CPAD, Curated Protein Aggregation Database: A Repository of Manually Curated Experimental Data on Protein and Peptide Aggregation

Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

Insoluble amyloid aggregates are the hallmarks of more than fifty human diseases, including the most common neurodegenerative disorders. The process by which soluble proteins nucleate to form amyloid fibrils is, however, quite poorly characterized. Relatively few sequences are known that form amyloids with high propensity and this data shortage likely limits our capacity to understand, predict, engineer, and prevent the formation of amyloid fibrils. Here we quantify the nucleation of amyloids at an unprecedented scale and use the data to train a deep learning model of amyloid nucleation. In total, we quantify the nucleation rates of >100,000 20-amino-acid-long peptides. This large and diverse dataset allows us to train CANYA, a convolution-attention hybrid neural network. CANYA is fast and outperforms existing methods with stable performance across diverse prediction tasks. Interpretability analyses reveal CANYA's decision-making process and learned grammar, providing mechanistic insights into amyloid nucleation. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict amyloid nucleation.

Using yeasts for the studies of nonfunctional factors in protein evolution

The evolution of protein sequence is driven not only by factors directly related to protein function and shape but also by nonfunctional factors. Such factors in protein evolution might be categorized as those connected to energetic costs, synthesis efficiency, and avoidance of misfolding and toxicity. A common approach to studying them is correlational analysis contrasting them with some characteristics of the protein, like amino acid composition, but these features are interdependent. To avoid possible bias, empirical studies are needed, and not enough work has been done to date. In this review, we describe the role of nonfunctional factors in protein evolution and present an experimental approach using yeast as a suitable model organism. The focus of the proposed approach is on the potential negative impact on the fitness of mutations that change protein properties not related to function and the frequency of mutations that change these properties. Experimental results of testing the misfolding avoidance hypothesis as an explanation for why highly expressed proteins evolve slowly are inconsistent with correlational research results. Therefore, more efforts should be made to empirically test the effects of nonfunctional factors in protein evolution and to contrast these results with the results of the correlational analysis approach.

Machine Learning-Guided Protein Engineering

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

Are fibrinaloid microclots a cause of autoimmunity in Long Covid and other post-infection diseases?

It is now well established that the blood-clotting protein fibrinogen can polymerise into an anomalous form of fibrin that is amyloid in character; the resultant clots and microclots entrap many other molecules, stain with fluorogenic amyloid stains, are rather resistant to fibrinolysis, can block up microcapillaries, are implicated in a variety of diseases including Long COVID, and have been referred to as fibrinaloids. A necessary corollary of this anomalous polymerisation is the generation of novel epitopes in proteins that would normally be seen as 'self', and otherwise immunologically silent. The precise conformation of the resulting fibrinaloid clots (that, as with prions and classical amyloid proteins, can adopt multiple, stable conformations) must depend on the existing small molecules and metal ions that the fibrinogen may (and is some cases is known to) have bound before polymerisation. Any such novel epitopes, however, are likely to lead to the generation of autoantibodies. A convergent phenomenology, including distinct conformations and seeding of the anomalous form for initiation and propagation, is emerging to link knowledge in prions, prionoids, amyloids and now fibrinaloids. We here summarise the evidence for the above reasoning, which has substantial implications for our understanding of the genesis of autoimmunity (and the possible prevention thereof) based on the primary process of fibrinaloid formation.

Nucleolar- and Nuclear-Stress-Induced Membrane-Less Organelles: A Proteome Analysis through the Prism of Liquid-Liquid Phase Separation

Radical changes in the idea of the organization of intracellular space that occurred in the early 2010s made it possible to consider the formation and functioning of so-called membrane-less organelles (MLOs) based on a single physical principle: the liquid-liquid phase separation (LLPS) of biopolymers. Weak non-specific inter- and intramolecular interactions of disordered polymers, primarily intrinsically disordered proteins, and RNA, play a central role in the initiation and regulation of these processes. On the other hand, in some cases, the "maturation" of MLOs can be accompanied by a "liquid-gel" phase transition, where other types of interactions can play a significant role in the reorganization of their structure. In this work, we conducted a bioinformatics analysis of the propensity of the proteomes of two membrane-less organelles, formed in response to stress in the same compartment, for spontaneous phase separation and examined their intrinsic disorder predispositions. These MLOs, amyloid bodies (A-bodies) formed in the response to acidosis and heat shock and nuclear stress bodies (nSBs), are characterized by a partially overlapping composition, but show different functional activities and morphologies. We show that the proteomes of these biocondensates are differently enriched in proteins, and many have high potential for spontaneous LLPS that correlates with the different morphology and function of these organelles. The results of these analyses allowed us to evaluate the role of weak interactions in the formation and functioning of these important organelles.

DES-Amyloidoses “Amyloidoses through the looking-glass”: A knowledgebase developed for exploring and linking information related to human amyloid-related diseases

More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer’s disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities’ influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.

Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins

Several computational methods have been developed to predict amyloid propensity of a protein or peptide. These bioinformatics tools are time- and cost-saving alternatives to expensive and laborious experimental methods which are used to confirm self-aggregation of a protein. Computational approaches not only allow preselection of reliable candidates for amyloids but, most importantly, are capable of a thorough and informative analysis of a protein, indicating the sequence determinants of protein aggregation, identifying the potential causal mutations and likely mechanisms. Bioinformatics modeling applies several different approaches, which most typically include physicochemical or structure-based modeling, machine learning, or statistics based modeling. Bioinformatics methods typically use the amino acid sequence of a protein as an input, some also include additional information, for example, an available structure. This chapter describes the methods currently used to computationally predict amyloid propensity of a protein or peptide. Since the accuracy of bioinformatics methods may be highly dependent on reference data used to develop and evaluate the predictors, we also briefly present the main databases of amyloids used by the authors of bioinformatics tools.

Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets

Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.

SAPdb: A database of short peptides and the corresponding nanostructures formed by self-assembly

Nanostructures generated by self-assembly of peptides yield nanomaterials that have many therapeutic applications, including drug delivery and biomedical engineering, due to their low cytotoxicity and higher uptake by targeted cells owing to their high affinity and specificity towards cell surface receptors. Despite the promising implications of this rapidly expanding field, there is no dedicated resource to study peptide nanostructures. This study endeavours to create a repository of short peptides, which may prove to be the best models to study ordered nanostructures formed by peptide self-assembly. SAPdb has a repertoire of 1049 entries of experimentally validated nanostructures formed by the self-assembly of small peptides. It consists of 328 tripeptides, 701 dipeptides, and 20 single amino acids with some conjugate partners. Each entry encompasses comprehensive information about the peptide, such as chemical modifications, the type of nanostructure formed, experimental conditions like pH, temperature, solvent required for the self-assembly, etc. Our analysis indicates that peptides containing aromatic amino acids favour the formation of self-assembling nanostructures. Additionally, we observed that these peptides form different nanostructures under different experimental conditions. SAPdb provides this comprehensive information in a hassle-free tabulated manner at a glance. User-friendly browsing, searching, and analysis modules have been integrated for easy data retrieval, data comparison, and examination of properties. We anticipate SAPdb to be a valuable repository for researchers engaged in the burgeoning arena of nanobiotechnology. It is freely available at https://webs.iiitd.edu.in/raghava/sapdb.

Protein aggregation: in silico algorithms and applications

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.

ANuPP: A Versatile Tool to Predict Aggregation Nucleating Regions in Peptides and Proteins

Short aggregation prone sequence motifs can trigger aggregation in peptide and protein sequences. Most algorithms developed so far to identify potential aggregation prone regions (APRs) use amino acid residue composition and/or sequence pattern features. In this work, we have investigated the importance of atomic-level characteristics rather than residue level to understand the initiation of aggregation in proteins and peptides. Using atomic-level features an ensemble-classifier, ANuPP has been developed to predict the aggregation-nucleating regions in peptides and proteins. In a dataset of 1279 hexapeptides, ANuPP achieved an area under the curve (AUC) of 0.831 with 77% accuracy on 10-fold cross-validation and an AUC of 0.883 with 83% accuracy in a blind test dataset of 142 hexapeptides. Further, it showed an average SOV of 48.7% on identifying APR regions in 37 proteins. The performance of ANuPP is better than other methods reported in the literature on both amyloidogenic hexapeptide prediction and APR identification. We have developed a web server for ANuPP and it is available at https://web.iitm.ac.in/bioinfo2/ANuPP/. Insights gained from this work demonstrate the importance of atomic and functional group characteristics towards diversity of atomic level origins as well as mechanisms of protein aggregation.

AggreRATE-Pred: a mathematical model for the prediction of change in aggregation rate upon point mutation

MOTIVATION

Protein aggregation is a major unsolved problem in biochemistry with implications for several human diseases, biotechnology and biomaterial sciences. A majority of sequence-structural properties known for their mechanistic roles in protein aggregation do not correlate well with the aggregation kinetics. This limits the practical utility of predictive algorithms.

RESULTS

We analyzed experimental data on 183 unique single point mutations that lead to change in aggregation rates for 23 polypeptides and proteins. Our initial mathematical model obtained a correlation coefficient of 0.43 between predicted and experimental change in aggregation rate upon mutation (P-value <0.0001). However, when the dataset was classified based on protein length and conformation at the mutation sites, the average correlation coefficient almost doubled to 0.82 (range: 0.74-0.87; P-value <0.0001). We observed that distinct sequence and structure-based properties determine protein aggregation kinetics in each class. In conclusion, the protein aggregation kinetics are impacted by local factors and not by global ones, such as overall three-dimensional protein fold, or mechanistic factors such as the presence of aggregation-prone regions.

AVAILABILITY AND IMPLEMENTATION

The web server is available at http://www.iitm.ac.in/bioinfo/aggrerate-pred/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Relevance of Electrostatic Charges in Compactness, Aggregation, and Phase Separation of Intrinsically Disordered Proteins

The abundance of intrinsic disorder in the protein realm and its role in a variety of physiological and pathological cellular events have strengthened the interest of the scientific community in understanding the structural and dynamical properties of intrinsically disordered proteins (IDPs) and regions (IDRs). Attempts at rationalizing the general principles underlying both conformational properties and transitions of IDPs/IDRs must consider the abundance of charged residues (Asp, Glu, Lys, and Arg) that typifies these proteins, rendering them assimilable to polyampholytes or polyelectrolytes. Their conformation strongly depends on both the charge density and distribution along the sequence (i.e., charge decoration) as highlighted by recent experimental and theoretical studies that have introduced novel descriptors. Published experimental data are revisited herein in the frame of this formalism, in a new and possibly unitary perspective. The physicochemical properties most directly affected by charge density and distribution are compaction and solubility, which can be described in a relatively simplified way by tools of polymer physics. Dissecting factors controlling such properties could contribute to better understanding complex biological phenomena, such as fibrillation and phase separation. Furthermore, this knowledge is expected to have enormous practical implications for the design, synthesis, and exploitation of bio-derived materials and the control of natural biological processes.

CPAD 2.0: a repository of curated experimental data on aggregating proteins and peptides

The Curated Protein Aggregation Database (CPAD) is a manually curated and open-access database dedicated to providing comprehensive information related to mechanistic, kinetic and structural aspects of protein and peptide aggregation. The database has been updated to CPAD 2.0 by significantly expanding datasets and improving the user-interface. Key features of CPAD 2.0 are (i) 83,098 data points on aggregation kinetics experiments, (ii) 565 structures related to aggregation, which are classified into proteins, fibrils, and protein-ligand complexes, (iii) 2031 aggregating/non-aggregating peptides with pre-calculated aggregation properties, and (iv) 912 aggregation-prone regions in amyloidogenic proteins. This database will help the scientific community (a) by facilitating research leading to improved understanding of protein aggregation, (b) by helping develop, validate and benchmark mechanistic and kinetic models of protein aggregation, and (c) by assisting experimentalists with design of their investigations and dissemination of data generated by their studies. CPAD 2.0 can be accessed at https://web.iitm.ac.in/bioinfo2/cpad2/index.html.

WALTZ-DB 2.0: an updated database containing structural information of experimentally determined amyloid-forming peptides

Transition of soluble proteins into insoluble amyloid fibrils is driven by self-propagating short sequence stretches. However, accurate prediction of aggregation determinants remains challenging. Here, we describe WALTZ-DB 2.0, an updated and significantly expanded open-access database providing information on experimentally determined amyloid-forming hexapeptide sequences (http://waltzdb.switchlab.org/). We have updated WALTZ-DB 2.0 with new entries, including: (i) experimental validation of an in-house developed dataset of 229 hexapeptides, using electron microscopy and Thioflavin-T binding assays; (ii) manual curation of 98 amyloid-forming peptides isolated from literature. Furthermore, the content has been expanded by adding novel structural information for peptide entries, including sequences of the previous version. Using a computational methodology developed in the Switch lab, we have generated 3D-models of the putative amyloid fibril cores of WALTZ-DB 2.0 entries. Structural models, coupled with information on the energetic contributions and fibril core stabilities, can be accessed through individual peptide entries. Customized filtering options for subset selections and new modelling graphical features were added to upgrade online accessibility, providing a user-friendly interface for browsing, downloading and updating. WALTZ-DB 2.0 remains the largest open-access repository for amyloid fibril formation determinants and will continue to enhance the development of new approaches focused on accurate prediction of aggregation prone sequences.

AmyPro: a database of proteins with validated amyloidogenic regions

Soluble functional proteins may transform into insoluble amyloid fibrils that deposit in a variety of tissues. Amyloid formation is a hallmark of age-related degenerative disorders. Perhaps surprisingly, amyloid fibrils can also be beneficial and are frequently exploited for diverse functional roles in organisms. Here we introduce AmyPro, an open-access database providing a comprehensive, carefully curated collection of validated amyloid fibril-forming proteins from all kingdoms of life classified into broad functional categories (http://amypro.net). In particular, AmyPro provides the boundaries of experimentally validated amyloidogenic sequence regions, short descriptions of the functional relevance of the proteins and their amyloid state, a list of the experimental techniques applied to study the amyloid state, important structural/functional/variation/mutation data transferred from UniProt, a list of relevant PDB structures categorized according to protein states, database cross-references and literature references. AmyPro greatly improves on similar currently available resources by incorporating both prions and functional amyloids in addition to pathogenic amyloids, and allows users to screen their sequences against the entire collection of validated amyloidogenic sequence fragments. By enabling further elucidation of the sequential determinants of amyloid fibril formation, we hope AmyPro will enhance the development of new methods for the precise prediction of amyloidogenic regions within proteins.

AntiTbPdb: a knowledgebase of anti-tubercular peptides

Tuberculosis is a global menace, caused by Mycobacterium tuberculosis, responsible for millions of premature deaths every year. In the era of drug-resistant tuberculosis, peptide-based therapeutics may provide alternate to small molecule based drugs. In order to create knowledgebase, AntiTbPdb (http://webs.iiitd.edu.in/raghava/antitbpdb/), experimentally validated anti-tubercular and anti-mycobacterial peptides were compiled from literature. We curate 10 652 research articles and 35 patents to extract anti-tubercular peptides and annotate these peptides manually. This knowledgebase has 1010 entries, each entry provides extensive information about an anti-tubercular peptide such as sequence, chemical modification, chirality, nature and source of origin. The tertiary structure of these anti-tubercular peptides containing natural as well as chemically modified residues was predicted using PEPstrMOD and I-TASSER. In addition to structural information, database maintains other properties of peptides like physiochemical properties. Numerous web-based tools have been integrated for data retrieval, browsing, sequence similarity search and peptide mapping. In order to assist wide range of user, we developed a responsive website suitable for smartphone, tablet and desktop.

Database URL: http://webs.iiitd.edu.in/raghava/antitbpdb/

The proteome of neurofilament-containing protein aggregates in blood

Protein aggregation in biofluids is a poorly understood phenomenon. Under normal physiological conditions, fluid-borne aggregates may contain plasma or cell proteins prone to aggregation. Recent observations suggest that neurofilaments (Nf), the building blocks of neurons and a biomarker of neurodegeneration, are included in high molecular weight complexes in circulation. The composition of these Nf-containing hetero-aggregates (NCH) may change in systemic or organ-specific pathologies, providing the basis to develop novel disease biomarkers. We have tested ultracentrifugation (UC) and a commercially available protein aggregate binder, Seprion PAD-Beads (SEP), for the enrichment of NCH from plasma of healthy individuals, and then characterised the Nf content of the aggregate fractions using gel electrophoresis and their proteome by mass spectrometry (MS). Western blot analysis of fractions obtained by UC showed that among Nf isoforms, neurofilament heavy chain (NfH) was found within SDS-stable high molecular weight aggregates. Shotgun proteomics of aggregates obtained with both extraction techniques identified mostly cell structural and to a lesser extent extra-cellular matrix proteins, while functional analysis revealed pathways involved in inflammatory response, phagosome and prion-like protein behaviour. UC aggregates were specifically enriched with proteins involved in endocrine, metabolic and cell-signalling regulation. We describe the proteome of neurofilament-containing aggregates isolated from healthy individuals biofluids using different extraction methods.

In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs

The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.

Mining databases for protein aggregation: a review

Protein aggregation is an active area of research in recent decades, since it is the most common and troubling indication of protein instability. Understanding the mechanisms governing protein aggregation and amyloidogenesis is a key component to the aetiology and pathogenesis of many devastating disorders, including Alzheimer's disease or type 2 diabetes. Protein aggregation data are currently found "scattered" in an increasing number of repositories, since advances in computational biology greatly influence this field of research. This review exploits the various resources of aggregation data and attempts to distinguish and analyze the biological knowledge they contain, by introducing protein-based, fragment-based and disease-based repositories, related to aggregation. In order to gain a broad overview of the available repositories, a novel comprehensive network maps and visualizes the current association between aggregation databases and other important databases and/or tools and discusses the beneficial role of community annotation. The need for unification of aggregation databases in a common platform is also addressed.

Usage of a dataset of NMR resolved protein structures to test aggregation versus solubility prediction algorithms

There has been an increased interest in computational methods for amyloid and (or) aggregate prediction, due to the prevalence of these aggregates in numerous diseases and their recently discovered functional importance. To evaluate these methods, several datasets have been compiled. Typically, aggregation-prone regions of proteins, which form aggregates or amyloids in vivo, are more than 15 residues long and intrinsically disordered. However, the number of such experimentally established amyloid forming and non-forming sequences are limited, not exceeding one hundred entries in existing databases. In this work, we parsed all available NMR-resolved protein structures from the PDB and assembled a new, sevenfold larger, dataset of unfolded sequences, soluble at high concentrations. We proposed to use these sequences as a negative set for evaluating methods for predicting aggregation in vivo. We also present the results of benchmarking cutting edge tools for the prediction of aggregation versus solubility propensity.

Visual Detection of Denatured Glutathione Peptides: A Facile Method to Visibly Detect Heat Stressed Biomolecules

Every year pharmaceutical companies use significant resources to mitigate aggregation of pharmaceutical drug products. Specifically, peptides and proteins that have been denatured or degraded can lead to adverse patient reactions such as undesired immune responses. Current methods to detect aggregation of biological molecules are limited to costly and time consuming processes such as high pressure liquid chromatography, ultrahigh pressure liquid chromatography and SDS-PAGE gels. Aggregation of pharmaceutical drug products can occur during manufacturing, processing, packaging, shipment and storage. Therefore, a facile in solution detection method was evaluated to visually detect denatured glutathione peptides, utilizing gold nanoparticle aggregation via 3-Aminopropyltreithoxysilane. Glutathione was denatured using a 70 °C water bath to create an accelerated heat stressed environment. The peptide, gold nanoparticle and aminosilane solution was then characterized via, UV-Vis spectroscopy, FTIR spectroscopy, dynamic light scattering and scanning electron microscopy. Captured images and resulting absorbance spectra of the gold nanoparticle, glutathione, and aminosilane complex demonstrated visual color changes detectable with the human eye as a function of the denaturation time. This work serves as an extended proof of concept for fast in solution detection methods for glutathione peptides that have experienced heat stress.

Challenges in structural approaches to cell modeling

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.