Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information Xintao Song, Lei Bao, Chenjie Feng, Qiang Huang, Fa Zhang, Xin Gao & Renmin Han Nature Communications volume 15, Article number: 5538 (2024) Cite this art...
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model MOTIVATION: Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is ... W Sheng,S Sun,L Zhen,... - 《Plos Computational Biology》...
However, the underlying structural principles governing such heterochiral protein–protein interactions remain largely unknown. In this study, we present the de novo design of-proteins consisting of 50–65 residues, aiming to target specific surface regions of-proteins or-peptides. Our designer-protein...
1d for the prediction of a 2,180-residue protein with no structural homologues). Finally, the model is able to provide precise, per-residue estimates of its reliability that should enable the confident use of these predictions. Fig. 1: AlphaFold produces highly accurate structures. a, The ...
At the per-protein level, 43.8% of proteins have a confident prediction on at least three quarters of their sequence, while 1,290 proteins contain a substantial region (more than 200 residues) with pLDDT ≥ 70 and no good template.
Indeed, even the most advanced protein structure prediction tools, such as AlphaFold [12] and RosettaFold [13] are struggling with correctly identifying the quaternary state of complexes [14, 15, 16], as compared to their performance in predicting a single chain structure. However, despite these...
(ΔΔG) rather than the native protein sequences. We reason that a score function specifically optimized for ΔΔGprediction may not do well for sequence design. Thus, we extended EvoEF into EvoEF2 by introducing extra energy terms and reoptimizing energy weights. Computational experiments show...
Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and ...
However, the prediction of amino acid or framshift changes has been performed for each SV separately without considering potential nearby SVs. It is known that additional nearby variants can compensate for frameshifts [29], thus the number of protein-changing SVs reported here might still be an ...
Protein–RNA and protein–DNA complexes play critical roles in biology. Despite considerable recent advances in protein structure prediction, the prediction of the structures of protein–nucleic acid complexes without homology to known complexes is a lar