Optimal timing of a colonoscopy screening schedule depends on adenoma detection, adenoma risk, adherence to screening and the screening objective: A microsimulation study.

Colonoscopy-based screening provides protection against colorectal cancer (CRC), but the optimal starting age and time intervals of screening colonoscopies are unknown. We aimed to determine an optimal screening schedule for the US population and its dependencies on the objective of screening (life years gained or incidence, mortality, or cost reduction) and the setting in which screening is performed. We used our established open-source microsimulation model CMOST to calculate optimized colonoscopy schedules with one, two, three or four screening colonoscopies between 20 and 90 years of age. A single screening colonoscopy was most effective in reducing life years lost from CRC when performed at 55 years of age. Two, three and four screening colonoscopy schedules saved a maximum number of life years when performed between 49-64 years; 44-69 years; and 40-72 years; respectively. However, for maximum incidence and mortality reduction, screening colonoscopies needed to be scheduled 4-8 years later in life. The optimum was also influenced by adenoma detection efficiency with lower values for these parameters favoring a later starting age of screening. Low adherence to screening consistently favored a later start and an earlier end of screening. In a personalized approach, optimal screening would start earlier for high-risk patients and later for low-risk individuals. In conclusion, our microsimulation-based approach supports colonoscopy screening schedule between 45 and 75 years of age but the precise timing depends on the objective of screening, as well as assumptions regarding individual CRC risk, efficiency of adenoma detection during colonoscopy and adherence to screening.

Base-promoted cyclization of ortho-hydroxyacetophenones with in situ generated cyclopropenes: diastereoselective access to spirobenzo[b]oxepines and related precursors.

An unprecedented [5 + 2] spirocyclization route to obtain a vital class of functionalized spirobenzo[b]oxepine-cyclopropanes in good to high yields with excellent diastereoselectivities is reported. This domino reaction proceeds through a regioselective oxa-Michael addition of ortho-hydroxyacetophenones as 1,5-binucleophiles to in situ produced highly reactive cyclopropenes from 2-aroyl-1-chlorocyclopropanecarboxylates triggered by Cs2CO3 and the subsequent intramolecular aldol reaction under heating conditions, enabling the formation of new C-O and C-C bonds for benzo[b]oxepine ring synthesis. Moreover, at ambient temperature, the above C-O/C-C bond-forming event takes place preferentially via a [4 + 2] annulation path over a spirocyclization route, leading to substituted fused-cyclopropanes with good diastereoselectivities. Gratifyingly, further alterations of the obtained spirobenzo[b]oxepines and tetrahydrocyclopropa[b]chromenes afford fascinating classes of 4H-chromen-4-ones and cyclopenta[c]chromenes, respectively, under metal-free conditions.

Swapping Copper-Catalytic Process: Selective Access to Pyrazoles and Conjugated Ketimines from Oxime Acetates and Cyclic Sulfamidate Imines.

A powerful CuCl-catalyzed sequential one-pot reaction of aryl methyl ketoxime acetates with cyclic N-sulfonyl imines followed by elimination in the presence of base is reported. This hydrazine-free method conveniently makes C-C and N-N bonds via a radical cleavage of the N-O bond, delivering a special class of C3-hydroxyarylated pyrazoles in good yields. Surprisingly, while employing CuI as a catalyst instead of CuCl, the reaction proceeds through a non-radical pathway which embodies a new tactic for the high-yielding access to value-added conjugated N-unsubstituted ketimines. Moreover, additive-free approach to sulfamidate-fused-pyrazoles was achieved by successfully catalyzing addition and oxidative N-N bond-making reactions by CuI and CuCl, respectively. Significantly, our novel technique could convert the prepared ketimines into the pharmacologically recognized 6H-benzo[c]chromene frameworks.

Using Atomistic Simulations to Explore the Role of Methylation and ATP in Chemotaxis Signal Transduction.

A bacterial chemotaxis mechanism is activated when nutrients bind to surface receptors. The sequence of intra- and interprotein events in this signal cascade from the receptors to the eventual molecular motors has been clearly identified. However, the atomistic details remain elusive, as in general may be expected of intraprotein signal transduction pathways, especially when fibrillar proteins are involved. We performed atomistic calculations of the methyl accepting chemoprotein (MCP)-CheA-CheW multidomain complex from Escherichia coli, simulating the methylated and unmethylated conditions in the chemoreceptors and the ATP-bound and apo conditions of the CheA. Our results indicate that these atomistic simulations, especially with one of the two force fields we tried, capture several relevant features of the downstream effects, such as the methylation favoring an intermediate structure that is more toward a dipped state and increases the chance of ATP hydrolysis. The results thus suggest the sensitivity of the model to reflect the nutrient signal response, a nontrivial validation considering the complexity of the system, encouraging even more detailed studies on the thermodynamic quantification of the effects and the identification of the signaling networks.

A substrate-dependent reaction of 1-aryl-2-alkyl-1,2-diketones with 2-aroyl-1-chlorocyclopropanecarboxylates: selective access to 2',5'-dicyclopropoxy-1,1':4',1''-teraryls and pentafulvenes.

An interesting substrate-controlled one-pot approach to highly substituted 2',5'-dicyclopropoxy-1,1':4',1''-teraryls and 6-hydroxypentafulvenes involving various 1,2-diketones and 2-aroyl-1-chlorocyclopropanecarboxylates as 3C Michael acceptors triggered by Cs2CO3 has been developed. We noticed that 1,2-diketones play a decisive role in this reaction to determine the product's selectivity. For example, aryl rings having electron-poor functionalities at the para and meta-positions of 1,2-diketones led to 2,5-diarylhydroquinones selectively via a cyclodimerization/double oxa-Michael process with highly strained cyclopropenes. However, when 1-naphthyl/electron-donating aryl/ortho-aryl-substituted 1,2-diketones were chosen, the Michael-initiated ring expansion reaction (C-C and CC bonds) took place under the same conditions that gave the corresponding pentafulvenes predominately. Moreover, this reaction has several imperative features such as good to high diastereoselectivities, wide substrate scope, good functional group tolerance, transition metal-free process, etc.

Early Bioinformatic Implication of Triacidic Amino Acid Motifs in Autophagy-Dependent Unconventional Secretion of Mammalian Proteins.

Several proteins are secreted outside the cell, and in many cases, they may be identified by a characteristic signal peptide. However, more and more studies point to the evidence for an "unconventional" secretion, where proteins without a hitherto unknown signal are secreted, possibly in conditions of starvation. In this work, we analyse a set of 202 RNA binding mammalian proteins, whose unconventional secretion has recently been established. Analysis of these proteins secreted by LC3 mediation, the largest unconventionally secreted dataset to our knowledge, identifies the role of KKX motif as well as triacidic amino acid motif in unconventional secretion, the latter being an extension of the recent implicated diacidic amino acid motif. Further data analysis evolves a hypothesis on the sequence or structural proximity of the triacidic or KKX motifs to the LC3 interacting region, and a phosphorylatable amino acid such as serine as a statistically significant feature among these unconventionally secreted proteins. This hypothesis, although needs to be validated in experiments that challenge the specific details of each of these aspects, appears to be one of the early steps in defining what may be a plausible signal for unconventional protein secretion.

Cu(OAc)2/DABCO-mediated domino reaction of vinyl malononitriles with cyclic sulfamidate imines: access to 6-hydroxyaryl-2-aminonicotinonitriles.

A novel Cu(II)-salt/DABCO-mediated one-pot access to a myriad of highly substituted biologically relevant 2-aminonicotinonitriles possessing a resourceful phenolic moiety with satisfactory yields is reported. This method involves cyclic sulfamidate imines as 1C1N sources and different kinds of acyclic/cyclic vinyl malononitriles as 4C sources for pyridine synthesis via a vinylogous Mannich-cycloaromatization sequence process, creating two new C-N bonds under mild conditions. Importantly, this de novo strategy is applicable to gram-scale syntheses, underlining the method's practicability and allowing for a wide range of substrates with excellent functional group tolerance.

Diastereoselective desymmetrization reactions of prochiral para-quinamines with cyclopropenes generated in situ: access to fused hydroindol-5-one scaffolds.

Interesting desymmetric [3 + 2] annulation reactions between p-quinamines as prochiral N-donors and 2-aroyl-1-chlorocyclopropanecarboxylates facilitated by a base are reported. This successive double Michael reaction delivered a unique class of cyclopropane-fused hydoindol-5-one frameworks, each having four contiguous stereogenic centers, with three of them being fully substituted. Moreover, this method was found to provide acceptable chemical yields with promising diastereoselectivities (dr of up to ≤95 : 5) and to work with a variety of substrates. Importantly, a polycyclic tacrine analogue used to treat Alzheimer's disease was synthesized using our developed method.

Substrate-Controlled Domino Reaction of N-Sulfonyl Ketimines with 2-Aroyl-1-chlorocyclopropanecarboxylates: Access to Cyclopenta[c]chromenes and Benzo[f]cyclopenta[d][1,2]thiazepine Dioxides.

An unprecedented substrate-controlled annulation method for the synthesis of fascinating classes of angularly fused cyclopenta[c]chromenes and benzo[f]cyclopenta[d][1,2]thiazepine 5,5-dioxide derivatives in good to high chemical yields is reported. This Michael-initiated ring-expansion reaction would enable two C-C and one C-O or C-N bonds by a judicious choice of carbonucleophiles, either 4-alkyl or 3-alkyl-substituted N-sulfonyl ketimines, respectively, with a series of donor-acceptor cyclopropane scaffolds as 4C sources promoted by DBU. Moreover, this eco-friendly method is mild enough to protect different kinds of functionalities. Importantly, the prepared fused fulvene scaffolds were smoothly transformed into a special class of hexahydrocyclopenta[c]chromenes as single cis-cis-cis-cis diastereomers in excellent yields by a simple catalytic hydrogenation reaction.

Disentangling the Contribution of Each Descriptive Characteristic of Every Single Mutation to Its Functional Effects.

Mutational effects predictions continue to improve in accuracy as advanced artificial intelligence (AI) algorithms are trained on exhaustive experimental data. The next natural questions to ask are if it is possible to gain insights into which attribute of the mutation contributes how much to the mutational effects and if one can develop universal rules for mapping the descriptors to mutational effects. In this work, we mainly address the former aspect using a framework of interpretable AI. Relations between the physicochemical descriptors and their contributions to the mutational effects are extracted by analyzing the data on 29,832 variants from eight systematic deep mutational scan studies. An opposite trend in the dependence of fitness and solubility on the distance of the amino acid from the catalytic sites could be extracted and quantified. The dependence of the mutational effect contributions on the position-specific scoring matrix (PSSM) score for the amino acid after mutation or the BLOSUM score of the substitution showed universal trends. Our attempts in the present work to explain the quantitative differences in the dependence on conservation and SASA across proteins were not successful. The work nevertheless brings transparency into the predictions and development of rules, and will hopefully lead to empirically uncovering the universalities among these rules.

Estimating the herd immunity threshold by accounting for the hidden asymptomatics using a COVID-19 specific model.

A quantitative COVID-19 model that incorporates hidden asymptomatic patients is developed, and an analytic solution in parametric form is given. The model incorporates the impact of lock-down and resulting spatial migration of population due to announcement of lock-down. A method is presented for estimating the model parameters from real-world data, and it is shown that the various phases in the observed epidemiological data are captured well. It is shown that increase of infections slows down and herd immunity is achieved when active symptomatic patients are 10-25% of the population for the four countries we studied. Finally, a method for estimating the number of asymptomatic patients, who have been the key hidden link in the spread of the infections, is presented.

Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data.

Protein structure and function can be severely altered by even a single amino acid mutation. Predictions of mutational effects using extensive artificial intelligence (AI)-based models, although accurate, remain as enigmatic as the experimental observations in terms of improving intuitions about the contributions of various factors. Inspired by Lipinski's rules for drug-likeness, we devise simple thresholding criteria on five different descriptors such as conservation, which have so far been limited to qualitative interpretations such as high conservation implies high mutational effect. We analyze systematic deep mutational scanning data of all possible single amino acid substitutions on seven proteins (25153 mutations) to first define these thresholds and then to evaluate the scope and limits of the predictions. At this stage, the approach allows us to comment easily and with a low error rate on the subset of mutations classified as neutral or deleterious by all of the descriptors. We hope that complementary to the accurate AI predictions, these thresholding rules or their subsequent modifications will serve the purpose of codifying the knowledge about the effects of mutations.

Modelling a pandemic with asymptomatic patients, impact of lockdown and herd immunity, with applications to SARS-CoV-2.

The SARS-CoV-2 is a type of coronavirus that has caused the pandemic known as the Coronavirus Disease of 2019, or COVID-19. In traditional epidemiological models such as SEIR (Susceptible, Exposed, Infected, Removed), the exposed group E does not infect the susceptible group S. A distinguishing feature of COVID-19 is that, unlike with previous viral diseases, there is a distinct "asymptomatic" group A, which does not show any symptoms, but can nevertheless infect others, at the same rate as infected symptomatic patients. This situation is captured in a model known as SAIR (Susceptible, Asymptomatic, Infected, Removed), introduced in Robinson and Stillianakis (2013). The dynamical behavior of the SAIR model is quite different from that of the SEIR model. In this paper, we use Lyapunov theory to establish the global asymptotic stabililty of the SAIR model, both without and with vital dynamics. Then we develop compartmental SAIR models to cater to the migration of population across geographic regions, and once again establish global asymptotic stability. Next, we go beyond long-term asymptotic analysis and present methods for estimating the parameters in the SAIR model. We apply these estimation methods to data from several countries including India, and demonstrate that the predicted trajectories of the disease closely match actual data. We show that "herd immunity" (defined as the time when the number of infected persons is maximum) can be achieved when the total of infected, symptomatic and asymptomatic persons is as low as 25% of the population. Previous estimates are typically 50% or higher. We also conclude that "lockdown" as a way of greatly reducing inter-personal contact has been very effective in checking the progress of the disease.

Minimal and adaptive numerical strategy for critical resource planning in a pandemic.

Current epidemiological models can in principle model the temporal evolution of a pandemic. However, any such model will rely on parameters that are unknown, which in practice are estimated using stochastic and poorly measured quantities. As a result, an early prediction of the long-term evolution of a pandemic will quickly lose relevance, while a late model will be too late to be useful for disaster management. Unless a model is designed to be adaptive, it is bound either to lose relevance over time, or lose trust and thus not have a second chance for retraining. We propose a strategy for estimating the number of infections and the number of deaths, that does away with time-series modeling, and instead makes use of a "phase portrait approach." We demonstrate that, with this approach, there is a universality to the evolution of the disease across countries, that can then be used to make reliable predictions. These same models can also be used to plan the requirements for critical resources during the pandemic. The approach is designed for simplicity of interpretation, and adaptivity over time. Using our model, we predict the number of infections and deaths in Italy and New York State, based on an adaptive algorithm which uses early available data, and show that our predictions closely match the actual outcomes. We also carry out a similar exercise for India, where in addition to projecting the number of infections and deaths, we also project the expected range of critical resource requirements for hospitalizations in a location.

Deep2Full: Evaluating strategies for selecting the minimal mutational experiments for optimal computational predictions of deep mutational scan outcomes.

Performing a complete deep mutational scan with all single point mutations may not be practical, and may not even be required, especially if predictive computational models can be developed. Computational models are however naive to cellular response in the myriads of assay-conditions. In a realistic paradigm of assay context-aware predictive hybrid models that combine minimal experimental data from deep mutational scans with structure, sequence information and computational models, we define and evaluate different strategies for choosing this minimal set. We evaluated the trivial strategy of a systematic reduction in the number of mutational studies from 85% to 15%, along with several others about the choice of the types of mutations such as random versus site-directed with the same 15% data completeness. Interestingly, the predictive capabilities by training on a random set of mutations and using a systematic substitution of all amino acids to alanine, asparagine and histidine (ANH) were comparable. Another strategy we explored, augmenting the training data with measurements of the same mutants at multiple assay conditions, did not improve the prediction quality. For the six proteins we analyzed, the bin-wise error in prediction is optimal when 50-100 mutations per bin are used in training the computational model, suggesting that good prediction quality may be achieved with a library of 500-1000 mutations.

NH4OAc-Promoted Domino Route to Hydroxyarylated Unsymmetrical Pyridines under Neat Conditions.

A new metal-, oxidant-, and solvent-free ecofriendly domino method has been established for modular synthesis of a diverse range of medicinally promising hydroxyarylated unsymmetrical pyridines in good to high chemical yields with an excellent regioselectivity. This domino process involves a range of N-sulfonyl ketimines as C,N-binucleophiles, enolizable ketones, and aromatic/heteroaromatic aldehydes using ammonium acetate as an ideal promoter under neat conditions, which creates two new C-C bonds and one C-N bond. Notably, the neutral reaction conditions are mild enough to tolerate a range of functionalities and cover a variety of substrates, thus bestowing a powerful avenue to access tri- and tetrasubstituted pyridines including carbo- and heterocyclic fused ones. Interestingly, a practical, scalable, and high-yielding synthesis of pyridylphenol derivatives was successfully accomplished by our unique method.

Modelling the COVID-19 Pandemic: Asymptomatic Patients, Lockdown and Herd Immunity.

The SARS-Cov-2 is a type of coronavirus that has caused the COVID-19 pandemic. In traditional epidemiological models such as SEIR (Susceptible, Exposed, Infected, Removed), the exposed group E does not infect the susceptible group S. A distinguishing feature of COVID-19 is that, unlike with previous viruses, there is a distinct "asymptomatic" group A, who do not show any symptoms, but can nevertheless infect others, at the same rate as infected patients. This situation is captured in a model known as SAIR (Susceptible, Asymptomatic, Infected, Removed), introduced in Robinson and Stilianakis (2013). The dynamical behavior of the SAIR model is quite different from that of the SEIR model. In this paper, we use Lyapunov theory to establish the global asymptotic stabiilty of the SAIR model. Next, we present methods for estimating the parameters in the SAIR model. We apply these estimation methods to data from several countries including India, and show that the predicted trajectories of the disease closely match actual data.

Statistical characteristics of amino acid covariance as possible descriptors of viral genomic complexity.

At the sequence level it is hard to describe the complexity of viruses which allows them to challenge host immune system, some for a few weeks and others up to a complete compromise. Paradoxically, viral genomes are both complex and simple. Complex because amino acid mutation rates are very high, and yet viruses remain functional. Simple because they have barely around 10 types of proteins, so viral protein-protein interaction networks are not insightful. In this work we use fine-grained amino acid level information and their evolutionary characteristics obtained from large-scale genomic data to develop a statistical panel, towards the goal of developing quantitative descriptors for the biological complexity of viruses. Networks were constructed from pairwise covariation of amino acids and were statistically analyzed. Three differentiating factors arise: predominantly intra- vs inter-protein covariance relations, the nature of the node degree distribution and network density. Interestingly, the covariance relations were primarily intra-protein in avian influenza and inter-protein in HIV. The degree distributions showed two universality classes: a power-law with exponent -1 in HIV and avian-influenza, random behavior in human flu and dengue. The calculated covariance network density correlates well with the mortality strengths of viruses on the viral-Richter scale. These observations suggest the potential utility of the statistical metrics for describing the covariance patterns in viruses. Our host-virus interaction analysis point to the possibility that host proteins which can interact with multiple viral proteins may be responsible for shaping the inter-protein covariance relations. With the available data, it appears that network density might be a surrogate for the virus Richter scale, however the hypothesis needs a re-examination when large scale complete genome data for more viruses becomes available.

Computational screening of antimicrobial peptides for Acinetobacter baumannii.

Acinetobacter baumannii, has been developing resistance to even the last line of drugs. Antimicrobial peptides (AMPs) to which bacteria do not develop resistance easily may be the last hope. A few independent experimental studies have designed and studied the activity of AMPs on A. baumannii, however the number of such studies are still limited. With the goal of developing a rational approach to the screening of AMPs against A. baumannii, we carefully curated the drug activity data from 75 cationic AMPs, all measured with a similar protocol, and on the same ATCC 19606 strain. A quantitative model developed and validated with a part of the data. While the model may be used for predicting the activity of any designed AMPs, in this work, we perform an in silico screening for the entire database of naturally occurring AMPs, to provide a rational guidance in this urgently needed drug development.

Amino acid impact factor.

Amino acid mutations in proteins are random and those mutations which are beneficial or neutral survive during the course of evolution. Conservation or co-evolution analyses are performed on the multiple sequence alignment of homologous proteins to understand how important different amino acids or groups of them are. However, these traditional analyses do not explore the directed influence of amino acid mutations, such as compensatory effects. In this work we develop a method to capture the directed evolutionary impact of one amino acid on all other amino acids, and provide a visual network representation for it. The method developed for these directed networks of inter- and intra-protein evolutionary interactions can also be used for noting the differences in amino acid evolution between the control and experimental groups. The analysis is illustrated with a few examples, where the method identifies several directed interactions of functionally critical amino acids. The impact of an amino acid is quantified as the number of amino acids that are influenced as a consequence of its mutation, and it is intended to summarize the compensatory mutations in large evolutionary sequence data sets as well as to rationally identify targets for mutagenesis when their functional significance can not be assessed using structure or conservation.