Atomic Clashes Research Articles

Learning torus PCA-based classification for multiscale RNA correction with application to SARS-CoV-2

Abstract Three-dimensional RNA structures frequently contain atomic clashes. Usually, corrections approximate the biophysical chemistry, which is computationally intensive and often does not correct all clashes. We propose fast, data-driven reconstructions from clash-free benchmark data with two-scale shape analysis: microscopic (suites) dihedral backbone angles, mesoscopic sugar ring centre landmarks. Our analysis relates concentrated mesoscopic scale neighbourhoods to microscopic scale clusters, correcting within-suite-backbone-to-backbone clashes exploiting angular shape and size-and-shape Fréchet means. Validation shows that learned classes highly correspond with literature clusters and reconstructions are well within physical resolution. We illustrate the power of our method using cutting-edge SARS-CoV-2 RNA.

Interface refinement of low- to medium-resolution Cryo-EM complexes using HADDOCK2.4

A wide range of cellular processes requires the formation of multimeric protein complexes. The rise of cryo-electron microscopy (cryo-EM) has enabled the structural characterization of these protein assemblies. The density maps produced can, however, still suffer from limited resolution, impeding the process of resolving structures at atomic resolution. In order to solve this issue, monomers can be fitted into low- to medium-resolution maps. Unfortunately, the models produced frequently contain atomic clashes at the protein-protein interfaces (PPIs), as intermolecular interactions are typically not considered during monomer fitting. Here, we present a refinement approach based on HADDOCK2.4 to remove intermolecular clashes and optimize PPIs. A dataset of 14 cryo-EM complexes was used to test eight protocols. The best-performing protocol, consisting of a semi-flexible simulated annealing refinement with centroid restraints on the monomers, was able to decrease intermolecular atomic clashes by 98% without significantly deteriorating the quality of the cryo-EM density fit.

Refinement of RNA Structures Using Amber Force Fields.

Atomic models for nucleic acids derived from X-ray diffraction data at low resolution provide much useful information, but the observed scattering intensities can be fit with models that can differ in structural detail. Tradtional geometric restraints favor models that have bond length and angle terms derived from small molecule crystal structures. Here we explore replacing these restraints with energy gradients derived from force fields, including recently developed integral equation models to account for the effects of water molecules and ions that are not part of the explicit model. We compare conventional and force-field based refinements for 22 RNA crystals, ranging in resolution from 1.1 to 3.6 Å. As expected, it can be important to account for solvent screening of charge–charge interactions, especially in the crowded environment of a nucleic acid crystal. The newly refined models can show improvements in torsion angles and hydrogen-bonding interactions, and can significantly reduce unfavorable atomic clashes, while maintaining or improving agreement with observed scattering intensities.

Using restraints in EROS-DOCK improves model quality in pairwise and multicomponent protein docking.

Protein docking algorithms aim to predict the 3D structure of a protein complex from the structures of its separated components. In the past, most docking algorithms focused on docking pairs of proteins to form dimeric complexes. However, attention is now turning towards the more difficult problem of using docking methods to predict the structures of multicomponent complexes. In both cases, however, the constituent proteins often change conformation upon complex formation, and this can cause many algorithms to fail to detect near-native binding orientations due to the high number of atomic steric clashes in the list of candidate solutions. An increasingly popular way to retain more near-native orientations is to define one or more restraints that serve to modulate or override the effect of steric clashes. Here, we present an updated version of our "EROS-DOCK" docking algorithm which has been extended to dock arbitrary dimeric and trimeric complexes, and to allow the user to define residue-residue or atom-atom interaction restraints. Our results show that using even just one residue-residue restraint in each interaction interface is sufficient to increase the number of cases with acceptable solutions within the top 10 from 51 to 121 out of 173 pairwise docking cases, and to successfully dock 8 out of 11 trimeric complexes.

Computational protein structure refinement: Almost there, yet still so far to go.

Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

Re-evaluation of low-resolution crystal structures via interactive molecular-dynamics flexible fitting (iMDFF): a case study in complement C4.

While the rapid proliferation of high-resolution structures in the Protein Data Bank provides a rich set of templates for starting models, it remains the case that a great many structures both past and present are built at least in part by hand-threading through low-resolution and/or weak electron density. With current model-building tools this task can be challenging, and the de facto standard for acceptable error rates (in the form of atomic clashes and unfavourable backbone and side-chain conformations) in structures based on data with dmax not exceeding 3.5 Å reflects this. When combined with other factors such as model bias, these residual errors can conspire to make more serious errors in the protein fold difficult or impossible to detect. The three recently published 3.6-4.2 Å resolution structures of complement C4 (PDB entries 4fxg, 4fxk and 4xam) rank in the top quartile of structures of comparable resolution both in terms of Rfree and MolProbity score, yet, as shown here, contain register errors in six β-strands. By applying a molecular-dynamics force field that explicitly models interatomic forces and hence excludes most physically impossible conformations, the recently developed interactive molecular-dynamics flexible fitting (iMDFF) approach significantly reduces the complexity of the conformational space to be searched during manual rebuilding. This substantially improves the rate of detection and correction of register errors, and allows user-guided model building in maps with a resolution lower than 3.5 Å to converge to solutions with a stereochemical quality comparable to atomic resolution structures. Here, iMDFF has been used to individually correct and re-refine these three structures to MolProbity scores of <1.7, and strategies for working with such challenging data sets are suggested. Notably, the improved model allowed the resolution for complement C4b to be extended from 4.2 to 3.5 Å as demonstrated by paired refinement.

Modeling and experimental assessment of a buried Leu–Ile mutation in dengue envelope domain III

Envelope protein domain III (ED3) of the dengue virus is important for both antibody binding and host cell interaction. Here, we focused on how a L387I mutation in the protein core could take place in DEN4 ED3, but cannot be accommodated in DEN3 ED3 without destabilizing its structure. To this end, we modeled a DEN4_L387I structure using the Penultimate Rotamer Library and taking the DEN4 ED3 main-chain as a fixed template. We found that three out of seven Ile387 conformers fit in DEN4 ED3 without introducing the severe atomic clashes that are observed when DEN3 serotype’s ED3 is used as a template. A more extensive search using 273 side-chain rotamers of the residues surrounding Ile387 confirmed this prediction. In order to assess the prediction, we determined the crystal structure of DEN4_L387I at 2 Å resolution. Ile387 indeed adopted one of the three predicted rotamers. Altogether, this study demonstrates that the effects of single mutations are to a large extent successfully predicted by systematically modeling the side-chain structures of the mutated as well as those of its surrounding residues using fixed main-chain structures and assessing inter-atomic steric clashes. More accurate and reliable predictions require considering sub-angstrom main-chain deformation, which remains a challenging task.

Evaluation of GalaxyDock Based on the Community Structure-Activity Resource 2013 and 2014 Benchmark Studies.

We analyze the results of the GalaxyDock protein-ligand docking program in the two recent experiments of Community Structure-Activity Resource (CSAR), CSAR 2013 and 2014. GalaxyDock performs global optimization of a modified AutoDock3 energy function by employing the conformational space annealing method. The energy function of GalaxyDock is quite sensitive to atomic clashes. Such energy functions can be effective for sampling physically correct conformations but may not be effective for scoring when conformations are not fully optimized. In phase 1 of CSAR 2013, we successfully selected all four true binders of digoxigenin along with three false positives. However, the energy values were rather high due to insufficient optimization of the conformations docked to homology models. A posteriori relaxation of the model complex structures by GalaxyRefine improved the docking energy values and differentiated the true binders from the false positives better. In the scoring test of CSAR 2013 phase 2, we selected the best poses for each of the two targets. The results of CSAR 2013 phase 3 suggested that an improved method for generating initial conformations for GalaxyDock is necessary for targets involving bulky ligands. Finally, combining existing binding information with GalaxyDock energy-based optimization may be needed for more accurate binding affinity prediction.

A Novel Unified Ab Initio and Template-Based Approach to GPCR Modeling: Case of EDG-LPA Receptors.

G-protein-coupled receptors (GPCRs) mediate diverse biological functions through intracellular signal cascades initiated by intracellular G-protein coupling following extracellular agonist binding. GPCRs are quintessential targets for drug design due to their involvement in pathophysiological conditions. The difficulty associated with GPCR crystallization and lack of accurate computational method for GPCR modeling constitutes the major setbacks for GPCRbased drug development. Here, we reported the combination of previously known ab initio and template-based methods as a novel approach applicable for modeling geometrically optimized full-length GPCR. First, geometry-optimized transmembrane helices (7Tms) of full-length GPCR are modeled using the GPCR server (http://gpcr.usc.es) followed by loop-refinement. A second structure is generated via the Iterative approach as implemented on I-TASSER (http://zhanglab.ccmb.med.umich.edu/I-TASSER/) server. The best Structures are then selected from the servers based on DOPE-score (GPCR server) and C-score (I-TASSER server) and piped into ModRefiner algorithm as initial and reference models respectively. ModRefiner drives the folding of the N- and C-termini regions of the initial model towards the reference model without altering the local geometries of the 7Tms and the loop regions as evaluated by Local-Global Alignment (LGA) algorithm. Finally, atomic clashes in the ModelRefiner output are resolved using Fragment-Guided Molecular Dynamics (FG-MD) simulation. Comparatively, FG-MD output structures of our test proteins (Endothelial Differentiation Gene-class (EDG) Lysophosphatidic acid receptors) have better model qualities than the initial and reference structures as evaluated by the QmeanScore6 algorithm. Keywords: C-and N-terminal optimization, EDG-LPAs, GPCR modeling, mod refiner, I-TASSER.

Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments

Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.

AN EVOLUTIONARY CONSERVATION-BASED METHOD FOR REFINING AND RERANKING PROTEIN COMPLEX STRUCTURES

Detection of protein complexes and their structures is crucial for understanding their role in the basic biology of organisms. Computational docking methods can provide researchers with a good starting point for the analysis of protein complexes. However, these methods are often not accurate and their results need to be further refined to improve interface packing. In this paper, we introduce a refinement method that incorporates evolutionary information into a novel scoring function by employing Evolutionary Trace (ET)-based scores. Our method also takes Van der Waals interactions into account to avoid atomic clashes in refined structures. We tested our method on docked candidates of eight protein complexes and the results suggest that the proposed scoring function helps bias the search toward complexes with native interactions. We show a strong correlation between evolutionary-conserved residues and correct interface packing. Our refinement method is able to produce structures with better lRMSD (least RMSD) with respect to the known complexes and lower energies than initial docked structures. It also helps to filter out false-positive complexes generated by docking methods, by detecting little or no conserved residues on false interfaces. We believe this method is a step toward better ranking and prediction of protein complexes.

Optimization of van der Waals Energy for Protein Side-Chain Placement and Design

Computational determination of optimal side-chain conformations in protein structures has been a long-standing and challenging problem. Solving this problem is important for many applications including homology modeling, protein docking, and for placing small molecule ligands on protein-binding sites. Programs available as of this writing are very fast and reasonably accurate, as measured by deviations of side-chain dihedral angles; however, often due to multiple atomic clashes, they produce structures with high positive energies. This is problematic in applications where the energy values are important, for example when placing small molecules in docking applications; the relatively small binding energy of the small molecule is drowned by the large energy due to atomic clashes that hampers finding the lowest energy state of the docked ligand. To address this we have developed an algorithm for generating a set of side-chain conformations that is dense enough that at least one of its members would have a root mean-square deviation of no more than R Å from any possible side-chain conformation of the amino acid. We call such a set a side-chain cover set of order R for the amino acid. The size of the set is constrained by the energy of the interaction of the side chain to the backbone atoms. Then, side-chain cover sets are used to optimize the conformation of the side chains given the coordinates of the backbone of a protein. The method we use is based on a variety of dead-end elimination methods and the recently discovered dynamic programming algorithm for this problem. This was implemented in a computer program called Octopus where we use side-chain cover sets with very small values for R, such as 0.1 Å, which ensures that for each amino-acid side chain the set contains a conformation with a root mean-square deviation of, at most, R from the optimal conformation. The side-chain dihedral-angle accuracy of the program is comparable to other implementations; however, it has the important advantage that the structures produced by the program have negative energies that are very close to the energies of the crystal structure for all tested proteins.

RASP: rapid modeling of protein side chain conformations

Modeling of side chain conformations constitutes an indispensable effort in protein structure modeling, protein-protein docking and protein design. Thanks to an intensive attention to this field, many of the existing programs can achieve reasonably good and comparable prediction accuracy. Moreover, in our previous work on CIS-RR, we argued that the prediction with few atomic clashes can complement the current existing methods for subsequent analysis and refinement of protein structures. However, these recent efforts to enhance the quality of predicted side chains have been accompanied by a significant increase of computational cost. In this study, by mainly focusing on improving the speed of side chain conformation prediction, we present a RApid Side-chain Predictor, called RASP. To achieve a much faster speed with a comparable accuracy to the best existing methods, we not only employ the clash elimination strategy of CIS-RR, but also carefully optimize energy terms and integrate different search algorithms. In comprehensive benchmark testings, RASP is over one order of magnitude faster (~ 40 times over CIS-RR) than the recently developed methods, while achieving comparable or even better accuracy.

Combined Approach Using Ligand Efficiency, Cross-Docking, and Antitarget Hits for Wild-Type and Drug-Resistant Y181C HIV-1 Reverse Transcriptase

New hits against HIV-1 wild-type and Y181C drug-resistant reverse transcriptases were predicted taking into account the possibility of some of the known metabolism interactions. In silico hits against a set of antitargets (i.e., proteins or nucleic acids that are off-targets from the desired pharmaceutical target objective) are used to predict a simple, visual measure of possible interactions for the ligands, which helps to introduce early safety considerations into the design of compounds before lead optimization. This combined approach consists of consensus docking and scoring: cross-docking to a group of wild-type and drug-resistant mutant proteins, ligand efficiency (also called binding efficiency) indices as new ranking measures, pre- and postdocking filters, a set of antitargets and estimation, and minimization of atomic clashes. Diverse, small-molecule compounds with new chemistry (such as a triazine core with aromatic side chains) as well as known drugs for different applications (oxazepam, chlorthalidone) were highly ranked to the targets having binding interactions and functional group spatial arrangements similar to those of known inhibitors, while being moderate to low binders to the antitargets. The results are discussed on the basis of their relevance to medicinal and computational chemistry. Optimization of ligands to targets and off-targets or antitargets is foreseen to be critical for compounds directed at several simultaneous sites.

PROSESS: a protein structure evaluation suite and server

PROSESS (PROtein Structure Evaluation Suite and Server) is a web server designed to evaluate and validate protein structures generated by X-ray crystallography, NMR spectroscopy or computational modeling. While many structure evaluation packages have been developed over the past 20 years, PROSESS is unique in its comprehensiveness, its capacity to evaluate X-ray, NMR and predicted structures as well as its ability to evaluate a variety of experimental NMR data. PROSESS integrates a variety of previously developed, well-known and thoroughly tested methods to evaluate both global and residue specific: (i) covalent and geometric quality; (ii) non-bonded/packing quality; (iii) torsion angle quality; (iv) chemical shift quality and (v) NOE quality. In particular, PROSESS uses VADAR for coordinate, packing, H-bond, secondary structure and geometric analysis, GeNMR for calculating folding, threading and solvent energetics, ShiftX for calculating chemical shift correlations, RCI for correlating structure mobility to chemical shift and PREDITOR for calculating torsion angle-chemical shifts agreement. PROSESS also incorporates several other programs including MolProbity to assess atomic clashes, Xplor-NIH to identify and quantify NOE restraint violations and NAMD to assess structure energetics. PROSESS produces detailed tables, explanations, structural images and graphs that summarize the results and compare them to values observed in high-quality or high-resolution protein structures. Using a simplified red–amber–green coloring scheme PROSESS also alerts users about both general and residue-specific structural problems. PROSESS is intended to serve as a tool that can be used by structure biologists as well as database curators to assess and validate newly determined protein structures. PROSESS is freely available at http://www.prosess.ca.

Feature space resampling for protein conformational search

De novo protein structure prediction requires location of the lowest energy state of the polypeptide chain among a vast set of possible conformations. Powerful approaches include conformational space annealing, in which search progressively focuses on the most promising regions of conformational space, and genetic algorithms, in which features of the best conformations thus far identified are recombined. We describe a new approach that combines the strengths of these two approaches. Protein conformations are projected onto a discrete feature space which includes backbone torsion angles, secondary structure, and beta pairings. For each of these there is one "native" value: the one found in the native structure. We begin with a large number of conformations generated in independent Monte Carlo structure prediction trajectories from Rosetta. Native values for each feature are predicted from the frequencies of feature value occurrences and the energy distribution in conformations containing them. A second round of structure prediction trajectories are then guided by the predicted native feature distributions. We show that native features can be predicted at much higher than background rates, and that using the predicted feature distributions improves structure prediction in a benchmark of 28 proteins. The advantages of our approach are that features from many different input structures can be combined simultaneously without producing atomic clashes or otherwise physically inviable models, and that the features being recombined have a relatively high chance of being correct.

Improved side-chain modeling by coupling clash-detection guided iterative search with rotamer relaxation

Side-chain modeling has seen wide applications in computational structure biology. Most of the popular side-chain modeling programs explore the conformation space using discrete rigid rotamers for speed and efficiency. However, in the tightly packed environments of protein interiors, these methods will inherently lead to atomic clashes and hinder the prediction accuracy. We present a side-chain modeling method (CIS-RR), which couples a novel clash-detection guided iterative search (CIS) algorithm with continuous torsion space optimization of rotamers (RR). Benchmark testing shows that compared with the existing popular side-chain modeling methods, CIS-RR removes atomic clashes much more effectively and achieves comparable or even better prediction accuracy while having comparable computational cost. We believe that CIS-RR could be a useful method for accurate side-chain modeling. CIS-RR is available to non-commercial users at our website: http://jianglab.ibp.ac.cn/lims/cisrr/cisrr.html.

Conformation dependence of backbone geometry in proteins.

Protein structure determination and predictive modeling have long been guided by the paradigm that the peptide backbone has a single, context-independent ideal geometry. Both quantum-mechanics calculations and empirical analyses have shown this is an incorrect simplification in that backbone covalent geometry actually varies systematically as a function of the phi and Psi backbone dihedral angles. Here, we use a nonredundant set of ultrahigh-resolution protein structures to define these conformation-dependent variations. The trends have a rational, structural basis that can be explained by avoidance of atomic clashes or optimization of favorable electrostatic interactions. To facilitate adoption of this paradigm, we have created a conformation-dependent library of covalent bond lengths and bond angles and shown that it has improved accuracy over existing methods without any additional variables to optimize. Protein structures derived from crystallographic refinement and predictive modeling both stand to benefit from incorporation of the paradigm.

Solution- and Adsorbed-State Structural Ensembles Predicted for the Statherin-Hydroxyapatite System

We have developed a multiscale structure prediction technique to study solution- and adsorbed-state ensembles of biomineralization proteins. The algorithm employs a Metropolis Monte Carlo-plus-minimization strategy that varies all torsional and rigid-body protein degrees of freedom. We applied the technique to fold statherin, starting from a fully extended peptide chain in solution, in the presence of hydroxyapatite (HAp) (001), (010), and (100) monoclinic crystals. Blind (unbiased) predictions capture experimentally observed macroscopic and high-resolution structural features and show minimal statherin structural change upon adsorption. The dominant structural difference between solution and adsorbed states is an experimentally observed folding event in statherin's helical binding domain. Whereas predicted statherin conformers vary slightly at three different HAp crystal faces, geometric and chemical similarities of the surfaces allow structurally promiscuous binding. Finally, we compare blind predictions with those obtained from simulation biased to satisfy all previously published solid-state NMR (ssNMR) distance and angle measurements (acquired from HAp-adsorbed statherin). Atomic clashes in these structures suggest a plausible, alternative interpretation of some ssNMR measurements as intermolecular rather than intramolecular. This work demonstrates that a combination of ssNMR and structure prediction could effectively determine high-resolution protein structures at biomineral interfaces.

A simple reference state makes a significant improvement in near‐native selections from structurally refined docking decoys

Near-native selections from docking decoys have proved challenging especially when unbound proteins are used in the molecular docking. One reason is that significant atomic clashes in docking decoys lead to poor predictions of binding affinities of near native decoys. Atomic clashes can be removed by structural refinement through energy minimization. Such an energy minimization, however, will lead to an unrealistic bias toward docked structures with large interfaces. Here, we extend an empirical energy function developed for protein design to protein–protein docking selection by introducing a simple reference state that removes the unrealistic dependence of binding affinity of docking decoys on the buried solvent accessible surface area of interface. The energy function called EMPIRE (EMpirical Protein-InteRaction Energy), when coupled with a refinement strategy, is found to provide a significantly improved success rate in near native selections when applied to RosettaDock and refined ZDOCK docking decoys. Our work underlines the importance of removing nonspecific interactions from specific ones in near native selections from docking decoys.