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Background. Indoor air quality is controlled in the clean operating room (OR) to reduce the risk of surgical-site infections (SSIs). The aim of this study is to assess the usage and management of clean ORs in China and to identify factors associated with the risk of SSIs. Methods. An online survey was distributed to hospitals in China from August 5 to September 5, 2018 via the WeChat account of the Shanghai International Forum for Infection Control and Prevention. The questionnaire consisted of two parts: basic information (hospital type, level, and number of beds) and usage and management (number of ORs, usage time, maintenance mode, test frequency, compliance with current standards, and comfort of healthcare workers). The significance of factors associated with the cleanliness and maintenance of clean ORs was assessed by univariate and multivariate logistic regression analyses. Results. Among 1,308 responding hospitals, 25.7% failed to comply with current standards. “Maintenance mode” had a significant effect on compliance with current standards for clean ORs ( p < 0.0001 ) and “professional” maintenance was superior to “outsource or no” maintenance (odds ratio = 0.511, 95% confidence interval = 0.367–0.711). There was a significant difference in the comfort of healthcare workers in clean ORs that complied with current standards vs. those that did not (39.92% [388/972] vs. 64.28% [216/336], respectively, p < 0.0001 ). Humidity was the chief complaint among healthcare workers. Conclusion. Maintenance of clean ORs was significantly associated with the compliance of current standards. Noncompliance with current standards was associated with greater risks of SSIs. Maintenance of ORs for prevention of SSIs should consider the costs and benefits.

SUMMARYMicroRNAs (miRNAs) are a class of short noncoding RNAs that regulate gene expression through the binding of their 5’-end to mRNA. However, the biological effects of miRNA’s 3’-end binding to mRNA remain unclear. Here we discover that the pairing of miRNA’s 3’-end with RNA could serve as a primer to initiate the production of miRNA-derived RNAs (midRs), in the opposite direction of its originating RNA. midRs could consequently translate into miRNA-derived proteins (midPs). Starting from 2,656Homo SapiensmiRNAs, we predicted 11,453 and 1,239 unique midRs and midPs for humans using a 15-nucleotide-pairing threshold. We verified the bona-fide existence of example midRs and midPs in human cells. Of clinical relevance, we demonstrate that midP0188 is highly expressed in human lung and breast cancer tissues and cells and that midP0188 and its encoding midRs represent novel anti-cancer targets. Our findings propose a miRNA→midR→midP axis that expands the central dogma and reveals thousands of novel RNAs and proteins that have immense potential for playing crucial biological and pathological roles in human cells, as well as other biological systems.

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

Computational protein design (CPD) methods hold great promise in protein engineering. A critical first step in CRISPR–Cas mediated gene editing is recognizing a preferred protospacer adjacent motif (PAM) sequence on the target nucleotides by the protein’s PAM interacting amino acids (PIAAs). Such PAM requirement defines the editable sequence and the PAM specificity of each CRISPR–Cas protein. A deep understanding of the PAM recognition process through computational design methods is therefore useful in assisting CRISPR–Cas engineering to either relax or tighten the PAM requirement. Here we describe a universal CPD framework (UniDesign) suitable for modeling and designing the protein–nucleic acid interaction. As a proof of concept, we applied UniDesign to decode the PAM–PIAA interactions in eight CRISPR–Cas9 proteins. We first show that, given native PIAAs, the computationally determined PAM sequences by UniDesign are largely identical to those of known natural PAMs of all tested CRISPR–Cas9 proteins. We next subjected the natural PAM sequences of different CRISPR–Cas9 proteins to UniDesign and computationally determined the preferred PIAA residues for each, which again largely recapitulated the key residues (>70% by identity or >80% by similarity). These results demonstrate that UniDesign faithfully captures the mutual preference between the PAM and the PIAAs, thus suggest it as a useful tool for engineering CRISPR­–Cas and other proteins that interact with nucleic acids. UniDesign is open-sourced at https://github.com/tommyhuangthu/UniDesign.

The phosphorylation of 14-3-3 binding motif is involved in many cellular processes. A strategy that enables targeted degradation of phospho-14-3-3-binding-motif embedded (P-14-3-3-BME) proteins for studying their functions is highly desirable for basic research. Here, we report a phosphorylation-induced, ubiquitin-proteasome-system-mediated targeted protein degradation (TPD) strategy that allows specific degradation of P-14-3-3-BME proteins. Specifically, by ligating a modified von Hippel-Lindau E3-ligase with an engineered 14-3-3 bait, we generated a protein chimera referred to as Targeted Degradation of P-14-3-3-BME Protein (TDPP). TDPP can serve as a universal degrader for P-14-3-3-BME proteins based on the specific recognition of the phosphorylation in 14-3-3 binding motifs. TDPP shows high efficiency and specificity to a difopein-EGFP reporter, general and specific P-14-3-3-BME proteins. TDPP can also be applied for the validation of P-14-3-3-BME proteins. These results strongly support TDPP as a powerful tool for 14-3-3 related research.

To summarize the research progress of femoral neck system (FNS) in the treatment of femoral neck fracture in young and middle-aged patients.The literature on FNS at home and abroad in recent years was extensively reviewed, and the results of mechanical and clinical studies on FNS were summarized based on clinical experience.FNS has good mechanical stability, which can reduce complications such as femoral neck shortening, internal fixation failure, and varus caused by mechanical instability. At present, FNS is mainly selected for comparison with cannulated compression screws and dynamic hip screws in clinical research. The results show that FNS has the advantages of minimally invasive, short operation time, less intraoperative fluoroscopy, earlier postoperative weight-bearing and fracture healing, and better hip function recovery.As a new internal fixator, FNS has achieved satisfactory results in the current research. FNS has good mechanical advantages, which is beneficial to fracture healing and the recovery of hip joint function after operation. However, whether FNS can reduce the incidence of nonunion and osteonecrosis of the femoral head remains to be further clarified.总结股骨颈动力交叉钉系统（femoral neck system，FNS）治疗青壮年股骨颈骨折的研究进展。.广泛查阅近年来国内外关于FNS的相关文献，结合临床经验对FNS的生物力学研究及临床研究结果进行总结。.FNS具有较好的力学稳定性，能减少因力学不稳定导致的股骨颈短缩、内固定失效、髋关节内翻等并发症。目前临床研究主要与空心加压螺钉以及动力髋螺钉比较，结果显示FNS具有微创、手术时间短、术中透视次数少、患者术后负重及骨折愈合较早、髋关节功能恢复好等优点。.FNS作为一种新的内固定物，目前研究显示治疗青壮年股骨颈骨折可取得较满意疗效，有利于骨折愈合及术后髋关节功能恢复。但对于FNS能否减少术后骨不连及股骨头坏死的发生，有待进一步研究明确。.

The antiobesity effect of celastrol has been reported in numerous studies, but the underlying mechanism remains unclear. It is widely accepted that gut dysbiosis is closely related to obesity. The potential effect of celastrol on microbiota is worth exploring. In this study, the celastrol-induced weight loss was validated in high-fat diet (HFD)-induced obese mice, with the detection of reported phenotypes including a reduction in food intake, augments in dyslipidemia and glucose metabolism, and adipose thermogenesis. The anti-inflammatory effect of celastrol was also proved based on the alterations in serum cytokines. Antibiotic interference showed that gut microbiota contributes to celastrol-induced weight loss. Several key bacteria were identified using shotgun metagenomic sequencing to display the alterations of the intestinal microbiome in obese mice treated with celastrol. Meanwhile, the fecal and serum metabolic profiles were generated by pseudotargeted metabolomics, and changes in some critical metabolites related to appetite and metabolism were detected. Importantly, we applied in silico bidirectional mediation analysis to identify the precise connections among the alterations in gut microbes, serum metabolome, and host phenotypes induced by celastrol treatment for the first time. Therefore, we concluded that the celastrol-induced microbial changes partially contribute to the antiobesity effect via the serum metabolome. The mass spectrometry data are deposited on MetaboLights (ID: MTBLS3278).

For the diagnosis and prognosis of glioma, the development of prognostic biomarkers is critical. The N-type calcium channel, whose predominant subunit is encoded by calcium voltage-gated channel subunit alpha1 B (CACNA1B), is mostly found in the nervous system and is closely associated with neurosensory functions. However, the link between the expression of CACNA1B and glioma remains unknown. We used ONCOMINE to explore the differences in CACNA1B expression among different cancers. We then conducted survival analysis and COX analysis using TCGA_LGG and TCGA_GBM datasets, which were divided into CACNA1Bhigh and CACNA1Blow based on the median. We examined the differences in other favorable prognostic markers or clinical characteristics between CACNA1Bhigh and CACNA1Blow using t tests. Differentially expressed genes were identified, and KEGG pathway enrichment was performed. We compared the expression of methyltransferases and analyzed the differentially methylated regions. Immunohistochemistry results were retrieved from the Human Protein Atlas database for validation purposes. CACNA1B was expressed at lower levels in gliomas, and, for the first time, we found that high expression of CACNA1B in gliomas predicts a good prognosis. Other favorable prognostic markers, such as isocitrate dehydrogenase mutation, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase promoter methylation, were increased in tandem with high expression of CACNA1B. Differentially expressed genes were enriched in multiple pathways related to cancer progression and aberrant epigenetic alterations were significantly associated with CACNA1B. High expression of N-type calcium channels indicates a favorable prognosis for gliomas. This study provides a better understanding of the link between gliomas and N-type calcium channels and may offer guidance for the future treatment of gliomas.

Graphical abstract, Highlights • Evolutionary design of new SARS-CoV-2 spike proteins by perturbing the core protein sequence without changing the surface conformation. • Computationally designed SARS-CoV-2 spike proteins contain additional MHC-II T cell promiscuous epitopes with enhanced T cell immunity. • Structurally designed SARS-CoV-2 spike proteins preserve function and B cell immunity. • The newly designed SARS-CoV-2 spike proteins can be explored as new COVID-19 vaccine candidates., The development of effective and safe vaccines is the ultimate way to efficiently stop the ongoing COVID-19 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Built on the fact that SARS-CoV-2 utilizes the association of its Spike (S) protein with the human Angiotensin-converting enzyme 2 (ACE2) receptor to invade host cells, we computationally redesigned the S protein sequence to improve its immunogenicity and antigenicity. Toward this purpose, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants that perturb the core protein sequence but keep the surface conformation and B cell epitopes. The T cell epitope content and similarity scores of the perturbed sequences were calculated and evaluated. Out of 22,914 designs with favorable stability energy, 301 candidates contained at least two pre-existing immunity-related epitopes and had promising immunogenic potential. The benchmark tests showed that, although the epitope restraints were not included in the scoring function of EvoDesign, the top S protein design successfully recovered 31 out of the 32 major histocompatibility complex (MHC) -II T cell promiscuous epitopes in the native S protein, where two epitopes were present in all seven human coronaviruses. Moreover, the newly designed S protein introduced nine new MHC-II T cell promiscuous epitopes that do not exist in the wildtype SARS-CoV-2. These results demonstrated a new and effective avenue to enhance a target protein’s immunogenicity using rational protein design, which could be applied for new vaccine design against COVID-19 and other human viruses.

De novo protein design generally consists of two steps, including structure and sequence design. However, many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. Here we developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent exposed areas that closely matched their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds that were completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses demonstrated that the major contributions to the successful fold design lay in the optimal energy force field, which contains a balanced set of fragment and secondary structure packing terms, and the REMC simulations, which utilize multiple auxiliary movements to efficiently search the conformational space. These results demonstrate FoldDesign’s strong potential to explore both structural and functional space through computational design simulations that natural proteins have not reached through evolution.SignificanceNatural proteins were generated following billions of years of evolution and therefore possess limited structural folds and biological functions. There is considerable interest in de novo protein design to generate artificial proteins with novel structures and functions beyond those created by nature. However, the success rate of computational de novo protein design remains low, where extensive user-intervention and large-scale experimental optimization are typically required to achieve successful designs. To address this issue, we developed a new automated open-source program, FoldDesign, for de novo protein fold design which shows improved performance in creating high fidelity stable folds compared to other state-of-the-art methods. The success of FoldDesign should enable the creation of desired protein structures with promising clinical and industrial potential.

There is a growing interest in developing cooperative chemoenzymatic reactions to harness the reactivity of chemical catalysts and the selectivity of enzymes for the synthesis of nonracemic chiral ...

The outbreak of COVID-19 has now become a global pandemic that has severely impacted lives and economic stability. There is, however, no effective antiviral drug that can be used to treat COVID-19 to date. Built on the fact that SARS-CoV-2 initiates its entry into human cells by the receptor binding domain (RBD) of its spike protein binding to the angiotensin-converting enzyme 2 (hACE2), we extended a recently developed approach, EvoDesign, to design multiple peptide sequences that can competitively bind to the SARS-CoV-2 RBD to inhibit the virus from entering human cells. The protocol starts with the construction of a hybrid peptidic scaffold by linking two fragments grafted from the interface of the hACE2 protein (a.a. 22-44 and 351-357) with a linker glycine, which is followed by the redesign and refinement simulations of the peptide sequence to optimize its binding affinity to the interface of the SARS-CoV-2 RBD. The binding experiment analyses showed that the designed peptides exhibited a significantly stronger binding potency to hACE2 than the wild-type hACE2 receptor (with -53.35 vs. -46.46 EvoEF2 energy unit scores for the top designed and wild-type peptides, respectively). This study demonstrates a new avenue to utilize computationally designed peptide motifs to treat the COVID-19 disease by blocking the critical spike-RBD and hACE2 interactions.

Enzymes are increasingly explored for use in asymmetric synthesis1-3, but their applications are generally limited by the reactions available to naturally occurring enzymes. Recently, interest in photocatalysis4 has spurred the discovery of novel reactivity from known enzymes5. However, so far photoinduced enzymatic catalysis6 has not been used for the cross-coupling of two molecules. For example, the intermolecular coupling of alkenes with α-halo carbonyl compounds through a visible-light-induced radical hydroalkylation, which could provide access to important γ-chiral carbonyl compounds, has not yet been achieved by enzymes. The major challenges are the inherent poor photoreactivity of enzymes and the difficulty in achieving stereochemical control of the remote prochiral radical intermediate7. Here we report a visible-light-induced intermolecular radical hydroalkylation of terminal alkenes that does not occur naturally, catalysed by an 'ene' reductase using readily available α-halo carbonyl compounds as reactants. This method provides an efficient approach to the synthesis of various carbonyl compounds bearing a γ-stereocentre with excellent yields and enantioselectivities (up to 99 per cent yield with 99 per cent enantiomeric excess), which otherwise are difficult to access using chemocatalysis. Mechanistic studies suggest that the formation of the complex of the substrates (α-halo carbonyl compounds) and the 'ene' reductase triggers the enantioselective photoinduced radical reaction. Our work further expands the reactivity repertoire of biocatalytic, synthetically useful asymmetric transformations by the merger of photocatalysis and enzyme catalysis.

The integration of biocatalysis with chemocatalysis combines the excellent selectivity of the former with the robust reactivity of the latter and offers many advantages, such as lower cost, higher yield, enhanced selectivity, as well as less waste generation. In spite of the challenge of incompatibilities between different classes of catalysts, recent advances in synthetic chemistry and biology provide ample opportunities for multistep cascade transformations that combine biocatalysis and chemocatalysis. Herein, we review recent progress in merging biocatalysis with chemocatalysis, highlighting selected examples of photo-/electricity-driven biotransformations and recently developed strategies for addressing the catalyst incompatibility issue.

As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, the careful analysis of its transmission and cellular mechanisms is sorely needed. In this Communication, we first analyzed two recent studies that concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions share a unique similarity to HIV-1. However, the reimplementation of the analyses, built on larger scale data sets using state-of-the-art bioinformatics methods and databases, presents clear evidence that rebuts these conclusions. Next, using metagenomic samples from Manis javanica, we assembled a draft genome of the 2019-nCoV-like coronavirus, which shows 73% coverage and 91% sequence identity to the 2019-nCoV genome. In particular, the alignments of the spike surface glycoprotein receptor binding domain revealed four times more variations in the bat coronavirus RaTG13 than in the Manis coronavirus compared with 2019-nCoV, suggesting the pangolin as a missing link in the transmission of 2019-nCoV from bats to human.

Harnessing biocatalysts for novel abiological transformations is a longstanding goal of synthetic chemistry. Combining the merits of biocatalysis and photocatalysis allows for selective transformations fueled by visible light and offers many advantages including new reactivity, high enantioselectivity, greener syntheses, and high yields. Photoinduced electron or energy transfer enables synthetic methodologies that complement conventional two electron processes or offer orthogonal pathways for developing new reactions. Enzymes are well suited and can be tuned by directed evolution to exert control over open-shell intermediates, thereby suppressing undesirable reactions and delivering high chemo- and stereoselectivities. Within the past decade, the combination of biocatalysis and photocatalysis was mainly focused on exploiting light-regenerated cofactors to function native enzymatic activity. However, recent developments have demonstrated that the combination can unlock new-to-nature chemistry. Particularly, the discovery and application of new strategies are well poised to expand the applications of photobiocatalysis.In the past five years, our lab has been studying the combinations of photocatalysis and biocatalysis that can be applied to create new synthetic methodologies and solve challenges in synthetic organic chemistry. Our efforts have expanded the strategies for combining external photocatalysts with enzymes through the construction of a synergistic cooperative stereoconvergent reduction system consisting of photosensitized energy transfer and ene-reductase-catalyzed alkene reduction. Additionally, our efforts have also extended the capability of cofactor-dependent photoenzymatic systems to include enantioselective bimolecular radical hydroalkylations of alkenes by irradiating electron donor-acceptor complexes comprised of enzymatic redox active cofactors and unnatural substrates.In this Account, we highlight strategies developed by our group and others for combining biocatalysis and photocatalysis with the aim of introducing non-natural reactivity to enzymes. Presently, strategies applied to achieve this goal include the repurposing of natural photoenzymes, the elucidation of new photoreactivity within cofactor-dependent enzymes, the combination of external photocatalysts with enzymes, and the construction of artificial photoenzymes. By demonstrating the successful applications of these strategies for achieving selective new-to-nature transformations, we hope to spur interest in expanding the scope of photobiocatalytic systems through the use and extension of these strategies and creation of new strategies. Additionally, we hope to elucidate the intuition in synergizing the unique capabilities of biocatalysis and photocatalysis so that photobiocatalysis can be recognized as a potential solution to difficult challenges in synthetic organic chemistry.

In this article, we report 3D structure prediction results by two of our best server groups (“Zhang-Server” and “QUARK”) in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (i) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (ii) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (iii) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (iv) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.

Since December 2019, the COVID-19 caused by SARS-CoV-2 has been widely spread all over the world. It is reported that SARS-CoV-2 infection affects a series of human tissues, including lung, gastrointestinal tract, kidney, etc. ACE2 has been identified as the primary receptor of the SARS-CoV-2 Spike (S) protein. The relatively low expression level of this known receptor in the lungs, which is the predominantly infected organ in COVID-19, indicates that there may be some other co-receptors or alternative receptors of SARS-CoV-2 to work in coordination with ACE2. Here, we identified twenty-one candidate receptors of SARS-CoV-2, including ACE2-interactor proteins and SARS-CoV receptors. Then we investigated the protein expression levels of these twenty-one candidate receptors in different human tissues and found that five of which CAT, MME, L-SIGN, DC-SIGN, and AGTR2 were specifically expressed in SARS-CoV-2 affected tissues. Next, we performed molecular simulations of the above five candidate receptors with SARS-CoV-2 S protein, and found that the binding affinities of CAT, AGTR2, L-SIGN and DC-SIGN to S protein were even higher than ACE2. Interestingly, we also observed that CAT and AGTR2 bound to S protein in different regions with ACE2 conformationally, suggesting that these two proteins are likely capable of the co-receptors of ACE2. Conclusively, we considered that CAT, AGTR2, L-SIGN and DC-SIGN were the potential receptors of SARS-CoV-2. Moreover, AGTR2 and DC-SIGN tend to be highly expressed in the lungs of smokers, which is consistent with clinical phenomena of COVID-19, and further confirmed our conclusion. Besides, we also predicted the binding hot spots for these putative protein-protein interactions, which would help develop drugs against SARS-CoV-2.

Protein rotamers refer to the conformational isomers taken by the side-chains of amino acids to accommodate specific structural folding environments. Since accurate modeling of atomic interactions is difficult, rotamer information collected from experimentally solved protein structures is often used to guide side-chain packing in protein folding and sequence design studies. Many rotamer libraries have been built in the literature but there is little quantitative guidance on which libraries should be chosen for different structural modeling studies. Here, we performed a comparative study of six widely used rotamer libraries and systematically examined their suitability for protein folding and sequence design in four aspects: (1) side-chain match accuracy, (2) side-chain conformation prediction, (3) de novo protein sequence design, and (4) computational time cost. We demonstrated that, compared to the backbone-dependent rotamer libraries (BBDRLs), the backbone-independent rotamer libraries (BBIRLs) generated conformations that more closely matched the native conformations due to the larger number of rotamers in the local rotamer search spaces. However, more practically, using an optimized physical energy function incorporated into a simulated annealing Monte Carlo searching scheme, we showed that utilization of the BBDRLs could result in higher accuracies in side-chain prediction and higher sequence recapitulation rates in protein design experiments. Detailed data analyses showed that the major advantage of BBDRLs lies in the energy term derived from the rotamer probabilities that are associated with the individual backbone torsion angle subspaces. This term is important for distinguishing between amino acid identities as well as the rotamer conformations of an amino acid. Meanwhile, the backbone torsion angle subspace-specific rotamer search drastically speeds up the searching time, despite the significantly larger number of total rotamers in the BBDRLs. These results should provide important guidance for the development and selection of rotamer libraries for practical protein design and structure prediction studies.