Motivation Molecular dynamics (MD) simulations model the physical movements of atoms in biomolecular systems over time, providing atomic-resolution insight into conformational changes, binding events, and dynamic behaviors that cannot be captured by static structures alone. As such, MD simulations are playing an increasingly important role in understanding the functional roles and molecular interactions of proteins. However, trajectories from these simulations can be extremely large, often reaching tens of gigabytes for a single simulation of modest duration. This creates substantial challenges for storage and data transfer, motivating efficient compression strategies. Furthermore, many downstream analyses require extraction of only a subset of frames or specific atoms from the full trajectory, so an ideal compression format should support rapid random-access decompression of such samplings without requiring full file decompression.Results Here, we introduce MDCompress, a new trajectory compression format and accompanying software implementation that meets these goals. MDCompress produces compressed trajectory files that are 15-37% smaller than those generated by the widely-used XTC format, while achieving faster compression and decompression speeds through efficient multithreading.Availability and implementation The MDCompress software and library are released under an open license (BSD-3) and may be downloaded at https://github.com/refresh-bio/mdcompress and is also available as a Zenodo repository at 10.5281/zenodo.19218347

Repbase and Dfam, the two foundational resources for transposable element annotation, are being unified into a single, fully open access framework, with Repbase released under CC-0 and its core curation team joining the Dfam project.

In eukaryotes, alternative splicing allows a single gene to encode multiple protein isoforms, by conditionally using only a subset of the gene's exons. In some cases, distinct isoforms utilize the same exon(s) in a different reading frame, thus encoding a distinct sequence of amino acids. Here, we provide a genome-wide view of such dual coding regions (DCRs) in humans. By mapping all reviewed human UniProtKB/Swiss-Prot isoforms to the human genome and tracking reading frames used by each isoform, we identified 1296 DCR-containing genes. Though it is possible for an exon to contain multiple reading frames that lead to DCRs simply due to noisy splicing, (i) mouse orthologs to human DCR genes appear to share a dual-coding nature with much greater frequency than is expected by chance and (ii) many human and mouse DCR isoforms show differential tissue-specific expression levels, suggesting a conserved functional role. DCRs are typically short (average: 95nt), confined to a single exon, and mostly appear to introduce early stop codons that lead to loss of C-terminal coding regions. At least one third of DCRs are likely to cause nonsense-mediated decay. DCR genes are not restricted to any particular functional category, suggesting that dual coding is broadly permissive rather than confined to specialized pathways. Structure prediction indicates that most amino acids produced by canonical-frame regions are involved in some secondary structural element, while non-canonical reading frames generally produce disordered peptides, supporting a model in which dual coding primarily rewires terminal regions and isoform stability rather than creating new folded domains. Our work characterizes DCRs as a fairly common byproduct of alternative splicing, sporadically co-opted and conserved in eukaryotes through evolution, contributing to gene regulation and functional diversity. We also provide web interfaces to enable visual exploration of DCR architecture and usage patterns.

ABSTRACT Introducing non-native organisms into natural or designed microbial communities holds enormous potential but faces challenges in establishment, persistence, and containment. We propose the “persistence landscape”—the functional composition of a target microbiome that promotes or discourages engineered organism persistence. Building on existing ecological concepts, this framework predicts the fitness of introduced organisms. Application of this concept would enable controlled introduction of desired taxa and phenotypes, addressing pressing challenges in agronomy, biomanufacturing, and human health.

The accurate determination of biological molecular function remains one of the most significant challenges in computational biology, with vast areas of biological "dark matter" persisting in microbiomes, viruses, and unexplored sequence space. To meet this challenge, we developed at PSB session to address the limitations of traditional sequence similarity-based functional annotation methods and explores how recent advances in AI/ML and high-throughput data generation are transforming the field. We highlight four innovative contributions presented in this session: a geometric framework using signed distance functions for modeling protein surfaces; a reinforcement learning-based approach for steering protein generative models to design functional sequences; an ensemble framework combining sequence, structural, and network features for subcellular localization prediction; and a scalable factorization method integrating gene-gene interaction data for analyzing high-dimensional genetic perturbation profiles. Together, these methodologies showcase the potential for computational and AI-driven tools to address the complex and multiscale nature of molecular function prediction, paving the way for new discoveries in understanding and engineering biological systems.

Recent advances in computer vision have enabled the development of automated animal behavior observation tools. Several software packages currently exist for concurrently tracking pose in multiple animals; however, existing tools still face challenges in maintaining animal identities across frames and can demand extensive human oversight and editing. Here we report on DIPLOMAT, a Deep learning-based, Identity-Preserving, Labeled-Object Multi-Animal Tracker, which implements automated algorithms to improve identity continuity, supplemented by an efficient human interface to help eliminate remaining errors. DIPLOMAT is designed to perform multi-animal tracking by building on the per-frame pose prediction models of two state-of-the-art tools, DeepLabCut and SLEAP, applying novel methods to tolerate occlusion and preserve animal identity across frames. Notable features include leveraging model-derived positional probabilities to compute independent maximum probability traces across frames of a video, use of video-specific skeletal constraints, and implementation of an efficient user interface for resolving errors. On the MABe mouse tracking benchmark, automated tracking with DIPLOMAT reduces body identity swaps by >75%, while remaining errors are easily eradicated with manual correction.

The introduction of non-native organisms into complex microbiome communities holds enormous potential to benefit society. However, microbiome engineering faces several challenges including successful establishment of the organism into the community, its persistence in the microbiome to serve a specified purpose, and constraint of the organism and its activity to the intended environment. A theoretical framework is needed to represent the complex interactions that drive these dynamics. Building on the concept of the community functional landscape, we define the persistence landscape as the metabolic, genetic, and broader functional composition and ecological context of the target microbiome that can be used to predict the environmental fitness of an introduced organism. Here, we discuss critical aspects of persistence landscapes that impact interactions between an introduced organism and the target microbiome, including the communitys genetic and metabolic complementation potential, cellular defense strategies, spatial and temporal dynamics, and the introduced organisms ability to compete for resources to survive. Finally, we highlight important knowledge gaps in the fields of microbial ecology and microbiome engineering that limit characterization and engineering of persistence landscapes. As a model for understanding microbiome structure and interaction in the context of microbiome engineering, the persistence landscape model should enable development of novel containment approaches while improving controlled colonization of a complex microbiome community to address pressing challenges in human health, agronomy, and biomanufacturing.

Building on established methods for molecular docking, structure-based deep learning has recently yielded important advances in virtual drug screening. We present simpatico, a method that follows an alternate approach, based on the field of Representation Learning, to dramatically speed the process of accurate drug screening. Simpatico employs graph neural networks to produce high-dimensional embeddings for the atoms of proteins and small molecules, and uses these embeddings to rapidly produce accurate predictions of the interaction potential for drug candidates with target protein pockets. Simpatico can search a database containing 600 million drugs for good binding candidates to a single protein pocket in 2.5 hours on a single GPU. Despite being >1000x faster than state of the art docking and diffusion-based methods, simpatico is competitive with the most accurate of those methods. We also observe that simpatico embeddings can be used to explore toxicity risk and to identify proteins with similar binding potential. Simpatico is open source software; all code, weights, and data may be accessed at https://github.com/TravisWheelerLab/Simpatico.

Advances in whole genome sequencing have led to a rapid and ongoing increase in the amount of sequence data available, but 40-50% of known genes have no functional annotation and only 25-30% have specific functional annotations. Current functional annotation approaches typically rely on computationally expensive pairwise or multiple sequence alignments, preventing rapid development of models for novel protein functions and sometimes limiting methods to one ontology. Representation of sequence in short segments (kmers) has been used in many applications for nucleotide sequence, and more recently has been applied to protein sequence as well. We previously developed Snekmer, a tool which uses kmer patterns to develop alignment-free individual protein family models. Other approaches, such as MMSeqs2 and DIAMOND, use protein kmers as a fast filter to reduce search space for subsequent sequence alignment. Here, we describe a novel addition to the Snekmer tool which builds kmer libraries for protein families and uses those libraries to map functional annotations to new sequences. We first demonstrate that our method accurately applies TIGRFAMs annotations to protein fragments and to a low-sequence identity benchmark dataset, and further use it to annotate a set of drought stress associated soil and rhizosphere metagenome sequences with higher sensitivity towards several important protein function classes than that shown by HMMs. We have incorporated this workflow into Snekmer. ### Competing Interest Statement The authors have declared no competing interest. Office of Biological and Environmental Research, Persistence Control of Engineered Function in Complex Soil Microbiomes Animal and Plant Health Inspection Service, https://ror.org/0599wfz09, A systems approach to understanding farm animal-environmental drivers of SARS-CoV-2 transmission in the food supply chain

In the Big Data era, a change of paradigm in the use of molecular dynamics is required. Trajectories should be stored under FAIR (findable, accessible, interoperable and reusable) requirements to favor its reuse by the community under an open science paradigm.

SUMMARY:Protein language models (PLMs) have recently demonstrated potential to supplant classical protein database search methods based on sequence alignment, but are slower than common alignment-based tools and appear to be prone to a high rate of false labeling. Here, we present Neural Embeddings for Amino acid Relationships (NEAR), a method based on neural representation learning that is designed to improve both speed and accuracy of search for likely homologs in a large protein sequence database. NEAR's ResNet embedding model is trained using contrastive learning guided by trusted sequence alignments. It computes per-residue embeddings for target and query protein sequences, and identifies alignment candidates with a pipeline consisting of residue-level k-NN search and a simple neighbor aggregation scheme. Tests on a benchmark consisting of trusted remote homologs and randomly shuffled decoy sequences reveal that NEAR substantially improves accuracy relative to state-of-the-art PLMs, with lower memory requirements and faster embedding and search speed. While these results suggest that the NEAR model may be useful for standalone homology detection with increased sensitivity over standard alignment-based methods, in this manuscript, we focus on a more straightforward analysis of the model's value as a high-speed pre-filter for sensitive annotation. In that context, NEAR is at least 5x faster than the pre-filter currently used in the widely used profile hidden Markov model (pHMM) search tool HMMER3, and also outperforms the pre-filter used in our fast pHMM tool, nail. AVAILABILITY AND IMPLEMENTATION:NEAR is under an open-source license. Code and data curation instructions can be found at https://github.com/TravisWheelerLab/NEAR.

Immune checkpoint inhibitors (ICI) have changed the treatment paradigm for many cancers but have not shown benefit in prostate cancer. Chronic inflammation contributes to the immunosuppressive prostate tumor microenvironment and is associated with poor response to ICIs. The primary source of inflammatory cytokine production is the inflammasome. In this study, we identify the proviral integration site for Moloney murine leukemia virus (PIM) kinases as regulators of inflammasome activation in tumor-associated macrophages (TAM). The analysis of clinical data from a cohort of patients with treatment-naïve, hormone-responsive prostate cancer revealed that tumors from patients with high PIM1/2/3 displayed an immunosuppressive tumor microenvironment characterized by high inflammation and a high density of repressive immune cells, most notably TAMs. Macrophage-specific knockout of PIM reduced tumor growth in syngeneic models of prostate cancer. Transcriptional analyses indicated that eliminating PIM from macrophages enhanced the adaptive immune response and increased cytotoxic immune cells. Combined treatment with PIM inhibitors and ICIs synergistically reduced tumor growth. Immune profiling revealed that PIM inhibitors sensitized prostate cancer tumors to ICIs by increasing tumor suppressive TAMs and increasing the activation of cytotoxic T cells. Our data implicate macrophage PIM as a driver of inflammation that limits ICI potency and provide preclinical evidence that PIM inhibitors are an effective strategy to improve the ICI efficacy in prostate cancer.

The volume of biological data being generated by the scientific community is growing exponentially, reflecting technological advances and research activities. The National Institutes of Health's (NIH) Sequence Read Archive (SRA), which is maintained by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM), is a rapidly growing public database that researchers use to drive scientific discovery across all domains of life. This increase in available data has great promise for pushing scientific discovery but also introduces new challenges that scientific communities need to address. As genomic datasets have grown in scale and diversity, a parade of new methods and associated software have been developed to address the challenges posed by this growth. These methodological advances are vital for maximally leveraging the power of next-generation sequencing (NGS) technologies. With the goal of laying a foundation for evaluation of methods for petabyte-scale sequence search, the Department of Energy (DOE) Office of Biological and Environmental Research (BER), the NIH Office of Data Science Strategy (ODSS), and NCBI held a virtual codeathon 'Petabyte Scale Sequence Search: Metagenomics Benchmarking Codeathon' on September 27 - Oct 1 2021, to evaluate emerging solutions in petabyte scale sequence search. The codeathon attracted experts from national laboratories, research institutions, and universities across the world to (a) develop benchmarking approaches to address challenges in conducting large-scale analyses of metagenomic data (which comprises approximately 20 that benefit from SRA-wide searches and the tools required to execute the search, and (c) produce community resources i.e. a public facing repository with information to rebuild and reproduce the problems addressed by each team challenge.

" Fast is fine, but accuracy is final. " -- Wyatt Earp. Background:The extreme diversity of newly sequenced organisms and considerable scale of modern sequence databases lead to a tension between competing needs for sensitivity and speed in sequence annotation, with multiple tools displacing the venerable BLAST software suite on one axis or another. Alignment based on profile hidden Markov models (pHMMs) has demonstrated state of art sensitivity, while recent algorithmic advances have resulted in hyper-fast annotation tools with sensitivity close to that of BLAST. Results:Here, we introduce a new tool that bridges the gap between advances in these two directions, reaching speeds comparable to fast annotation methods such as MMseqs2 while retaining most of the sensitivity offered by pHMMs. The tool, called nail, implements a heuristic approximation of the pHMM Forward/Backward (FB) algorithm by identifying a sparse subset of the cells in the FB dynamic programming matrix that contains most of the probability mass. The method produces an accurate approximation of pHMM scores and E-values with high speed and small memory requirements. On a protein benchmark, nail recovers the majority of recall difference between MMseqs2 and HMMER, with run time ~26x faster than HMMER3 (only ~2.4x slower than MMseqs2's sensitive variant). nail is released under the open BSD-3-clause license and is available for download at https://github.com/TravisWheelerLab/nail.

Computational approaches for small-molecule drug discovery now regularly scale to consideration of libraries containing billions of candidate small molecules. One promising approach to increased speed in evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly-used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active. We also demonstrate that, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on similarity to other such molecules.. ### Competing Interest Statement The authors have declared no competing interest. * (TT) : Topological torsion (ECFP) : Extended Connectivity Fingerprint (FCFP) : Functional Class Fingerprint (AP2D) : Atom pair (AT2D) : Atom Triplet (ASP) : All Star Paths (DFS) : Depth First Search

In the age of long read sequencing, genomics researchers now have access to accurate repetitive DNA sequence (including satellites) that, due to the limitations of short read sequencing, could previously be observed only as unmappable fragments. Tools that annotate repetitive sequence are now more important than ever, so that we can better understand newly uncovered repetitive sequences, and also so that we can mitigate errors in bioinformatic software caused by those repetitive sequences. To that end, we introduce the 1.0 release of our tool for identifying and annotating locally-repetitive sequence, ULTRA (ULTRA Locates Tandemly Repetitive Areas). ULTRA is fast enough to use as part of an efficient annotation pipeline, produces state-of-the-art reliable coverage of repetitive regions containing many mutations, and provides interpretable statistics and labels for repetitive regions. It released under an open license, and available for download at https://github.com/TravisWheelerLab/ULTRA.

Bacteriophages are viruses that infect bacteria. Many bacteriophages integrate their genomes into the bacterial chromosome and become prophages. Prophages may substantially burden or benefit host bacteria fitness, acting in some cases as parasites and in others as mutualists. Some prophages have been demonstrated to increase host virulence. The increasing ease of bacterial genome sequencing provides an opportunity to deeply explore prophage prevalence and insertion sites. Here we present VIBES (Viral Integrations in Bacterial genomES), a workflow intended to automate prophage annotation in complete bacterial genome sequences. VIBES provides additional context to prophage annotations by annotating bacterial genes and viral proteins in user-provided bacterial and viral genomes. The VIBES pipeline is implemented as a Nextflow-driven workflow, providing a simple, unified interface for execution on local, cluster and cloud computing environments. For each step of the pipeline, a container including all necessary software dependencies is provided. VIBES produces results in simple tab-separated format and generates intuitive and interactive visualizations for data exploration. Despite VIBES's primary emphasis on prophage annotation, its generic alignment-based design allows it to be deployed as a general-purpose sequence similarity search manager. We demonstrate the utility of the VIBES prophage annotation workflow by searching for 178 Pf phage genomes across 1072 Pseudomonas spp. genomes.

Background Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). The Forward algorithm that provides much of pHMMs’ sensitivity is relatively slow, motivating extensive efforts to increase speed. Numerous researchers have devised methods to improve pHMM alignment speed using hardware accelerators such as graphics processing units (GPUs) and field programmable gate arrays (FPGAs). Here, we describe an FPGA hardware accelerator for a key bottleneck step in the analysis pipeline employed by the popular pHMM aligment tool, HMMER. HMMER accelerates pHMM Forward alignment by screening most sequence with a series of filters that rapidly approximate the result of computing full Forward alignment. The first of these filters, the Single Segment ungapped Viterbi (SSV) algorithm, is designed to filter out 98% of non-related inputs and accounts for 70% of the overall runtime of the DNA search tool nhmmer in common use cases. SSV is an ideal target for hardware acceleration due to its limited data dependency structure. Results We present Hardware Accelerated single segment Viterbi Additional Coprocessor (HAVAC), an FPGA-based hardware accelerator for the SSV algorithm. The core HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a Xilinx Alveo U50 FPGA accelerator card, ∼ 227x faster than the optimized SSV implementation in nhmmer . Accounting for PCI-e data transfer data processing, HAVAC is 65x faster than nhmmer’s SSV with one thread and 35x faster than nhmmer with four threads, and uses ∼ 31% the energy of a traditional high end Intel CPU. Because these computations are performed on a co-processor, the host CPU remain free to simultaneously compute downstream pHMM alignment and later post-processing. Author summary Sequence alignment lies at the heart of genome sequence annotation, and must be both fast and accurate. Signals of relationships between sequences are obscured over time by mutational forces, so that alignment and annotation of the full diversity of life demands highly sensitive tools. Profile hidden Markov models (pHMMs) provide the greatest sensitivity in the face of diversity, but are relatively slow. Here, we describe an approach to improving the speed of pHMM search that leverages field programmable gate arrays - hardware devices that can be configured to implement arbitrary digital circuits to achieve impressive parallelism and energy efficiency. Our tool, HAVAC, accelerates one key bottleneck step in the analysis pipeline employed by the popular pHMM aligment tool, HMMER. HAVAC produces a ∼ 60x speedup over the analogous stage in HMMER. HAVAC can be implemented as a part of a larger sequence homology search tool for faster search times and reduced energy usage. Interested users can download HAVAC on github at https://github.com/TravisWheelerLab/HAVAC .

We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long read sequencing data and in the context of pseudogenes.