Intrinsically disordered proteins and novel strategies for drug discovery

Long way up: rethink diseases in light of phase separation and phase transition

Biomolecular condensation, driven by multivalency, serves as a fundamental mechanism within cells, facilitating the formation of distinct compartments, including membraneless organelles that play essential roles in various cellular processes. Perturbations in the delicate equilibrium of condensation, whether resulting in gain or loss of phase separation, have robustly been associated with cellular dysfunction and physiological disorders. As ongoing research endeavors wholeheartedly embrace this newly acknowledged principle, a transformative shift is occurring in our comprehension of disease. Consequently, significant strides have been made in unraveling the profound relevance and potential causal connections between abnormal phase separation and various diseases. This comprehensive review presents compelling recent evidence that highlight the intricate associations between aberrant phase separation and neurodegenerative diseases, cancers, and infectious diseases. Additionally, we provide a succinct summary of current efforts and propose innovative solutions for the development of potential therapeutics to combat the pathological consequences attributed to aberrant phase separation.

Rational peptide design for targeting cancer cell invasion

Cell invasion is an important process in cancer progression and recurrence. Invasion and implantation of cancer cells from their original place to other tissues, by disabling vital organs, challenges the treatment of cancer patients. Given the importance of the matter, many molecular treatments have been developed to inhibit cancer cell invasion. Because of their low production cost and ease of production, peptides are valuable therapeutic molecules for inhibiting cancer cell invasion. In recent years, advances in the field of computational biology have facilitated the design of anti-cancer peptides. In our investigation, using computational biology approaches such as evolutionary analysis, residue scanning, protein-peptide interaction analysis, molecular dynamics, and free energy analysis, our team designed a peptide library with about 100 000 candidates based on A6 (acetyl-KPSSPPEE-amino) sequence which is an anti-invasion peptide. During computational studies, two of the designed peptides that give the highest scores and showed the greatest sequence similarity to A6 were entered into the experimental analysis workflow for further analysis. In experimental analysis steps, the anti-metastatic potency and other therapeutic effects of designed peptides were evaluated using MTT assay, RT-qPCR, zymography analysis, and invasion assay. Our study disclosed that the IK1 (acetyl-RPSFPPEE-amino) peptide, like A6, has great potency to inhibit the invasion of cancer cells.

Uncovering the structural stability of Magnaporthe oryzae effectors: a secretome-wide in silico analysis

Rice blast, caused by the ascomycete fungus Magnaporthe oryzae, is a deadly disease and a major threat to global food security. The pathogen secretes small proteinaceous effectors, virulence factors, inside the host to manipulate and perturb the host immune system, allowing the pathogen to colonize and establish a successful infection. While the molecular functions of several effectors are characterized, very little is known about the structural stability of these effectors. We analyzed a total of 554 small secretory proteins (SSPs) from the M. oryzae secretome to decipher key features of intrinsic disorder (ID) and the structural dynamics of the selected putative effectors through thorough and systematic in silico studies. Our results suggest that out of the total SSPs, 66% were predicted as effector proteins, released either into the apoplast or cytoplasm of the host cell. Of these, 68% were found to be intrinsically disordered effector proteins (IDEPs). Among the six distinct classes of disordered effectors, we observed peculiar relationships between the localization of several effectors in the apoplast or cytoplasm and the degree of disorder. We determined the degree of structural disorder and its impact on protein foldability across all the putative small secretory effector proteins from the blast pathogen, further validated by molecular dynamics simulation studies. This study provides definite clues toward unraveling the mystery behind the importance of structural distortions in effectors and their impact on plant-pathogen interactions. The study of these dynamical segments may help identify new effectors as well.Communicated by Ramaswamy H. Sarma.

IDP-LM: Prediction of protein intrinsic disorder and disorder functions based on language models

Intrinsically disordered proteins (IDPs) and regions (IDRs) are a class of functionally important proteins and regions that lack stable three-dimensional structures under the native physiologic conditions. They participate in critical biological processes and thus are associated with the pathogenesis of many severe human diseases. Identifying the IDPs/IDRs and their functions will be helpful for a comprehensive understanding of protein structures and functions, and inform studies of rational drug design. Over the past decades, the exponential growth in the number of proteins with sequence information has deepened the gap between uncharacterized and annotated disordered sequences. Protein language models have recently demonstrated their powerful abilities to capture complex structural and functional information from the enormous quantity of unlabelled protein sequences, providing opportunities to apply protein language models to uncover the intrinsic disorders and their biological properties from the amino acid sequences. In this study, we proposed a computational predictor called IDP-LM for predicting intrinsic disorder and disorder functions by leveraging the pre-trained protein language models. IDP-LM takes the embeddings extracted from three pre-trained protein language models as the exclusive inputs, including ProtBERT, ProtT5 and a disorder specific language model (IDP-BERT). The ablation analysis shown that the IDP-BERT provided fine-grained feature representations of disorder, and the combination of three language models is the key to the performance improvement of IDP-LM. The evaluation results on independent test datasets demonstrated that the IDP-LM provided high-quality prediction results for intrinsic disorder and four common disordered functions.

Thermodynamic and kinetic approaches for drug discovery to target protein misfolding and aggregation

INTRODUCTION

Protein misfolding diseases, including Alzheimer's and Parkinson's diseases, are characterized by the aberrant aggregation of proteins. These conditions are still largely untreatable, despite having a major impact on our healthcare systems and societies.

AREAS COVERED

We describe drug discovery strategies to target protein misfolding and aggregation. We compare thermodynamic approaches, which are based on the stabilization of the native states of proteins, with kinetic approaches, which are based on the slowing down of the aggregation process. This comparison is carried out in terms of the current knowledge of the process of protein misfolding and aggregation, the mechanisms of disease and the therapeutic targets.

EXPERT OPINION

There is an unmet need for disease-modifying treatments that target protein misfolding and aggregation for the over 50 human disorders known to be associated with this phenomenon. With the approval of the first drugs that can prevent misfolding or inhibit aggregation, future efforts will be focused on the discovery of effective compounds with these mechanisms of action for a wide range of conditions.

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.

The influence of random-coil chemical shifts on the assessment of structural propensities in folded proteins and IDPs

In studying secondary structural propensities of proteins by nuclear magnetic resonance (NMR) spectroscopy, secondary chemical shifts (SCSs) serve as the primary atomic scale observables. For SCS calculation, the selection of an appropriate random coil chemical shift (RCCS) dataset is a crucial step, especially when investigating intrinsically disordered proteins (IDPs). The scientific literature is abundant in such datasets, however, the effect of choosing one over all the others in a concrete application has not yet been studied thoroughly and systematically. Hereby, we review the available RCCS prediction methods and to compare them, we conduct statistical inference by means of the nonparametric sum of ranking differences and comparison of ranks to random numbers (SRD-CRRN) method. We try to find the RCCS predictors best representing the general consensus regarding secondary structural propensities. The existence and the magnitude of resulting differences on secondary structure determination under varying sample conditions (temperature, pH) are demonstrated and discussed for globular proteins and especially IDPs.

Pre-Molten, Wet, and Dry Molten Globules en Route to the Functional State of Proteins

Transitions between the unfolded and native states of the ordered globular proteins are accompanied by the accumulation of several intermediates, such as pre-molten globules, wet molten globules, and dry molten globules. Structurally equivalent conformations can serve as native functional states of intrinsically disordered proteins. This overview captures the characteristics and importance of these molten globules in both structured and intrinsically disordered proteins. It also discusses examples of engineered molten globules. The formation of these intermediates under conditions of macromolecular crowding and their interactions with nanomaterials are also reviewed.

Functional benefit of structural disorder for the replication of measles, Nipah and Hendra viruses

Measles, Nipah and Hendra viruses are severe human pathogens within the Paramyxoviridae family. Their non-segmented, single-stranded, negative-sense RNA genome is encapsidated by the nucleoprotein (N) within a helical nucleocapsid that is the substrate used by the viral RNA-dependent-RNA-polymerase (RpRd) for transcription and replication. The RpRd is a complex made of the large protein (L) and of the phosphoprotein (P), the latter serving as an obligate polymerase cofactor and as a chaperon for N. Both the N and P proteins are enriched in intrinsically disordered regions (IDRs), i.e. regions devoid of stable secondary and tertiary structure. N possesses a C-terminal IDR (NTAIL), while P consists of a large, intrinsically disordered N-terminal domain (NTD) and a C-terminal domain (CTD) encompassing alternating disordered and ordered regions. The V and W proteins, two non-structural proteins that are encoded by the P gene via a mechanism of co-transcriptional edition of the P mRNA, are prevalently disordered too, sharing with P the disordered NTD. They are key players in the evasion of the host antiviral response and were shown to phase separate and to form amyloid-like fibrils in vitro. In this review, we summarize the available information on IDRs within the N, P, V and W proteins from these three model paramyxoviruses and describe their molecular partnership. We discuss the functional benefit of disorder to virus replication in light of the critical role of IDRs in affording promiscuity, multifunctionality, fine regulation of interaction strength, scaffolding functions and in promoting liquid-liquid phase separation and fibrillation.

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.

DMFpred: Predicting protein disorder molecular functions based on protein cubic language model

Intrinsically disordered proteins and regions (IDP/IDRs) are widespread in living organisms and perform various essential molecular functions. These functions are summarized as six general categories, including entropic chain, assembler, scavenger, effector, display site, and chaperone. The alteration of IDP functions is responsible for many human diseases. Therefore, identifying the function of disordered proteins is helpful for the studies of drug target discovery and rational drug design. Experimental identification of the molecular functions of IDP in the wet lab is an expensive and laborious procedure that is not applicable on a large scale. Some computational methods have been proposed and mainly focus on predicting the entropic chain function of IDRs, while the computational predictive methods for the remaining five important categories of disordered molecular functions are desired. Motivated by the growing numbers of experimental annotated functional sequences and the need to expand the coverage of disordered protein function predictors, we proposed DMFpred for disordered molecular functions prediction, covering disordered assembler, scavenger, effector, display site and chaperone. DMFpred employs the Protein Cubic Language Model (PCLM), which incorporates three protein language models for characterizing sequences, structural and functional features of proteins, and attention-based alignment for understanding the relationship among three captured features and generating a joint representation of proteins. The PCLM was pre-trained with large-scaled IDR sequences and fine-tuned with functional annotation sequences for molecular function prediction. The predictive performance evaluation on five categories of functional and multi-functional residues suggested that DMFpred provides high-quality predictions. The web-server of DMFpred can be freely accessed from http://bliulab.net/DMFpred/.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

INTRODUCTION

Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed.

AREAS COVERED

One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future.

EXPERT OPINION

Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

Correlating multi-functional role of cold shock domain proteins with intrinsically disordered regions

Cold shock proteins (CSPs) are an ancient and conserved family of proteins. They are renowned for their role in response to low-temperature stress in bacteria and nucleic acid binding activities. In prokaryotes, cold and non-cold inducible CSPs are involved in various cellular and metabolic processes such as growth and development, osmotic oxidation, starvation, stress tolerance, and host cell invasion. In prokaryotes, cold shock condition reduces cell transcription and translation efficiency. Eukaryotic cold shock domain (CSD) proteins are evolved form of prokaryotic CSPs where CSD is flanked by N- and C-terminal domains. Eukaryotic CSPs are multi-functional proteins. CSPs also act as nucleic acid chaperons by preventing the formation of secondary structures in mRNA at low temperatures. In human, CSD proteins play a crucial role in the progression of breast cancer, colon cancer, lung cancer, and Alzheimer's disease. A well-defined three-dimensional structure of intrinsically disordered regions of CSPs family members is still undetermined. In this article, intrinsic disorder regions of CSPs have been explored systematically to understand the pleiotropic role of the cold shock family of proteins.

Addiction of Cancer Stem Cells to MUC1-C in Triple-Negative Breast Cancer Progression

Triple-negative breast cancer (TNBC) is an aggressive malignancy with limited treatment options. TNBC progression is associated with expansion of cancer stem cells (CSCs). Few insights are available regarding druggable targets that drive the TNBC CSC state. This review summarizes the literature on TNBC CSCs and the compelling evidence that they are addicted to the MUC1-C transmembrane protein. In normal epithelia, MUC1-C is activated by loss of homeostasis and induces reversible wound-healing responses of inflammation and repair. However, in settings of chronic inflammation, MUC1-C promotes carcinogenesis. MUC1-C induces EMT, epigenetic reprogramming and chromatin remodeling in TNBC CSCs, which are dependent on MUC1-C for self-renewal and tumorigenicity. MUC1-C-induced lineage plasticity in TNBC CSCs confers DNA damage resistance and immune evasion by chronic activation of inflammatory pathways and global changes in chromatin architecture. Of therapeutic significance, an antibody generated against the MUC1-C extracellular domain has been advanced in a clinical trial of anti-MUC1-C CAR T cells and in IND-enabling studies for development as an antibody-drug conjugate (ADC). Agents targeting the MUC1-C cytoplasmic domain have also entered the clinic and are undergoing further development as candidates for advancing TNBC treatment. Eliminating TNBC CSCs will be necessary for curing this recalcitrant cancer and MUC1-C represents a promising druggable target for achieving that goal.

Functional inhibition of c-Myc using novel inhibitors identified through “hot spot” targeting

Protein–protein interactions drive various biological processes in healthy as well as disease states. The transcription factor c-Myc plays a crucial role in maintaining cellular homeostasis, and its deregulated expression is linked to various human cancers; therefore, it can be considered a viable target for cancer therapeutics. However, the structural heterogeneity of c-Myc due to its disordered nature poses a major challenge to drug discovery. In the present study, we used an in silico alanine scanning mutagenesis approach to identify “hot spot” residues within the c-Myc/Myc-associated factor X interface, which is highly disordered and has not yet been systematically analyzed for potential small molecule binding sites. We then used the information gained from this analysis to screen potential inhibitors using a conformation ensemble approach. The fluorescence-based biophysical experiments showed that the identified hit molecules displayed noncovalent interactions with these hot spot residues, and further cell-based experiments showed substantial in vitro potency against diverse c-Myc-expressing cancer/stem cells by deregulating c-Myc activity. These biophysical and computational studies demonstrated stable binding of the hit compounds with the disordered c-Myc protein. Collectively, our data indicated effective drug targeting of the disordered c-Myc protein via the determination of hot spot residues in the c-Myc/Myc-associated factor X heterodimer.

Surveying over 100 predictors of intrinsic disorder in proteins

INTRODUCTION

Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources.

AREAS COVERED

We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends.

EXPERT OPINION

We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

Revealing Intrinsic Disorder and Aggregation Properties of the DPF3a Zinc Finger Protein

Double PHD fingers 3 (DPF3) is a human epigenetic factor found in the multiprotein BRG1-associated factor (BAF) chromatin remodeling complex. It has two isoforms: DPF3b and DPF3a, but very little is known about the latter. Despite the lack of structural data, it has been established that DPF3a is involved in various protein-protein interactions and that it is subject to phosphorylation. These features are typical of intrinsically disordered proteins (IDPs) for which the disorder is essential to their functionality. IDPs are also prone to aggregation and can assemble into cytotoxic amyloid fibrils in specific pathological contexts. In the present work, the DPF3a disordered nature and propensity to aggregation have been investigated using a combination of disorder predictors and biophysical methods. The DPF3a-predicted disordered character has been correlated to a characteristic random coil signal in far-UV circular dichroism (CD) and to a fluorescence emission band typical of Trp residues fully exposed to the solvent. After DPF3a purification and 24 h of incubation at room temperature, dynamic light scattering confirmed the presence of DPF3a aggregates whose amyloid nature have been highlighted by a specific deep-blue autofluorescence signature, as well as by an increase in thioflavin T fluorescence upon binding. These results are supported by an enrichment in twisted β-sheets as observed in far-UV CD and a blue shift in intrinsic Trp fluorescence. Both indicate that DPF3a spontaneously tends to orderly aggregate into amyloid fibrils. The diversity of optical signatures originates from dynamical transitions between the disordered and aggregated states of the protein during the incubation. Transmission electron microscopy micrographs reveal that the DPF3a fibrillation process leads to the formation of short needle-shape filaments.

Analysis of the dark proteome of Chandipura virus reveals maximum propensity for intrinsic disorder in phosphoprotein

Chandipura virus (CHPV, a member of the Rhabdoviridae family) is an emerging pathogen that causes rapidly progressing influenza-like illness and acute encephalitis often leading to coma and death of the human host. Given several CHPV outbreaks in Indian sub-continent, recurring sporadic cases, neurological manifestation, and high mortality rate of this infection, CHPV is gaining global attention. The 'dark proteome' includes the whole proteome with special emphasis on intrinsically disordered proteins (IDP) and IDP regions (IDPR), which are proteins or protein regions that lack unique (or ordered) three-dimensional structures within the cellular milieu. These proteins/regions, however, play a number of vital roles in various biological processes, such as cell cycle regulation, control of signaling pathways, etc. and, therefore, are implicated in many human diseases. IDPs and IPPRs are also abundantly found in many viral proteins enabling their multifunctional roles in the viral life cycles and their capability to highjack various host systems. The unknown abundance of IDP and IDPR in CHPV, therefore, prompted us to analyze the dark proteome of this virus. Our analysis revealed a varying degree of disorder in all five CHPV proteins, with the maximum level of intrinsic disorder propensity being found in Phosphoprotein (P). We have also shown the flexibility of P protein using extensive molecular dynamics simulations up to 500 ns (ns). Furthermore, our analysis also showed the abundant presence of the disorder-based binding regions (also known as molecular recognition features, MoRFs) in CHPV proteins. The identification of IDPs/IDPRs in CHPV proteins suggests that their disordered regions may function as potential interacting domains and may also serve as novel targets for disorder-based drug designs.

A Tale of Loops and Tails: The Role of Intrinsically Disordered Protein Regions in R-Loop Recognition and Phase Separation

R-loops are non-canonical, three-stranded nucleic acid structures composed of a DNA:RNA hybrid, a displaced single-stranded (ss)DNA, and a trailing ssRNA overhang. R-loops perform critical biological functions under both normal and disease conditions. To elucidate their cellular functions, we need to understand the mechanisms underlying R-loop formation, recognition, signaling, and resolution. Previous high-throughput screens identified multiple proteins that bind R-loops, with many of these proteins containing folded nucleic acid processing and binding domains that prevent (e.g., topoisomerases), resolve (e.g., helicases, nucleases), or recognize (e.g., KH, RRMs) R-loops. However, a significant number of these R-loop interacting Enzyme and Reader proteins also contain long stretches of intrinsically disordered regions (IDRs). The precise molecular and structural mechanisms by which the folded domains and IDRs synergize to recognize and process R-loops or modulate R-loop-mediated signaling have not been fully explored. While studying one such modular R-loop Reader, the Fragile X Protein (FMRP), we unexpectedly discovered that the C-terminal IDR (C-IDR) of FMRP is the predominant R-loop binding site, with the three N-terminal KH domains recognizing the trailing ssRNA overhang. Interestingly, the C-IDR of FMRP has recently been shown to undergo spontaneous Liquid-Liquid Phase Separation (LLPS) assembly by itself or in complex with another non-canonical nucleic acid structure, RNA G-quadruplex. Furthermore, we have recently shown that FMRP can suppress persistent R-loops that form during transcription, a process that is also enhanced by LLPS via the assembly of membraneless transcription factories. These exciting findings prompted us to explore the role of IDRs in R-loop processing and signaling proteins through a comprehensive bioinformatics and computational biology study. Here, we evaluated IDR prevalence, sequence composition and LLPS propensity for the known R-loop interactome. We observed that, like FMRP, the majority of the R-loop interactome, especially Readers, contains long IDRs that are highly enriched in low complexity sequences with biased amino acid composition, suggesting that these IDRs could directly interact with R-loops, rather than being “mere flexible linkers” connecting the “functional folded enzyme or binding domains”. Furthermore, our analysis shows that several proteins in the R-loop interactome are either predicted to or have been experimentally demonstrated to undergo LLPS or are known to be associated with phase separated membraneless organelles. Thus, our overall results present a thought-provoking hypothesis that IDRs in the R-loop interactome can provide a functional link between R-loop recognition via direct binding and downstream signaling through the assembly of LLPS-mediated membrane-less R-loop foci. The absence or dysregulation of the function of IDR-enriched R-loop interactors can potentially lead to severe genomic defects, such as the widespread R-loop-mediated DNA double strand breaks that we recently observed in Fragile X patient-derived cells.

Tumor Susceptibility Gene 101 Regulates the Glucocorticoid Receptor through Disorder-Mediated Allostery

Tumor susceptibility gene 101 (TSG101) is involved in endosomal maturation and has been implicated in the transcriptional regulation of several steroid hormone receptors, although a detailed characterization of such regulation has yet to be conducted. Here we directly measure binding of TSG101 to one steroid hormone receptor, the glucocorticoid receptor (GR). Using biophysical and cellular assays, we show that the coiled-coil domain of TSG101 (1) binds and folds the disordered N-terminal domain of the GR, (2) upon binding improves the DNA binding of the GR in vitro, and (3) enhances the transcriptional activity of the GR in vivo. Our findings suggest that TSG101 is a bona fide transcriptional co-regulator of the GR and reveal how the underlying thermodynamics affect the function of the GR.

Reinforcement learning to boost molecular docking upon protein conformational ensemble

Intrinsically disordered proteins (IDPs) are widely involved in human diseases and thus are attractive therapeutic targets. In practice, however, it is computationally prohibitive to dock large ligand libraries to thousands and tens of thousands of conformations. Here, we propose a reversible upper confidence bound (UCB) algorithm for the virtual screening of IDPs to address the influence of the conformation ensemble. The docking process is dynamically arranged so that attempts are focused near the boundary to separate top ligands from the bulk accurately. It is demonstrated in the example of transcription factor c-Myc that the average docking number per ligand can be greatly reduced while the performance is merely slightly affected. This study suggests that reinforcement learning is highly efficient in solving the bottleneck of virtual screening due to the conformation ensemble in the rational drug design of IDPs.

Intrinsically disordered features of carbonic anhydrase IX proteoglycan-like domain

hCA IX is a multi-domain protein belonging to the family of hCAs which are ubiquitous zinc enzymes that catalyze the reversible hydration of CO2 to HCO3- and H+. hCA IX is a tumor-associated enzyme with a limited distribution in normal tissues, but over-expressed in many tumors, and is a promising drug target. Although many studies concerning the CA IX catalytic domain were performed, little is known about the proteoglycan-like (PG-like) domain of hCA IX which has been poorly investigated so far. Here we attempt to fill this gap by providing an overview on the functional, structural and therapeutic studies of the PG-like domain of hCA IX which represents a unique feature within the CA family. The main studies and recent advances concerning PG role in modulating hCA IX catalytic activity as well as in tumor spreading and migration are here reported. Special attention has been paid to the newly discovered disordered features of the PG domain which open new perspectives about its molecular mechanisms of action under physiological and pathological conditions, since disorder is likely involved in mediating interactions with partner proteins. The emerged disordered features of PG domain will be explored for putative diagnostic and therapeutic applications involving CA IX targeting in tumors.

Disaggregation mechanism of prion amyloid for tweezer inhibitor

The aggregation of amyloid has been an important event in the pathology of amyloidogenicity. A number of small molecules have been designed for Amyloidosis treatment. Molecular tweezer CLR01, a potential drug for misfolded β-amyloids inhibition, was reportedly bind directly to Lysine residues and interrupt oligomerization. However, the disaggregation mechanism of amyloid for this inhibitor is unclear. Here we used long timescale of molecular dynamic simulation to reveal the mechanism of disaggregation for pentamer prion amyloid. Molecular docking and molecular dynamics simulation demonstrate that CLR01 is attached with Lysine₂₂₂ nitrogen by π-cation interaction of its nine aromatic rings and formation of salt bridge/hydrogen bond of one of the two rotatable peripheral anionic phosphate groups. Upon CLR01 binding, we found a major shifting occurs in initial conformation of the oligomer and stretch out the N-terminal chain A from the rest of the amyloid which seems to be the first stage of disaggregated the fibrils slowly yet efficiently. Moreover, the CLR01 remodelled the pentamer Prion_220-272 into a compact structure which might be the resistant conformation for further oligomerization. Our work will contribute to better understand the interaction and deterioration mechanism of molecular tweezer for prions and similar amyloids, and offer significant insights into therapeutic development for Amyloidosis treatment.

Recent Developments in the Field of Intrinsically Disordered Proteins: Intrinsic Disorder-Based Emergence in Cellular Biology in Light of the Physiological and Pathological Liquid-Liquid Phase Transitions

This review deals with two important concepts-protein intrinsic disorder and proteinaceous membrane-less organelles (PMLOs). The past 20 years have seen an upsurge of scientific interest in these phenomena. However, neither are new discoveries made in this century, but instead are timely reincarnations of old ideas that were mostly ignored by the scientific community for a long time. Merging these concepts in the form of the intrinsic disorder-based biological liquid-liquid phase separation provides a basis for understanding the molecular mechanisms of PMLO biogenesis.

Critical assessment of protein intrinsic disorder prediction

Intrinsically disordered proteins, defying the traditional protein structure-function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has F_max = 0.483 on the full dataset and F_max = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F_max = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Computational strategy for intrinsically disordered protein ligand design leads to the discovery of p53 transactivation domain I binding compounds that activate the p53 pathway

Intrinsically disordered proteins or intrinsically disordered regions (IDPs) have gained much attention in recent years due to their vital roles in biology and prevalence in various human diseases. Although IDPs are perceived as attractive therapeutic targets, rational drug design targeting IDPs remains challenging because of their conformational heterogeneity. Here, we propose a hierarchical computational strategy for IDP drug virtual screening (IDPDVS) and applied it in the discovery of p53 transactivation domain I (TAD1) binding compounds. IDPDVS starts from conformation sampling of the IDP target, then it combines stepwise conformational clustering with druggability evaluation to identify potential ligand binding pockets, followed by multiple docking screening runs and selection of compounds that can bind multi-conformations. p53 is an important tumor suppressor and restoration of its function provides an opportunity to inhibit cancer cell growth. TAD1 locates at the N-terminus of p53 and plays key roles in regulating p53 function. No compounds that directly bind to TAD1 have been reported due to its highly disordered structure. We successfully used IDPDVS to identify two compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells. Our study demonstrates that IDPDVS is an efficient strategy for IDP drug discovery and p53 TAD1 can be directly targeted by small molecules.

A hierarchical computational strategy for IDP drug virtual screening (IDPDVS) was proposed and successfully applied to identify compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells.

Liquid-Liquid Phase Separation by Intrinsically Disordered Protein Regions of Viruses: Roles in Viral Life Cycle and Control of Virus-Host Interactions

Intrinsically disordered proteins (IDPs) are unable to adopt a unique 3D structure under physiological conditions and thus exist as highly dynamic conformational ensembles. IDPs are ubiquitous and widely spread in the protein realm. In the last decade, compelling experimental evidence has been gathered, pointing to the ability of IDPs and intrinsically disordered regions (IDRs) to undergo liquid-liquid phase separation (LLPS), a phenomenon driving the formation of membrane-less organelles (MLOs). These biological condensates play a critical role in the spatio-temporal organization of the cell, where they exert a multitude of key biological functions, ranging from transcriptional regulation and silencing to control of signal transduction networks. After introducing IDPs and LLPS, we herein survey available data on LLPS by IDPs/IDRs of viral origin and discuss their functional implications. We distinguish LLPS associated with viral replication and trafficking of viral components, from the LLPS-mediated interference of viruses with host cell functions. We discuss emerging evidence on the ability of plant virus proteins to interfere with the regulation of MLOs of the host and propose that bacteriophages can interfere with bacterial LLPS, as well. We conclude by discussing how LLPS could be targeted to treat phase separation-associated diseases, including viral infections.

Ensemble-Based Thermodynamics of the Fuzzy Binding between Intrinsically Disordered Proteins and Small-Molecule Ligands

In contrast to the "lock-and-key" model underlying the long-term success of structural biology and rational drug design, intrinsically disordered proteins (IDPs) exist in an ensemble of highly heterogeneous conformations even after binding with small-molecule ligands. It remains controversial how to characterize the thermodynamics of such fuzzy interactions. Here, we derive an ensemble-based thermodynamic framework to analyze the apparent affinity between IDPs and ligands. It is shown that the apparent affinity is related to the interaction free energy between the individual conformation and ligand in a way similar to Jarzynski's equality in nonequilibrium statistics. The oncoprotein c-Myc is adopted as an example to demonstrate the related properties, for example, the distribution of conformation-ligand interaction free energy, the entropic contribution from the ensemble, the conformation shift under ligand binding, and how to control the error under a limited number of sampled conformations.

The dark side of Alzheimer's disease: unstructured biology of proteins from the amyloid cascade signaling pathway

Alzheimer's disease (AD) is a leading cause of age-related dementia worldwide. Despite more than a century of intensive research, we are not anywhere near the discovery of a cure for this disease or a way to prevent its progression. Among the various molecular mechanisms proposed for the description of the pathogenesis and progression of AD, the amyloid cascade hypothesis, according to which accumulation of a product of amyloid precursor protein (APP) cleavage, amyloid β (Aβ) peptide, induces pathological changes in the brain observed in AD, occupies a unique niche. Although multiple proteins have been implicated in this amyloid cascade signaling pathway, their structure-function relationships are mostly unexplored. However, it is known that two major proteins related to AD pathology, Aβ peptide, and microtubule-associated protein tau belong to the category of intrinsically disordered proteins (IDPs), which are the functionally important proteins characterized by a lack of fixed, ordered three-dimensional structure. IDPs and intrinsically disordered protein regions (IDPRs) play numerous vital roles in various cellular processes, such as signaling, cell cycle regulation, macromolecular recognition, and promiscuous binding. However, the deregulation and misfolding of IDPs may lead to disturbed signaling, interactions, and disease pathogenesis. Often, molecular recognition-related IDPs/IDPRs undergo disorder-to-order transition upon binding to their biological partners and contain specific disorder-based binding motifs, known as molecular recognition features (MoRFs). Knowing the intrinsic disorder status and disorder-based functionality of proteins associated with amyloid cascade signaling pathway may help to untangle the mechanisms of AD pathogenesis and help identify therapeutic targets. In this paper, we have used multiple computational tools to evaluate the presence of intrinsic disorder and MoRFs in 27 proteins potentially relevant to the amyloid cascade signaling pathway. Among these, BIN1, APP, APOE, PICALM, PSEN1 and CD33 were found to be highly disordered. Furthermore, their disorder-based binding regions and associated short linear motifs have also been identified. These findings represent important foundation for the future research, and experimental characterization of disordered regions in these proteins is required to better understand their roles in AD pathogenesis.

Targeting Intrinsically Disordered Proteins through Dynamic Interactions

Intrinsically disordered proteins (IDPs) are over-represented in major disease pathways and have attracted significant interest in understanding if and how they may be targeted using small molecules for therapeutic purposes. While most existing studies have focused on extending the traditional structure-centric drug design strategies and emphasized exploring pre-existing structure features of IDPs for specific binding, several examples have also emerged to suggest that small molecules could achieve specificity in binding IDPs and affect their function through dynamic and transient interactions. These dynamic interactions can modulate the disordered conformational ensemble and often lead to modest compaction to shield functionally important interaction sites. Much work remains to be done on further elucidation of the molecular basis of the dynamic small molecule-IDP interaction and determining how it can be exploited for targeting IDPs in practice. These efforts will rely critically on an integrated experimental and computational framework for disordered protein ensemble characterization. In particular, exciting advances have been made in recent years in enhanced sampling techniques, Graphic Processing Unit (GPU)-computing, and protein force field optimization, which have now allowed rigorous physics-based atomistic simulations to generate reliable structure ensembles for nontrivial IDPs of modest sizes. Such de novo atomistic simulations will play crucial roles in exploring the exciting opportunity of targeting IDPs through dynamic interactions.

Structural facets of POU2F1 in light of the functional annotations and sequence-structure patterns

POU domain class 2 homebox 1 or POU2F1 is broadly known as an important transcription factor. Due to its association with different types of malignancies, POU2F1 became one of the key factors in pancancer analysis. However, in spite of considering this protein as a potential drug target, none of the drug targeting POU2F1 has been designed as of yet due to the extreme structural flexibility of this protein. In this article, we have proposed a three-level comprehensive framework for understanding the structural conservation and co-variation of POU2F1. First, a gene regulatory network based on the normal and pathological functions of POU2F1 has been created for better understanding the strong association between POU2F1 deregulation and cancers. After that, based on the evolutionary sequence space analysis, the comparative sequence dynamics of the protein members of POU domain family has been studied mostly between non-human and human species. Subsequently, the reciprocity effect of the residual co-variation has been identified through direct coupling analysis. Along with that, the structure of POU2F1 has been analyzed depending on quality assessment and normal mode-based structure network. Comparing the sequence and structure space information, the most significant set of residues viz., 3, 9, 13, 17, 20, 21, 28, 35, and 36 have been identified as structural facet for function. This study demonstrates that the structural malleability of POU2F1 serves as one of the prime reason behind its functional multiplicity in terms of protein moonlighting. Communicated by Ramaswamy H. Sarma.

A chemical approach for the synthesis of the DNA-binding domain of the oncoprotein MYC

We describe the first chemical synthesis of a functional mutant of the DNA binding domain of the oncoprotein MYC, using two alternative strategies which involve either one or two Native Chemical Ligations (NCLs). Both routes allowed the efficient synthesis of a miniprotein which is capable of heterodimerizing with MAX, and replicate the DNA binding of the native protein. The versatility of the reported synthetic approach enabled the straightforward preparation of MYC and Omomyc analogues, as well as fluorescently labeled derivatives.

Investigating into the molecular interactions of flavonoids targeting NS2B-NS3 protease from ZIKA virus through in-silico approaches

Zika virus (ZIKV), belongs to the flavivirus genus and Flaviviridae family that associated with serious diseased conditions like microcephaly and other neurological disorders (Guillan-Barré syndrome). As there is no vaccine or therapies available against ZIKV to date. Hence, it is an unmet need to find potential drug candidates and target sites against Zika virus infection. NS2B-NS3 protease making an attractive target for therapeutic intervention in ZIKV infections because of its critical role in hydrolysis of a single polyprotein encoded by Zika virus. Recently, there are some experimental evidence about the flavonoids as Zika virus NS2B-NS3 protease inhibitors. However, molecular interaction between protease complex and inhibitors at atomic levels has not been explored. Here, we have taken the experimentally validated thirty-eight flavonoids inhibitors against NS2B-NS3 protease to examine the molecular interaction using molecular docking and molecular dynamics simulations. We found out few flavonoids such as EGCG and its two derivatives, isoquercetin, rutin and sanggenon O showing interaction with catalytic triad (His51, Asp75, and Ser135) of the active site of NS2B-NS3 protease and found to be stable throughout the simulation. Therefore it is evident that interaction with the catalytic triad playing a vital role in the inhibition of the enzyme activity as a result inhibition of the virus propagation. However these compounds can be explored further for understanding the mechanism of action of these compounds targeting NS2B-NS3 protease for inhibition of Zika virus.

Identification of peptidomimetic compounds as potential inhibitors against MurA enzyme of Mycobacterium tuberculosis

Increasing prevalence of resistance to anti-tubercular drugs has become the foremost challenge now. According to WHO, over half a million of multidrug resistance cases (rifampicin, isoniazid, etc.) were reported in 2017, mostly emerging from countries such as China, India, and Russia. Therefore, developing new drugs or repurposing existing ones is need of the hour. The Mycobacterium cell wall biogenesis pathway offers many attractive targets for drug discovery against Tuberculosis (TB). MurA, a transferase enzyme that catalyzes the initial step of peptidoglycan (PG) biosynthesis, is one among them. A peptidoglycan layer resides over the plasma membrane and is an integral component of the bacterial cell wall. Therefore, disruption of their formation through inhibition of MurA enzyme should lead to deficiency in Mycobacterium cell synthesis. Based on this strategy, we have designed this study where two libraries of peptidomimetic compounds (Asinex & ChemDiv) were first screened against our modeled MurA structure and then validated through molecular dynamic simulations. From our virtual screening, top four compounds (ChemDiv: D675-0102, D675-0217; Asinex: BDE25373574, BDE 26717803) were selected based on their docking scores, binding energies, and interactions with catalytic site residues, for further evaluation. Results revealed stable ligand-MurA interactions throughout 50 ns of MD simulation and also druggability acceptable pharmacokinetic profile for all four compounds. Thus, based on our findings, these compounds could be considered as potential inhibitors of Mycobacterium MurA enzyme and hence be further tested for in vitro experimental validation as TB therapeutic drug candidate.Communicated by Ramaswamy H. Sarma.

Binding site matching in rational drug design: algorithms and applications

Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.

Rational discovery of antimetastatic agents targeting the intrinsically disordered region of MBD2

Although intrinsically disordered protein regions (IDPRs) are commonly engaged in promiscuous protein-protein interactions (PPIs), using them as drug targets is challenging due to their extreme structural flexibility. We report a rational discovery of inhibitors targeting an IDPR of MBD2 that undergoes disorder-to-order transition upon PPI and is critical for the regulation of the Mi-2/NuRD chromatin remodeling complex (CRC). Computational biology was essential for identifying target site, searching for promising leads, and assessing their binding feasibility and off-target probability. Molecular action of selected leads inhibiting the targeted PPI of MBD2 was validated in vitro and in cell, followed by confirming their inhibitory effects on the epithelial-mesenchymal transition of various cancer cells. Identified lead compounds appeared to potently inhibit cancer metastasis in a murine xenograft tumor model. These results constitute a pioneering example of rationally discovered IDPR-targeting agents and suggest Mi-2/NuRD CRC and/or MBD2 as a promising target for treating cancer metastasis.

Accuracy of protein-level disorder predictions

Experimental annotations of intrinsic disorder are available for 0.1% of 147 000 000 of currently sequenced proteins. Over 60 sequence-based disorder predictors were developed to help bridge this gap. Current benchmarks of these methods assess predictive performance on datasets of proteins; however, predictions are often interpreted for individual proteins. We demonstrate that the protein-level predictive performance varies substantially from the dataset-level benchmarks. Thus, we perform first-of-its-kind protein-level assessment for 13 popular disorder predictors using 6200 disorder-annotated proteins. We show that the protein-level distributions are substantially skewed toward high predictive quality while having long tails of poor predictions. Consequently, between 57% and 75% proteins secure higher predictive performance than the currently used dataset-level assessment suggests, but as many as 30% of proteins that are located in the long tails suffer low predictive performance. These proteins typically have relatively high amounts of disorder, in contrast to the mostly structured proteins that are predicted accurately by all 13 methods. Interestingly, each predictor provides the most accurate results for some number of proteins, while the best-performing at the dataset-level method is in fact the best for only about 30% of proteins. Moreover, the majority of proteins are predicted more accurately than the dataset-level performance of the most accurate tool by at least four disorder predictors. While these results suggests that disorder predictors outperform their current benchmark performance for the majority of proteins and that they complement each other, novel tools that accurately identify the hard-to-predict proteins and that make accurate predictions for these proteins are needed.

p53 Phosphomimetics Preserve Transient Secondary Structure but Reduce Binding to Mdm2 and MdmX

The disordered p53 transactivation domain (p53TAD) contains specific levels of transient helical secondary structure that are necessary for its binding to the negative regulators, mouse double minute 2 (Mdm2) and MdmX. The interactions of p53 with Mdm2 and MdmX are also modulated by posttranslational modifications (PTMs) of p53TAD including phosphorylation at S15, T18 and S20 that inhibits p53-Mdm2 binding. It is unclear whether the levels of transient secondary structure in p53TAD are changed by phosphorylation or other PTMs. We used phosphomimetic mutants to determine if adding a negative charge at positions 15 and 18 has any effect on the transient secondary structure of p53TAD and protein-protein binding. Using a combination of biophysical and structural methods, we investigated the effects of single and multisite phosphomimetics on the transient secondary structure of p53TAD and its interaction with Mdm2, MdmX, and the KIX domain. The phosphomimetics reduced Mdm2 and MdmX binding affinity by 3–5-fold, but resulted in minimal changes in transient secondary structure, suggesting that the destabilizing effect of phosphorylation on the p53TAD-Mdm2 interaction is primarily electrostatic. Phosphomimetics had no effect on the p53-KIX interaction, suggesting that increased binding of phosphorylated p53 to KIX may be influenced by decreased competition with its negative regulators.

Self-assembly in elastin-like recombinamers: a mechanism to mimic natural complexity

The topic of self-assembled structures based on elastin-like recombinamers (ELRs, i.e., elastin-like polymers recombinantly bio-produced) has released a noticeable amount of references in the last few years. Most of them are intended for biomedical applications. In this review, a complete revision of the bibliography is carried out. Initially, the self-assembly (SA) concept is considered from a general point of view, and then ELRs are described and characterized based on their intrinsic disorder. A classification of the different self-assembled ELR-based structures is proposed based on their morphologies, paying special attention to their tentative modeling. The impact of the mechanism of SA on these biomaterials is analyzed. Finally, the implications of ELR SA in biological systems are considered.

Conservation of Potentially Druggable Cavities in Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) exist in highly dynamic conformational ensembles, which pose a major obstacle for drug development targeting IDPs because traditional rational drug design relies on unique three-dimensional structures. Here, we analyzed the conservation (especially structural conservation) of potentially druggable cavities in 22 ensembles of IDPs. It was found that there is considerable conservation for potentially druggable cavities within each ensemble. The average common atom percentage of potentially druggable cavities is as high as 54%. The average root-mean-squared deviation of common atoms ranges between 1 and 8 Å for multichain IDPs, and a common pocket is kept after direct alignment of cavities. In addition, the conservation of potentially druggable cavities varies among different proteins. In the comparison of multi- and single-chain IDPs, some multichain IDPs have an extremely high conservation, whereas another multichain IDPs' conservation appears worse, and the single-chain IDPs have relatively moderate conservations. This study is a new attempt to generally assess the potentially druggable cavities in IDPs for taking IDPs as druggable targets, and this work also lends support to the opinion of IDPs tending to bind to "multiconformational affinity" compounds.

Targeting intrinsically disordered proteins at the edge of chaos

Intrinsically disordered proteins or intrinsically disordered regions (IDPs or IDRs) are those that do not fold into defined tertiary structures under physiological conditions. Given their prevalence in various diseases, IDPs are attractive therapeutic targets. However, because of the dynamic nature of the IDP structure, conventional structure-based drug design methods cannot be directly applied. Thanks to recent progress in understanding the mechanisms underlying IDP and ligand interactions, computational strategies for IDP-targeted rational drug discovery are emerging. Here, we summarize recent developments in computational IDP drug design strategies and their successful applications, analyze the typical properties of reported IDP-binding compounds (iIDPs), and discuss the major challenges ahead as well as possible solutions.

Deciphering the dark proteome of Chikungunya virus

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus. The outbreak of CHIKV infection has been seen in many tropical and subtropical regions of the biosphere. Current reports evidenced that after outbreaks in 2005-06, the fitness of this virus propagating in Aedes albopictus enhanced due to the epistatic mutational changes in its envelope protein. In our study, we evaluated the prevalence of intrinsically disordered proteins (IDPs) and IDP regions (IDPRs) in CHIKV proteome. IDPs/IDPRs are known as members of a 'Dark Proteome' that defined as a set of polypeptide segments or whole protein without unique three-dimensional structure within the cellular milieu but with significant biological functions, such as cell cycle regulation, control of signaling pathways, and maintenance of viral proteomes. However, the intrinsically disordered aspects of CHIKV proteome and roles of IDPs/IDPRs in the pathogenic mechanism of this important virus have not been evaluated as of yet. There are no existing reports on the analysis of intrinsic disorder status of CHIKV. To fulfil this goal, we have analyzed the abundance and functionality of IDPs/IDPRs in CHIKV proteins, involved in the replication and maturation. It is likely that these IDPs/IDPRs can serve as novel targets for disorder based drug design.

Understanding the interactability of chikungunya virus proteinsviamolecular recognition feature analysis

The chikungunya virus (CHIKV) is an alphavirus that has an enveloped icosahedral capsid and is transmitted byAedessp. mosquitos.

Functional Analysis of Human Hub Proteins and Their Interactors Involved in the Intrinsic Disorder-Enriched Interactions

Some of the intrinsically disordered proteins and protein regions are promiscuous interactors that are involved in one-to-many and many-to-one binding. Several studies have analyzed enrichment of intrinsic disorder among the promiscuous hub proteins. We extended these works by providing a detailed functional characterization of the disorder-enriched hub protein-protein interactions (PPIs), including both hubs and their interactors, and by analyzing their enrichment among disease-associated proteins. We focused on the human interactome, given its high degree of completeness and relevance to the analysis of the disease-linked proteins. We quantified and investigated numerous functional and structural characteristics of the disorder-enriched hub PPIs, including protein binding, structural stability, evolutionary conservation, several categories of functional sites, and presence of over twenty types of posttranslational modifications (PTMs). We showed that the disorder-enriched hub PPIs have a significantly enlarged number of disordered protein binding regions and long intrinsically disordered regions. They also include high numbers of targeting, catalytic, and many types of PTM sites. We empirically demonstrated that these hub PPIs are significantly enriched among 11 out of 18 considered classes of human diseases that are associated with at least 100 human proteins. Finally, we also illustrated how over a dozen specific human hubs utilize intrinsic disorder for their promiscuous PPIs.

The roles of intrinsic disorder-based liquid-liquid phase transitions in the "Dr. Jekyll-Mr. Hyde" behavior of proteins involved in amyotrophic lateral sclerosis and frontotemporal lobar degeneration

Pathological developments leading to amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are associated with misbehavior of several key proteins, such as SOD1 (superoxide dismutase 1), TARDBP/TDP-43, FUS, C9orf72, and dipeptide repeat proteins generated as a result of the translation of the intronic hexanucleotide expansions in the C9orf72 gene, PFN1 (profilin 1), GLE1 (GLE1, RNA export mediator), PURA (purine rich element binding protein A), FLCN (folliculin), RBM45 (RNA binding motif protein 45), SS18L1/CREST, HNRNPA1 (heterogeneous nuclear ribonucleoprotein A1), HNRNPA2B1 (heterogeneous nuclear ribonucleoprotein A2/B1), ATXN2 (ataxin 2), MAPT (microtubule associated protein tau), and TIA1 (TIA1 cytotoxic granule associated RNA binding protein). Although these proteins are structurally and functionally different and have rather different pathological functions, they all possess some levels of intrinsic disorder and are either directly engaged in or are at least related to the physiological liquid-liquid phase transitions (LLPTs) leading to the formation of various proteinaceous membrane-less organelles (PMLOs), both normal and pathological. This review describes the normal and pathological functions of these ALS- and FTLD-related proteins, describes their major structural properties, glances at their intrinsic disorder status, and analyzes the involvement of these proteins in the formation of normal and pathological PMLOs, with the ultimate goal of better understanding the roles of LLPTs and intrinsic disorder in the "Dr. Jekyll-Mr. Hyde" behavior of those proteins.