Improving peptide-protein docking with AlphaFold-Multimer using forced sampling

A Stable Dehydratase Complex Catalyzes the Formation of Dehydrated Amino Acids in a Class V Lanthipeptide

Lanthipeptides are ribosomally synthesized and post-translationally modified peptides that bear the characteristic lanthionine (Lan) or methyllanthionine (MeLan) thioether linkages. (Me)Lan moieties bestow lanthipeptides with robust stability and diverse antimicrobial, anticancer, and antiallodynic activities. Installation of (Me)Lan requires dehydration of serine and threonine residues to 2,3-dehydroalanine (Dha) and (Z)-2,3-dehydrobutyrine (Dhb), respectively. LxmK and LxmY enzymes comprise the biosynthetic machinery of a newly discovered class V lanthipeptide, lexapeptide, and are proposed to catalyze the dehydration of serine and threonine residues in the precursor peptide. We demonstrate that LxmK and LxmY form a stable dehydratase complex to dehydrate precursor peptides. In addition, we present crystal structures of the LxmKY heterodimer, revealing structural and mechanistic features that enable iterative phosphorylation and elimination by the LxmKY complex. These findings provide molecular insights into class V lanthionine synthetases and lay the foundation for their applications as enzymatic tools in the biosynthesis of exquisitely modified peptides.

Differential prolyl hydroxylation by six Physcomitrella prolyl-4 hydroxylases

Hydroxylation of prolines to 4-trans-hydroxyproline (Hyp) is mediated by prolyl-4 hydroxylases (P4Hs). In plants, Hyps occur in Hydroxyproline-rich glycoproteins (HRGPs), and are frequently O-glycosylated. While both modifications are important, e.g. for cell wall stability, they are undesired in plant-made pharmaceuticals. Sequence motifs for prolyl-hydroxylation were proposed but did not include data from mosses, such as Physcomitrella. We identified six moss P4Hs by phylogenetic reconstruction. Our analysis of 73 Hyps in 24 secretory proteins from multiple mass spectrometry datasets revealed that prolines near other prolines, alanine, serine, threonine and valine were preferentially hydroxylated. About 95 % of Hyps were predictable with combined established methods. In our data, AOV was the most frequent pattern. A combination of 443 AlphaFold models and MS data with 3000 prolines found Hyps mainly on protein surfaces in disordered regions. Moss-produced human erythropoietin (EPO) exhibited O-glycosylation with arabinose chains on two Hyps. This modification was significantly reduced in a p4h1 knock-out (KO) Physcomitrella mutant. Quantitative proteomics with different p4h mutants revealed specific changes in protein amounts, and a modified prolyl-hydroxylation pattern, suggesting a differential function of the Physcomitrella P4Hs. Quantitative RT-PCR revealed a differential effect of single p4h KOs on the expression of the other five p4h genes, suggesting a partial compensation of the mutation. AlphaFold-Multimer models for Physcomitrella P4H1 and its target EPO peptide superposed with the crystal structure of Chlamydomonas P4H1 suggested significant amino acids in the active centre of the enzyme and revealed differences between P4H1 and the other Physcomitrella P4Hs.

Predicting multiple conformations of ligand binding sites in proteins suggests that AlphaFold2 may remember too much

The goal of this paper is predicting the conformational distributions of ligand binding sites using the AlphaFold2 (AF2) protein structure prediction program with stochastic subsampling of the multiple sequence alignment (MSA). We explored the opening of cryptic ligand binding sites in 16 proteins, where the closed and open conformations define the expected extreme points of the conformational variation. Due to the many structures of these proteins in the Protein Data Bank (PDB), we were able to study whether the distribution of X-ray structures affects the distribution of AF2 models. We have found that AF2 generates both a cluster of open and a cluster of closed models for proteins that have comparable numbers of open and closed structures in the PDB and not too many other conformations. This was observed even with default MSA parameters, thus without further subsampling. In contrast, with the exception of a single protein, AF2 did not yield multiple clusters of conformations for proteins that had imbalanced numbers of open and closed structures in the PDB, or had substantial numbers of other structures. Subsampling improved the results only for a single protein, but very shallow MSA led to incorrect structures. The ability of generating both open and closed conformations for six out of the 16 proteins agrees with the success rates of similar studies reported in the literature. However, we showed that this partial success is due to AF2 "remembering" the conformational distributions in the PDB and that the approach fails to predict rarely seen conformations.

Cryo-EM structure of PML RBCC dimer reveals CC-mediated octopus-like nuclear body assembly mechanism

Promyelocytic leukemia protein (PML) nuclear bodies (NBs) are essential in regulating tumor suppression, antiviral response, inflammation, metabolism, aging, and other important life processes. The re-assembly of PML NBs might lead to an ~100% cure of acute promyelocytic leukemia. However, until now, the molecular mechanism underpinning PML NB biogenesis remains elusive due to the lack of structural information. In this study, we present the cryo-electron microscopy (cryo-EM) structure of the PML dimer at an overall resolution of 5.3 Å, encompassing the RING, B-box1/2 and part of the coiled-coil (RBCC) domains. The integrated approach, combining crosslinking and mass spectrometry (XL-MS) and functional analyses, enabled us to observe a unique folding event within the RBCC domains. The RING and B-box1/2 domains fold around the α3 helix, and the α6 helix serves as a pivotal interface for PML dimerization. More importantly, further characterizations of the cryo-EM structure in conjugation with AlphaFold2 prediction, XL-MS, and NB formation assays, help unveil an unprecedented octopus-like mechanism in NB assembly, wherein each CC helix of a PML dimer (PML dimer A) interacts with a CC helix from a neighboring PML dimer (PML dimer B) in an anti-parallel configuration, ultimately leading to the formation of a 2 µm membrane-less subcellular organelle.

Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation

Recent technological advancements, including computer-assisted drug discovery, gene-editing techniques, and high-throughput screening approaches, have greatly expanded the palette of methods for the discovery of peptides available to researchers. These emerging strategies, driven by recent advances in bioinformatics and multi-omics, have significantly improved the efficiency of peptide drug discovery when compared with traditional in vitro and in vivo methods, cutting costs and improving their reliability. An added benefit of peptide-based drugs is the ability to precisely target protein-protein interactions, which are normally a particularly challenging aspect of drug discovery. Another recent breakthrough in this field is targeted protein degradation through proteolysis-targeting chimeras. These revolutionary compounds represent a noteworthy advancement over traditional small-molecule inhibitors due to their unique mechanism of action, which allows for the degradation of specific proteins with unprecedented specificity. The inclusion of a peptide as a protein-of-interest-targeting moiety allows for improved versatility and the possibility of targeting otherwise undruggable proteins. In this review, we discuss various novel wet-lab and computational multi-omic methods for peptide drug discovery, provide an overview of therapeutic agents discovered through these cutting-edge techniques, and discuss the potential for the therapeutic delivery of peptide-based drugs.

MassiveFold: unveiling AlphaFold's hidden potential with optimized and parallelized massive sampling

Massive sampling in AlphaFold enables access to increased structural diversity. In combination with its efficient confidence ranking, this unlocks elevated modeling capabilities for monomeric structures and foremost for protein assemblies. However, the approach struggles with GPU cost and data storage. Here we introduce MassiveFold, an optimized and customizable version of AlphaFold that runs predictions in parallel, reducing the computing time from several months to hours. MassiveFold is scalable and able to run on anything from a single computer to a large GPU infrastructure, where it can fully benefit from all the computing nodes.

Massively parallel measurement of protein-protein interactions by sequencing using MP3-seq

Protein-protein interactions (PPIs) regulate many cellular processes and engineered PPIs have cell and gene therapy applications. Here, we introduce massively parallel PPI measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Lastly, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics-based energy terms to predict MP3-seq values. We find that AF-M-based models could be valuable for prescreening interactions but experimentally measuring interactions remains necessary to rank their strengths quantitatively.

Cognitive Impact of Neurotropic Pathogens: Investigating Molecular Mimicry through Computational Methods

Neurotropic pathogens, notably, herpesviruses, have been associated with significant neuropsychiatric effects. As a group, these pathogens can exploit molecular mimicry mechanisms to manipulate the host central nervous system to their advantage. Here, we present a systematic computational approach that may ultimately be used to unravel protein-protein interactions and molecular mimicry processes that have not yet been solved experimentally. Toward this end, we validate this approach by replicating a set of pre-existing experimental findings that document the structural and functional similarities shared by the human cytomegalovirus-encoded UL144 glycoprotein and human tumor necrosis factor receptor superfamily member 14 (TNFRSF14). We began with a thorough exploration of the Homo sapiens protein database using the Basic Local Alignment Search Tool (BLASTx) to identify proteins sharing sequence homology with UL144. Subsequently, we used AlphaFold2 to predict the independent three-dimensional structures of UL144 and TNFRSF14. This was followed by a comprehensive structural comparison facilitated by Distance-Matrix Alignment and Foldseek. Finally, we used AlphaFold-multimer and PPIscreenML to elucidate potential protein complexes and confirm the predicted binding activities of both UL144 and TNFRSF14. We then used our in silico approach to replicate the experimental finding that revealed TNFRSF14 binding to both B- and T-lymphocyte attenuator (BTLA) and glycoprotein domain and UL144 binding to BTLA alone. This computational framework offers promise in identifying structural similarities and interactions between pathogen-encoded proteins and their host counterparts. This information will provide valuable insights into the cognitive mechanisms underlying the neuropsychiatric effects of viral infections.

AlphaFold-Multimer accurately captures interactions and dynamics of intrinsically disordered protein regions

Interactions mediated by intrinsically disordered protein regions (IDRs) pose formidable challenges in structural characterization. IDRs are highly versatile, capable of adopting diverse structures and engagement modes. Motivated by recent strides in protein structure prediction, we embarked on exploring the extent to which AlphaFold-Multimer can faithfully reproduce the intricacies of interactions involving IDRs. To this end, we gathered multiple datasets covering the versatile spectrum of IDR binding modes and used them to probe AlphaFold-Multimer's prediction of IDR interactions and their dynamics. Our analyses revealed that AlphaFold-Multimer is not only capable of predicting various types of bound IDR structures with high success rate, but that distinguishing true interactions from decoys, and unreliable predictions from accurate ones is achievable by appropriate use of AlphaFold-Multimer's intrinsic scores. We found that the quality of predictions drops for more heterogeneous, fuzzy interaction types, most likely due to lower interface hydrophobicity and higher coil content. Notably though, certain AlphaFold-Multimer scores, such as the Predicted Aligned Error and residue-ipTM, are highly correlated with structural heterogeneity of the bound IDR, enabling clear distinctions between predictions of fuzzy and more homogeneous binding modes. Finally, our benchmarking revealed that predictions of IDR interactions can also be successful when using full-length proteins, but not as accurate as with cognate IDRs. To facilitate identification of the cognate IDR of a given partner, we established "minD," which pinpoints potential interaction sites in a full-length protein. Our study demonstrates that AlphaFold-Multimer can correctly identify interacting IDRs and predict their mode of engagement with a given partner.

Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data

Protein-polypeptide interactions, including those involving intrinsically-disordered peptides and intrinsically-disordered regions of protein binding partners, are crucial for many biological functions. However, experimental structure determination of protein-peptide complexes can be challenging. Computational methods, while promising, generally require experimental data for validation and refinement. Here we present CSP_Rank, an integrated modeling approach to determine the structures of protein-peptide complexes. This method combines AlphaFold2 (AF2) enhanced sampling methods with a Bayesian conformational selection process based on experimental Nuclear Magnetic Resonance (NMR) Chemical Shift Perturbation (CSP) data and AF2 confidence metrics. Using a curated dataset of 108 protein-peptide complexes from the Biological Magnetic Resonance Data Bank (BMRB), we observe that while AF2 typically yields models with excellent consistency with experimental CSP data, applying enhanced sampling followed by data-guided conformational selection routinely results in ensembles of structures with improved agreement with NMR observables. For two systems, we cross-validate the CSP-selected models using independently acquired nuclear Overhauser effect (NOE) NMR data and demonstrate how CSP and NMR can be combined using our Bayesian framework for model selection. CSP_Rank is a novel method for integrative modeling of protein-peptide complexes and has broad implications for studies of protein-peptide interactions and aiding in understanding their biological functions.

AlphaCRV: a pipeline for identifying accurate binder topologies in mass-modeling with AlphaFold

Motivation

The speed and accuracy of deep learning-based structure prediction algorithms make it now possible to perform in silico "pull-downs" to identify protein-protein interactions on a proteome-wide scale. However, on such a large scale, existing scoring algorithms are often insufficient to discriminate biologically relevant interactions from false positives.

Results

Here, we introduce AlphaCRV, a Python package that helps identify correct interactors in a one-against-many AlphaFold screen by clustering, ranking, and visualizing conserved binding topologies, based on protein sequence and fold.

Availability and implementation

AlphaCRV is a Python package for Linux, freely available at https://github.com/strubelab/AlphaCRV.

Artificial Intelligence Learns Protein Prediction

From AlphaGO over StableDiffusion to ChatGPT, the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: AlphaFold2 leaped forward in protein structure prediction, and protein language models (pLMs) replaced expertise and evolutionary information from multiple sequence alignments with information learned from reoccurring patterns in databases of billions of proteins without experimental annotations other than the amino acid sequences. None of those tools could have been developed 10 years ago; all will increase the wealth of experimental data and speed up the cycle from idea to proof. AI is affecting molecular and medical biology at giant steps, and the most important might be the leap toward more powerful protein design.

Structure prediction of alternative protein conformations

Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins with conformations most likely to exist in the PDB. However, almost all protein structures with multiple conformations represented in the PDB have been used while training these models. Therefore, it is unclear whether alternative protein conformations can be genuinely predicted using these networks, or if they are simply reproduced from memory. Here, we train a structure prediction network, Cfold, on a conformational split of the PDB to generate alternative conformations. Cfold enables efficient exploration of the conformational landscape of monomeric protein structures. Over 50% of experimentally known nonredundant alternative protein conformations evaluated here are predicted with high accuracy (TM-score > 0.8).

Leveraging coevolutionary insights and AI-based structural modeling to unravel receptor-peptide ligand-binding mechanisms

Secreted signaling peptides are central regulators of growth, development, and stress responses, but specific steps in the evolution of these peptides and their receptors are not well understood. Also, the molecular mechanisms of peptide-receptor binding are only known for a few examples, primarily owing to the limited availability of protein structural determination capabilities to few laboratories worldwide. Plants have evolved a multitude of secreted signaling peptides and corresponding transmembrane receptors. Stress-responsive SERINE RICH ENDOGENOUS PEPTIDES (SCOOPs) were recently identified. Bioactive SCOOPs are proteolytically processed by subtilases and are perceived by the leucine-rich repeat receptor kinase MALE DISCOVERER 1-INTERACTING RECEPTOR-LIKE KINASE 2 (MIK2) in the model plant Arabidopsis thaliana. How SCOOPs and MIK2 have (co)evolved, and how SCOOPs bind to MIK2 are unknown. Using in silico analysis of 350 plant genomes and subsequent functional testing, we revealed the conservation of MIK2 as SCOOP receptor within the plant order Brassicales. We then leveraged AI-based structural modeling and comparative genomics to identify two conserved putative SCOOP-MIK2 binding pockets across Brassicales MIK2 homologues predicted to interact with the "SxS" motif of otherwise sequence-divergent SCOOPs. Mutagenesis of both predicted binding pockets compromised SCOOP binding to MIK2, SCOOP-induced complex formation between MIK2 and its coreceptor BRASSINOSTEROID INSENSITIVE 1-ASSOCIATED KINASE 1, and SCOOP-induced reactive oxygen species production, thus, confirming our in silico predictions. Collectively, in addition to revealing the elusive SCOOP-MIK2 binding mechanism, our analytic pipeline combining phylogenomics, AI-based structural predictions, and experimental biochemical and physiological validation provides a blueprint for the elucidation of peptide ligand-receptor perception mechanisms.

PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling

Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. The computational design of protein-based binders presents unique opportunities to access "undruggable" targets, but have often relied on stable 3D structures or structure-influenced latent spaces for effective binder generation. In this work, we introduce PepMLM, a target sequence-conditioned generator of de novo linear peptide binders. By employing a novel span masking strategy that uniquely positions cognate peptide sequences at the C-terminus of target protein sequences, PepMLM fine-tunes the state-of-the-art ESM-2 pLM to fully reconstruct the binder region, achieving low perplexities matching or improving upon validated peptide-protein sequence pairs. After successful in silico benchmarking with AlphaFold-Multimer, outperforming RFDiffusion on structured targets, we experimentally verify PepMLM's efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of emergent viral phosphoproteins and Huntington's disease-driving proteins. In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream therapeutic applications.

De Novo Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins via three-dimensional structures of target proteins, but as a result, struggle to design binders to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model, and subsequently screen these novel linear sequences for target-selective interaction activity via a CLIP-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly-ranked, target-specific peptides experimentally, both as inhibitory peptides and as fusions to E3 ubiquitin ligase domains, demonstrating functionally potent binding and degradation of conformationally diverse protein targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding linear peptides to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.

Explaining Conformational Diversity in Protein Families through Molecular Motions

Proteins play a central role in biological processes, and understanding their conformational variability is crucial for unraveling their functional mechanisms. Recent advancements in high-throughput technologies have enhanced our knowledge of protein structures, yet predicting their multiple conformational states and motions remains challenging. This study introduces Dimensionality Analysis for protein Conformational Exploration (DANCE) for a systematic and comprehensive description of protein families conformational variability. DANCE accommodates both experimental and predicted structures. It is suitable for analysing anything from single proteins to superfamilies. Employing it, we clustered all experimentally resolved protein structures available in the Protein Data Bank into conformational collections and characterized them as sets of linear motions. The resource facilitates access and exploitation of the multiple states adopted by a protein and its homologs. Beyond descriptive analysis, we assessed classical dimensionality reduction techniques for sampling unseen states on a representative benchmark. This work improves our understanding of how proteins deform to perform their functions and opens ways to a standardised evaluation of methods designed to sample and generate protein conformations.

Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile

Structure prediction of protein complexes has improved significantly with AlphaFold2 and AlphaFold-multimer (AFM), but only 60% of dimers are accurately predicted. Here, we learn a bias to the MSA representation that improves the predictions by performing gradient descent through the AFM network. We demonstrate the performance on seven difficult targets from CASP15 and increase the average MMscore to 0.76 compared to 0.63 with AFM. We evaluate the procedure on 487 protein complexes where AFM fails and obtain an increased success rate (MMscore>0.75) of 33% on these difficult targets. Our protocol, AFProfile, provides a way to direct predictions towards a defined target function guided by the MSA. We expect gradient descent over the MSA to be useful for different tasks.

Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR

Recent advances in molecular modeling using deep learning can revolutionize our understanding of dynamic protein structures. NMR is particularly well-suited for determining dynamic features of biomolecular structures. The conventional process for determining biomolecular structures from experimental NMR data involves its representation as conformation-dependent restraints, followed by generation of structural models guided by these spatial restraints. Here we describe an alternative approach: generating a distribution of realistic protein conformational models using artificial intelligence-(AI-) based methods and then selecting the sets of conformers that best explain the experimental data. We applied this conformational selection approach to redetermine the solution NMR structure of the enzyme Gaussia luciferase. First, we generated a diverse set of conformer models using AlphaFold2 (AF2) with an enhanced sampling protocol. The models that best-fit NOESY and chemical shift data were then selected with a Bayesian scoring metric. The resulting models include features of both the published NMR structure and the standard AF2 model generated without enhanced sampling. This "AlphaFold-NMR" protocol also generated an alternative "open" conformational state that fits nearly as well to the overall NMR data but accounts for some NOESY data that is not consistent with first "closed" conformational state; while other NOESY data consistent with this second state are not consistent with the first conformational state. The structure of this "open" structural state differs from that of the "closed" state primarily by the position of a thumb-shaped loop between α-helices H5 and H6, revealing a cryptic surface pocket. These alternative conformational states of Gluc are supported by "double recall" analysis of NOESY data and AF2 models. Additional structural states are also indicated by backbone chemical shift data indicating partially-disordered conformations for the C-terminal segment. Considered as a multistate ensemble, these multiple states of Gluc together fit the NOESY and chemical shift data better than the "restraint-based" NMR structure and provide novel insights into its structure-dynamic-function relationships. This study demonstrates the potential of AI-based modeling with enhanced sampling to generate conformational ensembles followed by conformer selection with experimental data as an alternative to conventional restraint satisfaction protocols for protein NMR structure determination.

Benchmarking AlphaFold-Generated Structures of Chemokine-Chemokine Receptor Complexes

AlphaFold and AlphaFold-Multimer have become two essential tools for the modeling of unknown structures of proteins and protein complexes. In this work, we extensively benchmarked the quality of chemokine-chemokine receptor structures generated by AlphaFold-Multimer against experimentally determined structures. Our analysis considered both the global quality of the model, as well as key structural features for chemokine recognition. To study the effects of template and multiple sequence alignment parameters on the results, a new prediction pipeline called LIT-AlphaFold (https://github.com/LIT-CCM-lab/LIT-AlphaFold) was developed, allowing extensive input customization. AlphaFold-Multimer correctly predicted differences in chemokine binding orientation and accurately reproduced the unique binding orientation of the CXCL12-ACKR3 complex. Further, the predictions of the full receptor N-terminus provided insights into a putative chemokine recognition site 0.5. The accuracy of chemokine N-terminus binding mode prediction varied between complexes, but the confidence score permitted the distinguishing of residues that were very likely well positioned. Finally, we generated a high-confidence model of the unsolved CXCL12-CXCR4 complex, which agreed with experimental mutagenesis and cross-linking data.

The Rapid Evolution of De Novo Proteins in Structure and Complex

Recent studies in the rice genome-wide have established that de novo genes, evolving from noncoding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their evolution in protein structure over time remain unclear. Here, we addressed these issues within a surprisingly short evolutionary timescale (<1 million years for 97% of Oryza de novo genes) with comparative approaches to gene duplicates. We found that de novo genes evolve faster than gene duplicates in the intrinsically disordered regions (such as random coils), secondary structure elements (such as α helix and β strand), hydrophobicity, and molecular recognition features. In de novo proteins, specifically, we observed an 8% to 14% decay in random coils and intrinsically disordered region lengths and a 2.3% to 6.5% increase in structured elements, hydrophobicity, and molecular recognition features, per million years on average. These patterns of structural evolution align with changes in amino acid composition over time as well. We also revealed higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions showed that most de novo proteins, though not typically well folded on their own, readily form low-energy and compact complexes with other proteins facilitated by extensive residue contacts and conformational flexibility, suggesting a faster-binding scenario in de novo proteins to promote interaction. These analyses illuminate a rapid evolution of protein structure in de novo genes in rice genomes, originating from noncoding sequences, highlighting their quick transformation into active, protein complex-forming components within a remarkably short evolutionary timeframe.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Peptriever: a Bi-Encoder approach for large-scale protein-peptide binding search

MOTIVATION

Peptide therapeutics hinge on the precise interaction between a tailored peptide and its designated receptor while mitigating interactions with alternate receptors is equally indispensable. Existing methods primarily estimate the binding score between protein and peptide pairs. However, for a specific peptide without a corresponding protein, it is challenging to identify the proteins it could bind due to the sheer number of potential candidates.

RESULTS

We propose a transformers-based protein embedding scheme in this study that can quickly identify and rank millions of interacting proteins. Furthermore, the proposed approach outperforms existing sequence- and structure-based methods, with a mean AUC-ROC and AUC-PR of 0.73.

AVAILABILITY AND IMPLEMENTATION

Training data, scripts, and fine-tuned parameters are available at https://github.com/RoniGurvich/Peptriever. The proposed method is linked with a web application available for customized prediction at https://peptriever.app/.

Evolution of the ribbon-like organization of the Golgi apparatus in animal cells

The "ribbon," a structural arrangement in which Golgi stacks connect to each other, is considered to be restricted to vertebrate cells. Although ribbon disruption is linked to various human pathologies, its functional role in cellular processes remains unclear. In this study, we investigate the evolutionary origin of the Golgi ribbon. We observe a ribbon-like architecture in the cells of several metazoan taxa suggesting its early emergence in animal evolution predating the appearance of vertebrates. Supported by AlphaFold2 modeling, we propose that the evolution of Golgi reassembly and stacking protein (GRASP) binding by golgin tethers may have driven the joining of Golgi stacks resulting in the ribbon-like configuration. Additionally, we find that Golgi ribbon assembly is a shared developmental feature of deuterostomes, implying a role in embryogenesis. Overall, our study points to the functional significance of the Golgi ribbon beyond vertebrates and underscores the need for further investigations to unravel its elusive biological roles.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

A druggable conformational switch in the c-MYC transactivation domain

The c-MYC oncogene is activated in over 70% of all human cancers. The intrinsic disorder of the c-MYC transcription factor facilitates molecular interactions that regulate numerous biological pathways, but severely limits efforts to target its function for cancer therapy. Here, we use a reductionist strategy to characterize the dynamic and structural heterogeneity of the c-MYC protein. Using probe-based Molecular Dynamics (MD) simulations and machine learning, we identify a conformational switch in the c-MYC amino-terminal transactivation domain (termed coreMYC) that cycles between a closed, inactive, and an open, active conformation. Using the polyphenol epigallocatechin gallate (EGCG) to modulate the conformational landscape of coreMYC, we show through biophysical and cellular assays that the induction of a closed conformation impedes its interactions with the transformation/transcription domain-associated protein (TRRAP) and the TATA-box binding protein (TBP) which are essential for the transcriptional and oncogenic activities of c-MYC. Together, these findings provide insights into structure-activity relationships of c-MYC, which open avenues towards the development of shape-shifting compounds to target c-MYC as well as other disordered transcription factors for cancer treatment.

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

Modeling the SDF-1/CXCR4 protein using advanced artificial intelligence and antagonist screening for Japanese anchovy

SDF-1/CXCR4 chemokine signaling are indispensable for cell migration, especially the Primordial Germ Cell (PGC) migration towards the gonadal ridge during early development. We earlier found that this signaling is largely conserved in the Japanese anchovy (Engraulis japonicus, EJ), and a mere treatment of CXCR4 antagonist, AMD3100, leads to germ cell depletion and thereafter gonad sterilization. However, the effect of AMD3100 was limited. So, in this research, we scouted for CXCR4 antagonist with higher potency by employing advanced artificial intelligence deep learning-based computer simulations. Three potential candidates, AMD3465, WZ811, and LY2510924, were selected and in vivo validation was conducted using Japanese anchovy embryos. We found that seven transmembrane motif of EJ CXCR4a and EJ CXCR4b were extremely similar with human homolog while the CXCR4 chemokine receptor N terminal (PF12109, essential for SDF-1 binding) was missing in EJ CXCR4b. 3D protein analysis and cavity search predicted the cavity in EJ CXCR4a to be five times larger (6,307 Å³) than that in EJ CXCR4b (1,241 Å³). Docking analysis demonstrated lower binding energy of AMD3100 and AMD3465 to EJ CXCR4a (Vina score -9.6) and EJ CXCR4b (Vina score -8.8), respectively. Furthermore, we observed significant PGC mismigration in microinjected AMD3465 treated groups at 10, 100 and 1 × 105 nM concentration in 48 h post fertilized embryos. The other three antagonists showed various degrees of PGC dispersion, but no significant effect compared to their solvent control at tested concentrations was observed. Cumulatively, our results suggests that AMD3645 might be a better candidate for abnormal PGC migration in Japanese anchovy and warrants further investigation.

From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2

The revolution brought about by AlphaFold2 opens promising perspectives to unravel the complexity of protein-protein interaction networks. The analysis of interaction networks obtained from proteomics experiments does not systematically provide the delimitations of the interaction regions. This is of particular concern in the case of interactions mediated by intrinsically disordered regions, in which the interaction site is generally small. Using a dataset of protein-peptide complexes involving intrinsically disordered regions that are non-redundant with the structures used in AlphaFold2 training, we show that when using the full sequences of the proteins, AlphaFold2-Multimer only achieves 40% success rate in identifying the correct site and structure of the interface. By delineating the interaction region into fragments of decreasing size and combining different strategies for integrating evolutionary information, we manage to raise this success rate up to 90%. We obtain similar success rates using a much larger dataset of protein complexes taken from the ELM database. Beyond the correct identification of the interaction site, our study also explores specificity issues. We show the advantages and limitations of using the AlphaFold2 confidence score to discriminate between alternative binding partners, a task that can be particularly challenging in the case of small interaction motifs.

DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model

Motivation

Protein-Protein interactions (PPIs) play critical roles in numerous cellular processes. By modelling the 3D structures of the correspond protein complexes valuable insights can be obtained, providing, e.g. starting points for drug and protein design. One challenge in the modelling process is however the identification of near-native models from the large pool of generated models. To this end we have previously developed DeepRank-GNN, a graph neural network that integrates structural and sequence information to enable effective pattern learning at PPI interfaces. Its main features are related to the Position Specific Scoring Matrices (PSSMs), which are computationally expensive to generate, significantly limits the algorithm's usability.

Results

We introduce here DeepRank-GNN-esm that includes as additional features protein language model embeddings from the ESM-2 model. We show that the ESM-2 embeddings can actually replace the PSSM features at no cost in-, or even better performance on two PPI-related tasks: scoring docking poses and detecting crystal artifacts. This new DeepRank version bypasses thus the need of generating PSSM, greatly improving the usability of the software and opening new application opportunities for systems for which PSSM profiles cannot be obtained or are irrelevant (e.g. antibody-antigen complexes).

Availability and implementation

DeepRank-GNN-esm is freely available from https://github.com/DeepRank/DeepRank-GNN-esm.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.

Structural study of UFL1-UFC1 interaction uncovers the role of UFL1 N-terminal helix in ufmylation

Ufmylation plays a crucial role in various cellular processes including DNA damage response, protein translation, and ER homeostasis. To date, little is known about how the enzymes responsible for ufmylation coordinate their action. Here, we study the details of UFL1 (E3) activity, its binding to UFC1 (E2), and its relation to UBA5 (E1), using a combination of structural modeling, X-ray crystallography, NMR, and biochemical assays. Guided by Alphafold2 models, we generate an active UFL1 fusion construct that includes its partner DDRGK1 and solve the crystal structure of this critical interaction. This fusion construct also unveiled the importance of the UFL1 N-terminal helix for binding to UFC1. The binding site suggested by our UFL1-UFC1 model reveals a conserved interface, and competition between UFL1 and UBA5 for binding to UFC1. This competition changes in the favor of UFL1 following UFM1 charging of UFC1. Altogether, our study reveals a novel, terminal helix-mediated regulatory mechanism, which coordinates the cascade of E1-E2-E3-mediated transfer of UFM1 to its substrate and provides new leads to target this modification.

Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning

Protein-peptide interactions play a key role in biological processes. Understanding the interactions that occur within a receptor-peptide complex can help in discovering and altering their biological functions. Various computational methods for modeling the structures of receptor-peptide complexes have been developed. Recently, accurate structure prediction enabled by deep learning methods has significantly advanced the field of structural biology. AlphaFold (AF) is among the top-performing structure prediction methods and has highly accurate structure modeling performance on single-chain targets. Shortly after the release of AlphaFold, AlphaFold-Multimer (AFM) was developed in a similar fashion as AF for prediction of protein complex structures. AFM has achieved competitive performance in modeling protein-peptide interactions compared to previous computational methods; however, still further improvement is needed. Here, we present DistPepFold, which improves protein-peptide complex docking using an AFM-based architecture through a privileged knowledge distillation approach. DistPepFold leverages a teacher model that uses native interaction information during training and transfers its knowledge to a student model through a teacher-student distillation process. We evaluated DistPepFold's docking performance on two protein-peptide complex datasets and showed that DistPepFold outperforms AFM. Furthermore, we demonstrate that the student model was able to learn from the teacher model to make structural improvements based on AFM predictions.

Improved multimer prediction using massive sampling with AlphaFold in CASP15

AlphaFold2 has revolutionized structure prediction by achieving high accuracy comparable to experimentally determined structures. However, there is still room for improvement, especially for challenging cases like multimers. A key to the success of AlphaFold is its ability to assess and rank its own predictions. Our basic idea for the Wallner group in CASP15 was to exploit this excellent scoring function in AlphaFold by massive sampling. To achieve this goal, we conducted AlphaFold runs using six different settings, using templates, without templates, and with an increased number of recycles for both multimer v1 and v2 weights. In all instances, we enabled dropout layers during inference, allowing for sampling of uncertainty and enhancing the diversity of the generated models. In total, 274 289 models were generated for the 38 targets in CASP15, with a median of 4810 models per target. Of these 38 targets, 10 were high quality, 11 were medium quality, 11 were acceptable, and only 6 were incorrect. The improvement over the baseline method, NBIS-AF2-multimer, is substantial, with the mean DockQ increasing from 0.43 to 0.56, with several targets showing a DockQ score increase of +0.6 units. Remarkable, considering Wallner and NBIS-AF2-multimer were using identical input data. The success can be attributed to the diversified sampling using dropout with different settings and, in particular, the use of multimer v1, which is much more susceptible to sampling compared with v2. The method is available here: http://wallnerlab.org/AFsample/.

Protein target highlights in CASP15: Analysis of models by structure providers

We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three-dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.

Representing structures of the multiple conformational states of proteins

Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges associated with representing single- and multiple-state models in data archives, with a particular focus on NMR structures. Establishing standardized representations and annotations will facilitate effective communication and understanding of these complex models to the broader scientific community.

Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15

For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.

Transcription factor 12-mediated self-feedback regulatory mechanism is required in DUX4 fusion leukaemia

BACKGROUND

IGH::DUX4 is frequently observed in 4% B-cell acute lymphoblastic leukaemia patients. Regarding the IGH::DUX4-driven transactivation and alternative splicing, which are the main reasons behind this acute leukaemia outbreak, it remains unclear how transcriptional cofactors contribute to this oncogenic process. Further investigation is required to elucidate their specific role in leukaemogenesis.

METHODS

In order to investigate the cofactors of IGH::DUX4, integrated mining of Chromatin immunoprecipitation (ChIP)-sequencing and RNA-sequencing of leukaemia cells and patient samples were conducted. Furthermore, to elucidate the synergistic interaction between transcription factor 12 (TCF12) and IGH::DUX4, knockdown and knockout experiment, mammalian two-hybridisation assay, co-immunoprecipitation and in situ proximity ligation assays were carried out. Additionally, to further investigate the direct interaction between TCF12 and IGH::DUX4, AI-based structural simulations were utilised. Finally, to validate the synergistic role of TCF12 in promoting IGH::DUX4 leukaemia, cell proliferation, apoptosis and drug sensitivity experiments were performed.

RESULTS

In this study, we observed that the IGH::DUX4 target gene TCF12 might be an important cofactor/helper for this oncogenic driver. The co-expression of IGH::DUX4 and TCF12 resulted in enhanced DUX4-driven transactivation. Supportively, knockdown and knockout of TCF12 significantly reduced expression of IGH::DUX4-driven target genes in leukaemia REH (a precursor B-cell leukaemia cell line) and NALM-6 cells (a precursor B-cell leukaemia cell line). Consistently, in TCF12 knockout cells, the expression of structure-based TCF12 mutant, but not wild-type TCF12, failed to restore the TCF12-IGH::DUX4 crosstalk and the synergistic transactivation. More importantly, the breakdown in TCF12-IGH::DUX4 cooperation impaired IGH::DUX4-driven leukaemia cell survival, caused sensitivity to the chemotherapy.

CONCLUSIONS

Altogether, these results helped to define a previously unrecognised TCF12-mediated positive self-feedback regulatory mechanism in IGH::DUX4 leukaemia, which holds the potential to function as a pivotal drug target for the management of this particular form of leukaemia.

HIGHLIGHTS

Transcription factor 12 (TCF12) is a new novel cofactor in IGH::DUX4 transcriptional complexes/machinery. TCF12 mediates a positive self-feedback regulatory mechanism in IGH::DUX4-driven oncogenic transaction. IGH::DUX4-TCF12 structure/cooperation might represent a potent target/direction in future drug design against B-cell acute lymphoblastic leukaemia.

Automated benchmarking of combined protein structure and ligand conformation prediction

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.

Assessing the fast prediction of peptide conformers and the impact of non-natural modifications

We present an assessment of different approaches to predict peptide structures using modeling tools. Several small molecule, protein, and peptide-focused methodologies were used for the fast prediction of conformers for peptides shorter than 30 amino acids. We assessed the effect of including restraints based on annotated or predicted secondary structure motifs. A number of peptides in bound conformations and in solution were collected to compare the tools. In addition, we studied the impact of changing single amino acids to non-natural residues using molecular dynamics simulations. Deep learning methods such as AlphaFold2, or the combination of physics-based approaches with secondary structure information, produce the most accurate results for natural sequences. In the case of peptides with non-natural modifications, modeling the peptide containing natural amino acids first and then modifying and simulating the peptide using benchmarked force fields is a recommended pipeline. The results can guide the modeling of oligopeptides for drug discovery projects.

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

Being able to accurately predict the three-dimensional structure of an Ab can facilitate Ab characterization and epitope prediction, with important diagnostic and clinical implications. In this study, we evaluated the ability of AlphaFold to predict the structures of 222 recently published, high-resolution Fab H and L chain structures of Abs from different species directed against different Ags. We show that although the overall Ab prediction quality is in line with the results of CASP14, regions such as the complementarity-determining regions (CDRs) of the H chain, which are prone to higher variation, are predicted less accurately. Moreover, we discovered that AlphaFold mispredicts the bending angles between the variable and constant domains. To evaluate the ability of AlphaFold to model Ab-Ag interactions based only on sequence, we used AlphaFold-Multimer in combination with ZDOCK to predict the structures of 26 known Ab-Ag complexes. ZDOCK, which was applied on bound components of both the Ab and the Ag, succeeded in assembling 11 complexes, whereas AlphaFold succeeded in predicting only 2 of 26 models, with significant deviations in the docking contacts predicted in the rest of the molecules. Within the 11 complexes that were successfully predicted by ZDOCK, 9 involved short-peptide Ags (18-mer or less), whereas only 2 were complexes of Ab with a full-length protein. Docking of modeled unbound Ab and Ag was unsuccessful. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict Ab-Ag interactions and suggests areas for possible improvement.

Anoikis resistance and immune escape mediated by Epstein-Barr virus-encoded latent membrane protein 1-induced stabilization of PGC-1α promotes invasion and metastasis of nasopharyngeal carcinoma

BACKGROUND

Epstein-Barr virus (EBV) is the first discovered human tumor virus that is associated with a variety of malignancies of both lymphoid and epithelial origin including nasopharyngeal carcinoma (NPC). The EBV-encoded latent membrane protein 1 (LMP1) has been well-defined as a potent oncogenic protein, which is intimately correlated with NPC pathogenesis. Anoikis is considered to be a physiological barrier to metastasis, and avoiding anoikis is a major hallmark of metastasis. However, the role of LMP1 in anoikis-resistance and metastasis of NPC has not been fully identified.

METHODS

Trypan blue staining, colony formation assay, flow cytometry, and TUNEL staining, as well as the detection of apoptosis and anoikis resistance-related markers was applied to evaluate the anoikis-resistant capability of NPC cells cultured in ultra-low adhesion condition. Co-immunoprecipitation (Co-IP) experiment was performed to determine the interaction among LMP1, PRMT1 and PGC-1α. Ex vivo ubiquitination assay was used to detect the ubiquitination level of PGC-1α. Anoikis- resistant LMP1-positive NPC cell lines were established and applied for the xenograft and metastatic animal experiments.

RESULTS

Our current findings reveal the role of LMP1-stabilized peroxisome proliferator activated receptor coactivator-1a (PGC-1α) in anoikis resistance and immune escape to support the invasion and metastasis of NPC. Mechanistically, LMP1 enhances PGC-1α protein stability by promoting the interaction between arginine methyltransferase 1 (PRMT1) and PGC-1α to elevate the methylation modification of PGC-1α, thus endowing NPC cells with anoikis-resistance. Meanwhile, PGC-1α mediates the immune escape induced by LMP1 by coactivating with STAT3 to transcriptionally up-regulate PD-L1 expression.

CONCLUSION

Our work provides insights into how virus-encoded proteins recruit and interact with host regulatory elements to facilitate the malignant progression of NPC. Therefore, targeting PGC-1α or PRMT1-PGC-1α interaction might be exploited for therapeutic gain for EBV-associated malignancies.

Disrupting Dimeric β-Amyloid by Electric Fields

The early oligomers of the amyloid Aβ peptide are implicated in Alzheimer's disease, but their transient nature complicates the characterization of their structure and toxicity. Here, we investigate the stability of the minimal toxic species, i.e., β-amyloid dimers, in the presence of an oscillating electric field. We first use deep learning (AlphaFold-multimer) for generating initial models of Aβ42 dimers. The flexibility and secondary structure content of the models are then analyzed by multiple runs of molecular dynamics (MD). Structurally stable models are similar to ensemble representatives from microsecond-long MD sampling. Finally, we employ the validated model as the starting structure of MD simulations in the presence of an external oscillating electric field and observe a fast decay of β-sheet content at high field strengths. Control simulations using the helical dimer of the 42-residue leucine zipper peptide show higher structural stability than the Aβ42 dimer. The simulation results provide evidence that an external electric field (oscillating at 1 GHz) can disrupt amyloid oligomers which should be further investigated by experiments with brain organoids in vitro and eventually in vivo.

Discovery and pharmacophoric characterization of chemokine network inhibitors using phage-display, saturation mutagenesis and computational modelling

CC and CXC-chemokines are the primary drivers of chemotaxis in inflammation, but chemokine network redundancy thwarts pharmacological intervention. Tick evasins promiscuously bind CC and CXC-chemokines, overcoming redundancy. Here we show that short peptides that promiscuously bind both chemokine classes can be identified from evasins by phage-display screening performed with multiple chemokines in parallel. We identify two conserved motifs within these peptides and show using saturation-mutagenesis phage-display and chemotaxis studies of an exemplar peptide that an anionic patch in the first motif and hydrophobic, aromatic and cysteine residues in the second are functionally necessary. AlphaFold2-Multimer modelling suggests that the peptide occludes distinct receptor-binding regions in CC and in CXC-chemokines, with the first and second motifs contributing ionic and hydrophobic interactions respectively. Our results indicate that peptides with broad-spectrum anti-chemokine activity and therapeutic potential may be identified from evasins, and the pharmacophore characterised by phage display, saturation mutagenesis and computational modelling.

AFsample: improving multimer prediction with AlphaFold using massive sampling

SUMMARY

The AlphaFold2 neural network model has revolutionized structural biology with unprecedented performance. We demonstrate that by stochastically perturbing the neural network by enabling dropout at inference combined with massive sampling, it is possible to improve the quality of the generated models. We generated ∼6000 models per target compared with 25 default for AlphaFold-Multimer, with v1 and v2 multimer network models, with and without templates, and increased the number of recycles within the network. The method was benchmarked in CASP15, and compared with AlphaFold-Multimer v2 it improved the average DockQ from 0.41 to 0.55 using identical input and was ranked at the very top in the protein assembly category when compared with all other groups participating in CASP15. The simplicity of the method should facilitate the adaptation by the field, and the method should be useful for anyone interested in modeling multimeric structures, alternate conformations, or flexible structures.

AVAILABILITY AND IMPLEMENTATION

AFsample is available online at http://wallnerlab.org/AFsample.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Lipoylation is dependent on the ferredoxin FDX1 and dispensable under hypoxia in human cells

Iron-sulfur clusters (ISC) are essential cofactors that participate in electron transfer, environmental sensing, and catalysis. Amongst the most ancient ISC-containing proteins are the ferredoxin (FDX) family of electron carriers. Humans have two FDXs- FDX1 and FDX2, both of which are localized to mitochondria, and the latter of which is itself important for ISC synthesis. We have previously shown that hypoxia can eliminate the requirement for some components of the ISC biosynthetic pathway, but FDXs were not included in that study. Here, we report that FDX1, but not FDX2, is dispensable under 1% O2 in cultured human cells. We find that FDX1 is essential for production of the lipoic acid cofactor, which is synthesized by the ISC-containing enzyme lipoyl synthase. While hypoxia can rescue the growth phenotype of either FDX1 or lipoyl synthase KO cells, lipoylation in these same cells is not rescued, arguing against an alternative biosynthetic route or salvage pathway for lipoate in hypoxia. Our work reveals the divergent roles of FDX1 and FDX2 in mitochondria, identifies a role for FDX1 in lipoate synthesis, and suggests that loss of lipoic acid can be tolerated under low oxygen tensions in cell culture.

Pan-cancer structurome reveals overrepresentation of beta sandwiches and underrepresentation of alpha helical domains

The recent progress in the prediction of protein structures marked a historical milestone. AlphaFold predicted 200 million protein models with an accuracy comparable to experimental methods. Protein structures are widely used to understand evolution and to identify potential drug targets for the treatment of various diseases, including cancer. Thus, these recently predicted structures might convey previously unavailable information about cancer biology. Evolutionary classification of protein domains is challenging and different approaches exist. Recently our team presented a classification of domains from human protein models released by AlphaFold. Here we evaluated the pan-cancer structurome, domains from over and under expressed proteins in 21 cancer types, using the broadest levels of the ECOD classification: the architecture (A-groups) and possible homology (X-groups) levels. Our analysis reveals that AlphaFold has greatly increased the three-dimensional structural landscape for proteins that are differentially expressed in these 21 cancer types. We show that beta sandwich domains are significantly overrepresented and alpha helical domains are significantly underrepresented in the majority of cancer types. Our data suggest that the prevalence of the beta sandwiches is due to the high levels of immunoglobulins and immunoglobulin-like domains that arise during tumor development-related inflammation. On the other hand, proteins with exclusively alpha domains are important elements of homeostasis, apoptosis and transmembrane transport. Therefore cancer cells tend to reduce representation of these proteins to promote successful oncogeneses.