Abstract
Proteins are complex biomolecules which perform critical tasks in living organisms. Knowledge of a protein’s structure is essential for understanding its physiological function in detail. Despite the incredible progress in experimental techniques, protein structure determination is still expensive, time-consuming, and arduous. That is why computer simulations are often used to complement or interpret experimental data. Here, we explore how in silico protein structure determination based on replica-exchange molecular dynamics (REMD) can benefit from including contact information derived from theoretical and experimental sources, such as direct coupling analysis or NMR spectroscopy. To reflect the influence from erroneous and noisy data we probe how false-positive contacts influence the simulated ensemble. Specifically, we integrate varying numbers of randomly selected native and non-native contacts and explore how such a bias can guide simulations towards the native state. We investigate the number of contacts needed for a significant enrichment of native-like conformations and show the capabilities and limitations of this method. Adhering to a threshold of approximately 75% true-positive contacts within a simulation, we obtain an ensemble with native-like conformations of high quality. We find that contact-guided REMD is capable of delivering physically reasonable models of a protein’s structure.
Highlights
Knowledge of protein structures is crucial for understanding their various functions within living organisms and the biological processes they take part in
We find that the replica-exchange molecular dynamics (REMD) simulations benefited from restraints with a true-positive rate (TPR) of 75% or higher, resulting in significantly better Global Distance Test (GDT) scores according to Eq (13)
We showed that including such information into replica-exchange molecular dynamics (REMD) delivers proper structural models of a protein’s conformational state by significantly enriching native-like folds
Summary
Knowledge of protein structures is crucial for understanding their various functions within living organisms and the biological processes they take part in. Structural knowledge is critical in related fields such as pharmacology to understand pathogenesis on a molecular level as an essential prerequisite to effective drug design. Both protein structure and function are intrinsically encoded in the corresponding amino acid sequence [1–3]. Experimental sequencing techniques have become exceptionally efficient and lead to fast growing sequence databases, e.g., GenBank [4] and UniProt [5].
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