305 research outputs found
Identification of direct residue contacts in protein-protein interaction by message passing
Understanding the molecular determinants of specificity in protein-protein
interaction is an outstanding challenge of postgenome biology. The availability
of large protein databases generated from sequences of hundreds of bacterial
genomes enables various statistical approaches to this problem. In this context
covariance-based methods have been used to identify correlation between amino
acid positions in interacting proteins. However, these methods have an
important shortcoming, in that they cannot distinguish between directly and
indirectly correlated residues. We developed a method that combines covariance
analysis with global inference analysis, adopted from use in statistical
physics. Applied to a set of >2,500 representatives of the bacterial
two-component signal transduction system, the combination of covariance with
global inference successfully and robustly identified residue pairs that are
proximal in space without resorting to ad hoc tuning parameters, both for
heterointeractions between sensor kinase (SK) and response regulator (RR)
proteins and for homointeractions between RR proteins. The spectacular success
of this approach illustrates the effectiveness of the global inference approach
in identifying direct interaction based on sequence information alone. We
expect this method to be applicable soon to interaction surfaces between
proteins present in only 1 copy per genome as the number of sequenced genomes
continues to expand. Use of this method could significantly increase the
potential targets for therapeutic intervention, shed light on the mechanism of
protein-protein interaction, and establish the foundation for the accurate
prediction of interacting protein partners.Comment: Supplementary information available on
http://www.pnas.org/content/106/1/67.abstrac
Anchored Design of Protein-Protein Interfaces
Few existing protein-protein interface design methods allow for extensive backbone rearrangements during the design process. There is also a dichotomy between redesign methods, which take advantage of the native interface, and de novo methods, which produce novel binders.Here, we propose a new method for designing novel protein reagents that combines advantages of redesign and de novo methods and allows for extensive backbone motion. This method requires a bound structure of a target and one of its natural binding partners. A key interaction in this interface, the anchor, is computationally grafted out of the partner and into a surface loop on the design scaffold. The design scaffold's surface is then redesigned with backbone flexibility to create a new binding partner for the target. Careful choice of a scaffold will bring experimentally desirable characteristics into the new complex. The use of an anchor both expedites the design process and ensures that binding proceeds against a known location on the target. The use of surface loops on the scaffold allows for flexible-backbone redesign to properly search conformational space.This protocol was implemented within the Rosetta3 software suite. To demonstrate and evaluate this protocol, we have developed a benchmarking set of structures from the PDB with loop-mediated interfaces. This protocol can recover the correct loop-mediated interface in 15 out of 16 tested structures, using only a single residue as an anchor
Identification of hot-spot residues in protein-protein interactions by computational docking
<p>Abstract</p> <p>Background</p> <p>The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex.</p> <p>Results</p> <p>We have applied here normalized interface propensity (<it>NIP</it>) values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value), and the advantage of not requiring any prior structural knowledge of the complex.</p> <p>Conclusion</p> <p>The <it>NIP </it>values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.</p
Knowledge-based energy functions for computational studies of proteins
This chapter discusses theoretical framework and methods for developing
knowledge-based potential functions essential for protein structure prediction,
protein-protein interaction, and protein sequence design. We discuss in some
details about the Miyazawa-Jernigan contact statistical potential,
distance-dependent statistical potentials, as well as geometric statistical
potentials. We also describe a geometric model for developing both linear and
non-linear potential functions by optimization. Applications of knowledge-based
potential functions in protein-decoy discrimination, in protein-protein
interactions, and in protein design are then described. Several issues of
knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe
Integrating water exclusion theory into βcontacts to predict binding free energy changes and binding hot spots
10.1186/1471-2105-15-57BMC Bioinformatics151-BBMI
An effective all-atom potential for proteins
We describe and test an implicit solvent all-atom potential for simulations
of protein folding and aggregation. The potential is developed through studies
of structural and thermodynamic properties of 17 peptides with diverse
secondary structure. Results obtained using the final form of the potential are
presented for all these peptides. The same model, with unchanged parameters, is
furthermore applied to a heterodimeric coiled-coil system, a mixed alpha/beta
protein and a three-helix-bundle protein, with very good results. The
computational efficiency of the potential makes it possible to investigate the
free-energy landscape of these 49--67-residue systems with high statistical
accuracy, using only modest computational resources by today's standards
Four small puzzles that Rosetta doesn't solve
A complete macromolecule modeling package must be able to solve the simplest
structure prediction problems. Despite recent successes in high resolution
structure modeling and design, the Rosetta software suite fares poorly on
deceptively small protein and RNA puzzles, some as small as four residues. To
illustrate these problems, this manuscript presents extensive Rosetta results
for four well-defined test cases: the 20-residue mini-protein Trp cage, an even
smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease
inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies,
several lines of evidence indicate that conformational sampling is not the
major bottleneck in modeling these small systems. Instead, approximations and
omissions in the Rosetta all-atom energy function currently preclude
discriminating experimentally observed conformations from de novo models at
atomic resolution. These molecular "puzzles" should serve as useful model
systems for developers wishing to make foundational improvements to this
powerful modeling suite.Comment: Published in PLoS One as a manuscript for the RosettaCon 2010 Special
Collectio
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
Specialized dynamical properties of promiscuous residues revealed by simulated conformational ensembles
The ability to interact with different partners is one of the most important features in proteins. Proteins that bind a large number of partners (hubs) have been often associated with intrinsic disorder. However, many examples exist of hubs with an ordered structure, and evidence of a general mechanism promoting promiscuity in ordered proteins is still elusive. An intriguing hypothesis is that promiscuous binding sites have specific dynamical properties, distinct from the rest of the interface and pre-existing in the protein isolated state. Here, we present the first comprehensive study of the intrinsic dynamics of promiscuous residues in a large protein data set. Different computational methods, from coarse-grained elastic models to geometry-based sampling methods and to full-atom Molecular Dynamics simulations, were used to generate conformational ensembles for the isolated proteins. The flexibility and dynamic correlations of interface residues with a different degree of binding promiscuity were calculated and compared considering side chain and backbone motions, the latter both on a local and on a global scale. The study revealed that (a) promiscuous residues tend to be more flexible than nonpromiscuous ones, (b) this additional flexibility has a higher degree of organization, and (c) evolutionary conservation and binding promiscuity have opposite effects on intrinsic dynamics. Findings on simulated ensembles were also validated on ensembles of experimental structures extracted from the Protein Data Bank (PDB). Additionally, the low occurrence of single nucleotide polymorphisms observed for promiscuous residues indicated a tendency to preserve binding diversity at these positions. A case study on two ubiquitin-like proteins exemplifies how binding promiscuity in evolutionary related proteins can be modulated by the fine-tuning of the interface dynamics. The interplay between promiscuity and flexibility highlighted here can inspire new directions in protein-protein interaction prediction and design methods. © 2013 American Chemical Society
Predicting the Tolerated Sequences for Proteins and Protein Interfaces Using RosettaBackrub Flexible Backbone Design
Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s) are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface), interactions between and within parts of the structure (e.g. domains) can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others
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