pwc-archive/papers-with-abstracts huggingface dataset:
Computational evidence on repurposing the anti-influenza drugs baloxavir acid and baloxavir marboxil against COVID-19
Authors: Suban K Sahoo, Seshu Vardhan
Date: 2020-09-02 00:00:00
Tasks: Molecular Docking

The main reasons for the ongoing COVID-19 (coronavirus disease 2019) pandemic are the unavailability of recommended efficacious drugs or vaccines along with the human to human transmission nature of SARS-CoV-2 virus. So, there is urgent need to search appropriate therapeutic approach by repurposing approved drugs. In this communication, molecular docking analyses of two influenza antiviral drugs baloxavir acid (BXA) and baloxavir marboxil (BXM) were performed with the three therapeutic target proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), i.e., main protease (Mpro), papain-like protease (PLpro) and RNA-dependent RNA polymerase (RdRp). The molecular docking results of both the drugs BXA and BXM were analysed and compared. The investigational drug BXA binds at the active site of Mpro and RdRp, whereas the approved drug BXM binds only at the active site of RdRp. Also, comparison of dock score revealed that BXA is binding more effectively at the active site of RdRp than BXM. The computational molecular docking revealed that the drug BXA may be more effective against COVID-19 as compared to BXM.
Computational screening of repurposed drugs and natural products against SARS-Cov-2 main protease (Mpro) as potential COVID-19 therapies
Authors: Sakshi Piplani, Puneet Singh, Nikolai Petrovsky, David A. Winkler
Date: 2020-09-01 00:00:00

There remains an urgent need to identify existing drugs that might be suitable for treating patients suffering from COVID-19 infection. Drugs rarely act at a single molecular target, with off target effects often being responsible for undesirable side effects and sometimes, beneficial synergy between targets for a specific illness. Off target activities have also led to blockbuster drugs in some cases, e.g. Viagra for erectile dysfunction and Minoxidil for male pattern hair loss. Drugs already in use or in clinical trials plus approved natural products constitute a rich resource for discovery of therapeutic agents that can be repurposed for existing and new conditions, based on the rationale that they have already been assessed for safety in man. A key question then is how to rapidly and efficiently screen such compounds for activity against new pandemic pathogens such as COVID-19. Here we show how a fast and robust computational process can be used to screen large libraries of drugs and natural compounds to identify those that may inhibit the main protease of SARS-Cov-2 (3CL pro, Mpro). We show how the resulting shortlist of candidates with strongest binding affinities is highly enriched in compounds that have been independently identified as potential antivirals against COVID-19. The top candidates also include a substantial number of drugs and natural products not previously identified as having potential COVID-19 activity, thereby providing additional targets for experimental validation. This in silico screening pipeline may also be useful for repurposing of existing drugs and discovery of new drug candidates against other medically important pathogens and for use in future pandemics.
High-Throughput Virtual Screening of 4487 flavonoids: New insights on the structural inhibition of SARS-CoV-2 Main Protease
Authors: Gabriel M. Jiménez-Avalos, Ana P. Vargas-Ruiz, Nicolás E. Delgado-Pease, Gustavo E. Olivos-Ramirez, Patricia Sheen-Cortavarría, Manolo Fernandez, Mirko J. Zimic-Peralta
Date: 2020-08-30 00:00:00

COVID-19 presents a great threat to public health worldwide and the infectious agent SARS-CoV-2 is currently the target of much research aiming at inhibition. The virus' main protease is a dimeric enzyme that has only recently begun to be thoroughly described, opening the door for virtual screening more broadly. Here, a PAIN-filtered flavonoid database was screened against four sites of the protease: a free (normal) conformation of the Substrate Binding Site (NSBS), an induced-fit state of the SBS (ISBS), a Dimerization Site (DS) and a Cryptic Site (CS). The mean binding energies of the top five ligands from each site were -9.52, -11.512, -7.042 and -10.348 kcal/mol for the NSBS, the ISBS, the DS and the CS, respectively. For the DS and CS, these top five compounds were selected as candidates to bind their respective site. In the case of SBS, the top 30 ligands with the lowest binding energies from NSBS and ISBS were contrasted and the ones present in both lists were selected as the final candidates. The final list was: Dorsilurin E (FL3FQUNP0001), Euchrenone a11 (FL2FALNP0014), Kurziflavolactone C (FL2FA9NC0016), Licorice glycoside E (FL2F1AGSN001) and Taxifolin 3'- (6"-phenyl- acetylglucoside) (FL4DACGS0020) for the SBS; Sanggenol O (FL2FALNP0020), CHEMBL2171573, Kanzonol E (FL3F1ANP0001), CHEMBL2171584 and Abyssynoflavanone VI (FL2FACNP0014) for DS and CHEMBL2171598, CHEMBL2171577, Denticulaflavanol (FL5FAANR0001), Kurzichalcolactone (FL1CA9NC0001) and CHEMBL2171578 for CS. Virtual screening integrated several confirmation methods, including cross-docking assays and positive and negative controls. All 15 compounds are currently subjected to molecular dynamics so as to theoretically validate their binding to the protease.
Recognition of potential Covid-19 drug treatments through the study of existing protein-drug and protein-protein structures: an analysis of kinetically active residues
Authors: Ognjen Perišić
Date: 2020-04-21 00:00:00
Tasks: Drug Design

We report the results of our study of approved drugs as potential treatments for COVID 19, based on the application of various bioinformatics predictive methods. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and {\alpha}-difluoromethylornithine (DMFO). Our results indicate that these small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets and that some prefer hydrophobic over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general.
BPPart and BPMax: RNA-RNA Interaction Partition Function and Structure Prediction for the Base Pair Counting Model
Authors: Ali Ebrahimpour-Boroojeny, Sanjay Rajopadhye, Hamidreza Chitsaz
Date: 2019-04-02 00:00:00

RNA-RNA interaction (RRI) is ubiquitous and has complex roles in the cellular functions. In human health studies, miRNA-target and lncRNAs are among an elite class of RRIs that have been extensively studied. Bacterial ncRNA-target and RNA interference are other classes of RRIs that have received significant attention. In recent studies, mRNA-mRNA interaction instances have been observed, where both partners appear in the same pathway without any direct link between them, or any prior knowledge about their relationship. Those recently discovered cases suggest that RRI scope is much wider than those aforementioned elite classes. We revisit our RNA-RNA interaction partition function algorithm, piRNA, which computes the partition function, base-pairing probabilities, and structure for the comprehensive Turner energy model using 96 different dynamic programming tables. In this study, we strategically retreat from sophisticated thermodynamic models to the much simpler base pair counting model. That might seem counter-intuitive at the first glance; our idea is to benefit from the advantages of such simple models in terms of running time and memory footprint and compensate for the associated information loss by adding machine learning components in the future. Here, simple weighted base pair counting is considered to obtain BPPart for Base-pair Partition function and BPMax for Base-pair Maximization, which use 9 and 2 tables respectively. They are empirically 225 and 1350 fold faster than piRNA. A correlation of 0.855 and 0.836 was achieved between piRNA and BPPart and between piRNA and BPMax, respectively, in 37 degrees, and 0.920 and 0.904 in -180 degrees. We also discover two partner RNAs, SNORD3D and TRAF3, and hypothesize their potential roles in genetic diseases. We envision fusion of machine learning methods with the proposed algorithms in the future.
COVID-19 Docking Server: A meta server for docking small molecules, peptides and antibodies against potential targets of COVID-19
Authors: Ren Kong, Guangbo Yang, Rui Xue, Ming Liu, Feng Wang, Jianping Hu, Xiaoqiang Guo, Shan Chang
Date: 2020-02-29 00:00:00
Tasks: Drug Discovery

Motivation: The coronavirus disease 2019 (COVID-19) caused by a new type of coronavirus has been emerging from China and led to thousands of death globally since December 2019. Despite many groups have engaged in studying the newly emerged virus and searching for the treatment of COVID-19, the understanding of the COVID-19 target-ligand interactions represents a key chal-lenge. Herein, we introduce COVID-19 Docking Server, a web server that predicts the binding modes between COVID-19 targets and the ligands including small molecules, peptides and anti-bodies. Results: Structures of proteins involved in the virus life cycle were collected or constructed based on the homologs of coronavirus, and prepared ready for docking. The meta platform provides a free and interactive tool for the prediction of COVID-19 target-ligand interactions and following drug discovery for COVID-19.
How do intrinsically disordered protein regions encode a driving force for liquid-liquid phase separation?
Authors: Wade Borcherds, Anne Bremer, Madeleine B. Borgia, Tanja Mittag
Date: 2020-08-07 00:00:00

Liquid-liquid phase separation is the mechanism underlying the formation of biomolecular condensates. Disordered protein regions often drive phase separation, but molecular interactions of disordered protein regions are not well understood, sometimes leading to the conflation that all disordered protein regions drive phase separation. Given the critical role of phase separation in many cellular processes, and that dysfunction of phase separation can lead to debilitating diseases, it is important that we understand the interactions and sequence properties underlying phase behavior. A conceptual framework that divides IDRs into interacting and solvating regions has proven particularly useful, and analytical instantiations and coarse-grained models can test our understanding of the driving forces against experimental phase behavior. Validated simulation paradigms enable the exploration of sequence space to help our understanding of how disordered protein regions can encode phase behavior, which IDRs may mediate phase separation in cells, and which IDRs are in contrast highly soluble.
First-Passage Time Distributions in Two-State Protein Folding Kinetics: Exploring the Native-Like States vs Overcoming the Free Energy Barrier
Authors: Sergei F. Chekmarev
Date: 2020-05-27 00:00:00
Tasks: Protein Folding

Using a beta-hairpin protein as a representative example of two-state folders, we studied how the exploration of native-like states affects the folding kinetics. It has been found that the first-passage time (FPT) distributions are essentially single-exponential not only for the times to overcome the free energy barrier that separates unfolded and native-like states but also for the times to find the native state among the native-like ones. If the protein explores native-like states for a time much longer than the time to overcome the free energy barrier, which was found to be characteristic of high temperatures, the resulting FPT distribution to reach the native state remains close to exponential but the mean FPT (MFPT) is determined not by the height of the free energy barrier but by the time to explore native-like states. The mean time to overcome the free energy barrier is found to be in reasonable agreement with the Kramers rate formula and generally far shorter than the MFPT to reach the native state. The time to find the native state among native-like ones increases with temperature, which explains the known U-shape dependence of the MFPTs on temperature.
In Silico Investigation of Phytoconstituents from Indian Medicinal Herb 'Tinospora cordifolia (Giloy)' against SARS-CoV-2 (COVID-19) by Molecular Dynamics Approach
Authors: Papia Chowdhury
Date: 2020-05-29 00:00:00
Tasks: Molecular Docking

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 $\rm{M^{pro}}$ or $\rm{3CL^{pro}}$ 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, $\rm{3CL^{pro}}$ 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 $\rm{(C_{20}H_{18}NO_{4})}$, $\beta$-sitosterol $\rm{(C_{29}H_{50}O)}$, choline $\rm{(C_{5}H_{14}NO)}$, tetrahydropalmatine $\rm{(C_{21}H_{25}NO_{4})}$ and octacosanol $\rm{(C_{28}H_{58}O))}$ 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 $\rm{3CL^{pro}}$ 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.
Quantitative Structure Property Analysis of Anti-Covid-19 Drugs
Authors: Sunilkumar M. Hosamani
Date: 2020-08-06 00:00:00

Inspired by recent work on anti-covid-19 drugs \cite{2} here we study the Quantitative-structure property relationships(QSPR) of phytochemicals screened against SARS-CoV-2 $3CL^{pro}$ with the help of topological indices like the first Zagreb index $M_{1}$, second Zagreb index $M_{2}$, Randi$\acute{c}$ index $R$, Balban index $J$ and sum-connectivity index $SCI(G)$. Our study has raveled that the sum-connectivity index $(SCI)$ and the first Zagreb index $(M_{1})$ are two important parameters to predict the molecular weight and the topological polar surface area of phytochemicals respectively.
Weak carbohydrate-carbohydrate interactions in membrane adhesion are fuzzy and generic
Authors: Batuhan Kav, Andrea Grafmüller, Emanuel Schneck, Thomas R. Weikl
Date: 2020-08-04 00:00:00

Carbohydrates such as the trisaccharide motif LeX are key constituents of cell surfaces. Despite intense research, the interactions between carbohydrates of apposing cells or membranes are not well understood. In this article, we investigate carbohydrate-carbohydrate interactions in membrane adhesion as well as in solution with extensive atomistic molecular dynamics simulations that exceed the simulation times of previous studies by orders of magnitude. For LeX, we obtain association constants of soluble carbohydrates, adhesion energies of lipid-anchored carbohydrates, and maximally sustained forces of carbohydrate complexes in membrane adhesion that are in good agreement with experimental results in the literature. Our simulations thus appear to provide a realistic, detailed picture of LeX-LeX interactions in solution and during membrane adhesion. In this picture, the LeX-LeX interactions are fuzzy, i.e. LeX pairs interact in a large variety of short-lived, bound conformations. For the synthetic tetrasaccharide Lac 2, which is composed of two lactose units, we observe similarly fuzzy interactions and obtain association constants of both soluble and lipid-anchored variants that are comparable to the corresponding association constants of LeX. The fuzzy, weak carbohydrate-carbohydrate interactions quantified in our simulations thus appear to be a generic feature of small, neutral carbohydrates such as LeX and Lac 2.
Analyzing ribosome remodeling in health and disease
Authors: Aleksandra A. Petelski, Nikolai Slavov
Date: 2020-07-11 00:00:00

regulation largely unexplored, in part due to methodological limitations. Indeed, we review evidence demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. We review such methods, focusing on mass spectrometry and emphasizing method-specific biases and approaches to control these biases. We argue that using multiple complementary methods can help reduce the danger of interpreting reproducible systematic biases as evidence for ribosome remodeling.
Attribution Methods Reveal Flaws in Fingerprint-Based Virtual Screening
Authors: Vikram Sundar, Lucy Colwell
Date: 2020-07-02 00:00:00

Fingerprint-based models for protein-ligand binding have demonstrated outstanding success on benchmark datasets; however, these models may not learn the correct binding rules. To assess this concern, we use in silico datasets with known binding rules to develop a general framework for evaluating model attribution. This framework identifies fragments that a model considers necessary to achieve a particular score, sidestepping the need for a model to be differentiable. Our results confirm that high-performing models may not learn the correct binding rule, and suggest concrete steps that can remedy this situation. We show that adding fragment-matched inactive molecules (decoys) to the data reduces attribution false negatives, while attribution false positives largely arise from the background correlation structure of molecular data. Normalizing for these background correlations helps to reveal the true binding logic. Our work highlights the danger of trusting attributions from high-performing models and suggests that a closer examination of fingerprint correlation structure and better decoy selection may help reduce misattributions.
Analytical Theory for Sequence-Specific Binary Fuzzy Complexes of Charged Intrinsically Disordered Proteins
Authors: Alan N. Amin, Yi-Hsuan Lin, Suman Das, Hue Sun Chan
Date: 2019-10-24 00:00:00

Intrinsically disordered proteins (IDPs) are important for biological functions. In contrast to folded proteins, molecular recognition among certain IDPs is "fuzzy" in that their binding and/or phase separation are stochastically governed by the interacting IDPs' amino acid sequences while their assembled conformations remain largely disordered. To help elucidate a basic aspect of this fascinating yet poorly understood phenomenon, the binding of a homo- or hetero-dimeric pair of polyampholytic IDPs is modeled statistical mechanically using cluster expansion. We find that the binding affinities of binary fuzzy complexes in the model correlate strongly with a newly derived simple "jSCD" parameter readily calculable from the pair of IDPs' sequence charge patterns. Predictions by our analytical theory are in essential agreement with coarse-grained explicit-chain simulations. This computationally efficient theoretical framework is expected to be broadly applicable to rationalizing and predicting sequence-specific IDP-IDP polyelectrostatic interactions.
GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research
Authors: Scott LeGrand, Aaron Scheinberg, Andreas F. Tillack, Mathialakan Thavappiragasam, Josh V. Vermaas, Rupesh Agarwal, Jeff Larkin, Duncan Poole, Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, Stefano Forli, Oscar Hernandez, Jeremy C. Smith, Ada Sedova
Date: 2020-07-06 00:00:00
Tasks: Drug Discovery, GPU

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.
Attacking COVID-19 Progression using Multi-Drug Therapy for Synergetic Target Engagement
Authors: Mathew Coban, Juliet Morrison PhD, William D. Freeman MD, Evette Radisky PhD, Karine G. Le Roch PhD, Thomas R. Caulfield PhD
Date: 2020-07-06 00:00:00

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 6 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 11.6 million (25% located in United States) and killed more than 540K people around the world. As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors; S/Ace2, Tmprss2, Cathepsins L and K, and Mpro to prevent binding, membrane fusion and replication of the virus, respectively. All together we generated an ensemble of structural conformations that increase high quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.