scholarly journals Fragment-based computational design of antibodies targeting structured epitopes

2021 ◽  
Author(s):  
Mauricio Aguilar Rangel ◽  
Alice Bedwell ◽  
Elisa Costanzi ◽  
Stefano Ricagno ◽  
Judith Frydman ◽  
...  
Keyword(s):  
De Novo ◽  
Computational Design ◽  
Computational Procedure ◽  
Affinity Maturation ◽  
Combinatorial Design ◽  
Starting Point ◽  
Antibody Discovery ◽  
Single Domain Antibodies ◽  
Rapid Generation

De novo design methods hold the promise of reducing the time and cost of antibody discovery, while enabling the facile and precise targeting of specific epitopes. Here we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterisation showed that all designs are highly stable, and bind their intended targets with affinities in the nanomolar range without any in vitro affinity maturation. We further show that a high-resolution input antigen structure is not required, as our method yields similar predictions when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides the starting point for the rapid generation of lead antibodies binding to pre-selected epitopes.

scholarly journals De novo design of self-assembling helical protein filaments

Science ◽  
2018 ◽  
Vol 362 (6415) ◽  
pp. 705-709 ◽  
Author(s):  
Hao Shen ◽  
Jorge A. Fallas ◽  
Eric Lynch ◽  
William Sheffler ◽  
Bradley Parry ◽  
...  
Keyword(s):  
De Novo ◽  
Computational Design ◽  
Building Blocks ◽  
Repeat Units ◽  
Self Assembling ◽  
Wide Range ◽  
Helical Protein ◽  
Monomeric Proteins

We describe a general computational approach to designing self-assembling helical filaments from monomeric proteins and use this approach to design proteins that assemble into micrometer-scale filaments with a wide range of geometries in vivo and in vitro. Cryo–electron microscopy structures of six designs are close to the computational design models. The filament building blocks are idealized repeat proteins, and thus the diameter of the filaments can be systematically tuned by varying the number of repeat units. The assembly and disassembly of the filaments can be controlled by engineered anchor and capping units built from monomers lacking one of the interaction surfaces. The ability to generate dynamic, highly ordered structures that span micrometers from protein monomers opens up possibilities for the fabrication of new multiscale metamaterials.

scholarly journals Development of High Affinity and High Specificity Inhibitors of Matrix Metalloproteinase 14 through Computational Design and Directed Evolution

2017 ◽  
Vol 292 (8) ◽  
pp. 3481-3495 ◽  
Author(s):  
Valeria Arkadash ◽  
Gal Yosef ◽  
Jason Shirian ◽  
Itay Cohen ◽  
Yuval Horev ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Surface Display ◽  
High Specificity ◽  
Computational Design ◽  
Computational Method ◽  
Yeast Surface Display ◽  
Mmp Inhibitor ◽  
Starting Point ◽  
Yeast Surface

Degradation of the extracellular matrices in the human body is controlled by matrix metalloproteinases (MMPs), a family of more than 20 homologous enzymes. Imbalance in MMP activity can result in many diseases, such as arthritis, cardiovascular diseases, neurological disorders, fibrosis, and cancers. Thus, MMPs present attractive targets for drug design and have been a focus for inhibitor design for as long as 3 decades. Yet, to date, all MMP inhibitors have failed in clinical trials because of their broad activity against numerous MMP family members and the serious side effects of the proposed treatment. In this study, we integrated a computational method and a yeast surface display technique to obtain highly specific inhibitors of MMP-14 by modifying the natural non-specific broad MMP inhibitor protein N-TIMP2 to interact optimally with MMP-14. We identified an N-TIMP2 mutant, with five mutations in its interface, that has an MMP-14 inhibition constant (Ki) of 0.9 pm, the strongest MMP-14 inhibitor reported so far. Compared with wild-type N-TIMP2, this variant displays ∼900-fold improved affinity toward MMP-14 and up to 16,000-fold greater specificity toward MMP-14 relative to other MMPs. In an in vitro and cell-based model of MMP-dependent breast cancer cellular invasiveness, this N-TIMP2 mutant acted as a functional inhibitor. Thus, our study demonstrates the enormous potential of a combined computational/directed evolution approach to protein engineering. Furthermore, it offers fundamental clues into the molecular basis of MMP regulation by N-TIMP2 and identifies a promising MMP-14 inhibitor as a starting point for the development of protein-based anticancer therapeutics.

scholarly journals A cell-free antibody engineering platform rapidly generates SARS-CoV-2 neutralizing antibodies

2020 ◽  
Author(s):  
Xun Chen ◽  
Matteo Gentili ◽  
Nir Hacohen ◽  
Aviv Regev
Keyword(s):  
Neutralizing Antibodies ◽  
Affinity Maturation ◽  
Antibody Engineering ◽  
Pseudotyped Virus ◽  
Free Antibody ◽  
A Cell ◽  
Heavy Chain Antibody ◽  
Rapid Generation ◽  
Unique Capability

AbstractAntibody engineering technologies face increasing demands for speed, reliability and scale. We developed CeVICA, a cell-free antibody engineering platform that integrates a novel generation method and design for camelid heavy-chain antibody VHH domain-based synthetic libraries, optimized in vitro selection based on ribosome display and a computational pipeline for binder prediction based on CDR-directed clustering. We applied CeVICA to engineer antibodies against the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike proteins and identified >800 predicted binder families. Among 14 experimentally-tested binders, 6 showed inhibition of pseudotyped virus infection. Antibody affinity maturation further increased binding affinity and potency of inhibition. Additionally, the unique capability of CeVICA for efficient and comprehensive binder prediction allowed retrospective validation of the fitness of our synthetic VHH library design and revealed direction for future refinement. CeVICA offers an integrated solution to rapid generation of divergent synthetic antibodies with tunable affinities in vitro and may serve as the basis for automated and highly parallel antibody generation.

scholarly journals Identification of Antimalarial Inhibitors Using Late-Stage Gametocytes in a Phenotypic Live/Dead Assay

2018 ◽  
Vol 24 (1) ◽  
pp. 38-46 ◽  
Author(s):  
Timothy P. Spicer ◽  
Donald L. Gardiner ◽  
Frank J. Schoenen ◽  
Sudeshna Roy ◽  
Patrick R. Griffin ◽  
...  
Keyword(s):  
De Novo ◽  
Infection Prevalence ◽  
Mosquito Vector ◽  
Original Activity ◽  
Small Molecule Drug ◽  
Starting Point ◽  
Specialized Equipment ◽  
Low Levels

Malaria remains a major cause of morbidity and mortality worldwide with ~3.3 billion people at risk of contracting malaria and an estimated 450,000 deaths each year. While tools to reduce the infection prevalence to low levels are currently under development, additional efforts will be required to interrupt transmission. Transmission between human host and vector by the malaria parasite involves gametogenesis in the host and uptake of gametocytes by the mosquito vector. This stage is a bottleneck for reproduction of the parasite, making it a target for small-molecule drug discovery. Targeting this stage, we used whole Plasmodium falciparum gametocytes from in vitro culture and implemented them into 1536-well plates to create a live/dead phenotypic antigametocyte assay. Using specialized equipment and upon further validation, we screened ~150,000 compounds from the NIH repository currently housed at Scripps Florida. We identified 100 primary screening hits that were tested for concentration response. Additional follow-up studies to determine specificity, potency, and increased efficacy of the antigametocyte candidate compounds resulted in a starting point for initial medicinal chemistry intervention. From this, 13 chemical analogs were subsequently tested as de novo powders, which confirmed original activity from the initial analysis and now provide a point of future engagement.

scholarly journals Computational design of a modular protein sense-response system

Science ◽  
2019 ◽  
Vol 366 (6468) ◽  
pp. 1024-1028 ◽  
Author(s):  
Anum A. Glasgow ◽  
Yao-Ming Huang ◽  
Daniel J. Mandell ◽  
Michael Thompson ◽  
Ryan Ritterson ◽  
...  
Keyword(s):  
De Novo ◽  
Protein Structures ◽  
Computational Design ◽  
Metabolic Intermediate ◽  
Protein Interfaces ◽  
Substantial Progress ◽  
Valuable Compounds ◽  
Protein Sensors ◽  
New Protein

Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site closely matches the design model. Our computational design strategy opens broad avenues to link biological outputs to new signals.

scholarly journals Computational design of genomic transcriptional networks with adaptation to varying environments

2012 ◽  
Vol 109 (38) ◽  
pp. 15277-15282 ◽  
Author(s):  
Javier Carrera ◽  
Santiago F. Elena ◽  
Alfonso Jaramillo
Keyword(s):  
De Novo ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Profiling ◽  
Computational Design ◽  
Computational Procedure ◽  
Transcriptional Networks ◽  
E Coli ◽  
Environmental Perturbations ◽  
Transcriptional Regulatory ◽  
Varying Environments

Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome. Combining transcriptomic and signaling data, we develop an evolutionary computational procedure that allows obtaining alternative genomic transcriptional regulatory network (GTRN) that still maintains its adaptability to dynamic environments. We apply our methodology to an E. coli GTRN and show that it could be rewired to simpler transcriptional regulatory structures. These rewired GTRNs still maintain the global physiological response to fluctuating environments. Rewired GTRNs contain 73% fewer regulated operons. Genes with similar functions and coordinated patterns of expression across environments are clustered into longer regulated operons. These synthetic GTRNs are more sensitive and show a more robust response to challenging environments. This result illustrates that the natural configuration of E. coli GTRN does not necessarily result from selection for robustness to environmental perturbations, but that evolutionary contingencies may have been important as well. We also discuss the limitations of our methodology in the context of the demand theory. Our procedure will be useful as a novel way to analyze global transcription regulation networks and in synthetic biology for the de novo design of genomes.

scholarly journals A cell-free nanobody engineering platform rapidly generates SARS-CoV-2 neutralizing nanobodies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xun Chen ◽  
Matteo Gentili ◽  
Nir Hacohen ◽  
Aviv Regev
Keyword(s):  
In Vitro Selection ◽  
Affinity Maturation ◽  
Antibody Engineering ◽  
Pseudotyped Virus ◽  
Receptor Binding Domain ◽  
Computational Pipeline ◽  
A Cell ◽  
Rapid Generation ◽  
Selection Of

AbstractAntibody engineering technologies face increasing demands for speed, reliability and scale. We develop CeVICA, a cell-free nanobody engineering platform that uses ribosome display for in vitro selection of nanobodies from a library of 1011 randomized sequences. We apply CeVICA to engineer nanobodies against the Receptor Binding Domain (RBD) of SARS-CoV-2 spike protein and identify >800 binder families using a computational pipeline based on CDR-directed clustering. Among 38 experimentally-tested families, 30 are true RBD binders and 11 inhibit SARS-CoV-2 pseudotyped virus infection. Affinity maturation and multivalency engineering increase nanobody binding affinity and yield a virus neutralizer with picomolar IC50. Furthermore, the capability of CeVICA for comprehensive binder prediction allows us to validate the fitness of our nanobody library. CeVICA offers an integrated solution for rapid generation of divergent synthetic nanobodies with tunable affinities in vitro and may serve as the basis for automated and highly parallel nanobody engineering.

De novo design of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike protein

2020 ◽  
Author(s):  
Veda Sheersh Boorla ◽  
Ratul Chowdhury ◽  
Costas D. Maranas
Keyword(s):  
Viral Entry ◽  
De Novo ◽  
Point Mutations ◽  
Software Tool ◽  
Computational Design ◽  
Affinity Maturation ◽  
Binding Energies ◽  
Spike Protein ◽  
Variable Regions ◽  
High Affinity

AbstractThe emergence of SARS-CoV-2 is responsible for the pandemic of respiratory disease known as COVID-19, which emerged in the city of Wuhan, Hubei province, China in late 2019. Both vaccines and targeted therapeutics for treatment of this disease are currently lacking. Viral entry requires binding of the viral spike receptor binding domain (RBD) with the human angiotensin converting enzyme (hACE2). In an earlier paper1, we report on the specific residue interactions underpinning this event. Here we report on the de novo computational design of high affinity antibody variable regions through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike protein using the software tool OptMAVEn-2.02. Subsequently, we carry out computational affinity maturation of the designed prototype variable regions through point mutations for improved binding with the target epitope. Immunogenicity was restricted by preferring designs that match sequences from a 9-mer library of “human antibodies” based on H-score (human string content, HSC)3. We generated 106 different designs and report in detail on the top five that trade-off the greatest affinity for the spike RBD epitope (quantified using the Rosetta binding energies) with low H-scores. By grafting the designed Heavy (VH) and Light (VL) chain variable regions onto a human framework (Fc), high-affinity and potentially neutralizing full-length monoclonal antibodies (mAb) can be constructed. Having a potent antibody that can recognize the viral spike protein with high affinity would be enabling for both the design of sensitive SARS-CoV-2 detection devices and for their deployment as therapeutic antibodies.

De novo design of ACE2 protein decoys to neutralize SARS-CoV-2

2020 ◽  
Author(s):  
Thomas W. Linsky ◽  
Renan Vergara ◽  
Nuria Codina ◽  
Jorgen W. Nelson ◽  
Matthew J. Walker ◽  
...  
Keyword(s):  
De Novo ◽  
Rapid Development ◽  
Computational Design ◽  
Design Strategy ◽  
De Novo Design ◽  
Surface Proteins ◽  
Receptor Binding Domain ◽  
Angiotensin Converting Enzyme 2 ◽  
Designed Proteins

AbstractThere is an urgent need for the ability to rapidly develop effective countermeasures for emerging biological threats, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. We have developed a generalized computational design strategy to rapidly engineer de novo proteins that precisely recapitulate the protein surface targeted by biological agents, like viruses, to gain entry into cells. The designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape. Using our novel platform, in less than ten weeks, we engineered, validated, and optimized de novo protein decoys of human angiotensin-converting enzyme 2 (hACE2), the membrane-associated protein that SARS-CoV-2 exploits to infect cells. Our optimized designs are hyperstable de novo proteins (∼18-37 kDa), have high affinity for the SARS-CoV-2 receptor binding domain (RBD) and can potently inhibit the virus infection and replication in vitro. Future refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.

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