Molecular dynamics simulations identify the topological weak spots of a protease CN2S8A.

Thermophilic enzymes are highly desired in industrial applications due to their efficient catalytic activity at high temperature. However, most enzymes exhibit inferior thermostability and it remains challenging to identify the optimal sites for designing mutations to improve protein stability. To tackle this issue, we integrated topological analysis and all-atom molecular dynamics simulations to efficiently pinpoint the thermally-unstable regions in protein structures. Using a protease CN2S8A as the model, we analyzed the intramolecular hydrogen bonding interactions between adjacent secondary structure elements, and then identified the topological weak spots of CN2S8A where weak hydrogen bonding interactions were formed. To examine the role of these sites in protein structural stability, we designed three virtual mutations at different weak spots and characterized the effects of these mutations on the structural properties of CN2S8A. The results showed that all three mutations increased the protein structural stability. In conclusion, these findings provide a novel method to identify the topological weak spots of proteins, with implications in the rational design of biocatalysts with superior thermostability.

Halogen Bonds Exist between Noncovalent Ligands and Natural Nucleic Acids.

Because of their strong electron-rich properties, nucleic acids (NAs) can theoretically serve as halogen bond (XB) acceptors. From a PDB database survey, Kolár found that no XBs are formed between noncovalent ligands and NAs. Through statistical database analysis, quantum-mechanics/molecular-mechanics (QM/MM) optimizations, and energy calculations, we find that XBs formed between natural NAs and noncovalent ligands are primarily underestimated and that NAs can serve as XB acceptors to interact with noncovalent halogen ligands. Finally, through energy calculations, natural bond orbital analysis, and noncovalent interaction analysis, XBs are confirmed in 13 systems, among which two systems (445D and 4Q9Q) have relatively strong XBs. In addition, on the basis of energy scanning of four model systems, we explore the geometric rule for XB formation in NAs. This work will inspire researchers to utilize XBs in rational drug design targeting NAs.

Identification and mechanistic analysis of an inhibitor of the CorC Mg2+ transporter.

The CorC/CNNM family of Na+-dependent Mg2+ transporters is ubiquitously conserved from bacteria to humans. CorC, the bacterial CorC/CNNM family of proteins, is involved in resistance to antibiotic exposure and in the survival of pathogenic microorganisms in their host environment. The CorC/CNNM family proteins possess a cytoplasmic region containing the regulatory ATP-binding site. CorC and CNNM have attracted interest as therapeutic targets, whereas inhibitors targeting the ATP-binding site have not been identified. Here, we performed a virtual screening of CorC by targeting its ATP-binding site, identified a compound named IGN95a with inhibitory effects on ATP binding and Mg2+ export, and determined the cytoplasmic domain structure in complex with IGN95a. Furthermore, a chemical cross-linking experiment indicated that with ATP bound to the cytoplasmic domain, the conformational equilibrium of CorC was shifted more toward the inward-facing state of the transmembrane domain. In contrast, IGN95a did not induce such a shift.

Ligand-based approach for predicting drug targets and for virtual screening against COVID-19.

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing coronavirus disease 2019 (COVID-19). Protein structure-based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by e.g. various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure-based platform for COVID-19, termed as D3Docking in our previous work, we developed in this study a ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses, viz., severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human betacoronavirus 2c EMC/2012 (HCoV-EMC), human CoV 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), some of which have target or mechanism information but some do not. Based on the two-dimensional (2D) and three-dimensional (3D) similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity by using 2D × 3D value as score for drug discovery and target prediction against COVID-19. The database, which will be updated regularly, is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.

Computational Insights into the Conformational Accessibility and Binding Strength of SARS-CoV-2 Spike Protein to Human Angiotensin-Converting Enzyme 2.

The spike protein of SARS-CoV-2 (CoV-2-S) mediates the virus entry into human cells. Experimental studies have shown the stronger binding affinity of the RBD (receptor binding domain) of CoV-2-S to angiotensin-converting enzyme 2 (ACE2) as compared to that of SARS-CoV spike (CoV-S). However, a similar or weaker binding affinity of CoV-2-S compared to that of CoV-S is observed if entire spikes are used in the bioassay. To explore the underlying mechanism, we calculated the binding affinities of the RBDs to ACE2 and simulated the transitions between ACE2-inaccessible and -accessible conformations. We found that the ACE2-accessible angle of CoV-2-S is 52.2° and that the ACE2 binding strength of CoV-2-S RBD is much stronger than that of CoV-S RBD. However, CoV-2-S has much less of an ACE2-accessible conformation and is much more difficult to shift from ACE2-inaccessible to -accessible than CoV-S, making the binding affinity of the entire protein decrease. Further analysis revealed key interactional residues for strong binding and five potential ligand-binding pockets for drug research.

D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19.

A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å3 on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19.