Computational de novo design of antibodies binding to a peptide with high affinity

Poosarla, Venkata Giridhar; Li, Tong; Goh, Boon Chong; Schulten, Klaus; Wood, Thomas K.; Maranas, Costas D.

doi:10.1002/bit.26244

Cited by 27 publications

(17 citation statements)

References 42 publications

(55 reference statements)

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“…Then the heavy-atom RMSD of the antigen residues within the interface was computed (Figure 6). RMSDs of the native complex and 5 gzn_R0 were similar and remained below 6 Å in every frame examined, indicating that these complexes were stable throughout the entire simulations, according to a previous definition of stable binding by Poosarla et al [52]. RMSD of 5gzn_R27 exceeded 6 Å but did not exceed 12 Å, indicating that the antigen remained partially bound [52].…”

Section: Resultssupporting

confidence: 54%

OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes

2018

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Monoclonal antibodies are becoming increasingly important therapeutic agents for the treatment of cancers, infectious diseases, and autoimmune disorders. However, laboratory-based methods of developing therapeutic monoclonal antibodies (e.g., immunized mice, hybridomas, and phage display) are time-consuming and are often unable to target a specific antigen epitope or reach (sub)nanomolar levels of affinity. To this end, we developed Optimal Method for Antibody Variable region Engineering (OptMAVEn) for de novo design of humanized monoclonal antibody variable regions targeting a specific antigen epitope. In this work, we introduce OptMAVEn-2.0, which improves upon OptMAVEn by (1) reducing computational resource requirements without compromising design quality; (2) clustering the designs to better identify high-affinity antibodies; and (3) eliminating intra-antibody steric clashes using an updated set of clashing parts from the Modular Antibody Parts (MAPs) database. Benchmarking on a set of 10 antigens revealed that OptMAVEn-2.0 uses an average of 74% less CPU time and 84% less disk storage relative to OptMAVEn. Testing on 54 additional antigens revealed that computational resource requirements of OptMAVEn-2.0 scale only sub-linearly with respect to antigen size. OptMAVEn-2.0 was used to design and rank variable antibody fragments targeting five epitopes of Zika envelope protein and three of hen egg white lysozyme. Among the top five ranked designs for each epitope, recovery of native residue identities is typically 45–65%. MD simulations of two designs targeting Zika suggest that at least one would bind with high affinity. OptMAVEn-2.0 can be downloaded from our GitHub repository and webpage as (links in Summary and Discussion section).

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Section: Resultssupporting

confidence: 54%

OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes

2018

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“…Consequently, the sequence of scFv antibody against fusion loop of DENV was created from a pre-existing antibody by homology modelling. Previously, a novel anti-TNF scFv was constructed from human antibody frameworks and antagonistic peptides [25] suggesting a common approach to enhance the specificity and binding efficiency of scFv [26–29]. Interaction analysis between scFv and DENV envelope protein showed a stable interaction which assured that after conversion from Fab to scFv fragment, scFv antibody retained the binding property of the original antibody [30].…”

Section: Discussionmentioning

confidence: 99%

Designing antibody against highly conserved region of dengue envelope protein by in silico screening of scFv mutant library

2019

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Dengue being one of the deadliest diseases of tropical regions, enforces to put continuous efforts for the development of vaccine and effective therapeutics. Most of the antibodies generated during dengue infection are non-neutralizing and cause antibody dependent enhancement. Hence, making a potent neutralizing antibody against all four dengue serotypes could be very effective for the treatment. However, designing a single antibody for all serotypes is difficult due to variation in protein sequences. Therefore, the objective is to identify conserved region of dengue envelope protein and then develop an antibody against that conserved region. Before advancing to the development of such an antibody, it is desirable to validate the interactions between antibody and dengue envelope protein. In silico analysis of such interactions provides a good platform to find out a suitable region to design and construct an antibody against it by analyzing antigen-antibody interaction before synthesizing the antibody. In this study, two highly conserved regions of dengue envelope protein were identified and an scFv was constructed against it. Both scFv and FuBc proteins were expressed in bacterial expression system and binding efficiency was analyzed by SPR analysis with KD value 2.3 μM. In order to improve binding efficiency, an in silico scFv mutant library was created which was virtually screened for higher binding efficiency. Six mutants with high binding efficiency were selected for further analysis. The binding ability of these mutants were predicted using simulation analysis which shows these mutations were stabilizing scFv-FuBc complex.

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“…We next used the previously developed OptMAVEn-2.0 2 software to computationally identify the combination of VDJ genes forming the variable region that best binds the desired epitope. OptMAVEn 20 has been used before successfully to design five high affinity CDRs against a FLAG tetrapeptide 21 , three thermally and conformationally stable antibody variable regions (sharing less than 75% sequence similarity to any naturally occurring antibody sequence) against a dodecapeptide mimic of carbohydrates 22 and two thermostable, high affinity variable heavy chain domains (V H H) against α-synuclein peptide responsible for Parkinson’s disease 23 . All these designs were experimentally constructed and nanomolar affinities for their respective target antigens was demonstrated.…”

Section: Mainmentioning

confidence: 99%

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

Boorla

Chowdhury

Maranas

2020

Preprint

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1The emergence of SARS-CoV-2 is responsible for the pandemic of respiratory disease known as COVID-1 2 19, which emerged in the city of Wuhan, Hubei province, China in late 2019. Both vaccines and targeted 1 3 therapeutics for treatment of this disease are currently lacking. Viral entry requires binding of the viral 1 4 spike receptor binding domain (RBD) with the human angiotensin converting enzyme (ACE2). In an 1 5 3 1 (Cellex, GeneTex etc.) for the development of antibody-based tests for SARS-CoV-2 detection are 3 2 ongoing 8 . At the same time, significant progress towards the isolation and design of vaccines (mRNA-3 3 1273 vaccine © 2020 Moderna, Inc) and neutralizing antibodies 9 has been made. A computational study : bioRxiv preprint identified the structural basis for multi-epitope vaccines 10,11 whereas in another study, the glycosylation 3 5 patterns of the spike SARS-CoV-2 protein were computationally deduced 12 . In one study 13 , fully human 3 6 single domain anti-SARS-CoV-2 antibodies with sub-nanomolar affinities were identified from a phage-3 7 displayed single-domain antibody library by grafting naïve CDRs into framework regions of an identified 3 8 human germline IGHV allele using SARS-CoV-2 RBD and S1 protein as antigens. In another study 14 , a 3 9 human antibody 47D11 was identified to have cross neutralizing effect on SARS-CoV-2 by screening 4 0 through a library of SARS-Cov-1 antibodies. In two other studies, potent neutralizing antibodies were 4 1 isolated from the sera of convalescent COVID-19 patients 15,16 . To the best of our knowledge, none of 4 2 these neutralizing antibody sequences are publicly available. In another very encouraging study 17 , human 4 3 antibody CR3022 (which is neutralizing against SARS-CoV-1 18 ) has been shown to bind to SARS-CoV-2 4 4 RBD in a cryptic epitope but without a neutralizing effect for SARS-CoV-2 in vitro. Moreover, we did 4 5 not find any studies that performed guided design of high affinity antibodies against specific epitopes of 4 6 SARS-CoV-2 proteins such as targeting the spike protein and subsequently prevent its binding with 4 7 human ACE2. 8 9Motivated by these shortcomings, here we explore the de novo design of antibody variable regions For each one of the 3,234 spike poses, OptMAVEn-2.0 identified a variable region combination 9 3 composed of end-to-end joined V*, CDR3, and J* region parts that minimized the Rosetta binding energy 9 4 between the variable region and spike epitope formed by the seven residues. As part of OptMAVEn-2.0, 9 5 the combinatorial optimization problem was posed and solved as a mixed-integer linear programming 9 6 (MILP) problem using the cplex solver 26 . The solution of this problem identifies, for each one of the spike 9 7 poses, the complete design of the variable region using parts denoted as HV*, HCDR3, HJ* for the heavy 9 8 chain H and L/KV*, L/KCDR3 and L/KJ* for the light chain-L/K. Only 173 antigen-presenting poses out 9 9 of 3,234 explored, yielded non-clashing antigen-antibody complexes. These 173 poses ...

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Computational de novo design of antibodies binding to a peptide with high affinity

Cited by 27 publications

References 42 publications

OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes

OptMAVEn-2.0: De novo Design of Variable Antibody Regions Against Targeted Antigen Epitopes

Designing antibody against highly conserved region of dengue envelope protein by in silico screening of scFv mutant library

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

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