Accurately positioning functional residues with robotics-inspired computational protein design

Accurate positioning of functional residues is critical for the design of new protein functions, but has remained difficult because of the prevalence of irregular local geometries in active sites. Here we introduce two computational methods that build local protein geometries from sequence with atomic accuracy: fragment kinematic closure (FKIC) and loophash kinematic closure (LHKIC). FKIC and LHKIC integrate two approaches: robotics-inspired kinematics of protein backbones and insertion of peptide fragments, and show up to 140-fold improvements in native-like predictions over either approach alone. We then integrate these methods into a new design protocol, pull-into-place (PIP), to position functionally important sidechains via design of new structured loop conformations. We validate PIP by remodeling a sizeable active site region in an enzyme and confirming the engineered new conformations of two designs with crystal structures. The described methods can be applied broadly to the design of many new protein geometries and functions.

Toward the computational design of protein crystals with improved resolution

Substantial advances have been made in the computational design of protein interfaces over the last 20 years. However, the interfaces targeted by design have typically been stable and high-affinity. Here, we report the development of a generic computational design method to stabilize the weak interactions at crystallographic interfaces. Initially, we analyzed structures reported in the Protein Data Bank to determine whether crystals with more stable interfaces result in higher resolution structures. We found that for 22 variants of a single protein crystallized by a single individual, the Rosetta-calculated `crystal score' correlates with the reported diffraction resolution. We next developed and tested a computational design protocol, seeking to identify point mutations that would improve resolution in a highly stable variant of staphylococcal nuclease (SNase). Using a protocol based on fixed protein backbones, only one of the 11 initial designs crystallized, indicating modeling inaccuracies and forcing us to re-evaluate our strategy. To compensate for slight changes in the local backbone and side-chain environment, we subsequently designed on an ensemble of minimally perturbed protein backbones. Using this strategy, four of the seven designed proteins crystallized. By collecting diffraction data from multiple crystals per design and solving crystal structures, we found that the designed crystals improved the resolution modestly and in unpredictable ways, including altering the crystal space group. Post hoc, in silico analysis of the three observed space groups for SNase showed that the native space group was the lowest scoring for four of six variants (including the wild type), but that resolution did not correlate with crystal score, as it did in the preliminary results. Collectively, our results show that calculated crystal scores can correlate with reported resolution, but that the correlation is absent when the problem is inverted. This outcome suggests that more comprehensive modeling of the crystallographic state is necessary to design high-resolution protein crystals from poorly diffracting crystals.

ragp: Pipeline for mining of plant hydroxyproline-rich glycoproteins with implementation in R

Hydroxyproline-rich glycoproteins (HRGPs) are one of the most complex families of macromolecules found in plants, due to the diversity of glycans decorating the protein backbone, as well as the heterogeneity of the protein backbones. While this diversity is responsible for a wide array of physiological functions associated with HRGPs, it hinders attempts for homology-based identification. Current approaches, based on identifying sequences with characteristic motifs and biased amino acid composition, are limited to prototypical sequences. Ragp is an R package for mining and analysis of HRGPs, with emphasis on arabinogalactan proteins. The ragp filtering pipeline exploits one of the HRGPs key features, the presence of hydroxyprolines which represent glycosylation sites. Main package features include prediction of proline hydroxylation sites, amino acid motif and bias analyses, efficient communication with web servers for prediction of N-terminal signal peptides, glycosylphosphatidylinositol modification sites and disordered regions and the ability to annotate sequences through hmmscan and subsequent GO enrichment, based on predicted Pfam domains. As such, ragp extends R’s rich ecosystem for high-throughput sequence data analyses. The ragp R package is available under the MIT Open Source license and is freely available to download from GitHub at: https://github.com/missuse/ragp.

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI Rounds 37–45

CAPRI Rounds 37 through 45 introduced larger complexes, new macromolecules, and multi-stage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since Round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pre-generated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide–protein complexes in Round 41. While the results were broadly encouraging, they also highlighted the pressing need to invest in (1) flexible docking algorithms with the ability to model loop and linker motions and in (2) new sampling and scoring methods for oligosaccharide–protein interactions.

Path Components and the Fundamental Group

This chapter introduces some of the basic concepts of algebraic topology and describes datatypes and algorithms for implementing them on a computer. The basic concepts include: regular CW-complex, non-regular CW-complex, simplicial complex, cubical complex, permutahedral complex, simple homotopy, set of path-components, fundamental group, van Kampen’s theorem, knot quandle, Alexander polynomial of a knot, covering space. These are illustrated using computer examples involving digital images, protein backbones, high-dimensional point cloud data, knot complements, discrete groups, and random simplicial complexes.

Cellular Homology

This chapter introduces more basic concepts of algebraic topology and describes datatypes and algorithms for implementing them on a computer. The basic concepts include: chain complex, chain mapping, chain homotopy, homology of a (simplicial or cubical or permutahedral or CW-) space, persistent homology of a filtered space, cohomology ring of a space, van Kampen diagrams, excision. These are illustrated using computer examples involving digital images, protein backbones, high-dimensional point cloud data, knot complements, discrete groups, and random simplicial complexes.

The BackMAP Python module: how a simpler Ramachandran number can simplify the life of a protein simulator

Protein backbones occupy diverse conformations, but compact metrics to describe such conformations and transitions between them have been missing. This report re-introduces the Ramachandran number (ℛ) as a residue-level structural metric that could simply the life of anyone contending with large numbers of protein backbone conformations (e.g., ensembles from NMR and trajectories from simulations). Previously, the Ramachandran number (ℛ) was introduced using a complicated closed form, which made the Ramachandran number difficult to implement. This report discusses a much simpler closed form of ℛ that makes it much easier to calculate, thereby making it easy to implement. Additionally, this report discusses how ℛ dramatically reduces the dimensionality of the protein backbone, thereby making it ideal for simultaneously interrogating large numbers of protein structures. For example, 200 distinct conformations can easily be described in one graphic using ℛ (rather than 200 distinct Ramachandran plots). Finally, a new Python-based backbone analysis tool—BackMAP—is introduced, which reiterates how ℛ can be used as a simple and succinct descriptor of protein backbones and their dynamics.