Decoding protein-protein interactions (PPIs) at the residue level is crucial for understanding cellular mechanisms and developing targeted therapeutics. We present Seq2Bind Webserver, a computational framework that leverages fine-tuned protein language models (PLMs) to determine binding affinity between proteins and identify critical binding residues directly from sequences, eliminating the structural requirements that limit most affinity prediction tools. We fine-tuned four architectures including ProtBERT, ProtT5, ESM2, and BiLSTM on the SKEMPI 2.0 dataset containing 5,387 protein pairs with experimental binding affinities. Through systematic alanine mutagenesis on each residue for 14 therapeutically relevant protein complexes, we evaluated each model's ability to identify interface residues. Performance was assessed using N-factor metrics, where N-factor=3 evaluates whether true residues appear within 3n top predictions for n interface residues. ESM2 achieved 49.5% accuracy at N-factor=3, with both ESM2 (37.2%) and ProtBERT (35.1%) outperforming structural docking method HADDOCK3 (32.1%) at N-factor=2. Our sequence-based approach enables rapid screening (minutes versus hours for docking), handles disordered proteins, and provides comparable accuracy, making Seq2Bind a valuable prior to steer blind docking protocols to identify putative binding residues from each protein for therapeutic targets. Seq2Bind Webserver is accessible at this https URL under StructF suite.

Power domination in graphs arises from the problem of monitoring an electric power system by placing as few measurement devices in the system as possible. A power dominating set of a graph is a set of vertices that observes every vertex in the graph, following a set of rules for power system monitoring. A practical problem of interest is to determine the minimum number of additional measurement devices needed to monitor a power network when the network is expanded and the existing devices remain in place. In this paper, we study the problem of finding the smallest power dominating set that contains a given set of vertices X. We also study the related problem of finding the smallest zero forcing set that contains a given set of vertices X. The sizes of such sets in a graph G are respectively called the restricted power domination number and restricted zero forcing number of G subject to X. We derive several tight bounds on the restricted power domination and zero forcing numbers of graphs, and relate them to other graph parameters. We also present exact and algorithmic results for computing the restricted power domination number, including integer programs for general graphs and a linear time algorithm for graphs with bounded treewidth. We also use restricted power domination to obtain a parallel algorithm for finding minimum power dominating sets in trees.