Knowledge-based potentials in protein fold recognition

An accurate potential function is essential for protein folding problem and structure prediction. Two different types of potential energy functions are currently in use. The first type is based on the law of physics and second type is referred to as statistical potentials or knowledge based potentials.  In the latter type, the energy function is extracted from statistical analysis of experimental data of known protein structures. By increasing the amount of three dimensional protein structures, this approach is growing rapidly.There are various forms of knowledge based potentials depending on how statistics are calculated and how proteins are modeled. In this review, we explain how the knowledge based potentials are extracted by using known protein structures and briefly compare many of the potentials in theory

Orientation-dependent backbone-only residue pair scoring functions for fixed backbone protein design

<p>Abstract</p> <p>Background</p> <p>Empirical scoring functions have proven useful in protein structure modeling. Most such scoring functions depend on protein side chain conformations. However, backbone-only scoring functions do not require computationally intensive structure optimization and so are well suited to protein design, which requires fast score evaluation. Furthermore, scoring functions that account for the distinctive relative position and orientation preferences of residue pairs are expected to be more accurate than those that depend only on the separation distance.</p> <p>Results</p> <p>Residue pair scoring functions for fixed backbone protein design were derived using only backbone geometry. Unlike previous studies that used spherical harmonics to fit 2D angular distributions, Gaussian Mixture Models were used to fit the full 3D (position only) and 6D (position and orientation) distributions of residue pairs. The performance of the 1D (residue separation only), 3D, and 6D scoring functions were compared by their ability to identify correct threading solutions for a non-redundant benchmark set of protein backbone structures. The threading accuracy was found to steadily increase with increasing dimension, with the 6D scoring function achieving the highest accuracy. Furthermore, the 3D and 6D scoring functions were shown to outperform side chain-dependent empirical potentials from three other studies. Next, two computational methods that take advantage of the speed and pairwise form of these new backbone-only scoring functions were investigated. The first is a procedure that exploits available sequence data by averaging scores over threading solutions for homologs. This was evaluated by applying it to the challenging problem of identifying interacting transmembrane alpha-helices and found to further improve prediction accuracy. The second is a protein design method for determining the optimal sequence for a backbone structure by applying Belief Propagation optimization using the 6D scoring functions. The sensitivity of this method to backbone structure perturbations was compared with that of fixed-backbone all-atom modeling by determining the similarities between optimal sequences for two different backbone structures within the same protein family. The results showed that the design method using 6D scoring functions was more robust to small variations in backbone structure than the all-atom design method.</p> <p>Conclusions</p> <p>Backbone-only residue pair scoring functions that account for all six relative degrees of freedom are the most accurate and including the scores of homologs further improves the accuracy in threading applications. The 6D scoring function outperformed several side chain-dependent potentials while avoiding time-consuming and error prone side chain structure prediction. These scoring functions are particularly useful as an initial filter in protein design problems before applying all-atom modeling.</p

Fast Atomic Charge Calculation for Implementation into a Polarizable Force Field and Application to an Ion Channel Protein

Polarization of atoms plays a substantial role in molecular interactions. Class I and II force fields mostly calculate with fixed atomic charges which can cause inadequate descriptions for highly charged molecules, for example, ion channels or metalloproteins. Changes in charge distributions can be included into molecular mechanics calculations by various methods. Here, we present a very fast computational quantum mechanical method, the Bond Polarization Theory (BPT). Atomic charges are obtained via a charge calculation method that depend on the 3D structure of the system in a similar way as atomic charges of ab initio calculations. Different methods of population analysis and charge calculation methods and their dependence on the basis set were investigated. A refined parameterization yielded excellent correlation of R=0.9967. The method was implemented in the force field COSMOS-NMR and applied to the histidine-tryptophan-complex of the transmembrane domain of the M2 protein channel of influenza A virus. Our calculations show that moderate changes of side chain torsion angle χ1 and small variations of χ2 of Trp-41 are necessary to switch from the inactivated into the activated state; and a rough two-side jump model of His-37 is supported for proton gating in accordance with a flipping mechanism

Assessment of atomic charge models for gas-phase computations on polypeptides

The concept of the atomic charge is extensively used to model the electrostatic properties of proteins. Atomic charges are not only the basis for the electrostatic energy term in biomolecular force fields but are also derived from quantum mechanical computations on protein fragments to get more insight into their electronic structure. Unfortunately there are many atomic charge schemes which lead to significantly different results, and it is not trivial to determine which scheme is most suitable for biomolecular studies. Therefore, we present an extensive methodological benchmark using a selection of atomic charge schemes [Mulliken, natural, restrained electrostatic potential, Hirshfeld-I, electronegativity equalization method (EEM), and split-charge equilibration (SQE)] applied to two sets of penta-alanine conformers. Our analysis clearly shows that Hirshfeld-I charges offer the best compromise between transferability (robustness with respect to conformational changes) and the ability to reproduce electrostatic properties of the penta-alanine. The benchmark also considers two charge equilibration models (EEM and SQE), which both clearly fail to describe the locally charged moieties in the zwitterionic form of penta-alanine. This issue is analyzed in detail because charge equilibration models are computationally much more attractive than the Hirshfeld-I scheme. Based on the latter analysis, a straightforward extension of the SQE model is proposed, SQE+Q0, that is suitable to describe biological systems bearing many locally charged functional groups

Computational studies of biomembrane systems: Theoretical considerations, simulation models, and applications

This chapter summarizes several approaches combining theory, simulation and experiment that aim for a better understanding of phenomena in lipid bilayers and membrane protein systems, covering topics such as lipid rafts, membrane mediated interactions, attraction between transmembrane proteins, and aggregation in biomembranes leading to large superstructures such as the light harvesting complex of green plants. After a general overview of theoretical considerations and continuum theory of lipid membranes we introduce different options for simulations of biomembrane systems, addressing questions such as: What can be learned from generic models? When is it expedient to go beyond them? And what are the merits and challenges for systematic coarse graining and quasi-atomistic coarse grained models that ensure a certain chemical specificity

Membrane-mediated interactions

Interactions mediated by the cell membrane between inclusions, such as membrane proteins or antimicrobial peptides, play important roles in their biological activity. They also constitute a fascinating challenge for physicists, since they test the boundaries of our understanding of self-assembled lipid membranes, which are remarkable examples of two-dimensional complex fluids. Inclusions can couple to various degrees of freedom of the membrane, resulting in different types of interactions. In this chapter, we review the membrane-mediated interactions that arise from direct constraints imposed by inclusions on the shape of the membrane. These effects are generic and do not depend on specific chemical interactions. Hence, they can be studied using coarse-grained soft matter descriptions. We deal with long-range membrane-mediated interactions due to the constraints imposed by inclusions on membrane curvature and on its fluctuations. We also discuss the shorter-range interactions that arise from the constraints on membrane thickness imposed by inclusions presenting a hydrophobic mismatch with the membrane.Comment: 38 pages, 10 figures, pre-submission version. In: Bassereau P., Sens P. (eds) Physics of Biological Membranes. Springer, Cha

Rotamer-specific Statistical Potentials for Protein Structure Modeling.

Knowledge-based (or statistical) potentials are widely used as essential tools in protein structure modeling and quality assessment. They are derived from experimentally determined protein structures aiming to extract relevant structural features that characterize the tightly folded structures. Since the surrounding circumstances are inhomogeneous and anisotropic, multibody contributions are important for accurate account of cooperative effects of molecular interactions. On the other hand, protein residues have great flexibility. It is energetically favorable for residues to adopt only a limited number of staggered conformations, known as rotamers. Depending on the rotameric state, the residue conformation and intra-residue interaction vary significantly within protein structures, resulting in different solvent accessibility and different electric polarization effect as well as different steric effect on residue elements. The major goal of this thesis is the design and development of statistical potentials that take into account the rotamer-dependence of interactions. We hypothesized that the rotameric state of residues is related to the specificity of interactions within protein structures. We first investigated how amino acid residues in PDB structures show different interaction patterns with the environment depending on their rotameric states. Observed rotamer-specific environmental features were incorporated to a scoring function, ProtGrid for protein designs. Our tests demonstrated that the ProtGrid is superior to widely used Rosetta energy function in prediction of the native amino acid types and rotameric states. Next, we formulated a rotamer-specific atomic statistical potential, named ROTAS that extends an existing orientation-dependent atomic potential (GOAP) by including the influence of rotameric states of residues on the specificity of interactions. The results showed that ROTAS performs better than other competing potentials not only in native structure recognition, but also in best model selection and correlation coefficients between energy and model quality. Finally, we applied the ROTAS potential to the problem of side-chain prediction. Compared with the existing side-chain modeling programs, ROTAS achieved comparable or even better prediction accuracy. We expect that the effectiveness of our energy functions would provide insightful information for the development of many applications which require accurate side-chain modeling such as homology modeling, protein design, mutation analysis, protein-protein docking and flexible ligand docking.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102342/1/jungkap_1.pd

Membrane-mediated interactions

International audienceInteractions mediated by the cell membrane between inclusions, such as membrane proteins or antimicrobial peptides, play important roles in their biological activity. They also constitute a fascinating challenge for physicists, since they test the boundaries of our understanding of self-assembled lipid membranes, which are remarkable examples of two-dimensional complex fluids. Inclusions can couple to various degrees of freedom of the membrane, resulting in different types of interactions. In this chapter, we review the membrane-mediated interactions that arise from direct constraints imposed by inclusions on the shape of the membrane. These effects are generic and do not depend on specific chemical interactions. Hence, they can be studied using coarse-grained soft matter descriptions. We deal with long-range membrane-mediated interactions due to the constraints imposed by inclusions on membrane curvature and on its fluctuations. We also discuss the shorter-range interactions that arise from the constraints on membrane thickness imposed by inclusions presenting a hydrophobic mismatch with the membrane