Quantitative Biology

Debye-Waller factor, a measure of X-ray attenuation, can be experimentally observed in protein X-ray crystallography. Previous theoretical models have made strong inroads in the analysis of B-factors by linearly fitting protein B-factors from experimental data. However, the blind prediction of B-factors for unknown proteins is an unsolved problem. This work integrates machine learning and advanced graph theory, namely, multiscale weighted colored graphs (MWCGs), to blindly predict B-factors of unknown proteins. MWCGs are local features that measure the intrinsic flexibility due to a protein structure. Global features that connect the B-factors of different proteins, e.g., the resolution of X-ray crystallography, are introduced to enable the cross-protein B-factor predictions. Several machine learning approaches, including ensemble methods and deep learning, are considered in the present work. The proposed method is validated with hundreds of thousands of experimental B-factors. Extensive numerical results indicate that the blind B-factor predictions obtained from the present method are more accurate than the least squares fittings using traditional methods.

An ideal signature material and method, which can be used to prove the authenticity of a physical item and against forgery, should be immune to the fast developments in digital and engineering technologies. Herein, the design of signature material combining cryptographic hash function and DNA steganography is proposed. The encrypting materials are used to construct a series of time-stamped records (blockchain) associated with published hash values, while each DNA-encrypted block is associated with a set of DNA keys. The decrypted DNA information, as digital keys, can be validated through a hash function to compare with the published hash values. The blocks can also be cross-referenced among different related signatures. While both digital cryptography and DNA steganography can have large key size, automated brutal force search is far more labor intensive and expensive for DNA steganography with wet lab experiments, as compared to its digital counterpart. Moreover, the time-stamped blockchain structure permits the incorporation of new cryptographic functions and DNA steganographies over time, thus can evolve over time without losing the continuous history line.

Although non-canonical residues, caps, crosslinks, and nicks play an important role in the function of many DNA, RNA, proteins, and complexes, we do not fully understand how networks of non-canonical macromolecules generate behavior. One barrier is our limited formats, such as IUPAC, for abstractly describing macromolecules. To overcome this barrier, we developed BpForms and BcForms, a toolkit of ontologies, grammars, and software for abstracting the primary structure of polymers and complexes as combinations of residues, caps, crosslinks, and nicks. The toolkit can help quality control, exchange, and integrate information about the primary structure of macromolecules into fine-grained global networks of intracellular biochemistry.

The native state structures of globular proteins are stable and well-packed indicating that self-interactions are favored over protein-solvent interactions under folding conditions. We use this as a guiding principle to derive the geometry of the building blocks of protein structures, alpha-helices and strands assembled into beta-sheets, with no adjustable parameters, no amino acid sequence information, and no chemistry. There is an almost perfect fit between the dictates of mathematics and physics and the rules of quantum chemistry. Our theory establishes an energy landscape that channels protein evolution by providing sequence-independent platforms for elaborating sequence-dependent functional diversity. Our work highlights the vital role of discreteness in life and has implications for the creation of artificial life and on the nature of life elsewhere in the cosmos.

Classical simulations of protein flexibility remain computationally expensive, especially for large proteins. A few years ago, we developed a fast method for predicting protein structure fluctuations that uses a single protein model as the input. The method has been made available as the CABS-flex web server and applied in numerous studies of protein structure-function relationships. Here, we present a major update of the CABS-flex web server to version 2.0. The new features include: extension of the method to significantly larger and multimeric proteins, customizable distance restraints and simulation parameters, contact maps and a new, enhanced web server interface. CABS-flex 2.0 is freely available at this http URL

Motivation: The coronavirus disease 2019 (COVID-19) caused by a new type of coronavirus has been emerging from China and led to thousands of death globally since December 2019. Despite many groups have engaged in studying the newly emerged virus and searching for the treatment of COVID-19, the understanding of the COVID-19 target-ligand interactions represents a key chal-lenge. Herein, we introduce COVID-19 Docking Server, a web server that predicts the binding modes between COVID-19 targets and the ligands including small molecules, peptides and anti-bodies. Results: Structures of proteins involved in the virus life cycle were collected or constructed based on the homologs of coronavirus, and prepared ready for docking. The meta platform provides a free and interactive tool for the prediction of COVID-19 target-ligand interactions and following drug discovery for COVID-19.

SARS-CoV-2 (COVID-19), a positive single stranded RNA virus, member of corona virus family, is spreading its tentacles across the world due to lack of drugs at present. Being associated with cough, fever, and respiratory distress, this disease caused more than 15 % mortality worldwide. Due to its vital role in virus replication, Mpro/3CLpro has recently been regarded as a suitable target for drug design. The current study focused on the inhibitory activity of Calotropin, a component from milk of Calotropis gigantean, against Mpro protein from SARS-CoV-2. Till date there is no work is undertaken on in-silico analysis of this compound against Mpro of COVID-19 protein. In the present study, molecular docking studies were conducted by using Patchdock tool. Protein Interactions tool was used for protein interactions. The calculated parameters such as docking score indicated effective binding of Calotropin to Mpro protein. Interactions results indicated that, Mpro/ Calotropin complexes forms hydrophobic interactions. Therefore, Calotropin may represent potential herbal treatment to act as COVID-19 Mpro inhibitor. However, further research is necessary to investigate their potential medicinal use.

We inquire to what extent protein peptide plane and side chain dynamics can be reconstructed from knowledge of C-alpha dynamics. Due to lack of experimental data we analyze all atom molecular dynamics trajectories from Anton supercomputer, and for clarity we limit our attention to the peptide plane O atoms and side chain C-beta atoms. We try and reconstruct their dynamics using four different approaches. Three of these are the publicly available reconstruction programs Pulchra, Remo Scwrl4. The fourth, Statistical Method, builds entirely on statistical analysis of Protein Data Bank (PDB) structures. All four methods place the O and C-beta atoms accurately along the Anton trajectories. However, the Statistical Method performs best. The results suggest that under physiological conditions, the all atom dynamics is slaved to that of C-alpha atoms. The results can help improve all atom force fields, and advance reconstruction and refinement methods for reduced protein structures. The results provide impetus for development of effective coarse grained force fields in terms of reduced coordinates.

Pepducins are cell-penetrating, membrane-tethered lipopeptides designed to target the intracellular region of a G protein-coupled receptor (GPCR) in order to allosterically modulate the receptor's signaling output. In this proof-of-concept study, we explored the pain-relief potential of a pepducin series derived from the first intracellular loop of neurotensin receptor type 1 (NTS1), a class A GPCR that mediates many of the effects of the neurotensin (NT) tridecapeptide, including hypothermia, hypotension and analgesia. We used BRET-based biosensors to determine the pepducins' ability to engage G protein signaling pathways associated with NTS1 activation. We observed partial Gq and G13 activation at a 10 {\mu}M concentration, indicating that these pepducins may act as allosteric agonists of NTS1. Additionally, we used surface plasmon resonance (SPR) as a label-free assay to monitor pepducin-induced responses in CHO-K1 cells stably expressing hNTS1. This whole-cell integrated assay enabled us to subdivide our pepducin series into three profile response groups. In order to determine the pepducins' antinociceptive potential, we then screened the series in an acute pain model (tail-flick test) by measuring tail withdrawal latencies to a thermal nociceptive stimulus, following intrathecal pepducin administration (275 nmol/kg). We further evaluated promising pepducins in a tonic pain model (formalin test), as well as in neuropathic (Chronic Constriction Injury) and inflammatory (Complete Freund's Adjuvant) chronic pain models. We report one pepducin, PP-001, that consistently reduced rat nociceptive behaviors, even in chronic pain paradigm. Altogether, these results suggest that NTS1-derived pepducins may represent a promising strategy in pain-relief.

Deep generative models are increasingly becoming integral parts of the in silico molecule design pipeline and have dual goals of learning the chemical and structural features that render candidate molecules viable while also being flexible enough to generate novel designs. Specifically, Variational Auto Encoders (VAEs) are generative models in which encoder-decoder network pairs are trained to reconstruct training data distributions in such a way that the latent space of the encoder network is smooth. Therefore, novel candidates can be found by sampling from this latent space. However, the scope of architectures and hyperparameters is vast and choosing the best combination for in silico discovery has important implications for downstream success. Therefore, it is important to develop a principled methodology for distinguishing how well a given generative model is able to learn salient molecular features. In this work, we propose a method for measuring how well the latent space of deep generative models is able to encode structural and chemical features of molecular datasets by correlating latent space metrics with metrics from the field of topological data analysis (TDA). We apply our evaluation methodology to a VAE trained on SMILES strings and show that 3D topology information is consistently encoded throughout the latent space of the model.