Variant Effect Prediction in the Age of Machine Learning.

Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein "sentences"? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.

Investigation on Thermally Radiative Mixed Convective Flow of Carbon Nanotubes/Al2O3 Nanofluid in Water Past a Stretching Plate with Joule Heating and Viscous Dissipation.

The nature of this prevailing inquisition is to scrutinize the repercussion of MHD mixed convective flow of CNTs/Al2O3 nanofluid in water past a heated stretchy plate with injection/suction, heat consumption and radiation. The Joule heating and viscous dissipation are included in our investigation. The Navier-Stokes equations are implemented to frame the governing flow expressions. These flow expressions are non-dimensioned by employing suitable transformations. The converted flow expressions are computed numerically by applying the MATLAB bvp4c procedure and analytically by the HAM scheme. The impacts of relevant flow factors on fluid velocity, fluid temperature, skin friction coefficient, and local Nusselt number are illustrated via graphs, tables and charts. It is unequivocally shown that the fluid speed declines when escalating the size of the magnetic field parameter; however, it is enhanced by strengthening the Richardson number. The fluid warmness shows a rising pattern when enriching the Biot number and heat consumption/generation parameter. The findings conclusively demonstrate that the surface drag force improves for a larger scale of Richardson number and is suppressed when heightening the unsteady parameter. In addition, it is evident from the outcomes that the heat transfer gradient decreases to increase the quantity of the Eckert number in the convective heating case; however, the opposite nature is obtained in the convective cooling case. Our numerical results are novel, unique and applied in microfluid devices such as micro-instruments, sleeve electrodes, nerve growth electrodes, etc.

Ab-CoV: a curated database for binding affinity and neutralization profiles of coronavirus-related antibodies.

SUMMARY: We have developed a database, Ab-CoV, which contains manually curated experimental interaction profiles of 1780 coronavirus-related neutralizing antibodies. It contains more than 3200 datapoints on half maximal inhibitory concentration (IC50), half maximal effective concentration (EC50) and binding affinity (KD). Each data with experimentally known three-dimensional structures are complemented with predicted change in stability and affinity of all possible point mutations of interface residues. Ab-CoV also includes information on epitopes and paratopes, structural features of viral proteins, sequentially similar therapeutic antibodies and Collier de Perles plots. It has the feasibility for structure visualization and options to search, display and download the data. AVAILABILITY AND IMPLEMENTATION: Ab-CoV database is freely available at https://web.iitm.ac.in/bioinfo2/ab-cov/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Understanding the mutational frequency in SARS-CoV-2 proteome using structural features.

The prolonged transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in the human population has led to demographic divergence and the emergence of several location-specific clusters of viral strains. Although the effect of mutation(s) on severity and survival of the virus is still unclear, it is evident that certain sites in the viral proteome are more/less prone to mutations. In fact, millions of SARS-CoV-2 sequences collected all over the world have provided us a unique opportunity to understand viral protein mutations and develop novel computational approaches to predict mutational patterns. In this study, we have classified the mutation sites into low and high mutability classes based on viral isolates count containing mutations. The physicochemical features and structural analysis of the SARS-CoV-2 proteins showed that features including residue type, surface accessibility, residue bulkiness, stability and sequence conservation at the mutation site were able to classify the low and high mutability sites. We further developed machine learning models using above-mentioned features, to predict low and high mutability sites at different selection thresholds (ranging 5-30% of topmost and bottommost mutated sites) and observed the improvement in performance as the selection threshold is reduced (prediction accuracy ranging from 65 to 77%). The analysis will be useful for early detection of variants of concern for the SARS-CoV-2, which can also be applied to other existing and emerging viruses for another pandemic prevention.

Effect of charged mutation on aggregation of a pentapeptide: Insights from molecular dynamics simulations.

Aggregation of therapeutic monoclonal antibodies (mAbs) can negatively affect their chemistry, manufacturing, and control attributes and lead to undesirable immune responses in patients. Therefore, optimization of lead mAb drug candidates during discovery stages to mitigate aggregation is increasingly becoming an integral part of their developability assessments. The disruption of short sequence motifs called aggregation prone regions (APRs) found in amino acid sequences of mAb candidates can potentially mitigate their aggregation. In this work, we have performed molecular dynamics simulations to study the aggregation of an APR (VLVIY) found in λ light chains of human antibodies and its single point mutant KLVIY. Eighteen different multicopy peptide simulation systems of "VLVIY" and "KLVIY" were constructed by varying their concentrations, temperatures, termini capping, and flanking gate-keeper regions. Within 20 ns of the simulation, peptide "VLVIY" formed an aggregate of 100 peptides at ~0.1 M concentration with a 60% reduction in solvent accessible surface area (SASA). Furthermore, analysis of the SASA change, peptide cluster distribution, and water residence time demonstrated how Val ➔ Lys mutation resists aggregation and improves solubility. Presence of Lys slows down aggregation kinetics via charge-charge repulsions and by raising the kinetic barrier to formation of large oligomers. However, the effect of the Val ➔ Lys mutation is dependent on sequence and structural contexts around the APR. This mutation also alters the solvation shell around the peptide by favoring solute-solvent interactions, thereby increasing its solubility. This work has provided a detailed mechanistic explanation of how APR disruption can mitigate aggregation in biotherapeutics and improve their developability.

A novel hybrid SEIQR model incorporating the effect of quarantine and lockdown regulations for COVID-19.

Mitigating the devastating effect of COVID-19 is necessary to control the infectivity and mortality rates. Hence, several strategies such as quarantine of exposed and infected individuals and restricting movement through lockdown of geographical regions have been implemented in most countries. On the other hand, standard SEIR based mathematical models have been developed to understand the disease dynamics of COVID-19, and the proper inclusion of these restrictions is the rate-limiting step for the success of these models. In this work, we have developed a hybrid Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model to explore the influence of quarantine and lockdown on disease propagation dynamics. The model is multi-compartmental, and it considers everyday variations in lockdown regulations, testing rate and quarantine individuals. Our model predicts a considerable difference in reported and actual recovered and deceased cases in qualitative agreement with recent reports.

Exploring the sequence features determining amyloidosis in human antibody light chains.

The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, ß-propensity, etc.) calculated for the CDR, FR or VL regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called "VLAmY-Pred" ( https://web.iitm.ac.in/bioinfo2/vlamy-pred/ ). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.

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.

AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins.

Protein aggregation has two aspects, namely, mechanistic and kinetics. Understanding protein aggregation kinetics is critical for prediction of progression of diseases caused by amyloidosis, accumulation of aggregates in biotherapeutics during storage and engineering commercial nano-biomaterials. In this work, we have collected experimentally determined absolute protein aggregation rates and developed an SVM based regression model to predict absolute rates of protein and peptide aggregation near-physiological conditions. The regression model achieved a correlation coefficient of 0.72 with MAE of 0.91 (natural log of kapp, where kapp is in hour-1) using leave-one-out cross-validation on a dataset of 82 non-redundant proteins/peptides. The model accounts for the experimental conditions (such as temperature, pH, ionic and protein concentration) and sequence-based properties. The amino acid sequence features revealed by this model as being important for aggregation kinetics, are also associated with the aggregation mechanism. In particular, inherent aggregation propensity of the protein/peptide sequence and number of aggregation prone regions (APRs) unpunctuated by the gatekeeping residues, were found to play important roles in the prediction of the absolute aggregation rates. This analysis shows that mechanism and kinetics of protein aggregation are coupled via common sequence attributes. The aggregation kinetic prediction method developed in this work is available at https://web.iitm.ac.in/bioinfo2/absolurate-pred/index.html.

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.

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.

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.

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.

Interpretation of Kidney Biopsy in Indian Patients Older than 60 Years: A Tertiary Care Experience.

The adult population above the age of 60 years has significantly increased in India, with a life expectancy of 68.4 years in 2016. Data regarding the renal histopathology in these patients are scarce though the number of native kidney biopsies done in this subset of population is increasing. The present study is a retrospective analysis of 231 biopsies from a total of 700 biopsies, from patients above 60 years of age (M = 65.8%; F = 34.2%) with a mean age of 64 ± 6.03 years. The indications for kidney biopsy included nephrotic syndrome (NS) (30.4%), nephritic syndrome (19.1%), rapidly progressive renal failure (11.7%), acute kidney injury (AKI) (15.7%), and acute worsening of preexisting chronic kidney disease (CKD) (23%). The median percentage of glomerulosclerosis was 22% (5%-45%), and interstitial fibrosis and tubular atrophy was 30% (10%-50%). The most common cause for nephrotic syndrome was membranous nephropathy (31.4%) and for nephritic syndrome was benign arterionephrosclerosis (22.7%). Postinfectious glomerulonephritis (29.6%) was the leading cause for rapidly progressive renal failure. Acute injury on CKD was notable in patients with diabetic nephropathy (30.2%). The predominant causes for AKI were acute tubulointerstitial nephritis (33.3%), acute tubular necrosis (22.2%), and acute pyelonephritis (19.4%). The biopsy proven histopathological features enabled us in tailoring the therapy. None of the patients developed life-threatening complications following ultrasonography-guided biopsy.

Aggregation prone regions in human proteome: Insights from large-scale data analyses.

Protein aggregation leads to several burdensome human maladies, but a molecular level understanding of how human proteome has tackled the threat of aggregation is currently lacking. In this work, we survey the human proteome for incidence of aggregation prone regions (APRs), by using sequences of experimentally validated amyloid-fibril forming peptides and via computational predictions. While approximately 30 human proteins are currently known to be amyloidogenic, we found that 260 proteins (∼1% of human proteome) contain at least one experimentally validated amyloid-fibril forming segment. Computer predictions suggest that more than 80% of the human proteins contain at least one potential APR and approximately two-thirds (65%) contain two or more APRs; spanning 3-5% of their sequences. Sequence randomizations show that this apparently high incidence of APRs has been actually significantly reduced by unique amino acid composition and sequence patterning of human proteins. The human proteome has utilized a wide repertoire of sequence-structural optimization strategies, most of them already known, to minimize deleterious consequences due to the presence of APRs while simultaneously taking advantage of their order promoting properties. This survey also found that APRs tend to be located near the active and ligand binding sites in human proteins, but not near the post translational modification sites. The APRs in human proteins are also preferentially found at heterotypic interfaces rather than homotypic ones. Interestingly, this survey reveals that APRs play multiple, often opposing, roles in the human protein sequence-structure-function relationships. Insights gained from this work have several interesting implications towards novel drug discovery and development. Proteins 2017; 85:1099-1118. © 2017 Wiley Periodicals, Inc.

Synthesis and magnetic characterizations of La(1-x)Sr(x)MnO3 nanoparticles for biomedical applications.

The La(1-x)Sr(x)MnO3 (LSMO) nanoparticles have been synthesized by citric gel process followed by ball milling method. These nanoparticles demonstrated high crystalline quality. Nanoparticle size was further decreased by ball milling technique as observed by the field-emission scanning electron microscopic studies. The ball milled and silica coated LSMO nanoparticles show magnetic transition at about 370 K with a superparamagnetic properties. The ferromagnetic resonance (FMR) spectra analysis of LSMO nanoparticles shows large FMR linewidth due to the surface strain of the nanoparticles. Both magnetization and FMR studies demonstrate that the LSMO nanoparticles are highly anisotropic. The toxicity of the nanoparticles was studied for safe biomedical applications. Measurement of intracellular reactive oxygen species (ROS) and MTT assay results show that LSMO nanoparticles are relatively nontoxic and the toxicity is further reduced by SiO2 coating. These results are very important for applications in the field of biotechnology.