Biology subjects

Adhikari, B.

Publications and source records attributed to Adhikari, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Managing the genetic diversity of Dioscorea yams for adaptation to climate change

Understanding adaptation to environmental variation is fundamental to climate-resilient agriculture. Here, we analyzed a georeferenced collection of 167 Dioscorea accessions spanning a wide range of bioclimatic and biophysical variation, from dry sahelian environment to rainforest environment. Genome-wide association analyses revealed that the genetic architecture of environmental adaptation is largely driven by large-effect loci, although precipitation-related traits were associated with many small-effect loci. This mixed architecture could lead to multiple breeding strategies, including marker-assisted backcrossing and the targeted use of wild and semi-wild relatives. We identified a core collection of accessions that captures much of the genetic and environmental diversity, and highlighted accessions as potential parents for abiotic stress tolerance. Projections under future climate scenarios using genomic offset identified regions and accessions at elevated risk of maladaptation, as well as others with broad adaptive potentiality. Maximizing the use of available material may facilitate the development of climate-resilient cultivars.

plant biology↗

bioRxiv2026

MYC-MIZ1 Complexes at Enhancers Tune Neuroendocrine Identity of Small Cell Lung Cancer

MYC family members have been extensively studied as undruggable transcription factors regulating oncogenic signaling in highly aggressive tumors such as small cell lung cancer (SCLC), via promoter binding. Here, leveraging the previously described Myc-driven SCLC mouse model (RPM), we generated RPM-Miz1{Delta}POZ (RPMM) mice to uncover a Myc-dependent regulation of neuroendocrine (NE) differentiation, via enhancers. Our functional and genomic analyses reveal that Miz1 facilitates Myc binding to low-affinity E-boxes at distal chromosomal regions, thereby enabling Myc occupancy at sites with otherwise limited intrinsic affinity. We further show that SCLC patients and cellular models share an enrichment of low-affinity E-Box Myc binding motifs at enhancer regions that loop to genes of classic neuroendocrine differentiation. Integrated epigenetic and genomic analyses with AI-modeling implicate Myc/Miz1 binding at enhancers as the determinant for the expression of bona-fide neuroendocrine genes. In RPMM tumors, the suppression of neuroendocrine identity is paralleled by a redistribution of Myc protein towards promoter-proximal regions, hyper-activation of Myc transcriptional programs, apoptotic priming and enhanced sensitivity to etoposide. Together, these findings uncover Miz1/Myc-engaged enhancers as a central hub for neuroendocrine lineage programs and provide a mechanistic basis for a targeted inhibition of Miz1 to boost chemosensitivity in SCLC.

cancer biology↗

bioRxiv2026

PhytoFam: A Nextflow Pipeline for Genome-Wide Analysis of Plant Gene Families

Genome-wide identification of plant gene families is essential for functional and evolutionary studies but often requires the use of multiple independent tools for homolog detection, domain validation, orthology assignment, and phylogenetic analysis. This fragmented approach involves extensive manual scripting, complicates reproducibility and parameter tracking, and may require additional steps to remove redundant protein isoforms. To address these challenges, we developed PhytoFam, a Nextflow-based workflow that automates gene family identification from proteome input through phylogenetic reconstruction. The pipeline integrates HMMER for candidate sequence identification, isoform-aware deduplication, InterProScan for domain confirmation, BLAST reciprocal best hit (RBH) analysis for orthology assignment, MUSCLE for multiple sequence alignment with optional outgroup incorporation, TrimAl for alignment trimming, and IQ-TREE3 for phylogenetic reconstruction. PhytoFam is portable across local workstations and high-performance computing environments and supports deployment through Conda, Docker, and Singularity. We validated the workflow using the Morus alba MADS-box gene family, where the complete analysis finished in 1 h 10 min (9 CPU h). IQ-TREE3 accounted for most of the execution time, whereas InterProScan showed the highest memory requirement with a peak resident set size of 4.5 GB. PhytoFam provides a reproducible, automated, and scalable solution for plant gene family identification and phylogenetic analysis. The pipeline is freely available at https://github.com/sanamparajuli/PhytoFam.

bioinformatics↗

bioRxiv2026

Patchy distribution of a Madrean Sky Island squirrel shaped by historical habitat configuration

Sky islands, mountain-top forests isolated by surrounding lowlands, offer unique opportunities to test how past and present landscapes shape species distributions. We examined the distribution of the Arizona gray squirrel (Sciurus arizonensis) across the Madrean Archipelago to test the constraint-based dynamic island biogeography (C-DIB) model, which posits that current occupancy in the sky islands reflects historical habitat size and connectivity. Using verified specimen records, we modeled climatically suitable habitats across four time periods: the Last Glacial Maximum (LGM), Mid-Holocene (MH), Present, and Future. For each mountain, we quantified suitable habitat area and estimated least-cost dispersal distances to assess both persistence and colonization potential. Our results suggest that species presence is best explained by LGM habitat metrics, which marginally outperformed models based on current conditions. Mountains that were large or well-connected during the LGM continue to support S. arizonensis, whereas historically isolated ranges remain unoccupied despite suitable contemporary habitat. These findings indicate a legacy of Pleistocene connectivity and reveal patterns of distributional disequilibrium. Furthermore, climatically suitable habitat for S. arizonensis has shifted both elevationally and geographically through time, reflecting long-term responses to climatic change. Together, these results emphasize the importance of protecting historically connected refugia, restoring riparian corridors that facilitate dispersal, and developing mountain range-specific management strategies that account for elevational shifts and potential downslope habitat recovery under future climate scenarios.

ecology↗

bioRxiv2025

Minute-scale control of ubiquitin-mediated degradation reveals dynamics of bacterial secreted effector-functions

Precise temporal control of protein abundance is essential for dissecting dynamic cellular processes. While degron-based systems enable rapid protein depletion in eukaryotic cells, comparable tools are lacking for bacterial effectors delivered into host cells during infection. Here, we establish AIDE (Auxin-Inducible Degradation of Effectors), a host-directed degradation platform that harnesses the ubiquitin-proteasome system to selectively eliminate secreted bacterial proteins, including membrane-integrated effectors. By integrating a minimal auxin-inducible degron (AID) tag into effector genes, AIDE enables rapid, reversible, and spatially confined degradation while preserving native expression and secretion. Applied to Chlamydia trachomatis, AIDE revealed that the membrane-integrated deubiquitinase Cdu1 suppresses autophagy early and later promotes developmental transitions, whereas the integral membrane fusogen IncA is continuously required for inclusion integrity. This AIDE platform provides minute-scale, spatiotemporal control over bacterial effector activity, offering a broadly applicable framework for dissecting virulence mechanisms and host-pathogen interactions across diverse secretion-dependent pathogens.

microbiology↗

bioRxiv2025

PathPCNet: Pathway Principal Component-Based Interpretable Framework for Drug Sensitivity Prediction

Precision medicine aims to identify significant biomarkers and effective drugs tailored to individual genomic profiles, thereby enabling personalized treatment strategies. Drug efficacy is often attributed to drug response, commonly measured as the concentration of a drug required to inhibit a biological activity. In contrast, drug sensitivity reflects how strongly a tumor responds to a drug, where a lower effective dose indicates higher sensitivity. Machine learning-based drug response prediction has the potential to accelerate biomarker discovery and facilitate the development of more effective therapeutics. In this study, we present PathPCNet, a novel interpretable deep learning framework that integrates multi-omics data (copy number variation, mutation, and RNA sequencing) fused with biological pathways, drug molecular structure, and Principal Component Analysis for drug response prediction. Our model achieves a Pearson correlation coefficient of 0.941 and an R-squared of 0.885, outperforming the existing pathway-based approaches. We employ SHAP-based model interpretation to quantify the contributions of omics and drug features, uncovering key pathways and gene-drug interactions associated with resistance mechanisms. These results demonstrate the utility of integrative deep learning models not only for accurate prediction but also for generating biologically meaningful insights, which can advance drug discovery and precision oncology. In addition, the framework also facilitates the identification of important pathways, genes, and atomic attributes of drugs related to drug sensitivity and different cancer types.

bioinformatics↗

bioRxiv2025

Adaptive P-element insertions in a long non-coding RNA are associated with unique silencing properties

Transposable elements are genetic parasites whose mobilization throughout the genome is a major source of deleterious mutations. However, some TE insertions are beneficial because they improve host fitness. Adaptive TE insertions sometimes alter the function of adjacent genes by positively and negatively impacting their expression, or by altering their encoding proteins. Alternatively, individual TE insertions can also be adaptive because they occur in piRNA clusters and lead to piRNA-mediated silencing of transposition. In a recent laboratory evolution experiment, we discovered that the long non-coding RNA CR43651 is an adaptive insertion hot spot for P-element DNA transposons in Drosophila melanogaster. The functional effects of these insertions on P-element repression and CR43651 function were unknown. In this study, we examined the effects of CR43651 insertions on P-element transcriptional regulation, piRNA biogenesis, and viability. We determined that although CR43651 is not a canonical piRNA cluster, chromosomes containing antisense P-element insertions in CR43651 exhibit enhanced piRNA-like silencing in the stage of oogenesis when P-elements transpose, potentially explaining their adaptive benefit. We also discovered that the fitness benefit provided by P-element repression is offset by recessive viability effects of insertion chromosomes, potentially due to disrupted production of mir14, a miRNA produced from CR43651.

genetics↗

bioRxiv2025

Targeted degradation of USP7 in solid cancer cells reveals disparate effects of deubiquitinase inhibition vs. acute protein depletion

Proteolysis-targeting chimeras (PROTACs) co-op the ubiquitin system for targeted protein degradation, creating opportunities to interrogate cellular functions of proteins through "chemical knockdown". However, matched pairs of protein degraders and inhibitors, that possess high specificity and chemical complementarity, for individual components of the ubiquitin system have remained scarce. This includes reagents to modulate activity and abundance of deubiquitinases (DUBs), which critically regulate ubiquitin-mediated signaling. Here, using an integrated chemical biology approach, we explored the cellular function of the DUB USP7 as a case study comparing inhibition and degradation of this DUB in melanoma and pancreatic cancer cells. Through the synthesis of a degrader library, we identified potent USP7 PROTACs for each cancer type, established BRET-based ternary complex formation and quantified degradation efficiency. USP7 degraders and their cognate inhibitor were subsequently employed to characterize treatment-induced phenotypic alterations. Proteomic and cellular analyses revealed that highly specific degradation of USP7 modulated both shared and distinct protein sets across cancer cell types, without impacting cell growth. Notably, cellular responses to USP7 degradation differed markedly from those to USP7 inhibition. Moreover, our data uncovered broad proteomic and metabolic changes induced by prolonged USP7 inhibitor treatment. Collectively, our work provides a chemical toolbox of comprehensively characterized reagents to distinguish on-target phenotypes which will aid the understanding of the role of USP7 in malignant diseases. More broadly, our data emphasize the importance of increased specificity via PROTAC-mediated degradation and the potential of this modality to distinguish catalytic from non-catalytic as well as cell-line specific functions of DUBs.

biochemistry↗

bioRxiv2024

Identification of Suitable Target/E3 Ligase Pairs for PROTAC Development using a Rapamycin-induced Proximity Assay (RiPA)

The development of proteolysis targeting chimeras (PROTACs), which induce the degradation of target proteins by bringing them into proximity with cellular E3 ubiquitin ligases, has revolutionized drug development. While the human genome encodes more than 600 different E3 ligases, current PROTACs use only a handful of them, drastically limiting their full potential. Furthermore, many PROTAC development campaigns fail because the selected E3 ligase candidates are unable to induce degradation of the particular target of interest. As more and more ligands for novel E3 ligases are discovered, the chemical effort to identify the best E3 ligase for a given target is exploding. Therefore, a genetic system to identify degradation-causing E3 ligases and suitable target/E3 ligase pairs is urgently needed. Here we used the well-established dimerization of the FKBP12 protein and FRB domain by rapamycin to bring the target protein WDR5 into proximity with candidate E3 ligases. Strikingly, this rapamycin-induced proximity assay (RiPA) revealed that VHL, but not Cereblon, is able to induce WDR5 degradation - a finding previously made by PROTACs, demonstrating its predictive power. By optimizing the steric arrangement of all components and fusing the target protein with a minimal luciferase, RiPA can identify the ideal E3 for any target protein of interest in living cells, significantly reducing and focusing the chemical effort in the early stages of PROTAC development.

biochemistry↗

bioRxiv2023

Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Functional magnetic resonance imaging (fMRI) studies often estimate brain intrinsic connectivity networks (ICNs) from temporal relationships between hemodynamic signals using approaches such as independent component analysis (ICA). While ICNs are thought to represent functional sources that play important roles in various psychological phenomena, current approaches have been tailored to identify ICNs that mainly reflect linear statistical relationships. However, the elements comprising neural systems often exhibit remarkably complex nonlinear interactions that may be involved in cognitive operations and altered in psychiatric conditions such as schizophrenia. Consequently, there is a need to develop methods capable of effectively capturing ICNs from measures that are sensitive to nonlinear relationships. Here, we advance a novel approach to estimate ICNs from explicitly nonlinear whole-brain functional connectivity (ENL-wFC) by transforming resting-state fMRI (rsfMRI) data into the connectivity domain, allowing us to capture unique information from distance correlation patterns that would be missed by linear whole-brain functional connectivity (LIN-wFC) analysis. Our findings provide evidence that ICNs commonly extracted from linear (LIN) relationships are also reflected in explicitly nonlinear (ENL) connectivity patterns. ENL ICN estimates exhibit higher reliability and stability, highlighting our approachs ability to effectively quantify ICNs from rsfMRI data. Additionally, we observed a consistent spatial gradient pattern between LIN and ENL ICNs with higher ENL weight in core ICN regions, suggesting that ICN function may be subserved by nonlinear processes concentrated within network centers. We also found that a uniquely identified ENL ICN distinguished individuals with schizophrenia from healthy controls while a uniquely identified LIN ICN did not, emphasizing the valuable complementary information that can be gained by incorporating measures that are sensitive to nonlinearity in future analyses. Moreover, the ENL estimates of ICNs associated with auditory, linguistic, sensorimotor, and self-referential processes exhibit heightened sensitivity towards differentiating between individuals with schizophrenia and controls compared to LIN counterparts, demonstrating the translational value of our approach and of the ENL estimates of ICNs that are frequently reported as disrupted in schizophrenia. In summary, our findings underscore the tremendous potential of connectivity domain ICA and nonlinear information in resolving complex brain phenomena and revolutionizing the landscape of clinical FC analysis.

neuroscience↗

bioRxiv2023

Retinoid X Receptor activation prevents diabetic retinopathy in murine models.

Previously, the RXR agonist UAB126 demonstrated therapeutic potential to treat obese mice by controlling blood glucose levels (BGL) and altering the expression of genes associated with lipid metabolism and inflammatory response. The purpose of the study was to assess UAB126 effect in progression of diabetic retinopathy (DR) in rodent models of Type1 diabetes (T1D), streptozotocin-induced, and Type2 diabetes (T2D), the db/db mice. UAB126 treatment was delivered either by oral gavage for 6 weeks or by topical application of eye drops for 2 weeks. At the end of the treatment, the retinal function of diabetic mice was assessed by electroretinography (ERG), and their retinal tissue was harvested for protein and gene expression analyses. Bone-marrow cells were isolated and differentiated into bone marrow-derived macrophages (BMDMs). The glycolysis stress test and the 2-DG glucose uptake analysis were performed. Our results demonstrated that in the UAB126-treated diabetic BMDMs, the ECAR rate and the 2-DG uptake were improved as compared to untreated diabetic BMDMs. In UAB126-treated diabetic mice, hyperglycemia was reduced and associated with the preservation of ERG amplitudes and enhanced AMPK activity. Retinas from diabetic mice treated with topical UAB126 demonstrated an increase in Rxr and Ppar, and expression of genes associated with lipid metabolism. Altogether, our data indicate that RXR activation is beneficial to preclinical models of DR.

molecular biology↗

bioRxiv2022

Extraction and characterization of cellulose from invasive weeds from central Nepal: A potential prospect of environmental management.

The cellulose is made up of long chains of polysaccharide of glucose molecules. Microfibrils are formed when numerous hydrogen-bonded cellulose chains unite, they are extremely stiff and contribute to physical stability, due to this general ability of forming these microfibrils to form long chains, cellulose is an ideal molecule for the manufacturing of packaging materials and bioplastics. On the other hand, Invasive plant species are one of the major constituents for environmental degradation and its application seems outmost. The main purpose of this study is to extract and identify the composition of cellulose fiber and characterize the fiber of invasive plant species that could be used as a replacement for plastics and textiles in some cases. In this study, Cellulose was isolated from 6 invasive species collected in Nepals Ramechhap district using various techniques, the composition of the fiber was identified using AOAC method 973.18, ASTM method D1106-96 and ASTM method E1755-01 and characterized using FTIR spectroscopy with weight analyses. Acid hydrolysis, chlorination, alkaline extraction, and bleaching were among the chemical methods adopted. In all of the samples, there were two primary absorbance peaks. The first occurred at low wavelengths in the 700-1,800 cm-1 range, while the second occurred at higher wavelengths in the 2,700-3,500 cm-1 range.The percentage of lignin within the final sample was determined in the range of 4.4-3.1% and the percentage yield of cellulose was determined within the range of 78-62%.The study shows that the cellulose can be extracted from the taken invasive plant species and can be used for further applications.

biochemistry↗

bioRxiv2022

Computational modeling of macrophage iron sequestration during host defense against Aspergillus

Iron is essential to the virulence of Aspergillus species, and restricting iron availability is a critical mechanism of antimicrobial host defense. Macrophages recruited to the site of infection are at the crux of this process, employing multiple intersecting mechanisms to orchestrate iron sequestration from pathogens. To gain an integrated understanding of how this is achieved in invasive aspergillosis, we generated a transcriptomic time-series of the response of human monocyte-derived macrophages to Aspergillus and used this and the available literature to construct a mechanistic computational model of iron handling of macrophages during this infection. We found an overwhelming macrophage response beginning 2-4 hours after exposure to the fungus, which included upregulated transcription of iron import proteins transferrin receptor-1, divalent metal transporter-1, and ZIP family transporters, and downregulated transcription of the iron exporter ferroportin. The computational model, based on a discrete dynamical systems framework, consisted of 21 3-state nodes, and was validated with additional experimental data that were not used in model generation. The model accurately captures the steady state and the trajectories of most of the quantitatively measured nodes. In the experimental data, we surprisingly found that transferrin receptor-1 upregulation preceded the induction of inflammatory cytokines, a feature that deviated from model predictions. Model simulations suggested that direct induction of TfR1 after fungal recognition, independent of the Iron Regulatory Protein - Labile Iron Pool system, explains this finding. We anticipate that this model will contribute to a quantitative understanding of iron regulation as a fundamental host defense mechanism during aspergillosis. ImportanceInvasive pulmonary aspergillosis is a major cause of death among immunosuppressed individuals despite the best available therapy. Depriving the pathogen of iron is an essential component of host defense in this infection, but the mechanisms by which the host achieves this are complex. To understand how recruited macrophages mediate iron deprivation during the infection, we developed and validated a mechanistic computational model that integrates the available information in the field. The insights provided by this approach can help in designing iron modulation therapies as anti-fungal treatments.

immunology↗

bioRxiv2021

Scoring Protein Sequence Alignments Using Deep Learning

BackgroundA high-quality sequence alignment (SA) is the most important input feature for accurate protein structure prediction. For a protein sequence, there are many methods to generate a SA. However, when given a choice of more than one SA for a protein sequence, there are no methods to predict which SA may lead to more accurate models without actually building the models. In this work, we describe a method to predict the quality of a proteins SA. MethodsWe created our own dataset by generating a variety of SAs for a set of 1,351 representative proteins and investigated various deep learning architectures to predict the local distance difference test (lDDT) scores of distance maps predicted with SAs as the input. These lDDT scores serve as indicators of the quality of the SAs. ResultsUsing two independent test datasets consisting of CASP13 and CASP14 targets, we show that our method is effective for scoring and ranking SAs when a pool of SAs is available for a protein sequence. With an example, we further discuss that SA selection using our method can lead to improved structure prediction.

bioinformatics↗

bioRxiv2021

The Innate Immune Response to Invasive Pulmonary Aspergillosis: A Systems Modeling Approach

Aspergillus species are ubiquitous environmental molds, the spores of which are inhaled daily by most humans. Immunocompromised hosts can develop an invasive infection resulting in high mortality. There is, therefore, a pressing need for host-centric therapeutics for this infection. To address this need, we created a multi-scale computational model of the infection, focused on its interaction with the innate immune system and iron, a critical nutrient for the pathogen. The model, parameterized using published data, was found to recapitulate a wide range of biological features and was experimentally validated in vivo. Conidial swelling was identified as critical in fungal strains with high growth, whereas the siderophore secretion rate is an essential prerequisite for establishment of the infection in low-growth strains. In immunocompetent hosts, high growth, high swelling probability, and impaired leukocyte activation lead to a high conidial germination rate. Similarly, in neutropenic mice, high fungal growth was achieved through synergy between high growth rate, high swelling probability, slow leukocyte activation, and high siderophore secretion. In summary, the model reveals a small set of parameters related to fungal growth, iron acquisition, and leukocyte activation as key determinants of the fate of the infection.

systems biology↗

bioRxiv2021

Design, Synthesis and Evaluation of WD-repeat containing protein 5 (WDR5) degraders

Histone H3K4 methylation serves as post-translational hallmark of actively transcribed genes and is introduced by histone methyltransferases (HMT) and its regulatory scaffolding proteins. One of these is the WD-repeat containing protein 5 (WDR5) that has also been associated with controlling long non-coding RNAs and transcription factors including MYC. The wide influence of dysfunctional HMTs complexes and the typically upregulated MYC levels in diverse tumor types suggested WDR5 as an attractive drug target. Indeed, protein-protein interface inhibitors for two protein interaction interfaces on WDR5 have been developed. While such compounds only inhibit a subset of WDR5 interactions, chemically induced proteasomal degradation of WDR5 might represent an elegant way to target all oncogenic function. This study presents the design, synthesis and evaluation of two diverse WDR5 degrader series based on two WIN site binding scaffolds and shows that linker nature and length strongly influence degradation efficacy.

cancer biology↗

bioRxiv2020

CONCENTRATION OF THIOUREA IS EFFECTIVE IN BREAKING THE DORMANCY OF POTATO VARIETIES.

Potato germination is highly sensitive to ecological conditions. High altitude and low annual average temperature results in tuber dormancy and poor sprouting. Dormancy has become a significant constraint for lowering potato production that hinders the possibility of growing two crops per year. An experiment was conducted from February to April 2020, where two major potato varieties (Desiree and Cardinal) were treated with four concentrations of Thiourea (0, 1, 2, and 3%) in a two factorial completely randomized block design with three replications. Results showed that Thiourea has a significant effect on all observed attributes as per varieties of potato. For Desiree variety, Thiourea (1%) decreased breaking of dormancy by 22 days compared to control (Desiree*Thiourea 0%) and produced the longest average sprout of 7.36cm at 49 days after treatment (DAT). On the other hand, for the Cardinal variety, Thiourea (3%) decreased tuber dormancy by 27 days compared to control (Cardinal*Thiourea 0%) and produced a sprout of 7.75 cm at 49 DAT. In conclusion, for breaking dormancy and enhancing sprouting of potato varieties Desiree and Cardinal, Thiourea concentration of 1% and 3% is recommended, respectively. Author summaryThis work is the combined effort of all the authors; conceptualization and designing the plot experiments, S.R. and M.D.; performing the experiment and data collection, S.R. and A.A.; statistical analysis and preparation of presentation table and figure, S.R. and B.A.; writing the original draft and editing the whole manuscript, S.R., M.D., R.S., S.S., and A.A. All the author have read and agreed to the published version of the manuscript.

plant biology↗

bioRxiv2020

REALDIST: Real-valued protein distance prediction

Protein structure prediction continues to stand as an unsolved problem in bioinformatics and biomedicine. Deep learning algorithms and the availability of metagenomic sequences have led to the development of new approaches to predict inter-residue distances--the key intermediate step. Different from the recently successful methods which frame the problem as a multi-class classification problem, this article introduces a real-valued distance prediction method REALDIST. Using a representative set of 43 thousand protein chains, a variant of deep ResNet is trained to predict real-valued distance maps. The contacts derived from the real-valued distance maps predicted by this method, on the most difficult CASP13 free-modeling protein datasets, demonstrate a long-range top-L precision of 52%, which is 17% higher than the top CASP13 predictor Raptor-X and slightly higher than the more recent trRosetta method. Similar improvements are observed on the CAMEO hard and very hard datasets. Three-dimensional (3D) structure prediction guided by real-valued distances reveals that for short proteins the mean accuracy of the 3D models is slightly higher than the top human predictor AlphaFold and server predictor Quark in the CASP13 competition.

bioinformatics↗