Signaling from RAS to RAF: The Molecules and Their Mechanisms.

RAF family protein kinases are a key node in the RAS/RAF/MAP kinase pathway, the signaling cascade that controls cellular proliferation, differentiation, and survival in response to engagement of growth factor receptors on the cell surface. Over the past few years, structural and biochemical studies have provided new understanding of RAF autoregulation, RAF activation by RAS and the SHOC2 phosphatase complex, and RAF engagement with HSP90-CDC37 chaperone complexes. These studies have important implications for pharmacologic targeting of the pathway. They reveal RAF in distinct regulatory states and show that the functional RAF switch is an integrated complex of RAF with its substrate (MEK) and a 14-3-3 dimer. Here we review these advances, placing them in the context of decades of investigation of RAF regulation. We explore the insights they provide into aberrant activation of the pathway in cancer and RASopathies (developmental syndromes caused by germline mutations in components of the pathway).

An atlas of protein homo-oligomerization across domains of life.

Protein structures are essential to understanding cellular processes in molecular detail. While advances in artificial intelligence revealed the tertiary structure of proteins at scale, their quaternary structure remains mostly unknown. We devise a scalable strategy based on AlphaFold2 to predict homo-oligomeric assemblies across four proteomes spanning the tree of life. Our results suggest that approximately 45% of an archaeal proteome and a bacterial proteome and 20% of two eukaryotic proteomes form homomers. Our predictions accurately capture protein homo-oligomerization, recapitulate megadalton complexes, and unveil hundreds of homo-oligomer types, including three confirmed experimentally by structure determination. Integrating these datasets with omics information suggests that a majority of known protein complexes are symmetric. Finally, these datasets provide a structural context for interpreting disease mutations and reveal coiled-coil regions as major enablers of quaternary structure evolution in human. Our strategy is applicable to any organism and provides a comprehensive view of homo-oligomerization in proteomes.

Structural evolution of fibril polymorphs during amyloid assembly.

Cryoelectron microscopy (cryo-EM) has provided unprecedented insights into amyloid fibril structures, including those associated with disease. However, these structures represent the endpoints of long assembly processes, and their relationship to fibrils formed early in assembly is unknown. Consequently, whether different fibril architectures, with potentially different pathological properties, form during assembly remains unknown. Here, we used cryo-EM to determine structures of amyloid fibrils at different times during in vitro fibrillation of a disease-related variant of human islet amyloid polypeptide (IAPP-S20G). Strikingly, the fibrils formed in the lag, growth, and plateau phases have different structures, with new forms appearing and others disappearing as fibrillation proceeds. A time course with wild-type hIAPP also shows fibrils changing with time, suggesting that this is a general property of IAPP amyloid assembly. The observation of transiently populated fibril structures has implications for understanding amyloid assembly mechanisms with potential new insights into amyloid progression in disease.

Structural and biochemical analyses of the nuclear IκBζ protein in complex with the NF-κB p50 homodimer.

As part of the efforts to understand nuclear IκB function in NF-κB-dependent gene expression, we report an X-ray crystal structure of the IκBζ ankyrin repeat domain in complex with the dimerization domain of the NF-κB p50 homodimer. IκBζ possesses an N-terminal α helix that conveys domain folding stability. Affinity and specificity of the complex depend on a small portion of p50 at the nuclear localization signal. The model suggests that only one p50 subunit supports binding with IκBζ, and biochemical experiments confirm that IκBζ associates with DNA-bound NF-κB p50:RelA heterodimers. Comparisons of IκBζ:p50 and p50:κB DNA complex crystallographic models indicate that structural rearrangement is necessary for ternary complex formation of IκBζ and p50 with DNA.

Limitations in membrane protein structure determination by lipid nanodiscs.

Lipid nanodiscs are popular mimetics of biological membranes for determining membrane protein structures. However, a recent study revealed that the choice of nanodisc scaffold directly influenced the structure of an ion channel. This finding prompts us to be cautious and calls for improved membrane mimetics for structure determination.

AlphaFold two years on: Validation and impact.

Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations.

Bioengineering a plant NLR immune receptor with a robust binding interface toward a conserved fungal pathogen effector.

Bioengineering of plant immune receptors has emerged as a key strategy for generating novel disease resistance traits to counteract the expanding threat of plant pathogens to global food security. However, current approaches are limited by rapid evolution of plant pathogens in the field and may lack durability when deployed. Here, we show that the rice nucleotide-binding, leucine-rich repeat (NLR) immune receptor Pik-1 can be engineered to respond to a conserved family of effectors from the multihost blast fungus pathogen Magnaporthe oryzae. We switched the effector binding and response profile of the Pik NLR from its cognate rice blast effector AVR-Pik to the host-determining factor pathogenicity toward weeping lovegrass 2 (Pwl2) by installing a putative host target, OsHIPP43, in place of the native integrated heavy metal-associated domain (generating Pikm-1OsHIPP43). This chimeric receptor also responded to other PWL alleles from diverse blast isolates. The crystal structure of the Pwl2/OsHIPP43 complex revealed a multifaceted, robust interface that cannot be easily disrupted by mutagenesis, and may therefore provide durable, broad resistance to blast isolates carrying PWL effectors in the field. Our findings highlight how the host targets of pathogen effectors can be used to bioengineer recognition specificities that have more robust properties compared to naturally evolved disease resistance genes.

Control of biofilm formation by an Agrobacterium tumefaciens pterin-binding periplasmic protein conserved among diverse Proteobacteria.

Biofilm formation and surface attachment in multiple Alphaproteobacteria is driven by unipolar polysaccharide (UPP) adhesins. The pathogen Agrobacterium tumefaciens produces a UPP adhesin, which is regulated by the intracellular second messenger cyclic diguanylate monophosphate (c-di-GMP). Prior studies revealed that DcpA, a diguanylate cyclase-phosphodiesterase, is crucial in control of UPP production and surface attachment. DcpA is regulated by PruR, a protein with distant similarity to enzymatic domains known to coordinate the molybdopterin cofactor (MoCo). Pterins are bicyclic nitrogen-rich compounds, several of which are produced via a nonessential branch of the folate biosynthesis pathway, distinct from MoCo. The pterin-binding protein PruR controls DcpA activity, fostering c-di-GMP breakdown and dampening its synthesis. Pterins are excreted, and we report here that PruR associates with these metabolites in the periplasm, promoting interaction with the DcpA periplasmic domain. The pteridine reductase PruA, which reduces specific dihydro-pterin molecules to their tetrahydro forms, imparts control over DcpA activity through PruR. Tetrahydromonapterin preferentially associates with PruR relative to other related pterins, and the PruR-DcpA interaction is decreased in a pruA mutant. PruR and DcpA are encoded in an operon with wide conservation among diverse Proteobacteria including mammalian pathogens. Crystal structures reveal that PruR and several orthologs adopt a conserved fold, with a pterin-specific binding cleft that coordinates the bicyclic pterin ring. These findings define a pterin-responsive regulatory mechanism that controls biofilm formation and related c-di-GMP-dependent phenotypes in A. tumefaciens and potentially acts more widely in multiple proteobacterial lineages.

The molecular architecture of Lactobacillus S-layer: Assembly and attachment to teichoic acids.

S-layers are crystalline arrays found on bacterial and archaeal cells. Lactobacillus is a diverse family of bacteria known especially for potential gut health benefits. This study focuses on the S-layer proteins from Lactobacillus acidophilus and Lactobacillus amylovorus common in the mammalian gut. Atomic resolution structures of Lactobacillus S-layer proteins SlpA and SlpX exhibit domain swapping, and the obtained assembly model of the main S-layer protein SlpA aligns well with prior electron microscopy and mutagenesis data. The S-layer's pore size suggests a protective role, with charged areas aiding adhesion. A highly similar domain organization and interaction network are observed across the Lactobacillus genus. Interaction studies revealed conserved binding areas specific for attachment to teichoic acids. The structure of the SlpA S-layer and the suggested incorporation of SlpX as well as its interaction with teichoic acids lay the foundation for deciphering its role in immune responses and for developing effective treatments for a variety of infectious and bacteria-mediated inflammation processes, opening opportunities for targeted engineering of the S-layer or lactobacilli bacteria in general.

ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

Capturing the catalytic intermediates of parkin ubiquitination.

Parkin is an E3 ubiquitin ligase implicated in early-onset forms of Parkinson's disease. It catalyzes a transthiolation reaction by accepting ubiquitin (Ub) from an E2 conjugating enzyme, forming a short-lived thioester intermediate, and transfers Ub to mitochondrial membrane substrates to signal mitophagy. A major impediment to the development of Parkinsonism therapeutics is the lack of structural and mechanistic detail for the essential, short-lived transthiolation intermediate. It is not known how Ub is recognized by the catalytic Rcat domain in parkin that enables Ub transfer from an E2~Ub conjugate to the catalytic site and the structure of the transthiolation complex is undetermined. Here, we capture the catalytic intermediate for the Rcat domain of parkin in complex with ubiquitin (Rcat-Ub) and determine its structure using NMR-based chemical shift perturbation experiments. We show that a previously unidentified α-helical region near the Rcat domain is unmasked as a recognition motif for Ub and guides the C-terminus of Ub toward the parkin catalytic site. Further, we apply a combination of guided AlphaFold modeling, chemical cross-linking, and single turnover assays to establish and validate a model of full-length parkin in complex with UbcH7, its donor Ub, and phosphoubiquitin, trapped in the process of transthiolation. Identification of this catalytic intermediate and orientation of Ub with respect to the Rcat domain provides important structural insights into Ub transfer by this E3 ligase and explains how the previously enigmatic Parkinson's pathogenic mutation T415N alters parkin activity.

Single-sequence protein structure prediction by integrating protein language models.

Protein structure prediction has been greatly improved by deep learning in the past few years. However, the most successful methods rely on multiple sequence alignment (MSA) of the sequence homologs of the protein under prediction. In nature, a protein folds in the absence of its sequence homologs and thus, a MSA-free structure prediction method is desired. Here, we develop a single-sequence-based protein structure prediction method RaptorX-Single by integrating several protein language models and a structure generation module and then study its advantage over MSA-based methods. Our experimental results indicate that in addition to running much faster than MSA-based methods such as AlphaFold2, RaptorX-Single outperforms AlphaFold2 and other MSA-free methods in predicting the structure of antibodies (after fine-tuning on antibody data), proteins of very few sequence homologs, and single mutation effects. By comparing different protein language models, our results show that not only the scale but also the training data of protein language models will impact the performance. RaptorX-Single also compares favorably to MSA-based AlphaFold2 when the protein under prediction has a large number of sequence homologs.

Machine learning in biological physics: From biomolecular prediction to design.

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is "learnable" and propose its future use in the generation of unique sequences that can fold into a target structure.

An all-atom protein generative model.

Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which represents all sidechain states at once as a "superposition" state; superpositions defining a protein are collapsed into individual residue types and conformations during sample generation. When combined with sequence design methods, our model is able to codesign all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model to conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.

Learning the shape of protein microenvironments with a holographic convolutional neural network.

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.

B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769).

Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.

Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the challenge, machine learning approaches have been proposed to automate this process. Currently, the majority of the experimental maps in the database lack atomic resolution features, making it challenging for machine learning-based methods to precisely determine protein structures from cryogenic electron microscopy density maps. On the other hand, protein structure prediction methods, such as AlphaFold2, leverage evolutionary information from protein sequences and have recently achieved groundbreaking accuracy. However, these methods often require manual refinement, which is labor intensive and time consuming. In this study, we present DeepTracer-Refine, an automated method that refines AlphaFold predicted structures by aligning them to DeepTracers modeled structure. Our method was evaluated on 39 multi-domain proteins and we improved the average residue coverage from 78.2 to 90.0% and average local Distance Difference Test score from 0.67 to 0.71. We also compared DeepTracer-Refine with Phenixs AlphaFold refinement and demonstrated that our method not only performs better when the initial AlphaFold model is less precise but also surpasses Phenix in run-time performance.

DEMO-EM2: assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting.

The breakthrough in cryo-electron microscopy (cryo-EM) technology has led to an increasing number of density maps of biological macromolecules. However, constructing accurate protein complex atomic structures from cryo-EM maps remains a challenge. In this study, we extend our previously developed DEMO-EM to present DEMO-EM2, an automated method for constructing protein complex models from cryo-EM maps through an iterative assembly procedure intertwining chain- and domain-level matching and fitting for predicted chain models. The method was carefully evaluated on 27 cryo-electron tomography (cryo-ET) maps and 16 single-particle EM maps, where DEMO-EM2 models achieved an average TM-score of 0.92, outperforming those of state-of-the-art methods. The results demonstrate an efficient method that enables the rapid and reliable solution of challenging cryo-EM structure modeling problems.

MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry.

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.