Gaussian Network Model (GNM) is a powerful tool to sample conformational dynamics based on contact topology in a coarse-grained presentation. Here we present a method to consider protein dynamics in the presence of ‘environment’ (1). The ‘environment’ here can be crystal contacts defined in structures solved by x-ray crystallography, a protein in homo-/hetero-dimers, a part of a protein complex, the DNA in complexed with transcription factors, or even a large ligand (or ligands) in a protein. The protein dynamics, in the presence of these ‘environments’, can be assessed in a more rigorous physics ground (the size and number of the orthogonal normal modes are the same as those in unperturbed systems (in the absence of the environment)) with our theories (2) that guarantee our implementations to be more efficient (saving 3/4 time, assuming equal size of system and environment), more memory friendly (saving >1/2 time) and more accurate (enhanced correlation between predictions and B-factors). The environment consideration in ANM theory was published (1) and reviewed (2); its GNM counterpart is first derived in this work and proven to be more accurate in the B-factor predictions than conventional GNM.
