Sequence-Based Prediction of Fuzzy Protein Interactions

An Analysis of Intrinsic Protein Disorder in Antimicrobial Peptides

Antibiotic resistance, driven by the rise of pathogens like VRE and MRSA, poses a global health threat, prompting the exploration of antimicrobial peptides (AMPs) as alternatives to traditional antibiotics. AMPs, known for their broad-spectrum activity and structural flexibility, share characteristics with intrinsically disordered proteins, which lack a rigid structure and play diverse roles in cellular processes. This study aims to quantify the intrinsic disorder and liquid-liquid phase separation (LLPS) propensity in AMPs, advancing our understanding of their antimicrobial mechanisms and potential therapeutic applications. To investigate the propensity for intrinsic disorder and LLPS in AMPs, we compared the AMPs to the human proteome. The AMP sequences were retrieved from the AMP database (APD3), while the human proteome was obtained from the UniProt database. We analyzed amino acid composition using the Composition Profiler tool and assessed intrinsic disorder using various predictors, including PONDR® and IUPred, through the Rapid Intrinsic Disorder Analysis Online (RIDAO) platform. For LLPS propensity, we employed FuzDrop, and FuzPred was used to predict context-dependent binding behaviors. Statistical analyses, such as ANOVA and χ2 tests, were performed to determine the significance of observed differences between the two groups. We analyzed over 3000 AMPs and 20,000 human proteins to investigate differences in amino acid composition, intrinsic disorder, and LLPS potential. Composition analysis revealed distinct differences in amino acid abundance, with AMPs showing an enrichment in both order-promoting and disorder-promoting amino acids compared to the human proteome. Intrinsic disorder analysis, performed using a range of predictors, consistently demonstrated that AMPs exhibit higher levels of predicted disorder than human proteins, with significant differences confirmed by statistical tests. LLPS analysis, conducted using FuzDrop, showed that AMPs had a lower overall propensity for LLPS compared to human proteins, although specific subsets of AMPs exhibited high LLPS potential. Additionally, redox-dependent disorder predictions highlighted significant differences in how AMP and human proteins respond to oxidative conditions, further suggesting functional divergences between the two proteomes. CH-CDF plot analysis revealed that AMPs and human proteins occupy distinct structural categories, with AMPs showing a greater proportion of highly disordered proteins compared to the human proteome. These findings underscore key molecular differences between AMPs and human proteins, with implications for their antimicrobial activity and potential therapeutic applications. Our study reveals that AMPs possess a significantly higher degree of intrinsic disorder and specific subsets exhibit LLPS potential, distinguishing them from the human proteome. These molecular characteristics likely contribute to their antimicrobial function and adaptability, offering valuable insights for developing novel therapeutic strategies to combat antibiotic resistance.

Intrinsically disordered regions as facilitators of the transcription factor target search

Transcription factors (TFs) contribute to organismal development and function by regulating gene expression. Despite decades of research, the factors determining the specificity and speed at which eukaryotic TFs detect their target binding sites remain poorly understood. Recent studies have pointed to intrinsically disordered regions (IDRs) within TFs as key regulators of the process by which TFs find their target sites on DNA (the TF target search). However, IDRs are challenging to study because they can confer specificity despite low sequence complexity and can be functionally conserved despite rapid sequence divergence. Nevertheless, emerging computational and experimental approaches are beginning to elucidate the sequence-function relationship within the IDRs of TFs. Additional insights are informing potential mechanisms underlying the IDR-directed search for the DNA targets of TFs, including incorporation into biomolecular condensates, facilitating TF co-localization, and the hypothesis that IDRs recognize and directly interact with specific genomic regions.

Comparative Analysis of the Intrinsic Disorder Within the Layers of the Human Cornea

PURPOSE

The human cornea is essential for vision, providing structural integrity and refractive power to the eye. Recent advancements have deepened our understanding of the corneal molecular composition, yet the role of intrinsically disordered proteins within the cornea is unexplored.

METHODS

We analyzed 3,250 corneal proteins identified by Dyrlund et al, focusing on the epithelium, stroma, and endothelium layers. We performed a bioinformatics analysis to characterize the amino acid composition, the propensity for intrinsic protein disorder, and the distribution of protein types in 3 corneal layer proteome.

RESULTS

Our study demonstrates that each corneal layer exhibited unique patterns in amino acid composition related to protein disorder. Order-promoting amino acids were generally depleted except for leucine, whereas disorder-promoting amino acids like arginine and glutamic acid were enriched across all layers. Significant variations were observed in the levels of intrinsic disorder among the different corneal layers, with substantial proportions of highly disordered proteins present in each. Analysis of protein class type in each layers revealed that no significant differences were detected in the distribution of protein classifications across the layers, suggesting a consistent population of the protein types across all corneal layers.

CONCLUSIONS

Our findings reveal a sophisticated landscape of protein structures where intrinsic disorder varies across layers, suggesting an adaptation of the corneal proteome to the unique physiological demands of each layer. These structural variations may reflect the intricate requirements for corneal transparency, biomechanical stability, and environmental responsiveness.

A deep learning method for predicting interactions for intrinsically disordered regions of proteins

Intrinsically disordered proteins or regions (IDPs/IDRs) adopt diverse binding modes with different partners, ranging from ordered to multivalent to fuzzy conformations in the bound state. Characterizing IDR interfaces is challenging experimentally and computationally. Alphafold-multimer and Alphafold3, the state-of-the-art structure prediction methods, are less accurate at predicting IDR binding sites at their benchmarked confidence cutoffs. Their performance improves upon lowering the confidence cutoffs. Here, we developed Disobind, a deep-learning method that predicts inter-protein contact maps and interface residues for an IDR and a partner protein, given their sequences. It outperforms AlphaFold-multimer and AlphaFold3 at multiple confidence cutoffs. Combining the Disobind and AlphaFold-multimer predictions further improves the performance. In contrast to most current methods, Disobind considers the context of the binding partner and does not depend on structures and multiple sequence alignments. Its predictions can be used to localize IDRs in integrative structures of large assemblies and characterize and modulate IDR-mediated interactions.

Evaluation of predictions of disordered binding regions in the CAID2 experiment

A large portion of the Intrinsically Disordered Regions (IDRs) in protein sequences interact with proteins, nucleic acids, and other types of ligands. Correspondingly, dozens of sequence-based predictors of binding IDRs were developed. A recently completed second community-based Critical Assessments of protein Intrinsic Disorder prediction (CAID2) evaluated 32 predictors of binding IDRs. However, CAID2 considered a rather narrow scenario by testing on 78 proteins with binding IDRs and not differentiating between different ligands, in spite that virtually all predictors target IDRs that interact with specific types of ligands. In that scenario, several intrinsic disorder predictors predict binding IDRs with accuracy equivalent to the best predictors of binding IDRs since large majority of IDRs in the 78 test proteins are binding. We substantially extended the CAID2's evaluation by using the entire CAID2 dataset of 348 proteins and considering several arguably more practical scenarios. We assessed whether predictors accurately differentiate binding IDRs from other types of IDRs and how they perform when predicting IDRs that interact with different ligand types. We found that intrinsic disorder predictors cannot accurately identify binding IDRs among other disordered regions, majority of the predictors of binding IDRs are ligand type agnostic (i.e., they cross predict binding in IDRs that interact with ligands that they do not cover), and only a handful of predictors of binding IDRs perform relatively well and generate reasonably low amounts of cross predictions. We also suggest a number of future research directions that would move this active field of research forward.

Nucleo-cytoplasmic environment modulates spatiotemporal p53 phase separation

Liquid-liquid phase separation of various transcription factors into biomolecular condensates plays an essential role in gene regulation. Here, using cellular models and in vitro studies, we show the spatiotemporal formation and material properties of p53 condensates that might dictate its function. In particular, p53 forms liquid-like condensates in the nucleus of cells, which can bind to DNA and perform transcriptional activity. However, cancer-associated mutations promote misfolding and partially rigidify the p53 condensates with impaired DNA binding ability. Irrespective of wild-type and mutant forms, the partitioning of p53 into cytoplasm leads to the condensate formation, which subsequently undergoes rapid solidification. In vitro studies show that abundant nuclear components such as RNA and nonspecific DNA promote multicomponent phase separation of the p53 core domain and maintain their liquid-like property, whereas specific DNA promotes its dissolution into tetrameric functional p53. This work provides mechanistic insights into how the life cycle and DNA binding properties of p53 might be regulated by phase separation.

PICNIC accurately predicts condensate-forming proteins regardless of their structural disorder across organisms

Biomolecular condensates are membraneless organelles that can concentrate hundreds of different proteins in cells to operate essential biological functions. However, accurate identification of their components remains challenging and biased towards proteins with high structural disorder content with focus on self-phase separating (driver) proteins. Here, we present a machine learning algorithm, PICNIC (Proteins Involved in CoNdensates In Cells) to classify proteins that localize to biomolecular condensates regardless of their role in condensate formation. PICNIC successfully predicts condensate members by learning amino acid patterns in the protein sequence and structure in addition to the intrinsic disorder. Extensive experimental validation of 24 positive predictions in cellulo shows an overall ~82% accuracy regardless of the structural disorder content of the tested proteins. While increasing disorder content is associated with organismal complexity, our analysis of 26 species reveals no correlation between predicted condensate proteome content and disorder content across organisms. Overall, we present a machine learning classifier to interrogate condensate components at whole-proteome levels across the tree of life.

Inherently disordered regions of axonemal dynein assembly factors

The dynein-driven beating of cilia is required to move individual cells and to generate fluid flow across surfaces and within cavities. These motor enzymes are highly complex and can contain upwards of 20 different protein components with a total mass approaching 2 MDa. The dynein heavy chains are enormous proteins consisting of ~4500 residues and ribosomes take approximately 15 min to synthesize one. Studies in a broad array of organisms ranging from the green alga Chlamydomonas to humans has identified 19 cytosolic factors (DNAAFs) that are needed to specifically build axonemal dyneins; defects in many of these proteins lead to primary ciliary dyskinesia in mammals which can result in infertility, severe bronchial problems, and situs inversus. How all these factors cooperate in a spatially and temporally regulated manner to promote dynein assembly in cytoplasm remains very uncertain. These DNAAFs contain a variety of well-folded domains many of which provide protein interaction surfaces. However, many also exhibit large regions that are predicted to be inherently disordered. Here I discuss the nature of these unstructured segments, their predicted propensity for driving protein phase separation, and their potential for adopting more defined conformations during the dynein assembly process.

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.

Structural study of the intrinsically disordered tardigrade damage suppressor protein (Dsup) and its complex with DNA

Studies of proteins, found in one of the most stress-resistant animals tardigrade Ramazzottius varieornatus, aim to reveal molecular principles of extreme tolerance to various types of stress and developing applications based on them for medicine, biotechnology, pharmacy, and space research. Tardigrade DNA/RNA-binding damage suppressor protein (Dsup) reduces DNA damage caused by reactive oxygen spices (ROS) produced upon irradiation and oxidative stresses in Dsup-expressing transgenic organisms. This work is focused on the determination of structural features of Dsup protein and Dsup-DNA complex, which refines details of protective mechanism. For the first time, intrinsically disordered nature of Dsup protein with highly flexible structure was experimentally proven and characterized by the combination of small angle X-ray scattering (SAXS) technique, circular dichroism spectroscopy, and computational methods. Low resolution models of Dsup protein and an ensemble of conformations were presented. In addition, we have shown that Dsup forms fuzzy complex with DNA.

StrIDR: a database of intrinsically disordered regions of proteins with experimentally resolved structures

Motivation

Intrinsically disordered regions (IDRs) of proteins exist as an ensemble of conformations, and not as a single structure. Existing databases contain extensive, experimentally derived annotations of intrinsic disorder for millions of proteins at the sequence level. However, only a tiny fraction of these IDRs are associated with an experimentally determined protein structure. Moreover, even if a structure exists, parts of the disordered regions may still be unresolved.

Results

Here we organize Structures of Intrinsically Disordered Regions (StrIDR), a database of IDRs confirmed via experimental or homology-based evidence, resolved in experimentally determined structures. The database can provide useful insights into the dynamics, folding, and interactions of IDRs. It can also facilitate computational studies on IDRs, such as those using molecular dynamics simulations and/or machine learning.

Availability

StrIDR is available at https://isblab.ncbs.res.in/stridr. The web UI allows for downloading PDB structures and SIFTS mappings of individual entries. Additionally, the entire database can be downloaded in a JSON format. The source code for creating and updating the database is available at https://github.com/isblab/stridr.

Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning

Intrinsically disordered proteins and regions (IDP/IDRs) are ubiquitous across all domains of life. Characterized by a lack of a stable tertiary structure, IDP/IDRs populate a diverse set of transiently formed structural states that can promiscuously adapt upon binding with specific interaction partners and/or certain alterations in environmental conditions. This malleability is foundational for their role as tunable interaction hubs in core cellular processes such as signaling, transcription, and translation. Tracing the conformational ensemble of an IDP/IDR and its perturbation in response to regulatory cues is thus paramount for illuminating its function. However, the conformational heterogeneity of IDP/IDRs poses several challenges. Here, we review experimental and computational methods devised to disentangle the conformational landscape of IDP/IDRs, highlighting recent computational advances that permit proteome-wide scans of IDP/IDRs conformations. We briefly evaluate selected computational methods using the disordered N-terminal of the human copper transporter 1 as a test case and outline further challenges in IDP/IDRs ensemble prediction.

Context-dependent, fuzzy protein interactions: Towards sequence-based insights

Predicting protein interactions in the cellular environment still remains a challenge in the AlphaFold era. Protein interactions, similarly to their structures, sample a continuum from ordered to disordered states, with specific partners in many bound configurations. A multiplicity of binding modes (MBM) enables transition between these states under different cellular conditions. This review focuses on how the cellular environment affects protein interactions, highlighting the molecular mechanisms, biophysical origin, and sequence-based principles of context-dependent, fuzzy interactions. It summarises experimental and computational approaches to address the challenge of interaction heterogeneity and its contribution to a wide range of biological functions. These insights will help in understanding complex cellular processes, involving conversions between protein assembly states, such as from liquid-like droplet state to the amyloid state.

Intrinsic disorder in the human vitreous proteome

The vitreous is a vital component of the eye, occupying a substantial portion of its volume and maintaining its structure. This study delves into the presence and significance of intrinsically disordered proteins (IDPs) within the vitreous, utilizing a dataset of 1240 vitreous proteins previously discovered in the vitreous proteome by Murthy et al.in five healthy subjects. The results indicate that 26.9 % of vitreous proteins are highly disordered, 68.8 % possess moderate disorder, and only 4.3 % are highly ordered. A complex interaction network among these proteins suggests their biological importance, and approximately 25 % may undergo liquid-liquid phase separation (LLPS). These findings offer new perspectives on the vitreous' molecular composition and behavior, potentially impacting our understanding of eye-related diseases, physiological changes such as vitreous syneresis. Further research is needed to translate these insights into clinical applications, although the intrinsic protein disorder and its association with LLPS appears to play a role in vitreous proteome function.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Deep mutational scanning of the RNase III-like domain in Trypanosoma brucei RNA editing protein KREPB4

Kinetoplastid pathogens including Trypanosoma brucei, T. cruzi, and Leishmania species, are early diverged, eukaryotic, unicellular parasites. Functional understanding of many proteins from these pathogens has been hampered by limited sequence homology to proteins from other model organisms. Here we describe the development of a high-throughput deep mutational scanning approach in T. brucei that facilitates rapid and unbiased assessment of the impacts of many possible amino acid substitutions within a protein on cell fitness, as measured by relative cell growth. The approach leverages several molecular technologies: cells with conditional expression of a wild-type gene of interest and constitutive expression of a library of mutant variants, degron-controlled stabilization of I-SceI meganuclease to mediate highly efficient transfection of a mutant allele library, and a high-throughput sequencing readout for cell growth upon conditional knockdown of wild-type gene expression and exclusive expression of mutant variants. Using this method, we queried the effects of amino acid substitutions in the apparently non-catalytic RNase III-like domain of KREPB4 (B4), which is an essential component of the RNA Editing Catalytic Complexes (RECCs) that carry out mitochondrial RNA editing in T. brucei. We measured the impacts of thousands of B4 variants on bloodstream form cell growth and validated the most deleterious variants containing single amino acid substitutions. Crucially, there was no correlation between phenotypes and amino acid conservation, demonstrating the greater power of this method over traditional sequence homology searching to identify functional residues. The bloodstream form cell growth phenotypes were combined with structural modeling, RECC protein proximity data, and analysis of selected substitutions in procyclic form T. brucei. These analyses revealed that the B4 RNaseIII-like domain is essential for maintenance of RECC integrity and RECC protein abundances and is also involved in changes in RECCs that occur between bloodstream and procyclic form life cycle stages.

Protein structure-function continuum model: Emerging nexuses between specificity, evolution, and structure

The rationale for replacing the old binary of structure-function with the trinity of structure, disorder, and function has gained considerable ground in recent years. A continuum model based on the expanded form of the existing paradigm can now subsume importance of both conformational flexibility and intrinsic disorder in protein function. The disorder is actually critical for understanding the protein-protein interactions in many regulatory processes, formation of membrane-less organelles, and our revised notions of specificity as amply illustrated by moonlighting proteins. While its importance in formation of amyloids and function of prions is often discussed, the roles of intrinsic disorder in infectious diseases and protein function under extreme conditions are also becoming clear. This review is an attempt to discuss how our current understanding of protein function, specificity, and evolution fit better with the continuum model. This integration of structure and disorder under a single model may bring greater clarity in our continuing quest for understanding proteins and molecular mechanisms of their functionality.

Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-Based Algorithms

The identification and characterization of the main conformations from a protein population are a challenging and inherently high-dimensional problem. Here, we evaluate the performance of the Secondary sTructural Ensembles with machine LeArning (StELa) double-clustering method, which clusters protein structures based on the relationship between the φ and ψ dihedral angles in a protein backbone and the secondary structure of the protein, thus focusing on the local properties of protein structures. The classification of states as vectors composed of the clusters' indices arising naturally from the Ramachandran plot is followed by the hierarchical clustering of the vectors to allow for the identification of the main features of the corresponding free energy landscape (FEL). We compare the performance of StELa with the established root-mean-squared-deviation (RMSD)-based clustering algorithm, which focuses on global properties of protein structures and with Combinatorial Averaged Transient Structure (CATS), the combinatorial averaged transient structure clustering method based on distributions of the φ and ψ dihedral angle coordinates. Using ensembles of conformations from molecular dynamics simulations of intrinsically disordered proteins (IDPs) of various lengths (tau protein fragments) or short fragments from a globular protein, we show that StELa is the clustering method that identifies many of the minima and relevant energy states around the minima from the corresponding FELs. In contrast, the RMSD-based algorithm yields a large number of clusters that usually cover most of the FEL, thus being unable to distinguish between states, while CATS does not sample well the FELs for long IDPs and fragments from globular proteins.

Protein nanocondensates: the next frontier

Over the past decade, myriads of studies have highlighted the central role of protein condensation in subcellular compartmentalization and spatiotemporal organization of biological processes. Conceptually, protein condensation stands at the highest level in protein structure hierarchy, accounting for the assembly of bodies ranging from thousands to billions of molecules and for densities ranging from dense liquids to solid materials. In size, protein condensates range from nanocondensates of hundreds of nanometers (mesoscopic clusters) to phase-separated micron-sized condensates. In this review, we focus on protein nanocondensation, a process that can occur in subsaturated solutions and can nucleate dense liquid phases, crystals, amorphous aggregates, and fibers. We discuss the nanocondensation of proteins in the light of general physical principles and examine the biophysical properties of several outstanding examples of nanocondensation. We conclude that protein nanocondensation cannot be fully explained by the conceptual framework of micron-scale biomolecular condensation. The evolution of nanocondensates through changes in density and order is currently under intense investigation, and this should lead to the development of a general theoretical framework, capable of encompassing the full range of sizes and densities found in protein condensates.

Towards sequence-based principles for protein phase separation predictions

The phenomenon of protein phase separation, which underlies the formation of biomolecular condensates, has been associated with numerous cellular functions. Recent studies indicate that the amino acid sequences of most proteins may harbour not only the code for folding into the native state but also for condensing into the liquid-like droplet state and the solid-like amyloid state. Here we review the current understanding of the principles for sequence-based methods for predicting the propensity of proteins for phase separation. A guiding concept is that entropic contributions are generally more important to stabilise the droplet state than they are for the native and amyloid states. Although estimating these entropic contributions has proven difficult, we describe some progress that has been recently made in this direction. To conclude, we discuss the challenges ahead to extend sequence-based prediction methods of protein phase separation to include quantitative in vivo characterisations of this process.

A sequence-based foldability score combined with AlphaFold2 predictions to disentangle the protein order/disorder continuum

Order and disorder govern protein functions, but there is a great diversity in disorder, from regions that are-and stay-fully disordered to conditional order. This diversity is still difficult to decipher even though it is encoded in the amino acid sequences. Here, we developed an analytic Python package, named pyHCA, to estimate the foldability of a protein segment from the only information of its amino acid sequence and based on a measure of its density in regular secondary structures associated with hydrophobic clusters, as defined by the hydrophobic cluster analysis (HCA) approach. The tool was designed by optimizing the separation between foldable segments from databases of disorder (DisProt) and order (SCOPe [soluble domains] and OPM [transmembrane domains]). It allows to specify the ratio between order, embodied by regular secondary structures (either participating in the hydrophobic core of well-folded 3D structures or conditionally formed in intrinsically disordered regions) and disorder. We illustrated the relevance of pyHCA with several examples and applied it to the sequences of the proteomes of 21 species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes, for which structure models are provided in the AlphaFold protein structure database. Cases of low-confidence scores related to disorder were distinguished from those of sequences that we identified as foldable but are still excluded from accurate modeling by AlphaFold2 due to a lack of sequence homologs or to compositional biases. Overall, our approach is complementary to AlphaFold2, providing guides to map structural innovations through evolutionary processes, at proteome and gene scales.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Alternatively spliced exon regulates context-dependent MEF2D higher-order assembly during myogenesis

During muscle cell differentiation, the alternatively spliced, acidic β-domain potentiates transcription of Myocyte-specific Enhancer Factor 2 (Mef2D). Sequence analysis by the FuzDrop method indicates that the β-domain can serve as an interaction element for Mef2D higher-order assembly. In accord, we observed Mef2D mobile nuclear condensates in C2C12 cells, similar to those formed through liquid-liquid phase separation. In addition, we found Mef2D solid-like aggregates in the cytosol, the presence of which correlated with higher transcriptional activity. In parallel, we observed a progress in the early phase of myotube development, and higher MyoD and desmin expression. In accord with our predictions, the formation of aggregates was promoted by rigid β-domain variants, as well as by a disordered β-domain variant, capable of switching between liquid-like and solid-like higher-order states. Along these lines, NMR and molecular dynamics simulations corroborated that the β-domain can sample both ordered and disordered interactions leading to compact and extended conformations. These results suggest that β-domain fine-tunes Mef2D higher-order assembly to the cellular context, which provides a platform for myogenic regulatory factors and the transcriptional apparatus during the developmental process.

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

NMR insights into dynamic, multivalent interactions of intrinsically disordered regions: from discrete complexes to condensates

The spatial and temporal organization of interactions between proteins underlie the regulation of most cellular processes. The requirement for such interactions to be specific predisposes a view that protein-protein interactions are relatively static and are formed through the stable complementarity of the interacting partners. A growing body of reports indicate, however, that many interactions lead to fuzzy complexes with an ensemble of conformations in dynamic exchange accounting for the observed binding. Here, we discuss how NMR has facilitated the characterization of these discrete, dynamic complexes and how such characterization has aided the understanding of dynamic, condensed phases of phase-separating proteins with exchanging multivalent interactions.

Protein interactions: anything new?

How do proteins interact in the cellular environment? Which interactions stabilize liquid-liquid phase separated condensates? Are the concepts, which have been developed for specific protein complexes also applicable to higher-order assemblies? Recent discoveries prompt for a universal framework for protein interactions, which can be applied across the scales of protein communities. Here, we discuss how our views on protein interactions have evolved from rigid structures to conformational ensembles of proteins and discuss the open problems, in particular related to biomolecular condensates. Protein interactions have evolved to follow changes in the cellular environment, which manifests in multiple modes of interactions between the same partners. Such cellular context-dependence requires multiplicity of binding modes (MBM) by sampling multiple minima of the interaction energy landscape. We demonstrate that the energy landscape framework of protein folding can be applied to explain this phenomenon, opening a perspective toward a physics-based, universal model for cellular protein behaviors.

The MDMX Acidic Domain Uses Allovalency to Bind Both p53 and MDMX

Autoinhibition of p53 binding to MDMX requires two short-linear motifs (SLiMs) containing adjacent tryptophan (WW) and tryptophan-phenylalanine (WF) residues. NMR spectroscopy was used to show the WW and WF motifs directly compete for the p53 binding site on MDMX and circular dichroism spectroscopy was used to show the WW motif becomes helical when it is bound to the p53 binding domain (p53BD) of MDMX. Binding studies using isothermal titration calorimetry showed the WW motif is a stronger inhibitor of p53 binding than the WF motif when they are both tethered to p53BD by the natural disordered linker. We also investigated how the WW and WF motifs interact with the DNA binding domain (DBD) of p53. Both motifs bind independently to similar sites on DBD that overlap the DNA binding site. Taken together our work defines a model for complex formation between MDMX and p53 where a pair of disordered SLiMs bind overlapping sites on both proteins.

MobiDB: 10 years of intrinsically disordered proteins

The MobiDB database (URL: https://mobidb.org/) is a knowledge base of intrinsically disordered proteins. MobiDB aggregates disorder annotations derived from the literature and from experimental evidence along with predictions for all known protein sequences. MobiDB generates new knowledge and captures the functional significance of disordered regions by processing and combining complementary sources of information. Since its first release 10 years ago, the MobiDB database has evolved in order to improve the quality and coverage of protein disorder annotations and its accessibility. MobiDB has now reached its maturity in terms of data standardization and visualization. Here, we present a new release which focuses on the optimization of user experience and database content. The major advances compared to the previous version are the integration of AlphaFoldDB predictions and the re-implementation of the homology transfer pipeline, which expands manually curated annotations by two orders of magnitude. Finally, the entry page has been restyled in order to provide an overview of the available annotations along with two separate views that highlight structural disorder evidence and functions associated with different binding modes.

New insights into disordered proteins and regions according to the FOD-M model

A collection of intrinsically disordered proteins (IDPs) having regions with the status of intrinsically disordered (IDR) according to the Disprot database was analyzed from the point of view of the structure of hydrophobic core in the structural unit (chain / domain). The analysis includes all the Homo Sapiens as well as Mus Musculus proteins present in the DisProt database for which the structure is available. In the analysis, the fuzzy oil drop modified model (FOD-M) was used, taking into account the external force field, modified by the presence of other factors apart from polar water, influencing protein structuring. The paper presents an alternative to secondary-structure-based classification of intrinsically disordered regions (IDR). The basis of our classification is the ordering of hydrophobic core as calculated by the FOD-M model resulting in FOD-ordered or FOD-unordered IDRs.

Nanomedicines Meet Disordered Proteins: A Shift from Traditional Materials and Concepts to Innovative Polymers

Water-soluble nanomedicines have been widely studied for the targeted delivery of drugs for a very long time. As a notable example, biomaterials based on N-(2-hydroxypropyl) methacrylamide (HPMA) copolymers have been under investigation for nearly half a century. In particular, anticancer drug carriers have been developed under the assumption that the leading mechanism with a therapeutic impact on solid tumors is the enhanced permeability and retention (EPR) effect, which dates back more than three decades. Nevertheless, these (and other) materials and concepts have encountered several barriers in their successful translation into clinical practice, and future nanomedicines need improvements in both passive and active targeting to their site of action. Notions borrowed from recent studies on intrinsically disordered proteins (IDPs) seem promising for enhancing the self-assembly, stimuli-responsiveness, and recognition properties of protein/peptide-based copolymers. Accordingly, IDP-based nanomedicines are ready to give new impetus to more traditional research in this field.

Protein conformation and biomolecular condensates

Protein conformation and cell compartmentalization are fundamental concepts and subjects of vast scientific endeavors. In the last two decades, we have witnessed exciting advances that unveiled the conjunction of these concepts. An avalanche of studies highlighted the central role of biomolecular condensates in membraneless subcellular compartmentalization that permits the spatiotemporal organization and regulation of myriads of simultaneous biochemical reactions and macromolecular interactions. These studies have also shown that biomolecular condensation, driven by multivalent intermolecular interactions, is mediated by order-disorder transitions of protein conformation and by protein domain architecture. Conceptually, protein condensation is a distinct level in protein conformational landscape in which collective folding of large collections of molecules takes place. Biomolecular condensates arise by the physical process of phase separation and comprise a variety of bodies ranging from membraneless organelles to liquid condensates to solid-like conglomerates, spanning lengths from mesoscopic clusters (nanometers) to micrometer-sized objects. In this review, we summarize and discuss recent work on the assembly, composition, conformation, material properties, thermodynamics, regulation, and functions of these bodies. We also review the conceptual framework for future studies on the conformational dynamics of condensed proteins in the regulation of cellular processes.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.

Are casein micelles extracellular condensates formed by liquid-liquid phase separation?

Casein micelles are extracellular polydisperse assemblies of unstructured casein proteins. Caseins are the major component of milk. Within casein micelles, casein molecules are stabilised by binding to calcium phosphate nanoclusters and, by acting as molecular chaperones, through multivalent interactions. In light of such interactions, we discuss whether casein micelles can be considered as extracellular condensates formed by liquid-liquid phase separation. We analyse the sequence, structure and interactions of caseins in comparison to proteins forming intracellular condensates. Furthermore, we review the similarities between caseins and small heat-shock proteins whose chaperone activity is linked to phase separation of proteins. By bringing these observations together, we describe a regulatory mechanism for protein condensates, as exemplified by casein micelles.

Mapping the deformability of natural and designed cellulosomes in solution

BACKGROUND

Natural cellulosome multi-enzyme complexes, their components, and engineered 'designer cellulosomes' (DCs) promise an efficient means of breaking down cellulosic substrates into valuable biofuel products. Their broad uptake in biotechnology relies on boosting proximity-based synergy among the resident enzymes, but the modular architecture challenges structure determination and rational design.

RESULTS

We used small angle X-ray scattering combined with molecular modeling to study the solution structure of cellulosomal components. These include three dockerin-bearing cellulases with distinct substrate specificities, original scaffoldins from the human gut bacterium Ruminococcus champanellensis (ScaA, ScaH and ScaK) and a trivalent cohesin-bearing designer scaffoldin (Scaf20L), followed by cellulosomal complexes comprising these components, and the nonavalent fully loaded Clostridium thermocellum CipA in complex with Cel8A from the same bacterium. The size analysis of Rg and Dmax values deduced from the scattering curves and corresponding molecular models highlight their variable aspects, depending on composition, size and spatial organization of the objects in solution.

CONCLUSIONS

Our data quantifies variability of form and compactness of cellulosomal components in solution and confirms that this native plasticity may well be related to speciation with respect to the substrate that is targeted. By showing that scaffoldins or components display enhanced compactness compared to the free objects, we provide new routes to rationally enhance their stability and performance in their environment of action.

FuzDrop on AlphaFold: visualizing the sequence-dependent propensity of liquid-liquid phase separation and aggregation of proteins

Many proteins perform their functions within membraneless organelles, where they form a liquid-like condensed state, also known as droplet state. The FuzDrop method predicts the probability of spontaneous liquid-liquid phase separation of proteins and provides a sequence-based score to identify the regions that promote this process. Furthermore, the FuzDrop method estimates the propensity of conversion of proteins to the amyloid state, and identifies aggregation hot-spots, which can drive the irreversible maturation of the liquid-like droplet state. These predictions can also identify mutations that can induce formation of amyloid aggregates, including those implicated in human diseases. To facilitate the interpretation of the predictions, the droplet-promoting and aggregation-promoting regions can be visualized on protein structures generated by AlphaFold. The FuzDrop server (https://fuzdrop.bio.unipd.it) thus offers insights into the complex behavior of proteins in their condensed states and facilitates the understanding of the functional relationships of proteins.

Fuzzy RNA recognition by the Trypanosoma brucei editosome

The assembly of high molecular mass ribonucleoprotein complexes typically relies on the binary interaction of defined RNA sequences or precisely folded RNA motifs with dedicated RNA-binding domains on the protein side. Here we describe a new molecular recognition principle of RNA molecules by a high molecular mass protein complex. By chemically probing the solvent accessibility of mitochondrial pre-mRNAs when bound to the Trypanosoma brucei editosome, we identified multiple similar but non-identical RNA motifs as editosome contact sites. However, by treating the different motifs as mathematical graph objects we demonstrate that they fit a consensus 2D-graph consisting of 4 vertices (V) and 3 edges (E) with a Laplacian eigenvalue of 0.5477 (λ₂). We establish that synthetic 4V(3E)-RNAs are sufficient to compete for the editosomal pre-mRNA binding site and that they inhibit RNA editing in vitro. Furthermore, we demonstrate that only two topological indices are necessary to predict the binding of any RNA motif to the editosome with a high level of confidence. Our analysis corroborates that the editosome has adapted to the structural multiplicity of the mitochondrial mRNA folding space by recognizing a fuzzy continuum of RNA folds that fit a consensus graph descriptor.

SRC homology 3 domains: multifaceted binding modules

The assembly of complexes following the detection of extracellular signals is often controlled by signaling proteins comprising multiple peptide binding modules. The SRC homology (SH)3 family represents the archetypical modular protein interaction module, with ~300 annotated SH3 domains in humans that regulate an impressive array of signaling processes. We review recent findings regarding the allosteric contributions of SH3 domains host protein context, their phosphoregulation, and their roles in phase separation that challenge the simple model in which SH3s are considered to be portable domains binding to specific proline-rich peptide motifs.

AlphaFold2: A Role for Disordered Protein/Region Prediction?

The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs).

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

The key proteins involved in transcriptional regulation play convergent roles in cellular homeostasis, and their dysfunction mediates aberrant gene expressions that underline the hallmarks of tumorigenesis. As tumor progression is dependent on such abnormal regulation of transcription, it is important to discover novel chemical entities as antitumor drugs that target key tumor-associated proteins involved in transcriptional regulation. Despite most key proteins (especially transcription factors) involved in transcriptional regulation are historically recognized as undruggable targets, multiple targeting approaches at diverse levels of transcriptional regulation, such as epigenetic intervention, inhibition of DNA-binding of transcriptional factors, and inhibition of the protein-protein interactions (PPIs), have been established in preclinically or clinically studies. In addition, several new approaches have recently been described, such as targeting proteasomal degradation and eliciting synthetic lethality. This review will emphasize on accentuating these developing therapeutic approaches and provide a thorough conspectus of the drug development to target key proteins involved in transcriptional regulation and their impact on future oncotherapy.

Intrinsically disordered signaling proteins: Essential hub players in the control of stress responses in Saccharomyces cerevisiae

Cells have developed diverse mechanisms to monitor changes in their surroundings. This allows them to establish effective responses to cope with adverse environments. Some of these mechanisms have been well characterized in the budding yeast Saccharomyces cerevisiae, an excellent experimental model to explore and elucidate some of the strategies selected in eukaryotic organisms to adjust their growth and development in stressful conditions. The relevance of structural disorder in proteins and the impact on their functions has been uncovered for proteins participating in different processes. This is the case of some transcription factors (TFs) and other signaling hub proteins, where intrinsically disordered regions (IDRs) play a critical role in their function. In this work, we present a comprehensive bioinformatic analysis to evaluate the significance of structural disorder in those TFs (170) recognized in S. cerevisiae. Our findings show that 85.2% of these TFs contain at least one IDR, whereas ~30% exhibit a higher disorder level and thus were considered as intrinsically disordered proteins (IDPs). We also found that TFs contain a higher number of IDRs compared to the rest of the yeast proteins, and that intrinsically disordered TFs (IDTFs) have a higher number of protein-protein interactions than those with low structural disorder. The analysis of different stress response pathways showed a high content of structural disorder not only in TFs but also in other signaling proteins. The propensity of yeast proteome to undergo a liquid-liquid phase separation (LLPS) was also analyzed, showing that a significant proportion of IDTFs may undergo this phenomenon. Our analysis is a starting point for future research on the importance of structural disorder in yeast stress responses.

Topological frustration leading to backtracking in a coupled folding-binding process

Intrinsically disordered proteins (IDPs) are abundant in all species. Their discovery challenges the traditional "sequence-structure-function" paradigm of protein science because IDPs play important roles in various biological processes without preformed folded structures. Bioinformatic analysis reveals that the intrinsically conformational disorder of IDPs as well as their conformational transition upon binding to their targets is encoded by their amino acid sequences. The rRNase domain of colicin E3 and the immunity protein Im3 are a pair of proteins involved in bacterial survival. While the N-terminal segment and the central segment of E3 make comparable intermolecular contacts with Im3 in the bound state, binding of E3 with Im3 is dominantly triggered by the central segment of E3. In this work, to further investigate the binding mechanism of disordered E3 with Im3, we performed systematic free energy and transition path analysis through coarse-grained molecular dynamics simulations. We observed backtracking of the N-terminal segment of E3 in the binding process, whose occurrence depends on salt concentration. Conformational analysis revealed that initial binding of the N-terminal segment of E3 to Im3 usually leads to misorientation of a central hairpin of E3 on Im3, which generates topological frustration and results in backtracking of the N-terminal segment. Our results not only provide deeper mechanistic insights into the coupled folding-binding process of the E3/Im3 complex, but also suggest that topological frustration could be present in the coupled folding-binding process of IDPs and play an important role in regulating the binding transition pathways.

Molecular Determinants of Selectivity in Disordered Complexes May Shed Light on Specificity in Protein Condensates

Biomolecular condensates challenge the classical concepts of molecular recognition. The variable composition and heterogeneous conformations of liquid-like protein droplets are bottlenecks for high-resolution structural studies. To obtain atomistic insights into the organization of these assemblies, here we have characterized the conformational ensembles of specific disordered complexes, including those of droplet-driving proteins. First, we found that these specific complexes exhibit a high degree of conformational heterogeneity. Second, we found that residues forming contacts at the interface also sample many conformations. Third, we found that different patterns of contacting residues form the specific interface. In addition, we observed a wide range of sequence motifs mediating disordered interactions, including charged, hydrophobic and polar contacts. These results demonstrate that selective recognition can be realized by variable patterns of weakly defined interaction motifs in many different binding configurations. We propose that these principles also play roles in determining the selectivity of biomolecular condensates.

Characterization of the High-Affinity Fuzzy Complex between the Disordered Domain of the E7 Oncoprotein from High-Risk HPV and the TAZ2 Domain of CBP

The intrinsically disordered N-terminal region of the E7 protein from high-risk human papillomavirus (HPV) strains is responsible for oncogenic transformation of host cells through its interaction with a number of cellular factors, including the TAZ2 domain of the transcriptional coactivator CREB-binding protein. Using a variety of spectroscopic and biochemical tools, we find that despite its nanomolar affinity, the HPV16 E7 complex with TAZ2 is disordered and highly dynamic. The disordered domain of HPV16 E7 protein does not adopt a single conformation on the surface of TAZ2 but engages promiscuously with its target through multiple interactions involving two conserved motifs, termed CR1 and CR2, that occupy an extensive binding surface on TAZ2. The fuzzy nature of the complex is a reflection of the promiscuous binding repertoire of viral proteins, which must efficiently dysregulate host cell processes by binding to a variety of host factors in the cellular environment.