The second and subsequent waves of the coronavirus disease (COVID-19) have caused problems worldwide. Here, using objective analysis, we present the changes that occurred in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus has mutated in three major directions, resulting in three groups to date. The basic viral genome was identified in April and shared across all continents. However, the virus continued to mutate independently in each country after the closure of borders. Some variants with greater infectivity replaced the earlier ones and caused second waves of the disease. Some of them slowly entered other countries and caused epidemics. Going forward, these viruses could also serve as sources of further mutations.

Diffusion is a spontaneous process and one of the physicochemical phenomena responsible for molecular transport, the rate of which is governed mainly by the diffusion coefficient; however, few coefficients are available for small molecules. We have constructed a simple and convenient experimental system with agar-gel to measure the diffusion coefficients of sugars.

A theoretical method has also been developed to estimate diffusion coefficients by a combination of molecular modeling and the Stokes–Einstein equation, by which the coefficients of several sugars, amino acids, and drug molecules have been obtained. This time we have applied both experimental and theoretical approaches to estimate the diffusion coefficients of an additional 10 amino acids. The measured and calculated values are consistent with small deviations, i.e., the diffusion coefficients estimated by molecular modeling correspond well to the experimental data, which suggests that the potential use of the diffusion coefficient as an additional molecular property in drug screening has been enhanced.

Among the various methods so far proposed for genetic network inference, this study focuses on the random-forest-based methods. Confidence values are assigned to all of the candidate regulations when taking the random-forest-based approach. To our knowledge, all of the random-forest-based methods make the assignments using the standard variable importance measure defined in tree-based machine learning techniques. Therefore, the sum of the confidence values of the candidate regulations of a certain gene from the other genes, that are computed from a single random forest, is always restricted to a value of almost 1. We think that this feature is inconvenient for the genetic network inference that requires to compare the confidence values computed from multiple random forests. In this study we therefore propose an alternative measure, what we call ``the random-input variable importance measure,'' and design a new inference method that uses the proposed measure in place of the standard measure in the existing random-forest-based inference method. We show, through numerical experiments, that the use of the random-input variable importance measure improves the performance of the existing random-forest-based inference method by as much as 45.5% with respect to the area under the recall-precision curve (AURPC).

We investigated electronic states of a complex of zinc metalloprotease ubiquitin ligase 2(UBR2) with its peptide ligand using ab initio fragment molecular orbital (FMO) calculations. UBR2 possesses three Zn ions and several residues of UBR2 are coordinated to each Zn ion to form an active site of UBR2. To provide a precise description of these coordination bonds, we included these residues in the same fragment as Zn ion in FMO calculations. The results revealed that all coordinated residues should be included in the same fragment as Zn ion for obtaining the converged results. This fact can be applicable equally to metalloproteases including other metal ions.

Medium-sized molecules have attracted significant attention as new chemical modalities. In this study, we compared the performances of three methods of 3D structure generation for medium-sized molecules using free and commercial software. The benchmark dataset consisted of 2131 protein-binding ligands with molecular weights greater than 600, which were selected from the Protein Data Bank (PDB). When selecting the smallest root mean square deviation between the generated 3D conformers and the PDB ligand structures, 43% of the conformations determined with the software CORINA were within 1 Å, followed by 10% from OMEGA and 5% from RDKit. According to our results, comparing the polar solvent-accessible surface area (PSA) and normalized principal moment of inertia ratio (NPR) among the three methods, 83% of the conformers generated with CORINA were within 20 in PSA, and 53% of the conformers from CORINA were within 0.05 in the NPR1 and NPR2 spaces. Thus, we concluded that CORINA has the highest performance in terms of efficient conformer generation. We also examined 3D descriptor calculation using Mordred, which is a free descriptor computation tool. The results showed that OMEGA-generated conformers exhibited the highest success rate, indicating that OMEGA is a suitable conformer generation tool for various 3D descriptors. Our results could contribute to the selection of conformation generators for the rapid construction of various predictive models for medium-sized molecules and can be shared with the research community for further validation.

We investigated fragmentation methods around metals when performing fragment molecular orbital (FMO) calculations for metal-containing proteins, as well as appropriate structural preprocessing. The protein structure data was employed from the peptidyl prolyl cis/trans isomerase (PPlase) domain of human Cyclophilin G, which contains Mg²⁺ ions (PDBID: 2WFI). The results of the present study revealed three issues: First, it was better to contain Mg²⁺ ions in the same fragment as that for water molecules in the first hydration shell, which was revealed thorough PIEDA (pair interaction energy decomposition analysis) and atomic charge analysis obtained by FMO calculations. Second, while there were two conformers of Phe72 in the PPlase domain of Cyclophilin, we could determine the more appropriate conformer for computational analysis by comparing each energy component of PIEDA. Finally, we derived the optimal constraint conditions for the structural optimization of this molecular system in which the exchange repulsion (EX) component of PIEDA took relevant values without deforming the initial structure too much. These findings could be applied to FMO calculations for other proteins as well.

The development of next generation non-steroidal anti-inflammatory drugs (NSAIDs) is one active area of research as inflammatory diseases continue to afflict over 1.5 billion people worldwide. The publicly available and computationally accessible chemical and biological data provide a wellspring of information for any research pursuit that could expedite the discovery of new anti-inflammatory drugs. Computational statistics is a handy tool in establishing quantitative relationship between the anti-inflammatory activity and the key molecular features that determine the compound’s medicinal property. In this work, Multiple Logistic Regression (MLogR) was employed to develop a mathematical model of the inhibitory activity of a compound on cyclooxygenase-2 (COX-2), an enzyme that facilitates the production of inflammatory prostanoids. The best model with hit ratio of 94% and 91% on the train and test set, respectively, was used to predict the classification (i.e. active or inactive) of newly designed coxib Derivatives and Similars obtained through similarity search. The predicted actives were further screened based on their quantitative estimate of druglikeness (QED), synthetic accessibility, and ADMETox properties. The selected top 15 hits have superior confidence as actives, are highly druglike and easy to synthesize, and generally possess outstanding drug profile.

Machine learning (ML) models are cost-effective methods that have accelerated the identification of novel drug candidates in pharmaceutical research. These in silico methods estimate the characteristics of chemical compounds using calculated physicochemical features or using molecular sub-structure fingerprints. This rise in the deployment of machine learning models is facilitated by the development of numerous ML packages that enables researchers to build local models to meet their requirements. Despite the growing ease of building ML models, programming these informatics-driven solutions can be arduous for wet-lab researchers. In this study, we present a template for ML model construction that would enable researchers to efficiently reproduce ML models. We constructed prototype models to estimate the fraction of absorption and membrane permeability of a chemical compound using Mordred descriptors.

Structural biology comprises “Structured biology” based on folded structure and “Unstructured biology” based on unfolded structure. Principal of “Unstructured biology” is intrinsically disordered protein (IDP), in which the polypeptide chain is highly disordered under physiological conditions. The length of the disordered polypeptide may extend to several hundred residues or more. Further, in protein comprising multiple domains, long disordered polypeptides exist between domains, even if each domain has a regularly folded structure. Such polypeptide region is called intrinsically disordered region (IDR). Several IDPs and IDRs exist in living cells and play important biological roles in intracellular networks. Here, the biological significance of IDP and IDR from various viewpoints are described. Overall, this review aims to encourage further studies on “Unstructured biology” based on unfolded/disordered protein structure.