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Evolutionary and dynamic constraints converge on a conserved coupling sector in thermally adapted GH1 β-glucosidases.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches
  1. [1] § 3. Results › 3.3. Thermostability emerges from preservation of a distributed dynamic interaction network ↔ NRI/plot_edge_probability_distribution.py, lines 180 to 193 · score 0.65 · Threshold sensitivity, edge probability distributions, selected threshold, directed edge probabilities, fraction, temperature · Report a mismatch
  2. [2] § 2. Materials and methods › 2.4. Network inference from molecular dynamics trajectories ↔ NRI/plot_edge_probability_distribution.py, lines 180 to 193 · score 0.64 · Edge probability distributions, selected threshold, probability thresholds, directed edges, Sensitivity, fraction · Report a mismatch
  3. [3] § 2. Materials and methods › 2.4. Network inference from molecular dynamics trajectories ↔ NRI/9UPT/300/main.py, lines 1 to 84 · score 0.54 · sparsity prior, batch, decoder, encoder, velocity, dimensions · Report a mismatch
  4. [4] § 2. Materials and methods › 2.4. Network inference from molecular dynamics trajectories ↔ NRI/BS/300/main.py, lines 1 to 84 · score 0.54 · sparsity prior, batch, decoder, encoder, velocity, dimensions · Report a mismatch

Paper

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The authors' code

Python · 267 lines · 10 KB · no license · 2 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. plot_edge_probability_distribution.py
  5. 读取3种蛋白（BsBgl1A、9UPT、LQBG8）在300/325/350 K下的
  6. out_probs_train.npy，生成：
  7. - 9张单条件Histogram
  8. - 3张蛋白内三温度叠加Histogram
  9. - 3张CDF
  10. - 3张阈值敏感性曲线
  11. - 1张所有条件超过阈值边比例图
  12. - 1个汇总CSV
  14. 默认总有边概率 = 1 - P(no-edge)，适用于class 0为无边、其余class为不同有边类型。
  15. """
  17. from pathlib import Path
  18. import re
  19. import numpy as np
  20. import pandas as pd
  21. import matplotlib.pyplot as plt
  23. ROOT_DIR = Path(r"D:/GH1/nri")
  24. OUTPUT_DIR = ROOT_DIR / "edge_probability_distribution_results"
  25. SPLIT = "train"
  26. EDGE_THRESHOLD = 0.1
  27. SAMPLE_CHUNK_SIZE = 16
  28. HIST_BINS = 100
  30. PROTEIN_ORDER = ["BsBgl1A", "9UPT", "LQBG8"]
  31. TEMPERATURE_ORDER = [300, 325, 350]
  32. PROTEIN_ALIASES = {
  33. "bsm": "BsBgl1A", "bsbgl1a": "BsBgl1A", "bs": "BsBgl1A",
  34. "9upt": "9UPT", "upt": "9UPT",
  35. "lq": "LQBG8", "lqbg8": "LQBG8",
  36. }
  38. # 概率模式：
  39. # "one_minus_no_edge" -> 1 - P(class0)
  40. # "single_class" -> 取某一类别
  41. PROBABILITY_MODE = "one_minus_no_edge"
  42. NO_EDGE_CLASS_INDEX = 0
  43. EDGE_CLASS_INDEX = 1
  45. THRESHOLD_WINDOW = (0.08, 0.12)
  46. SENSITIVITY_THRESHOLDS = np.arange(0.02, 0.301, 0.01)
  49. def find_files():
  50. return sorted(ROOT_DIR.rglob(f"out_probs_{SPLIT}.npy"))
  53. def parse_condition(path: Path):
  54. parts = [p.lower() for p in path.parts]
  55. protein = "unknown"
  56. for part in parts:
  57. for alias, canonical in PROTEIN_ALIASES.items():
  58. if alias == part or alias in part:
  59. protein = canonical
  60. break
  61. if protein != "unknown":
  62. break
  64. temperature = None
  65. for part in parts:
  66. m = re.search(r"(?<!\d)(300|325|350)(?:k)?(?!\d)", part)
  67. if m:
  68. temperature = int(m.group(1))
  69. break
  70. return protein, temperature
  73. def extract_edge_probability(class_array: np.ndarray) -> np.ndarray:
  74. if PROBABILITY_MODE == "one_minus_no_edge":
  75. p = 1.0 - class_array[..., NO_EDGE_CLASS_INDEX]
  76. elif PROBABILITY_MODE == "single_class":
  77. p = class_array[..., EDGE_CLASS_INDEX]
  78. else:
  79. raise ValueError("PROBABILITY_MODE设置错误")
  80. return np.clip(p, 0.0, 1.0)
  83. def mean_edge_probabilities(path: Path):
  84. arr = np.load(path, mmap_mode="r")
  85. info = {"array_shape": str(arr.shape), "n_samples": 1, "n_classes": None}
  87. if arr.ndim == 3 and arr.shape[-1] <= 10:
  88. n_samples, n_edges, n_classes = arr.shape
  89. total = np.zeros(n_edges, dtype=np.float64)
  90. for start in range(0, n_samples, SAMPLE_CHUNK_SIZE):
  91. stop = min(start + SAMPLE_CHUNK_SIZE, n_samples)
  92. chunk = np.asarray(arr[start:stop], dtype=np.float64)
  93. total += extract_edge_probability(chunk).sum(axis=0)
  94. probs = total / n_samples
  95. info.update({"n_samples": n_samples, "n_classes": n_classes})
  97. elif arr.ndim == 2 and arr.shape[-1] <= 10:
  98. probs = extract_edge_probability(np.asarray(arr, dtype=np.float64))
  99. info.update({"n_samples": 1, "n_classes": arr.shape[-1]})
  101. elif arr.ndim == 2 and arr.shape[0] != arr.shape[1]:
  102. probs = np.asarray(arr, dtype=np.float64).mean(axis=0)
  103. info.update({"n_samples": arr.shape[0], "n_classes": None})
  105. elif arr.ndim == 1:
  106. probs = np.asarray(arr, dtype=np.float64)
  108. else:
  109. raise ValueError(f"暂不支持数组shape={arr.shape}")
  111. probs = np.asarray(probs, dtype=np.float64).reshape(-1)
  112. probs = probs[np.isfinite(probs)]
  113. return np.clip(probs, 0.0, 1.0), info
  116. def summarize(probs: np.ndarray):
  117. q = np.quantile(probs, [0.01,0.05,0.10,0.25,0.50,0.75,0.90,0.95,0.99])
  118. lo, hi = THRESHOLD_WINDOW
  119. return {
  120. "mean": float(np.mean(probs)),
  121. "sd": float(np.std(probs, ddof=1)),
  122. "median": float(np.median(probs)),
  123. "q01": float(q[0]), "q05": float(q[1]), "q10": float(q[2]),
  124. "q25": float(q[3]), "q50": float(q[4]), "q75": float(q[5]),
  125. "q90": float(q[6]), "q95": float(q[7]), "q99": float(q[8]),
  126. "fraction_ge_threshold": float(np.mean(probs >= EDGE_THRESHOLD)),
  127. "percent_ge_threshold": float(np.mean(probs >= EDGE_THRESHOLD) * 100.0),
  128. "percent_0.08_to_0.12": float(np.mean((probs >= lo) & (probs <= hi)) * 100.0),
  129. "percent_0.08_to_below_0.10": float(np.mean((probs >= lo) & (probs < EDGE_THRESHOLD)) * 100.0),
  130. "percent_0.10_to_0.12": float(np.mean((probs >= EDGE_THRESHOLD) & (probs <= hi)) * 100.0),
  131. }
  134. def plot_single_hist(probs, protein, temp, out):
  135. fig, ax = plt.subplots(figsize=(7,5.2))
  136. ax.hist(probs, bins=HIST_BINS, range=(0,1), alpha=0.85)
  137. ax.axvline(EDGE_THRESHOLD, linestyle="--", linewidth=1.8,
  138. label=f"Threshold = {EDGE_THRESHOLD}")
  139. frac = np.mean(probs >= EDGE_THRESHOLD) * 100
  140. ax.text(0.97, 0.94, f"Edges ≥ {EDGE_THRESHOLD}: {frac:.2f}%",
  141. transform=ax.transAxes, ha="right", va="top")
  142. ax.set_xlabel("Mean edge probability")
  143. ax.set_ylabel("Number of directed edges")
  144. ax.set_title(f"{protein}, {temp} K")
  145. ax.legend(frameon=False)
  146. fig.tight_layout(); fig.savefig(out, dpi=600); plt.close(fig)
  149. def plot_overlay_hist(data, protein, out):
  150. fig, ax = plt.subplots(figsize=(7.5,5.5))
  151. for temp in TEMPERATURE_ORDER:
  152. if temp in data:
  153. ax.hist(data[temp], bins=HIST_BINS, range=(0,1), density=True,
  154. histtype="step", linewidth=1.8, label=f"{temp} K")
  155. ax.axvline(EDGE_THRESHOLD, linestyle="--", linewidth=1.8,
  156. label=f"Threshold = {EDGE_THRESHOLD}")
  157. ax.set_xlabel("Mean edge probability")
  158. ax.set_ylabel("Probability density")
  159. ax.set_title(f"{protein}: edge-probability distributions")
  160. ax.legend(frameon=False)
  161. fig.tight_layout(); fig.savefig(out, dpi=600); plt.close(fig)
  164. def plot_cdf(data, protein, out):
  165. fig, ax = plt.subplots(figsize=(7.5,5.5))
  166. for temp in TEMPERATURE_ORDER:
  167. if temp in data:
  168. x = np.sort(data[temp])
  169. y = np.arange(1, len(x)+1) / len(x)
  170. ax.plot(x, y, linewidth=1.8, label=f"{temp} K")
  171. ax.axvline(EDGE_THRESHOLD, linestyle="--", linewidth=1.8,
  172. label=f"Threshold = {EDGE_THRESHOLD}")
  173. ax.set_xlabel("Mean edge probability")
  174. ax.set_ylabel("Cumulative fraction of directed edges")
  175. ax.set_title(f"{protein}: edge-probability CDF")
  176. ax.legend(frameon=False)
  177. fig.tight_layout(); fig.savefig(out, dpi=600); plt.close(fig)
  180. def plot_threshold_sensitivity(data, protein, out):
  181. fig, ax = plt.subplots(figsize=(7.5,5.5))
  182. for temp in TEMPERATURE_ORDER:
  183. if temp in data:
  184. fractions = [np.mean(data[temp] >= t) for t in SENSITIVITY_THRESHOLDS]
  185. ax.plot(SENSITIVITY_THRESHOLDS, fractions, linewidth=1.8,
  186. label=f"{temp} K")
  187. ax.axvline(EDGE_THRESHOLD, linestyle="--", linewidth=1.8,
  188. label=f"Selected threshold = {EDGE_THRESHOLD}")
  189. ax.set_xlabel("Edge-probability threshold")
  190. ax.set_ylabel("Fraction of retained directed edges")
  191. ax.set_title(f"{protein}: threshold sensitivity")
  192. ax.legend(frameon=False)
  193. fig.tight_layout(); fig.savefig(out, dpi=600); plt.close(fig)
  196. def plot_all_conditions(summary_df, out):
  197. df = summary_df.copy()
  198. df["protein"] = pd.Categorical(df["protein"], PROTEIN_ORDER, ordered=True)
  199. df["temperature"] = pd.Categorical(df["temperature"], TEMPERATURE_ORDER, ordered=True)
  200. df = df.sort_values(["protein","temperature"])
  201. labels = [f"{r['protein']}\n{int(r['temperature'])} K" for _, r in df.iterrows()]
  202. vals = df["percent_ge_threshold"].to_numpy(float)
  203. x = np.arange(len(df))
  204. fig, ax = plt.subplots(figsize=(11,6))
  205. ax.bar(x, vals)
  206. ax.set_xticks(x); ax.set_xticklabels(labels, rotation=35, ha="right")
  207. ax.set_ylabel(f"Directed edges ≥ {EDGE_THRESHOLD} (%)")
  208. ax.set_title("Fraction of retained NRI edges")
  209. proteins = df["protein"].astype(str).tolist()
  210. for i in range(1, len(proteins)):
  211. if proteins[i] != proteins[i-1]:
  212. ax.axvline(i-0.5, linestyle="--", linewidth=1, alpha=0.5)
  213. fig.tight_layout(); fig.savefig(out, dpi=600); plt.close(fig)
  216. def main():
  217. OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
  218. single_dir = OUTPUT_DIR / "single_condition_histograms"
  219. compare_dir = OUTPUT_DIR / "protein_comparison_plots"
  220. single_dir.mkdir(exist_ok=True); compare_dir.mkdir(exist_ok=True)
  222. files = find_files()
  223. if not files:
  224. raise FileNotFoundError(f"在{ROOT_DIR}下未找到out_probs_{SPLIT}.npy")
  226. all_probs = {p:{} for p in PROTEIN_ORDER}
  227. rows = []
  229. for path in files:
  230. protein, temp = parse_condition(path)
  231. if protein not in PROTEIN_ORDER or temp not in TEMPERATURE_ORDER:
  232. continue
  233. print(f"处理 {protein}, {temp} K: {path}")
  234. probs, info = mean_edge_probabilities(path)
  235. all_probs[protein][temp] = probs
  236. np.save(OUTPUT_DIR / f"{protein}_{temp}K_mean_edge_probability.npy", probs)
  237. row = {"protein": protein, "temperature": temp, "split": SPLIT,
  238. "source_file": str(path), "probability_mode": PROBABILITY_MODE,
  239. "edge_threshold": EDGE_THRESHOLD, **info, **summarize(probs)}
  240. rows.append(row)
  241. plot_single_hist(probs, protein, temp,
  242. single_dir / f"{protein}_{temp}K_edge_probability_histogram.png")
  244. summary_df = pd.DataFrame(rows)
  245. if summary_df.empty:
  246. raise RuntimeError("没有成功识别任何蛋白和温度")
  247. summary_df.to_csv(OUTPUT_DIR / "edge_probability_summary.csv", index=False)
  249. for protein in PROTEIN_ORDER:
  250. if not all_probs[protein]:
  251. continue
  252. plot_overlay_hist(all_probs[protein], protein,
  253. compare_dir / f"{protein}_edge_probability_histogram_all_temperatures.png")
  254. plot_cdf(all_probs[protein], protein,
  255. compare_dir / f"{protein}_edge_probability_CDF.png")
  256. plot_threshold_sensitivity(all_probs[protein], protein,
  257. compare_dir / f"{protein}_threshold_sensitivity.png")
  259. plot_all_conditions(summary_df,
  260. OUTPUT_DIR / "fraction_edges_above_threshold_all_conditions.png")
  262. print(f"\n完成，结果位于：{OUTPUT_DIR}")
  263. print("重点文件：edge_probability_summary.csv、3张CDF、3张threshold_sensitivity图")
  266. if __name__ == "__main__":
  267. main()

plot_edge_probability_distribution.py at commit 07be281, no license · at the source

Overview

Authors: Li-Ting Zheng1,2,3,4, Yi Li3,4,5, Xian He2,3,4, Lian-Tao Yang5, De-Rui Zhao2,3,4, Meng-Ting Liu1,3,4, Peng Sang2,3,4, Li-Quan Yang2,3,4
ORCID iDs: Li-Ting Zheng
  1. College of Pharmacy, Dali University, Dali, China
  2. College of Agriculture and Biological Science, Dali University, Dali, China
  3. Key Laboratory of Bioinformatics and Computational Biology, Department of Education of Yunnan Province, Dali University, Dali, China
  4. Co-Innovation Center for Cangshan Mountain and Erhai Lake Integrated Protection and Green Development of Yunnan Province, Dali University, Dali, China
  5. College of Mathematics and Computer Science, Dali University, Dali, China
Institutions: Dali University (China)
Journal: PLoS computational biology, volume 22, issue 10, article e1014879
Dates: received 4 February 2026; accepted 5 October 2026; published online 27 October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014879 · PMID 42945199 · PMCID PMC13712516 · OpenAlex W7169763534
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: computational modeling (no new data) (modality), none (in silico) (organism)
Methods: Preprocessing, Connectivity, Graphs, Machine learning
MeSH: beta-Glucosidase*, Cellulases*, Evolution, Molecular*, Enzyme Stability, Molecular Dynamics Simulation, Protein Conformation, Temperature (* major topic)
Topic: Protein Structure and Dynamics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China; Dali University; Applied Basic Research Key Project of Yunnan
Citations: not cited yet (Europe PMC); 50 references in the paper

Abstract

How protein sequences evolve to maintain functional conformational dynamics under thermal stress remains an important question in molecular evolution. Thermophilic enzymes are often considered to achieve stability primarily through enhanced structural rigidity, yet thermal adaptation may also involve the preservation or reorganization of correlated motions across the protein scaffold. Here, we compared three glycoside hydrolase family 1 β-glucosidases—BsBgl1A, 9UPT, and LQBG8—that share a conserved (β/α)8 TIM-barrel fold but display distinct optimal temperatures of approximately 45, 65, and 70 °C, respectively. By integrating comparative structural analysis, three independent 1 μs molecular dynamics simulations at 300, 325, and 350 K, neural relational inference of residue-level dynamic coupling, and family-level coevolutionary analysis, we identified distinct dynamic strategies associated with their thermal phenotypes. All three enzymes retained their global compactness and principal secondary-structure framework across the simulated temperatures, indicating that differences in RMSD and residue flexibility did not reflect large-scale unfolding. BsBgl1A showed the strongest temperature sensitivity, with increased structural deviation and residue mobility at 350 K. Its inferred interaction network exhibited a non-monotonic response, characterized by pronounced overcoupling at 325 K followed by reduced connectivity at 350 K. In contrast, 9UPT maintained relatively restrained residue dynamics and a highly reproducible coupling topology across temperatures. LQBG8 combined substantial local conformational flexibility with persistent long-range coupling and retained the highest directed network density at 350 K. Across the three enzymes, the βα5–βα7 region repeatedly emerged as a shared structural and dynamic center. Coevolutionary couplings were also enriched in TIM-barrel-associated regions that spatially converged on this sector. These findings indicate that thermal adaptation in GH1 β-glucosidases is not adequately explained by uniform structural rigidification. Instead, enzymes with the same conserved fold may adopt distinct organizations of correlated motion, while greater thermal adaptation is associated with the persistence or reorganization of a βα5–βα7-centered dynamic coupling pattern under elevated temperature. The correspondence between dynamic coupling and coevolutionary enrichment is consistent with long-term evolutionary constraint on this structural sector, although it does not establish direct selection on the network as an independent functional unit. Our results therefore provide a testable framework in which thermostability reflects a balance among local stabilization, regional flexibility, and the resilience of higher-order dynamic organization.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

zh-l-t/GH1--Glucosidase

License: none: the authors keep all their rights
State: the link answers, verified on 30 October 2026
Evidence: files inventoried
Commit: 07be281025e64bc5535e122d74809706936fa9ed, 19 July 2026
Languages: Python (114), Shell (3)
Size: 128 files, 117 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: environment (coevolution/requirements.txt)
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (90 files), Matplotlib (47 files), pandas (36 files), PyTorch (27 files), seaborn (23 files), SciPy (18 files), MDAnalysis (13 files), NetworkX (9 files), Biopython (2 files), statsmodels (1 file)
Availability: 1 check, the latest on 30 October 2026: the link answers
  • 30 October 2026: the link answers
117 files
At the source: github.com/zh-l-t/GH1--Glucosidase

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 117 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

All data and code required to reproduce the principal analyses and figures reported in this study are publicly available. The complete research dataset is distributed across two linked Zenodo records because the molecular dynamics trajectory collection exceeds the storage capacity of a single record. The primary record ([https://doi.org/10.5281/zenodo.21441624]) contains the initial structures, sequence alignments, GH1 family and coevolutionary data, BsBgl1A molecular dynamics data, NRI scripts and outputs, final network files, analysis scripts, and the numerical results underlying the figures. The supplementary molecular dynamics record ([https://doi.org/10.5281/zenodo.21441686]) contains the processed trajectories and associated analysis files for 9UPT and LQBG8 at 300, 325, and 350 K. The executable analysis code and configuration files are additionally available from GitHub at [https://github.com/zh-l-t/GH1--Glucosidase]. The two Zenodo records together provide the complete archived dataset required to reproduce the principal analyses. The repository contains the initial protein structures, sequence and structural alignment data, processed molecular dynamics trajectories, trajectory-derived structural metrics, Neural Relational Inference outputs, coevolutionary analysis data, final network files, analysis scripts, and the numerical results underlying the main and supplementary figures. Specifically, the Zenodo repository includes: (i) the initial homology models of BsBgl1A and LQBG8 and the experimentally resolved structure of 9UPT used to initiate the molecular dynamics simulations; (ii) the protein sequences, multiple sequence alignments, representative GH1 family structures, structural-filtering results, and coevolutionary coupling matrices; (iii) processed molecular dynamics trajectories or representative trajectory subsets for BsBgl1A, 9UPT, and LQBG8 at 300, 325, and 350 K, together with the corresponding topology and structure files; (iv) trajectory-derived data for RMSD, radius of gyration, RMSF, secondary-structure evolution, native-contact fraction, RMSD–Q conformational landscapes, contact survival, and catalytic-residue distance analyses; (v) the complete Neural Relational Inference workflow, including data-preprocessing scripts, model-training and inference scripts, trained encoder and decoder parameters, predicted edge probabilities, training and held-out test outputs, threshold-sensitivity analyses, and network-level summary metrics; (vi) residue- and module-level adjacency matrices, GraphML network files, node and edge tables, Cytoscape-compatible files, and the processed results used to generate the network figures; and (vii) the scripts and processed outputs used for ESM-MSA-1b-based coevolutionary analysis and comparison with the NRI-derived coupling patterns.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 7 MeSH terms, 3 funders, 46 references.

Cite

This paper

Zheng, L.-T., Li, Y., He, X., Yang, L.-T., Zhao, D.-R., Liu, M.-T., Sang, P., & Yang, L.-Q. (2026). Evolutionary and dynamic constraints converge on a conserved coupling sector in thermally adapted GH1 β-glucosidases. PLoS computational biology, 22(10), e1014879. https://doi.org/10.1371/journal.pcbi.1014879

BibTeX

@article{zheng2026evolutionary,
author = {Zheng, Li-Ting and Li, Yi and He, Xian and Yang, Lian-Tao and Zhao, De-Rui and Liu, Meng-Ting and Sang, Peng and Yang, Li-Quan},
title = {{Evolutionary and dynamic constraints converge on a conserved coupling sector in thermally adapted GH1 β-glucosidases}},
journal = {PLoS computational biology},
year = {2026},
month = oct,
volume = {22},
number = {10},
pages = {e1014879},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014879},
url = {https://doi.org/10.1371/journal.pcbi.1014879},
pmid = {42945199},
pmcid = {PMC13712516}
}

RIS

TY - JOUR
AU - Zheng, Li-Ting
AU - Li, Yi
AU - He, Xian
AU - Yang, Lian-Tao
AU - Zhao, De-Rui
AU - Liu, Meng-Ting
AU - Sang, Peng
AU - Yang, Li-Quan
TI - Evolutionary and dynamic constraints converge on a conserved coupling sector in thermally adapted GH1 β-glucosidases
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/10/27
VL - 22
IS - 10
SP - e1014879
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014879
UR - https://doi.org/10.1371/journal.pcbi.1014879
LA - en
ER -

CSL-JSON

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"id": "10.1371/journal.pcbi.1014879",
"type": "article-journal",
"title": "Evolutionary and dynamic constraints converge on a conserved coupling sector in thermally adapted GH1 β-glucosidases",
"container-title": "PLoS computational biology",
"author": [
{
"family": "Zheng",
"given": "Li-Ting"
},
{
"family": "Li",
"given": "Yi"
},
{
"family": "He",
"given": "Xian"
},
{
"family": "Yang",
"given": "Lian-Tao"
},
{
"family": "Zhao",
"given": "De-Rui"
},
{
"family": "Liu",
"given": "Meng-Ting"
},
{
"family": "Sang",
"given": "Peng"
},
{
"family": "Yang",
"given": "Li-Quan"
}
],
"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "10",
"page": "e1014879",
"DOI": "10.1371/journal.pcbi.1014879",
"PMID": "42945199",
"PMCID": "PMC13712516",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1014879",
"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
27
]
]
}
}

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