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).

Protein-to-protein interaction (PPI) prediction aims to predict whether two given proteins interact or not. Compared with traditional experimental methods of high cost and low efficiency, the current deep learning based approach makes it possible to discover massive potential PPIs from large-scale databases. However, deep PPI prediction models perform poorly on unseen species, as their proteins are not in the training set. Targetting on this issue, the paper first proposes PPITrans, a Transformer based PPI prediction model that exploits a language model pre-trained on proteins to conduct binary PPI prediction. To validate the effectiveness on unseen species, PPITrans is trained with Human PPIs and tested on PPIs of other species. Experimental results show that PPITrans significantly outperforms the previous state-of-the-art on various metrics, especially on PPIs of unseen species. For example, the AUPR improves 0.339 absolutely on Fly PPIs. Aiming to explore the knowledge learned by PPITrans from PPI data, this paper also designs a series of probes belonging to three categories. Their results reveal several interesting findings, like that although PPITrans cannot capture the spatial structure of proteins, it can obtain knowledge of PPI type and binding affinity, learning more than binary PPI.

Summary As data analysis scenarios keep increasing on high‐performance computing systems, the ability to select a small fraction of data from a large volume of scientific data sets is vital to accelerate scientific discovery. However, parallel file systems lack the ability to provide efficient data locating services at the granularity of both a file and a record. Existing methods for identifying and indexing data are often domain‐specific and do not scale to large scientific data sets. In this paper, we describe the design and implementation of UniIndex framework, which combines our proposed techniques for user‐annotation extraction, in‐memory cache layer, in‐situ indexing, and parallel query processing. Acting as middleware on top of production file systems, UniIndex enables efficient data locating services with minimal user effort. Our evaluations show that UniIndex can locate target files from directories containing millions of files in microseconds. By applying in situ indexing and the lightweight range‐bitmap index, record‐level index building time can be dramatically reduced while maintaining up to two orders of magnitude query speedup than scanning the entire data set.