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We present a method to rapidly identify hydrogen-mediated interactions in proteins (e.g., hydrogen bonds, hydrogen bonds, water-mediated hydrogen bonds, salt bridges, and aromatic π-hydrogen interactions) through heavy atom geometry alone, that is, without needing to explicitly determine hydrogen atom positions using either experimental or theoretical methods. By including specific real (or virtual) partner atoms as defined by the atom type of both the donor and acceptor heavy atoms, a set of unique angles can be rapidly calculated. By comparing the distance between the donor and the acceptor and these unique angles to the statistical preferences observed in the Protein Data Bank (PDB), we were able to identify a set of conserved geometries (15 for donor atoms and 7 for acceptor atoms) for hydrogen-mediated interactions in proteins. This set of identified interactions includes every polar atom type present in the Protein Data Bank except OE1 (glutamate/glutamine sidechain) and a clear geometric preference for the methionine sulfur atom (SD) to act as a hydrogen bond acceptor. This method could be readily applied to protein design efforts.

The planarity of peptide bonds is an assumption that underlies decades of theoretical modeling of proteins. Peptide bonds strongly deviating from planarity are considered very rare features of protein structure that occur for functional reasons. Here, empirical analyses of atomic-resolution protein structures reveal that trans peptide groups can vary by more than 25° from planarity and that the true extent of nonplanarity is underestimated even in 1.2 Å resolution structures. Analyses as a function of the φ,ψ-backbone dihedral angles show that the expected value deviates by ±8° from planar as a systematic function of conformation, but that the large majority of variation in planarity depends on tertiary effects. Furthermore, we show that those peptide bonds in proteins that are most nonplanar, deviating by over 20° from planarity, are not strongly associated with active sites. Instead, highly nonplanar peptides are simply integral components of protein structure related to local and tertiary structural features that tend to be conserved among homologs. To account for the systematic φ,ψ-dependent component of nonplanarity, we present a conformation-dependent library that can be used in crystallographic refinement and predictive protein modeling.

Oligomerization plays an important role in the function of many proteins. Thus, understanding, predicting, and, ultimately, engineering oligomerization presents a long-standing interest. From the perspective of structural biology, protein–protein interactions have mainly been analyzed in terms of the biophysical nature and evolution of protein interfaces. Here, our aim is to quantify the importance of the larger structural context of protein interfaces in protein interaction evolution. Specifically, we ask to what extent intersubunit geometry affects oligomerization state. We define a set of structural parameters describing the overall geometry and relative positions of interfaces of homomeric complexes with different oligomeric states. This allows us to quantify the contribution of direct sequence changes in interfaces versus indirect changes outside the interface that affect intersubunit geometry. We find that such indirect, or allosteric mutations affecting intersubunit geometry via indirect mechanisms are as important as interface sequence changes for evolution of oligomeric states.

Statistical analysis reveals that the set of differences between the secondary shifts of the alpha- and beta-carbons for residues i of a protein (Deltadelta13C(alpha)i - Deltadelta13C(beta)i) provides the means to detect and correct referencing errors for 1H and 13C nuclei within a given dataset. In a correctly referenced protein dataset, linear regression plots of Deltadelta13C(alpha)i, Deltadelta13C(beta)i, or Deltadelta1H(alpha)i vs. (Deltadelta13C(alpha)i - Deltadelta13C(beta)i) pass through the origin from two directions, the helix-to-coil and strand-to-coil directions. Thus, linear analysis of chemical shifts (LACS) can be used to detect referencing errors and to recalibrate the 1H and 13C chemical shift scales if needed. The analysis requires only that the signals be identified with distinct residue types (intra-residue spin systems). LACS allows errors in calibration to be detected and corrected in advance of sequence-specific assignments and secondary structure determinations. Signals that do not fit the linear model (outliers) deserve scrutiny since they could represent errors in identifying signals with a particular residue, or interesting features such as a cis-peptide bond. LACS provides the basis for the automated detection of such features and for testing reassignment hypotheses. Early detection and correction of errors in referencing and spin system identifications can improve the speed and accuracy of chemical shift assignments and secondary structure determinations. We have used LACS to create a database of offset-corrected chemical shifts corresponding to nearly 1800 BMRB entries: 300 with and 1500 without corresponding three-dimensional (3D) structures. This database can serve as a resource for future analysis of the effects of amino acid sequence and protein secondary and tertiary structure on NMR chemical shifts.

This chapter describes the molecular fractionation with conjugate caps (MFCC)‐based fragmentation methods and their applications to biological systems. To account for the environmental polarization effect for each fragment calculation, electrostatic embedding was introduced into the GMFCC/molecular mechanics (MM) method which became the latest electrostatically embedded generalized molecular fractionation with conjugate caps (EE‐GMFCC) method for more accurate calculation of the protein energy. The EE‐GMFCC method is computationally efficient and linear‐scaling with a low prefactor, and has been successfully applied to protein geometry optimization, molecular dynamics simulation, protein‐ligand binding affinity calculation, and protein vibrational spectrum calculation at QM levels. The chapter combines the more accurate EE‐GMFCC method with the CPCM model, denoted as EE‐GMFCC‐CPCM, for accurate calculation of protein solvation energy. To reduce the computational cost, the mechanical embedded (ME)‐quantum mechanical (QM)/MM approach is used to describe the protein dynamics in explicit solvent while the water molecules are described by mechanical mechanics.

This chapter contains sections titled: Introduction Historical Development Structure Calculation Algorithms Automated NOE Assignment Nonclassical Approaches Fully Automated Structure Analysis References

Molecular self-organziation, also regarded as pattern formation, is crucial for the correct distribution of cellular content. The processes leading to spatiotemporal patterns often involve a multitude of molecules interacting in complex networks, so that only very few cellular pattern-forming systems can be regarded as well understood. Due to its compositional simplicity, the Escherichia coli MinCDE system has, thus, become a paradigm for protein pattern formation. This biological reaction diffusion system spatiotemporally positions the division machinery in E. coli and is closely related to ParA-type ATPases involved in most aspects of spatiotemporal organization in bacteria. The ATPase MinD and the ATPase-activating protein MinE self-organize on the membrane as a reaction matrix. In vivo, these two proteins typically oscillate from pole-to-pole, while in vitro they can form a variety of distinct patterns. MinC is a passenger protein supposedly operating as a downstream cue of the system, coupling it to the division machinery. The MinCDE system has helped to extract not only the principles underlying intracellular patterns, but also how they are shaped by cellular boundaries. Moreover, it serves as a model to investigate how patterns can confer information through specific and non-specific interactions with other molecules. Here, we review how the three Min proteins self-organize to form patterns, their response to geometric boundaries, and how these patterns can in turn induce patterns of other molecules, focusing primarily on experimental approaches and developments.

We recently described general principles for designing ideal protein structures stabilized by completely consistent local and nonlocal interactions. The principles relate secondary structure patterns to tertiary packing motifs and enable design of different protein topologies. To achieve fine control over protein shape and size within a particular topology, we have extended the design rules by systematically analyzing the codependencies between the lengths and packing geometry of successive secondary structure elements and the backbone torsion angles of the loop linking them. We demonstrate the control afforded by the resulting extended rule set by designing a series of proteins with the same fold but considerable variation in secondary structure length, loop geometry, β-strand registry, and overall shape. Solution NMR structures of four designed proteins for two different folds show that protein shape and size can be precisely controlled within a given protein fold. These extended design principles provide the foundation for custom design of protein structures performing desired functions.

With the recent development of accurate protein structure prediction tools, virtually all protein sequences now have an experimental or a modeled structure. It has therefore become essential to develop fast algorithms capable of detecting non-covalent interactions not only within proteins but also in protein-protein, protein-DNA, protein-RNA, and protein-ligand complexes. Interactions involving aromatic compounds, particularly their π molecular orbitals, hold unique significance among molecular interactions due to the electron delocalization, which is known to play a key role in processes such as protein aggregation. In this paper, we present PInteract, an algorithm that detects π-involving interactions in input structures based on geometric criteria, including π-π, cation-π, amino-π, His-π, and sulfur-π interactions. In addition, it is capable of detecting chains and clusters of π interactions as well as particular recurrent motifs at protein-DNA and protein-RNA interfaces, called stair motifs, consisting of a particular combination of π-π stacking, cation/amino/His-π and H-bond interactions.

Background Disulfide engineering is an important biotechnological tool that has advanced a wide range of research. The introduction of novel disulfide bonds into proteins has been used extensively to improve protein stability, modify functional characteristics, and to assist in the study of protein dynamics. Successful use of this technology is greatly enhanced by software that can predict pairs of residues that will likely form a disulfide bond if mutated to cysteines. Results We had previously developed and distributed software for this purpose: Disulfide by Design (DbD). The original DbD program has been widely used; however, it has a number of limitations including a Windows platform dependency. Here, we introduce Disulfide by Design 2.0 (DbD2), a web-based, platform-independent application that significantly extends functionality, visualization, and analysis capabilities beyond the original program. Among the enhancements to the software is the ability to analyze the B-factor of protein regions involved in predicted disulfide bonds. Importantly, this feature facilitates the identification of potential disulfides that are not only likely to form but are also expected to provide improved thermal stability to the protein. Conclusions DbD2 provides platform-independent access and significantly extends the original functionality of DbD. A web server hosting DbD2 is provided at http://cptweb.cpt.wayne.edu/DbD2/ .