Title: xTrimoABFold: Improving Antibody Structure Prediction without Multiple Sequence Alignments . Abstract: Antibody, used by the immune system to identify and neutralize foreign objects such as pathogenic bacteria and viruses, plays an important role in immune system. In the field of drug engineering, the essential task is designing a novel antibody to make sure its paratope (substructures in the antibody) binds to the epitope of the specific antigen with high precision. Also, understanding the structure of antibody and its paratope can facilitate a mechanistic understanding of the function. Therefore, antibody structure prediction has always been a highly valuable problem for drug discovery. AlphaFold2, a breakthrough in the field of structural biology, provides a feasible solution to predict protein structure based on protein sequences and computationally expensive coevolutionary multiple sequence alignments (MSAs). However, the computational efficiency and undesirable prediction accuracy on antibody, especially on the complementarity-determining regions (CDRs) of antibody limit its applications on the industrially high-throughput drug design. In this paper, we present a novel method named xTrimoABFold to predict antibody structure from antibody sequence based on a pretrained antibody language model (ALM) as well as homologous templates, which are searched from protein database (PDB) via fast and cheap algorithms. xTrimoABFold outperforms the MSA-based AlphaFold2 and the protein language model based SOTAs, e.g., OmegaFold, HelixFold-Single and IgFold with a large significant margin (30+% improvement on RMSD) while performs 151x faster than AlphaFold2. To the best of our knowledge, xTrimoABFold is the best antibody structure predictor to date in the world.