Computational Modeling of Multifarious Protein Interactions

Protein-protein interactions are fundamental to cellular processes. Computational methods are useful for studying protein-protein interactions, but the majority of protein-protein docking software programs are limited to pairwise protein complexes. However, there are many other classes of protein complexes that are important to study, including multimeric protein complexes and disordered protein complexes. This dissertation introduces our computational method to model disordered protein complexes and presents our refinement of a method to predict the assembly order of multimeric protein complexes. Our research applies computational methods to a wider class of proteins than has been demonstrated previously. The methods developed in this work can be applied to obtain new biological insights into disordered protein complexes and multimeric complexes

Modeling disordered protein interactions from biophysical principles

<div><p>Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.</p></div

IDP-LZerD consists of four steps.

<p>1. fragment structure prediction, 2. fragment docking, 3. path assembly, and 4. refinement. Steps 1 and 2 correspond to “dock” and Steps 3 and 4 correspond to “coalesce.”</p

Examples of successful bound and unbound cases.

<p>Green: native IDP; orange: modeled IDP. a-d: bound cases; e-h: unbound cases. a: Rank 1 model of MDM2 with bound P53 (PDB ID: 1ycr). <i>f</i>_<i>nat</i> 0.42, I-RMSD 1.48 Å, L-RMSD 3.60 Å (medium quality). b: Rank 4 model of PKA C-<i>α</i> with bound protein kinase inhibitor <i>α</i> (2cpk). <i>f</i>_<i>nat</i> 0.56, I-RMSD 1.95 Å, L-RMSD 4.41 Å (medium quality). c: Rank 6 model of RAP1 with bound SIR3 (3owt). <i>f</i>_<i>nat</i> 0.33, I-RMSD 3.30 Å, L-RMSD 6.02 Å. d: Rank 5 model of BoNT/A with bound SNAP-25 (1xtg). <i>f</i>_<i>nat</i> 0.17, I-RMSD 3.79 Å, L-RMSD 9.22 Å. e: Rank 1 model of DRA/DRB5 with unbound myelin basic protein (4ah2). <i>f</i>_<i>nat</i> 0.39, I-RMSD 2.46 Å, L-RMSD 5.83 Å. f: Rank 9 model of <i>α</i>-actin-1 with unbound Cibulot (1ijj). <i>f</i>_<i>nat</i> 0.55, I-RMSD 2.51 Å, L-RMSD 5.15 Å. g: Rank 3 model of Cbp/p300 with unbound CITED2 (1l3e). <i>f</i>_<i>nat</i> 0.25, I-RMSD 6.31 Å, L-RMSD 7.43 Å. h: Rank 2 model of SycE with unbound YopE (1jya). <i>f</i>_<i>nat</i> 0.21, I-RMSD 5.44 Å, L-RMSD 9.97 Å.</p

L-RMSD vs Model Score and IDP RMSD.

<p>Inc: incorrect; Acc: acceptable; Med: medium. PDB ID: 2bzw.</p

9-residue IDR complex test set selected from ELM.

<p>9-residue IDR complex test set selected from ELM.</p

Biological case studies.

<p><b>A</b>: <i>β</i>-catenin in complex with TCF7L2. Green: native TCF7L2; orange: rank 1 model of TCF7L2; <i>f</i>_<i>nat</i> 0.38, I-RMSD 2.85 Å, L-RMSD 7.94 Å. PDB ID: 1jpw. <b>B-E</b>: Human and mouse Cbp/p300 TAZ1 domain in complex with CITED2 and Hif-1<i>α</i>. Green/cyan: native CITED2/Hif-1<i>α</i>; orange/yellow: model CITED2/Hif-1<i>α</i>. Ball and stick: LPXL motif. <b>B-C</b>: Human TAZ1 and CITED2. <b>B</b>: bound (1p4qB); rank 5 model; <i>f</i>_<i>nat</i> 0.27, I-RMSD: 4.2 Å, L-RMSD: 7.6 Å. <b>C</b>: unbound (1l3eB); rank 9 model; <i>f</i>_<i>nat</i> 0.17, I-RMSD: 7.1 Å, L-RMSD: 9.6 Å. <b>D-E</b>: Mouse TAZ1 and Hif-1<i>α</i>. <b>D</b>: bound (1l8cA); rank 16 model; <i>f</i>_<i>nat</i> 0.05, I-RMSD 11.7 Å, L-RMSD 20.1 Å. <b>E</b>: unbound (1u2nA); rank 9 model; <i>f</i>_<i>nat</i> 0.20, I-RMSD 6.4 Å, L-RMSD 10.4 Å. <b>F</b>: Unbound complex between BoNT/A and sn2. Green: native sn2; orange: rank 1 model of sn2; <i>f</i>_<i>nat</i> 0.00, I-RMSD 15.7 Å, L-RMSD 38.2 Å. Receptor PDB ID 1xtfA (unbound).</p

Performance of IDP-LZerD on ≥ 11 amino acid protein-peptide complexes from MD test sets.

<p>Performance of IDP-LZerD on ≥ 11 amino acid protein-peptide complexes from MD test sets.</p

Fragment modeling and docking accuracy for 9-residue IDR complexes from ELM.

<p>Fragment modeling and docking accuracy for 9-residue IDR complexes from ELM.</p

Complex between Bcl2-L-1 and BAD.

<p><b>(A)</b>: A model of the bound structure (2bzw) before (purple) and after (orange) refinement vs. native (green). <b>(B)</b>: Unbound (1pq0); blue-to-red (N-terminus on the left): native BAD; rainbow: top 7 models of BAD.</p