A variant in human AIOLOS impairs adaptive immunity by interfering with IKAROS.

In the present study, we report a human-inherited, impaired, adaptive immunity disorder, which predominantly manifested as a B cell differentiation defect, caused by a heterozygous IKZF3 missense variant, resulting in a glycine-to-arginine replacement within the DNA-binding domain of the encoded AIOLOS protein. Using mice that bear the corresponding variant and recapitulate the B and T cell phenotypes, we show that the mutant AIOLOS homodimers and AIOLOS-IKAROS heterodimers did not bind the canonical AIOLOS-IKAROS DNA sequence. In addition, homodimers and heterodimers containing one mutant AIOLOS bound to genomic regions lacking both canonical motifs. However, the removal of the dimerization capacity from mutant AIOLOS restored B cell development. Hence, the adaptive immunity defect is caused by the AIOLOS variant hijacking IKAROS function. Heterodimeric interference is a new mechanism of autosomal dominance that causes inborn errors of immunity by impairing protein function via the mutation of its heterodimeric partner.

Long-timescale atomistic simulations uncover loss-of-function mechanisms of uncharacterized Angiogenin mutants associated with ALS.

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease characterized by progressive degeneration of motor neurons, resulting in respiratory failure and mortality within 3-5 years. Mutations in the Angiogenin (ANG) cause loss of ribonucleolytic and nuclear translocation activities, contributing to ALS pathogenesis. This study focused on investigating two uncharacterized ANG mutations, T11S and R122H, newly identified in the Project Mine consortium. Using extensive computational analysis, including structural modeling and microsecond-timescale molecular dynamics (MD) simulations, we observed conformational changes in the catalytic residue His114 of ANG induced by T11S and R122H mutations. These alterations impaired ribonucleolytic activity, as inferred through molecular docking and binding free energy calculations. Gibbs free energy landscape and residue-residue interaction network analysis further supported our findings, revealing the energetic states and allosteric pathway from the mutated site to His114. Additionally, we assessed the binding of NCI-65828, an inhibitor of ribonucleolytic activity of ANG, and found reduced effectiveness in binding to T11S and R122H mutants when His114 assumed a non-native conformation. This highlights the crucial role of His114 and its association with ALS. Elucidating the relationship between physical structure and functional dynamics of frequently mutated ANG mutants is essential for understanding ALS pathogenesis and developing more effective therapeutic interventions.

Interface design of SARS-CoV-2 symmetrical nsp7 dimer and machine learning-guided nsp7 sequence prediction reveals physicochemical properties and hotspots for nsp7 stability, adaptation, and therapeutic design.

The COVID-19 pandemic, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates a profound understanding of the virus and its lifecycle. As an RNA virus with high mutation rates, SARS-CoV-2 exhibits genetic variability leading to the emergence of variants with potential implications. Among its key proteins, the RNA-dependent RNA polymerase (RdRp) is pivotal for viral replication. Notably, RdRp forms dimers via non-structural protein (nsp) subunits, particularly nsp7, crucial for efficient viral RNA copying. Similar to the main protease (Mpro) of SARS-CoV-2, there is a possibility that the nsp7 might also undergo mutational selection events to generate more stable and adaptable versions of nsp7 dimer during virus evolution. However, efforts to obtain such cohesive and comprehensive information are lacking. To address this, we performed this study focused on deciphering the molecular intricacies of nsp7 dimerization using a multifaceted approach. Leveraging computational protein design (CPD), machine learning (ML), AlphaFold v2.0-based structural analysis, and several related computational approaches, we aimed to identify critical residues and mutations influencing nsp7 dimer stability and adaptation. Our methodology involved identifying potential hotspot residues within the dimeric nsp7 interface using an interface-based CPD approach. Through Rosetta-based symmetrical protein design, we designed and modulated nsp7 dimerization, considering selected interface residues. Analysis of physicochemical features revealed acceptable structural changes and several structural and residue-specific insights emphasizing the intricate nature of such protein-protein complexes. Our ML models, particularly the random forest regressor (RFR), accurately predicted binding affinities and ML-guided sequence predictions corroborated CPD findings, elucidating potential nsp7 mutations and their impact on binding affinity. Validation against clinical sequencing data demonstrated the predictive accuracy of our approach. Moreover, AlphaFold v2.0 structural analyses validated optimal dimeric configurations of affinity-enhancing designs, affirming methodological precision. Affinity-enhancing designs exhibited favourable energetics and higher binding affinity as compared to their counterparts. The obtained physicochemical properties, molecular interactions, and sequence predictions advance our understanding of SARS-CoV-2 evolution and inform potential avenues for therapeutic intervention against COVID-19.

Comprehensive analysis of liquid-liquid phase separation propensities of HSV-1 proteins and their interaction with host factors.

In recent years, it has been shown that the liquid-liquid phase separation (LLPS) of virus proteins plays a crucial role in their life cycle. It promotes the formation of viral replication organelles, concentrating viral components for efficient replication and facilitates the assembly of viral particles. LLPS has emerged as a crucial process in the replication and assembly of herpes simplex virus-1 (HSV-1). Recent studies have identified several HSV-1 proteins involved in LLPS, including the myristylated tegument protein UL11 and infected cell protein 4; however, a complete proteome-level understanding of the LLPS-prone HSV-1 proteins is not available. We provide a comprehensive analysis of the HSV-1 proteome and explore the potential of its proteins to undergo LLPS. By integrating sequence analysis, prediction algorithms and an array of tools and servers, we identified 10 HSV-1 proteins that exhibit high LLPS potential. By analysing the amino acid sequences of the LLPS-prone proteins, we identified specific sequence motifs and enriched amino acid residues commonly found in LLPS-prone regions. Our findings reveal a diverse range of LLPS-prone proteins within the HSV-1, which are involved in critical viral processes such as replication, transcriptional regulation and assembly of viral particles. This suggests that LLPS might play a crucial role in facilitating the formation of specialized viral replication compartments and the assembly of HSV-1 virion. The identification of LLPS-prone proteins in HSV-1 opens up new avenues for understanding the molecular mechanisms underlying viral pathogenesis. Our work provides valuable insights into the LLPS landscape of HSV-1, highlighting potential targets for further experimental validation and enhancing our understanding of viral replication and pathogenesis.

An integrated computational pipeline for designing high-affinity nanobodies with expanded genetic codes.

Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson's r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody's structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

Multi-pathogen based chimeric vaccine to fight against COVID-19 and concomitant coinfections.

BACKGROUND: COVID-19 has proved to be a fatal disease of the year 2020, due to which thousands of people globally have lost their lives, and still, the infection cases are at a high rate. Experimental studies suggested that SARS-CoV-2 interacts with various microorganisms, and this coinfection is accountable for the augmentation of infection severity. METHODS AND RESULTS: In this study, we have designed a multi-pathogen vaccine by involving the immunogenic proteins from S. pneumonia, H. influenza, and M. tuberculosis, as they are dominantly associated with SARS-CoV-2. A total of 8 antigenic protein sequences were selected to predict B-cell, HTL, and CTL epitopes restricted to the most prevalent HLA alleles. The selected epitopes were antigenic, non-allergenic, and non-toxic and were linked with adjuvant and linkers to make the vaccine protein more immunogenic, stable, and flexible. The tertiary structure, Ramachandran plot, and discontinuous B-cell epitopes were predicted. Docking and MD simulation study has shown efficient binding of the chimeric vaccine with the TLR4 receptor. CONCLUSION: The in silico immune simulation analysis has shown a high level of cytokines and IgG after a three-dose injection. Hence, this strategy could be a better way to decrease the disease's severity and could be used as a weapon to prevent this pandemic.

Hotspot residues and resistance mutations in the nirmatrelvir-binding site of SARS-CoV-2 main protease: Design, identification, and correlation with globally circulating viral genomes.

Shortly after the onset of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has acquired numerous variations in its intracellular proteins to adapt quickly, become more infectious, and ultimately develop drug resistance by mutating certain hotspot residues. To keep the emerging variants at bay, including Omicron and subvariants, FDA has approved the antiviral nirmatrelvir for mild-to-moderate and high-risk COVID-19 cases. Like other viruses, SARS-CoV-2 could acquire mutations in its main protease (Mpro) to adapt and develop resistance against nirmatrelvir. Employing a unique high-throughput protein design technique, the hotspot residues, and signatures of adaptation of Mpro having the highest probability of mutating and rendering nirmatrelvir ineffective were identified. Our results show that ∼40% of the designed mutations in Mpro already exist in the globally circulating SARS-CoV-2 lineages and several predicted mutations. Moreover, several high-frequency, designed mutations were found to be in corroboration with the experimentally reported nirmatrelvir-resistant mutants and are naturally occurring. Our work on the targeted design of the nirmatrelvir-binding site offers a comprehensive picture of potential hotspot sites and resistance mutations in Mpro and is thus crucial in comprehending viral adaptation, robust antiviral design, and surveillance of evolving Mpro variations.

Scanning the RBD-ACE2 molecular interactions in Omicron variant.

The emergence of new SARS-CoV-2 variants poses a threat to the human population where it is difficult to assess the severity of a particular variant of the virus. Spike protein and specifically its receptor binding domain (RBD) which makes direct interaction with the ACE2 receptor of the human has shown prominent amino acid substitutions in most of the Variants of Concern. Here, by using all-atom molecular dynamics simulations we compare the interaction of Wild-type RBD/ACE2 receptor complex with that of the latest Omicron variant of the virus. We observed a very interesting diversification of the charge, dynamics and energetics of the protein complex formed upon mutations. These results would help us in understanding the molecular basis of binding of the Omicron variant with that of SARS-CoV-2 Wild-type.

High-throughput design of symmetrical dimeric SARS-CoV-2 main protease: structural and physical insights into hotspots for adaptation and therapeutics.

Dimerization of SARS-CoV-2 main protease (Mpro) is a prerequisite for its processing activity. With >2000 mutations already reported in Mpro, SARS-CoV-2 may accumulate mutations in the Mpro dimeric interface to stabilize it further. We employed high-throughput protein design strategies to design the symmetrical dimeric interface of Mpro (300 000 designs) to identify mutational hotspots that render the Mpro more stable. We found that ∼22% of designed mutations that yield stable Mpro dimers already exist in SARS-CoV-2 genomes and are currently circulating. Our multi-parametric analyses highlight potential Mpro mutations that SARS-CoV-2 may develop, providing a foundation for assessing viral adaptation and mutational surveillance.

Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase.

The extant complex proteins must have evolved from ancient short and simple ancestors. The double-ψ ß-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its plausible evolutionary pathway started by "interlacing homodimerization" of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

Targeted design of drug binding sites in the main protease of SARS-CoV-2 reveals potential signatures of adaptation.

Several existing drugs are currently being tested worldwide to treat COVID-19 patients. Recent data indicate that SARS-CoV-2 is rapidly evolving into more transmissible variants. It is therefore highly possible that SARS-CoV-2 can accumulate adaptive mutations modulating drug susceptibility and hampering viral antigenicity. Thus, it is vital to predict potential non-synonymous mutation sites and predict the evolution of protein structural modifications leading to drug tolerance. As two FDA-approved anti-hepatitis C virus (HCV) drugs, boceprevir, and telaprevir, have been shown to effectively inhibit SARS-CoV-2 by targeting the main protease (Mpro), here we used a high-throughput interface-based protein design strategy to identify mutational hotspots and potential signatures of adaptation in these drug binding sites of Mpro. Several mutants exhibited reduced binding affinity to these drugs, out of which hotspot residues having a strong tendency to undergo positive selection were identified. The data further indicated that these anti-HCV drugs have larger footprints in the mutational landscape of Mpro and hence encompass the highest potential for positive selection and adaptation. These findings are crucial in understanding the potential structural modifications in the drug binding sites of Mpro and thus its signatures of adaptation. Furthermore, the data could provide systemic strategies for robust antiviral design and discovery against COVID-19 in the future.

Design of a peptide-based subunit vaccine against novel coronavirus SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan, China, and has subsequently spread worldwide. In the absence of any antiviral or immunomodulatory therapies, the disease is spreading at an alarming rate. A possibility of a resurgence of COVID-19 in places where lockdowns have already worked is also developing. Thus, for controlling COVID-19, vaccines may be a better option than drugs. An mRNA-based anti-COVID-19 candidate vaccine has entered a phase 1 clinical trial. However, its efficacy and potency have to be evaluated and validated. Since vaccines have high failure rates, as an alternative, we are presenting a new, designed multi-peptide subunit-based epitope vaccine against COVID-19. The recombinant vaccine construct comprises an adjuvant, cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes joined by linkers. The computational data suggest that the vaccine is non-toxic, non-allergenic, thermostable, with the capability to elicit a humoral and cell-mediated immune response. The stabilization of the vaccine construct is validated with molecular dynamics simulation studies. This unique vaccine is made up of 33 highly antigenic epitopes from three proteins that have a prominent role in host-receptor recognition, viral entry, and pathogenicity. We advocate this vaccine must be synthesized and tested urgently as a public health priority.

Insights into the role of d-amino acid oxidase mutations in amyotrophic lateral sclerosis.

Missense mutations in the coding region of d-amino acid oxidase (DAO) have been found in patients suffering from amyotrophic lateral sclerosis (ALS). Mutations primarily impair the enzymatic activity of DAO and cause neurodegeneration due to an abnormal accumulation of d-serine in the spinal cord. However, the structural and dynamic changes that lead to impaired enzymatic activity are not fully understood. We present here extensive molecular dynamics simulations of wild-type, and all reported ALS-associated DAO mutants to elucidate the plausible mechanisms of impaired enzymatic activity, a critical function needed for neuroprotection. Simulation results show that DAO mutations disrupt several key interactions with the active site residues and decrease the conformational flexibility of active site loop comprising 216 to 228 residues, necessary for substrate binding and product release. This conformational restriction of the active site loop in the mutants is mainly due to the distortion of critical salt bridge and hydrogen bond interactions compared with wild-type. Furthermore, binding free energy calculations show that DAO mutants have a lower binding affinity toward cofactor flavin adenine dinucleotide and substrate imino-serine than the wild-type. A closer look at the cofactor and substrate interaction profiles further show that DAO mutants have lost several critical interactions with the neighboring residues as compared with wild-type. Taken together, this study provides first-hand explanation of crucial structural features that lead to the loss of enzymatic function in DAO mutants and highlights the need of further genomic scans of patients with ALS to map the association of novel DAO variants in ALS pathophysiology.

Identification and characterization of novel and rare susceptible variants in Indian amyotrophic lateral sclerosis patients.

Rare missense variants play a crucial role in amyotrophic lateral sclerosis (ALS) pathophysiology. We report rare/novel missense variants from 154 Indian ALS patients, identified through targeted sequencing of 25 ALS-associated genes. As pathogenic variants could explain only a small percentage of ALS pathophysiology in our cohort, we investigated the frequency of tolerated and benign novel/rare variants, which could be potentially ALS susceptible. These variants were identified in 5.36% (8/149) of sporadic ALS (sALS) cases; with one novel variant each in ERBB4, SETX, DCTN1, and MATR3; four rare variants, one each in PON2 and ANG and two different rare variants in SETX. Identified variants were either absent or present at extremely rare frequencies (MAF < 0.01) in large population databases and were absent in 50 healthy controls sequenced through Sanger method. Furthermore, an oligogenic basis of ALS was observed in three sALS, with co-occurrence of intermediate-length repeat expansions in ATXN2 and a rare/novel variant in DCTN1 and SETX genes. Additionally, molecular dynamics and biochemical functional analysis of an angiogenin variant (R21G) identified from our cohort demonstrated loss of ribonucleolytic and nuclear translocation activities. Our findings suggest that rare variants could be potentially pathogenic and functional studies are warranted to decisively establish the pathogenic mechanisms associated with them.

Rare Angiogenin and Ribonuclease 4 variants associated with amyotrophic lateral sclerosis exhibit loss-of-function: a comprehensive in silico study.

Amyotrophic Lateral Sclerosis (ALS), a debilitating neurodegenerative disorder is related to mutations in a number of genes, and certain genes of the Ribonuclease (RNASE) superfamily trigger ALS more frequently. Even though missense mutations in Angiogenin (ANG) and Ribonuclease 4 (RNASE4) have been previously shown to cause ALS through loss-of-function mechanisms, understanding the role of rare variants with a plausible explanation of their functional loss mechanisms is an important mission. The study aims to understand if any of the rare ANG and RNASE4 variants catalogued in Project MinE consortium caused ALS due to loss of ribonucleolytic or nuclear translocation or both these activities. Several in silico analyses in combination with extensive molecular dynamics (MD) simulations were performed on wild-type ANG and RNASE4, along with six rare variants (T11S-ANG, R122H-ANG, D2E-RNASE4, N26K-RNASE4, T79A-RNASE4 and G119S-RNASE4) to study the structural and dynamic changes in the catalytic triad and nuclear localization signal residues responsible for ribonucleolytic and nuclear translocation activities respectively. Our comprehensive analyses comprising 1.2 µs simulations with a focus on physicochemical, structural and dynamic properties reveal that T11S-ANG, N26K-RNASE4 and T79A-RNASE4 variants would result in loss of ribonucleolytic activity due to conformational switching of catalytic His114 and His116 respectively but none of the variants would lose their nuclear translocation activity. Our study not only highlights the importance of rare variants but also demonstrates that elucidating the structure-function relationship of mutant effectors is crucial to gain insights into ALS pathophysiology and in developing effective therapeutics.

The VP4 peptide of hepatitis A virus ruptures membranes through formation of discrete pores.

UNLABELLED: Membrane-active peptides, components of capsid structural proteins, assist viruses in overcoming the host membrane barrier in the initial stages of infection. Several such peptides have been identified, and their roles in membrane fusion or disruption have been characterized through biophysical studies. In several members of the Picornaviridae family, the role of the VP4 structural peptide in cellular-membrane penetration is well established. However, there is not much information on the membrane-penetrating capsid components of hepatitis A virus (HAV), an unusual member of this family. The VP4 peptide of HAV differs from its analogues in other picornaviruses in being significantly shorter in length and in lacking a signal for myristoylation, thought to be a critical requisite for VP4-mediated membrane penetration. Here we report, for the first time, that the atypical VP4 in HAV contains significant membrane-penetrating activity. Using a combination of biophysical assays and molecular dynamics simulation studies, we show that VP4 integrates into membrane vesicles through its N-terminal region to finally form discrete pores of 5- to 9-nm diameter, which induces leakage in the vesicles without altering their overall size or shape. We further demonstrate that the membrane activity of VP4 is specific toward vesicles mimicking the lipid content of late endosomes at acidic pH. Taken together, our data indicate that VP4 might be essential for the penetration of host endosomal membranes and release of the viral genome during HAV entry. IMPORTANCE: Hepatitis A virus causes acute hepatitis in humans through the fecal-oral route and is particularly prevalent in underdeveloped regions with poor hygienic conditions. Although a vaccine for HAV exists, its high cost makes it unsuitable for universal application in developing countries. Studies on host-virus interaction for HAV have been hampered due to a lack of starting material, since the virus is extremely slow growing in culture. Among the unknown aspects of the HAV life cycle is its manner of host membrane penetration, which is one of the most important initial steps in viral infection. Here, we present data to suggest that a small peptide, VP4, a component of the HAV structural polyprotein, might be essential in helping the viral genome cross cell membranes during entry. It is hoped that this work might help in elucidating the manner of initial host cell interaction by HAV.

Uncovering the secrets of resistance: An introduction to computational methods in infectious disease research.

Antimicrobial resistance (AMR) is a growing global concern with significant implications for infectious disease control and therapeutics development. This chapter presents a comprehensive overview of computational methods in the study of AMR. We explore the prevalence and statistics of AMR, underscoring its alarming impact on public health. The role of AMR in infectious disease outbreaks and its impact on therapeutics development are discussed, emphasizing the need for novel strategies. Resistance mutations are pivotal in AMR, enabling pathogens to evade antimicrobial treatments. We delve into their importance and contribution to the spread of AMR. Experimental methods for quantitatively evaluating resistance mutations are described, along with their limitations. To address these challenges, computational methods provide promising solutions. We highlight the advantages of computational approaches, including rapid analysis of large datasets and prediction of resistance profiles. A comprehensive overview of computational methods for studying AMR is presented, encompassing genomics, proteomics, structural bioinformatics, network analysis, and machine learning algorithms. The strengths and limitations of each method are briefly outlined. Additionally, we introduce ResScan-design, our own computational method, which employs a protein (re)design protocol to identify potential resistance mutations and adaptation signatures in pathogens. Case studies are discussed to showcase the application of ResScan in elucidating hotspot residues, understanding underlying mechanisms, and guiding the design of effective therapies. In conclusion, we emphasize the value of computational methods in understanding and combating AMR. Integration of experimental and computational approaches can expedite the discovery of innovative antimicrobial treatments and mitigate the threat posed by AMR.

NeuroDNet - an open source platform for constructing and analyzing neurodegenerative disease networks.

BACKGROUND: Genetic networks control cellular functions. Aberrations in normal cellular function are caused by mutations in genes that disrupt the fine tuning of genetic networks and cause disease or disorder. However, the large number of signalling molecules, genes and proteins that constitute such networks, and the consequent complexity of interactions, has restrained progress in research elucidating disease mechanisms. Hence, carrying out a systematic analysis of how diseases alter the character of these networks is important. We illustrate this through our work on neurodegenerative disease networks. We created a database, NeuroDNet, which brings together relevant information about signalling molecules, genes and proteins, and their interactions, for constructing neurodegenerative disease networks. DESCRIPTION: NeuroDNet is a database with interactive tools that enables the creation of interaction networks for twelve neurodegenerative diseases under one portal for interrogation and analyses. It is the first of its kind, which enables the construction and analysis of neurodegenerative diseases through protein interaction networks, regulatory networks and Boolean networks. The database has a three-tier architecture - foundation, function and interface. The foundation tier contains the human genome data with 23857 protein-coding genes linked to more than 300 genes reported in clinical studies of neurodegenerative diseases. The database architecture was designed to retrieve neurodegenerative disease information seamlessly through the interface tier using specific functional information. Features of this database enable users to extract, analyze and display information related to a disease in many different ways. CONCLUSIONS: The application of NeuroDNet was illustrated using three case studies. Through these case studies, the construction and analyses of a PPI network for angiogenin protein in amyotrophic lateral sclerosis, a signal-gene-protein interaction network for presenilin protein in Alzheimer's disease and a Boolean network for a mammalian cell cycle was demonstrated. NeuroDNet is accessible at http://bioschool.iitd.ac.in/NeuroDNet/.