Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data.

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15 N- 1 H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research. Highlights: AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.

AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures.

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2.

NMR studies can provide unique information about protein conformations in solution. In CASP14, three reference structures provided by solution NMR methods were available (T1027, T1029, and T1055), as well as a fourth data set of NMR-derived contacts for an integral membrane protein (T1088). For the three targets with NMR-based structures, the best prediction results ranged from very good (GDT_TS = 0.90, for T1055) to poor (GDT_TS = 0.47, for T1029). We explored the basis of these results by comparing all CASP14 prediction models against experimental NMR data. For T1027, NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction methods. The analysis of T1029 motivated exploration of a novel method of "inverse structure determination," in which an AlphaFold2 model was used to guide NMR data analysis. NMR data provided to CASP predictor groups for target T1088, a 238-residue integral membrane porin, was also used to assess several NMR-assisted prediction methods. Most groups involved in this exercise generated similar beta-barrel models, with good agreement with the experimental data. However, as was also observed in CASP13, some pure prediction groups that did not use any NMR data generated models for T1088 that better fit the NMR data than the models generated using these experimental data. These results demonstrate the remarkable power of modern methods to predict structures of proteins with accuracies rivaling solution NMR structures, and that it is now possible to reliably use prediction models to guide and complement experimental NMR data analysis.

Protein structure prediction assisted with sparse NMR data in CASP13.

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

An ELISA-Based Screening Platform for Ligand-Receptor Discovery.

Cell surface molecules are important for development and function of multicellular organisms. Although several methods are available to identify ligand-receptor pairs, ELISA-based methods are particularly amenable to high-throughput screens. ELISA-based methods have high sensitivity and low false-positive rates for detecting protein-protein interaction (PPI) complexes. Here, we provide a detailed protocol for a 384-well ELISA-based PPI screening protocol for the identification of novel cell surface ligand-receptor interactions, together with considerations for validation of PPIs by biophysical methods. This PPI screen has been developed and tested for discovery of novel ligand-receptor pairs between human synaptic adhesion proteins, believed to play crucial roles in many steps of neurodevelopment, from neuronal maturation, to axon guidance, synapse connectivity, and pruning.

Combining Evolutionary Covariance and NMR Data for Protein Structure Determination.

Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data.

While 3D structure determination of small (<15 kDa) proteins by solution NMR is largely automated and routine, structural analysis of larger proteins is more challenging. An emerging hybrid strategy for modeling protein structures combines sparse NMR data that can be obtained for larger proteins with sequence co-variation data, called evolutionary couplings (ECs), obtained from multiple sequence alignments of protein families. This hybrid "EC-NMR" method can be used to accurately model larger (15-60 kDa) proteins, and more rapidly determine structures of smaller (5-15 kDa) proteins using only backbone NMR data. The resulting structures have accuracies relative to reference structures comparable to those obtained with full backbone and sidechain NMR resonance assignments. The requirement that evolutionary couplings (ECs) are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, potentially also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

Protein structure determination by combining sparse NMR data with evolutionary couplings.

Accurate determination of protein structure by NMR spectroscopy is challenging for larger proteins, for which experimental data are often incomplete and ambiguous. Evolutionary sequence information together with advances in maximum entropy statistical methods provide a rich complementary source of structural constraints. We have developed a hybrid approach (evolutionary coupling-NMR spectroscopy; EC-NMR) combining sparse NMR data with evolutionary residue-residue couplings and demonstrate accurate structure determination for several proteins 6-41 kDa in size.

Guiding automated NMR structure determination using a global optimization metric, the NMR DP score.

ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases (15)N-(1)H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta.

Solution NMR structures of homeodomains from human proteins ALX4, ZHX1, and CASP8AP2 contribute to the structural coverage of the Human Cancer Protein Interaction Network.

High-quality solution NMR structures of three homeodomains from human proteins ALX4, ZHX1 and CASP8AP2 were solved. These domains were chosen as targets of a biomedical theme project pursued by the Northeast Structural Genomics Consortium. This project focuses on increasing the structural coverage of human proteins associated with cancer.

DisMeta: a meta server for construct design and optimization.

Intrinsically disordered or unstructured regions in proteins are both common and biologically important, particularly in regulation, signaling, and modulating intermolecular recognition processes. From a practical point of view, however, such disordered regions often can pose significant challenges for crystallization. Disordered regions are also detrimental to NMR spectral quality, complicating the analysis of resonance assignments and three-dimensional protein structures by NMR methods. The DisMeta Server has been used by Northeastern Structural Genomics (NESG) consortium as a primary tool for construct design and optimization in preparing samples for both NMR and crystallization studies. It is a meta-server that generates a consensus analysis of eight different sequence-based disorder predictors to identify regions that are likely to be disordered. DisMeta also identifies predicted secretion signal peptides, transmembrane segments, and low-complexity regions. Identification of disordered regions, by either experimental or computational methods, is an important step in the NESG structure production pipeline, allowing the rational design of protein constructs that have improved expression and solubility, improved crystallization, and better quality NMR spectra.

RPF: a quality assessment tool for protein NMR structures.

We describe the RPF web server, a quality assessment tool for protein NMR structures. The RPF server measures the 'goodness-of-fit' of the 3D structure with NMR chemical shift and unassigned NOESY data, and calculates a discrimination power (DP) score, which estimates the differences between the fits of the query structures and random coil structures to these experimental data. The DP-score is an accuracy predictor of the query structure. The RPF server also maps local structure quality measures onto the 3D structure using an online molecular viewer, and onto the NMR spectra, allowing refinement of the structure and/or NOESY peak list data. The RPF server is available at: http://nmr.cabm.rutgers.edu/rpf.

Human retinoblastoma binding protein 9, a serine hydrolase implicated in pancreatic cancers.

Human retinoblastoma binding protein 9 (RBBP9) is an interacting partner of the retinoblastoma susceptibility protein (Rb). RBBP9 is a tumor-associated protein required for pancreatic neoplasia, affects cell cycle control, and is involved in the TGF-ß signalling pathway. Sequence analysis suggests that RBBP9 belongs to the α/ß hydrolase superfamily of enzymes. The serine hydrolase activity of RBBP9 is required for development of pancreatic carcinomas in part by inhibiting TGF-ß antiproliferative signaling through suppressing Smad2/3 phosphorylation. The crystal structure of human RBBP9 confirms the α/ß hydrolase fold, with a six-stranded parallel ß-sheet flanked by α helixes. The structure of RBBP9 resembles that of the YdeN protein from Bacillus subtilis, which is suggested to have carboxylesterase activity. RBBP9 contains a Ser75-His165-Asp138 catalytic triad, situated in a prominent pocket on the surface of the protein. The side chains of the LxCxE sequence motif that is important for interaction with Rb is mostly buried in the structure. Structure- function studies of RBBP9 suggest possible routes for novel cancer drug discovery programs.

UNC119 is required for G protein trafficking in sensory neurons.

UNC119 is widely expressed among vertebrates and other phyla. We found that UNC119 recognized the acylated N terminus of the rod photoreceptor transducin α (Tα) subunit and Caenorhabditis elegans G proteins ODR-3 and GPA-13. The crystal structure of human UNC119 at 1.95-Å resolution revealed an immunoglobulin-like ß-sandwich fold. Pulldowns and isothermal titration calorimetry revealed a tight interaction between UNC119 and acylated Gα peptides. The structure of co-crystals of UNC119 with an acylated Tα N-terminal peptide at 2.0 Å revealed that the lipid chain is buried deeply into UNC119's hydrophobic cavity. UNC119 bound Tα-GTP, inhibiting its GTPase activity, thereby providing a stable UNC119-Tα-GTP complex capable of diffusing from the inner segment back to the outer segment after light-induced translocation. UNC119 deletion in both mouse and C. elegans led to G protein mislocalization. Thus, UNC119 is a Gα subunit cofactor essential for G protein trafficking in sensory cilia.

Preparation of protein samples for NMR structure, function, and small-molecule screening studies.

In this chapter, we concentrate on the production of high-quality protein samples for nuclear magnetic resonance (NMR) studies. In particular, we provide an in-depth description of recent advances in the production of NMR samples and their synergistic use with recent advancements in NMR hardware. We describe the protein production platform of the Northeast Structural Genomics Consortium and outline our high-throughput strategies for producing high-quality protein samples for NMR studies. Our strategy is based on the cloning, expression, and purification of 6×-His-tagged proteins using T7-based Escherichia coli systems and isotope enrichment in minimal media. We describe 96-well ligation-independent cloning and analytical expression systems, parallel preparative scale fermentation, and high-throughput purification protocols. The 6×-His affinity tag allows for a similar two-step purification procedure implemented in a parallel high-throughput fashion that routinely results in purity levels sufficient for NMR studies (>97% homogeneity). Using this platform, the protein open reading frames of over 17,500 different targeted proteins (or domains) have been cloned as over 28,000 constructs. Nearly 5000 of these proteins have been purified to homogeneity in tens of milligram quantities (see Summary Statistics, http://nesg.org/statistics.html), resulting in more than 950 new protein structures, including more than 400 NMR structures, deposited in the Protein Data Bank. The Northeast Structural Genomics Consortium pipeline has been effective in producing protein samples of both prokaryotic and eukaryotic origin. Although this chapter describes our entire pipeline for producing isotope-enriched protein samples, it focuses on the major updates introduced during the last 5 years (Phase 2 of the National Institute of General Medical Sciences Protein Structure Initiative). Our advanced automated and/or parallel cloning, expression, purification, and biophysical screening technologies are suitable for implementation in a large individual laboratory or by a small group of collaborating investigators for structural biology, functional proteomics, ligand screening, and structural genomics research.

The high-throughput protein sample production platform of the Northeast Structural Genomics Consortium.

We describe the core Protein Production Platform of the Northeast Structural Genomics Consortium (NESG) and outline the strategies used for producing high-quality protein samples. The platform is centered on the cloning, expression and purification of 6X-His-tagged proteins using T7-based Escherichia coli systems. The 6X-His tag allows for similar purification procedures for most targets and implementation of high-throughput (HTP) parallel methods. In most cases, the 6X-His-tagged proteins are sufficiently purified (>97% homogeneity) using a HTP two-step purification protocol for most structural studies. Using this platform, the open reading frames of over 16,000 different targeted proteins (or domains) have been cloned as>26,000 constructs. Over the past 10 years, more than 16,000 of these expressed protein, and more than 4400 proteins (or domains) have been purified to homogeneity in tens of milligram quantities (see Summary Statistics, http://nesg.org/statistics.html). Using these samples, the NESG has deposited more than 900 new protein structures to the Protein Data Bank (PDB). The methods described here are effective in producing eukaryotic and prokaryotic protein samples in E. coli. This paper summarizes some of the updates made to the protein production pipeline in the last 5 years, corresponding to phase 2 of the NIGMS Protein Structure Initiative (PSI-2) project. The NESG Protein Production Platform is suitable for implementation in a large individual laboratory or by a small group of collaborating investigators. These advanced automated and/or parallel cloning, expression, purification, and biophysical screening technologies are of broad value to the structural biology, functional proteomics, and structural genomics communities.

Outcome of a workshop on applications of protein models in biomedical research.

We describe the proceedings and conclusions from the "Workshop on Applications of Protein Models in Biomedical Research" (the Workshop) that was held at the University of California, San Francisco on 11 and 12 July, 2008. At the Workshop, international scientists involved with structure modeling explored (i) how models are currently used in biomedical research, (ii) the requirements and challenges for different applications, and (iii) how the interaction between the computational and experimental research communities could be strengthened to advance the field.

Targeting the human cancer pathway protein interaction network by structural genomics.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Solution NMR structure of the junction between tropomyosin molecules: implications for actin binding and regulation.

Tropomyosin is a coiled-coil protein that binds head-to-tail along the length of actin filaments in eukaryotic cells, stabilizing them and providing protection from severing proteins. Tropomyosin cooperatively regulates actin's interaction with myosin and mediates the Ca2+ -dependent regulation of contraction by troponin in striated muscles. The N-terminal and C-terminal ends are critical functional determinants that form an "overlap complex". Here we report the solution NMR structure of an overlap complex formed of model peptides. In the complex, the chains of the C-terminal coiled coil spread apart to allow insertion of 11 residues of the N-terminal coiled coil into the resulting cleft. The plane of the N-terminal coiled coil is rotated 90 degrees relative to the plane of the C terminus. A consequence of the geometry is that the orientation of postulated periodic actin binding sites on the coiled-coil surface is retained from one molecule to the next along the actin filament when the overlap complex is modeled into the X-ray structure of tropomyosin determined at 7 Angstroms. Nuclear relaxation NMR data reveal flexibility of the junction, which may function to optimize binding along the helical actin filament and to allow mobility of tropomyosin on the filament surface as it switches between regulatory states.