Search

The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design. [ABSTRACT FROM AUTHOR]

Background: The stability of protein sequence structure plays an important role in the prevention and treatment of diseases. Results: In this paper, particle swarm optimization and tabu search are combined to propose a new method for protein structure prediction. The experimental results show that: for four groups of artificial protein sequences with different lengths, this method obtains the lowest potential energy value and stable structure prediction results, and the effect is obviously better than the other two comparison methods. Taking the first group of protein sequences as an example, our method improves the prediction of minimum potential energy by 127% and 7% respectively. Conclusions: Therefore, the method proposed in this paper is more suitable for the prediction of protein structural stability. [ABSTRACT FROM AUTHOR]

Protein sequence similarity analysis (PSSA) is a significant task in bioinformatics, which can obtain information about unknown sequences such as protein structures and homology relationships. Protein sequence refers to the series of amino acids with rich physical and chemical properties, namely the basic structure of proteins. However, sequence similarity analysis and phylogenetic analysis between different species which have complex amino acid sequences is a challenging problem. In this paper, nine properties of amino acids were considered and the sequence was converted into numerical values by principal component analysis (PCA); with Haar Wavelet Transform, and Higuchi fractal dimension (HFD), a new feature vector is constructed to represent the sequence; Spearman distance was selected to calculate the distance matrix and the phylogenetic tree was constructed. In this paper, two representative protein sequences (9 ND5 (NADH dehydrogenase 5) and 8 ND6 (NADH dehydrogenase 6)) were selected for similarity analysis and phylogenetic analysis, and compared with MEGA software and other existing methods. The extensive results show that our method is outperforming and results consistent with the known facts. [ABSTRACT FROM AUTHOR]

In recent years, protein structure problems have become a hotspot for understanding protein folding and function mechanisms. It has been observed that most of the protein structure works rely on and benefit from co-evolutionary information obtained by multiple sequence alignment (MSA). As an example, AlphaFold2 (AF2) is a typical MSA-based protein structure tool which is famous for its high accuracy. As a consequence, these MSA-based methods are limited by the quality of the MSAs. Especially for orphan proteins that have no homologous sequence, AlphaFold2 performs unsatisfactorily as MSA depth decreases, which may pose a barrier to its widespread application in protein mutation and design problems in which there are no rich homologous sequences and rapid prediction is needed. In this paper, we constructed two standard datasets for orphan and de novo proteins which have insufficient/none homology information, called Orphan62 and Design204, respectively, to fairly evaluate the performance of the various methods in this case. Then, depending on whether or not utilizing scarce MSA information, we summarized two approaches, MSA-enhanced and MSA-free methods, to effectively solve the issue without sufficient MSAs. MSA-enhanced model aims to improve poor MSA quality from the data source by knowledge distillation and generation models. MSA-free model directly learns the relationship between residues on enormous protein sequences from pre-trained models, bypassing the step of extracting the residue pair representation from MSA. Next, we evaluated the performance of four MSA-free methods (trRosettaX-Single, TRFold, ESMFold and ProtT5) and MSA-enhanced (Bagging MSA) method compared with a traditional MSA-based method AlphaFold2, in two protein structure-related prediction tasks, respectively. Comparison analyses show that trRosettaX-Single and ESMFold which belong to MSA-free method can achieve fast prediction (⁠|$\sim\! 40$| s) and comparable performance compared with AF2 in tertiary structure prediction, especially for short peptides, |$\alpha $| -helical segments and targets with few homologous sequences. Bagging MSA utilizing MSA enhancement improves the accuracy of our trained base model which is an MSA-based method when poor homology information exists in secondary structure prediction. Our study provides biologists an insight of how to select rapid and appropriate prediction tools for enzyme engineering and peptide drug development. Contact guofei@csu.edu.cn , jj.tang@siat.ac.cn [ABSTRACT FROM AUTHOR]

As biological macromolecules, proteins are involved in important cellular functions ranging from DNA replication and biosynthesis to metabolic signalling and environmental sensing. Protein sequencing can help understand the relationship between protein function and structure, and provide key information for disease diagnosis and new drug design. Nanopore sensors are a novel technology to achieve the goal of label‐free and high‐throughput protein sequencing. In recent years, nanopore‐based biosensors have been widely used in the detection and analysis of biomolecules such as DNA, RNA, and proteins. At the same time, computer simulations can describe the transport of proteins through nanopores at the atomic level. This paper reviews the applications of nanopore sensors in protein sequencing over the past decade and the solutions to key problems from a computer simulation perspective, with the aim of pointing the way to the future of nanopore protein sequencing. [ABSTRACT FROM AUTHOR]

The formation of mature mRNA requires cutting introns and splicing exons. The occurrence of splicing involves the participation of the spliceosome. Common spliceosomes mainly include five snRNPs: U1, U2, U4/U6, and U5. SF3a2, an essential component of spliceosome U2 snRNP, participates in splicing a series of genes. There is no definition of SF3a2 in plants. The paper elaborated on SF3a2s from a series of plants through protein sequence similarity. We constructed the evolutionary relationship of SF3a2s in plants. Moreover, we analyzed the similarities and differences in gene structure, protein structure, the cis-element of the promoter, and expression pattern; we predicted their interacting proteins and constructed their collinearity. We have preliminarily analyzed SF3a2s in plants and clarified the evolutionary relationship between different species; these studies can better serve for in-depth research on the members of the spliceosome in plants. [ABSTRACT FROM AUTHOR]

Directed laboratory evolution applies iterative rounds of mutation and selection to explore the protein fitness landscape and provides rich information regarding the underlying relationships between protein sequence, structure, and function. Laboratory evolution data consist of protein sequences sampled from evolving populations over multiple generations and this data type does not fit into established supervised and unsupervised machine learning approaches. We develop a statistical learning framework that models the evolutionary process and can infer the protein fitness landscape from multiple snapshots along an evolutionary trajectory. We apply our modeling approach to dihydrofolate reductase (DHFR) laboratory evolution data and the resulting landscape parameters capture important aspects of DHFR structure and function. We use the resulting model to understand the structure of the fitness landscape and find numerous examples of epistasis but an overall global peak that is evolutionarily accessible from most starting sequences. Finally, we use the model to perform an in silico extrapolation of the DHFR laboratory evolution trajectory and computationally design proteins from future evolutionary rounds. Author summary: Laboratory evolution has revolutionized our understanding of protein structure, function, and evolution, and has generated countless useful proteins broad applications in medicine, biocatalysis, and biotechnology. These experiments explore protein sequence space through iterative rounds of mutation and selection and can provide rich data of populations traversing the fitness landscape. In this paper, we present a statistical learning framework that models the evolutionary process and can infer the structure of the underlying protein fitness landscape from multiple snapshots along a laboratory evolution trajectory. We generate a dihydrofolate reductase (DHFR) laboratory evolution data set and apply our modeling approach to infer the landscape parameters. The estimated parameters pinpoint key residues that dictate DHFR structure and function. We use the resulting model to understand the local and global structure of the fitness landscape and to perform in silico directed evolution for protein engineering. [ABSTRACT FROM AUTHOR]

Background: The revolution in molecular biology has shown how protein function and structure are based on specific sequences of amino acids. Thus, an important feature in many papers is the mention of the significance of individual amino acids in the context of the entire sequence of the protein. MutationFinder is a widely used program for finding mentions of specific mutations in texts. We report on augmenting the positive attributes of MutationFinder with a more inclusive regular expression list to create ResidueFinder, which finds mentions of native amino acids as well as mutations. We also consider parameter options for both ResidueFinder and MutationFinder to explore trade-offs between precision, recall, and computational efficiency. We test our methods and software in full text as well as abstracts. Results: We find there is much more variety of formats for mentioning residues in the entire text of papers than in abstracts alone. Failure to take these multiple formats into account results in many false negatives in the program. Since MutationFinder, like several other programs, was primarily tested on abstracts, we found it necessary to build an expanded regular expression list to achieve acceptable recall in full text searches. We also discovered a number of artifacts arising from PDF to text conversion, which we wrote elements in the regular expression library to address. Taking into account those factors resulted in high recall on randomly selected primary research articles. We also developed a streamlined regular expression (called "cut") which enables a several hundredfold speedup in both MutationFinder and ResidueFinder with only a modest compromise of recall. All regular expressions were tested using expanded F-measure statistics, i.e., we compute Fβ for various values of where the larger the value of β the more recall is weighted, the smaller the value of β the more precision is weighted. Conclusions: ResidueFinder is a simple, effective, and efficient program for finding individual residue mentions in primary literature starting with text files, implemented in Python, and available in SourceForge.net. The most computationally efficient versions of ResidueFinder could enable creation and maintenance of a database of residue mentions encompassing all articles in PubMed. [ABSTRACT FROM AUTHOR]

Predicting a protein structure using primary method like X-ray crystallography and Nuclear Magnetic Resonance (NMR) are very costly and time taking for predicting a 3D protein structure, these primary methods takes three to five years, which is too much time. So, to improve the prediction time of the protein structure Deep Mind has developed a computational tool called alphafold colab to predict the 3D protein structure of the unknown protein sequence using computational method which are based on Artificial Intelligence (AI). In this paper we are reviewing homology modelling and thread modelling based predicted 3D protein structure of the unknown protein sequence based on AlphaFold Database (AFDB). [ABSTRACT FROM AUTHOR]

The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple experimental steps, which requires a lot of manpower and material resources. In addition, studies have shown that not all proteins can form crystals under experimental conditions, and the success rate of the final crystallization of proteins is only <10%. Although some protein crystallization predictors have been developed, not many tools capable of predicting multi-stage protein crystallization propensity are available and the accuracy of these tools is not satisfactory. In this paper, we propose a novel deep learning framework, named SADeepcry, for predicting protein crystallization propensity. The framework can be used to estimate the three steps (protein material production, purification and crystallization) in protein crystallization experiments and the success rate of the final protein crystallization. SADeepcry uses the optimized self-attention and auto-encoder modules to extract sequence, structure and physicochemical features from the proteins. Compared with other state-of-the-art protein crystallization propensity prediction models, SADeepcry can obtain more complex global spatial long-distance dependence of protein sequence information. Our computational results show that SADeepcry has increased Matthews correlation coefficient and area under the curve, by 100.3% and 13.4%, respectively, over the DCFCrystal method on the benchmark dataset. The codes of SADeepcry are available at https://github.com/zhc940702/SADeepcry. [ABSTRACT FROM AUTHOR]

Understanding gene functions and their associated abnormal phenotypes is crucial in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. However, the current HPO annotations are far from completion, and only a small fraction of human protein-coding genes has HPO annotations. Thus, it is necessary to predict protein-phenotype associations using computational methods. Protein sequences can indicate the structure and function of the proteins, and interacting proteins are more likely to have same function. It is promising to integrate these features for predicting HPO annotations of human protein. We developed GraphPheno, a semi-supervised method based on graph autoencoders, which does not require feature engineering to capture deep features from protein sequences, while also taking into account the topological properties in the protein–protein interaction network to predict the relationships between human genes/proteins and abnormal phenotypes. Cross validation and independent dataset tests show that GraphPheno has satisfactory prediction performance. The algorithm is further confirmed on automatic HPO annotation for no-knowledge proteins under the benchmark of the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2), where GraphPheno surpasses most existing methods. Further bioinformatics analysis shows that predicted certain phenotype-associated genes using GraphPheno share similar biological properties with known ones. In a case study on the phenotype of abnormality of mitochondrial respiratory chain, top prioritized genes are validated by recent papers. We believe that GraphPheno will help to reveal more associations between genes and phenotypes, and contribute to the discovery of drug targets. [ABSTRACT FROM AUTHOR]

Background: Recently, drug repositioning has received considerable attention for its advantage to pharmaceutical industries in drug development. Artificial intelligence techniques have greatly enhanced drug reproduction by discovering therapeutic drug profiles, side effects, and new target proteins. However, as the number of drugs increases, their targets and enormous interactions produce imbalanced data that might not be preferable as an input to a prediction model immediately. Methods: This paper proposes a novel scheme for predicting drug–target interactions (DTIs) based on drug chemical structures and protein sequences. The drug Morgan fingerprint, drug constitutional descriptors, protein amino acid composition, and protein dipeptide composition were employed to extract the drugs and protein's characteristics. Then, the proposed approach for extracting negative samples using a support vector machine one-class classifier was developed to tackle the imbalanced data problem feature sets from the drug–target dataset. Negative and positive samplings were constructed and fed into different prediction algorithms to identify DTIs. A 10-fold CV validation test procedure was applied to assess the predictability of the proposed method, in addition to the study of the effectiveness of the chemical and physical features in the evaluation and discovery of the drug–target interactions. Results: Our experimental model outperformed existing techniques concerning the curve for receiver operating characteristic (AUC), accuracy, precision, recall F-score, mean square error, and MCC. The results obtained by the AdaBoost classifier enhanced prediction accuracy by 2.74%, precision by 1.98%, AUC by 1.14%, F-score by 3.53%, and MCC by 4.54% over existing methods. [ABSTRACT FROM AUTHOR]

Most intracellular activities of living organisms are performed by proteins that have unique and complex 3-Dimensional (3D) structures, playing very important roles in their operations. Due to importance and challenges of predicting 3D structures for proteins, laboratory methods with limitations such as time consuming and high cost have been developed. Computational methods use a sequence of amino acids to obtain the 3D structure. They encounter with a nondeterministic problem having polynomial completion time (NP-Complete problem); with a chain of amino acids as input, and a protein, with 3D structure, as output. In this paper, a new population-based algorithm, under Predatory Search Strategy-Particle Swarm Optimization (PSS-PSO) framework, is presented for predicting the 3D structure; using Hydrophobic-Polar (HP) model on Face-Centered Cubic (FCC) lattice. In this approach, name TRL-PSSPSO, two new moves are proposed to direct each solution toward the native structure: Local Move for reaching a dense hydrophobic core and large H-H contacts and Meta Move for reaching optimal structure, by using Transfer learning. Two datasets are considered for evaluating and the results on some set of standard protein benchmarks show outperforming the state-of-the-art approaches. They show that the proposed approach can improve the accuracy of template-free prediction in an acceptable manner. [ABSTRACT FROM AUTHOR]

A deep insight into the 3D structures of the proteins is essential to understand their functions and will be helpful in drug design and making decisions for the treatment of diseases. The 3D structure for less than one-thousandth of the protein sequences has been determined so far due to the slow structure elucidation procedures with experimental methods, such as X-ray. For this reason, different computational methods were used to determine the structure of the proteins, including template-based and de novo approaches. Much software has been developed to facilitate the computational approaches in protein modeling, some of which need user expertise and extensive knowledge in bioinformatics. The present study attempted to provide a user-friendly environment to motivate the researchers in computational biology, a Python-based interface based on MODELLER software. PyProtModel can be used as a toolbox in structural bioinformatics for protein modeling from sequence to the prediction of 3D structure. In addition, the users can take benefits of PyProtModel in order to prepare and evaluate protein PDB files prior to other in silico experiments. PyProtModel allows the user to apply different MODELLER options, automates, and speeds up time-consuming homology modeling steps. Different aspects of PyProtModel and a comprehensive view of the application will be discussed in this paper. The application is available for download and use at: https://github.com/msisakht/pyprotmodel.git. [Display omitted] • PyProtModel is a new interface to handle different homology modeling tasks. • Different features of PyProtModel with other interfaces were discussed. • A workflow to all modules of PyProtModel was depicted for the users. [ABSTRACT FROM AUTHOR]

This article examines the presence of the empirical tendency known as the Menzerath–Altmann Law (MAL) on protein secondary structures. MAL is related to optimization principles observed in natural languages and in genetic information on chromosomes or protein domains. The presence of MAL is examined on a non-redundant dataset of 4728 proteins by verifying significant, negative correlations and testing classical and newly proposed formulas by fitting the observed trend. We conclude that the lengths of secondary structures are specifically dependent on their number inside the protein sequence, while possibly reflecting the formula proposed in this paper. This behavior is observed on average but is individually avoidable and possibly driven by a latent cost function. The data suggest that MAL could provide a useful guiding principle in protein design. [ABSTRACT FROM AUTHOR]

Protein secondary structure prediction is an important topic in bioinformatics. This paper proposed a novel model named WS-BiLSTM, which combined the wavelet scattering convolutional network and the long-short-term memory network for the first time to predict protein secondary structure. This model captures nonlocal interactions between amino acid sequences and remembers long-range interactions between amino acids. In our WS-BiLSTM model, the wavelet scattering convolutional network is used to extract protein features from the PSSM sliding window; the extracted features are combined with the original PSSM data as the input features of the long-short-term memory network to predict protein secondary structure. It is worth noting that the wavelet scattering convolutional network is asymmetric as a member of the continuous wavelet family. The Q3 accuracy on the test set CASP9, CASP10, CASP11, CASP12, CB513, and PDB25 reached 85.26%, 85.84%, 84.91%, 85.13%, 86.10%, and 85.52%, which were higher 2.15%, 2.16%, 3.5%, 3.19%, 4.22%, and 2.75%, respectively, than using the long-short-term memory network alone. Comparing our results with the state-of-art methods shows that our proposed model achieved better results on the CB513 and CASP12 data sets. The experimental results show that the features extracted from the wavelet scattering convolutional network can effectively improve the accuracy of protein secondary structure prediction. [ABSTRACT FROM AUTHOR]

The prediction of protein subcellular localization not only is important for the study of protein structure and function but also can facilitate the design and development of new drugs. In recent years, feature extraction methods based on protein evolution information have attracted much attention and made good progress. Based on the protein position-specific score matrix (PSSM) obtained by PSI-BLAST, PSSM-GSD method is proposed according to the data distribution characteristics. In order to reflect the protein sequence information as much as possible, AAO method, PSSM-AAO method, and PSSM-GSD method are fused together. Then, conditional entropy-based classifier chain algorithm and support vector machine are used to locate multilabel proteins. Finally, we test Gpos-mPLoc and Gneg-mPLoc datasets, considering the severe imbalance of data, and select SMOTE algorithm to expand a few sample; the experiment shows that the AAO + PSSM ∗ method in the paper achieved 83.1% and 86.8% overall accuracy, respectively. After experimental comparison of different methods, AAO + PSSM ∗ has good performance and can effectively predict protein subcellular location. [ABSTRACT FROM AUTHOR]