A human transcription factor in search mode

A potent pan-sarbecovirus neutralizing antibody resilient to epitope diversification

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution has resulted in viral escape from clinically authorized monoclonal antibodies (mAbs), creating a need for mAbs that are resilient to epitope diversification. Broadly neutralizing coronavirus mAbs that are sufficiently potent for clinical development and retain activity despite viral evolution remain elusive. We identified a human mAb, designated VIR-7229, which targets the viral receptor-binding motif (RBM) with unprecedented cross-reactivity to all sarbecovirus clades, including non-ACE2-utilizing bat sarbecoviruses, while potently neutralizing SARS-CoV-2 variants since 2019, including the recent EG.5, BA.2.86, and JN.1. VIR-7229 tolerates extraordinary epitope variability, partly attributed to its high binding affinity, receptor molecular mimicry, and interactions with RBM backbone atoms. Consequently, VIR-7229 features a high barrier for selection of escape mutants, which are rare and associated with reduced viral fitness, underscoring its potential to be resilient to future viral evolution. VIR-7229 is a strong candidate to become a next-generation medicine.

Nonspecific vs. specific DNA binding free energetics of a transcription factor domain protein

Transcription factor (TF) proteins regulate gene expression by binding to specific sites on the genome. In the facilitated diffusion model, an optimized search process is achieved by the TF alternating between 3D diffusion in the bulk and 1D diffusion along DNA. While undergoing 1D diffusion, the protein can switch from a search mode for fast diffusion along nonspecific DNA to a recognition mode for stable binding to specific DNA. It was recently noticed that, for a small TF domain protein, reorientations on DNA happen between the nonspecific and specific DNA binding. We here conducted all-atom molecular dynamics simulations with steering forces to reveal the protein-DNA binding free energetics, confirming that the search and recognition modes are distinguished primarily by protein orientations on the DNA. As the binding free energy difference between the specific and nonspecific DNA system slightly deviates from that being estimated directly from dissociation constants on 15-bp DNA constructs, we hypothesize that the discrepancy can come from DNA sequences flanking the 6-bp central binding sites that impact on the dissociation kinetics measurements. The hypothesis is supported by a simplified spherical protein-DNA model along with stochastic simulations and kinetic modeling.

Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .

Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved variants with substitutions in the spike receptor-binding domain (RBD) that affect its affinity for angiotensin-converting enzyme 2 (ACE2) receptor and recognition by antibodies. These substitutions could also shape future evolution by modulating the effects of mutations at other sites-a phenomenon called epistasis. To investigate this possibility, we performed deep mutational scans to measure the effects on ACE2 binding of all single-amino acid mutations in the Wuhan-Hu-1, Alpha, Beta, Delta, and Eta variant RBDs. Some substitutions, most prominently Asn501→Tyr (N501Y), cause epistatic shifts in the effects of mutations at other sites. These epistatic shifts shape subsequent evolutionary change-for example, enabling many of the antibody-escape substitutions in the Omicron RBD. These epistatic shifts occur despite high conservation of the overall RBD structure. Our data shed light on RBD sequence-function relationships and facilitate interpretation of ongoing SARS-CoV-2 evolution.

Searching for Low Probability Opening Events in a DNA Sliding Clamp

The β subunit of E. coli DNA polymererase III is a DNA sliding clamp associated with increasing the processivity of DNA synthesis. In its free form, it is a circular homodimer structure that can accomodate double-stranded DNA in a nonspecific manner. An open state of the clamp must be accessible before loading the DNA. The opening mechanism is still a matter of debate, as is the effect of bound DNA on opening/closing kinetics. We use a combination of atomistic, coarse-grained, and enhanced sampling strategies in both explicit and implicit solvents to identify opening events in the sliding clamp. Such simulations of large nucleic acid and their complexes are becoming available and are being driven by improvements in force fields and the creation of faster computers. Different models support alternative opening mechanisms, either through an in-plane or out-of-plane opening event. We further note some of the current limitations, despite advances, in modeling these highly charged systems with implicit solvent.

Differential Scaling of Gene Expression with Cell Size May Explain Size Control in Budding Yeast

Yeast cells must grow to a critical size before committing to division. It is unknown how size is measured. We find that as cells grow, mRNAs for some cell-cycle activators scale faster than size, increasing in concentration, while mRNAs for some inhibitors scale slower than size, decreasing in concentration. Size-scaled gene expression could cause an increasing ratio of activators to inhibitors with size, triggering cell-cycle entry. Consistent with this, expression of the CLN2 activator from the promoter of the WHI5 inhibitor, or vice versa, interfered with cell size homeostasis, yielding a broader distribution of cell sizes. We suggest that size homeostasis comes from differential scaling of gene expression with size. Differential regulation of gene expression as a function of cell size could affect many cellular processes.

Multiscale effects of heating and cooling on genes and gene networks

Fluctuating environments such as changes in ambient temperature represent a fundamental challenge to life. Cells must protect gene networks that protect them from such stresses, making it difficult to understand how temperature affects gene network function in general. Here, we focus on single genes and small synthetic network modules to reveal four key effects of nonoptimal temperatures at different biological scales: (i) a cell fate choice between arrest and resistance, (ii) slower growth rates, (iii) Arrhenius reaction rates, and (iv) protein structure changes. We develop a multiscale computational modeling framework that captures and predicts all of these effects. These findings promote our understanding of how temperature affects living systems and enables more robust cellular engineering for real-world applications.

Most organisms must cope with temperature changes. This involves genes and gene networks both as subjects and agents of cellular protection, creating difficulties in understanding. Here, we study how heating and cooling affect expression of single genes and synthetic gene circuits in Saccharomyces cerevisiae. We discovered that nonoptimal temperatures induce a cell fate choice between stress resistance and growth arrest. This creates dramatic gene expression bimodality in isogenic cell populations, as arrest abolishes gene expression. Multiscale models incorporating population dynamics, temperature-dependent growth rates, and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating in resistant cells. Molecular-dynamics simulations revealed how heating alters the conformational dynamics of the TetR repressor, fully explaining the experimental observations. Overall, nonoptimal temperatures induce a cell fate decision and corrupt gene and gene network function in computationally predictable ways, which may aid future applications of engineered microbes in nonstandard temperatures.

Characterization of Biomolecular Helices and Their Complementarity Using Geometric Analysis

A general method is presented to characterize the helical properties of potentially irregular helices, such as those found in protein secondary and tertiary structures and nucleic acids. The method was validated using artificial helices with varying numbers of points, points per helical turn, pitch, and radius. The sensitivity of the method was validated by applying increasing amounts of random perturbation to the coordinates of these helices; 399 360 helices in total were evaluated. In addition, the helical parameters of protein secondary structure elements and nucleic acid helices were analyzed. Generally, at least seven points were required to recapitulate the parameters of a helix using our method. The method can also be used to calculate the helical parameters of nucleic acid-binding proteins, like TALE, enabling direct analysis of their helix complementarity to sequence-dependent DNA distortions.

Every transcription factor deserves its map: Scaling up epitope tagging of proteins to bypass antibody problems

Genome-wide identification of transcription factor binding sites with the ChIP-seq method is an extremely important scientific endeavor - one that should ideally be performed for every transcription factor in as many cell types as possible. A major hurdle on the way to this goal is the necessity for a specific, ChIP-grade antibody for each transcription factor of interest, which is often not available. Here, we describe CETCh-seq, a recently published method utilizing genome engineering with the CRISPR/Cas9 system to circumvent the need for a specific antibody. Using the CETCh-seq method, targeted genomic editing results in an epitope-tagged transcription factor, which is recognized by a well-characterized, standard antibody, efficacious for ChIP-seq. We have used CETCh-seq in human cancer cell lines as well as mouse embryonic stem cells. We find that roughly 60% of transcription factors tagged using CETCh-seq produce a high quality ChIP-seq map, a significant improvement over traditional antibody-based methods.