What is the smallest protein? This is actually not such a simple question to answer, because there is no established consensus among scientists as to the definition of a protein. We describe here a designed molecule consisting of only 10 amino acids. Despite its small size, its essential characteristics, revealed by its crystal structure, solution structure, thermal stability, free energy surface, and folding pathway network, are consistent with the properties of natural proteins. The existence of this kind of molecule deepens our understanding of proteins and impels us to define an “ideal protein” without inquiring whether the molecule actually occurs in nature.

Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from large chemical library for further biological experiments. However, the accuracy of current virtual screening methods is insufficient. In this study, we develop a new virtual screening method named Similarity of Interaction Energy VEctor Score (SIEVE-Score), in which proteinligand interaction energies are extracted to represent of docking poses for machine learning. SIEVE-Score offers substantial improvements compared to other state-of-theart virtual screening methods, namely, other machine-learning-based scoring functions, interaction fingerprints, and docking software, for the enrichment factor 1% results on the Directory of Useful Decoys, Enhanced (DUD-E). The screening results are also human-interpretable in the form of important interactions for distinguishing between active and inactive compounds. The source code is available at: https://github.com/ sekijima-lab/SIEVE-Score.

The number of cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) has reached over 114,000. SARS-CoV-2 caused a pandemic in Wuhan, China, in December 2019 and is rapidly spreading globally. It has been reported that peptide-like anti-HIV-1 drugs are effective against SARS-CoV Main protease (M^pro). Due to the close phylogenetic relationship between SARS-CoV and SARS-CoV-2, their main proteases share many structural and functional features. Thus, these drugs are also regarded as potential drug candidates targeting SARS-CoV-2 M^pro. However, the mechanism of action of SARS-CoV-2 M^pro at the atomic-level is unknown. In the present study, we revealed key interactions between SARS-CoV-2 M^pro and three drug candidates by performing pharmacophore modeling and 1 μs molecular dynamics (MD) simulations. His41, Gly143, and Glu166 formed interactions with the functional groups that were common among peptide-like inhibitors in all MD simulations. These interactions are important targets for potential drugs against SARS-CoV-2 M^pro.

A central theme in prion protein research is the detection of the process that underlies the conformational transition from the normal cellular prion form (PrP(C)) to its pathogenic isoform (PrP(Sc)). Although the three-dimensional structures of monomeric and dimeric human prion protein (HuPrP) have been revealed by NMR spectroscopy and x-ray crystallography, the process underlying the conformational change from PrP(C) to PrP(Sc) and the dynamics and functions of PrP(C) remain unknown. The dimeric form is thought to play an important role in the conformational transition. In this study, we performed molecular dynamics (MD) simulations on monomeric and dimeric HuPrP at 300 K and 500 K for 10 ns to investigate the differences in the properties of the monomer and the dimer from the perspective of dynamic and structural behaviors. Simulations were also undertaken with Asp178Asn and acidic pH, which is known as a disease-associated factor. Our results indicate that the dynamics of the dimer and monomer were similar (e.g., denaturation of helices and elongation of the beta-sheet). However, additional secondary structure elements formed in the dimer might result in showing the differences in dynamics and properties between the monomer and dimer (e.g., the greater retention of dimeric than monomeric tertiary structure).

Motivation: Protein–protein interactions are essential for the cell and mediate various functions. However, mutations can disrupt these interactions and may cause diseases. Currently available computational methods require a complex structure as input for predicting the change in binding affinity. Further, they have not included the functional class information for the protein–protein complex. To address this, we have developed a method, ProAffiMuSeq, which predicts the change in binding free energy using sequence-based features and functional class. Results: Our method shows an average correlation between predicted and experimentally determined ΔΔG of 0.73 and mean absolute error (MAE) of 0.86 kcal/mol in 10-fold cross-validation and correlation of 0.75 with MAE of 0.94 kcal/mol in the test dataset. ProAffiMuSeq was also tested on an external validation set and showed results comparable to structure-based methods. Our method can be used for large-scale analysis of disease-causing mutations in protein–protein complexes without structural information. Availability and implementation: Users can access the method at https://web.iitm.ac.in/bioinfo2/proaffimuseq/. Supplementary information: Supplementary data are available at Bioinformatics online.

B-cell lymphoma 2 (Bcl-2) family proteins are potential drug targets in cancer and have a relatively flat and flexible binding site. ABT-199 is one of the most promising selective Bcl-2 inhibitors, and A-1155463 selectively inhibits Bcl-XL. Although the amino acid sequences of the binding sites of these two inhibitors are similar, the inhibitors selectively bind the target protein. In order to determine the origin of the selectivity of these inhibitors, we conducted molecular dynamics simulations using protein-inhibitor modeling. We confirmed that ASP103 of Bcl-2 is a key residue and that hydrogen bonding between ASP103 and ABT-199 confers the Bcl-2 selectivity of this inhibitor. For Bcl-XL selectivity, the secondary structure of α-helix 3 is a key factor. PHE105, SER106, and LEU108 in the loose α-helix 3 interact with A-1155463 to confer Bcl-XL selectivity. These findings provide important insights into the molecular mechanisms of selective inhibitors of Bcl-2 family proteins.