MOTIVATION: Generative models have demonstrated considerable promise in de novo protein design. Traditional approaches typically focus on either sequence or structure in isolation, limiting the capacity to explore the intricate sequence-structure landscape and achieve optimal designs. However, joint protein sequence and structure co-design remains a largely underexplored challenge. RESULTS: We present CoFlow, a discrete model for protein co-design from scratch or given constraints. CoFlow employs a joint discrete flow and integrates a multi-modal protein masked language model to facilitate co-design in the discrete space. Comprehensive experiments demonstrate that CoFlow outperforms previous design methods across multiple evaluation metrics. Notably, CoFlow achieves a consistency approximately eight times higher than that of ESM3 in unconditional generation. Moreover, CoFlow exhibits competitive performance in conditional generation tasks, including motif-scaffolding, protein folding, and inverse folding. AVAILABILITY AND IMPLEMENTATION: The source code of CoFlow, including data preprocessing and model, is available at https://github.com/LtECoD/CoFlow and https://zenodo.org/records/14842367. (DOI: 10.5281/zenodo.14842367).

The social and environmental interactions, as well as the pedestrian goal are crucial for pedestrian trajectory prediction. This is because they could learn both complex interactions in the scenes and the intentions of the pedestrians. However, most existing methods either learn the one-moment social interactions, or supervise the pedestrian trajectories using long-term goal, resulting in suboptimal prediction performances. In this paper, we propose a novel network named Completed Interaction Network (CINet) to simultaneously consider the social interactions in all moments, the environmental interactions and the short-term goal of pedestrians in a unified framework for pedestrian trajectory prediction. Specifically, we propose the Spatio-Temporal Transformer Layer (STTL) to fully mine the spatio-temporal information among historical trajectories of all pedestrians in order to obtain the social interactions in all moments. Additionally, we present the Gradual Goal Module (GGM) to capture the environmental interactions under the supervision of the short-term goal, which is beneficial to understanding the intentions of the pedestrian. Afterwards, we employ the cross-attention to effectively integrate the all-moment social and environmental interactions. The experimental results on three standard pedestrian datasets, i.e., ETH/UCY, SDD and inD demonstrate that our method achieves a new state-of-the-art performance. Furthermore, the visualization results indicate that our method could predict trajectories more reasonably in complex scenarios such as sharp turns, infeasible areas and so on.