Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies.

Coronavirus disease 2019 (COVID-19) caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide as a severe pandemic and caused enormous global health and economical damage. Since December 2019, more than 197 million cases have been reported, causing 4.2 million deaths.

De novo prediction of cancer-associated T cell receptors for noninvasive cancer detection.

The adaptive immune system recognizes tumor antigens at an early stage to eradicate cancer cells. This process is accompanied by systemic proliferation of the tumor antigen-specific T lymphocytes. While detection of asymptomatic early-stage cancers is challenging due to small tumor size and limited somatic alterations, tracking peripheral T cell repertoire changes may provide an attractive solution to cancer diagnosis.

Estimation of the protein-ligand interaction energy for model building and validation.

Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein-ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode.

Synthesis, SAR and molecular docking study of novel non-β-lactam inhibitors of TEM type β-lactamase.

The novel classes of acylated phenoxyanilide and thiourea compounds were investigated for their ability to inhibit TEM type β-lactamase enzyme. Two compounds 4g and 5c reveal the inhibition potency in micromolar range and show their action by non-covalent binding in the vicinity of the TEM-171 active site. The structure activity relationship around carbon chain length and different substituents in ortho- and para-positions of acylated phenoxyanilide as well as molecular modelling study has been performed.

Regulation of the nucleosome repeat length in vivo by the DNA sequence, protein concentrations and long-range interactions.

The nucleosome repeat length (NRL) is an integral chromatin property important for its biological functions. Recent experiments revealed several conflicting trends of the NRL dependence on the concentrations of histones and other architectural chromatin proteins, both in vitro and in vivo, but a systematic theoretical description of NRL as a function of DNA sequence and epigenetic determinants is currently lacking. To address this problem, we have performed an integrative biophysical and bioinformatics analysis in species ranging from yeast to frog to mouse where NRL was studied as a function of various parameters.

Nucleosome repositioning links DNA (de)methylation and differential CTCF binding during stem cell development.

During differentiation of embryonic stem cells, chromatin reorganizes to establish cell type-specific expression programs. Here, we have dissected the linkages between DNA methylation (5mC), hydroxymethylation (5hmC), nucleosome repositioning, and binding of the transcription factor CTCF during this process. By integrating MNase-seq and ChIP-seq experiments in mouse embryonic stem cells (ESC) and their differentiated counterparts with biophysical modeling, we found that the interplay between these factors depends on their genomic context.

Taking into account nucleosomes for predicting gene expression.

The eukaryotic genome is organized in a chain of nucleosomes that consist of 145-147 bp of DNA wrapped around a histone octamer protein core. Binding of transcription factors (TF) to nucleosomal DNA is frequently impeded, which makes it a challenging task to calculate TF occupancy at a given regulatory genomic site for predicting gene expression. Here, we review methods to calculate TF binding to DNA in the presence of nucleosomes.

A novel computational approach "BP-STOCH" to study ligand binding to finite lattice.

We report a novel computational algorithm "BP-STOCH" to be used for studying single-type ligand binding with biopolymers of finite lengths, such as DNA oligonucleotides or oligopeptides. It is based on an idea to represent any type of ligand-biopolymer complex in a form of binary number, where "0" and "1" bits stand for vacant and engaged monomers of the biopolymer, respectively. Cycling over all binary numbers from the lowest 0 up to the highest 2(N) - 1 means a sequential generating of all possible configurations of vacant/engaged monomers, which, after proper filtering, results in a full set of possible types of complexes in solution between the ligand and the N-site lattice.

Does the ligand-biopolymer equilibrium binding constant depend on the number of bound ligands?

Conventional methods, such as Scatchard or McGhee-von Hippel analyses, used to treat ligand-biopolymer interactions, indirectly make the assumption that the microscopic binding constant is independent of the number of ligands, i, already bound to the biopolymer. Recent results on the aggregation of aromatic molecules (Beshnova et al., J Chem Phys 2009, 130, 165105) indicated that the equilibrium constant of self-association depends intrinsically on the number of molecules in an aggregate due to loss of translational and rotational degrees of freedom on formation of the complex.

Profiles of equilibrium constants for self-association of aromatic molecules.

Analysis of the noncovalent, noncooperative self-association of identical aromatic molecules assumes that the equilibrium self-association constants are either independent of the number of molecules (the EK-model) or change progressively with increasing aggregation (the AK-model). The dependence of the self-association constant on the number of molecules in the aggregate (i.e.