In Silico Methods in Antibody Design

Zhao, Jun; Nussinov, Ruth; Wu, Wenjin; Ma, Buyong

doi:10.3390/antib7030022

Cited by 32 publications

(34 citation statements)

References 135 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, because of recent advances in computational power and algorithms, computational design is becoming an alternative method in antibody engineering. [13][14][15][16][17][18] One of the advantages to computational methods is that their use can be a rational approach when combined with a structure. Antibodies and their structures should be governed by physical laws, and based on physical principles, we should be able to predict behavior of antibodies in solution and in our body.…”

Section: Introductionmentioning

confidence: 99%

Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design

Kuroda

Tsumoto

2020

Journal of Pharmaceutical Sciences

View full text Add to dashboard Cite

In recent years, computational methods have garnered much attention in protein engineering. A large number of computational methods have been developed to analyze the sequences and structures of proteins and have been used to predict the various properties. Antibodies are one of the emergent protein therapeutics, and thus, methods to control their physicochemical properties are highly desirable. However, despite the tremendous efforts of past decades, computational methods to predict the physicochemical properties of antibodies are still in their infancy. Experimental validations are certainly required for real-world applications, and the results should be interpreted with caution. Among the various properties of antibodies, we focus in this review on stability, viscosity, and immunogenicity, and we present the current status of computational methods to engineer such properties.

show abstract

Section: Introductionmentioning

confidence: 99%

Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design

Kuroda

Tsumoto

2020

Journal of Pharmaceutical Sciences

View full text Add to dashboard Cite

show abstract

“…Antibody therapies with high functional efficiency and potential low toxicity are becoming one of the major approaches in mAb therapeutics [ 47 ]. Based on high-throughput sequencing and increased availability of experimental structures of antibodies/antibody-antigen complexes, computational approaches are now being used to predict antibody/antigen structures, engineer the function of antibodies, and design antibody–antigen complexes with improved properties [ 47 ]. However, the field of in silico prediction of adverse effects in humans from mAb structures is still in early stages.…”

Section: Mab Structure and Predictive Safety/toxicitymentioning

confidence: 99%

“…Large datasets are used to derive and form the basis of accuracy for the predictions, which could include toxicities ranging from hepatotoxicity to genotoxicity and potential carcinogenicity. Zhao and colleagues [ 47 ] summarized recent progress in the field of in silico design of antibodies, including antibody structure modeling, antibody–antigen complex prediction, and antibody stability evaluation. Allosteric effects in antibodies and functions force fields are becoming more accurate to model molecular behaviors, especially local rearrangement.…”

Section: Mab Structure and Predictive Safety/toxicitymentioning

confidence: 99%

“…Allosteric effects in antibodies and functions force fields are becoming more accurate to model molecular behaviors, especially local rearrangement. Thus, Zhao and colleagues [ 47 ] reported that in silico molecular modeling techniques are becoming more popular to engineer antibodies such as Fc-based antibody domains and fragments [ 48 ], disulfide bonds [ 49 ], and T-cell receptor (TCR) mimic antibodies [ 50 ] with desired properties. These properties include viscosity and phase separation [ 51 ].…”

Section: Mab Structure and Predictive Safety/toxicitymentioning

confidence: 99%

“…These properties include viscosity and phase separation [ 51 ]. They also suggested that an alternative computational approach to physical modeling is the knowledge-based residue pair preference on epitope–paratope interfaces [ 47 ]. With the increasing availability of crystal structures of antibody–antigen complexes in the Protein Data Bank (PDB), a statistical amino acid interaction preference matrix can be used to predict the antibody–antigen recognition [ 47 ].…”

Section: Mab Structure and Predictive Safety/toxicitymentioning

confidence: 99%

See 2 more Smart Citations

Biotherapeutics: Challenges and Opportunities for Predictive Toxicology of Monoclonal Antibodies

Johnson

2018

IJMS

View full text Add to dashboard Cite

Biotherapeutics are a rapidly growing portion of the total pharmaceutical market accounting for almost one-half of recent new drug approvals. A major portion of these approvals each year are monoclonal antibodies (mAbs). During development, non-clinical pharmacology and toxicology testing of mAbs differs from that done with chemical entities since these biotherapeutics are derived from a biological source and therefore the animal models must share the same epitopes (targets) as humans to elicit a pharmacological response. Mechanisms of toxicity of mAbs are both pharmacological and non-pharmacological in nature; however, standard in silico predictive toxicological methods used in research and development of chemical entities currently do not apply to these biotherapeutics. Challenges and potential opportunities exist for new methodologies to provide a more predictive program to assess and monitor potential adverse drug reactions of mAbs for specific patients before and during clinical trials and after market approval.

show abstract

From monomer to fibril: Abeta‐amyloid binding to Aducanumab antibody studied by molecular dynamics simulation

Frost

Zacharias

2020

Proteins

View full text Add to dashboard Cite

Alzheimer's disease is one of the most common causes of dementia. It is believed that the aggregation of short Aβ-peptides to form oligomeric and protofibrillar amyloid assemblies plays a central role for disease-relevant neurotoxicity. In recent years, passive immunotherapy has been introduced as a potential treatment strategy with anti-amyloid antibodies binding to Aβ-amyloids and inducing their subsequent degradation by the immune system. Although so far mostly unsuccessful in clinical studies, the high-dosed application of the monoclonal antibody Aducanumab has shown therapeutic potential that might be attributed to its much greater affinity to Aβ-aggregates vs monomeric Aβ-peptides. In order to better understand how Aducanumab interacts with aggregated Aβ-forms compared to monomers, we have generated structural model complexes based on the known structure of Aducanumab in complex with an Aβ 2 − 7-eptitope. Structural models of Aducanumab bound to full-sequence Aβ 1 − 40-monomers, oligomers, protofilaments and mature fibrils were generated and investigated using extensive molecular dynamics simulations to characterize the flexibility and possible additional interactions. Indeed, an aggregate-specific N-terminal binding motif was found in case of Aducanumab binding to oligomers, protofilaments and fibrils that is located next to but not overlapping with the epitope binding site found in the crystal structure with Aβ 2 − 7. Analysis of binding energetics indicates that this motif binds weaker than the epitope but likely contributes to Aducanumab's preference for aggregated Aβspecies. The predicted aggregate-specific binding motif could potentially serve as a basis to reengineer Aducanumab for further enhanced preference to bind Aβaggregates vs monomers.

show abstract

In Silico Methods in Antibody Design

Cited by 32 publications

References 135 publications

Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design

Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design

Biotherapeutics: Challenges and Opportunities for Predictive Toxicology of Monoclonal Antibodies

From monomer to fibril: Abeta‐amyloid binding to Aducanumab antibody studied by molecular dynamics simulation

Contact Info

Product

Resources

About