Quantitative Biology

The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by different machine learning techniques. Successful state inference of RNA sequences can be used to generate auxiliary information for data-directed RNA secondary structure prediction. Bidirectional long short-term memory (LSTM) neural networks have emerged as a powerful tool that can model global nonlinear sequence dependencies and have achieved state-of-the-art performances on many different classification problems. This paper presents a practical approach to RNA secondary structure inference centered around a deep learning method for state inference. State predictions from a deep bidirectional LSTM are used to generate synthetic SHAPE data that can be incorporated into RNA secondary structure prediction via the Nearest Neighbor Thermodynamic Model (NNTM). This method produces predicted secondary structures for a diverse test set of 16S ribosomal RNA that are, on average, 25 percentage points more accurate than undirected MFE structures. These improvements range from several percentage points for some sequences to nearly 50 percentage points for others. Accuracy is highly dependent on the success of our state inference method, and investigating the global features of our state predictions reveals that accuracy of both our state inference and structure inference methods are highly dependent on the similarity of the sequence to the dataset. This paper presents a deep learning state inference tool, trained and tested on 16S ribosomal RNA. Converting these state predictions into synthetic SHAPE data with which to direct NNTM can result in large improvements in secondary structure prediction accuracy, as shown on a test set of 16S rRNA.

In recent years machine learning (ML) took bio- and cheminformatics fields by storm, providing new solutions for a vast repertoire of problems related to protein sequence, structure, and interactions analysis. ML techniques, deep neural networks especially, were proven more effective than classical models for tasks like predicting binding affinity for molecular complex. In this work we investigated the earlier stage of drug discovery process - finding druggable pockets on protein surface, that can be later used to design active molecules. For this purpose we developed a 3D fully convolutional neural network capable of binding site segmentation. Our solution has high prediction accuracy and provides intuitive representations of the results, which makes it easy to incorporate into drug discovery projects. The model's source code, together with scripts for most common use-cases is freely available at this http URL

The recent appearance of COVID-19 virus has created a global crisis due to unavailability of any vaccine or drug that can effectively and deterministically work against it. Naturally, different possibilities (including herbal medicines having known therapeutic significance) have been explored by the scientists. The systematic scientific study (beginning with in silico study) of herbal medicines in particular and any drug in general is now possible as the structural components (proteins) of COVID-19 are already characterized. The main protease of COVID-19 virus is M pro or 3C L pro which is a key CoV enzyme and an attractive drug target as it plays a pivotal role in mediating viral replication and transcription. In the present study, 3C L pro is used to study drug:3CLpro interactions and thus to investigate whether all or any of the main chemical constituents of Tinospora cordifolia (e.g., berberine ( C 20 H 18 N O 4 ) , β -sitosterol ( C 29 H 50 O) , choline ( C 5 H 14 NO) , tetrahydropalmatine ( C 21 H 25 N O 4 ) and octacosanol ( C 28 H 58 O)) can be used as an anti-viral drug against SARS-CoV-2. The in silico study performed using tools of network pharmacology, molecular docking including molecular dynamics have revealed that among all considered phytochemicals in Tinospora cordifolia, berberine can regulate 3C L pro protein's function due to its easy inhibition and thus can control viral replication. The selection of Tinospora cordifolia was motivated by the fact that the main constituents of it are known to be responsible for various antiviral activities and the treatment of jaundice, rheumatism, diabetes, etc.

SARS-COV-2 identified as COVID-19 in Wuhan city of China in the month of December, 2019 has now been declared as pandemic by World Health Organization whose transmission chain and cure both have emerged as a tough problem for the medical fraternity. The reports pertaining to the treatment of this pandemic are still lacking. We firmly believe that Nature itself provides a simple solution for any complicated problem created in it which motivated us to carry out In Silico investigations on some bioactive natural compounds reportedly found in the fruits and leaves of Anthocephalus Cadamba which is a miraculous plant found on the earth aiming to predict the potential inhibitors against aforesaid virus. Having modeled the ground state ligand structure of the such nine natural compounds applying density functional theory at B3LYP/631+G (d, p) level we have performed their molecular docking with SARS-COV-2 protease to calculate the binding affinity as well as to screen the binding at S-protein site during ligand-protein interactions. Out of these nine studied naturally occurring compounds; Oleanic Acid has been appeared to be potential inhibitor for COVID-19 followed by Ursolic Acid, IsoVallesiachotamine,Vallesiachotamine,Cadambine,Vincosamide-N-Oxide, Isodihydroamino-cadambine, Pentyle Ester of Chlorogenic Acid and D-Myo-Inositol. Hence these bioactive natural compounds or their structural analogs may be explored as anti-COVID19 drug agent which will be possessing the peculiar feature of cost-less synthesis and less or no side effect due to their natural occurrence. The solubility and solvent-effect related to the phytochemicals may be the point of concern. The In-vivo investigations on these proposed natural compounds or on their structural analogs are invited for designing and developing the potential medicine/vaccine for the treatment of COVID-19 pandemic.

Virtual screening of phytochemicals was performed through molecular docking, simulation, in silico ADMET and drug-likeness prediction to identify the potential hits that can inhibit the effects of SARS-CoV-2. Considering the published literature on medicinal importance, total 154 phytochemicals with analogous structure from limonoids and triterpenoids were selected to search potential inhibitors for the five therapeutic protein targets of SARS-CoV-2, i.e., 3CLpro (main protease), PLpro (papain-like protease), SGp-RBD (spike glycoprotein-receptor binding domain), RdRp (RNA dependent RNA polymerase) and ACE2 (angiotensin-converting enzyme 2). The in silico computational results revealed that the phytochemicals such as glycyrrhizic acid, limonin, 7-deacetyl-7-benzoylgedunin, maslinic acid, corosolic acid, obacunone and ursolic acid were found to be effective against the target proteins of SARS-CoV-2. The protein-ligand interaction study revealed that these phytochemicals bind with the amino acid residues at the active site of the target proteins. Therefore, the core structure of these potential hits can be used for further lead optimization to design drugs for SARS-CoV-2. Also, the medicinal plants containing these phytochemicals like licorice, neem, tulsi, citrus and olives can be used to formulate suitable therapeutic approaches in traditional medicines.

The devastating impact of the COVID-19 pandemic caused by SARS coronavirus 2 (SARS CoV 2) has raised important questions about viral origin, mechanisms of zoonotic transfer to humans, whether companion or commercial animals can act as reservoirs for infection, and why there are large variations in SARS-CoV-2 susceptibilities across animal species. Powerful in silico modelling methods can rapidly generate information on newly emerged pathogens to aid countermeasure development and predict future behaviours. Here we report an in silico structural homology modelling, protein-protein docking, and molecular dynamics simulation study of the key infection initiating interaction between the spike protein of SARS-Cov-2 and its target, angiotensin converting enzyme 2 (ACE2) from multiple species. Human ACE2 has the strongest binding interaction, significantly greater than for any species proposed as source of the virus. Binding to pangolin ACE2 was the second strongest, possibly due to the SARS-CoV-2 spike receptor binding domain (RBD) being identical to pangolin CoV spike RDB. Except for snake, pangolin and bat for which permissiveness has not been tested, all those species in the upper half of the affinity range (human, monkey, hamster, dog, ferret) have been shown to be at least moderately permissive to SARS-CoV-2 infection, supporting a correlation between binding affinity and permissiveness. Our data indicates that the earliest isolates of SARS-CoV-2 were surprisingly well adapted to human ACE2, potentially explaining its rapid transmission.

The COVID-19 pandemic triggered by SARS-CoV-2 is a worldwide health disaster. Main protease is an attractive drug target among coronaviruses, due to its vital role in processing the polyproteins that are translated from the viral RNA. There is presently no exact drug or treatment for this diseases caused by SARS-CoV-2. In the present study, we report the potential inhibitory activity of some FDA approved drugs against SARS-CoV-2 main protease by molecular docking study to investigate their binding affinity in protease active site. Docking studies revealed that drug Oseltamivir (anti-H1N1 drug), Rifampin (anti-TB drug), Maraviroc, Etravirine, Indinavir, Rilpivirine (anti-HIV drugs) and Atovaquone, Quinidine, Halofantrine, Amodiaquine, Tetracylcine, Azithromycin, hydroxycholoroquine (anti-malarial drugs) among others binds in the active site of the protease with similar or higher affinity. However, the in-silico abilities of the drug molecules tested in this study, further needs to be validated by carrying out in vitro and in vivo studies. Moreover, this study spreads the potential use of current drugs to be considered and used to comprise the fast expanding SARS-CoV-2 infection.

Presently, there are no approved drugs or vaccines to treat COVID-19 which has spread to over 200 countries and is responsible for over 3,65,000 deaths worldwide. Recent studies have shown that two human proteases, TMPRSS2 and cathepsin L, play a key role in host cell entry of SARS-CoV-2. Importantly, inhibitors of these proteases were shown to block SARS-CoV-2 infection. Here, we perform virtual screening of 14010 phytochemicals produced by Indian medicinal plants to identify natural product inhibitors of TMPRSS2 and cathepsin L. We built a homology model of TMPRSS2 as an experimentally determined structure is not available. AutoDock Vina was used to perform molecular docking of phytochemicals against TMPRSS2 model structure and cathepsin L crystal structure. Potential phytochemical inhibitors were filtered by comparing their docked binding energies with those of known inhibitors of TMPRSS2 and cathepsin L. Further, the ligand binding site residues and non-covalent protein-ligand interactions were used as an additional filter to identify phytochemical inhibitors that either bind to or form interactions with residues important for the specificity of the target proteases. We have identified 96 inhibitors of TMPRSS2 and 9 inhibitors of cathepsin L among phytochemicals of Indian medicinal plants. The top inhibitors of TMPRSS2 are Edgeworoside C, Adlumidine and Qingdainone, and of cathepsin L is Ararobinol. Interestingly, several herbal sources of identified phytochemical inhibitors have antiviral or anti-inflammatory use in traditional medicine. Further in vitro and in vivo testing is needed before clinical trials of the promising phytochemical inhibitors identified here.

The design of antibacterial-releasing coatings or wrapping materials with controlled drug release capability is a promising strategy to minimise risks of infection and medical device failure in vivo. Collagen fibres have been employed as medical device building block, although they still fail to display controlled release capability, competitive wet-state mechanical properties, and retained triple helix organisation. We investigated this challenge by pursuing a multiscale design approach integrating drug encapsulation, in-situ covalent crosslinking and fibre spinning. By selecting ciprofloxacin (Cip) as a typical antibacterial drug, wet spinning was selected as a triple helix-friendly route towards Cip-encapsulated collagen fibres; whilst in situ crosslinking of fibre-forming triple helices with 1,3 phenylenediacetic acid (Ph) was hypothesised to yield Ph-Cip {\pi}-{\pi} stacking aromatic interactions and enable controlled drug release. Higher tensile modulus and strength were measured in Ph crosslinked fibres compared to state-of-the-art carbodiimide crosslinked controls. Cip-encapsulated Ph-crosslinked fibres revealed decreased elongation at break and significantly-enhanced drug retention in vitro with respect to Cip-free variants and carbodiimide-crosslinked controls, respectively. This multiscale manufacturing strategy provides new insight aiming at wet spun collagen triple helices with nanoscale-regulated tensile properties and drug release capability.

Functional protein-protein interactions are crucial in most cellular processes. They enable multi-protein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partners, and thus in correlations between their sequences. Pairwise maximum-entropy based models have enabled successful inference of pairs of amino-acid residues that are in contact in the three-dimensional structure of multi-protein complexes, starting from the correlations in the sequence data of known interaction partners. Recently, algorithms inspired by these methods have been developed to identify which proteins are functional interaction partners among the paralogous proteins of two families, starting from sequence data alone. Here, we demonstrate that a slightly higher performance for partner identification can be reached by an approximate maximization of the mutual information between the sequence alignments of the two protein families. Our mutual information-based method also provides signatures of the existence of interactions between protein families. These results stand in contrast with structure prediction of proteins and of multi-protein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information. Our findings entail that the statistical dependences allowing interaction partner prediction from sequence data are not restricted to the residue pairs that are in direct contact at the interface between the partner proteins.