“Dividing and Conquering” and “Caching” in Molecular Modeling

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes “dividing and conquering” and/or “caching” in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of “dividing and conquering” and “caching” along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution “caching” of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for “dividing and conquering” and “caching” in complex molecular systems.     

Slaying (Yet Again) the Brain-Eating Zombie Called the “Isochore Theory”: A Segmentation Algorithm Used to “Confirm” the Existence of Isochores Creates “Isochores” Where None Exist

The isochore theory, which was proposed more than 40 years ago, depicts the mammalian genome as a mosaic of long, homogeneous regions that are characterized by their guanine and cytosine (GC) content. The human genome, for instance, was claimed to consist of five compositionally distinct isochore families. The isochore theory, in all its reincarnations, has been repeatedly falsified in the literature, yet isochore proponents have persistently resurrected it by either redefining isochores or by proposing alternative means of testing the theory. Here, I deal with the latest attempt to salvage this seemingly immortal zombie—a sequence segmentation method called isoSegmenter, which was claimed to “identify” isochores while at the same time disregarding the main characteristic attribute of isochores—compositional homogeneity. I used a series of controlled, randomly generated simulated sequences as a benchmark to study the performance of isoSegmenter. The main advantage of using simulated sequences is that, unlike real data, the exact start and stop point of any isochore or homogeneous compositional domain is known. Based on three key performance metrics—sensitivity, precision, and Jaccard similarity index—isoSegmenter was found to be vastly inferior to isoPlotter, a segmentation algorithm with no user input. Moreover, isoSegmenter identified isochores where none exist and failed to identify compositionally homogeneous sequences that were shorter than 100−200 kb. Will this zillionth refutation of “isochores” ensure a final and permanent entombment of the isochore theory? This author is not holding his breath.     

A Regulator Based “Semi-Targeted” Approach to Activate Silent Biosynthetic Gene Clusters

By culturing microorganisms under standard laboratory conditions, most biosynthetic gene clusters (BGCs) are not expressed, and thus, the products are not produced. To explore this biosynthetic potential, we developed a novel “semi-targeted” approach focusing on activating “silent” BGCs by concurrently introducing a group of regulator genes into streptomycetes of the Tübingen strain collection. We constructed integrative plasmids containing two classes of regulatory genes under the control of the constitutive promoter ermE*p (cluster situated regulators (CSR) and Streptomyces antibiotic regulatory proteins (SARPs)). These plasmids were introduced into Streptomyces sp. TÜ17, Streptomyces sp. TÜ10 and Streptomyces sp. TÜ102. Introduction of the CSRs-plasmid into strain S. sp. TÜ17 activated the production of mayamycin A. By using the individual regulator genes, we proved that Aur1P, was responsible for the activation. In strain S. sp. TÜ102, the introduction of the SARP-plasmid triggered the production of a chartreusin-like compound. Insertion of the CSRs-plasmid into strain S. sp. TÜ10 resulted in activating the warkmycin-BGC. In both recombinants, activation of the BGCs was only possible through the simultaneous expression of aur1PR3 and griR in S. sp. TÜ102 and aur1P and pntR in of S. sp. TÜ10.     

From Molecular Recognition to the “Vehicles” of Evolutionary Complexity: An Informational Approach

Countless informational proposals and models have explored the singular characteristics of biological systems: from the initial choice of information terms in the early days of molecular biology to the current bioinformatic avalanche in this “omic” era. However, this was conducted, most often, within partial, specialized scopes or just metaphorically. In this paper, we attempt a consistent informational discourse, initially based on the molecular recognition paradigm, which addresses the main stages of biological organization in a new way. It considers the interconnection between signaling systems and information flows, between informational architectures and biomolecular codes, between controlled cell cycles and multicellular complexity. It also addresses, in a new way, a central issue: how new evolutionary paths are opened by the cumulated action of multiple variation engines or mutational ‘vehicles’ evolved for the genomic exploration of DNA sequence space. Rather than discussing the possible replacement, extension, or maintenance of traditional neo-Darwinian tenets, a genuine informational approach to evolutionary phenomena is advocated, in which systemic variation in the informational architectures may induce differential survival (self-construction, self-maintenance, and reproduction) of biological agents within their open ended environment.     

Platelets Contribution to Thrombin Generation in Philadelphia-Negative Myeloproliferative Neoplasms: The “Circulating Wound” Model

Current cytoreductive and antithrombotic strategies in MPNs are mostly based on cell counts and on patient’s demographic and clinical history. Despite the numerous studies conducted on platelet function and on the role of plasma factors, an accurate and reliable method to dynamically quantify the hypercoagulability states of these conditions is not yet part of clinical practice. Starting from our experience, and after having sifted through the literature, we propose an in-depth narrative report on the contribution of the clonal platelets of MPNs—rich in tissue factor (TF)—in promoting a perpetual procoagulant mechanism. The whole process results in an unbalanced generation of thrombin and is self-maintained by Protease Activated Receptors (PARs). We chose to define this model as a “circulating wound”, as it indisputably links the coagulation, inflammation, and fibrotic progression of the disease, in analogy with what happens in some solid tumours. The platelet contribution to thrombin generation results in triggering a vicious circle supported by the PARs/TGF-beta axis. PAR antagonists could therefore be a good option for target therapy, both to contain the risk of vascular events and to slow the progression of the disease towards end-stage forms. Both the new and old strategies, however, will require tools capable of measuring procoagulant or prohaemorrhagic states in a more extensive and dynamic way to favour a less empirical management of MPNs and their potential clinical complications.     

The “Hitchhiker’s Guide to the Galaxy” of Endothelial Dysfunction Markers in Human Fertility

Endothelial dysfunction is an early event in the pathogenesis of atherosclerosis and represents the first step in the pathogenesis of cardiovascular diseases. The evaluation of endothelial health is fundamental in clinical practice and several direct and indirect markers have been suggested so far to identify any alterations in endothelial homeostasis. Alongside the known endothelial role on vascular health, several pieces of evidence have demonstrated that proper endothelial functioning plays a key role in human fertility and reproduction. Therefore, this state-of-the-art review updates the endothelial health markers discriminating between those available for clinical practice or for research purposes and their application in human fertility. Moreover, new molecules potentially helpful to clarify the link between endothelial and reproductive health are evaluated herein.     

“Molecular Masks” for ACE2 to Effectively and Safely Block SARS-CoV-2 Virus Entry

Coronavirus Disease 2019 (COVID-19) remains a global health crisis, despite the development and success of vaccines in certain countries. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, uses its spike protein to bind to the human cell surface receptor angiotensin-converting enzyme 2 (ACE2), which allows the virus to enter the human body. Using our unique cell screening technology, we identified two ACE2-binding peptoid compounds and developed dimeric derivatives (ACE2P1D1 and ACE2P2D1) that effectively blocked spike protein-ACE2 interaction, resulting in the inhibition of SARS-CoV-2 pseudovirus entry into human cells. ACE2P1D1 and ACE2P2D1 also blocked infection by a D614G mutant pseudovirus. More importantly, these compounds do not decrease ACE2 expression nor its enzyme activity (which is important in normal blood pressure regulation), suggesting safe applicability in humans