Structure, function and dynamics in acyl carrier proteins.

Carrier proteins are four-helix bundles that covalently hold metabolites and secondary metabolites, such as fatty acids, polyketides and non-ribosomal peptides. These proteins mediate the production of many pharmaceutically important compounds including antibiotics and anticancer agents. Acyl carrier proteins (ACPs) can be found as part of a multi-domain polypeptide (Type I ACPs), or as part of a multiprotein complex (Type II). Here, the main focus is on ACP2 and ACP3, domains from the type I trans-AT polyketide synthase MmpA, which is a core component of the biosynthetic pathway of the antibiotic mupirocin. During molecular dynamics simulations of their apo, holo and acyl forms ACP2 and ACP3 both form a substrate-binding surface-groove. The substrates bound to this surface-groove have polar groups on their acyl chain exposed and forming hydrogen bonds with the solvent. Bulky hydrophobic residues in the GXDS motif common to all ACPs, and similar residues on helix III, appear to prohibit the formation of a deep tunnel in type I ACPs and type II ACPs from polyketide synthases. In contrast, the equivalent positions in ACPs from type II fatty acid synthases, which do form a deep solvent-excluded substrate-binding tunnel, have the small residue alanine. During simulation, ACP3 with mutations I61A L36A W44L forms a deep tunnel that can fully bury a saturated substrate in the core of the ACP, in contrast to the surface groove of the wild type ACP3. Similarly, in the ACP from E. coli fatty acid synthase, a type II ACP, mutations can change ligand binding from being inside a deep tunnel to being in a surface groove, thus demonstrating how changing a few residues can modify the possibilities for ligand binding.

DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.

Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving the prediction of the amino acid residues that contact in their 3D structure. For an average globular protein, around 92% of all residue pairs are non-contacting, therefore accurate prediction of only a small percentage of inter-amino acid distances could increase the number of constraints to guide structure determination. We have trained deep neural networks to predict inter-residue contacts and distances. Distances are predicted with an accuracy better than most contact prediction techniques. Addition of distance constraints improved de novo structure predictions for test sets of 158 protein structures, as compared to using the best contact prediction methods alone. Importantly, usage of distance predictions allows the selection of better models from the structure pool without a need for an external model assessment tool. The results also indicate how the accuracy of distance prediction methods might be improved further.