Atomic-accuracy prediction of protein loop structures through an RNA-inspired ansatz

Consistently predicting biopolymer structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative modeling and protein design, can become intractable as loop lengths exceed 10 residues and if surrounding side-chain conformations are erased. This article introduces a modeling strategy based on a 'stepwise ansatz', recently developed for RNA modeling, which posits that any realistic all-atom molecular conformation can be built up by residue-by-residue stepwise enumeration. When harnessed to a dynamic-programming-like recursion in the Rosetta framework, the resulting stepwise assembly (SWA) protocol enables enumerative sampling of a 12 residue loop at a significant but achievable cost of thousands of CPU-hours. In a previously established benchmark, SWA recovers crystallographic conformations with sub-Angstrom accuracy for 19 of 20 loops, compared to 14 of 20 by KIC modeling with a comparable expenditure of computational power. Furthermore, SWA gives high accuracy results on an additional set of 15 loops highlighted in the biological literature for their irregularity or unusual length. Successes include cis-Pro touch turns, loops that pass through tunnels of other side-chains, and loops of lengths up to 24 residues. Remaining problem cases are traced to inaccuracies in the Rosetta all-atom energy function. In five additional blind tests, SWA achieves sub-Angstrom accuracy models, including the first such success in a protein/RNA binding interface, the YbxF/kink-turn interaction in the fourth RNA-puzzle competition. These results establish all-atom enumeration as a systematic approach to protein structure that can leverage high performance computing and physically realistic energy functions to more consistently achieve atomic resolution.Comment: Identity of four-loop blind test protein and parts of figures 5 have been omitted in this preprint to ensure confidentiality of the protein structure prior to its public releas

Conformational changes in the Hepatitis B virus core protein are consistent with a role for allostery in virus assembly

In infected cells, virus components must be organized at the right place and time to ensure assembly of infectious virions. From a different perspective, assembly must be prevented until all components are available. Hypothetically, this can be achieved by allosterically controlling assembly. Consistent with this hypothesis, here we show that the structure of hepatitis B virus (HBV) core protein dimer, which can spontaneously self-assemble, is incompatible with capsid assembly. Systematic differences between core protein in dimer and capsid conformations demonstrate linkage between the intradimer interface and interdimer contact surface. These structures also provide explanations for the capsid-dimer selectivity of some antibodies and activity of assembly effectors. Solution studies suggest that the assembly-inactive state is more accurately an ensemble of conformations. Simulations show that allostery supports controlled assembly and results in capsids that are resistant to dissociation. We propose that allostery, as demonstrated in HBV, is common to most self-assembling viruses

Introduction to protein folding for physicists

The prediction of the three-dimensional native structure of proteins from the knowledge of their amino acid sequence, known as the protein folding problem, is one of the most important yet unsolved issues of modern science. Since the conformational behaviour of flexible molecules is nothing more than a complex physical problem, increasingly more physicists are moving into the study of protein systems, bringing with them powerful mathematical and computational tools, as well as the sharp intuition and deep images inherent to the physics discipline. This work attempts to facilitate the first steps of such a transition. In order to achieve this goal, we provide an exhaustive account of the reasons underlying the protein folding problem enormous relevance and summarize the present-day status of the methods aimed to solving it. We also provide an introduction to the particular structure of these biological heteropolymers, and we physically define the problem stating the assumptions behind this (commonly implicit) definition. Finally, we review the 'special flavor' of statistical mechanics that is typically used to study the astronomically large phase spaces of macromolecules. Throughout the whole work, much material that is found scattered in the literature has been put together here to improve comprehension and to serve as a handy reference.Comment: 53 pages, 18 figures, the figures are at a low resolution due to arXiv restrictions, for high-res figures, go to http://www.pabloechenique.co

Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.

ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements ("flips" of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation

Conformational sampling by a general linearized embedding algorithm

Linearized embedding is a variant on the usual distance geometry methods for finding atomic Cartesian coordinates given constraints on interatomic distances. Instead of dealing primarily with the matrix of interatomic distances, linearized embedding concentrates on properties of the metric matrix, the matrix of inner products between pairs of vectors defining local coordinate systems within the molecule. We developed a pair of general computer programs that first convert a given arbitrary conformation of any covalent molecule from atomic Cartesian coordinates representation to internal local coordinate systems enforcing rigid valence geometry and then generate a random sampling of conformers in terms of atomic Cartesian coordinates that satisfy the rigid local geometry and a given list of interatomic distance constraints. We studied the sampling properties of this linearized embedding algorithm vs. a standard metric matrix embedding program, DGEOM, on cyclohexane, cycloheptane, and a cyclic pentapeptide. Linearized embedding always produces exactly correct bond lengths, bond angles, planarities, and chiralities; it runs at least two times faster per structure generated, and is successful as much as four times as often at refining these structures to full agreement with the constraints. It samples the full range of allowed conformations broadly, although not perfectly uniformly. Because local geometry is rigid, linearized embedding's sampling in terms of torsion angles is more restricted than that of DGEOM, but it finds in some instances conformations missed by DGEOM. © 1992 by John Wiley & Sons, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/38284/1/540131010_ftp.pd

Chemical distance geometry: Current realization and future projection

Since the 1988 monograph “Distance Geometry and Molecular Conformation” by Crippen and Havel, there have been significant changes in the application of distance geometry to problems of chemical interest. This review attempts to outline what the current state of the art is, in both the underlying mathematical methods and chemical applications, and to indicate future developments. Rather than go into details concerning algorithms and theorems, the emphasis is on defining the kinds of problems we can solve or would like to, and then guiding the interested reader to the recent literature. Special emphasis is given to the problem of determining macromolecular conformation in solution by NMR, including energy functions, and dealing with conformational flexibility.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43067/1/10910_2005_Article_BF01192588.pd

Computational Protein Design and Molecular Dynamics Simulations: A Study of Membrane Proteins, Small Peptides and Molecular Systems

Molecular design and modeling can provide stringent assessment of our understanding of the structure and function of proteins. Due to the subtleness of the interactions that largely stabilize proteins, computational methods have been particularly valuable in establishing practical, formal and physically grounded protocols to study the structure and function of these biomolecules. Especifically, computational protein design seeks to identify sequences that fold into a desired structure and have specific structural and functional properties using computational methodologies. Among current techniques, an entropy-based formalism that efficiently determines the number and composition of sequences satisfying a predefined set of constraints seems particularly promising and powerful. Complementary to this methodology are the well-established molecular dynamics simulation techniques that have been extensively used to study structure, function and dynamics of biologically relevant systems. Herein different studies of systems using computational techniques to address particular molecular problems are described. Efforts to redesign membrane proteins to generate water-soluble variants were applied to a widely studied pentameric ligand-gated ion channel, the nicotinic acetylchoilne receptor (nAChR). NMR structures and binding studies demostrated the robustness and applicability of the computational design approach. Toward the creation of water-soluble variants of a G protein–coupled receptor (GPCR), comparative modeling and docking calculations were used to investigate the structure of the human μ opioid receptor and presented in light of previous mutagenesis studies of structure and agonist-induced activation. Candidate peptides for possible therapeutic agents were computationally analyzed. Peptide design, loop modeling and MD simulations were applied to investigate the stromal cell-derived factor-1&a; (SDF-1&a;). SDF-1&a; displays promising therapeutic benefits to treat blood-supply related heart disease and elicit growth of microvasculature. Simplified analogs of SDF-1&a; exhibit enhanced therapeutic properties in cell-based assays. MD simulations provide insights about the molecular features of this enhancement. One simplified peptide offers a potentially clinically translatable neovasculogenic therapy. Lastly, MD simulations were utilized to analyze a molecule with hindered internal rotors, a tribenzylamine hemicryptophane. The molecule was characterized by different experimental and computational techniques. The structural and dynamic features of the hemicryptophane molecule make it an attractive starting point for controlling internal rotation of aromatic rings within molecular systems