Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640008
L. Ordínez, Ivana González Bagur, Marisa Castillo, Federico Amandi, Carlos Buckle
The aim of this work is to characterize and analyze the flow of arrivals to the city of Puerto Madryn (Patagonia - Argentina), during the first six month of social isolation while the pandemic for Covid-19 was occurring. Results remarked how simple traffic data collection based on provenance, local destination, age and gender is more important to understand the spread of the virus and for the design of health policies than reinforcing traffic restrictions. The intention is to expose the potentialities of a more comprehensive analogous analysis of this kind of mobility for local public health policy and urban planning in a post-pandemic context.
{"title":"A space-time analysis of Puerto Madryn arrivals during COVID-19","authors":"L. Ordínez, Ivana González Bagur, Marisa Castillo, Federico Amandi, Carlos Buckle","doi":"10.1109/CLEI53233.2021.9640008","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640008","url":null,"abstract":"The aim of this work is to characterize and analyze the flow of arrivals to the city of Puerto Madryn (Patagonia - Argentina), during the first six month of social isolation while the pandemic for Covid-19 was occurring. Results remarked how simple traffic data collection based on provenance, local destination, age and gender is more important to understand the spread of the virus and for the design of health policies than reinforcing traffic restrictions. The intention is to expose the potentialities of a more comprehensive analogous analysis of this kind of mobility for local public health policy and urban planning in a post-pandemic context.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"37 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85725381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640086
Gabriela Pereyra, Claudina Rattaro, P. Belzarena
The fifth generation of mobile communications (5G) is the new 3GPP technology designed to solve a wide range of requirements. On the one hand, it must be able to support high bit rates and ultra-low latency services, and on the other hand, it should be able to connect a massive amount of devices with loose bandwidth and delay requirements. In this context, as scheduling is always a delicate vendor topic and there are not so many free and complete simulation tools to support all 5G features, in this paper we present Py5cheSim. Py5cheSim is a flexible and open-source simulator based on Python and specially oriented to simulate cell capacity in 3GPP 5G networks and beyond. To the best of our knowledge, Py5cheSim is the first simulator that supports Network Slicing at the Radio Access Network (RAN), one of the main innovations of 5G. The present work describes its design and implementation choices and the principal validation results. Finally, as another contribution, we present an exhaustive analysis of the existing available simulation tools highlighting the novelty of Py5cheSim comparing with the others existing simulation software for 5G.
{"title":"Py5cheSim: a 5G Multi-Slice Cell Capacity Simulator","authors":"Gabriela Pereyra, Claudina Rattaro, P. Belzarena","doi":"10.1109/CLEI53233.2021.9640086","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640086","url":null,"abstract":"The fifth generation of mobile communications (5G) is the new 3GPP technology designed to solve a wide range of requirements. On the one hand, it must be able to support high bit rates and ultra-low latency services, and on the other hand, it should be able to connect a massive amount of devices with loose bandwidth and delay requirements. In this context, as scheduling is always a delicate vendor topic and there are not so many free and complete simulation tools to support all 5G features, in this paper we present Py5cheSim. Py5cheSim is a flexible and open-source simulator based on Python and specially oriented to simulate cell capacity in 3GPP 5G networks and beyond. To the best of our knowledge, Py5cheSim is the first simulator that supports Network Slicing at the Radio Access Network (RAN), one of the main innovations of 5G. The present work describes its design and implementation choices and the principal validation results. Finally, as another contribution, we present an exhaustive analysis of the existing available simulation tools highlighting the novelty of Py5cheSim comparing with the others existing simulation software for 5G.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"8 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87572973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640100
Marks Dextre, Oscar Rosas, Jesus Lazo, J. C. Gutiérrez
Automating the detection of weapons from video surveillance images is a difficult task due to: lighting, focus, resolution, among others. Solving this problem would be very useful for citizen security purposes. In this sense, this research work trains a weapon detection system based on YOLOv5 (You Only Look Once) for different data sources, reaching an accuracy of 98.56 % in video surveillance images, performing Real-Time inferences reaching 33 fps on Nvidia's Jetson AGX Xavier which is a good result compared to other existing research in the state of the art.
从视频监控图像中自动检测武器是一项艰巨的任务，因为:照明，焦点，分辨率等。解决这个问题将对公民安全非常有用。从这个意义上说，本研究工作训练了一个基于YOLOv5 (You Only Look Once)的武器检测系统，针对不同的数据源，在视频监控图像中达到98.56%的准确率，在Nvidia的Jetson AGX Xavier上执行实时推理，达到33 fps，与其他现有的研究相比，这是一个很好的结果。
{"title":"Gun Detection in Real-Time, using YOLOv5 on Jetson AGX Xavier","authors":"Marks Dextre, Oscar Rosas, Jesus Lazo, J. C. Gutiérrez","doi":"10.1109/CLEI53233.2021.9640100","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640100","url":null,"abstract":"Automating the detection of weapons from video surveillance images is a difficult task due to: lighting, focus, resolution, among others. Solving this problem would be very useful for citizen security purposes. In this sense, this research work trains a weapon detection system based on YOLOv5 (You Only Look Once) for different data sources, reaching an accuracy of 98.56 % in video surveillance images, performing Real-Time inferences reaching 33 fps on Nvidia's Jetson AGX Xavier which is a good result compared to other existing research in the state of the art.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"42 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84266239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640083
Solange Ramos-Cooper, Guillermo Cámara Chávez
Ear recognition has gained attention in recent years. The possibility of being captured from a distance, contactless, without the cooperation of the subject and not be affected by facial expressions makes ear recognition a captivating choice for surveillance and security applications, and even more in the current COVID-19 pandemic context where modalities like face recognition fail due to mouth and facial covering masks usage. Applying any deep learning (DL) algorithm usually demands a large amount of training data and appropriate network architectures, therefore we introduce a large-scale database and explore fine-tuning pre-trained convolutional neural networks (CNNs) looking for a robust representation of ear images taken under uncontrolled conditions. Taking advantage of the face recognition field, we built an ear dataset based on the VGGFace dataset and use the Mask-RCNN for ear detection. Besides, adapting the VGGFace model to the ear domain leads to a better performance than using a model trained for general image recognition. Experiments on the UERC dataset have shown that fine-tuning from a face recognition model and using a larger dataset leads to a significant improvement of around 9% compared to state-of-the-art methods on the ear recognition field. In addition, we have explored score-level fusion by combining matching scores of the fine-tuning models which leads to an improvement of around 4% more. Open-set and close-set experiments have been performed and evaluated using Rank-1 and Rank-5 recognition rate metrics.
{"title":"Ear Recognition In The Wild with Convolutional Neural Networks","authors":"Solange Ramos-Cooper, Guillermo Cámara Chávez","doi":"10.1109/CLEI53233.2021.9640083","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640083","url":null,"abstract":"Ear recognition has gained attention in recent years. The possibility of being captured from a distance, contactless, without the cooperation of the subject and not be affected by facial expressions makes ear recognition a captivating choice for surveillance and security applications, and even more in the current COVID-19 pandemic context where modalities like face recognition fail due to mouth and facial covering masks usage. Applying any deep learning (DL) algorithm usually demands a large amount of training data and appropriate network architectures, therefore we introduce a large-scale database and explore fine-tuning pre-trained convolutional neural networks (CNNs) looking for a robust representation of ear images taken under uncontrolled conditions. Taking advantage of the face recognition field, we built an ear dataset based on the VGGFace dataset and use the Mask-RCNN for ear detection. Besides, adapting the VGGFace model to the ear domain leads to a better performance than using a model trained for general image recognition. Experiments on the UERC dataset have shown that fine-tuning from a face recognition model and using a larger dataset leads to a significant improvement of around 9% compared to state-of-the-art methods on the ear recognition field. In addition, we have explored score-level fusion by combining matching scores of the fine-tuning models which leads to an improvement of around 4% more. Open-set and close-set experiments have been performed and evaluated using Rank-1 and Rank-5 recognition rate metrics.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"29 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84383665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640221
J. Merlino, P. Rodríguez-Bocca
Word embeddings are widely used in natural language processing (NLP) to group semantically similar words but have been applied in other areas to find semantic similarity between entities. In this paper we create a vector embedding for Internet Domain Names (DNS) using a corpus of real anonymized DNS log queries from a large Internet Service Provider (ISP). We then use this embedding as a layer of a recurrent neural network (RNN) that works as a Language Model for the DNS queries generated by the users. We show that this RNN can be used to predict the next DNS query generated by a user with good accuracy (considering the size of the problem). Moreover, we show that training the same RNN without using the pre-trained vector model takes more time and is substantially less accurate. The results presented in this work can have practical applications in many engineering activities related to DNS architecture design. For example, latency reduction in address resolution, optimization of cache systems in recursive DNS servers, automatic filtering of inappropriate domains, and detecting anomalies in traffic.
{"title":"Short-time prediction of DNS queries using deep learning and pre-trained word embedding","authors":"J. Merlino, P. Rodríguez-Bocca","doi":"10.1109/CLEI53233.2021.9640221","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640221","url":null,"abstract":"Word embeddings are widely used in natural language processing (NLP) to group semantically similar words but have been applied in other areas to find semantic similarity between entities. In this paper we create a vector embedding for Internet Domain Names (DNS) using a corpus of real anonymized DNS log queries from a large Internet Service Provider (ISP). We then use this embedding as a layer of a recurrent neural network (RNN) that works as a Language Model for the DNS queries generated by the users. We show that this RNN can be used to predict the next DNS query generated by a user with good accuracy (considering the size of the problem). Moreover, we show that training the same RNN without using the pre-trained vector model takes more time and is substantially less accurate. The results presented in this work can have practical applications in many engineering activities related to DNS architecture design. For example, latency reduction in address resolution, optimization of cache systems in recursive DNS servers, automatic filtering of inappropriate domains, and detecting anomalies in traffic.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"23 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89093413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9639977
Rafael Alfaro-Flores, José Salas-Bonilla, Loic Juillard, Juan Esquivel-Rodríguez
We present an end-to-end experimentation framework to improve the human annotation of data sets used in the training process of Machine Learning models. It covers the instrumentation of the annotation tool, the aggregation of metrics that highlight usage patterns and hypothesis-testing tools that enable the comparison of experimental groups, to decide whether improvements in the annotation process significantly impact the overall results. We show the potential of the protocol using two real-life annotation use cases.
{"title":"Experiment-driven improvements in Human-in-the-loop Machine Learning Annotation via significance-based A/B testing","authors":"Rafael Alfaro-Flores, José Salas-Bonilla, Loic Juillard, Juan Esquivel-Rodríguez","doi":"10.1109/CLEI53233.2021.9639977","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9639977","url":null,"abstract":"We present an end-to-end experimentation framework to improve the human annotation of data sets used in the training process of Machine Learning models. It covers the instrumentation of the annotation tool, the aggregation of metrics that highlight usage patterns and hypothesis-testing tools that enable the comparison of experimental groups, to decide whether improvements in the annotation process significantly impact the overall results. We show the potential of the protocol using two real-life annotation use cases.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"74 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79953129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640171
D. Silva, W. Watanabe
When the same web application is rendered in different browsers, inconsistencies detected in the layout or behavior of pages are known as (XBIs Cross Browser Incompatibilities). Currently, there are different classification models in the literature for the identification and automatic correction of XBIs. These models have evolved with the aim of reducing false positives and negatives. This paper proposes to compare these different models, focusing on those that use the classification of layout XBIs, through machine learning algorithms. There is still no paper in the literature to compare them, identifying their main advantages and disadvantages. This paper consists of an experiment that compares the results of models and presents metrics that allow to affirm how effective they are, aiming also to bring important information as contributions to propose future works regarding the evolution of the explored models. The result of the experiment is the metric of F-Score. For this metric, the higher values imply greater efficiency in detecting incompatibilities between browsers, and the C5.0 10 iterations - X configuration obtained the best result in the experiment.
{"title":"Cross-Browser Incompatibilities Classification Layout: A comparative study between different models","authors":"D. Silva, W. Watanabe","doi":"10.1109/CLEI53233.2021.9640171","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640171","url":null,"abstract":"When the same web application is rendered in different browsers, inconsistencies detected in the layout or behavior of pages are known as (XBIs Cross Browser Incompatibilities). Currently, there are different classification models in the literature for the identification and automatic correction of XBIs. These models have evolved with the aim of reducing false positives and negatives. This paper proposes to compare these different models, focusing on those that use the classification of layout XBIs, through machine learning algorithms. There is still no paper in the literature to compare them, identifying their main advantages and disadvantages. This paper consists of an experiment that compares the results of models and presents metrics that allow to affirm how effective they are, aiming also to bring important information as contributions to propose future works regarding the evolution of the explored models. The result of the experiment is the metric of F-Score. For this metric, the higher values imply greater efficiency in detecting incompatibilities between browsers, and the C5.0 10 iterations - X configuration obtained the best result in the experiment.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"34 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86423589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640070
F. Giraldo, Cesar Villegas, María C. López-Tavera, Dairo A. Gil
This paper presents the development and validation of a Domain Specific Language (DSL) for managing procedures that are commonly used in the traceability of the coffee. For the development of the DSL, a conceptual model that is built from domain knowledge is presented. A controlled experiment is performed to verify a DSL to support traceability procedures in the production chain for coffee. The first version of this DSL, and its support and understandability are analyzed. Some principles from the Physics of Notations (PoN) approach have been used in the experimental design.
{"title":"Using a visual domain-specific language to support traceability in coffee production chain","authors":"F. Giraldo, Cesar Villegas, María C. López-Tavera, Dairo A. Gil","doi":"10.1109/CLEI53233.2021.9640070","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640070","url":null,"abstract":"This paper presents the development and validation of a Domain Specific Language (DSL) for managing procedures that are commonly used in the traceability of the coffee. For the development of the DSL, a conceptual model that is built from domain knowledge is presented. A controlled experiment is performed to verify a DSL to support traceability procedures in the production chain for coffee. The first version of this DSL, and its support and understandability are analyzed. Some principles from the Physics of Notations (PoN) approach have been used in the experimental design.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"6 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74901304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640109
Néstor Fabián Riveros, Carlos Rodríguez
The huge amount of information regarding software vulnerabilities, the multiple and heterogeneous information sources, and the lack of awareness about the dangers of software vulnerabilities, exacerbates the risks of security threats being materialized. In this complex context, this paper approaches the problem of managing early alerts for software vulnerablities by leveraging existing vulnerability information found in vulnerability repositories and social networks. To this end, we propose a solution based on techniques that stem from automated retrieval of information about vulneratilities from the above sources, userdefined preferences regarding their technological environment and intelligent vulnerability tagging. Our user studies reveal the feasibility of our approach as a tool for managing early alerts regarding software vulnerabilities and keeping security professionals aware of them.
{"title":"An Early Alert System for Software Vulnerabilities based on Vulnerability Repositories and Social Networks","authors":"Néstor Fabián Riveros, Carlos Rodríguez","doi":"10.1109/CLEI53233.2021.9640109","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640109","url":null,"abstract":"The huge amount of information regarding software vulnerabilities, the multiple and heterogeneous information sources, and the lack of awareness about the dangers of software vulnerabilities, exacerbates the risks of security threats being materialized. In this complex context, this paper approaches the problem of managing early alerts for software vulnerablities by leveraging existing vulnerability information found in vulnerability repositories and social networks. To this end, we propose a solution based on techniques that stem from automated retrieval of information about vulneratilities from the above sources, userdefined preferences regarding their technological environment and intelligent vulnerability tagging. Our user studies reveal the feasibility of our approach as a tool for managing early alerts regarding software vulnerabilities and keeping security professionals aware of them.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"25 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73121108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1109/CLEI53233.2021.9640088
Clarice de Azevedo Souza, J. Bessa, Rosiane de Freitas, Micael Oliveira, Kelson Mota
A fast way to reconstruct the three-dimensional molecular conformation of SARS-CoV-2 virus proteins is addressed in this article, involving the most worrying variant discovered in patients from Brazil, the lineage $B$.1.1.28/$P$.1. The proposed methodology is based on the sequencing of virus proteins and that, through the incorporation of mutations in silico, which are then computationally reconstructed using an enumerative feasibility algorithm validated by the Ramachandran diagram and structural alignment, in addition to the subsequent study of structural stability through classical molecular dynamics. From the resulting structure to the ACE2-RBD complex, the valid solution presented 97.06% of the residues in the most favorable region while the reference crystallographic structure presented 95.0%, a difference therefore very small and revealing the great consistency of the developed algorithm. Another important result was the low RMSD alignment between the best solution by the BP algorithm and the reference structure, where we obtained 0.483Å. Finally, the molecular dynamics indicated greater structural stability in the ACE2-RBD interaction with the P.1 strain, which could be a plausible explanation for convergent evolution that provides an increase in the interaction affinity with the ACE2 receptor.
{"title":"Improvement of SARS-CoV-2 macromolecule conformation by algorithmic structural prediction","authors":"Clarice de Azevedo Souza, J. Bessa, Rosiane de Freitas, Micael Oliveira, Kelson Mota","doi":"10.1109/CLEI53233.2021.9640088","DOIUrl":"https://doi.org/10.1109/CLEI53233.2021.9640088","url":null,"abstract":"A fast way to reconstruct the three-dimensional molecular conformation of SARS-CoV-2 virus proteins is addressed in this article, involving the most worrying variant discovered in patients from Brazil, the lineage $B$.1.1.28/$P$.1. The proposed methodology is based on the sequencing of virus proteins and that, through the incorporation of mutations in silico, which are then computationally reconstructed using an enumerative feasibility algorithm validated by the Ramachandran diagram and structural alignment, in addition to the subsequent study of structural stability through classical molecular dynamics. From the resulting structure to the ACE2-RBD complex, the valid solution presented 97.06% of the residues in the most favorable region while the reference crystallographic structure presented 95.0%, a difference therefore very small and revealing the great consistency of the developed algorithm. Another important result was the low RMSD alignment between the best solution by the BP algorithm and the reference structure, where we obtained 0.483Å. Finally, the molecular dynamics indicated greater structural stability in the ACE2-RBD interaction with the P.1 strain, which could be a plausible explanation for convergent evolution that provides an increase in the interaction affinity with the ACE2 receptor.","PeriodicalId":6803,"journal":{"name":"2021 XLVII Latin American Computing Conference (CLEI)","volume":"6 1","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74377309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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