Glycosylation as a key for enhancing drug recognition into spike glycoprotein of SARS-CoV-2

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and its spread since 2019 represents the major public health problem worldwide nowadays, generating a high number of infections and deaths. That’s why, in addition to vaccination campaigns, the design of a drug to help in the treatment of severe cases of COVID-19 is being investigated. In relation to SARS-CoV-2, one of its most studied proteins is the spike protein (S protein), which mediates host-cell entry and is heavily glycosylated. Regarding the latter, several investigations have been carried out, since it plays an important role in the evasion of the host's immune system and contributes to protein folding and the thermostability of the viral particle. For that reason, our objective was to evaluate the impact of glycosylations on the drug recognition on two domains of the S protein, the receptor-binding domain (RBD) and the N-terminal domain (NTD) through molecular dynamics simulations and computational biophysics analysis. Our results show that glycosylations in the S protein induce structural stability and changes in rigidity/flexibility related to the number of glycosylations in the structure. These structural changes are important for its biological activity as well as the correct interaction of ligands in the RBD and NTD regions. Additionally, we evidenced a roto-translation phenomenon in the interaction of the ligand with RBD in the absence of glycosylation, which disappears due to the influence of glycosylation and the convergence of metastable states in RBM. Similarly, glycosylations in NTD promote an induced-fit phenomenon, which is not observed in the absence of glycosylations; this process is decisive for the activity of the ligand at the cryptic site. Altogether, these results provide an explanation of glycosylation relevance in biophysical properties and drug recognition to S protein of SARS-CoV-2 which must be considered in the rational drug development and virtual screening targeting S protein.

Identification of WDFY3 Neoantigens as Prognostic Markers in Longterm Survivors of Extrahepatic Cholangiocarcinoma

Background:: Neoantigens are newly formed antigens that have not been previously recognized by the immune system. They may arise from altered tumor proteins that form as a result of mutations. Although neoantigens have recently been linked to antitumor immunity in long-term survivors of cancers, such as melanoma and colorectal cancer, their prognostic and immune-modulatory role in many cancer types remains undefined. Objective: The purpose of this study is to identify prognostic markers for long-term extrahepatic cholangiocarcinoma (EHCC) survival. Methods: We investigated neoantigens in EHCC, a rare, aggressive cancer with a 5-year overall survival rate lower than 10%, using a combination of whole-exome sequencing (WES), RNA sequencing (RNA-seq), computational biophysics, and immunohistochemistry. Results: : Our analysis revealed a decreased neutrophil infiltration-related trend of high-quality neoantigen load with IC50 <500 nM (r=-0.445, P=0.043). Among 24 EHCC patients examined, we identified four long-term survivors with WDFY3 neoantigens and none with WDFY3 neoantigens in the short-term survivors. The WDFY3 neoantigens are associated with a lower infiltration of neutrophils (p=0.013), lower expression of CCL5 (p=0.025), CXCL9 (p=0.036) and TIGIT (p=0.016), and less favorable prognosis (p=0.030). In contrast, the prognosis was not significantly associated with tumor mutation burden, neoantigen load, or immune cell infiltration. Conclusion:: We suggest that the WDFY3 neoantigens may affect prognosis by regulating antitumor immunity and that the WDFY3 neoantigens may be harnessed as potential targets for immunotherapy of EHCC.

When a foreign gene meets its native counterpart: computational biophysics analysis of two PgiC loci in the grass Festuca ovina

Duplicative horizontal gene transfer may bring two previously separated homologous genes together, which may raise questions about the interplay between the gene products. One such gene pair is the “native” PgiC1 and “foreign” PgiC2 in the perennial grass Festuca ovina. Both PgiC1 and PgiC2 encode cytosolic phosphoglucose isomerase, a dimeric enzyme whose proper binding is functionally essential. Here, we use biophysical simulations to explore the inter-monomer binding of the two homodimers and the heterodimer that can be produced by PgiC1 and PgiC2 in F. ovina. Using simulated native-state ensembles, we examine the structural properties and binding tightness of the dimers. In addition, we investigate their ability to withstand dissociation when pulled by a force. Our results suggest that the inter-monomer binding is tighter in the PgiC2 than the PgiC1 homodimer, which could explain the more frequent occurrence of the foreign PgiC2 homodimer in dry habitats. We further find that the PgiC1 and PgiC2 monomers are compatible with heterodimer formation; the computed binding tightness is comparable to that of the PgiC1 homodimer. Enhanced homodimer stability and capability of heterodimer formation with PgiC1 are properties of PgiC2 that may contribute to the retaining of the otherwise redundant PgiC2 gene.

A Bioinformatics Study of Structural Perturbation of 3CL-Protease and the HR2-Domain of SARS-CoV-2 Induced by Synergistic Interaction with Ivermectins

The pandemic caused by SARS-CoV-2 forces drug research to combat it. Ivermectin, an FDA approved antiparasitic drug formulated as a mixture 80:20 of the equipotent homologous 22,23 dihydro ivermectin (B1_a and B1_b), which is known to inhibit SARS-CoV-2 in vitro with a mechanism of action to be defined. It draws attention powerfully that the energetic and structural perturbation that this drug induces by binding on SARS-COV-2 proteins of importance for its proliferation is ill unknown. Hence what we do an exhaustive computational biophysics study to discriminate the best docking of ivermectins to viral proteins and, subsequently, to analyze possible structural alterations with molecular dynamics. The results suggested that ivermectins are capable of docking to the superficial and internal pocket of the 3CL-protease and the HR2-domain, inducing unfolding/folding that change the native conformation in these proteins. In particular, ivermectin binds to the 3CL protease and leads this protein to an unfolded state, whereas the HR2-domain to a more compact conformation in comparison to the native state by refolding when the drug binding to this protein. The results obtained suggest a possible synergistic inhibitory against SARS-COV-2 owing to each role of ivermectins when favorably binding to these viral proteins. Given the importance of the results obtained about this new mechanism of action of ivermectin on SARS-CoV-2, experimental studies are needed that corroborate this proposal.

Mind reading of the proteins: Deep-learning to forecast molecular dynamics

Molecular dynamics (MD) simulations have emerged to become the back-bone of today’s computational biophysics. Simulation tools such as, NAMD, AMBER and GROMACS have accumulated more than 100,000 users. Despite this remarkable success, now also bolstered by compatibility with graphics processor units (GPUs) and exascale computers, even the most scalable simulations cannot access biologically relevant timescales - the number of numerical integration steps necessary for solving differential equations in a million-to-billion-dimensional space is computationally in-tractable. Recent advancements in Deep Learning has made it such that patterns can be found in high dimensional data. In addition, Deep Learning have also been used for simulating physical dynamics. Here, we utilize LSTMs in order to predict future molecular dynamics from current and previous timesteps, and examine how this physics-guided learning can benefit researchers in computational biophysics. In particular, we test fully connected Feed-forward Neural Networks, Recurrent Neural Networks with LSTM / GRU memory cells with TensorFlow and PyTorch frame-works trained on data from NAMD simulations to predict conformational transitions on two different biological systems. We find that non-equilibrium MD is easier to train and performance improves under the assumption that each atom is independent of all other atoms in the system. Our study represents a case study for high-dimensional data that switches stochastically between fast and slow regimes. Applications of resolving these sets will allow real-world applications in the interpretation of data from Atomic Force Microscopy experiments.

A deep learning approach to the structural analysis of proteins

Deep learning (DL) algorithms hold great promise for applications in the field of computational biophysics. In fact, the vast amount of available molecular structures, as well as their notable complexity, constitutes an ideal context in which DL-based approaches can be profitably employed. To express the full potential of these techniques, though, it is a prerequisite to express the information contained in a molecule’s atomic positions and distances in a set of input quantities that the network can process. Many of the molecular descriptors devised so far are effective and manageable for relatively small structures, but become complex and cumbersome for larger ones. Furthermore, most of them are defined locally, a feature that could represent a limit for those applications where global properties are of interest. Here, we build a DL architecture capable of predicting non-trivial and intrinsically global quantities, that is, the eigenvalues of a protein’s lowest-energy fluctuation modes. This application represents a first, relatively simple test bed for the development of a neural network approach to the quantitative analysis of protein structures, and demonstrates unexpected use in the identification of mechanically relevant regions of the molecule.

Envisioning data sharing for the biocomputing community

The scientific community is facing a revolution in several aspects of its modus operandi , ranging from the way science is done—data production, collection, analysis—to the way it is communicated and made available to the public, be that an academic audience or a general one. These changes have been largely determined by two key players: the big data revolution or, less triumphantly, the impressive increase in computational power and data storage capacity; and the accelerating paradigm switch in science publication, with people and policies increasingly pushing towards open access frameworks. All these factors prompt the undertaking of initiatives oriented to maximize the effectiveness of the computational efforts carried out worldwide. Taking the moves from these observations, we here propose a coordinated initiative, focusing on the computational biophysics and biochemistry community but general and flexible in its defining characteristics, which aims at addressing the growing necessity of collecting, rationalizing, sharing and exploiting the data produced in this scientific environment.

Structural propensity database of proteins

An overwhelming amount of experimental evidence suggests that elucidations of protein function, interactions, and pathology are incomplete without inclusion of intrinsic protein disorder and structural dynamics. Thus, to expand our understanding of intrinsic protein disorder, we have created a database of secondary structure (SS) propensities for proteins (dSPP) as a reference resource for experimental research and computational biophysics. The dSPP comprises SS propensities of 7,094 unrelated proteins, as gauged from NMR chemical shift measurements in solution and solid state. Here, we explain the concept of SS propensity and analyze dSPP entries of therapeutic relevance, α-synuclein, MOAG-4, and the ZIKA NS2B-NS3 complex to show: (1) how propensity mapping generates novel structural insights into intrinsically disordered regions of pathologically relevant proteins, (2) how computational biophysics tools can benefit from propensity mapping, and (3) how the residual disorder estimation based on NMR chemical shifts compares with sequence-based disorder predictors. This work demonstrates the benefit of propensity estimation as a method that reports both on protein structure, lability, and disorder.