Disulfide-constrained Fabs overcome target size limitation for high-resolution single particle cryoEM

This review provides the current status of structure-guided drug discovery, with examples from antibiotic drug discovery. The article aims to show where and when structural biology has been used “early and often” during the process of antibiotic drug discovery, primarily against novel antibacterial targets. The vast majority of examples are derived from the use of protein crystallization and diffraction information and the high-resolution protein structures that those tools provide. The review provides key examples and a range of methods that have effectively used structural information in various stages of the antibacterial drug discovery process. The starting point for structure-guided drug discovery is most often a high-resolution structure of the target protein. Over the past decade, the publicly funded Protein Structure Initiatives have solved a large number of bacterial protein structures, often from clinically important bacterial species. The public release of these high-resolution structures has enabled structure-guided efforts for antibacterial drug discovery. Many structure-guided methods overcome challenges raised by the limited chemical diversity found in existing chemical diversity libraries.

In drug discovery, determining the binding affinity and functional effects of small-molecule ligands on proteins is critical. Current computational methods can predict these protein–ligand interaction properties but often lose accuracy without high-resolution protein structures and falter in predicting functional effects. Here we introduce PSICHIC (PhySIcoCHemICal graph neural network), a framework incorporating physicochemical constraints to decode interaction fingerprints directly from sequence data alone. This enables PSICHIC to attain capabilities in decoding mechanisms underlying protein–ligand interactions, achieving state-of-the-art accuracy and interpretability. Trained on identical protein–ligand pairs without structural data, PSICHIC matched and even surpassed leading structure-based methods in binding-affinity prediction. In an experimental library screening for adenosine A1 receptor agonists, PSICHIC discerned functional effects effectively, ranking the sole novel agonist within the top three. PSICHIC’s interpretable fingerprints identified protein residues and ligand atoms involved in interactions, and helped in unveiling selectivity determinants of protein–ligand interaction. We foresee PSICHIC reshaping virtual screening and deepening our understanding of protein–ligand interactions. Predicting the binding affinity between small-molecule ligands and proteins is a key task in drug discovery; however, sequence-based methods are often less accurate than structure-based ones. Koh et al. develop a graph neural network using physicochemical constraints that discovers interactions between small molecules and proteins directly from sequence data and that can achieve state-of-the-art performance without the need for costly, experimental 3D structures.

Small membrane proteins (<100 kDa) are most often ensconced entirely within the plane of the lipid bilayer and are sensitive to changes in their native environment, making them unstable and recalcitrant targets of X-ray crystallography upon being extracted from the membrane. In the past decade, single particle cryo-electron microscopy (cryo-EM) has established itself as the method of choice for determination of high-resolution small membrane protein structures, primarily because it negates the requirement of high-quality diffracting crystals. However, the lack of conspicuous extra-membranous soluble domains, and the lack of asymmetry in the shape of the small membrane proteins by virtue of them being embedded entirely within detergent micelles, can affect particle alignment and cause challenges in data processing. Overcoming these challenges may require a combination of strategies that include protein expression construct engineering, optimization of sample preparation conditions, reconstitution of proteins in membrane mimetic environments, complexation with small molecule ligands and fiducial markers that can enhance the protein stability and size, and even employment of electron detectors with optimal detective quantum efficiency performance. My talk will use examples from my firsthand experience to demonstrate the applicability of these strategies to discover novel aspects, albeit sometimes serendipitously, of different classes of small membrane proteins from humans.

Structure determination of small proteins using NMR data is most commonly pursued by combining NOE derived distance constraints with inherent constraints based on chemical bonding. Ideally, one would make use of a variety of experimental observations, not just distance constraints. Here, coupling constant constraints have been added to molecular mechanics and molecular dynamics protocols for structure determination in the form of a psuedoenergy function that is minimized in a search for an optimum molecular conformation. Application is made to refinement of a structure for a 77 amino acid protein involved in fatty acid synthesis, Escherichia coli acyl carrier protein (ACP). 54 3JHN alpha coupling constants, 12 coupling constants for stereospecifically assigned side chain protons, and 450 NOE distance constraints were used to calculate the 3-D structure of ACP. A three-step protocol for a molecular dynamics calculation is described, in analogy to the protocol previously used in molecular mechanics calculations. The structures calculated with the molecular mechanics approach and the molecular dynamics approach using a rigid model for the protein show similar molecular energies and similar agreement with experimental distance and coupling constant constraints. The molecular dynamics approach shows some advantage in overcoming local minimum problems, but only when a two-state averaging model for the protein was used, did molecular energies drop significantly.

Editorial overview: New protein production tools for structural biology

Viruses with lipid envelopes must fuse their membranes with those of host cells to transfer their genomes and initiate infection. Depending on the virus, the membrane fusion process can occur at the plasma membrane, or at intracellular membranes following the internalization of virus particles. Not surprisingly, the viral proteins responsible for membrane fusion are highly diverse, as are the mechanisms by which the fusion processes can be triggered; however, common themes have emerged as our understanding of membrane fusion has developed. In particular, an expanding repertoire of high-resolution structures of viral fusion proteins (F proteins; VFPs) in pre- and postfusion conformations has driven the field forward. From these, we know that many VFPs undergo substantial conformational changes during fusion, forming highly stable rod-like structures to draw the membranes together (1⇓–3). Despite this, for VFPs other than influenza HA (4⇓⇓–7), high-resolution structures for all three major static conformations adopted during the virus life cycle, uncleaved prefusion, primed prefusion and postfusion, have remained incomplete. These limitations have hindered our development of insights into the fusion mechanism of viruses that, unlike influenza virus, enter cells at the plasma membrane at neutral pH. In a groundbreaking report in PNAS (8), Welch et al. solve the high-resolution X-ray crystal structure of the cleaved, prefusion form of the F protein of parainfluenza virus 5 (PIV5). This alters the state of affairs by providing an essential missing link in the understanding of paramyxovirus entry. In conjunction with previous work by these laboratories (9, 10), the new structure now affords us with a complete set of all three major static conformations of paramyxovirus F proteins (Fig. 1), a tour de force representing many years of effort …

Single particle electron cryomicroscopy (cryo-EM) has recently made significant progress in high-resolution structure determination of macromolecular complexes due to improvements in electron microscopic instrumentation and computational image analysis. However, cryo-EM structures can be highly non-uniform in local resolution and all structures available to date have been limited to resolutions above 3Å. Here we present the cryo-EM structure of the 70S ribosome from Escherichia coli in complex with elongation factor Tu, aminoacyl-tRNA and the antibiotic kirromycin at 2.65-2.9Å resolution using spherical aberration (Cs)-corrected cryo-EM. Overall, the cryo-EM reconstruction at 2.9Å resolution is comparable to the best-resolved X-ray structure of the E. coli 70S ribosome (2.8Å), but provides more detailed information (2.65Å) at the functionally important ribosomal core. The cryo-EM map elucidates for the first time the structure of all 35 rRNA modifications in the bacterial ribosome, explaining their roles in fine-tuning ribosome structure and function and modulating the action of antibiotics. We also obtained atomic models for flexible parts of the ribosome such as ribosomal proteins L9 and L31. The refined cryo-EM-based model presents the currently most complete high-resolution structure of the E. coli ribosome, which demonstrates the power of cryo-EM in structure determination of large and dynamic macromolecular complexes.

An Integrative Approach to Single-Molecule FRET Spectroscopy and Molecular Dynamics Simulations for the Study of Intrinsically Disordered Proteins

Inter-residue interactions play an essential role in driving protein folding, and analysis of these interactions increases our understanding of protein folding and stability and facilitates the development of tools for protein structure and function prediction. In this work, we systematically characterized the change of inter-residue interactions at various sequence separation cutoffs using two protein datasets. The first set included 100 diverse, nonredundant and high-resolution soluble protein structures, covering all four major structural classes, all-alpha, alpha/beta, alpha+beta, and all-beta; and the second set included 20 diverse, nonredundant and high-resolution membrane protein structures, representing 19 unique superfamilies. It was shown that the average number of inter-residue interactions in structures of both datasets displays the power-law behavior. Fitting parameters of the power-law function are directly related to the structural classes analyzed. These findings provided further insight into the distribution of short-, medium-, and long-range inter-residue interactions in both soluble and membrane proteins and could be used for protein structure prediction.

Characterizing Order and Disorder of Protein Structural Ensembles

Fourteen popular scoring functions, i.e., X-Score, DrugScore, five scoring functions in the Sybyl software (D-Score, PMF-Score, G-Score, ChemScore, and F-Score), four scoring functions in the Cerius2 software (LigScore, PLP, PMF, and LUDI), two scoring functions in the GOLD program (GoldScore and ChemScore), and HINT, were tested on the refined set of the PDBbind database, a set of 800 diverse protein-ligand complexes with high-resolution crystal structures and experimentally determined Ki or Kd values. The focus of our study was to assess the ability of these scoring functions to predict binding affinities based on the experimentally determined high-resolution crystal structures of proteins in complex with their ligands. The quantitative correlation between the binding scores produced by each scoring function and the known binding constants of the 800 complexes was computed. X-Score, DrugScore, Sybyl::ChemScore, and Cerius2::PLP provided better correlations than the other scoring functions with standard deviations of 1.8-2.0 log units. These four scoring functions were also found to be robust enough to carry out computation directly on unaltered crystal structures. To examine how well scoring functions predict the binding affinities for ligands bound to the same target protein, the performance of these 14 scoring functions were evaluated on three subsets of protein-ligand complexes from the test set: HIV-1 protease complexes (82 entries), trypsin complexes (45 entries), and carbonic anhydrase II complexes (40 entries). Although the results for the HIV-1 protease subset are less than desirable, several scoring functions are able to satisfactorily predict the binding affinities for the trypsin and the carbonic anhydrase II subsets with standard deviation as low as 1.0 log unit (corresponding to 1.3-1.4 kcal/mol at room temperature). Our results demonstrate the strengths as well as the weaknesses of current scoring functions for binding affinity prediction.

Analysis of Amino Acid Specific Energy Contributions to Native Conformations in High-Resolution Protein Structures

Molecular modeling of proteins including homology modeling, structure determination, and knowledge-based protein design requires tools to evaluate and refine three-dimensional protein structures. Steric clash is one of the artifacts prevalent in low-resolution structures and homology models. Steric clashes arise due to the unnatural overlap of any two nonbonding atoms in a protein structure. Usually, removal of severe steric clashes in some structures is challenging since many existing refinement programs do not accept structures with severe steric clashes. Here, we present a quantitative approach of identifying steric clashes in proteins by defining clashes based on the Van der Waals repulsion energy of the clashing atoms. We also define a metric for quantitative estimation of the severity of clashes in proteins by performing statistical analysis of clashes in high-resolution protein structures. We describe a rapid, automated, and robust protocol, Chiron, which efficiently resolves severe clashes in low-resolution structures and homology models with minimal perturbation in the protein backbone. Benchmark studies highlight the efficiency and robustness of Chiron compared with other widely used methods. We provide Chiron as an automated web server to evaluate and resolve clashes in protein structures that can be further used for more accurate protein design.

The structural analysis of a deamidated derivative of ribonuclease A, determined at 0.87 A resolution, reveals a highly significant negative correlation between CN and CO bond distances in peptide groups. This trend, i.e. the CO bond lengthens when the CN bond shortens, is also found in seven out of eight protein structures, determined at ultrahigh resolution (<0.95 A). In five of them the linear correlation is statistically significant at the 95% confidence level. The present findings are consistent with the traditional view of amide resonance and, although already found in small peptide structures, they represent a new and important result. In fact, in a protein structure the fine details of the peptide geometry are only marginally affected by the crystal field and they are mostly produced by intramolecular and solvent interactions. The analysis of very high-resolution protein structures can reveal subtle information about local electronic features of proteins which may be critical to folding, function or ligand binding.

Using statistical analysis of the Biological Macromolecular Crystallization Database, combined with previous knowledge about crystallization reagents, a crystallization screen called the Berkeley Screen has been created. Correlating crystallization conditions and high-resolution protein structures, it is possible to better understand the influence that a particular solution has on protein crystal formation. Ions and small molecules such as buffers and precipitants used in crystallization experiments were identified in electron density maps, highlighting the role of these chemicals in protein crystal packing. The Berkeley Screen has been extensively used to crystallize target proteins from the Joint BioEnergy Institute and the Collaborative Crystallography program at the Berkeley Center for Structural Biology, contributing to several Protein Data Bank entries and related publications. The Berkeley Screen provides the crystallographic community with an efficient set of solutions for general macromolecular crystallization trials, offering a valuable alternative to the existing commercially available screens.