Computational design and modeling of nanobodies toward SARS‐CoV‐2 receptor binding domain

Yang, Jingyi; Zhang, Zhao; Yang, Fengyuan; Zhang, Haiwei; Wu, Haibo; Zhu, Feng; Xue, Weiwei

The ongoing pandemic of coronavirus disease 2019 (COVID‐19) caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) has become a global health concern and pose a serious threat to humanity. There is an urgent need for developing therapeutic drugs and (or) biologics to prevent the spread of the virus. The life cycle of SARS‐CoV‐2 shows that the virus enters host cells by first binding to angiotensin‐converting enzyme 2 (ACE2) through its spike protein receptor‐binding domain (RBD). Therefore, blocking the binding between of ACE2 and SARS‐CoV‐2 RBD can inhibit the virus infection in the host cells. In this study, by grafting the complementarity‐determining regions (CDRs) of developed SARS‐CoV, MERS‐CoVs specific neutralizing antibodies (nAbs) include monoclonal antibodies (mAbs) as well as SARS‐CoV‐2 mAbs onto a known stable nanobody (Nb) scaffold, and a total of 16 Nbs sequences were designed. Five Nbs, namely CS01, CS02, CS03, CS10, and CS16, were selected based on the free energy landscape of protein docking verified by the recently reported Nb‐RBD cocrystal structures. CS01, CS02, and CS03 occupied the ACE2 binding site of RBD, while CS10 and CS16 were proposed to inhibit the interaction between RBD and ACE2 through an allosteric mechanism. Based on the structures of the five Nbs in complex with RBD, seven brand‐new Nbs with enhanced binding affinities (CS02_RD01, CS03_RD01, CS03_RD02, CS03_RD03, CS03_RD04, CS16_RD01, and CS16_RD02) were generated by redesign of residues on the interface of the five Nbs contact with SARS‐CoV‐2 RBD. In addition, the identified “hot spots” on the interface of each complex provide useful information to understand the binding mechanism of designed Nbs to SARS‐CoV‐2 RBD. In sum, the predicted stabilities and high binding affinities of the 11 (re)designed Nbs indicating the potential of the developed computational framework in this work to design effective agents to block the infection of SARS‐CoV‐2.