Rasmus Fonseca

Characterizing RNA ensembles from NMR data with kinematic models

Functional mechanisms of biomolecules often manifest themselves precisely in transient conformational substates. Researchers have long sought to structurally characterize dynamic processes in non-coding RNA, combining experimental data with computer algorithms. However, adequate exploration of conformational space for these highly dynamic molecules, starting from static crystal structures, remains challenging. Here, we report a new conformational sampling procedure, KGSrna, which can efficiently probe the native ensemble of RNA molecules in solution. We found that KGSrna ensembles accurately represent the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans-activation response element stem–loop. Ensemble-based interpretations of averaged data can aid in formulating and testing dynamic, motion-based hypotheses of functional mechanisms in RNAs with broad implications for RNA engineering and therapeutic intervention.

Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks

BackgroundPredicting the three-dimensional structure of a protein from its amino acid sequence is currently one of the most challenging problems in bioinformatics. The internal structure of helices and sheets is highly recurrent and help reduce the search space significantly. However, random coil segments make up nearly 40% of proteins and they do not have any apparent recurrent patterns, which complicates overall prediction accuracy of protein structure prediction methods. Luckily, previous work has indicated that coil segments are in fact not completely random in structure and flanking residues do seem to have a significant influence on the dihedral angles adopted by the individual amino acids in coil segments. In this work we attempt to predict a probability distribution of these dihedral angles based on the flanking residues. While attempts to predict dihedral angles of coil segments have been done previously, none have, to our knowledge, presented comparable results for the probability distribution of dihedral angles.ResultsIn this paper we develop an artificial neural network that uses an input-window of amino acids to predict a dihedral angle probability distribution for the middle residue in the input-window. The trained neural network shows a significant improvement (4-68%) in predicting the most probable bin (covering a 30° × 30° area of the dihedral angle space) for all amino acids in the data set compared to baseline statistics. An accuracy comparable to that of secondary structure prediction (≈ 80%) is achieved by observing the 20 bins with highest output values.ConclusionMany different protein structure prediction methods exist and each uses different tools and auxiliary predictions to help determine the native structure. In this work the sequence is used to predict local context dependent dihedral angle propensities in coil-regions. This predicted distribution can potentially improve tertiary structure prediction methods that are based on sampling the backbone dihedral angles of individual amino acids. The predicted distribution may also help predict local structure fragments used in fragment assembly methods.

Coupled Motions in β2AR:Gαs Conformational Ensembles

G protein-coupled receptors (GPCRs) act as conduits in the plasma membrane, facilitating cellular responses to physiological events by activating intracellular signal transduction pathways. Extracellular signaling molecules can induce conformational changes in GPCR, which allow it to selectively activate intracellular protein partners such as heterotrimeric protein G. However, a major unsolved problem is how GPCRs and G proteins form complexes and how their interaction results in G protein activation. Here, we show that an inactive, agonist-free β2AR:Gαs complex can collectively sample intermediate states of the receptor on an activation pathway. An in silico conformational ensemble around the inactive state manifests significant conformational coupling between structural elements implicated in G protein activation throughout the complex. While Gαs helix α5 has received much attention as a driver for nucleotide exchange, we also observe interactions between helix αN with Intra Cellular Loop 2, which can be transmitted by β1 to facilitate nucleotide exchange by disrupting a salt bridge between the P-loop and Switch I. These interactions are moderated in an active state ensemble. Collectively, our results support an alternative view of G protein activation, in which precoupling can allosterically modulate an agonist-free receptor. Subsequent selective agonist recruitment would result in collective activation of the complex. This alternative view can help us understand how distinct extracellular binding partners result in different but interdependent signaling pathways, with broad implications for GPCR drug discovery.

Ranking Beta Sheet Topologies with Applications to Protein Structure Prediction

One reason why ab initio protein structure predictors do not perform very well is their inability to reliably identify long-range interactions between amino acids. To achieve reliable long-range interactions, all potential pairings of β-strands (β-topologies) of a given protein are enumerated, including the native β-topology. Two very different β-topology scoring methods from the literature are then used to rank all potential β-topologies. This has not previously been attempted for any scoring method. The main result of this paper is a justification that one of the scoring methods, in particular, consistently top-ranks native β-topologies. Since the number of potential β-topologies grows exponentially with the number of β-strands, it is unrealistic to expect that all potential β-topologies can be enumerated for large proteins. The second result of this paper is an enumeration scheme of a subset of β-topologies. It is shown that native-consistent β-topologies often are among the top-ranked β-topologies of this subset. The presence of the native or native-consistent β-topologies in the subset of enumerated potential β-topologies relies heavily on the correct identification of β-strands. The third contribution of this paper is a method to deal with the inaccuracies of secondary structure predictors when enumerating potential β-topologies. The results reported in this paper are highly relevant for ab initio protein structure prediction methods based on decoy generation. They indicate that decoy generation can be heavily constrained using top-ranked β-topologies as they are very likely to contain native or native-consistent β-topologies.

Frustration‐guided motion planning reveals conformational transitions in proteins

Proteins exist as conformational ensembles, exchanging between substates to perform their function. Advances in experimental techniques yield unprecedented access to structural snapshots of their conformational landscape. However, computationally modeling how proteins use collective motions to transition between substates is challenging owing to a rugged landscape and large energy barriers. Here, we present a new, robotics‐inspired motion planning procedure called dCC‐RRT that navigates the rugged landscape between substates by introducing dynamic, interatomic constraints to modulate frustration. The constraints balance non‐native contacts and flexibility, and instantaneously redirect the motion towards sterically favorable conformations. On a test set of eight proteins determined in two conformations separated by, on average, 7.5 Å root mean square deviation (RMSD), our pathways reduced the Cα atom RMSD to the goal conformation by 78%, outperforming peer methods. We then applied dCC‐RRT to examine how collective, small‐scale motions of four side‐chains in the active site of cyclophilin A propagate through the protein. dCC‐RRT uncovered a spatially contiguous network of residues linked by steric interactions and collective motion connecting the active site to a recently proposed, non‐canonical capsid binding site 25 Å away, rationalizing NMR and multi‐temperature crystallography experiments. In all, dCC‐RRT can reveal detailed, all‐atom molecular mechanisms for small and large amplitude motions. Source code and binaries are freely available at https://github.com/ExcitedStates/KGS/.

KGSrna: Efficient 3D Kinematics-Based Sampling for Nucleic Acids

Noncoding ribonucleic acids (RNA) play a critical role in a wide variety of cellular processes, ranging from regulating gene expression to post-translational modification and protein synthesis. Their activity is modulated by highly dynamic exchanges between three-dimensional conformational substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules. Our procedure projects degrees of freedom onto a subspace of conformation space defined by distance constraints in the tertiary structure. The dimensionality reduction enables efficient exploration of conformational space. We show that the conformational distributions obtained with our method broadly sample the conformational landscape observed in NMR experiments. Compared to normal mode analysis-based exploration, our procedure diffuses faster through the experimental ensemble while also accessing conformational substates to greater precision. Our results suggest that conformational sampling with a highly reduced but fully atomistic representation of noncoding RNA expresses key features of their dynamic nature.

Bounding Volumes for Proteins: A Comparative Study

A chain tree is a data structure for representing changing protein conformations. It enables very fast detection of clashes and free potential energy calculations. The efficiency of chain trees is closely related to the bounding volumes associated with chain tree nodes. A protein subchain associated with a node of a chain tree will clash with another subchain only if their bounding volumes intersect. It is therefore essential that bounding volumes are as tight as possible while intersection tests can be carried out efficiently. We compare the performance of four different types of bounding volumes in connection with the rotation of protein bonds. It is observed that oriented bounding boxes are not as good as could be expected judging by their extensive use in various applications. Both rectangular- and line-swept spheres are shown to have very good tightness of fit but the line-swept, or even simple spheres, are shown to be significantly faster because of quick overlap checks. We also investigate how the performance of the recently introduced adjustable chain trees is affected by different bounding volume types.

Collision-free poisson motion planning in ultra high-dimensional molecular conformation spaces

The function of protein, RNA, and DNA is modulated by fast, dynamic exchanges between three‐dimensional conformations. Conformational sampling of biomolecules with exact and nullspace inverse kinematics, using rotatable bonds as revolute joints and noncovalent interactions as holonomic constraints, can accurately characterize these native ensembles. However, sampling biomolecules remains challenging owing to their ultra‐high dimensional configuration spaces, and the requirement to avoid (self‐) collisions, which results in low acceptance rates. Here, we present two novel mechanisms to overcome these limitations. First, we introduce temporary constraints between near‐colliding links. The resulting constraint varieties instantaneously redirect the search for collision‐free conformations, and couple motions between distant parts of the linkage. Second, we adapt a randomized Poisson‐disk motion planner, which prevents local oversampling and widens the search, to ultra‐high dimensions. Tests on several model systems show that the sampling acceptance rate can increase from 16% to 70%, and that the conformational coverage in loop modeling measured as average closeness to existing loop conformations doubled. Correlated protein motions identified with our algorithm agree with those from MD simulations.

Fast, clash-free RNA conformational morphing using molecular junctions

Motivation: Non‐coding ribonucleic acids (ncRNA) are functional RNA molecules that are not translated into protein. They are extremely dynamic, adopting diverse conformational substates, which enables them to modulate their interaction with a large number of other molecules. The flexibility of ncRNA provides a challenge for probing their complex 3D conformational landscape, both experimentally and computationally. Results: Despite their conformational diversity, ncRNAs mostly preserve their secondary structure throughout the dynamic ensemble. Here we present a kinematics‐based procedure to morph an RNA molecule between conformational substates, while avoiding inter‐atomic clashes. We represent an RNA as a kinematic linkage, with fixed groups of atoms as rigid bodies and rotatable bonds as degrees of freedom. Our procedure maintains RNA secondary structure by treating hydrogen bonds between base pairs as constraints. The constraints define a lower‐dimensional, secondary‐structure constraint manifold in conformation space, where motions are largely governed by molecular junctions of unpaired nucleotides. On a large benchmark set, we show that our morphing procedure compares favorably to peer algorithms, and can approach goal conformations to within a low all‐atom RMSD by directing fewer than 1% of its atoms. Our results suggest that molecular junctions can modulate 3D structural rearrangements, while secondary structure elements guide large parts of the molecule along the transition to the correct final conformation. Availability and Implementation: The source code, binaries and data are available at https://simtk.org/home/kgs. Contact: amelie.heliou@polytechnique.edu or vdbedem@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Adjustable Chain Trees for Proteins

A chain tree is a data structure for changing protein conformations. It enables very fast detection of clashes and free energy potential calculations. A modified version of chain trees that adjust themselves to the changing conformations of folding proteins is introduced. This results in much tighter bounding volume hierarchies and therefore fewer intersection checks. Computational results indicate that the efficiency of the adjustable chain trees is significantly improved compared to the traditional chain trees.