Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs)
are tremendous, the design and discovery of new candidates remain a time and cost …

Highlights•Protein Language Models (pLMs) tap into large unlabeled data to transform
protein science.•pLMs boost protein structure and function prediction performance and …

Data-centric approaches have been used to develop predictive methods for elucidating
uncharacterized properties of proteins; however, studies indicate that these methods should …

Proteins perform many essential functions in biological systems and can be successfully
developed as bio-therapeutics. It is invaluable to be able to predict their properties based on …

As one of the state-of-the-art automated function prediction (AFP) methods, NetGO 2.0
integrates multi-source information to improve the performance. However, it mainly utilizes …

Breakthrough methods in machine learning (ML), protein structure prediction, and novel
ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models …

Experimental protein function annotation does not scale with the fast-growing sequence
databases. Only a tiny fraction (< 0.1%) of protein sequences has experimentally determined …

Accurate identification of protein function is critical to elucidate life mechanisms and design
new drugs. We proposed a novel deep-learning method, ATGO, to predict Gene Ontology …

Experimental structures are leveraged through multiple sequence alignments, or more
generally through homology-based inference (HBI), facilitating the transfer of information …

Protein succinylation is an important post-translational modification (PTM) responsible for
many vital metabolic activities in cells, including cellular respiration, regulation, and repair …