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proteins VarCon calcualtes the HEXplorer score as an estimation. Besides

protein activity from gene expression data, including the VIPER and msVIPER

protein binding sites (clusters) are then resolved at high resolution

protein-protein interaction databases and integrative resources such as KEGG

Protein-Lipid Interaction Database. A large-scale experimentally validated dataset

protein translation, codon optimization, ligation calculations, molarity calculations, dilution calculations

protein structures. Protein structures are the key to understanding protein

protein properties, Sanger ab1 traces, HGVS conversion and variant annotation

protein, deduplicated and reordered before saving. Runs entirely in the browser

protein-structure assessment, binding-site detection, molecular docking, molecular dynamics

protein sequence and NCBI taxonomy IDs (or RefSeq assembly accessions

protein, plain or FASTA. Filters features by type, handles multi

protein-RNA binding with Deep Learning | This repository contains all DeepCLIP

method for prediction of ligand binding sites from protein structure.

protein lane translates target and consensus side by side. Reads

protein structure prediction, molecular docking, protein design, molecular dynamics, sequence

Protein-protein interaction data is essential for omics data analysis

proteins. This package contains useful functions to analyze patterns of paired

changes from RNA-binding protein perturbations in the KnockRBP database.

proteins, which are proteins that perform two or more distinct

protein structures by performing geometric matching of known enzyme catalytic

protein functional importance derived from UniProt annotations. It provides standardized

templates to annotate protein sequences with Gene Ontology (GO) terms.

proteins or lipids from live-cell time-lapse fluorescent imaging

protein design and predict protein fitness. It uses millions of protein

proteins using deep learning models. Users submit protein sequences in FASTA

protein embeddings (AlphaFold2, BioEmbeddings, ESM-2, ProtVec) for advanced systems

protein folding, conformational transitions, and RNA-protein dynamics using packages

protein chains based on AlphaFold predicted aligned error (PAE). It aids

protein-protein interaction (PPI) networks by modeling them as bipartite

protein transition pathways at atomistic resolution. It utilizes generative active

protein recognition mimicry. It uses High-Throughput (HTP) mutagenesis combined

protein structural data from PDB/mol2 files, creating residue-centered spherical

protein design and ligand screening by managing incompatible software environments

perform fast database searches and multiple alignments of protein structures.

protein structures, enabling high-throughput computational studies of enzyme catalysis

protein sequences, enabling visualization and comparison of results onto phylogenetic

alignments between UniProt Knowledgebase sequences and Protein Data Bank structures.

protein surfaces to reveal structural and sequence differences and similarities

protein structures. It combines statistical pLDDT score analysis with an interactive

protein sequences across the microbial tree of life using structural

extract entities like genes, proteins, and sequences from biomedical text.

involving successive, competing reactions in biomolecules like proteins and DNA.

number changes) alongside protein-defined cell states in intact tissues.

proteins, which are valuable targets for drug discovery. It utilizes

protein sets to anchor the manual curation of tiny, hard

protein structures and molecular dynamics trajectories. It calculates centrality measures

Protein-Protein Interactions (PPIs). It maps data from alternative splicing

protein therapeutics, such as antibodies and nanobodies, without relying on reference

protein pair language model that learns inter-protein context directly