. The potentials are further validated by ranking and clustering protein-protein docking poses.

Towards structure-based protein drug design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.