Protein structure prediction: making AWSEM AWSEM‐ER by adding evolutionary restraints

Abstract

Protein sequences have evolved to fold into functional structures, resulting in families of diverse protein sequences that all share the same overall fold. One can harness protein family sequence data to infer likely contacts between pairs of residues. In the current study, we combine this kind of inference from coevolutionary information with a coarse-grained protein force field ordinarily used with single sequence input, the Associative memory, Water mediated, Structure and Energy Model (AWSEM), to achieve improved structure prediction. The resulting Associative memory, Water mediated, Structure and Energy Model with Evolutionary Restraints (AWSEM-ER) yields a significant improvement in the quality of protein structure prediction over the single sequence prediction from AWSEM when a sufficiently large number of homologous sequences are available. Free energy landscape analysis shows that the addition of the evolutionary term shifts the free energy minimum to more native-like structures, which explains the improvement in the quality of structures when performing predictions using simulated annealing. Simulations using AWSEM without coevolutionary information have proved useful in elucidating not only protein folding behavior, but also mechanisms of protein function. The success of AWSEM-ER in de novo structure prediction suggests that the enhanced model opens the door to functional studies of proteins even when no experimentally solved structures are available.

Forging tools for refining predicted protein structures

“…The refinement protocol includes a coarse-grained modeling step for determining the mechanical deformations to be made in an all-atom refinement step where the molecule is distorted mechanically. The coarse-grained AWSEM with evolutionary restraints from ultradeep learning (AWSEMER-UD) potential (11) was simulated at a constant temperature of 300 K starting from the initial model structure taken from the CASP12 repository. The coarse-grained trajectory is then used to find the principal components of the backbone motions.…”

“…AWSEMER-UD was initially described in ref. 11. Here, we adopted a variant of AWSEMER-UD and initialized the simulations from the structures that were provided to participants in the CASP12 structure refinement competition (28).…”

“…3 is a prefactor that is proportional to the probability of contact formation as determined by RaptorX (50). r estimate is an estimated residue pair-type-dependent distance that was obtained by surveying a database of protein structures (11). σ ij = |i − j| 0.15Å is a sequence separation-dependent well width.…”

“…A variety of strategies, however, are bearing fruit in the quest for predicting 3D structures from sequence. At the outset, these strategies do not try slavishly to simulate the actual molecular dynamics of proteins at an all-atom level of detail, but rather use several tricks: analogy to known protein structures (1-3), neural nets and other types of machine learning (4)(5)(6)(7)(8)(9), evolutionary analysis (10,11), and coarse-grained simulations using reduced descriptions of protein tertiary structure (12,13) to produce initial predictions. These methods have grown in power over the last decades, as witnessed by the series of CASP experiments that aim to monitor the community's progress in protein structure prediction (14).…”

OpenAWSEM with Open3SPN2: a fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

“…AWSEM-MD has also been used to study protein association [13] and aggregation [14]. Recently, AWSEM-MD has been used to predict the folds of large proteins by incorporating co-evolutionary information [15] and 3D template information [16]. It has also performed quite well in recent CASP competitions.…”