Quantitative Biology

Endeavoring toward a transferable, predictive coarse-grained explicit-chain model for biomolecular condensates underlain by liquid-liquid phase separation (LLPS), we conducted multiple-chain simulations of the N-terminal intrinsically disordered region (IDR) of DEAD-box helicase Ddx4, as a test case, to assess the roles of electrostatic, hydrophobic, cation- π , and aromatic interactions in amino acid sequence-dependent LLPS. We evaluated 3 residue-residue interaction schemes with a shared electrostatic potential. Neither a common hydrophobicity scheme nor one augmented with arginine/lysine-aromatic cation- π interactions consistently accounted for the experimental LLPS data on the wildtype, a charge-scrambled, an FtoA, and an RtoK mutant of Ddx4 IDR. In contrast, interactions based on contact statistics among folded globular protein structures reproduce the overall experimental trend, including that the RtoK mutant has a much diminished LLPS propensity. Consistency between simulation and LLPS experiment was also found for RtoK mutants of P-granule protein LAF-1, underscoring that, to a degree, the important LLPS-driving π -related interactions are embodied in classical statistical potentials. Further elucidation will be necessary, however, especially of phenylalanine's role in condensate assembly because experiments on FtoA and YtoF mutants suggest that LLPS-driving phenylalanine interactions are significantly weaker than those posited by common statistical potentials. Protein-protein electrostatic interactions are modulated by relative permittivity, which depends on protein concentration. Analytical theory suggests that this dependence entails enhanced inter-protein interactions in the condensed phase but more favorable protein-solvent interactions in the dilute phase. The opposing trends lead to a modest overall impact on LLPS.

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.

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.

For fast development of COVID-19, it is only feasible to use drugs (off label use) or approved natural products that are already registered or been assessed for safety in previous human trials. These agents can be quickly assessed in COVID-19 patients, as their safety and pharmacokinetics should already be well understood. Computational methods offer promise for rapidly screening such products for potential SARS-CoV-2 activity by predicting and ranking the affinities of these compounds for specific virus protein targets. The RNA-dependent RNA polymerase (RdRP) is a promising target for SARS-CoV-2 drug development given it has no human homologs making RdRP inhibitors potentially safer, with fewer off-target effects that drugs targeting other viral proteins. We combined robust Vina docking on RdRP with molecular dynamic (MD) simulation of the top 80 identified drug candidates to yield a list of the most promising RdRP inhibitors. Literature reviews revealed that many of the predicted inhibitors had been shown to have activity in in vitro assays or had been predicted by other groups to have activity. The novel hits revealed by our screen can now be conveniently tested for activity in RdRP inhibition assays and if conformed testing for antiviral activity invitro before being tested in human trials

We study local conformational biases in the dynamics of {\alpha}-synuclein by using all-atom simulations with explicit and implicit solvents. The biases are related to the frequency of the specific contact formation. In both approaches, the protein is intrinsically disordered, and its strongest bias is to make bend and turn local structures. The explicit-solvent conformations can be substantially more extended which allows for formation of transient trefoil knots, both deep and shallow, that may last for up to 5 {\mu}s. The two-chain self-association events, both short- and long-lived, are dominated by formation of contacts in the central part of the sequence. This part tends to form helices when bound to a micelle.

Most known proteins in nature consist of multiple domains. Interactions between domains may lead to unexpected folding and misfolding phenomena. This study of human {\gamma}D-crystallin, a two-domain protein in the eye lens, revealed one such surprise: conformational catalysis of misfolding via intermolecular domain interface ''stealing''. An intermolecular interface between the more stable domains outcompetes the native intramolecular domain interface. Loss of the native interface in turn promotes misfolding and subsequent aggregation, especially in cataract-related {\gamma}D-crystallin variants. This phenomenon is likely a contributing factor in the development of cataract disease, the leading worldwide cause of blindness. However, interface stealing likely occurs in many proteins composed of two or more interacting domains.

We consider a measure of cooperativity based on the minimal absolute interaction required to generate an observed titration behavior. We describe the corresponding algebraic optimization problem and show how it can be solved using the nonlinear algebra tool \texttt{SCIP}. Moreover, we compute the minimal absolute interactions for various binding polynomials that describe the oxygen binding of various hemoglobins under different conditions. While calculated minimal absolute interactions are consistent with the expected outcome of the chemical modifications, it ranks the cooperativity of the molecules differently than the maximal Hill slope.

Computational prediction of RNA structures is an important problem in computational structural biology. Studies of RNA structure formation often assume that the process starts from a fully synthesized sequence. Experimental evidence, however, has shown that RNA folds concurrently with its elongation. We investigate RNA secondary structure formation, including pseudoknots, that takes into account the cotranscriptional effects. We propose a single-nucleotide resolution kinetic model of the folding process of RNA molecules, where the polymerase-driven elongation of an RNA strand by a new nucleotide is included as a primitive operation, together with a stochastic simulation method that implements this folding concurrently with the transcriptional synthesis. Numerical case studies show that our cotranscriptional RNA folding model can predict the formation of conformations that are favored in actual biological systems. Our new computational tool can thus provide quantitative predictions and offer useful insights into the kinetics of RNA folding.

We report the performance of our newly introduced Ensemble Docking with Enhanced sampling of pocket Shape (EDES) protocol coupled to a template-based algorithm to generate near-native ligand conformations in the 2019 iteration of the Grand Challenge organized by the D3R consortium. Using either AutoDock4.2 or HADDOCK2.2 docking programs (each software in two variants of the protocol) our method generated native-like poses among the top 5 submitted for evaluation for most of the 20 targets with similar performances. The protein selected for GC4 was the human beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1), a transmembrane aspartic-acid protease. We identified at least one pose whose heavy-atoms RMSD was less than 2.5 Å from the native conformation for 16 (80%) and 17 (85%) of the twenty targets using AutoDock and HADDOCK, respectively. Dissecting the possible sources of errors revealed that: i) our EDES protocol (with minor modifications) was able to sample sub-ångstrom conformations for all 20 protein targets, reproducing the correct conformation of the binding site within ~1 Å RMSD; ii) as already shown by some of us in GC3, even in the presence of near-native protein structures, a proper selection of ligand conformers is crucial for the success of ensemble-docking calculations. Importantly, our approach performed best among the protocols exploiting only structural information of the apo protein to generate conformations of the receptor for ensemble-docking calculations.

In the cell, proteins fold and perform complex functions through global structural rearrangements. Function requires a protein to be at the brink of stability to be susceptible to small environmental fluctuations, yet stable enough to maintain structural integrity. These apparently conflicting behaviors are exhibited by systems near a critical point, where distinct phases merge − a concept beyond previous studies indicating proteins have a well-defined folded/unfolded phase boundary in the pressure-temperature plane. Here, by modeling the protein phosphoglycerate kinase (PGK) on the temperature (T), pressure (P), and crowding volume-fraction ( ϕ ) phase diagram, we demonstrate a critical transition where phases merge, and PGK exhibits large structural fluctuations. Above the critical temperature (Tc), the difference between the intermediate and unfolded phases disappears. When ϕ increases, the Tc moves to a lower T. We verify the calculations with experiments mapping the T-P- ϕ space, which likewise reveal a critical point at 305 K and 170 MPa that moves to a lower T as ϕ increases. Crowding places PGK near a critical line in its natural parameter space, where large conformational changes can occur without costly free energy barriers. Specific structures are proposed for each phase based on simulation.