Roland L. Dunbrack

All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins †

New protein parameters are reported for the all-atom empirical energy function in the CHARMM program. The parameter evaluation was based on a self-consistent approach designed to achieve a balance between the internal (bonding) and interaction (nonbonding) terms of the force field and among the solvent-solvent, solvent-solute, and solute-solute interactions. Optimization of the internal parameters used experimental gas-phase geometries, vibrational spectra, and torsional energy surfaces supplemented with ab initio results. The peptide backbone bonding parameters were optimized with respect to data for N-methylacetamide and the alanine dipeptide. The interaction parameters, particularly the atomic charges, were determined by fitting ab initio interaction energies and geometries of complexes between water and model compounds that represented the backbone and the various side chains. In addition, dipole moments, experimental heats and free energies of vaporization, solvation and sublimation, molecular volumes, and crystal pressures and structures were used in the optimization. The resulting protein parameters were tested by applying them to noncyclic tripeptide crystals, cyclic peptide crystals, and the proteins crambin, bovine pancreatic trypsin inhibitor, and carbonmonoxy myoglobin in vacuo and in crystals. A detailed analysis of the relationship between the alanine dipeptide potential energy surface and calculated protein φ, χ angles was made and used in optimizing the peptide group torsional parameters. The results demonstrate that use of ab initio structural and energetic data by themselves are not sufficient to obtain an adequate backbone representation for peptides and proteins in solution and in crystals. Extensive comparisons between molecular dynamics simulations and experimental data for polypeptides and proteins were performed for both structural and dynamic properties. Energy minimization and dynamics simulations for crystals demonstrate that the latter are needed to obtain meaningful comparisons with experimental crystal structures. The presented parameters, in combination with the previously published CHARMM all-atom parameters for nucleic acids and lipids, provide a consistent set for condensed-phase simulations of a wide variety of molecules of biological interest.

PISCES: a protein sequence culling server.

PISCES is a public server for culling sets of protein sequences from the Protein Data Bank (PDB) by sequence identity and structural quality criteria. PISCES can provide lists culled from the entire PDB or from lists of PDB entries or chains provided by the user. The sequence identities are obtained from PSI-BLAST alignments with position-specific substitution matrices derived from the non-redundant protein sequence database. PISCES therefore provides better lists than servers that use BLAST, which is unable to identify many relationships below 40% sequence identity and often overestimates sequence identity by aligning only well-conserved fragments. PDB sequences are updated weekly. PISCES can also cull non-PDB sequences provided by the user as a list of GenBank identifiers, a FASTA format file, or BLAST/PSI-BLAST output.

A graph-theory algorithm for rapid protein side-chain prediction

Fast and accurate side‐chain conformation prediction is important for homology modeling, ab initio protein structure prediction, and protein design applications. Many methods have been presented, although only a few computer programs are publicly available. The SCWRL program is one such method and is widely used because of its speed, accuracy, and ease of use. A new algorithm for SCWRL is presented that uses results from graph theory to solve the combinatorial problem encountered in the side‐chain prediction problem. In this method, side chains are represented as vertices in an undirected graph. Any two residues that have rotamers with nonzero interaction energies are considered to have an edge in the graph. The resulting graph can be partitioned into connected subgraphs with no edges between them. These subgraphs can in turn be broken into biconnected components, which are graphs that cannot be disconnected by removal of a single vertex. The combinatorial problem is reduced to finding the minimum energy of these small biconnected components and combining the results to identify the global minimum energy conformation. This algorithm is able to complete predictions on a set of 180 proteins with 34,342 side chains in <7 min of computer time. The total χ1 and χ1 + 2 dihedral angle accuracies are 82.6% and 73.7% using a simple energy function based on the backbone‐dependent rotamer library and a linear repulsive steric energy. The new algorithm will allow for use of SCWRL in more demanding applications such as sequence design and ab initio structure prediction, as well addition of a more complex energy function and conformational flexibility, leading to increased accuracy.

Improved prediction of protein side-chain conformations with SCWRL4.

Determination of side‐chain conformations is an important step in protein structure prediction and protein design. Many such methods have been presented, although only a small number are in widespread use. SCWRL is one such method, and the SCWRL3 program (2003) has remained popular because of its speed, accuracy, and ease‐of‐use for the purpose of homology modeling. However, higher accuracy at comparable speed is desirable. This has been achieved in a new program SCWRL4 through: (1) a new backbone‐dependent rotamer library based on kernel density estimates; (2) averaging over samples of conformations about the positions in the rotamer library; (3) a fast anisotropic hydrogen bonding function; (4) a short‐range, soft van der Waals atom–atom interaction potential; (5) fast collision detection using k‐discrete oriented polytopes; (6) a tree decomposition algorithm to solve the combinatorial problem; and (7) optimization of all parameters by determining the interaction graph within the crystal environment using symmetry operators of the crystallographic space group. Accuracies as a function of electron density of the side chains demonstrate that side chains with higher electron density are easier to predict than those with low‐electron density and presumed conformational disorder. For a testing set of 379 proteins, 86% of χ1 angles and 75% of χ1+2 angles are predicted correctly within 40° of the X‐ray positions. Among side chains with higher electron density (25–100th percentile), these numbers rise to 89 and 80%. The new program maintains its simple command‐line interface, designed for homology modeling, and is now available as a dynamic‐linked library for incorporation into other software programs. Proteins 2009.

PONDR-FIT: A Meta-Predictor of Intrinsically Disordered Amino Acids

Protein intrinsic disorder is becoming increasingly recognized in proteomics research. While lacking structure, many regions of disorder have been associated with biological function. There are many different experimental methods for characterizing intrinsically disordered proteins and regions; nevertheless, the prediction of intrinsic disorder from amino acid sequence remains a useful strategy especially for many large-scale proteomic investigations. Here we introduced a consensus artificial neural network (ANN) prediction method, which was developed by combining the outputs of several individual disorder predictors. By eight-fold cross-validation, this meta-predictor, called PONDR-FIT, was found to improve the prediction accuracy over a range of 3 to 20% with an average of 11% compared to the single predictors, depending on the datasets being used. Analysis of the errors shows that the worst accuracy still occurs for short disordered regions with less than ten residues, as well as for the residues close to order/disorder boundaries. Increased understanding of the underlying mechanism by which such meta-predictors give improved predictions will likely promote the further development of protein disorder predictors. Access to PONDR-FIT is available at www.disprot.org.

Rotamer libraries in the 21st century

Rotamer libraries are widely used in protein structure prediction, protein design, and structure refinement. As the size of the structure data base has increased rapidly in recent years, it has become possible to derive well-refined rotamer libraries using strict criteria for data inclusion and for studying dependence of rotamer populations and dihedral angles on local structural features.

Cyclic coordinate descent: A robotics algorithm for protein loop closure

In protein structure prediction, it is often the case that a protein segment must be adjusted to connect two fixed segments. This occurs during loop structure prediction in homology modeling as well as in ab initio structure prediction. Several algorithms for this purpose are based on the inverse Jacobian of the distance constraints with respect to dihedral angle degrees of freedom. These algorithms are sometimes unstable and fail to converge. We present an algorithm developed originally for inverse kinematics applications in robotics. In robotics, an end effector in the form of a robot hand must reach for an object in space by altering adjustable joint angles and arm lengths. In loop prediction, dihedral angles must be adjusted to move the C‐terminal residue of a segment to superimpose on a fixed anchor residue in the protein structure. The algorithm, referred to as cyclic coordinate descent or CCD, involves adjusting one dihedral angle at a time to minimize the sum of the squared distances between three backbone atoms of the moving C‐terminal anchor and the corresponding atoms in the fixed C‐terminal anchor. The result is an equation in one variable for the proposed change in each dihedral. The algorithm proceeds iteratively through all of the adjustable dihedral angles from the N‐terminal to the C‐terminal end of the loop. CCD is suitable as a component of loop prediction methods that generate large numbers of trial structures. It succeeds in closing loops in a large test set 99.79% of the time, and fails occasionally only for short, highly extended loops. It is very fast, closing loops of length 8 in 0.037 sec on average.

PISCES: recent improvements to a PDB sequence culling server

PISCES is a database server for producing lists of sequences from the Protein Data Bank (PDB) using a number of entry- and chain-specific criteria and mutual sequence identity. Our goal in culling the PDB is to provide the longest list possible of the highest resolution structures that fulfill the sequence identity and structural quality cut-offs. The new PISCES server uses a combination of PSI-BLAST and structure-based alignments to determine sequence identities. Structure alignment produces more complete alignments and therefore more accurate sequence identities than PSI-BLAST. PISCES now allows a user to cull the PDB by-entry in addition to the standard culling by individual chains. In this scenario, a list will contain only entries that do not have a chain that has a sequence identity to any chain in any other entry in the list over the sequence identity cut-off. PISCES also provides fully annotated sequences including gene name and species. The server allows a user to cull an input list of entries or chains, so that other criteria, such as function, can be used. Results from a search on the re-engineered RCSBs site for the PDB can be entered into the PISCES server by a single click, combining the powerful searching abilities of the PDB with PISCESs utilities for sequence culling. The servers data are updated weekly. The server is available at .

Large‐scale comparison of protein sequence alignment algorithms with structure alignments

Sequence alignment programs such as BLAST and PSI‐BLAST are used routinely in pairwise, profile‐based, or intermediate‐sequence‐search (ISS) methods to detect remote homologies for the purposes of fold assignment and comparative modeling. Yet, the sequence alignment quality of these methods at low sequence identity is not known. We have used the CE structure alignment program (Shindyalov and Bourne, Prot Eng 1998;11:739) to derive sequence alignments for all superfamily and family‐level related proteins in the SCOP domain database. CE aligns structures and their sequences based on distances within each protein, rather than on interprotein distances. We compared BLAST, PSI‐BLAST, CLUSTALW, and ISS alignments with the CE structural alignments. We found that global alignments with CLUSTALW were very poor at low sequence identity (<25%), as judged by the CE alignments. We used PSI‐BLAST to search the nonredundant sequence database (nr) with every sequence in SCOP using up to four iterations. The resulting matrix was used to search a database of SCOP sequences. PSI‐BLAST is only slightly better than BLAST in alignment accuracy on a per‐residue basis, but PSI‐BLAST matrix alignments are much longer than BLASTs, and so align correctly a larger fraction of the total number of aligned residues in the structure alignments. Any two SCOP sequences in the same superfamily that shared a hit or hits in the nr PSI‐BLAST searches were identified as linked by the shared intermediate sequence. We examined the quality of the longest SCOP‐query/ SCOP‐hit alignment via an intermediate sequence, and found that ISS produced longer alignments than PSI‐BLAST searches alone, of nearly comparable per‐residue quality. At 10–15% sequence identity, BLAST correctly aligns 28%, PSI‐BLAST 40%, and ISS 46% of residues according to the structure alignments. We also compared CE structure alignments with FSSP structure alignments generated by the DALI program. In contrast to the sequence methods, CE and structure alignments from the FSSP database identically align 75% of residue pairs at the 10–15% level of sequence identity, indicating that there is substantial room for improvement in these sequence alignment methods. BLAST produced alignments for 8% of the 10,665 nonimmunoglobulin SCOP superfamily sequence pairs (nearly all <25% sequence identity), PSI‐BLAST matched 17% and the double‐PSI‐BLAST ISS method aligned 38% with E‐values <10.0. The results indicate that intermediate sequences may be useful not only in fold assignment but also in achieving more complete sequence alignments for comparative modeling. Proteins 2000;40:6–22.

Structural profiling of endogenous S-nitrosocysteine residues reveals unique features that accommodate diverse mechanisms for protein S-nitrosylation

S-nitrosylation, the selective posttranslational modification of protein cysteine residues to form S-nitrosocysteine, is one of the molecular mechanisms by which nitric oxide influences diverse biological functions. In this study, unique MS-based proteomic approaches precisely pinpointed the site of S-nitrosylation in 328 peptides in 192 proteins endogenously modified in WT mouse liver. Structural analyses revealed that S-nitrosylated cysteine residues were equally distributed in hydrophobic and hydrophilic areas of proteins with an average predicted pKa of 10.01 ± 2.1. S-nitrosylation sites were over-represented in α-helices and under-represented in coils as compared with unmodified cysteine residues in the same proteins (χ2 test, P < 0.02). A quantile–quantile probability plot indicated that the distribution of S-nitrosocysteine residues was skewed toward larger surface accessible areas compared with the unmodified cysteine residues in the same proteins. Seventy percent of the S-nitrosylated cysteine residues were surrounded by negatively or positively charged amino acids within a 6-Å distance. The location of cysteine residues in α-helices and coils in highly accessible surfaces bordered by charged amino acids implies site directed S-nitrosylation mediated by protein–protein or small molecule interactions. Moreover, 13 modified cysteine residues were coordinated with metals and 15 metalloproteins were endogenously modified supporting metal-catalyzed S-nitrosylation mechanisms. Collectively, the endogenous S-nitrosoproteome in the liver has structural features that accommodate multiple mechanisms for selective site-directed S-nitrosylation.