DisProt: the Database of Disordered Proteins

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.

Phase Separation and Aggregation of α-Synuclein Diverge at Different Salt Conditions

The coacervation and structural rearrangement of the protein alpha-synuclein (αSyn) into cytotoxic oligomers and amyloid fibrils are considered pathological hallmarks of Parkinson's disease. While aggregation is recognized as the key element of amyloid diseases, liquid-liquid phase separation (LLPS) and its interplay with aggregation have gained increasing interest. Previous work showed that factors promoting or inhibiting amyloid formation have similar effects on phase separation. Here, we provide a detailed scanning of a wide range of parameters including protein, salt and crowding concentrations at multiple pH values, revealing different salt dependencies of aggregation and phase separation. The influence of salt on aggregation under crowded conditions follows a non-monotonic pattern, showing increased effects at medium salt concentrations. This behavior can be elucidated through a combination of electrostatic screening and salting-out effects on the intramolecular interactions between the N-terminal and C-terminal regions of αSyn. By contrast, we find a monotonic salt dependence of phase separation due to the intermolecular interaction. Furthermore, we observe the time evolution of the two distinct assembly states, with macroscopic fibrillar-like bundles initially forming at medium salt concentration but subsequently converting into droplets after prolonged incubation. The droplet state is therefore capable of inhibiting aggregation or even dissolving the aggregates through a variety of heterotypic interactions, thus preventing αSyn from its dynamically arrested state.

The Proteomic Analysis of Cancer-Related Alterations in the Human Unfoldome

Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human proteome and are termed "the unfoldome". Herein, we probe the human breast cancer unfoldome and investigate relations between IDPs and key disease genes and pathways. We utilized bottom-up proteomics, MudPIT (Multidimensional Protein Identification Technology), to profile differentially expressed IDPs in human normal (MCF-10A) and breast cancer (BT-549) cell lines. Overall, we identified 2271 protein groups in the unfoldome of normal and cancer proteomes, with 148 IDPs found to be significantly differentially expressed in cancer cells. Further analysis produced annotations of 140 IDPs, which were then classified to GO (Gene Ontology) categories and pathways. In total, 65% (91 of 140) IDPs were related to various diseases, and 20% (28 of 140) mapped to cancer terms. A substantial portion of the differentially expressed IDPs contained disordered regions, confirmed by in silico characterization. Overall, our analyses suggest high levels of interactivity in the human cancer unfoldome and a prevalence of moderately and highly disordered proteins in the network.

Fine structures of intrinsically disordered proteins

We report simulation studies of 33 single intrinsically disordered proteins (IDPs) using coarse-grained bead-spring models where interactions among different amino acids are introduced through a hydropathy matrix and additional screened Coulomb interaction for the charged amino acid beads. Our simulation studies of two different hydropathy scales (HPS1, HPS2) [Dignon et al., PLoS Comput. Biol. 14, e1005941 (2018); Tesei et al. Proc. Natl. Acad. Sci. U. S. A. 118, e2111696118 (2021)] and the comparison with the existing experimental data indicate an optimal interaction parameter ϵ = 0.1 and 0.2 kcal/mol for the HPS1 and HPS2 hydropathy scales. We use these best-fit parameters to investigate both the universal aspects as well as the fine structures of the individual IDPs by introducing additional characteristics. (i) First, we investigate the polymer-specific scaling relations of the IDPs in comparison to the universal scaling relations [Bair et al., J. Chem. Phys. 158, 204902 (2023)] for the homopolymers. By studying the scaled end-to-end distances ⟨RN2⟩/(2Lℓp) and the scaled transverse fluctuations l̃⊥2=⟨l⊥2⟩/L, we demonstrate that IDPs are broadly characterized with a Flory exponent of ν ≃ 0.56 with the conclusion that conformations of the IDPs interpolate between Gaussian and self-avoiding random walk chains. Then, we introduce (ii) Wilson charge index (W) that captures the essential features of charge interactions and distribution in the sequence space and (iii) a skewness index (S) that captures the finer shape variation of the gyration radii distributions as a function of the net charge per residue and charge asymmetry parameter. Finally, our study of the (iv) variation of ⟨Rg⟩ as a function of salt concentration provides another important metric to bring out finer characteristics of the IDPs, which may carry relevant information for the origin of life.

Comprehensive amino acid composition analysis of seed storage proteins of cereals and legumes: identification and understanding of intrinsically disordered and allergenic peptides

The seed storage proteins of cereal and legumes are the primary source of amino acids which are required for sustaining the nitrogen and carbon demands during germination and growth. Humans derive most of their dietary proteins from storage proteins in form of a wide variety of foods, for consumption. The amino acid content of most of these proteins is biased and the need for this biasness is not understood. The high abundance of proline, glutamine, and cysteine in cereals makes the gluten fraction viscoelastic. The cereal proteins have less charge and legume proteins have more charge on them. Their non-polar amino acid distribution has large variations. These characteristics are strongly responsible for the partial and complete unfolding of several domains of the storage proteins. Many of the storage proteins share a highly conserved structural feature within the cupin superfamily spread across all kingdoms of life. The intrinsically disordered viscoelastic proteins help in making dough which is vital for the quality of bread. Unfolded regions harbor more immunogenic sequences and cause food-related allergies and intolerance. We have discussed these properties in terms of comparison of cereal and legume storage protein sequences and allergy. Our study supports the findings that large disordered regions contain allergen-representative peptides. Interestingly, a high number of allergen-representative peptides were cleavable by digestive enzymes. Furthermore, unfolded storage proteins mimic microbial immunogens to induce a memory immune response. Results findings can be used to guide the understanding of immunological characteristics of storage proteins and may assist in treatment decisions for food allergy.Communicated by Ramaswamy H. Sarma.

Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.

Disorder and amino acid composition in proteins: their potential role in the adaptation of extracellular pilins to the acidic media, where Acidithiobacillus thiooxidans grows

There are few biophysical studies or structural characterizations of the type IV pilin system of extremophile bacteria, such as the acidophilic Acidithiobacillus thiooxidans. We set out to analyze their pili-comprising proteins, pilins, because these extracellular proteins are in constant interaction with protons of the acidic medium in which At. thiooxidans grows. We used the web server Operon Mapper to analyze and identify the cluster codified by the minor pilin of At. thiooxidans. In addition, we carried an in-silico characterization of such pilins using the VL-XT algorithm of PONDR® server. Our results showed that structural disorder prevails more in pilins of At. thiooxidans than in non-acidophilic bacteria. Further computational characterization showed that the pilins of At. thiooxidans are significantly enriched in hydroxy (serine and threonine) and amide (glutamine and asparagine) residues, and significantly reduced in charged residues (aspartic acid, glutamic acid, arginine and lysine). Similar results were obtained when comparing pilins from other Acidithiobacillus and other acidophilic bacteria from another genus versus neutrophilic bacteria, suggesting that these properties are intrinsic to pilins from acidic environments, most likely by maintaining solubility and stability in harsh conditions. These results give guidelines for the application of extracellular proteins of acidophiles in protein engineering.

Functional small peptides for enhanced protein delivery, solubility, and secretion in microbial biotechnology

In microbial biotechnology, there is a constant demand for functional peptides to give new functionality to engineered proteins to address problems such as direct delivery of functional proteins into bacterial cells, enhanced protein solubility during the expression of recombinant proteins, and efficient protein secretion from bacteria. To tackle these critical issues, we selected three types of functional small peptides: cell-penetrating peptides (CPPs) enable the delivery of diverse cargoes into bacterial cytoplasm for a variety of purposes, protein-solubilizing peptide tags demonstrate remarkable efficiency in solubilizing recombinant proteins without folding interference, and signal peptides play a key role in enabling the secretion of recombinant proteins from bacterial cells. In this review, we introduced these three functional small peptides that offer effective solutions to address emerging problems in microbial biotechnology. Additionally, we summarized various engineering efforts aimed at enhancing the activity and performance of these peptides, thereby providing valuable insights into their potential for further applications.

Intrinsic Disorder in the Human Tear Proteome

Purpose

We aimed to characterize the proteome of human tears and assess for the presence of intrinsically disordered proteins (IDPs). IDPs, despite lacking a rigid three-dimensional structure, maintain biological functionality and could shed light on the molecular interactions within tears.

Methods

We analyzed a dataset of 1475 proteins identified in the tear film of three healthy subjects. We employed several computational tools, including the Compositional Profiler, Rapid Intrinsic Disorder Analysis Online, Search Tool for the Retrieval of Interacting Genes, and Database of Disordered Protein Predictors to evaluate the intrinsic disorder, protein interactions, and functional characterization of the disordered regions within this proteome.

Results

Our analysis showed a notable inclination toward intrinsic disorder. Two out of 10 order-promoting residues and five out of 10 disorder-promoting residues were found enriched. Using the Predictor of Natural Disordered Regions (PONDR) VSL2 output, 95% of these proteins were classified as highly or moderately disordered. We revealed an extensive protein-protein interaction network with significant interaction enrichment. The most disordered proteins exhibited higher disorder binding sites and diverse posttranslational modifications compared to the most ordered ones.

Conclusions

To the best of our knowledge, our study is the first comprehensive analysis of intrinsic disorder in the human tear film proteome, and it revealed an abundance of IDPs and their role in protein function and interaction networks. These findings suggest that variations in the intrinsic disorder of a tear film could be impacted by systemic and ocular conditions, offering promising avenues for disease biomarker identification and drug target development. Further research is needed to understand the implications of these findings in human health and disease.

Functional regulation of an intrinsically disordered protein via a conformationally excited state

A longstanding goal in the field of intrinsically disordered proteins (IDPs) is to characterize their structural heterogeneity and pinpoint the role of this heterogeneity in IDP function. Here, we use multinuclear chemical exchange saturation (CEST) nuclear magnetic resonance to determine the structure of a thermally accessible globally folded excited state in equilibrium with the intrinsically disordered native ensemble of a bacterial transcriptional regulator CytR. We further provide evidence from double resonance CEST experiments that the excited state, which structurally resembles the DNA-bound form of cytidine repressor (CytR), recognizes DNA by means of a "folding-before-binding" conformational selection pathway. The disorder-to-order regulatory switch in DNA recognition by natively disordered CytR therefore operates through a dynamical variant of the lock-and-key mechanism where the structurally complementary conformation is transiently accessed via thermal fluctuations.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.

DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction

Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions. The webserver is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER2/.

Interpreting Transient Interactions of Intrinsically Disordered Proteins

The flexible nature of intrinsically disordered proteins (IDPs) gives rise to a conformational ensemble with a diverse set of conformations. The simplest way to describe this ensemble is through a homopolymer model without any specific interactions. However, there has been growing evidence that the conformational properties of IDPs and their relevant functions can be affected by transient interactions between specific and even nonlocal pairs of amino acids. Interpreting these interactions from experimental methods, each of which is most sensitive to a different distance regime referred to as probing length, remains a challenging and unsolved problem. Here, we first show that transient interactions can be realized between short fragments of charged amino acids by generating conformational ensembles using model disordered peptides and coarse-grained simulations. Using these ensembles, we investigate how sensitive different types of experimental measurements are to the presence of transient interactions. We find methods with shorter probing lengths to be more appropriate for detecting these transient interactions, but one experimental method is not sufficient due to the existence of other weak interactions typically seen in IDPs. Finally, we develop an adjusted polymer model with an additional short-distance peak which can robustly reproduce the distance distribution function from two experimental measurements with complementary short and long probing lengths. This new model can suggest whether a homopolymer model is insufficient for describing a specific IDP and meets the challenge of quantitatively identifying specific, transient interactions from a background of nonspecific, weak interactions.

Structural differences between hypoxanthine phosphoribosyltransferase family members highlight opportunities for antiparasitic drug design in neglected diseases

Approaches to identify novel druggable targets for treating neglected diseases include computational studies that predict possible interactions of drugs and their molecular targets. Hypoxanthine phosphoribosyltransferase (HPRT) plays a central role in the purine salvage pathway. This enzyme is essential for the survival of the protozoan parasite T. cruzi, the causal agent of Chagas disease, and other parasites related to neglected diseases. Here we found dissimilar functional behaviours between TcHPRT and the human homologue, HsHPRT, in the presence of substrate analogues that can lie in differences in their oligomeric assemblies and structural features. To shed light on this issue, we carried out a comparative structural analysis between both enzymes. Our results show that HsHPRT is considerably more resistant to controlled proteolysis than TcHPRT. Moreover, we observed a variation in the length of two key loops depending on the structural arrangement of each protein (groups D1T1 and D1T1'). Such variations might be involved in inter-subunit communication or influencing the oligomeric state. Besides, to understand the molecular basis that govern D1T1 and D1T1' folding groups, we explored the distribution of charges on the interaction surfaces of TcHPRT and HsHPRT, respectively. To know whether the rigidity degree bears effect on the active site, we studied the flexibility of both proteins. The analysis performed here illuminates the underlying reasons and significance behind each protein's preference for one or the other quaternary arrangement that can be exploited for therapeutic approaches.

Computational prediction of disordered binding regions

One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.

Disease association and comparative genomics of compositional bias in human proteins

Background: The evolutionary rate of disordered proteins varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of intrinsically disordered regions (IDRs) across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards low complexity regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, low complexity proteins across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of low complexity, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

Liaisons dangereuses: Intrinsic Disorder in Cellular Proteins Recruited to Viral Infection-Related Biocondensates

Liquid-liquid phase separation (LLPS) is responsible for the formation of so-called membrane-less organelles (MLOs) that are essential for the spatio-temporal organization of the cell. Intrinsically disordered proteins (IDPs) or regions (IDRs), either alone or in conjunction with nucleic acids, are involved in the formation of these intracellular condensates. Notably, viruses exploit LLPS at their own benefit to form viral replication compartments. Beyond giving rise to biomolecular condensates, viral proteins are also known to partition into cellular MLOs, thus raising the question as to whether these cellular phase-separating proteins are drivers of LLPS or behave as clients/regulators. Here, we focus on a set of eukaryotic proteins that are either sequestered in viral factories or colocalize with viral proteins within cellular MLOs, with the primary goal of gathering organized, predicted, and experimental information on these proteins, which constitute promising targets for innovative antiviral strategies. Using various computational approaches, we thoroughly investigated their disorder content and inherent propensity to undergo LLPS, along with their biological functions and interactivity networks. Results show that these proteins are on average, though to varying degrees, enriched in disorder, with their propensity for phase separation being correlated, as expected, with their disorder content. A trend, which awaits further validation, tends to emerge whereby the most disordered proteins serve as drivers, while more ordered cellular proteins tend instead to be clients of viral factories. In light of their high disorder content and their annotated LLPS behavior, most proteins in our data set are drivers or co-drivers of molecular condensation, foreshadowing a key role of these cellular proteins in the scaffolding of viral infection-related MLOs.

CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information

One of key features of intrinsically disordered regions (IDRs) is facilitation of protein-protein and protein-nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/.

KIR signaling is regulated by electrostatic interaction of its cytosolic tail with the plasma membrane despite being neutral polyampholyte

Many receptors signal upon phosphorylation of tyrosine-based motifs in their cytosolic tail, with intrinsic disorder as a common feature. Studies on CD3ζ and CD3ε tails, which are disordered and polybasic, suggested regulation of phosphorylation through accessibility of tyrosines, governed by electrostatic interactions with membrane anionic lipids. We noticed characteristics of intrinsic disorder and previously unappreciated features in tyrosine-based motif-bearing cytosolic tails of many, especially, inhibitory receptors. They are neutral or acidic polyampholytes, with acidic and basic residues linearly segregated. To explore roles of these electrostatic features, we studied inhibitory killer-cell immunoglobulin-like receptor (KIR). Its cytosolic tail is a disordered neutrally charged polyampholyte, wherein juxtamembrane and membrane distal stretches are basic, and the intervening stretch is acidic. Despite lacking net charge, it interacted electrostatically with the plasma membrane. The juxtamembrane stretch was crucial for overall binding, which sequestered tyrosines in the lipid bilayer and restrained their constitutive phosphorylation. Human leukocyte antigen-C ligand binding to KIR released its tail from the plasma membrane to initiate signaling. Tail release occurred independently of KIR polymerization, clustering, or tyrosine phosphorylation, but required acidic residues of the acidic stretch. Tail interaction with the plasma membrane dictated signaling strength of KIR. These results revealed an electrostatic protein-lipid interaction that is unusual in being governed by segregated clusters of acidic and basic residues in polyampholytic disordered region of protein. In contrast to previously known, segregated distribution of oppositely charged residues made both binding and unbinding modules inherent to receptor tail, which could make the interaction an independent signaling switch.

Disease association and comparative genomics of compositional bias in human proteins

Background: The evolutionary rate of disordered protein regions varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of compositional bias, indicative of disorder, across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards biased regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, proteins with compositional bias across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of compositional bias, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

Intrinsically Disordered Proteins: An Overview

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

DeepCLD: An Efficient Sequence-Based Predictor of Intrinsically Disordered Proteins

Intrinsic disorder is common in proteins, plays important roles in protein functionality, and is commonly associated with various human diseases. To have an accurate tool for the annotation of intrinsic disorder in proteins, this paper proposes a novel algorithm, DeepCLD, for sequence-based prediction of intrinsically disordered proteins. This algorithm uses amino acid position specific scoring matrix (PSSM) to capture the intrinsic variability characteristic of sequence patterns, ResNet to preserve feature space structure, and bidirectional CudnnLSTM as recurrent layer to further improve the efficiency. Futhermore, DeepCLD also utilized the attention mechanism to solve the problem of gradient disappearing in deep network. Comparative analyses show that DeepCLD has faster training speed and higher prediction accuracy than comparable methods.

Prediction of Intrinsic Disorder Using Rosetta ResidueDisorder and AlphaFold2

The combination of deep learning and sequence data has transformed protein structure prediction and modeling, evidenced in the success of AlphaFold (AF). For this reason, many methods have been developed to take advantage of this success in areas where inaccurate structural modeling may limit computational predictiveness. For example, many methods have been developed to predict protein intrinsic disorder from sequence, including our Rosetta ResidueDisorder (RRD) approach. Intrinsically disordered regions in proteins are parts of the sequence that do not form ordered, folded structures under typical physiological conditions. In the original implementation of RRD, Rosetta ab initio models were generated, and disordered regions were predicted based on residue scores (disordered residues typically exist in regions of unfavorable scores). In this work, we show that by (i) replacing the ab initio modeling with AF (using the same scoring and disorder assignment approach) and (ii) updating the score function, the predictiveness improved significantly. Residues were better ranked by the order/disorder, evidenced by an improvement in receiver operating characteristic area-under-the-curve from 0.69 to 0.78 on a large (229 protein) and balanced data set (relatively even ordered versus disordered residues). Finally, the binary prediction accuracy also improved from 62% to 74% on the same data set. Our results show that the combined AF-RRD approach was as good as or better than all existing methods by these metrics (AF-RRD had the highest prediction accuracy).

Effect of Charge Distribution on the Dynamics of Polyampholytic Disordered Proteins

The stability and physiological function of many biomolecular coacervates depend on the structure and dynamics of intrinsically disordered proteins (IDPs) that typically contain a significant fraction of charged residues. Although the effect of relative arrangement of charged residues on IDP conformation is a well-studied problem, the associated changes in dynamics are far less understood. In this work, we systematically interrogate the effects of charge distribution on the chain-level and segmental dynamics of polyampholytic IDPs in dilute solutions. We study a coarse-grained model polyampholyte consisting of an equal fraction of two oppositely charged residues (glutamic acid and lysine) that undergoes a transition from an ideal chain-like conformation for uniformly charge-patterned sequences to a semi-compact conformation for highly charge-segregated sequences. Changes in the chain-level dynamics with increasing charge segregation correlate with changes in conformation. The chain-level and segmental dynamics conform to simple homopolymer models for uniformly charge-patterned sequences but deviate with increasing charge segregation, both in the presence and absence of hydrodynamic interactions. We discuss the significance of these findings, obtained for a model polyampholyte, in the context of a charge-rich intrinsically disordered region of the naturally occurring protein LAF-1. Our findings have important implications for understanding the effects of charge patterning on the dynamics of polyampholytic IDPs in dilute conditions using polymer scaling theories.

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.

Prediction of protein-protein interaction sites in intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) participate in many biological processes by interacting with other proteins, including the regulation of transcription, translation, and the cell cycle. With the increasing amount of disorder sequence data available, it is thus crucial to identify the IDP binding sites for functional annotation of these proteins. Over the decades, many computational approaches have been developed to predict protein-protein binding sites of IDP (IDP-PPIS) based on protein sequence information. Moreover, there are new IDP-PPIS predictors developed every year with the rapid development of artificial intelligence. It is thus necessary to provide an up-to-date overview of these methods in this field. In this paper, we collected 30 representative predictors published recently and summarized the databases, features and algorithms. We described the procedure how the features were generated based on public data and used for the prediction of IDP-PPIS, along with the methods to generate the feature representations. All the predictors were divided into three categories: scoring functions, machine learning-based prediction, and consensus approaches. For each category, we described the details of algorithms and their performances. Hopefully, our manuscript will not only provide a full picture of the status quo of IDP binding prediction, but also a guide for selecting different methods. More importantly, it will shed light on the inspirations for future development trends and principles.

Mapping the per-residue surface electrostatic potential of CAPRIN1 along its phase-separation trajectory

Electrostatic interactions and charge balance are important for the formation of biomolecular condensates involving proteins and nucleic acids. However, a detailed, atomistic picture of the charge distribution around proteins during the phase-separation process is lacking. Here, we use solution NMR spectroscopy to measure residue-specific near-surface electrostatic potentials (ϕ_ENS) of the positively charged carboxyl-terminal intrinsically disordered 103 residues of CAPRIN1, an RNA-binding protein localized to membraneless organelles playing an important role in messenger RNA (mRNA) storage and translation. Measured ϕ_ENS values have been mapped along the adenosine triphosphate (ATP)-induced phase-separation trajectory. In the absence of ATP, ϕ_ENS values for the mixed state of CAPRIN1 are positive and large and progressively decrease as ATP is added. This is coupled to increasing interchain interactions, particularly between aromatic-rich and arginine-rich regions of the protein. Upon phase separation, CAPRIN1 molecules in the condensed phase are neutral (ϕ_ENS [Formula: see text] 0 mV), with ∼five molecules of ATP associated with each CAPRIN1 chain. Increasing the ATP concentration further inverts the CAPRIN1 electrostatic potential, so that molecules become negatively charged, especially in aromatic-rich regions, leading to re-entrance into a mixed phase. Our results collectively show that a subtle balance between electrostatic repulsion and interchain attractive interactions regulates CAPRIN1 phase separation and provides insight into how nucleotides, such as ATP, can induce formation of and subsequently dissolve protein condensates.

Analysis of the Contribution of Intrinsic Disorder in Shaping Potyvirus Genetic Diversity

Intrinsically disordered regions (IDRs) are abundant in the proteome of RNA viruses. The multifunctional properties of these regions are widely documented and their structural flexibility is associated with the low constraint in their amino acid positions. Therefore, from an evolutionary stand point, these regions could have a greater propensity to accumulate non-synonymous mutations (NS) than highly structured regions (ORs, or 'ordered regions'). To address this hypothesis, we compared the distribution of non-synonymous mutations (NS), which we relate here to mutational robustness, in IDRs and ORs in the genome of potyviruses, a major genus of plant viruses. For this purpose, a simulation model was built and used to distinguish a possible selection phenomenon in the biological datasets from randomly generated mutations. We analyzed several short-term experimental evolution datasets. An analysis was also performed on the natural diversity of three different species of potyviruses reflecting their long-term evolution. We observed that the mutational robustness of IDRs is significantly higher than that of ORs. Moreover, the substitutions in the ORs are very constrained by the conservation of the physico-chemical properties of the amino acids. This feature is not found in the IDRs where the substitutions tend to be more random. This reflects the weak structural constraints in these regions, wherein an amino acid polymorphism is naturally conserved. In the course of evolution, potyvirus IDRs and ORs follow different evolutive paths with respect to their mutational robustness. These results have forced the authors to consider the hypothesis that IDRs and their associated amino acid polymorphism could constitute a potential adaptive reservoir.

Self-assembly of globular proteins with intrinsically disordered protein polyelectrolytes and block copolymers

Intrinsically disordered polypeptides are a versatile class of materials, combining the biocompatibility of peptides with the disordered structure and diverse phase behaviors of synthetic polymers. Synthetic polyelectrolytes are capable of complex phase behavior when mixed with oppositely charged polyelectrolytes, facilitating nanoparticle formation and bulk phase separation. However, there has been limited exploration of intrinsically disordered protein polyelectrolytes as potential bio-based replacements for synthetic polyelectrolytes. Here, we produce negatively charged, intrinsically disordered polypeptides, capable of high-yield expression in E. coli and use this intrinsically disordered peptide to produce entirely protein-based polyelectrolyte complexes. The complexes display rich phase behavior, showing sensitivity to charge density, salt concentration, temperature, and charge fraction. We characterize this behavior through a combination of turbidity assays, dynamic light scattering, and transmission electron microscopy. The robust expression profile and stimuli-responsive phase behavior of the intrinsically disordered peptides demonstrates their potential as easily producible, biocompatible substitutes for synthetic polyelectrolytes.

Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions

Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.

Genetic determinants of lung cancer: Understanding the oncogenic potential of somatic missense mutations

BACKGROUND

Treatment of lung cancer is getting more personalized nowadays and medical practitioners are moving away from conventional histology-driven empirical treatments, platinum-based chemotherapy, and other invasive surgical resections and have started adopting alternate therapies in which therapeutic targets are patient's molecular oncogenic drivers.

AIM

The aim of the current study is to extract meaningful information from the online somatic mutation data (retrieved from cBioPortal) of 16 most significantly mutated oncogenes in non-small-cell lung cancer (NSCLC), namely EGFR, NRAS, KRAS, HER2 (ERBB2), RET, MET, ROS1, FGFR1, BRAF, AKT1, MEK1 (MAP2K1), PIK3CA, PTEN, DDR2, LKB1 (STK11) and ALK, for improving our understanding of the pathobiology of the lung cancer that can aid decision-making on critical clinical and therapeutic considerations.

METHODS

Using an integrated approach comprising 4 steps, the oncogenic potential of 661 missense non-synonymous single nucleotide polymorphisms (nsSNPs) in 16 genes was ascertained using 2059 NSCLC (1575 lung adenocarcinomas, 484 lung squamous cell carcinomas) patients' online mutation data. The steps used comprise sequence/structure homology-based prediction, scoring of conservation of mutated residues and positions, prediction of resulting molecular and functional consequences using machine-learning and structure-guided approach.

RESULTS

Out of a total of 661 nsSNPs analyzed, a set of 29 nsSNPs has been identified as conserved high confidence mutations in 10 of 16 genes relevant to the under study. Out of 29 conserved high confidence nsSNPs, 4 nsSNPs (EGFR N1094Y, BRAF M620I, DDR2 R307L, ALK P1350T) have been found to be putative novel rare genetic markers for NSCLC.

CONCLUSIONS

The current study, the first of its kind, has provided a list of deleterious non-synonymous somatic mutations in a selected pool of oncogenes that can be considered as a promising target for future drug design and therapy for patients with lung adenocarcinomas and squamous cell carcinomas.

Distribution of Charged Residues Affects the Average Size and Shape of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are ensembles of interconverting conformers whose conformational properties are governed by several physico-chemical factors, including their amino acid composition and the arrangement of oppositely charged residues within the primary structure. In this work, we investigate the effects of charge patterning on the average compactness and shape of three model IDPs with different proline content. We model IDP ensemble conformations as ellipsoids, whose size and shape are calculated by combining data from size-exclusion chromatography and native mass spectrometry. For each model IDP, we analyzed the wild-type protein and two synthetic variants with permuted positions of charged residues, where positive and negative amino acids are either evenly distributed or segregated. We found that charge clustering induces remodeling of the conformational ensemble, promoting compaction and/or increasing spherical shape. Our data illustrate that the average shape and volume of the ensembles depend on the charge distribution. The potential effect of other factors, such as chain length, number of proline residues, and secondary structure content, is also discussed. This methodological approach is a straightforward way to model IDP average conformation and decipher the salient sequence attributes influencing IDP structural properties.

In Silico Characterisation of the Late Embryogenesis Abundant (LEA) Protein Families and Their Role in Desiccation Tolerance in Ramonda serbica Panc

Ramonda serbica Panc. is an ancient resurrection plant able to survive a long desiccation period and recover metabolic functions upon watering. The accumulation of protective late embryogenesis abundant proteins (LEAPs) is a desiccation tolerance hallmark. To propose their role in R. serbica desiccation tolerance, we structurally characterised LEAPs and evaluated LEA gene expression levels in hydrated and desiccated leaves. By integrating de novo transcriptomics and homologues LEAP domains, 318 R. serbica LEAPs were identified and classified according to their conserved motifs and phylogeny. The in silico analysis revealed that hydrophilic LEA4 proteins exhibited an exceptionally high tendency to form amphipathic α-helices. The most abundant, atypical LEA2 group contained more hydrophobic proteins predicted to fold into the defined globular domains. Within the desiccation-upregulated LEA genes, the majority encoded highly disordered DEH1, LEA1, LEA4.2, and LEA4.3 proteins, while the greatest portion of downregulated genes encoded LEA2.3 and LEA2.5 proteins. While dehydrins might chelate metals and bind DNA under water deficit, other intrinsically disordered LEAPs might participate in forming intracellular proteinaceous condensates or adopt amphipathic α-helical conformation, enabling them to stabilise desiccation-sensitive proteins and membranes. This comprehensive LEAPs structural characterisation is essential to understanding their function and regulation during desiccation aiming at crop drought tolerance improvement.

The aqueous humor proteome is intrinsically disordered

•

Our study demonstrated that intrinsic disorder is abundant in the aqueous humor.

•

The 749 aqueous proteins analyzed were enriched with disorder-promoting residues.

•

208 aqueous humor proteins were predicted to be highly intrinsically disordered.

•

Misregulation of IDPs may promote pathology in the aqueous humor.

•

IDPs in aqueous humor may serve as future targets for novel therapeutics.

Erythropoietin Interacts with Specific S100 Proteins

Erythropoietin (EPO) is a clinically significant four-helical cytokine, exhibiting erythropoietic, cytoprotective, immunomodulatory, and cancer-promoting activities. Despite vast knowledge on its signaling pathways and physiological effects, extracellular factors regulating EPO activity remain underexplored. Here we show by surface plasmon resonance spectroscopy, that among eighteen members of Ca2+-binding proteins of the S100 protein family studied, only S100A2, S100A6 and S100P proteins specifically recognize EPO with equilibrium dissociation constants ranging from 81 nM to 0.5 µM. The interactions occur exclusively under calcium excess. Bioinformatics analysis showed that the EPO-S100 interactions could be relevant to progression of neoplastic diseases, including cancer, and other diseases. The detailed knowledge of distinct physiological effects of the EPO-S100 interactions could favor development of more efficient clinical implications of EPO. Summing up our data with previous findings, we conclude that S100 proteins are potentially able to directly affect functional activities of specific members of all families of four-helical cytokines, and cytokines of other structural superfamilies.

Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks

Intrinsically disordered proteins (IDPs) possess at least one region that lacks a single stable structure in vivo, which makes them play an important role in a variety of biological functions. We propose a prediction method for IDPs based on convolutional neural networks (CNNs) and feature selection. The combination of sequence and evolutionary properties is used to describe the differences between disordered and ordered regions. Especially, to highlight the correlation between the target residue and adjacent residues, multiple windows are selected to preprocess the protein sequence through the selected properties. The shorter windows reflect the characteristics of the central residue, and the longer windows reflect the characteristics of the surroundings around the central residue. Moreover, to highlight the specificity of sequence and evolutionary properties, they are preprocessed, respectively. After that, the preprocessed properties are combined into feature matrices as the input of the constructed CNN. Our method is training as well as testing based on the DisProt database. The simulation results show that the proposed method can predict IDPs effectively, and the performance is competitive in comparison with IsUnstruct and ESpritz.

Predicting Protein Conformational Disorder and Disordered Binding Sites

In the last two decades it has become increasingly evident that a large number of proteins adopt either a fully or a partially disordered conformation. Intrinsically disordered proteins are ubiquitous proteins that fulfill essential biological functions while lacking a stable 3D structure. Their conformational heterogeneity is encoded by the amino acid sequence, thereby allowing intrinsically disordered proteins or regions to be recognized based on their sequence properties. The identification of disordered regions facilitates the functional annotation of proteins and is instrumental for delineating boundaries of protein domains amenable to crystallization. This chapter focuses on the methods currently employed for predicting protein disorder and identifying intrinsically disordered binding sites.

Probing ion channel functional architecture and domain recombination compatibility by massively parallel domain insertion profiling

Protein domains are the basic units of protein structure and function. Comparative analysis of genomes and proteomes showed that domain recombination is a main driver of multidomain protein functional diversification and some of the constraining genomic mechanisms are known. Much less is known about biophysical mechanisms that determine whether protein domains can be combined into viable protein folds. Here, we use massively parallel insertional mutagenesis to determine compatibility of over 300,000 domain recombination variants of the Inward Rectifier K⁺ channel Kir2.1 with channel surface expression. Our data suggest that genomic and biophysical mechanisms acted in concert to favor gain of large, structured domain at protein termini during ion channel evolution. We use machine learning to build a quantitative biophysical model of domain compatibility in Kir2.1 that allows us to derive rudimentary rules for designing domain insertion variants that fold and traffic to the cell surface. Positional Kir2.1 responses to motif insertion clusters into distinct groups that correspond to contiguous structural regions of the channel with distinct biophysical properties tuned towards providing either folding stability or gating transitions. This suggests that insertional profiling is a high-throughput method to annotate function of ion channel structural regions.

Implications derived from S-protein variants of SARS-CoV-2 from six continents

The spike (S) protein is a critical determinant of the infectivity and antigenicity of SARS-CoV-2. Several mutations in the S protein of SARS-CoV-2 have already been detected, and their effect in immune system evasion and enhanced transmission as a cause of increased morbidity and mortality are being investigated. From pathogenic and epidemiological perspectives, S proteins are of prime interest to researchers. This study focused on the unique variants of S proteins from six continents: Asia, Africa, Europe, Oceania, South America, and North America. In comparison to the other five continents, Africa had the highest percentage of unique S proteins (29.1%). The phylogenetic relationship implies that unique S proteins from North America are significantly different from those of the other five continents. They are most likely to spread to the other geographic locations through international travel or naturally by emerging mutations. It is suggested that restriction of international travel should be considered, and massive vaccination as an utmost measure to combat the spread of the COVID-19 pandemic. It is also further suggested that the efficacy of existing vaccines and future vaccine development must be reviewed with careful scrutiny, and if needed, further re-engineered based on requirements dictated by new emerging S protein variants.

Man does not live by intrinsically unstructured proteins alone: The role of structured regions in aggregation

Protein misfolding is a topic that is of primary interest both in biology and medicine because of its impact on fundamental processes and disease. In this review, we revisit the concept of protein misfolding and discuss how the field has evolved from the study of globular folded proteins to focusing mainly on intrinsically unstructured and often disordered regions. We argue that this shift of paradigm reflects the more recent realisation that misfolding may not only be an adverse event, as originally considered, but also may fulfil a basic biological need to compartmentalise the cell with transient reversible granules. We nevertheless provide examples in which structure is an important component of a much more complex aggregation behaviour that involves both structured and unstructured regions of a protein. We thus suggest that a more comprehensive evaluation of the mechanisms that lead to aggregation might be necessary.

Transient Electrostatic Interactions between Fcp1 and Rap74 Bias the Conformational Ensemble of the Complex with Minimal Impact on Binding Affinity

Intrinsically disordered protein (IDP) sequences often contain a high proportion of charged residues in conjunction with their high degree of hydrophilicity and solvation. For high net charge IDPs, long-range electrostatic interactions are thought to play a role in modulating the strength or kinetics of protein-protein interactions. In this work, we examined intramolecular interactions mediated by charged regions of a model IDP, the C-terminal tail of the phosphatase Fcp1. Specifically, this work focuses on intermolecular interactions between acidic and basic patches in the primary structure of Fcp1 and their contributions to binding its predominantly basic partner, the winged helix domain of Rap74. We observe both intramolecular and intermolecular interactions through paramagnetic relaxation enhancement (PRE) consistent with oppositely charged regions associating with one another, both in unbound Fcp1 and in the Fcp1-Rap74 complex. Formation of this complex is strongly driven by hydrophobic interactions in the minimal binding motif. Here, we test the hypothesis that charged residues in Fcp1 that flank the binding helix also contribute to the strength of binding. Charge inversion mutations in Fcp1 generally support this hypothesis, while PRE data suggest substitution of observed transient interactions in the unbound ensemble for similarly transient interactions with Rap74 in the complex.

On the roles of intrinsically disordered proteins and regions in cell communication and signaling

For proteins, the sequence → structure → function paradigm applies primarily to enzymes, transmembrane proteins, and signaling domains. This paradigm is not universal, but rather, in addition to structured proteins, intrinsically disordered proteins and regions (IDPs and IDRs) also carry out crucial biological functions. For these proteins, the sequence → IDP/IDR ensemble → function paradigm applies primarily to signaling and regulatory proteins and regions. Often, in order to carry out function, IDPs or IDRs cooperatively interact, either intra- or inter-molecularly, with structured proteins or other IDPs or intermolecularly with nucleic acids. In this IDP/IDR thematic collection published in Cell Communication and Signaling, thirteen articles are presented that describe IDP/IDR signaling molecules from a variety of organisms from humans to fruit flies and tardigrades ("water bears") and that describe how these proteins and regions contribute to the function and regulation of cell signaling. Collectively, these papers exhibit the diverse roles of disorder in responding to a wide range of signals as to orchestrate an array of organismal processes. They also show that disorder contributes to signaling in a broad spectrum of species, ranging from micro-organisms to plants and animals.

Salt-Dependent Conformational Changes of Intrinsically Disordered Proteins

The flexible structure of an intrinsically disordered protein (IDP) is known to be perturbed by salt concentrations, which can be understood by electrostatic screening on charged amino acids. However, an IDP usually contains more uncharged residues that are influenced by the salting-out effect. Here we have parametrized the salting-out effect into a coarse-grained model using a set of Förster resonance energy transfer data and verified with experimental salt-dependent liquid-liquid phase separation (LLPS) of 17 proteins. The new model can correctly capture the behavior of 6 more sequences, resulting in a total of 13 when varying salt concentrations. Together with a survey of more than 500 IDP sequences, we conclude that the salting-out effect, which was considered to be secondary to electrostatic screening, is important for IDP sequences with moderately charged residues at physiological salt concentrations. The presented scheme is generally applicable to other computational models for capturing salt-dependent IDP conformations.

Search for Highly Divergent Tandem Repeats in Amino Acid Sequences

We report a Method to Search for Highly Divergent Tandem Repeats (MSHDTR) in protein sequences which considers pairwise correlations between adjacent residues. MSHDTR was compared with some previously developed methods for searching for tandem repeats (TRs) in amino acid sequences, such as T-REKS and XSTREAM, which focus on the identification of TRs with significant sequence similarity, whereas MSHDTR detects repeats that significantly diverged during evolution, accumulating deletions, insertions, and substitutions. The application of MSHDTR to a search of the Swiss-Prot databank revealed over 15 thousand TR-containing amino acid sequences that were difficult to find using the other methods. Among the detected TRs, the most representative were those with consensus lengths of two and seven residues; these TRs were subjected to cluster analysis and the classes of patterns were identified. All TRs detected in this study have been combined into a databank accessible over the WWW.

Intrinsically Disordered Proteins as Regulators of Transient Biological Processes and as Untapped Drug Targets

Intrinsically disordered proteins (IDPs) are critical players in the dynamic control of diverse cellular processes, and provide potential new drug targets because their dysregulation is closely related to many diseases. This review focuses on several medicinal studies that have identified low-molecular-weight inhibitors of IDPs. In addition, clinically relevant liquid-liquid phase separations-which critically involve both intermolecular interactions between IDPs and their posttranslational modification-are analyzed to understand the potential of IDPs as new drug targets.

Prediction of secondary structure population and intrinsic disorder of proteins using multitask deep learning

Recent research in predicting protein secondary structure populations (SSP) based on Nuclear Magnetic Resonance (NMR) chemical shifts has helped quantitatively characterise the structural conformational properties of intrinsically disordered proteins and regions (IDP/IDR). Different from protein secondary structure (SS) prediction, the SSP prediction assumes a dynamic assignment of secondary structures that seem correlate with disordered states. In this study, we designed a single-task deep learning framework to predict IDP/IDR and SSP respectively; and multitask deep learning frameworks to allow quantitative predictions of IDP/IDR evidenced by the simultaneously predicted SSP. According to independent test results, single-task deep learning models improve the prediction performance of shallow models for SSP and IDP/IDR. Also, the prediction performance was further improved for IDP/IDR prediction when SSP prediction was simultaneously predicted in multitask models. With p53 as a use case, we demonstrate how predicted SSP is used to explain the IDP/IDR predictions for each functional region.

Biomineralization process in hard tissues: The interaction complexity within protein and inorganic counterparts

Biomineralization can be considered as nature's strategy to produce and sustain biominerals, primarily via creation of hard tissues for protection and support. This review examines the biomineralization process within the hard tissues of the human body with special emphasis on the mechanisms and principles of bone and teeth mineralization. We describe the detailed role of proteins and inorganic ions in mediating the mineralization process. Furthermore, we highlight the various available models for studying bone physiology and mineralization starting from the historical static cell line-based methods to the most advanced 3D culture systems, elucidating the pros and cons of each one of these methods. With respect to the mineralization process in teeth, enamel and dentin mineralization is discussed in detail. The key role of intrinsically disordered proteins in modulating the process of mineralization in enamel and dentine is given attention. Finally, nanotechnological interventions in the area of bone and teeth mineralization, diseases and tissue regeneration is also discussed. STATEMENT OF SIGNIFICANCE: This article provides an overview of the biomineralization process within hard tissues of the human body, which encompasses the detailed mechanism innvolved in the formation of structures like teeth and bone. Moreover, we have discussed various available models used for studying biomineralization and also explored the nanotechnological applications in the field of bone regeneration and dentistry.