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Designing sequences for specific protein backbones is a key step in creating new functional proteins. Here, we introduce GeoSeqBuilder, a deep learning framework that integrates protein sequence generation with side chain conformation prediction to produce the complete all-atom structures for designed sequences. GeoSeqBuilder uses spatial geometric features from protein backbones and explicitly includes three-body interactions of neighboring residues. GeoSeqBuilder achieves native residue type recovery rate of 51.6%, comparable to ProteinMPNN and  other leading methods, while accurately predicting side chain conformations. We first used GeoSeqBuilder to design sequences for thioredoxin and a hallucinated three-helical bundle protein. All the 15 tested sequences expressed as soluble monomeric proteins with high thermal stability, and the 2 high-resolution crystal structures solved closely match the designed models. The generated protein sequences exhibit low similarity (minimum 23%) to the original sequences, with significantly altered hydrophobic cores. We further redesigned the hydrophobic core of glutathione peroxidase 4, and 3 of the 5 designs showed improved enzyme activity. Although further testing is needed, the high experimental success rate in our testing demonstrates that GeoSeqBuilder is a powerful tool for designing novel sequences for predefined protein structures with atomic details. GeoSeqBuilder is available at https://github.com/PKUliujl/GeoSeqBuilder.

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The aim of this study was to effectively obtain monoamine oxidase A (MAO-A) inhibitory peptides from in vitro simulated gastrointestinal digestion and to assess the correspondences between in silico prediction and in vitro confirmation. Fractions (<3 kDa) from ultrafiltration of pepsin and simulated gastrointestinal enzymes hydrolysates exhibited the highest MAO-A inhibitory activity with IC50 values of 0.61 and 2.54 mg/mL, respectively. After sequencing and then screening by HPEPDOCK, 11 high-score peptides and 2 low-score peptides were selected for further synthesis. Remarkable correlation was found between (-)docking scores and MAO-A inhibitory activity of the synthesized peptides, and among which VVFEVFW showed the highest MAO-A inhibitory activity (IC50 = 0.405 mM). Current research suggested that in silico is an effective method to screen MAO-A inhibitory peptides from hairtail protein hydrolysates, and these peptides can be used as functional ingredients for MAO-A inhibition or potential alternatives for antidepressant.

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In this study, we de novo sequenced and analyzed the circular mitochondrial genome (mitogenome) of Tyrophagus putrescentiae. It was 14,156 bp long and contained a complete set of 37 genes, contrary to the initial published sequences; it included 22 tRNA sequences and the largest non-coding region. The mtDNA gene order of T. putrescentiae was found to be identical to that of Aleuroglyphus ovatus, Caloglyphus berlesei, and Rhizoglyphus robini (all Acaroidea). Most tRNAs of T. putrescentiae lack at least a D-arm or T-arm. Tyrophagus putrescentiae tRNAs also shared considerable structural and sequence similarity with the tRNAs of other reported Acaroidea species that have the full set of tRNAs. The largest non-coding region was located between trnF and trnS1, and it contained a microsatellite-like (AT)n sequence, short palindromic sequences, and several hairpin loops, as observed in other reported Acaroidea species (excepting Tyrophagus longior).

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BACKGROUND: De novo transcriptome assembly is an important technique for understanding gene expression in non-model organisms. Many de novo assemblers using the de Bruijn graph of a set of the RNA sequences rely on in-memory representation of this graph. However, current methods analyse the complete set of read-derived k-mer sequence at once, resulting in the need for computer hardware with large shared memory. RESULTS: We introduce a novel approach that clusters k-mers as the first step. The clusters correspond to small sets of gene products, which can be processed quickly to give candidate transcripts. We implement the clustering step using the MapReduce approach for parallelising the analysis of large datasets, which enables the use of compute clusters. The computational task is distributed across the compute system using the industry-standard MPI protocol, and no specialised hardware is required. Using this approach, we have re-implemented the Inchworm module from the widely used Trinity pipeline, and tested the method in the context of the full Trinity pipeline. Validation tests on a range of real datasets show large reductions in the runtime and per-node memory requirements, when making use of a compute cluster. CONCLUSIONS: Our study shows that MapReduce-based clustering has great potential for distributing challenging sequencing problems, without loss of accuracy. Although we have focussed on the Trinity package, we propose that such clustering is a useful initial step for other assembly pipelines.

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