Scaled Alternating Steepest Descent Algorithm Applied for Protein Structure Determination from Nuclear Magnetic Resonance Data

In silico design of potential Mcl-1 peptide-based inhibitors

CONTEXT

BIM (Bcl-2 interacting mediator of apoptosis)-derived peptides that specifically target over-expressed Mcl-1 (myeloid cell leukemia-1) protein and induce apoptosis are potentially anti-cancer agents. Since the helicity of BIM-derived peptides has a crucial role in their functionality, a range of strategies have been used to increase the helicity including the introduction of unnatural residues and stapling methods that have some drawbacks such as the accumulation in the liver. To avoid these drawbacks, this study aimed to design a more helical peptide by utilizing bioinformatics algorithms and molecular dynamics simulations without exploiting unnatural residues and stapling methods. MM-PBSA results showed that the mutations of A4fE and A2eE in analogue 5 demonstrate a preference towards binding with Mcl-1. As evidenced by Circular dichroism results, the helicity increases from 18 to 34%, these findings could enhance the potential of analogue 5 as an anti-cancer agent targeting Mcl-1. The applied strategies in this research could shed light on the in silico peptide design. Moreover, analogue 5 as a drug candidate can be evaluated in vitro and in vivo studies.

METHODS

The sequence of the lead peptide was determined using the ApInAPDB database and PRALINE program. Contact finder and PDBsum web server softwares were used to determine the contact involved amino acids in complex with Mcl-1. All identified salt bridge contributing residues were unaltered to preserve the binding affinity. After proposing novel analogues, their secondary structures were predicted by Cham finder web server software and GOR, Neural Network, and Chou-Fasman algorithms. Finally, molecular dynamics simulations run for 100 ns were done using the GROMACS, version 5.0.7, with the CHARMM36 force field. MM-PBSA was used to assess binding affinity specificity in targeting Mcl-1 and Bcl-xL (B-cell lymphoma extra-large).

Improved NMR-data-compliant protein structure modeling captures context-dependent variations and expands the scope of functional inference

Nuclear magnetic resonance (NMR) spectroscopy can reveal conformational states of a protein in physiological conditions. However, sparsely available NMR data for a protein with large degrees of freedom can introduce structural artifacts in the built models. Currently used state-of-the-art methods deriving protein structure and conformation from NMR deploy molecular dynamics (MD) coupled with simulated annealing for building models. We provide an alternate graph-based modeling approach, where we first build substructures from NMR-derived distance-geometry constraints combined in one shot to form the core structure. The remaining molecule with inadequate data is modeled using a hybrid approach respecting the observed distance-geometry constraints. One-shot structure building is rarely undertaken for large and sparse data systems, but our data-driven bottom-up approach makes this uniquely feasible by suitable partitioning of the problem. A detailed comparison of select models with state-of-art methods reveals differences in the secondary structure regions wherein the correctness of our models is confirmed by NMR data. Benchmarking of 106 protein-folds covering 38-282 length structures shows minimal experimental-constraint violations while conforming to other structure quality parameters such as the proper folding, steric clash, and torsion angle violation based on Ramachandran plot criteria. Comparative MD studies using select protein models from a state-of-art method and ours under identical experimental parameters reveal distinct conformational dynamics that could be attributed to protein structure-function. Our work is thus useful in building enhanced NMR-evidence-based models that encapsulate the contextual secondary and tertiary structure variations present during the experimentation and expand the scope of functional inference.

Ultralow-threshold green fluorescent protein laser based on high Q microbubble resonators

Biological lasers have attracted vast attention because of their potential medical application prospects, especially the low threshold biological laser that can be used for ultrasensitive biological detection while leaving the luminous gain medium undamaged by the pump light. By coupling the low concentration green fluorescent protein (GFP) solution with a high Q whispering gallery mode microbubble resonator, we managed to fabricate a miniature GFP laser with the lowest threshold and highest Q value compared to any known type of the GFP laser. The threshold energy is as low as 380 fJ, two orders of magnitude lower than any type of GFP laser at present. The Q value of the optical cavity in this biological laser is 5.3 × 10⁷, two orders higher than the highest Q value of GFP lasers. We further confirmed the long-term stability of the working characteristics of GFP laser. It can work well nearly a month in temperature 3-4°C. Finally, we measured the effects of different concentrations of fluorescent protein on laser threshold. The data show that this biological laser can be used for highly sensitive detection of GFP concentration, which is particularly useful when the GFP is used as tracers.

Prediction of hyaluronic acid target on sucrase-isomaltase (SI) with reverse docking and molecular dynamics simulations for inhibitors binding to SI

Auricularia cornea (E.) polysaccharide is an important component of A. cornea Ehrenb, a white mutant strain of Auricularia with biological activities, such as enhancement of human immune function and cancer prevention. The hyaluronic acids (HAs) are important components of the A. cornea polysaccharide and have extremely high medicinal value. In this study, we used HA to search the target protein sucrase-isomaltase (SI). In addition, we also performed molecular dynamics (MD) simulations to explore the binding of three inhibitors (HA, acarbose and kotalanol) to SI. The MD simulations indicated that the binding of the three inhibitors may induce the partial disappearance of α helix in residues 530–580. Hence, the hydrogen bond for Gly570-Asn572, which was near the catalytic base Asp471 in SI, was broken during the binding of the three inhibitors. We reveal a new inhibitor for SI and provide reasonable theoretical clues for inhibitor binding to SI.

Insight into molecular characteristics of SARS-CoV-2 spike protein following D614G point mutation, a molecular dynamics study

Undoubtedly, the SARS-CoV-2 has become a major concern for all societies due to its catastrophic effects on public health. In addition, mutations and changes in the structure of the virus make it difficult to design effective treatment. Moreover, the amino acid sequence of a protein is a major factor in the formation of the second and tertiary structure in a protein. Amino acid replacement can have noticeable effects on the folding of a protein, especially if an asymmetric change (substitution of polar residue with non-polar, charged with an uncharged, positive charge with a negative charge, or large residue with small residue) occurs. D614G as a spike mutant of SARS-CoV-2 previously identified as an associated risk factor with a high mortality rate of this virus. Using structural bioinformatics, our group determined that D614G mutation could cause extensive changes in SARS-CoV-2 behavior including the secondary structure, receptor binding pattern, 3D conformation, and stability of it.

Communicated by Ramaswamy H. Sarma

Protein structure determination using a Riemannian approach

Protein NMR structure determination is one of the most extensively studied problems. Here, we adopt a novel method based on a matrix completion technique - the Riemannian approach - to rebuild the protein structure from the nuclear Overhauser effect distance restraints and the dihedral angle restraints. In comparison with the cyana method, the results generated via the Riemannian approach are more similar to the standard X-ray crystallographic structures as a result of the simple but powerful internal calculation processing function. In addition, our results demonstrate that the Riemannian approach has a comparable or even better performance than the cyana method on other structural assessment metrics, including the stereochemical quality and restraint violations. The Riemannian approach software is available at: https://github.com/xubiaopeng/Protein_Recon_MCRiemman.