Allostery modulates the beat rate of a cardiac pacemaker

Pioneer in Molecular Biology: Conformational Ensembles in Molecular Recognition, Allostery, and Cell Function

In 1978, for my PhD, I developed the efficient O(n3) dynamic programming algorithm for the-then open problem of RNA secondary structure prediction. This algorithm, now dubbed the "Nussinov algorithm", "Nussinov plots", and "Nussinov diagrams", is still taught across Europe and the U.S. As sequences started coming out in the 1980s, I started seeking genome-encoded functional signals, later becoming a bioinformatics trend. In the early 1990s I transited to proteins, co-developing a powerful computer vision-based docking algorithm. In the late 1990s, I proposed the foundational role of conformational ensembles in molecular recognition and allostery. At the time, conformational ensembles and free energy landscapes were viewed as physical properties of proteins but were not associated with function. The classical view of molecular recognition and binding was based on only two conformations captured by crystallography: open and closed. I proposed that all conformational states preexist. Proteins always have not one folded form-nor two-but many folded forms. Thus, rather than inducing fit, binding can work by shifting the ensembles between states, and this shifting, or redistributing the ensembles to maintain equilibrium, is the origin of the allosteric effect and protein, thus cell, function. This transformative paradigm impacted community views in allosteric drug design, catalysis, and regulation. Dynamic conformational ensemble shifts are now acknowledged as the origin of recognition, allostery, and signaling, underscoring that conformational ensembles-not proteins-are the workhorses of the cell, pioneering the fundamental idea that dynamic ensembles are the driving force behind cellular processes. Nussinov was recognized as pioneer in molecular biology by JMB.

Altered cyclic nucleotide-binding and pore opening in a diseased human HCN4 channel

A growing number of nonsynonymous mutations in the human HCN4 channel gene, the major component of the funny channel of the sinoatrial node, are associated with disease but how they impact channel structure and function, and, thus, how they result in disease, is not clear for any of them. Here, we study the S672R mutation, in the cyclic nucleotide-binding domain of the channel, which has been associated with an inherited bradycardia in an Italian family. This may be the best studied of all known mutations, yet the underlying molecular and atomistic mechanisms remain unclear and controversial. We combine measurements of binding by isothermal titration calorimetry to a naturally occurring tetramer of the HCN4 C-terminal region with a mathematical model to show that weaker binding of cAMP to the mutant channel contributes to a lower level of facilitation of channel opening at submicromolar ligand concentrations but that, in general, facilitation occurs over a range that is similar between the mutant and wild-type because of enhanced opening of the mutant channel when liganded. We also show that the binding affinity for cGMP, which produces the same maximum facilitation of HCN4 opening as cAMP, is weaker in the mutant HCN4 channel but that, for both wild-type and mutant, high-affinity binding of cGMP occurs in a range of concentrations below 1 μM. Thus, binding of cGMP to the HCN4 channel may be relevant normally in vivo and reduced binding of cGMP, as well as cAMP, to the mutant channel may contribute to the reduced resting heart rate observed in the affected family.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Fetal Arrhythmias: Genetic Background and Clinical Implications

Fetal arrhythmias are a common phenomenon of pregnancies. However, debates remain with regard to the etiologies and early treatment of choices for severe fetal arrhythmias. The gene regulatory networks govern cardiac conduction system development to produce distinct nodal and fast conduction phenotypes. The slow conduction properties of nodes that display automaticity are determined by the cardiac ion channel genes, whereas the fast conduction properties are regulated by the transcription factors. Mutations of genes specific for the developmental processes and/or functional status of cardiac conduction system including ion channel promoter (minK-lacZ), GATA family of zinc finger proteins (GATA4), the homeodomain transcription factor (Nkx2.5), the homeodomain-only protein (Hop) and the T-box transcription factors (Tbx2, Tbx3 and Tbx5), hyperpolarization-activated cyclic nucleotide-gated channel 4 (HCN4) and connexins, may cause fetal arrhythmias. It is expected that development of investigational antiarrhythmic agents based on genetic researches on cardiac conduction system, and clinical application of percutaneously implantable fetal pacemaker for the treatment of fetal arrhythmias would come to true.

Precision medicine review: rare driver mutations and their biophysical classification

How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.