Prediction of the folding of short polypeptide segments by uniform conformational sampling (original) (raw)

Prediction of the folding of short polypeptide segments in proteins by systematic search

A procedure, CONGEN, for uniformly sampling the conformationd space of short polypeptide segments in proteins has been implemented. Because the time required for this sampling grows exponentially with the number of residues, parameters are introduced to limit the conformationd space that has to be explored. This is done by the use of the empirical energy function of CHARMM (1983) J . Comput. Chem. 4, 187-2171 and truncating the search when conformations of grossly unfavorable energy are sampled. Tests are made to determine control parameters that optimize the search without excluding important configurations. When applied to known protein structures, the resulting procedure is generally capable of generating conformations where the lowest energy conformation matches the known structure within a rms deviation of 1 A.

Conformational subspace in simulation of early‐stage protein folding

Proteins: Structure, Function, and Bioinformatics, 2004

A probability calculus was used to simulate the early stages of protein folding in ab initio structure prediction. The probabilities of particular ϕ and ψ angles for each of 20 amino acids as they occur in crystal forms of proteins were used to calculate the amount of information necessary for the occurrence of given ϕ and ψ angles to be predicted. It was found that the amount of information needed to predict ϕ and ψ angles with 5° precision is much higher than the amount of information actually carried by individual amino acids in the polypeptide chain. To handle this problem, a limited conformational space for the preliminary search for optimal polypeptide structure is proposed based on a simplified geometrical model of the polypeptide chain and on the probability calculus. These two models, geometric and probabilistic, based on different sources, yield a common conclusion concerning how a limited conformational space can represent an early stage of polypeptide chain‐folding simul...

Limited conformational space for early-stage protein folding simulation

Bioinformatics, 2004

Motivation: The problem of early-stage protein folding is critical for protein structure prediction. The model presented introduces a common definition of protein structures which may be treated as the possible in silico early-stage form of the polypeptide chain. Limitation of the conformational space to the ellipse path on the Ramachandran map was tested as a possible sub-space to represent the early-stage structure for simulation of protein folding. The proposed conformational sub-space was developed on the basis of the backbone conformation, with side-chain interactions excluded. Results: The ellipse-path-limited conformation of BPTI was created using the criterion of shortest distance between Phi, Psi angles in native form of protein and the Phi, Psi angles belonging to the ellipse. No knots were observed in the structure created according to ellipse-path conformational sub-space. The energy minimization procedure applied to ellipse-path derived conformation directed structural changes toward the native form of the protein with SS-bonds system introduced to the procedure.

LMProt: An Efficient Algorithm for Monte Carlo Sampling of Protein Conformational Space

Biophysical Journal, 2004

A new and efficient Monte Carlo algorithm for sampling protein configurations in the continuous space is presented; the efficiency of this algorithm, named Local Moves for Proteins (LMProt), was compared to other alternative algorithms. For this purpose, we used an intrachain interaction energy function that is proportional to the root mean square deviation (rmsd) with respect to a-carbons from native structures of real proteins. For phantom chains, the LMProt method is ;10 4 and 20 times faster than the algorithms Thrashing (no local moves) and Sevenfold Way (local moves), respectively. Additionally, the LMProt was tested for real chains (excluded-volume all-atoms model); proteins 5NLL (138 residues) and 1BFF (129 residues) were used to determine the folding success j as a function of the number h of residues involved in the chain movements, and as a function of the maximum amplitude of atomic displacement dr max. Our results indicate that multiple local moves associated with relative chain flexibility, controlled by appropriate adjustments for h and dr max , are essential for configurational search efficiency.

Guiding Probabilistic Search of the Protein Conformational Space With Structural Profiles

J. Bioinform. Comput. Biol., 2012

The roughness of the protein energy surface poses a signi¯cant challenge to search algorithms that seek to obtain a structural characterization of the native state. Recent research seeks to bias search toward near-native conformations through one-dimensional structural pro¯les of the protein native state. Here we investigate the e®ectiveness of such pro¯les in a structure prediction setting for proteins of various sizes and folds. We pursue two directions. We¯rst investigate the contribution of structural pro¯les in comparison to or in conjunction with physics-based energy functions in providing an e®ective energy bias. We conduct this investigation in the context of Metropolis Monte Carlo with fragment-based assembly. Second, we explore the e®ectiveness of structural pro¯les in providing projection coordinates through which to organize the conformational space. We do so in the context of a robotics-inspired search framework proposed in our lab that employs projections of the conformational space to guide search. Our¯ndings indicate that structural pro¯les are most e®ective in obtaining physically realistic near-native conformations when employed in conjunction with physics-based energy functions. Our¯ndings also show that these pro¯les are very e®ective when employed instead as projection coordinates to guide probabilistic search toward undersampled regions of the conformational space.

Computational techniques for efficient conformational sampling of proteins

Current Opinion in Structural Biology, 2008

In this review, we summarize the computational methods for sampling the conformational space of biomacromolecules. We discuss the methods applicable to find only lowest energy conformations (global minimization of the potential-energy function) and to generate canonical ensembles (canonical Monte Carlo method and canonical molecular dynamics method and their extensions). Special attention is devoted to the use of coarse-grained models that enable simulations to be enhanced by several orders of magnitude.

Equilibrium folding pathways for model proteins

Journal of Statistical Physics, 1983

Protein conformations have been generated with both a Monte Carlo scheme and a simpler two-state noninteracting globule-coil model. Conformational energies are taken to consist of intraresidue and interresidue terms. Interresidue energies are taken to be proportional to the number of nativelike contacts. To describe probable folding pathways, either energy or the number of native residues are employed as simple one-dimensional folding-unfolding coordinates. By considering only conformations at each point on these coordinates, it is possible to obtain detailed conformational descriptions of relatively rare intermediates on the folding pathway. This technique of "trapping" intermediates and statistically characterizing them is useful for studying conformational transitions. Equilibrium folding-unfolding pathways have been constructed by connecting most probable conformations in order along the folding coordinate. Calculations with the noninteracting globule-coil model have been performed with details chosen to correspond to those in the Monte Carlo calculation for pancreatic trypsin inhibitor. Both pathways are similar. The a helix appears prior to formation of the central beta sheet; beta sheet formation coincides with a large maximum in the free energy because of the attendant loss of conformational entropy. Subsequently the Monte Carlo method indicates two alternative pathways for growth toward either the amino or the carboxyl terminus, followed by completion of the native form. For the globule-coil model, the growth pattern differs somewhat, with the appearance of the single pathway for folding up to the carboxyl terminus prior to completion of folding. This difference may originate in the Monte Carlo sampling procedures or in the simplifications of the globule-coil model.

The two aspects of the protein folding problem

A physics-based approach to the protein folding problem is presented. It is concerned with the computation of folding pathways and final native structures, given the amino acid sequences, an empirical all-atom potential energy function, and a procedure to identify the global minimum of the potential energy. Whereas the all-atom approach has provided three-dimensional structures of relatively small molecules and for helical proteins containing up to 46 residues, it has been necessary to develop a hierarchical approach to treat larger proteins. In the hierarchical approach, global optimization was originally carried out with a simplified united residue (UNRES) description of a polypeptide chain to locate the region in which the global minimum lies. Conversion of the UNRES structures in this region to all-atom structures is followed by a local search in this region. The performance of this physics-based approach in successive CASP blind tests for predicting protein structure is described. More recently, a molecular dynamics treatment with UNRES has been introduced to compute not only native structures but also folding pathways.

Analysis of Conformations of Amino Acid Residues and Prediction of Backbone Topography in Proteins

Israel Journal of Chemistry, 1974

Methods for describing a discrete number of conformational states of amino acid residues in proteins are presented and used to investigate the topography of chain folding. The relative importance of short-range, medium-range and long-range interactions is discussed in the light of an analysis of the conformational states for the different amino acid residues in eight proteins of known structure. A prediction algorithm, which assigns four states to each residue of a protein chain (a-helix, extended structure, bend, or coil), has been developed from a consideration of both short-and medium-range interactions and applied to thirteen proteins of known three-dimensional structure. The prediction algorithm is simple to apply, and the assignment of a-helix and extended structure is considerably better than in most other predictive schemes. The prediction of chain reversal or bend regions was also better than with previous algorithms, but these assignments were not as good as those for a-helix and extended structure. The motivation for the development of this algorithm is not only to demonstrate the relative importance of short-and longer-range interactions but, more important, to begin to develop procedures for obtaining an approximate starting conformation for subsequent energy minimization to predict the three-dimensional structure of a protein. This procedure, as well as various other methods for the prediction of the backbone topography and conformational states of residues in proteins from the amino acid sequence, have been reviewed and evaluated by comparing the success of the methods to the success expected from a random assignment of conformational states.