Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal.

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development. Availability & Implementation: The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

CRISPR/Cas9-based genome editing and functional analysis of SlHyPRP1 and SlDEA1 genes of Solanum lycopersicum L. in imparting genetic tolerance to multiple stress factors.

CRISPR/Cas is a breakthrough genome editing system because of its precision, target specificity, and efficiency. As a speed breeding system, it is more robust than the conventional breeding and biotechnological approaches for qualitative and quantitative trait improvement. Tomato (Solanum lycopersicum L.) is an economically important crop, but its yield and productivity have been severely impacted due to different abiotic and biotic stresses. The recently identified SlHyPRP1 and SlDEA1 are two potential negative regulatory genes in response to different abiotic (drought and salinity) and biotic stress (bacterial leaf spot and bacterial wilt) conditions in S. lycopersicum L. The present study aimed to evaluate the drought, salinity, bacterial leaf spot, and bacterial wilt tolerance response in S. lycopersicum L. crop through CRISPR/Cas9 genome editing of SlHyPRP1 and SlDEA1 and their functional analysis. The transient single- and dual-gene SlHyPRP1 and SlDEA1 CRISPR-edited plants were phenotypically better responsive to multiple stress factors taken under the study. The CRISPR-edited SlHyPRP1 and SlDEA1 plants showed a higher level of chlorophyll and proline content compared to wild-type (WT) plants under abiotic stress conditions. Reactive oxygen species accumulation and the cell death count per total area of leaves and roots under biotic stress were less in CRISPR-edited SlHyPRP1 and SlDEA1 plants compared to WT plants. The study reveals that the combined loss-of-function of SlHyPRP1 along with SlDEA1 is essential for imparting significant multi-stress tolerance (drought, salinity, bacterial leaf spot, and bacterial wilt) in S. lycopersicum L. The main feature of the study is the detailed genetic characterization of SlDEA1, a poorly studied 8CM family gene in multi-stress tolerance, through the CRISPR/Cas9 gene editing system. The study revealed the key negative regulatory role of SlDEA1 that function together as an anchor gene with SlHyPRP1 in imparting multi-stress tolerance in S. lycopersicum L. It was interesting that the present study also showed that transient CRISPR/Cas9 editing events of SlHyPRP1 and SlDEA1 genes were successfully replicated in stably generated parent-genome-edited line (GEd0) and genome-edited first-generation lines (GEd1) of S. lycopersicum L. With these upshots, the study's key findings demonstrate outstanding value in developing sustainable multi-stress tolerance in S. lycopersicum L. and other crops to cope with climate change.

Green synthesis of nanocellulose supported cu-bionanocomposites and their profound applicability in the synthesis of amide derivatives and controlling of food-borne pathogens.

Copper bionanocomposites (CBNCS) were synthesized using Ipomoea carnea- sourced nanocellulose as support via an eco-friendly and cost-effective method. X-ray Diffractometer (XRD) pattern of CBNCS confirmed the octahedral structure of Cu2O, the face-centered cubic (FCC) crystal structure of Cu(0). XRD also revealed the crystal lattice of cellulose II. Surface Electron Microscope (SEM) and Transmission Electron Microscope (TEM) revealed the uniform distribution of copper nanoparticles (Cu NPs) with an average size of 10 nm due to the presence of nanocellulose. X-ray photoelectron spectroscopy (XPS) provided information about the electronic, chemical state and elemental composition of CBNCS. Thermogravimetric Analysis (TGA) showed the thermal stability of CBNCS. CBNCS catalyzed the rearrangement of oximes to primary amides in a very mild condition with a high yield of up to 92 %. CBNCS effectively inhibited the growth of Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) with lower minimum inhibitory concentration MIC values. Antioxidant activity and electrical conductivity of CBNCS were also determined.

ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT.

The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers are actively involved in the development of innovative models and strategies for material design. Recent advancements in analytical tools, experimentation, and computer technology additionally enhance the material design possibilities. Notably, data-driven techniques like artificial intelligence and machine learning have achieved substantial progress in exploring various applications within material science. One such approach, ChatGPT, a large language model, holds transformative potential for addressing complex queries. In this article, we explore ChatGPT's understanding of material science by assigning some simple tasks across various subareas of computational material science. The findings indicate that while ChatGPT may make some minor errors in accomplishing general tasks, it demonstrates the capability to learn and adapt through human interactions. However, issues like output consistency, probable hidden errors, and ethical consequences should be addressed.

Analyzing the cation-aromatic interactions in proteins: Cation-aromatic database V2.0.

The cation-aromatic database (CAD) is a comprehensive repository of cation-aromatic motifs found in experimentally determined protein structures, first reported in 2007 [Proteins, 2007, 67, 1179]. The present article is an update of CAD that contains information of approximately 27.26 million cation-aromatic motifs. CAD uses three distance parameters (r, d1, and d2) to determine the position of the cation relative to the centroid of the aromatic residue and classifies the motifs as cation-π or cation-σ interactions. As of June 2023, about 193 936 protein structures were retrieved from Protein Data Bank, and this resulted in the identification of an impressive number of 27 255 817 cation-aromatic motifs. Among these motifs, spherical motifs constituted 94.09%, while cylindrical motifs made up the remaining 5.91%. When considering the interaction of metal ions with aromatic residues, 965 564 motifs are identified. Remarkably, 82.08% of these motifs involved the binding of metal ions to the amino acid HIS. Moreover, the analysis of binding preferences between cations and aromatic residues revealed that the HIS-HIS, PHE-ARG, and TRP-ARG pairs exhibited a preferential geometry. The motif pair HIS-HIS was the most prevalent, accounting for 19.87% of the total, closely followed by TYR-LYS at 10.17%. Conversely, the motif pair TRP-HIS had the lowest occurrence, representing only 4.20% of the total. The data generated help in revealing the characteristics and biological functions of cation-aromatic interactions in biological molecules. The updated version of CAD (Cation-Aromatic Database V2.0) can be accessed at https://acds.neist.res.in/cadv2.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL): a structure-based classification of the chemical space.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL) is an open-source Galaxy-based cheminformatics web portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, obtained from 42 publicly available databases and curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions, has been done. These are further subjected to 56-bit fingerprint-based classification algorithm which led to the formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put into 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-AadharID, for each of these 149,169,443 molecules. MPDS-AadharID is akin to the unique number given to citizens in India (similar to SSN in the US and NINO in the UK). The unique features of MPDS-CL are (a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; (b) automatic generation of information for the processing for MPDS and other galaxy tools; (c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and (d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at https://mpds.neist.res.in:8086/ .

Analyzing the aromatic-aromatic interactions in proteins: A2ID 2.0.

The Aromatic-Aromatic Interactions Database (A2ID) is a comprehensive repository dedicated to documenting aromatic-aromatic (π-π) networks observed in experimentally determined protein structures. The first version of A2ID was reported in 2011 [Int J Biol Macromol, 2011, 48, 540]. It has undergone a series of significant updates, leading to its current version, which focuses on the identification and analysis of 3,444,619 π-π networks from proteins. The geometrical parameters such as centroid-centroid distances (r) and interplanar angles (ϕ) were used to identify and characterize π-π networks. It was observed that among the 84,500 proteins with at least one aromatic π-π network, about 92.50 % of the instances are found to be either 2π (77.34 %) or 3π (15.23 %) networks. The analysis of interacting amino acid pairs in 2π networks indicated a dominance of PHE residues followed by TYR. The updated version of A2ID incorporates analysis of π-π networks based on SCOP2 and ECOD classifiers, in addition to the existing SCOP, CATH, and EC classifications. This expanded scope allows researchers to explore the characteristics and functional implications of π-π networks in protein structures from multiple perspectives. The current version of A2ID along with its extensive dataset and detailed geometric information is publicly accessible using https://acds.neist.res.in/a2idv2.

Controlled Ni doping on a g-C3N4/CuWO4 photocatalyst for improved hydrogen evolution.

The development of a low-cost, environment-friendly and suitable semiconductor-based heterogeneous photocatalyst poses a great challenge towards extremely competent and substantial hydrogen evolution. A series of environment-friendly and proficient S-scheme Ni-doped CuWO4 nanocrystals supported on g-C3N4 nanocomposites (Ni-CuWO4/g-C3N4) were constructed to ameliorate the photocatalytic efficacy of pure g-C3N4 and Ni-CuWO4 and their activity in H2 generation through photocatalytic water splitting was evaluated. The Ni-CuWO4 nanoparticles were synthesized through doping of Ni2+ on wolframite CuWO4 crystals via the chemical precipitation method. An elevated hydrogen generation rate of 1980 µmol h-1 g-1 was accomplished over the 0.2Ni-CuWO4/g-C3N4 (0.2NCWCN) nanocomposite with an apparent quantum yield (AQY) of 6.49% upon visible light illumination (λ ≥ 420 nm), which is evidently 7.1 and 17.2 fold higher than those produced from pristine g-C3N4 and Ni-CuWO4. The substantial enhancement in the photocatalytic behaviour is primarily because of the large surface area, limited band gap energy of the semiconductor composite and magnified light harvesting capability towards visible light through the inclusion of g-C3N4, thus diminishing the reassembly rate of photoinduced excitons. Further, density functional theory (DFT) calculations were performed to investigate the structural, electronic and optical properties of the composite. Theoretical results confirmed that the Ni-CuWO4/g-C3N4 composite is a potential candidate for visible-light-driven photocatalysts and corroborated with the experimental findings. This research provides a meaningful and appealing perspective on developing cost-effective and very proficient two-dimensional (2D) g-C3N4-based materials for photocatalytic H2 production to accelerate the separation and transmission process of radiative charge carriers.

Polypharmacology guided drug repositioning approach for SARS-CoV2.

Drug repurposing has emerged as an important strategy and it has a great potential in identifying therapeutic applications for COVID-19. An extensive virtual screening of 4193 FDA approved drugs has been carried out against 24 proteins of SARS-CoV2 (NSP1-10 and NSP12-16, envelope, membrane, nucleoprotein, spike, ORF3a, ORF6, ORF7a, ORF8, and ORF9b). The drugs were classified into top 10 and bottom 10 drugs based on the docking scores followed by the distribution of their therapeutic indications. As a result, the top 10 drugs were found to have therapeutic indications for cancer, pain, neurological disorders, and viral and bacterial diseases. As drug resistance is one of the major challenges in antiviral drug discovery, polypharmacology and network pharmacology approaches were employed in the study to identify drugs interacting with multiple targets and drugs such as dihydroergotamine, ergotamine, bisdequalinium chloride, midostaurin, temoporfin, tirilazad, and venetoclax were identified among the multi-targeting drugs. Further, a pathway analysis of the genes related to the multi-targeting drugs was carried which provides insight into the mechanism of drugs and identifying targetable genes and biological pathways involved in SARS-CoV2.

Estimation of genetic variation in yield, its contributing characters and capsaicin content of Capsicum chinense Jacq. (ghost pepper) germplasm from Northeast India.

Capsicum chinense Jacq. (ghost pepper), a naturally occurring chili species of Northeast India is known throughout the world for its high pungency and a pleasant aroma. The economic importance is due to the high capsaicinoid levels, the main source for pharmaceutical industries. The present study focused on identifying important traits necessary for increasing the yield and pungency of ghost pepper and to determine the parameters for the selection of superior genotypes. A total of 120 genotypes with more than 1.2% capsaicin content (>1,92,000 Scoville Heat Unit, w/w on dry weight basis) collected from different northeast Indian regions were subjected to variability, divergence and correlation studies. Levene's homogeneity test of variance studied for three environments did not show significant deviation and so homogeneity of variance was reasonably met for analysis of variance study. Genotypic and phenotypic coefficient of variation was highest for fruit yield per plant (33.702, 36.200, respectively), followed by number of fruits per plant (29.583, 33.014, respectively) and capsaicin content (25.283, 26.362, respectively). The trait number of fruits per plant had maximum direct contribution to fruit yield per plant and the trait fruit yield per plant towards capsaicin content in the correlation study. High heritability with high genetic advance, which is the most favored selection criteria was observed for fruit yield per plant, number of fruits per plant, capsaicin content, fruit length and fruit girth. The genetic divergence study partitioned the genotypes into 20 clusters, where fruit yield per plant contributed maximum towards total divergence. Principal components analysis (PCA) studied to determine the largest contributor of variation showed 73.48% of the total variability, of which the PC1 and PC2 contributed 34.59% and 16.81% respectively.

In silico investigation on the mutational analysis of BRCA1-BARD1 RING domains and its effect on nucleosome recognition and ubiquitination.

The BRCA1-BARD1 complex is a crucial tumor suppressor E3 ubiquitin ligase involved in DNA double-stranded break repair. The BRCA1-BARD1 RING domains interact with UBE2D3 through the BRCA1 interface and this complex flexibly tether to the nucleosome core particle (NCP), where BRCA1 and BARD1 interacts with histone H2A and H2B of NCP. Mutations in the BRCA1-BARD1 RING domains have been linked to familial breast and ovarian cancer. Seven mutations were analyzed to understand their effect on the binding interface of protein partners and changes in conformational dynamics. Molecular dynamics simulations revealed that mutant complexes were less conformationally flexible than the wildtype complex. Protein-protein interaction profiling showed the importance of specific molecular interactions, hotspot and hub residues, and some of these were lost in the mutant complexes. Two mutations (BRCA1L51W-K65R and BARD1C53W) hindered significant interaction between protein partners and may prevent signaling for ubiquitination of histones in NCP and other cellular targets. The structural compactness and reduced significant interaction in mutant complexes may be the possible reason of preventing ubiquitination and hinder DNA repair, resulting cancer.

Machine learning based dynamic consensus model for predicting blood-brain barrier permeability.

The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, however, the reliability of these models is questionable due to the smaller and class-imbalance dataset which subsequently leads to a very high false positive rate. In this study, machine learning and deep learning-based predictive models were built using XGboost, Random Forest, Extra-tree classifiers and deep neural network. A dataset of 8153 compounds comprising both the BBB permeable and BBB non-permeable was curated and subjected to calculations of molecular descriptors and fingerprints for generating the features for machine learning and deep learning models. Three balancing techniques were then applied to the dataset to address the class-imbalance issue. A comprehensive comparison among the models showed that the deep neural network model generated on the balanced MACCS fingerprint dataset outperformed with an accuracy of 97.8% and a ROC-AUC score of 0.98 among all the models. Additionally, a dynamic consensus model was prepared with the machine learning models and validated with a benchmark dataset for predicting BBB permeability with higher confidence scores.

Galaxy for open-source computational drug discovery solutions.

INTRODUCTION: Drug discovery in academia and industry poses contrasting challenges. While academia focuses on producing new knowledge, industry is keen on product development and success in clinical trials. Galaxy is a web-based open-source computational workbench which is used to analyze large datasets and is customized to integrate analysis and visualization tools in a single framework. Depending on the methodology, one can generate customized and suitable workflows in the Galaxy platform. AREAS COVERED: Herein, the authors appraise the suitability of the Galaxy platform for developing a disease specific web portal called the Molecular Property Diagnostic Suite (MPDS). The authors include their future perspectives in the expert opinion section. EXPERT OPINION: Galaxy is ideally suited for community-based software development as the scripts, tools, and codes developed in the different programming languages can be integrated in an extremely efficient fashion. MPDS puts forth a new approach known as a disease-specific web portal which aims to implement a range of computational methods and algorithms that can be developed and shared freely across the community of computer aided drug design (CADD) scientists.

Molecular dynamics simulations reveal the effect of mutations in the RING domains of BRCA1-BARD1 complex and its relevance to the prognosis of breast cancer.

The N-terminal RING-RING domain of BRCA1-BARD1 is an E3 ubiquitin ligase complex that plays a critical role in tumor suppression through DNA double stranded repair mechanism. Mutations in the BRCA1-BARD1 heterodimer RING domains were found to have an association with breast and ovarian cancer by a way of hampering the E3 ubiquitin ligase activity. Herein, the molecular mechanism of interaction, conformational change due to the specific mutations on the BRCA1-BARD1 complex at atomic level has been examined by employing molecular modeling techniques. Sixteen mutations have been selected for the study. Molecular dynamics simulation results reveal that the mutant complexes have more local perturbation with a high residual fluctuation in the zinc binding sites and central helix. A few of the BRCA1 (V11A, I21V, I42V, R71G, I31M and L51W) mutants have been experimentally identified that do not impair E3 ligase activity, display an enhanced number of H-bonds and non-bonded contacts at the interacting interface as revealed by MD simulation. The mutation of BRCA1 (C61G, C64Y, C39Y and C24R) and BARD1 (C53W, C71Y and C83R) zinc binding residues displayed a smaller number of significant H-bonds, other interactions and also loss of some of the hotspot residues. Additionally, most of the mutant complexes display relatively lower electrostatic energy, H-bonding and total stabilizing energy as compared to wild-type. The current study attempts to unravel the role of BRCA1-BARD1 mutations and delineates the structural and conformational dynamics in the progression of breast cancer.Communicated by Ramaswamy H. Sarma.

Assessing machine learning approaches for predicting failures of investigational drug candidates during clinical trials.

One of the major challenges in drug development is having acceptable levels of efficacy and safety throughout all the phases of clinical trials followed by the successful launch in the market. While there are many factors such as molecular properties, toxicity parameters, mechanism of action at the target site, etc. that regulates the therapeutic action of a compound, a holistic approach directed towards data-driven studies will invariably strengthen the predictive toxicological sciences. Our quest for the current study is to find out various reasons as to why an investigational candidate would fail in the clinical trials after multiple iterations of refinement and optimization. We have compiled a dataset that comprises of approved and withdrawn drugs as well as toxic compounds and essentially have used time-split based approach to generate the training and validation set. Five highly robust and scalable machine learning binary classifiers were used to develop the predictive models that were trained with features like molecular descriptors and fingerprints and then validated rigorously to achieve acceptable performance in terms of a set of performance metrics. The mean AUC scores for all the five classifiers with the hold-out test set were obtained in the range of 0.66-0.71. The models were further used to predict the probability score for the clinical candidate dataset. The top compounds predicted to be toxic were analyzed to estimate different dimensions of toxicity. Apparently, through this study, we propose that with the appropriate use of feature extraction and machine learning methods, one can estimate the likelihood of success or failure of investigational drugs candidates thereby opening an avenue for future trends in computational toxicological studies. The models developed in the study can be accessed at https://github.com/gnsastry/predicting_clinical_trials.git.

OSADHI - An online structural and analytics based database for herbs of India.

The current study aims to develop a PAN India database of medicinal plants along with their phytochemicals and geographical availability. The database consists of 6959 unique medicinal plants belonging to 348 families which are available across 28 states and 8 union territories of India. The database sources the information on four different sections - traditional knowledge, geographical indications, phytochemicals, and chemoinformatics. The traditional knowledge reports the plant taxonomy with their vernacular names. A total of 27,440 unique phytochemicals associated with these plants were curated from various sources in this study. However, due to the non-availability of general information like IUPAC names, InChI key, etc. from reliable sources, only 22,314 phytochemicals have been currently reported in the database. Various analyses have been performed for the phytochemicals which include analysis of physicochemical and ADMET properties calculated from open-source web servers using in-house python scripts. The phytochemical data set has also been classified based on the class, superclass, and pathways respectively using NPClassifier, a deep learning framework. Additionally, the antiviral potency of the phytochemicals was also predicted using two machine learning models - Random Forest and XGBoost. The database aims to provide accurate and exhaustive data of the traditional practice of medicinal plants in India in a single platform integrating and analyzing the rich customary practices and facilitating the development and identification of plant-based therapeutics for a variety of diseases. The database can be accessed at https://neist.res.in/osadhi/.

Seroepidemiological and genomic investigation of SARS-CoV-2 spread in North East region of India.

PURPOSE: Seroepidemiology and genomic surveillance are valuable tools to investigate infection transmission during a pandemic. North East (NE) India is a strategically important region being the gateway connecting the country with Southeast Asia. Here, we examined the spread of SARS-CoV-2 in NE India during the first and second waves of COVID-19 using serological and whole genome sequencing approaches. METHODS: qRT-PCR analysis was performed on a selected population (n â€‹= â€‹16,295) from June 2020 to July 2021, and metadata was collected. Immunoassays were studied (n â€‹= â€‹2026) at three-time points (August 2020, February 2021, and June 2021) and in a cohort (n â€‹= â€‹35) for a year. SARS-CoV-2 whole genomes (n â€‹= â€‹914) were sequenced and analyzed with those obtained from the databases. RESULTS: Test positivity rates (TPR) in the first and second waves were 6.34% and 6.64% in Assam, respectively, and a similar pattern was observed in other NE states. Seropositivity in the three time points was 10.63%, 40.3%, and 46.33%, respectively, and neutralizing antibody prevalence was 90.91%, 52.14%, and 69.30%, respectively. Persistence of pan-IgG-N SARS-CoV-2 antibody for over a year was observed among three subjects in the cohort group. Normal variants dominated the first wave, while B.1.617.2 and AY-sublineages dominated the second wave in the region. The prevalence of the variants co-related well with high TPR and seropositivity rate in the region and identified mostly among vaccinated individuals. CONCLUSION: The COVID-19 first wave in the region witnessed low transmission with the evolution of diverse variants. Seropositivity increased during the study period with over half of the individuals carrying neutralizing antibodies against SARS-CoV-2. High infection and seroprevalence in NE India during the second wave were associated with the dominant emergence of variants of concern.

Binding propensity and selectivity of cationic, anionic, and neutral guests with model hydrophobic hosts: A first principles study.

Computations play a critical role in deciphering the nature of host-guest interactions both at qualitative and quantitative levels. Reliable quantum chemical computations were employed to assess the nature, binding strength, and selectivity of ionic, and neutral guests with benzenoid hosts. Optimized complex structures reveal that alkali and ammonium ions are found to be in the hydrophobic cavity, while halide ions are outside, while both complexes elicit substantial binding energy. The origin of the selectivity of host toward the guest has been traced to the interaction and deformation energies, and the nature of associated interactions is quantified using energy decomposition and the Quantum Theory of Atoms in Molecules analyses. While the larger hosts lead to loosely bound complexes, as assessed by the longer intermolecular distances, the binding strengths are proportional to the size of the host systems. The binding of cationic complexes is electrostatic or polarization driven while exchange term dominates the anionic complexes. In contrast, dispersion contribution is a key in neutral complexes and plays a pivotal role in stabilizing the polyatomic complexes.

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite.

A fragment-based drug discovery (FBDD) approach has traditionally been of utmost significance in drug design studies. It allows the exploration of large chemical space to find novel scaffolds and chemotypes which can be improved into selective inhibitors with good affinity. In the current work, several public domain chemical libraries (ChEMBL, DrugCentral, PDB ligands, COCONUT, and SAVI) comprising bioactive and virtual molecules were retrieved to develop a fragment library. A systematic fragmentation method that breaks a given molecule into rings, linkers, and substituents was used to cleave the molecules and the fragments were analyzed. Further, only the ring framework was taken into the consideration to develop a fragment library that consists of a total number of 107,614 unique fragments. This set represents a rich diverse structure framework that covers a wide variety of yet-to-be-explored fragments for a wide range of small molecule-based applications. This fragment library is an integral part of the molecular property diagnostic suite (MPDS) suite that can be used with other modeling and informatics methods for FBDD approaches. The fragment library module of MPDS can be accessed at http://mpds.neist.res.in:8085 .

Deciphering the importance of MD descriptors in designing Vitamin D Receptor agonists and antagonists using machine learning.

The Vitamin D Receptor (VDR) ligand-binding domain undergoes conformation change upon the binding of VDR agonists/antagonists. Helix 12 ((H)12) is one of the important helices at VDR ligand binding and its conformational changes are controlled by the binding of agonists and antagonists molecules. Various molecular modeling studies are available to explain the agonistic and antagonistic activity of vitamin D analogs. In this work, for the first time, we attempted to generate a machine learning model with fingerprints, 2D, 3D and MD descriptors that are specific to Vitamin D analogs and VDR. Initially, 2D and 3D descriptors and fingerprints of 1003 vitamin D analogs were calculated using CDK and RDKit. The machine learning model was generated using descriptors and fingerprints. Further, 80 Vitamin D analogs (40 VDR agonists + 40 VDR antagonists) were docked in the VDR active site. 50ns MD simulation was performed for each protein-ligand complex. Different MD descriptors such as Solvent Accessible Surface Area (SASA), radius of gyration, PC1 and PC2 were calculated and considered along with CDK and RDKit descriptors as features for machine learning calculations. A few other descriptors that are related to VDR conformational changes such as conformation of the (H)12, the angle at kink were considered for machine learning model generation. It was observed that the descriptors calculated from VDR conformational changes i) were able to distinguish between agonists and antagonists ii) provide key and comprehensive information about the unique binding characteristics of agonists and antagonists iii) provide a strong basis for the machine learning model generation. Overall, this study attempts the utilization of descriptors that are specific to a protein conformation will be helpful for the generation of an efficient machine learning model.