Dissecting contact potentials for proteins: relative contributions of individual amino acids.

Knowledge-based contact potentials are routinely used in fold recognition, binding of peptides to proteins, structure prediction, and coarse-grained models to probe protein folding kinetics. The dominant physical forces embodied in the contact potentials are revealed by eigenvalue analysis of the matrices, whose elements describe the strengths of interaction between amino acid side chains. We propose a general method to rank quantitatively the importance of various inter-residue interactions represented in the currently popular pair contact potentials. Eigenvalue analysis and correlation diagrams are used to rank the inter-residue pair interactions with respect to the magnitude of their relative contributions to the contact potentials. The amino acid ranking is shown to be consistent with a mean field approximation that is used to reconstruct the original contact potentials from the most relevant amino acids for several contact potentials. By providing a general, relative ranking score for amino acids, this method permits a detailed, quantitative comparison of various contact interaction schemes. For most contact potentials, between 7 and 9 amino acids of varying chemical character are needed to accurately reconstruct the full matrix. By correlating the identified important amino acid residues in contact potentials and analysis of about 7800 structural domains in the CATH database we predict that it is important to model accurately interactions between small hydrophobic residues. In addition, only potentials that take interactions involving the protein backbone into account can predict dense packing in protein structures.

Substrate-induced conformational changes and dynamics of UDP-N-acetylgalactosamine:polypeptide N-acetylgalactosaminyltransferase-2.

O-Glycan biosynthesis is initiated by the transfer of N-acetylgalactosamine (GalNAc) from a nucleotide sugar donor (UDP-GalNAc) to Ser/Thr residues of an acceptor substrate. The detailed transfer mechanism, catalyzed by the UDP-GalNAc polypeptide:N-acetyl-alpha-galactosaminyltransferases (ppGalNAcTs), remains unclear despite structural information available for several isoforms in complex with substrates at various stages along the catalytic pathway. We used all-atom molecular dynamics simulations with explicit solvent and counterions to study the conformational dynamics of ppGalNAcT-2 in several enzymatic states along the catalytic pathway. ppGalNAcT-2 is simulated both in the presence and in the absence of substrates and reaction products to examine the role of conformational changes in ligand binding. In multiple 40-ns-long simulations of more than 600 ns total run time, we studied systems ranging from 45,000 to 95,000 atoms. Our simulations accurately identified dynamically active regions of the protein, as previously revealed by the X-ray structures, and permitted a detailed, atomistic description of the conformational changes of loops near the active site and the characterization of the ensemble of structures adopted by the transferase complex on the transition pathway between the ligand-bound and ligand-free states. In particular, the conformational transition of a functional loop adjacent to the active site from closed (active) to open (inactive) is correlated with the rotameric state of the conserved residue W331. Analysis of water dynamics in the active site revealed that internal water molecules have an important role in enhancing the enzyme flexibility. We also found evidence that charged side chains in the active site rearrange during site opening to facilitate ligand binding. Our results are consistent with the single-displacement transfer mechanism previously proposed for ppGalNAcTs based on X-ray structures and mutagenesis data and provide new evidence for possible functional roles of certain amino acids conserved across several isoforms.

Development of novel statistical potentials for protein fold recognition.

The need to perform large-scale studies of protein fold recognition, structure prediction and protein-protein interactions has led to novel developments of residue-level minimal models of proteins. A minimum requirement for useful protein force-fields is that they be successful in the recognition of native conformations. The balance between the level of detail in describing the specific interactions within proteins and the accuracy obtained using minimal protein models is the focus of many current protein studies. Recent results suggest that the introduction of explicit orientation dependence in a coarse-grained, residue-level model improves the ability of inter-residue potentials to recognize the native state. New statistical and optimization computational algorithms can be used to obtain accurate residue-dependent potentials for use in protein fold recognition and, more importantly, structure prediction.

Continuous anisotropic representation of coarse-grained potentials for proteins by spherical harmonics synthesis.

A new method is presented for extracting statistical potentials dependent on the relative side chain and backbone orientations in proteins. Coarse-grained, anisotropic potentials are constructed for short-, medium-, and long-range interactions using the Boltzmann method and a database of non-homologous protein structures. The new orientation-dependent potentials are analyzed using a spherical harmonics decomposition method with real eigenfunctions. This method permits a more realistic, continuous angular representation of the coarse-grained potentials. Results of tests for discriminating the native protein conformations from large sets of decoy proteins, show that the new continuous distance- and orientation-dependent potentials present significantly improved performance. Novel graphical representations are developed and used to depict the orientational dependence of the interaction potentials. These new continuous anisotropic statistical potentials could be instrumental in developing new computational methods for structure prediction, threading and coarse-grained simulations.