Computational Molecular Biology Research Articles

Recombinant Antibody Fragments for Immunotherapy of Parkinson's Disease.

Parkinson's disease (PD) is the second most common age-related neurodegenerative disorder. Multiple genetic and environmental factors leading to progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SN) and consequent depletion of dopamine were described. Current clinical approaches, such as dopamine replacement or deep brain stimulation using surgically implanted probes, provide symptomatic relief but cannot modify disease progression. Therefore, disease-modifying therapeutic tools are urgently needed. Immunotherapy approaches, including passive transfer of protective antibodies and their fragments, have shown therapeutic efficacy in several animal models of neurodegenerative diseases, including PD. Recombinant antibody fragments are promising alternatives to conventional full-length antibodies. Modern computational approaches and molecular biology tools, directed evolution methodology, and the design of tissue-penetrating fusion peptides allowed for the development of recombinant antibody fragments with superior specificity and affinity, reduced immunogenicity, the capacity to target hidden epitopes and cross the blood-brain barrier (BBB), higher solubility and stability, the ability to refold after heat denaturation, and inexpensive large-scale production. In addition, antibody fragments do not induce microglia Fcγ receptor (FcγR)-mediated proinflammatory response and tissue damage in the central nervous system (CNS), because they lack the Fc portion of the immunoglobulin molecule. In the present review, we summarized data on recombinant antibody fragments evaluated as immunotherapeutics in preclinical models of PD and discussed their potential for developing therapeutic and preventive protocols for patients with PD.

Computational Molecular Biology in Data Science Applications on Bioinformatics in Genetic Sequencing of COVID Mutations

Computational Molecular Biology in Data Science Applications on Bioinformatics in Genetic Sequencing of COVID Mutations

Investigating performance metrics for container-based HPC environments using x86 and OpenPOWER systems

Container-based High-Performance Computing (HPC) is changing the way computation is performed and reproduced without sacrificing the raw performance compared to hypervisor-assisted virtualization technologies. It primarily supports continuously evolving data-intensive applications such as computational fluid dynamics, seismic tomography, molecular biology, and Proteomics. OpenPOWER systems, unlike the x86 systems, use the POWER-compliant processor to exploit instruction-level and thread-level parallelism heavily. In our previous work, we designed and developed a Containerized HPC environment (cHPCe) from the scratch using Linux namespaces on OpenPOWER systems. This paper aims to provide an in-depth performance analysis of the Containerized HPC environment using x86 systems and Containerized HPC environment using the OpenPOWER system, on systems’ subcomponents, processor, memory, interconnect, and IO. This sub-component analysis provides an insight on several aspects of the system performance. To the best of our knowledge, no research has been reported yet for such a comparative analysis that designs cHPCe for both x86 and OpenPOWER systems. The performance of the developed cHPCe is compared with BareMetals, and VMs using the benchmarks HPCC, and IOZone. Our experimental results achieve 0.13% less compute performance penalty at its peak performance on cHPCe compared to the BareMetal-based solution for x86 systems. In contrast, a VM-based solution introduces an overhead of 20% and 4.83% in x86 and OpenPOWER cases, respectively. Moreover, the x86 and OpenPOWER systems observe inconsistent behavior for memory performance with a worst-case penalty of 9.68% and 6.64% compared to achieved peak performance, respectively. However, similar behavior is reported for cHPCe with an overhead of less than 3% and 2% in the worst case for the latency and bandwidth, respectively, compared to the BareMetal for network and disk performance. Our experimental results reveal that the containerized OpenPOWER environment represents a viable alternative to the counterpart x86 environment for the HPC solution.

LISTER: Semiautomatic Metadata Extraction from Annotated Experiment Documentation in eLabFTW.

The availability of scientific methods, code, and data is key for reproducing an experiment. Research data should be made available following the FAIR principle (findable, accessible, interoperable, and reusable). For that, the annotation of research data with metadata is central. However, existing research data management workflows often require that metadata be created by the corresponding researchers, which takes effort and time. Here, we developed LISTER as a methodological and algorithmic solution to create and extract metadata from annotated, template-based experimental documentation using minimum effort. We focused on tailoring the integration between existing platforms by using eLabFTW as the electronic lab notebook and adopting the ISA (investigation, study, assay) model as the abstract data model framework. LISTER consists of four components: annotation language to support metadata extraction; customized eLabFTW entries using specific hierarchies, templates, and tags to structure reusable scientific documentation; a "container" concept in eLabFTW, making metadata of a particular container content extractable along with its underlying, related experiments via a single click; a Python-based app to enable easy-to-use, semiautomated metadata extraction from eLabFTW entries. LISTER outputs metadata in machine-readable .json and human-readable .xlsx formats, and Material and Methods (MM) descriptions in .docx format that could be used in a thesis or manuscript. The metadata can be used as a basis to create or extend ontologies, which, when applied to the published research data, will significantly enhance its value. DSpace is used as a data cataloging platform for hosting the extracted metadata and research data. We applied LISTER to computational biophysical chemistry, protein biochemistry, and molecular biology, and our concept should be extendable to other life science areas.

Quantum gate algorithm for reference-guided DNA sequence alignment

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4B bits or ∼760MB of storage (National Institute of General Medical Sciences, n.d.). Today’s most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed.

Chaining for accurate alignment of erroneous long reads to acyclic variation graphs.

Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit [Garrison et al. (Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875-9)] is a popular aligner of short reads, GraphAligner [Rautiainen and Marschall (GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 2020;21:253-28)] is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12-17% more reads, and 21-28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22, and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph [Li et al. (The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020;21:265-19.)] and minichain [Chandra and Jain (Sequence to graph alignment using gap-sensitive co-linear chaining. In: Proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2023). Springer, 2023, 58-73.)] obtain an accuracy of <60% on this setting. GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address.

DNA Image Encryption Scheme Based on a Chaotic LSTM Pseudo-Random Number Generator

Deoxyribonucleic Acid (DNA) coding technology is a new research field developed by the combination of computer science and molecular biology, that has been gradually applied in the field of image encryption in recent years. Furthermore, sensitivity to initial conditions, pseudo-random properties, and state ergodicity of coupled chaotic maps can help produce good pseudo-random number generators and meet the requirements of an image encryption system well. In this paper, an image encryption algorithm based on high-dimensional coupled chaotic maps and DNA coding is proposed. A pseudo-random sequence is generated by a long short-term memory (LSTM) architecture using the proposed maps and evaluated through a set of statistical tests to show the high performance of the proposed generator. All intensity values of an input image are converted to a binary sequence, which is scrambled globally by the high-dimensional coupled chaotic maps. The DNA operations are performed on the scrambled binary sequences instead of binary operations to increase the algorithm efficiency. Simulation results and performance analyses demonstrate that the proposed encryption scheme is extremely sensitive to small changes in secret keys, provides high security and can resist differential attack.

Analyzing Histone Chaperone ANTI-SILENCING FACTOR-1A and HISTONE REPRESSOR A Interactions and Their Roles in Arabidopsis thaliana Development via CRISPR/Cas9 Mutagenesis

The ASF1A and HIRA are histone chaperones highly conserved in many organisms. The protein interactions between ASF1a and HIRA and their role in plant development are not understood in Arabidopsis thaliana. Here, we present our attempt to generate mutations at ASF1a and HIRA genes using CRISPR/Cas9 technology and analyze their interactions through computational molecular biology. Our current efforts focus on preliminary sequence analyses, CRISPR/Cas9 system preparation, and transgene delivery.

LambdaPP: Fast and accessible protein-specific phenotype predictions.

The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP-leveraging ColabFold and computed in minutes-is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.

BOA: A partitioned view of genome assembly

SummaryDe novo genome assembly is a fundamental problem in computational molecular biology that aims to reconstruct an unknown genome sequence from a set of short DNA sequences (or reads) obtained from the genome. The relative ordering of the reads along the target genome is not known a priori, which is one of the main contributors to the increased complexity of the assembly process. In this article, with the dual objective of improving assembly quality and exposing a high degree of parallelism, we present a partitioning-based approach. Our framework, BOA (bucket-order-assemble), uses a bucketing alongside graph- and hypergraph-based partitioning techniques to produce a partial ordering of the reads. This partial ordering enables us to divide the read set into disjoint blocks that can be independently assembled in parallel using any state-of-the-art serial assembler of choice. Experimental results show that BOA improves both the overall assembly quality and performance.

Michael Waterman's Contributions to Computational Biology and Bioinformatics.

On the occasion of Dr. Michael Waterman's 80th birthday, we review his major contributions to the field of computational biology and bioinformatics including the famous Smith-Waterman algorithm for sequence alignment, the probability and statistics theory related to sequence alignment, algorithms for sequence assembly, the Lander-Waterman model for genome physical mapping, combinatorics and predictions of ribonucleic acid structures, word counting statistics in molecular sequences, alignment-free sequence comparison, and algorithms for haplotype block partition and tagSNP selection related to the International HapMap Project. His books Introduction to Computational Biology: Maps, Sequences and Genomes for graduate students and Computational Genome Analysis: An Introduction geared toward undergraduate students played key roles in computational biology and bioinformatics education. We also highlight his efforts of building the computational biology and bioinformatics community as the founding editor of the Journal of Computational Biology and a founding member of the International Conference on Research in Computational Molecular Biology (RECOMB).

Protein Structure Prediction Using Quantile Dragonfly and Structural Class-Based Deep Learning

Predicting three-dimensional structure of a protein in the field of computational molecular biology has received greater attention. Most of the recent research works aimed at exploring search space, however with the increasing nature and size of data, protein structure identification and prediction are still in the preliminary stage. This work is aimed at exploring search space to tackle protein structure prediction with minimum execution time and maximum accuracy by means of quantile regressive dragonfly and structural class homolog-based deep learning (QRD-SCHDL). The proposed QRD-SCHDL method consists of two distinct steps. They are protein structure identification and prediction. In the first step, protein structure identification is performed by means of QRD optimization model to identify protein structure with minimum error. Here the protein structure identification is first performed as the raw database contains sequence information and does not contain structural information. An optimization model is designed to obtain the structural information from the database. However, protein structure gives much more insight than its sequence. Therefore, to perform computational prediction of protein structure from its sequence, actual protein structure prediction is made. The second step involves the actual protein structure prediction via structural class and homolog-based deep learning. For each protein structure prediction, a scoring matrix is obtained by utilizing structural class maximum correlation coefficient. Finally, the proposed method is tested on a set of different unique numbers of protein data and compared to the state-of-the-art methods. The obtained results showed the potentiality of the proposed method in terms of metrics, error rate, protein structure prediction time, protein structure prediction accuracy, precision, specificity, recall, ROC, Kappa coefficient and [Formula: see text]-measure, respectively. It also shows that the proposed QRD-SCHDL method attains comparable results and outperformed in certain cases, thereby signifying the efficiency of the proposed work.

An Intelligent Optimization Model for Fragment Assembly Problem

problem as it requires rebuilding the original sequence of DNA from a massive number of fragments. This paper introduces an efficient hybridization between Harris Hawks Optimization (HHO) and Problem Aware Local Search (PALS) to be utilized in solving DNA Fragment Assembly Problem (FAP). The efficiency of the proposed hybrid algor In computational molecular biology, the sequencing of Deoxyribonucleic Acid (DNA) is a very challenging ithm (PHHO) is compared with the original PALS, Firefly Algorithm (FA), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and GAG50. The experimental results show the efficiency of the proposed algorithm compared to other approaches.

Computational Methods for Structure-Based Drug Design Through System Biology.

The advances in computational chemistry and biology, computer science, structural biology, and molecular biology go in parallel with the rapid progress in target-based systems. This technique has become a powerful tool in medicinal chemistry for the identification of hit molecules. The recent developments in target-based systems have played a major role in the creation of libraries of compounds, and it has also been widely applied for the design of molecular docking methods. The main advantage of this method is that it hits the fragment that has the strongest binding, has relatively small size, and leads to better compounds in terms of pharmacokinetic properties when compared with virtual screening (VS) and high-throughput screening (HTS) hits. De novo design is an essential aspect of target-based systems and requires the synthesis of chemical to allow the design of promising compound.

369. Alternative Workflow for SARS-CoV-2 Testing Using a Heat Lysis Protocol for Respiratory Specimens

BackgroundThe SARS-CoV-2 pandemic has demonstrated the need for streamlined workflows in high-throughput testing. In extraction-based testing, limited extraction reagents and required proprietary instrumentation may pose a bottleneck for labs. As a solution, ChromaCode developed a Direct Extraction protocol for the HDPCR™ SARS-CoV-2 Assay, distributed in accordance with the guidance on Policy for Coronavirus Disease-2019 Tests During the Public Health Emergency, Section IV.C., which allows for the processing of specimens without an extraction system. In lieu of an extraction system, the Direct Extraction protocol uses a thermal cycler to lyse and inactivate specimens which are directly added to the Polymerase Chain Reaction (PCR).MethodsThe Limit of Detection (LoD), Clinical Performance, and effect of Interfering Substances was determined for the Direct Extraction protocol. The LoD was established on 6 PCR platforms with dilutions of inactivated SARS-CoV-2 virus spiked into residual, negative nasopharyngeal swab (NPS) matrix. Clinical performance was assessed with 48 positive and 50 negative frozen retrospective samples using the Direct Extraction protocol compared to an external Emergency Use Authorized (EUA) comparator assays (cobas® Liat® SARS-CoV-2 & Influenza A/B assay and the Hologic Panther Fusion® SARS-CoV-2 Assay respectively) on three PCR platforms. The Direct Extraction protocol was evaluated for performance in the presence of 13 potentially interfering substances that can be present in a respiratory specimen.ResultsThe LoD of the Direct Extraction protocol ranges from 1000 – 3000 genomic equivalents (GE)/mL. The clinical performance of the assay was 95.8% positive agreement (95% CI of 84.6% - 99.3%) and 100% negative agreement (95% CI of 90.9% - 100% or 91.1% – 100%) across all three PCR platforms tested. The viral target was detected at 3X LoD for all interferents tested.ConclusionThe Direct Extraction protocol of ChromaCode’s SARS-CoV-2 Assay is a sensitive test that eliminates the need for sample extraction and performs very well against traditional extraction-based workflows. The inclusion of this protocol can reduce costs, reliance on extraction systems, and time associated with extraction-based protocols.Disclosures Meghna Yadav, Ph.D. Molecular Biology, ChromaCode Inc. (Employee, Shareholder) Tiffany Martinez, n/a, ChromaCode (Employee, Shareholder) Isabel Regoli, MS, Bioinformatics, ChromaCode (Employee, Shareholder) Osvaldo Hernandez, B.S., Molecular Biology, ChromaCode (Employee, Shareholder) Phuong Le, B.S., Biochemistry, ChromaCode (Employee, Shareholder) Heather Carolan, Masters, Computational Molecular Biology, ChromaCode (Employee, Shareholder) Brad Brown, Ph.D Biomedical Sciences, ChromaCode (Employee, Shareholder) Karen Menge, Ph.D. Biochemistry, ChromaCode (Employee, Shareholder)ChromaCode (Employee, Shareholder)

Protein secondary structure prediction using a lightweight convolutional network and label distribution aware margin loss

Protein secondary structure prediction (PSSP) is an important task in computational molecular biology. Recently, deep neural networks have demonstrated great potential in improving the performance of eight-class PSSP. However, the existing deep predictors usually have higher model complexity and ignore the class imbalance of eight-class secondary structure data in training. In addition, the current methods cannot guarantee that the features corresponding to the padded residue positions are always zero during the forward propagation of the network, which will cause the prediction results of the same protein chain to be different under varied zero-padding numbers. To this end, we propose a novel lightweight convolutional network ShuffleNet_SS, which adopts modified 1-dimensional batch normalization to eliminate the impact of padded residue positions on nonpadded residue positions and uses the label distribution aware margin loss to enhance the network’s ability to learn rare classes. In particular, in order to enable ShuffleNet_SS to fully achieve cross-group information exchange, we further improve the standard channel shuffle operation. Experimental results on the benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14 and CB513 show that the proposed method achieves state-of-the-art performance with much lower parameters compared to the five existing deep predictors.

Et Özgüllüğünün Belirlenmesinde Primer Setlerinin Tasarımına Yönelik Biyoinformatik Tabanlı Bir Yaklaşım

Polymerase chain reaction (PCR) and its derivatives are one of the most widely used DNA-based methods in species determination studies in meat and meat products. Chromosomal or mitochondrial genes of the species can be targeted in PCR-based analyzes used in species detection studies. Many researchers are able to realize oligonucleotide differences between species through online alignment programs on mitochondrial DNA. Using chromosomal DNA would provide more concise results in quantification studies. However, determining the marker regions for genomic DNA is challenging due to the large size of the chromosomes. Bioinformatics approaches are available for selected applications. However, using those approaches requires intensive knowledge of computer science, molecular biology, and bioinformatics in addition to high computational power. In this study, a pipeline is presented that will provide a user-friendly approach to be adopted by facilities where contamination analyzes are routinely performed.

Improved upper bound for sorting permutations by prefix transpositions

Modelling of chromosomes with permutations has triggered the research of sorting permutations using global rearrangement operations in computational molecular biology. One such rearrangement is transposition which swaps two adjacent substrings. If one of the substrings is restricted to be the prefix of the permutation then that operation is called a prefix transposition. The symmetric prefix transposition distance between two permutations is defined to be the minimum number of prefix transpositions required to transform one into another. Group theory dictates that an upper bound for prefix transposition distance between any pair of permutations in the symmetric group Sn is equivalent to the upper bound for sorting any permutation with prefix transpositions. In this paper, we improve the upper bound for sorting permutations with prefix transpositions to n−log2⁡n.

A novel method for inference of acyclic chemical compounds with bounded branch-height based on artificial neural networks and integer programming

Analysis of chemical graphs is becoming a major research topic in computational molecular biology due to its potential applications to drug design. One of the major approaches in such a study is inverse quantitative structure activity/property relationship (inverse QSAR/QSPR) analysis, which is to infer chemical structures from given chemical activities/properties. Recently, a novel two-phase framework has been proposed for inverse QSAR/QSPR, where in the first phase an artificial neural network (ANN) is used to construct a prediction function. In the second phase, a mixed integer linear program (MILP) formulated on the trained ANN and a graph search algorithm are used to infer desired chemical structures. The framework has been applied to the case of chemical compounds with cycle index up to 2 so far. The computational results conducted on instances with n non-hydrogen atoms show that a feature vector can be inferred by solving an MILP for up to n=40, whereas graphs can be enumerated for up to n=15. When applied to the case of chemical acyclic graphs, the maximum computable diameter of a chemical structure was up to 8. In this paper, we introduce a new characterization of graph structure, called “branch-height” based on which a new MILP formulation and a new graph search algorithm are designed for chemical acyclic graphs. The results of computational experiments using such chemical properties as octanol/water partition coefficient, boiling point and heat of combustion suggest that the proposed method can infer chemical acyclic graphs with around n=50 and diameter 30.

Closure properties of bonded sequential insertion-deletion systems

Through the years, formal language theory has evolved through continual interdisciplinary work in theoretical computer science, discrete mathematics and molecular biology. The combination of these areas resulted in the birth of DNA computing. Here, language generating devices that usually considered any set of letters have taken on extra restrictions or modified constructs to simulate the behavior of recombinant DNA. A type of these devices is an insertion-deletion system, where the operations of insertion and deletion of a word have been combined in a single construct. Upon appending integers to both sides of the letters in a word, bonded insertion-deletion systems were introduced to accurately depict chemical bonds in chemical compounds. Previously, it has been shown that bonded sequential insertion-deletion systems could generate up to recursively enumerable languages. However, the closure properties of these systems have yet to be determined. In this paper, it is shown that bonded sequential insertion-deletion systems are closed under union, concatenation, concatenation closure, λ-free concatenation closure, substitution and intersection with regular languages. Hence, the family of languages generated by bonded sequential insertion-deletion systems is shown to be a full abstract family of languages.