Advances in high-resolution cryo-electron microscopy (cryo-EM) require the development of validation metrics to independently assess map quality and model geometry. We report EMRinger, a tool that assesses the precise fitting of an atomic model into the map during refinement and shows how radiation damage alters scattering from negatively charged amino acids. EMRinger (https://github.com/fraser-lab/EMRinger) will be useful for monitoring progress in resolving and modeling high-resolution features in cryo-EM.

/pdf/emringer-side-chain-directed-model-and-map-validation-for-3d-4dbyok0s6n.pdf

EMRinger: side chain-directed model and map validation for 3D cryo-electron microscopy

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The Nature Of The Chemical Bond

Interactions between macromolecules in general, and between proteins in particular, are essential for any life process. Examples include transfer of information, inhibition or activation of function, molecular recognition as in the immune system, assembly of macromolecular structures and molecular machines, and more. Proteins interact with affinities ranging from millimolar to femtomolar and, because affinity determines the concentration required to obtain 50% binding, the amount of different complexes formed is very much related to local concentrations. Although the concentration of a specific binding partner is usually quite low in the cell (nanomolar to micromolar), the total concentration of other macromolecules is very high, allowing weak and non-specific interactions to play important roles. In this review we address the question of binding specificity, that is, how do some proteins maintain monogamous relations while others are clearly polygamous. We examine recent work that addresses the molecular and structural basis for specificity versus promiscuity. We show through examples how multiple solutions exist to achieve binding via similar interfaces and how protein specificity can be tuned using both positive and negative selection (specificity by demand). Binding of a protein to numerous partners can be promoted through variation in which residues are used for binding, conformational plasticity and/or post-translational modification. Natively unstructured regions represent the extreme case in which structure is obtained only upon binding. Many natively unstructured proteins serve as hubs in protein–protein interaction networks and such promiscuity can be of functional importance in biology.

Protein binding specificity versus promiscuity

The increasing availability of high-quality experimental data and first-principles calculations creates opportunities for developing more accurate empirical force fields for simulation of proteins. We developed the AMBER-FB15 protein force field by building a high-quality quantum chemical data set consisting of comprehensive potential energy scans and employing the ForceBalance software package for parameter optimization. The optimized potential surface allows for more significant thermodynamic fluctuations away from local minima. In validation studies where simulation results are compared to experimental measurements, AMBER-FB15 in combination with the updated TIP3P-FB water model predicts equilibrium properties with equivalent accuracy, and temperature dependent properties with significantly improved accuracy, in comparison with published models. We also discuss the effect of changing the protein force field and water model on the simulation results.

/pdf/building-a-more-predictive-protein-force-field-a-systematic-3avt5yk9kw.pdf

Building a More Predictive Protein Force Field: A Systematic and Reproducible Route to AMBER-FB15

It is well established that intramolecular hydrogen bonding and N-methylation play important roles in the passive permeability of cyclic peptides, but other structural features have been explored less intensively. Recent studies on the oral bioavailability of the cyclic heptapeptide sanguinamide A have raised the question of whether steric occlusion of polar groups via β-branching is an effective, yet untapped, tool in cyclic peptide permeability optimization. We report the structures of 17 sanguinamide A analogues designed to test the relative contributions of β-branching, N-methylation, and side chain size to passive membrane permeability and aqueous solubility. We demonstrate that β-branching has little effect on permeability compared to the effects of aliphatic carbon count and N-methylation of exposed NH groups. We highlight a new N-methylated analogue of sanguinamide A with a Leu substitution at position 2 that exhibits solvent-dependent flexibility and improved permeability over that of the natural product.

Going Out on a Limb: Delineating The Effects of β-Branching, N-Methylation, and Side Chain Size on the Passive Permeability, Solubility, and Flexibility of Sanguinamide A Analogues.

The pioneering work of Ramachandran and colleagues emphasized the dominance of steric constraints in specifying the structure of polypeptides. The ubiquitous Ramachandran plot of backbone dihedral angles (f and c) defined the allowed regions of conformational space. These predictions were subsequently confirmed in proteins of known structure. Ramachandran and colleagues also investigated the influence of the backbone angle s on the distribution of allowed f/c combinations. The ''bridge region'' (f � 0 and 220 � c � 40) was predicted to be particularly sensitive to the value of s. Here we present an analysis of the distribution of f/c angles in 850 non- homologous proteins whose structures are known to a resolution of 1.7 Aor less and sidechain B- factor less than 30 A ˚ 2 . We show that the distribution of f/c angles for all 87,000 residues in these proteins shows the same dependence on s as predicted by Ramachandran and colleagues. Our results are important because they make clear that steric constraints alone are sufficient to explain the backbone dihedral angle distributions observed in proteins. Contrary to recent suggestions, no additional energetic contributions, such as hydrogen bonding, need be invoked.

/pdf/revisiting-the-ramachandran-plot-from-a-new-angle-2xjn20n7ag.pdf

Revisiting the Ramachandran plot from a new angle

http://jamming.research.yale.edu/files/papers/hard_sphere_bio.pdf

The Power of Hard-Sphere Models: Explaining Side-Chain Dihedral Angle Distributions of Thr and Val

The side-chain dihedral angle distributions of all amino acids have been measured from myriad high-resolution protein crystal structures. However, we do not yet know the dominant interactions that determine these distributions. Here, we explore to what extent the defining features of the side-chain dihedral angle distributions of different amino acids can be captured by a simple physical model. We find that a hard-sphere model for a dipeptide mimetic that includes only steric interactions plus stereochemical constraints is able to recapitulate the key features of the back-bone dependent observed amino acid side-chain dihedral angle distributions of Ser, Cys, Thr, Val, Ile, Leu, Phe, Tyr, and Trp. We find that for certain amino acids, performing the calculations with the amino acid of interest in the central position of a short α-helical segment improves the match between the predicted and observed distributions. We also identify the atomic interactions that give rise to the differences between the predicted distributions for the hard-sphere model of the dipeptide and that of the α-helical segment. Finally, we point out a case where the hard-sphere plus stereochemical constraint model is insufficient to recapitulate the observed side-chain dihedral angle distribution, namely the distribution P(χ₃) for Met.

/pdf/predicting-the-side-chain-dihedral-angle-distributions-of-5a5bmu46h9.pdf

Predicting the side-chain dihedral angle distributions of nonpolar, aromatic, and polar amino acids using hard sphere models.

A fundamental question in protein science is what is the intrinsic propensity for an amino acid to be in an a-helix, b-sheet, or other backbone dihedral angle (/-w) conformation. This question has been hotly debated for many years because including all protein crystal structures from the protein database, increases the probabilities for a-helical structures, while experiments on small peptides observe that b-sheet-like conformations predominate. We perform molecular dynamics (MD) simulations of a hard-sphere model for Ala dipeptide mimetics that includes steric interactions between nonbonded atoms and bond length and angle constraints with the goal of evaluating the role of steric interactions in determining protein backbone conformational preferen- ces. We find four key results. For the hard-sphere MD simulations, we show that (1) b-sheet struc- tures are roughly three and half times more probable than a-helical structures, (2) transitions between a-helix and b-sheet structures only occur when the backbone bond angle s (NACaAC) is greater than 110 � , and (3) the probability distribution of s for Ala conformations in the "bridge" region of /-w space is shifted to larger angles compared to other regions. In contrast, (4) the distri- butions obtained from Amber and CHARMM MD simulations in the bridge regions are broader and have increased s compared to those for hard sphere simulations and from high-resolution protein crystal structures. Our results emphasize the importance of hard-sphere interactions and local stereochemical constraints that yield strong correlations between /-w conformations and s.

/pdf/intrinsic-a-helical-and-b-sheet-conformational-preferences-a-24u3zsmac3.pdf

Intrinsic α-helical and β-sheet conformational preferences: a computational case study of alanine.

http://jamming.research.yale.edu/files/papers/diego.pdf

Alice Qinhua Zhou

Papers

Revisiting the Ramachandran plot from a new angle

The Power of Hard-Sphere Models: Explaining Side-Chain Dihedral Angle Distributions of Thr and Val

Predicting the side-chain dihedral angle distributions of nonpolar, aromatic, and polar amino acids using hard sphere models.

Intrinsic α-helical and β-sheet conformational preferences: a computational case study of alanine.

New Insights into the Interdependence between Amino Acid Stereochemistry and Protein Structure