Biology subjects

Pang, Y. T.

Publications and source records attributed to Pang, Y. T..

7 recordsLinked to original sources

bioRxiv2026

Nesso-1: Accelerating Open-Source Binding Affinity Predictions

In this technical report, we introduce NO_SCPLOWESSOC_SCPLOW-1, a coarse-grained cofolding framework for binding- affinity prediction. NO_SCPLOWESSOC_SCPLOW-1 requires[~] 1 second per prediction on a single GPU. This offers more than one order of magnitude speed-up over the leading open-source baseline, Boltz-2, which significantly expands the regions of chemical space that can be explored during high-throughput virtual screening. Importantly, NO_SCPLOWESSOC_SCPLOW-1 matches or surpasses the accuracy of Boltz-2 over the same benchmarks adopted in their study--which we show reflect in-distribution scenarios--as well as over more challenging out-of-distribution data encompassing the OpenBind affinity benchmark and 25 internal biochemical assays. Notably, NO_SCPLOWESSOC_SCPLOW-1 maintains robust predictive accuracy even on assays with extremely low similarity to the training data. Moreover, we highlight examples where NO_SCPLOWESSOC_SCPLOW-1 demonstrates meaningful selectivity, separating the binding affinities of identical compounds between on-targets and related off-targets. Nonetheless, zero-shot generalization to real- world medicinal chemistry remains an inherently challenging task; consequently, we acknowledge specific assays where the models performance is limited. We open-source NO_SCPLOWESSOC_SCPLOW-1: code and weights are available at https://github.com/recursionpharma/nesso

molecular biology↗

bioRxiv2025

DeepPath: Overcoming data scarcity for protein transition pathway prediction using physics-based deep learning

The structural dynamics of proteins play a crucial role in their function, yet most experimental and deep learning methods produce only static models. While molecular dynamics (MD) simulations provide atomistic insight into conformational transitions, they remain computationally prohibitive, particularly for large-scale motions. Here, we introduce DeepPath, a deep-learning-based framework that rapidly generates physically realistic transition pathways between known protein states. Unlike conventional supervised learning approaches, DeepPath employs active learning to iteratively refine its predictions, leveraging molecular mechanical force fields as an oracle to guide pathway generation. We validated DeepPath on three biologically relevant test cases: SHP2 activation, CdiB H1 secretion, and the BAM complex lateral gate opening. DeepPath accurately predicted the transition pathways for all test cases, reproducing key intermediate structures and transient interactions observed in previous studies. Notably, DeepPath also predicted an intermediate between the BAM inwardand outward-open states that closely aligns with an experimentally observed hybrid-barrel structure (TMscore = 0.91). Across all cases, DeepPath achieved accurate pathway predictions within hours, showcasing an efficient alternative to MD simulations for exploring protein conformational transitions.

biophysics↗

bioRxiv2021

SARS-CoV-2 spike opening dynamics and energetics reveal the individual roles of glycans and their collective impact

The trimeric spike (S) glycoprotein, which protrudes from the SARS-CoV-2 viral envelope, binds to human ACE2, initiated by at least one protomers receptor binding domain (RBD) switching from a "down" (closed) to an "up" (open) state. Here, we used large-scale molecular dynamics simulations and two-dimensional replica exchange umbrella sampling calculations with more than a thousand windows and an aggregate total of 160 {micro}s of simulation to investigate this transition with and without glycans. We find that the glycosylated spike has a higher barrier to opening and also energetically favors the down state over the up state. Analysis of the S-protein opening pathway reveals that glycans at N165 and N122 interfere with hydrogen bonds between the RBD and the N-terminal domain in the up state, while glycans at N165 and N343 can stabilize both the down and up states. Finally we estimate how epitope exposure for several known antibodies changes along the opening path. We find that the BD-368-2 antibodys epitope is continuously exposed, explaining its high efficacy.

biophysics↗

bioRxiv2021

ACE2 glycans preferentially interact with the RBD of SARS-CoV-2 over SARS-CoV

We report a distinct difference in the interactions of the glycans of the host-cell receptor, ACE2, with SARS-CoV-2 and SARS-CoV S-protein receptor-binding domains (RBDs). Our analysis demonstrates that the ACE2 glycan at N90 may offer protection against infections of both coronaviruses, while the ACE2 glycan at N322 enhances interactions with the SARS-CoV-2 RBD. The interactions of the ACE2 glycan at N322 with SARS-CoV RBD are blocked by the presence of the RBD glycan at N357 of the SARS-CoV RBD. The absence of this glycosylation site on SARS-CoV-2 RBD may enhance its binding with ACE2.

biophysics↗

bioRxiv2021

Critical interactions for SARS-CoV-2 spike protein binding to ACE2 identified by machine learning

Both SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor. Based on high-resolution structures, the two viruses bind in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Here we have used molecular dynamics (MD) simulations, machine learning (ML), and free energy perturbation (FEP) calculations to elucidate the differences in RBD binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.

biophysics↗

bioRxiv2020

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of Mpro, a cysteine protease, have been determined, facilitating structure-based drug design. Mpro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41-Cys145, Mpro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nu-cleophile Cys145 have been debated in previous studies of SARS-CoV Mpro, but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 Mpro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of Mpro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an -ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N{delta} (HD) and N{epsilon} (HE) protonation of His41 and His164, respectively, the -ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 Mpro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts.

biophysics↗

bioRxiv2020

Combinatorial phosphorylation modulates the structure and function of the G protein gamma subunit in yeast

Protein intrinsically disordered regions (IDRs) are often targets of combinatorial post-translational modifications (PTMs) that serve to regulate protein structure and/or function. Emerging evidence suggests that the N-terminal tails of G protein {gamma} subunits - essential components of heterotrimeric G protein complexes - are intrinsically disordered, highly phosphorylated governors of G protein signaling. Here, we demonstrate that the yeast G{gamma} Ste18 undergoes combinatorial, multi-site phosphorylation within its N-terminal IDR. Phosphorylation at S7 is responsive to GPCR activation and osmotic stress while phosphorylation at S3 is responsive to glucose stress and is a quantitative indicator of intracellular pH. Each site is phosphorylated by a distinct set of kinases and both are also interactive, such that phosphomimicry at one site affects phosphorylation on the other. Lastly, we show that phosphorylation produces subtle yet clear changes in IDR structure and that different combinations of phosphorylation modulate the activation rate and amplitude of the scaffolded MAPK Fus3. These data place G{gamma} subunits among the growing list of intrinsically disordered proteins that exploit combinatorial post-translational modification to govern signaling pathway output.

molecular biology↗